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Published online by Cambridge University Press: 01 October 2020
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- River Networks as Ecological CorridorsSpecies, Populations, Pathogens, pp. 401 - 431Publisher: Cambridge University PressPrint publication year: 2020
References
[1]
Dasgupta, P. The nature of economic development and the economic development of nature. Economic & Political Weekly 47 (2013), 38–51.Google Scholar
[2]
Levin, S. The problem of pattern and scale in ecology. Ecology 76 (1992), 1943–1963.Google Scholar
[3]
Southwood, T., May, R., and Sugihara, G. Observations on related ecological exponents. Proceedings of the National Academy of Sciences of the USA 103, 1 (2006), 6931–6933.CrossRefGoogle ScholarPubMed
[4]
Banavar, J., Damuth, J., Maritan, A., and Rinaldo, A. Scaling in ecosystems and the linkage of macroecological laws. Physical Review Letters 98, 1 (2007), 068104.CrossRefGoogle ScholarPubMed
[5]
Newman, M. Power laws, Pareto distributions and Zipf’s law. Contemporary Physics 46, 5 (2005), 323–351.CrossRefGoogle Scholar
[6]
Fagan, W. Connectivity, fragmentation, and extinction risk in dendritic metapopulations. Ecology 83, 12 (2002), 3243–3249.Google Scholar
[7]
Bertuzzo, E., Maritan, A., Gatto, M., Rodriguez-Iturbe, I., and Rinaldo, A. River networks and ecological corridors: reactive transport on fractals, migration fronts, hydrochory. Water Resources Research 43 (2007), W04419.Google Scholar
[8]
Muneepeerakul, R., Bertuzzo, E., Lynch, H., Fagan, W., Rinaldo, A., and Rodriguez-Iturbe, I. Neutral metacommunity models predict fish diversity patterns in the Mississippi–Missouri basin. Nature 453, 7192 (2008), 220–224.Google Scholar
[9]
Bertuzzo, E., Suweis, S., Mari, L., Maritan, A., Rodriguez-Iturbe, I., and Rinaldo, A. Spatial effects for species persistence and implications for biodiversity. Proceedings of the National Academy of Sciences of the USA 108, 11 (2011), 4346–4351.Google Scholar
[10]
Chisholm, C., Lindo, Z., and Gonzalez, A. Metacommunity diversity depends on network connectivity arrangement in heterogeneous landscapes. Ecography 34 (2011), 415–424.Google Scholar
[11]
Ricklefs, R. Community diversity – relative roles of local and regional processes. Science 235, 4785 (1987), 167–171.Google Scholar
[12]
Holyoak, M., and Lawler, S. The contribution of laboratory experiments on protists to understanding population and metapopulation dynamics. In Advances in Ecological Research 37: Population Dynamics and Laboratory Ecology. vol. 37. Academic Press, 2005, pp. 245–271.Google Scholar
[13]
Altermatt, F., Bieger, A., Carrara, F., Rinaldo, A., and Holyoak, M. Effects of connectivity and recurrent local disturbances on community structure and population density in experimental metacommunities. PLoS ONE 6, 4 (2011), e19525.Google Scholar
[14]
Carrara, F., Altermatt, F., Rodriguez-Iturbe, I., and Rinaldo, A. Dendritic connectivity controls biodiversity patterns in experimental metacommunities. Proceedings of the National Academy of Sciences of the USA 109 (2012), 5761–5766.CrossRefGoogle ScholarPubMed
[15]
Giometto, A., et al. Generalized receptor law governs phototaxis in the phytoplankton Euglena gracilis. Proceedings of the National Academy of Sciences of the USA 112 (2015), 7045–7050.Google Scholar
[16]
Giometto, A., Altermatt, F., and Rinaldo, A. Demographic stochasticity and resource autocorrelation control biological invasions in heterogeneous landscapes. Oikos 126 (2017), 1554–1563.Google Scholar
[17]
Morrissey, M., and de Kerckhove, D. The maintenance of genetic variation due to asymmetric gene flow in dendritic metapopulations. American Naturalist 174 (2009), 875–889.CrossRefGoogle ScholarPubMed
[18]
Clarke, A., Mac Nally, R., Bond, N., and Lake, P. Macroinvertebrate diversity in headwater streams: a review. Freshwater Biology 53 (2008), 1707–1721.Google Scholar
[19]
Brown, B., and Swan, C. Dendritic network structure constrains metacommunity properties in riverine ecosystems. Journal of Animal Ecology 79 (2010), 571–580.Google Scholar
[20]
Finn, D., Bonada, N., Murria, C., and Hughes, J. Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. Journal of the North American Benthological Society 30 (2011), 963–980.Google Scholar
[21]
Rodriguez-Iturbe, I., Muneepeerakul, R., Bertuzzo, E., Levin, S., and Rinaldo, A. River networks as ecological corridors: a complex systems perspective for integrating hydrologic, geomorphologic, and ecologic dynamics. Water Resources Research 45 (2009), W01413.Google Scholar
[22]
Ammermann, A., and Cavalli-Sforza, L. The Neolithic Transition and the Genetics of Population in Europe. Princeton University Press.Google Scholar
[23]
Battin, T., Kaplan, L., Newbold, J., and Hansen, C. Contributions of microbial biofilms to ecosystem processes in stream mesocosms. Nature 426 (2003), 439–442.CrossRefGoogle ScholarPubMed
[24]
Battin, T., et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nature Geosciences 1 (2008), 95– 1 00.Google Scholar
[25]
Rinaldo, A., Bertuzzo, E., Blokesch, M., Mari, L., and Gatto, M. Modeling key drivers of cholera transmission dynamics provides new perspectives on parasitology. Trends in Parasitology 33, 8 (2017), 587–599.Google Scholar
[26]
Raymond, P., and Bauer, J. Riverine export of aged terrestrial organic matter to the north atlantic ocean. Nature 409 (2001), 497–501.Google Scholar
[27]
Alexander, R., Jones, P., Boyer, E., and Smith, R. Effect of stream channel size on the delivery of nitrogen to the Gulf of Mexico. Nature 403 (2000), 758–761.Google Scholar
[28]
D’Odorico, P., Laio, F., Porporato, A., Ridolfi, L., Rinaldo, A., and Rodriguez-Iturbe, I. Ecohydrology of terrestrial ecosystems. BioScience 11 (2010), 898–907.Google Scholar
[29]
Mari, L., Bertuzzo, E., Casagrandi, R., Gatto, M., Levin, S. A., Rodriguez-Iturbe, I., and Rinaldo, A. Hydrologic controls and anthropogenic drivers of the zebra mussel invasion of the Mississippi–Missouri river system. Water Resources Research 47 (2011), W03523.Google Scholar
[30]
Bertuzzo, E., et al. Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak. Geophysical Research Letters 38 (2011), L06403.Google Scholar
[32]
Vellend, M. Conceptual synthesis in community ecology. Quarterly Review of Biology 85, 2 (2010), 183–206.Google Scholar
[33]
Caswell, H. Community structure: a neutral model analysis. Ecological Monographs 46, 3 (1976), 327–354.Google Scholar
[34]
Hubbell, S. The Unified Theory of Biodiversity and Biogeography. Princeton University Press, 2001.Google Scholar
[35]
Economo, E., and Keitt, T. Species diversity in neutral metacommunities: a network approach. Ecology Letters 11, 1 (2008), 52–62.Google Scholar
[36]
Nee, S. The neutral theory of biodiversity: do the numbers add up? Functional Ecology 19, 1 (2005), 173–176.CrossRefGoogle Scholar
[37]
McGill, B., Maurer, B., and Weiser, M. Empirical evaluation of neutral theory. Ecology 87, 6 (2006), 1411–1423.CrossRefGoogle ScholarPubMed
[38]
Purves, D., and Turnbull, L. Different but equal: the implausible assumption at the heart of neutral theory. Journal of Animal Ecology 79, 6 (2010), 1215–1225.Google Scholar
[39]
Chave, J., Muller-Landau, H., and Levin, S. Comparing classical community models: theoretical consequences for patterns of diversity. American Naturalist 159, 1 (2002), 1–23.Google Scholar
[40]
Muneepeerakul, R., Weitz, J., Levin, S., Rinaldo, A., and Rodriguez-Iturbe, I. A neutral metapopulation model of biodiversity in river networks. Journal of Theoretical Biology 245 (2007), 351–363.CrossRefGoogle ScholarPubMed
[42]
Kimura, M. The Neutral Theory of Molecular Evolution. Cambridge University Press, 1983.Google Scholar
[43]
Hubbell, S. Tree dispersion, abundance and diversity in a tropical dry forest. Science 203 (1979), 1299–1309.Google Scholar
[44]
Kendall, D. On some modes of population growth leading to R. A. Fisher’s logarithmic series distribution. Biometrika 35, 1/2 (1948), 6–15.Google Scholar
[45]
Karlin, S., and McGregor, J. The number of mutant forms maintained in a population. Proceedings of the 5th Berkeley Symposium of Mathematical Statistics and Probability 4 (1967), 415–438.Google Scholar
[46]
Watterson, G. The sampling theory of selectively neutral alleles. Advances in Applied Probability 6, 3 (1974), 463–488.Google Scholar
[47]
Magurran, A. Ecological Diversity and Its Measurement. Princeton University Press, 1988.Google Scholar
[48]
Fisher, R., Corbet, A., and Williams, C. The relation between the number of species and the number of individuals in a random sample of an animal population. Journal of Animal Ecology 12 (1943), 42–58.Google Scholar
[49]
Volkov, I., Banavar, J., Hubbell, S., and Maritan, A. Neutral theory and relative species abundance in ecology. Nature 424, 6952 (2003), 1035–1037.Google Scholar
[50]
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. American Naturalist 142 (1993), 911–927.Google Scholar
[51]
Purves, D., and Pacala, S. Ecological drift in niche-structured communities: Neutral pattern does not imply neutral process. In Biotic Interactions in the Tropics, Burslem, D., Pinard, M., and Hartley, S., eds. Cambridge University Press, 2005, pp. 107–138.Google Scholar
[53]
Holt, R. Emergent neutrality. Trends in Ecology and Evolution 21 (2006), 451–457.Google Scholar
[54]
McGill, B. A test of the unified neutral theory of biodiversity. Nature 422 (2003), 881–885.Google Scholar
[55]
Chave, J. Neutral theory and community ecology. Ecology Letters 7 (2004), 241–253.Google Scholar
[56]
Dornelas, M., Connolly, S., and Hughes, T. Coral reef diversity refutes the neutral theory of biodiversity. Nature 440 (2006), 80–82.Google Scholar
[57]
Etienne, R., and Olff, H. Confronting different models of community structure to species-abundance data: a Bayesian model comparison. Ecology Letters 8 (2005), 493–504.Google Scholar
[58]
Volkov, I., Banavar, J., Hubbell, S., and Maritan, A. Patterns of relative species abundance in ecology. Nature 450 (2007), 45–49.Google Scholar
[59]
Guisan, A., Thuiller, W., and Zimmermann, N. Habitat Suitability and Distribution Models. Cambridge University Press, 2018.Google Scholar
[60]
Diamond, J. The present, past and future of human-caused extinctions. Philosophical Transactions of the Royal Society B 325, 1228 (1989), 469–477.Google Scholar
[61]
Burness, G., Diamond, J., and Flannery, T. Dinosaurs, dragons, and dwarfs: the evolution of maximal body size. Proceedings of the National Academy of Sciences of the USA 98 (2001), 14518–14523.CrossRefGoogle ScholarPubMed
[62]
Grant, P., and Grant, B. The secondary contact phase of allopatric speciation in Darwin finches. Proceedings of the National Academy of Sciences of the USA 106 (2009), 20141–20148.Google Scholar
[63]
Rodriguez-Iturbe, I., and Rinaldo, A. Fractal River Basins: Chance and Self-Organization. Cambridge University Press, 2001.Google Scholar
[64]
Rinaldo, A., Rigon, R., Banavar, J., Maritan, A., and Rodriguez-Iturbe, I. Evolution and selection of river networks: statics, dynamics, and complexity. Proceedings of the National Academy of Sciences of the USA 111, 7 (2014), 2417–2424.Google Scholar
[65]
Rodriguez-Iturbe, I., Rinaldo, A., Rigon, R., Bras, R., Ijjasz-Vasquez, E., and Marani, A. Fractal structures as least energy patterns – the case of river networks. Geophysical Research Letters 19, 9 (1992), 889–892.Google Scholar
[66]
Rinaldo, A., Rodriguez-Iturbe, I., Rigon, R., Bras, R., Vasquez, E., and Marani, A. Minimum energy and fractal structures of drainage networks. Water Resources Research 28, 9 (1992), 2183–2195.Google Scholar
[67]
Rinaldo, A., Rigon, R., and Rodriguez-Iturbe, I. Channel networks. Annual Review of Earth and Planetary Sciences 26 (1999), 289–306.Google Scholar
[68]
Kirchner, J. Statistical inevitability of Horton’s laws and the apparent randomness of stream channel networks. Geology 21 (1993), 591–594.Google Scholar
[69]
Rinaldo, A., Banavar, J., and Maritan, A. Trees, networks and hydrology. Water Resources Research 42 (2006), 88–93.Google Scholar
[70]
Montgomery, D., and Dietrich, W. Where do channels begin? Nature 336 (1988), 232–234.Google Scholar
[71]
Montgomery, D., and Dietrich, W. Channel initiation and the problem of landscape scale. Science 255 (1992), 826–830.CrossRefGoogle ScholarPubMed
[72]
Briggs, L., and Krishnamoorthy, M. Exploring network scaling through variations on optimal channel networks. Proceedings of the National Academy of Sciences of the USA 110 (2013), 19295–19300.Google Scholar
[73]
Rigon, R., Rinaldo, A., and Rodriguez-Iturbe, I. On landscape self-organization. Journal of Geophysical Research 99 (1994), 11971–11 99 3.Google Scholar
[74]
Banavar, J., Colaiori, F., Flammini, A., Maritan, A., and Rinaldo, A. Scaling, optimality, and landscape evolution. Journal of Statistical Physics 104 (2001), 1–48.Google Scholar
[75]
Balister, P., et al. River landscapes and optimal channel networks. Proceedings of the National Academy of Sciences of the USA 115 (2018), 6548–6553.CrossRefGoogle ScholarPubMed
[76]
Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 2 (1972), 213–251.Google Scholar
[77]
Anderson, M., et al. Navigating the multiple meanings of β-diversity: a roadmap for the practicing ecologist. Ecology Letters 14, 1 (2011), 19–28.Google Scholar
[78]
Grant, E., Lowe, W., and Fagan, W. Living in the branches: population dynamics and ecological processes in dendritic networks. Ecology Letters 10 (2007), 165–175.Google Scholar
[79]
Luczkovich, J., Borgatti, S., Johnson, J., and Everett, M. Defining and measuring trophic role similarity in food webs using regular equivalence. Journal of Theoretical Biology 220 (2003), 303–321.Google Scholar
[80]
MacArthur, R., and Wilson, E. The Theory of Island Biogeography. Princeton University Press, 1967.Google Scholar
[81]
Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the Entomogical Society of America 15 (1969), 237–240.Google Scholar
[82]
Jablonski, D. Extinction and the spatial dynamics of biodiversity. Proceedings of the National Academy of Sciences of the USA 105 (2008), 11528–11535.Google Scholar
[83]
Chandrasekhar, S. Stochastic problems in physics and astronomy. Reviews of Modern Physics 15, 1 (1943), 1–89.Google Scholar
[84]
Durrett, R., and Levin, S. Spatial models for species-area curves. Journal of Theoretical Biology 179, 2 (1996), 119–127.CrossRefGoogle Scholar
[85]
Pigolotti, S., Flammini, A., Marsili, M., and Maritan, A. Species lifetime distribution for simple models of ecologies. Proceedings of the National Academy of Sciences of the USA 102 (2005), 147–151.Google Scholar
[87]
Kerr, B., Riley, M., Feldman, M., and Bohannan, B. Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature 418, 6894 (2002), 171–174.Google Scholar
[88]
Tilman, D., May, R., Lehman, C., and Nowak, M. Habitat destruction and the extinction debt. Nature 371, 6492 (1994), 65–66.Google Scholar
[89]
Suweis, S., Bertuzzo, E., Mari, L., Maritan, A., and Rinaldo, A. On species persistence-time distributions. Journal of Theoretical Biology 303 (2012), 15–24.Google Scholar
[90]
Keitt, T., and Stanley, H. Dynamics of North American breeding bird populations. Nature 393, 6682 (1998), 257–260.Google Scholar
[91]
Department of the Interior, Geological Survey Patuxent Wildlife Research Center. North American breeding bird survey. www.pwrc.usgs.gov/bbs (2008).Google Scholar
[92]
Adler, P., Tyburczy, W., and Lauenroth, W. Long-term mapped quadrats from Kansas prairie: demographic information for herbaceous plants. Ecology 88, 10 (2007), 2673–2673.Google Scholar
[93]
Rodriguez-Iturbe, I., Cox, D., and Isham, V. Some models for rainfall based on stochastic point-processes. Proceedings of the Royal Society A 410, 1839 (1987), 269–288.Google Scholar
[94]
de Aguiar, M., Baranger, M., Baptestini, E. M., Kaufman, L., and Bar-Yam, Y. Global patterns of speciation and diversity. Nature 460, 7253 (2009), 384–387.Google Scholar
[95]
Moyle, P., and Chech, J. An Introduction to Ichthyology, 5th ed. Benjamin Cummings, 2003.Google Scholar
[96]
Benda, L., et al. The network dynamics hypothesis: how channel networks structure riverine habitats. Bioscience 54 (2004), 384–U98.Google Scholar
[97]
Harvey, E., Gounand, I., Fronhofer, E., and Altermatt, F. Population turnover reverses classic island biogeography predictions in river-like landscapes. BioRXiv (2018), 1–28.Google Scholar
[98]
Srivastava, D., et al. Are natural microcosms useful model systems for ecology? Trends in Ecology and Evolution 19 (2004), 379–384.Google Scholar
[99]
Altermatt, F., Schreiber, S., and Holyoak, M. Interactive effects of disturbance and dispersal directionality on species richness and composition in metacommunities. Ecology 92 (2011), 859–870.CrossRefGoogle ScholarPubMed
[100]
Carrara, F., Rinaldo, A., Giometto, A., and Altermatt, F. Complex interaction of dendritic connectivity and hierarchical patch size on biodiversity in river-like landscapes. American Naturalist 183, 1 (2014), 13–25.Google Scholar
[101]
Leopold, L., Wolman, M., and Miller, J. Fluvial Processes in Geomorphology. Freeman, 1964.Google Scholar
[102]
Zaoli, S., Giometto, A., Maritan, A., and Rinaldo, A. Covariations in ecological scaling laws fostered by community dynamics. Proceedings of the National Academy of Sciences of the USA 114 (2017), 10672–10677.Google Scholar
[103]
Maritan, A., Rinaldo, A., Rodriguez-Iturbe, I., Rigon, R., and Giacometti, A. Scaling in river networks. Physical Review E 53 (1996), 1501–1513.Google Scholar
[104]
Cardinale, B. Biodiversity improves water quality through niche partitioning. Nature 472 (2011), 86–91.Google Scholar
[105]
Erlander, S., and Stewart, N. F. The Gravity Model in Transportation Analysis – Theory and Extensions. VSP Books, 1990.Google Scholar
[106]
Altermatt, F., Pajunen, V., and Ebert, D. Climate change affects colonization dynamics in a metacommunity of three daphnia species. Global Change Biology 14 (2008), 1209–1220.Google Scholar
[107]
De Bie, T., De Meester, L., Brendonck, L., and Martens, K. E. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecology 15 (2012), 740–747.Google Scholar
[108]
Wilson, J., Dormontt, E., Prentis, P., Lowe, A., and Richardson, D. Something in the way you move: dispersal pathways affect invasion success. Trends in Ecology and Evolution 24 (2009), 136–144.Google Scholar
[109]
Gonzalez, A., Lawton, J., Gilbert, F., Blackburn, T., and Evans-Freke, I. Metapopulation dynamics, abundance, and distribution in a microecosystem. Science 281 (1998), 2045–2047.Google Scholar
[110]
Gonzalez, A., Rayfield, B., and Lindo, Z. The disentangled bank: how loss of habitat fragments and disassembles ecological networks. American Journal of Botany 98 (2011), 503–516.CrossRefGoogle ScholarPubMed
[111]
Matthiessen, B., and Hillebrand, H. Dispersal frequency affects local biomass production by controlling local diversity. Ecology Letters 9 (2006), 652–662.Google Scholar
[112]
Mouquet, N., and Loreau, M. Community patterns in source/sink metacommunities. American Naturalist 162 (2003), 544–557.Google Scholar
[113]
Vannote, R. L., Minshall, G., Cummins, K., Sedell, J., and Cushing, C. River continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37 (1998), 130–137.Google Scholar
[114]
Haddad, N. M., Holyoak, M., Mata, T., Davies, K, Melbourne, F., A, ., and Preston, K. Species traits predict the effects of disturbance and productivity on diversity. Ecology Letters 11 (2008), 348–356.Google Scholar
[115]
Mari, L., Casagrandi, R., Bertuzzo, E., Rinaldo, A., and Gatto, M. Metapopulation persistence and species spread in river networks. Ecology Letters 17, 4 (2014), 426–434.Google Scholar
[116]
Cadotte, M., Mai, D., Jantz, S., Collins, M., Keele, M., and Drake, J. On testing the competition-colonization trade-off in a multispecies assemblage. American Naturalist 168 (2006), 704–709.Google Scholar
[117]
Livingston, G., et al. Competition–colonization dynamics in experimental bacterial metacommunities. Nature Communications 3 (2012), doi:10.1038/ncomms2239.Google Scholar
[118]
Campos, D., Fort, J., and Mendez, V. Transport on fractal river networks: application to migration fronts. Theoretical Population Biology 69 (2006), 88–93.CrossRefGoogle ScholarPubMed
[119]
Kolmogorov, A., Petrovsky, I., and Piscounov, N. Étude de l’équation de la diffusion avec croissance de la quantité de matiére et son application á un probléme biologique. Moscow University Mathematics Bulletin 1 (1937), 1–25.Google Scholar
[122]
Fort, J., and Méndez, V. Time-delayed theory of the neolithic transition in Europe. Physical Review Letters 82 (1999), 867–870.Google Scholar
[125]
Fisher, R. The wave of advance of advantageous genes. Annals of Eugenics 7 (1937), 355–369.Google Scholar
[126]
Marani, A., Rigon, R., and Rinaldo, A. A note on fractal channel networks. Water Resources Research 27 (1991), 3041–3049.Google Scholar
[127]
Colaiori, F., Flammini, A., Banavar, J., and Maritan, A. Analytical and numerical study of optimal channel networks. Physical Review E 55 (1997), 1298–1310.Google Scholar
[128]
Peano, G. Sur une courbe qui remplit toute une aire plane. Math. Ann. 36 (1890), 157–160.Google Scholar
[129]
Méndez, V., Campos, D., and Fedotov, S. Front propagation in reaction-dispersal models with finite jump speed. Physical Review E 70 (2004), 036121.Google Scholar
[130]
Méndez, V., Campos, D., and Fedotov, S. Analysis of front in reaction-dispersal processes. Physical Review E 70 (2004), 066129.Google Scholar
[131]
Campos, D., and Mendez, V. Reaction-diffusion wavefronts on comblike structures. Physical Review E 71 (2005), 31–39.Google Scholar
[132]
Hughes, B. Random Walks and Random Environments. Random Walks 1. Oxford University Press, 1995.Google Scholar
[133]
Mathan, O., and Havlin, S. Mean first-passage time on loopless aggregates. Physical Review A 40 (1990), 6573–6579.Google Scholar
[134]
Van den Broeck, C. Waiting time for random walks on regular and fractal lattices. Physical Review Letters 62 (1989), 1421–1424.Google Scholar
[135]
Flammini, A., and Colaiori, F. Exact analysis of the Peano basin. Journal of Physics A: Mathematical and General 29 (1996), 6701–6708.Google Scholar
[137]
Van den Broeck, C. A new sample of males linked from the public use microdata sample of the 1850 US federal census of population to the 1860 US federal census. Historical Methods 29 (1996), 41–156.Google Scholar
[138]
Ackland, G., Signizer, M., Stratford, K., and Cohen, M. Cultural hitchhiking on the wave of advance of beneficial technologies. Proceedings of the National Academy of Sciences of the USA 104 (2007), 8714–8719.Google Scholar
[139]
Fang, Y., and Jawitz, J. The evolution of human population distance to water in the USA from 1790 to 2010. Nature Communications 10 (2019), 1–8.Google Scholar
[140]
NatureServe. Distribution of native US fishes by watershed. Tech. rep., USGS, 2004.Google Scholar
[141]
Seaber, P., Kapinos, F., and Knapp, G. Distribution of native US fishes by watershed. Tech. rep., USGS, 2004.Google Scholar
[142]
Guegan, J., Lek, S., and Oberdorff, T. Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature 39 (1998), 382–384.Google Scholar
[143]
Angermeier, P., and Winston, M. Local vs. regional influences on local diversity in stream fish communities of virginia. Ecology 79 (1998), 911–927.Google Scholar
[144]
Oberdorff, T., Guegan, J., and Hugueny, B. Global scale patterns of fish species richness in rivers. Ecography 18 (1995), 345–352.Google Scholar
[145]
Levin, S., Muller-Landau, H., Nathan, R., and Chave, J. The ecology and evolution of seed dispersal: a theoretical perspective. Annual Review of Ecology and Systematics 34 (2003), 575–604.Google Scholar
[146]
Gebert, W., Graczyk, D., and Krug, W. Average Annual Runoff in the United States. Tech. rep., USGS, 1987.Google Scholar
[147]
Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19 (1974), 716–723.Google Scholar
[148]
Corani, G., and Gatto, M. Structural risk minimization: a robust method for density-dependence detection and model selection. Ecography 30, 2 (2007), 400–416.Google Scholar
[149]
Casagrandi, R., and Gatto, M. A persistence criterion for metapopulations. Theoretical Population Biology 61 (2002), 115–125.Google Scholar
[150]
Campbell Grant, E., Nichols, J., Lowe, W., and Fagan, W. Use of multiple dispersal pathways facilitates amphibian persistence in stream networks. Proceedings of the National Academy of Sciences of the USA 107 (2010), 6936–6940.Google Scholar
[151]
Hack, J. Studies of longitudinal profiles in Virginia and Maryland. US Geological Survey Professional Paper 294-B (1957), 1–21.Google Scholar
[152]
Battin, T., et al. The boundless carbon cycle. Nature Geosciences 2 (2009), 598–600.Google Scholar
[154]
Ceola, S., Bertuzzo, E., Dinger, G., Battin, T., Montanari, A., and Rinaldo, A. Hydrologic variability affects invertebrate grazing on phototrophic biofilms in stream microcosms. PLoS ONE 8 (2014), e60629.Google Scholar
[155]
Ceola, S., Bertuzzo, E., Dinger, G., Battin, T., Montanari, A., and Rinaldo, A. Hydrologic controls on basin-scale distribution of benthic invertebrates. Water Resources Research 50 (2014), W015112.Google Scholar
[156]
Poff, N., Olden, J., Merritt, D., and Pepin, D. Homogenization of regional river dynamics by dams and global biodiversity implications. Proceedings of the National Academy of Sciences of the USA 104 (2007), 5732–5737.Google Scholar
[157]
Botter, G., Basso, S., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A. Natural streamflow regime alterations: damming of the Piave river basin (Italy). Water Resources Research 46 (2010), W06522.Google Scholar
[158]
Kupferberg, S., et al. Effects of flow regimes altered by dams on survival, population declines, and range-wide losses of California river-breeding frogs. Conservation Biology 26 (2012), 513–524.Google Scholar
[159]
Botter, G., Basso, S., Rodriguez-Iturbe, I., and Rinaldo, A. Resilience of river flow regimes. Proceedings of the National Academy of Sciences of the USA 110 (2013), 12925–12930.Google Scholar
[160]
Botter, G., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A. Basin-scale soil moisture dynamics and the probabilistic characterization of carrier hydrologic flows: slow, leaching-prone components of the hydrologic response. Water Resources Research 43 (2007), W06404.Google Scholar
[161]
Rodriguez-Iturbe, I., Cox, D., and Isham, V. Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation. Proceedings of the Royal Society A 455 (1999), 3789–3805.Google Scholar
[162]
Bertuzzo, E., Helton, A., Hall, R., and Battin, T. Scaling of dissolved organic carbon removal in river networks. Advances in Water Resources 110 (2018), 136–146.Google Scholar
[163]
Widder, S., et al. Fluvial network organization imprints on microbial co-occurrence networks. Proceedings of the National Academy of Sciences of the USA 111 (2014), 12799–12804.Google Scholar
[164]
Campbell Grant, E. Structural complexity, movement bias, and metapopulation extinction risk in dendritic ecological networks. Journal of the North American Benthological Society 30 (2011), 252–258.Google Scholar
[165]
Speirs, D., and Gurney, W. Population persistence in rivers and estuaries. Ecology 82 (2001), 1219–1237.Google Scholar
[166]
Lutscher, F., Nisbet, R., and Pachepsky, E. Population persistence in the face of advection. Theoretical Ecology 3 (2010), 271–284.Google Scholar
[167]
Müller, K. Investigations on the organic drift in North Swedish streams. Tech. rep., Institute of Freshwater Research, Drottningholm. 133–148 pages.Google Scholar
[168]
Müller, K. The colonization cycle of freshwater insects. Oecologia 53 (1982), 202–207.Google Scholar
[169]
Waters, T. The drift of stream insects. Annual Review of Entomology 17 (1972), 253–272.Google Scholar
[170]
Reynolds, C., Carling, P., and Beven, K. Flow in river channels: new insights into hydraulic retention. Archiv für Hydrobiologie 121 (1991), 171–179.Google Scholar
[171]
Lancaster, J., and Hildrew, A. Characterising instream flow refugia. Canadian Journal of Fisheries and Aquatic Science 50 (1993), 1663–1675.Google Scholar
[172]
Lancaster, J., and Hildrew, A. Flow refugia and the microdistribution of lotic macroinvertebrates. Journal of the North American Benthological Society 12 (1993), 385–393.Google Scholar
[173]
Fischer, H., List, N., Koh, R., Imberger, J., and Brooks, N. Mixing in Inland and Coastal Waters. Academic Press, 1979.Google Scholar
[174]
Rinaldo, A., Marani, A., and Rigon, R. Geomorphological dispersion. Water Resources Research 27 (1991), 513–525.Google Scholar
[175]
Peterson, E., et al. Modelling dendritic ecological networks in space: an integrated network perspective. Ecology Letters 16 (2013), 707–719.Google Scholar
[176]
Pachepsky, E., Lutscher, F., Nisbet, R., and Lewis, M. Persistence, spread and the drift paradox. Theoretical Population Biology 67 (2005), 61–73.Google Scholar
[177]
Lutscher, F., Pachepsky, E., and Lewis, M. The effect of dispersal patterns on stream populations. SIAM Journal on Applied Mathematics 65 (2005), 1305–1327.Google Scholar
[178]
Blasco-Costa, I., Waters, J. M., and Poulin, R. Swimming against the current: genetic structure, host mobility and the drift paradox in trematode parasites. Molecular Ecology 21 (2012), 207–271.Google Scholar
[179]
Goldberg, E., Lynch, H., Neubert, M., and Fagan, W. Effects of branching spatial structure and life history on the asymptotic growth rate of a population. Theoretical Ecology 3 (2010), 137–152.Google Scholar
[180]
Ramirez, J. Population persistence under advection-diffusion in river networks. Mathematical Biology 65 (2012), 919–942.Google Scholar
[181]
Collier, K., and Smith, B. Dispersal of adult caddisflies (Trichoptera) into forests alongside three New Zealand streams. Hydrobiologia 361 (1998), 53–65.Google Scholar
[182]
Didham, R., Blakely, T., Ewers, R., Hitchings, T., Ward, J., and Winterbourn, M. Horizontal and vertical structuring in the dispersal of adult aquatic insects in a fragmented landscape. Fundamental and Applied Limnology 180 (2012), 27–40.Google Scholar
[183]
Hanski, I., and Ovaskainen, O. The metapopulation capacity of a fragmented landscape. Nature 404 (2000), 755–758.Google Scholar
[184]
Casagrandi, R., and Gatto, M. A mesoscale approach to extinction risk in fragmented habitats. Nature 400 (1999), 560–562.Google Scholar
[185]
Farina, L., and Rinaldi, S. Positive Linear Systems: Theory and Applications. Wiley Interscience.Google Scholar
[186]
Silvester, J. Determinants of block matrices. The Mathematical Gazette 84 (2000), 460–467.Google Scholar
[188]
Kuznetsov, Y. A. Elements of Applied Bifurcation Theory (3rd ed.). Springer, 2004.Google Scholar
[189]
Casagrandi, R., and Gatto, M. The intermediate dispersal principle in spatially explicit metapopulations. Journal of Theoretical Biology 239 (2006), 22–32.Google Scholar
[190]
Hanski, I., and Ovaskainen, O. Extinction debt at extinction threshold. Conservation Biology 16, 3 (2002), 666–673.Google Scholar
[191]
Organ, J. Studies of the local distribution, life history, and population dynamics of the salamander genus Desmognathus in Virginia. Ecological Monographs 31 (1961), 189–220.Google Scholar
[192]
Benson, L., and Pearson, R. Drift and upstream movement in Yaccabine creek, an Australian tropical stream. Hydrobiologia 153 (1987), 225–239.Google Scholar
[193]
Mackay, R. Colonization by lotic macroinvertebrates: a review of processes and patterns. Canadian Journal of Fisheries and Aquatic Sciences 49 (1992), 617–628.Google Scholar
[194]
Williams, D., and Williams, N. The upstream/downstream movement paradox of lotic invertebrates: quantitative evidence from Welsh mountain stream. Freshwater Biology 30 (1993), 199–218.Google Scholar
[195]
Jackson, J., McElravvy, E., and Resh, V. Long-term movements of self-marked caddisfly larvae (Trichoptera: Sericostomatidae) in a California coastal mountain stream. Freshwater Biology 42 (1999), 525–536.Google Scholar
[196]
Sode, A., and Wiberg-Larsen, P. Dispersal of adult Trichoptera at a Danish forest brook. Freshwater Biology 30 (1993), 439–446.Google Scholar
[197]
Kovats, Z., Ciborowski, J., and Corkum, L. Inland dispersal of adult aquatic insects. Freshwater Biology 36 (1996), 265–276.Google Scholar
[198]
Caudill, C. Measuring dispersal in a metapopulation using stable isotope enrichment: high rates of sex-biased dispersal between patches in a mayfly metapopulation. Oikos 101 (2003), 624–630.Google Scholar
[199]
Petersen, I., Winterbottom, J., Orton, S., Friberg, N., Speirs, A. H. D., and Gurney, W. Emergence and lateral dispersal of adult Plecoptera and Trichoptera from Broadstone Stream, UK. Freshwater Biology 42 (1999), 401–416.Google Scholar
[200]
Kopp, M., Jenschke, J., and Gabriel, W. Exact compensation of stream drift as an evolutionarily stable strategy. Oikos 92 (2001), 522–530.Google Scholar
[201]
Briers, R., Cariss, H., and Gee, J. Dispersal of adult stoneflies (Plecoptera) from upland streams draining catchments with contrasting land-use. Archiv für Hydrobiologie 155 (2002), 627–644.Google Scholar
[202]
Macneale, K., Peckarsky, B., and Likens, G. Stable iosopes identify dispersal patterns of stonefly populations living along stream corridors. Freshwater Biology 50 (2005), 1117–1130.Google Scholar
[203]
Sweeney, B., Funk, D., and Vannote, R. Population genetic structure of two mayflies (Ephemerella subvaria, Eurylophella verisimilis) in the Delaware River drainage basin. Journal of the North American Benthological Society 5 (1986), 253–262.Google Scholar
[204]
Sweeney, B., Funk, D., and Vannote, R. Genetic variation in stream mayfly (Insecta: Ephemeroptera) populations in eastern North America. Annals of the Entomological Society of America 80 (1987), 600–612.Google Scholar
[205]
Jackson, J., and Resh, V. Variation in genetic structure among populations of the caddisfly Helicopsyche borealis from three streams in northern California, USA. Freshwater Biology 27 (1992), 29–42.Google Scholar
[206]
Schmidt, J., Hughes, J., and Bunn, S. Gene flow among conspecific populations of Baetis (Ephemeroptera): adult flight and larval drift. Journal of the North American Benthological Society 14 (1995), 147–157.Google Scholar
[207]
Bunn, S., and Hughes, J. Dispersal and recruitment in streams: evidence from genetic studies. Journal of the North American Benthological Society 16 (1997), 338–346.Google Scholar
[208]
Gibbs, H., Gibbs, K., Siebenmann, M., and Collins, L. Genetic differentiation among populations of the rare mayfly Siphlonisca aerodromia Needham. Journal of the North American Benthological Society 17 (1998), 461–474.Google Scholar
[209]
Miller, M., Blinn, D., and Keim, P. Correlation between observed dispersal capabilities and patterns of genetic differentiation in populations of four aquatic insect species from the Arizona White Mountains. Freshwater Biology 47 (2002), 1660–1673.Google Scholar
[210]
Chaput-Bardy, A., Lemaire, C., Picard, D., and Secondi, J. In-stream and overland dispersal across a river network influences gene flow in a freshwater insect, Calopteryx splendens. Molecular Ecology 17 (2008), 3496–3505.Google Scholar
[211]
Labonne, J., Ravigne, V., Parisi, B., and Gaucherel, C. Linking dendritic network structures to population demogenetics: the downside of connectivity. Oikos 17 (2008), 1479–1490.Google Scholar
[212]
Chaput-Bardy, A., Fleurant, C., Lemaire, C., and Secondi, J. Modelling the effect of in-stream and overland dispersal on gene flow in river networks. Ecological Modelling 220 (2009), 3589–3598.Google Scholar
[213]
Burnham, K., and Anderson, D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, 2002.Google Scholar
[214]
Band, L. Topographic partition of watersheds with digital elevation models. Water Resources Research 22 (1986), 15–24.Google Scholar
[215]
Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research 33, 2 (1997), 309–319.Google Scholar
[216]
Johnson, A., Hatfield, C., and Milne, B. Simulated diffusion dynamics in river networks. Ecological Modelling 83 (1995), 311–325.Google Scholar
[218]
Marquet, P., Nones, R. Q., Abades, S., Labra, F., Tognelli, M., Arim, M., and Rivadeneira, M. Scaling and power-laws in ecological systems. Journal of Experimental Biology 208 (2005), 1749–1769.Google Scholar
[220]
Kuussaari, M., et al.. Extinction debt: a challenge for biodiversity conservation. Trends in Ecology and Evolution 24 (2009), 564–571.Google Scholar
[221]
Hylander, K., and Ehrlén, J. The mechanisms causing extinction debts. Trends in Ecology and Evolution 28 (2013), 341–346.Google Scholar
[222]
Lutscher, F., Lewis, M., and McCauley, E. Effects of heterogeneity on spread and persistence in rivers. Bulletin of Mathematical Biology 68 (2006), 2129–2160.Google Scholar
[223]
Lutscher, F., and Seo, G. The effect of temporal variability on persistence conditions in rivers. Journal of Theoretical Biology 283 (2011), 53–59.Google Scholar
[224]
Klausmeier, C. Floquet theory: a useful tool for understanding nonequilibrium dynamics. Theoretical Ecology 1 (2008), 153–161.Google Scholar
[225]
Ferrière, R., and Gatto, M. Lyapunov exponents and the mathematics of invasion in oscillatory or chaotic populations. Theoretical Population Biology 48 (1995), 126–171.Google Scholar
[226]
White, J., Botsford, L., Hastings, A., and Largier, J. Population persistence in marine reserve networks: incorporating spatial heterogeneities in larval dispersal. Marine Ecology Progress Series 398 (2010), 49–67.Google Scholar
[227]
Aiken, C., and Navarrete, S. Environmental fluctuations and asymmetrical dispersal: generalized stability theory for studying metapopulation persistence and marine protected areas. Marine Ecology Progress Series 428 (2011), 77–88.Google Scholar
[228]
Naeem, S., Duffy, J., and Zavaleta, E. The functions of biological diversity in an age of extinction. Science 336 (2012), 1401–1406.Google Scholar
[229]
Bertuzzo, E., Carrara, F., Mari, L., Altermatt, F., Rodriguez-Iturbe, I., and Rinaldo, A. Geomorphic controls on elevational gradients of species richness. Proceedings of the National Academy of Sciences of the USA 113 (2016), 1737–1742.Google Scholar
[230]
Colwell, R., Rahbek, C., and Gotelli, N. The mid-domain effect and species richness patterns: what have we learned so far? American Naturalist 163 (2004), E1–E23.Google Scholar
[232]
Körner, C. Why are there global gradients in species richness? Mountains might hold the answer. Trends in Ecology and Evolution 15 (2000), 513–514.Google Scholar
[233]
Körner, C. The use of altitude in ecological research. Trends in Ecology and Evolution 22, 11 (2007), 569–574.Google Scholar
[234]
Lomolino, M. Elevation gradients of species-density: historical and prospective views. Global Ecology and Biogeography 10, 1 (2001), 3–13.Google Scholar
[235]
McCain, C. M., and Grytnes, J.-A. Elevational gradients in species richness. Encylcopedia of Life Sciences 15 (2010), 1–10.Google Scholar
[236]
Nogues-Bravo, D., Araujo, M., Romdal, T., and Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453 (2008), 216–219.Google Scholar
[237]
Rahbek, C. The role of spatial scale and the perception of large-scale species-richness patterns. Ecology Letters 8 (2005), 224–239.Google Scholar
[238]
Kraft, N., et al. Disentangling the drivers of beta diversity along latitudinal and elevational gradients. Science 333 (2011), 1755–1758.Google Scholar
[239]
Rahbek, C. The elevational gradient of species richness – a uniform pattern. Ecography 18 (1995), 200–205.Google Scholar
[240]
Sanders, N. Elevational gradients in ant species richness: area, geometry, and Rapoport’s rule. Ecography 25 (2002), 25–32.Google Scholar
[241]
Rosenzweig, M. Species Diversity in Space and Time. Cambridge University Press, 1995.Google Scholar
[242]
Romdal, T., and Grytnes, J. An indirect area effect on elevational species richness patterns. Ecography 30 (2007), 440–448.Google Scholar
[243]
Hutchinson, G. Population studies, animal ecology and demography: concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22 (1957), 415–427.Google Scholar
[244]
Marani, M., Da Lio, C., and D’Alpaos, A. Vegetation engineers marsh morphology through multiple competing stable states. Proceedings of the National Academy of Sciences of the USA 110 (2013), 3259–3263.Google Scholar
[245]
Nieto-Lugilde, D., et al. Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the alps. Ecography 38 (2015), 578–589.Google Scholar
[248]
Diamond, J. Ecological consequences of island colonisation by south-west Pacific birds. I. Types of niche shift. Proceedings of the National Academy of Sciences of the USA 67 (1970), 529–536.Google Scholar
[249]
Tilman, D. Niche tradeoffs, neutrality, and community structure: A stochastic theory of resource competition, invasion, and community assembly. Proceedings of the National Academy of Sciences of the USA 101, 30 (2004), 10854–10861.Google Scholar
[250]
Rosindell, J., Hubbell, S., and Etienne, R. The unified neutral theory of biodiversity and biogeography at age ten. Trends in Ecology and Evolution 26 (2011), 340–348.Google Scholar
[252]
Rybicki, J., and Hanski, I. Species–area relationships and extinctions caused by habitat loss and fragmentation. Ecology Letters 16 (2013), 27–38.Google Scholar
[253]
Bertuzzo, E., Rodriguez-Iturbe, I., and Rinaldo, A. Metapopulation capacity of evolving fluvial landscapes. Water Resources Research 51 (2015), 2696–2706.Google Scholar
[254]
Ovaskainen, O., and Hanski, I. Spatially structured metapopulation models: global and local assessment of metapopulation capacity. Theoretical Population Biology 60 (2001), 281–302.Google Scholar
[255]
Ovaskainen, O., and Hanski, I. Transient dynamics in metapopulation response to perturbation. Theoretical Population Biology 61 (2002), 285–295.Google Scholar
[257]
Banavar, J., Colaiori, F., Flammini, A., Maritan, A., and Rinaldo, A. Topology of the fittest transportation network. Physical Review Letters 84 (2000), 4745–4748.Google Scholar
[258]
Fraser, D., Lippe, C., and Bernatchez, L. Consequences of unequal population size, asymmetric gene flow and sex-biased dispersal on population structure in brook charr (Salvelinus fontinalis). Molecular Ecology 13 (2004), 67–80.Google Scholar
[259]
Haddad, N. Corridor and distance effects on interpatch movements: a landscape experiment with butterflies. Ecological Applications 9 (1999), 612–622.Google Scholar
[260]
Markwith, S., and Scanlon, M. Multiscale analysis of Hymenocallis coronaria (Amaryllidaceae) genetic diversity, genetic structure, and gene movement under the influence of unidirectional stream flow. American Journal of Botany 94 (2007), 151–160.Google Scholar
[262]
Rinaldo, A., Dietrich, W., Rigon, R., Vogel, G., and Rodriguez-Iturbe, I. Geomorphological signatures of varying climate. Nature 374 (1995), 632–635.Google Scholar
[263]
Giezendanner, J., Bertuzzo, E., Pasetto, D., Guisan, A., and Rinaldo, A. A minimalist model of extinction and range dynamics of virtual mountain species driven by warming temperatures. PLoS ONE 4 (2019), e0213775.Google Scholar
[264]
Elsen, P. R., and Tingley, M. W. Global mountain topography and the fate of montane species under climate change. Nature Climate Change 5, August (2015), 5–10.Google Scholar
[265]
Parmesan, C., and Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421 (2003), 37–42.Google Scholar
[266]
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics 37 (2012), 637–669.Google Scholar
[267]
Chen, I., Hill, J., Ohlemueller, R., Roy, D., and Thomas, C. Rapid range shift of species associated with high levels of climate warming. Science 20 (2011), 1024–1026.Google Scholar
[268]
Field, C., et al., eds. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Cambridge University Press, 2014.Google Scholar
[269]
Hanski, I. Messages from Islands: A Global Biodiversity Tour. University of Chicago Press, 2016.Google Scholar
[270]
Rumpf, S., et al. Range dynamics of mountain plants decrease with elevation. Proceedings of the National Academy of Sciences of the USA 115 (2018), 1–6.Google Scholar
[271]
McCain, C. M. Area and mammalian elevational diversity. Ecology 88, 1 (2007), 76–86.Google Scholar
[272]
McCain, C., and Colwell, R. Assessing montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecology Letters 14 (2007), 1236–1245.Google Scholar
[273]
Dullinger, S., et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nature Climate Change 2, 8 (2012), 619–622.Google Scholar
[274]
Guisan, A., and Theurillat, J.-P. J.-P. Assessing alpine plant vulnerability to climate change: a modeling perspective. Integrated Assessment 1, 1 (2001), 307–320.Google Scholar
[275]
Theurillat, J.-P., and Guisan, A. Potential impact of climate change on vegetation in the European Alps: a review. Climatic Change 50 (2001), 77–109.Google Scholar
[277]
Lenoir, J., Gegout, J., Marquet, P., de Ruffray, P., and Brisse, H. A significant upwardshift in plant species optimum elevation during the 20th century. Science 320, 5884 (2008), 1768–1771.Google Scholar
[278]
Engler, R., et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter? Ecography 32 (2009), 34–45.Google Scholar
[280]
Ovaskainen, O. Metapopulation dynamics in highly fragmented landscapes. In Ecology, Genetics and Evolution of Metapopulations. Elsevier, 2004, pp. 73–103.Google Scholar
[281]
Tischendorf, L., Bender, D. J., and Fahrig, L. Evaluation of patch isolation metrics in mosaic landscapes for specialist vs. generalist dispersers. Landscape Ecology 18 (2003), 41–50.Google Scholar
[283]
Shigesada, N., and Kawasaki, K. Biological Invasions: Theory and Practice. Oxford University Press, 1997.Google Scholar
[284]
Clobert, J., Danchin, E., Dhondt, A., and Nichols, J. Dispersal. Oxford University Press, 2001.Google Scholar
[285]
Okubo, A., and Levin, S. Diffusion and Ecological Problems: Modern Perspectives. Springer, 2002.Google Scholar
[286]
Méndez, V., Fedotov, S., and Horsthemke, W. Reaction-Transport Systems. Springer, 2010.Google Scholar
[287]
Schick, R., and Lindley, S. Directed connectivity among fish populations in a riverine network. Journal of Applied Ecology 44 (2007), 1116–1126.Google Scholar
[288]
Melbourne, B., and Hastings, A. Highly variable spread rates in replicated biological invasions: fundamental limits to predictability. Science 325, 5947 (2009), 1536–1539.Google Scholar
[289]
Giometto, A., Rinaldo, A., Carrara, F., and Altermatt, F. Emerging predictable features of replicated biological invasion fronts. Proceedings of the National Academy of Sciences of the USA 111, 1 (2014), 297–30 1.Google Scholar
[290]
Taylor, G. Diffusion by continuous movements. Proceedings of the London Mathematical Society A 20 (1921), 196–211.Google Scholar
[291]
Britton, N. Reaction-Diffusion Equations and Their Applications to Biology. Academic Press, 1986.Google Scholar
[292]
Newmark, W. Species–area relationship and its determinants for mammals in western North American national parks. Biological Journal of the Linnean Society 28 (1986), 83–98.Google Scholar
[293]
Teschl, G. Ordinary Differential Equations and Dynamical Systems. American Mathematical Society, 2012.Google Scholar
[294]
Skellam, J. Random dispersal in theoretical populations. Biometrika 38 (1951), 196–218.Google Scholar
[295]
Erickson, J. M. The displacement of native ant species by the introduced argentine ant Iridomyrmex humilis Mayr. Psyche 78 (1971), 257–266.Google Scholar
[296]
Grosholz, E. Contrasting rates of spread for introduced species in terrestrial and marine systems. Ecology 77, 6 (1996), 1680–1686.Google Scholar
[298]
Liggett, T. M. Stochastic models for large interacting systems and related correlation inequalities. Proceedings of the National Academy of Sciences of the USA 107, 38 (2010), 16413–16419.Google Scholar
[299]
Durrett, R., and Levin, S. Stochastic spatial models: a user’s guide to ecological applications. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 343, 1305 (1994), 329–350.Google Scholar
[300]
Schinazi, R. Classical and Spatial Stochastic Processes with Applications to Biology. Birkhäuser, 2014.Google Scholar
[301]
Neuhauser, C. Mathematical challenges in spatial ecology. Notices of the American Mathematical Society 48, 11 (2001), 1304–1314.Google Scholar
[303]
Durrett, R., and Neuhauser, C. Particle systems and reaction-diffusion equations. Annals of Probability 22 (1994), 289–333.Google Scholar
[304]
Ellner, S., Sasaki, A., Haraguchi, Y., and Matsuda, H. Speed of invasion in lattice population models: pair-edge approximation. Journal of Mathematical Biology 36 (1998), 469—484.Google Scholar
[306]
Stokstad, E. Feared quagga mussel turns up in western United States. Science 315 (2007), 453–454.Google Scholar
[307]
Stoeckel, J., Schneider, D., Soeken, L., Blodgett, K., and Sparks, R. Larval dynamics of a riverine metapopulation: implications for zebra mussel recruitment, dispersal, and control in a large-river system. Journal of the North American Benthological Society 16 (1997), 586–601.Google Scholar
[308]
Casagrandi, R., Mari, L., and Gatto, M. Modelling the local dynamics of the zebra mussel (Dreissena polymorpha). Freshwater Biology 52 (2007), 1223–1238.Google Scholar
[309]
Carlton, J. Dispersal mechanisms of the zebra mussel (Dreissena polymorpha). Pages 677–697 In Zebra Mussels: Biology, Impact, and Control, Nalepa, T. F. and Schloesser, D. W., eds. CRC Press, 1992, pp. 677–697.Google Scholar
[310]
Allen, Y. C., and Ramcharan, C. W. Dreissena distribution in commercial waterways of the US: using failed invasions to identify limiting factors. Canadian Journal of Fisheries and Aquatic Sciences 58 (2001), 898–907.Google Scholar
[311]
Chase, M. E., and Bailey, R. The ecology of the zebra mussel (Dreissena polymorpha) in the lower Great Lakes of North America: I. Population dynamics and growth. Journal of Great Lakes Research 293 (1999), 657–660.Google Scholar
[312]
Stoeckel, J., Padilla, D., Schneider, D., and Rehmann, C. Laboratory culture of Dreissena polymorpha larvae: spawning success, adult fecundity, and larval mortality patterns. Canadian Journal of Zoology 82 (2004), 1436–1443.Google Scholar
[313]
Stoeckel, J., Rehmann, C., Schneider, D., and Padilla, D. Retention and supply of zebra mussel larvae in a large river system: importance of an upstream lake. Freshwater Biology 49 (2004), 919–930.Google Scholar
[314]
Mari, L., Casagrandi, R., Pisani, M., Pucci, E., and Gatto, M. When will the zebra mussel reach Florence? A model for the spread of Dreissena polymorpha in the Arno water system (Italy). Ecohydrology 2 (2009), 428–439.Google Scholar
[315]
Mackie, G. L., and Schloesser, D. W. Comparative biology of zebra mussels in Europe and North America: an overview. American Zoologist 36 (1996), 244–258.Google Scholar
[316]
Lewis, M. A., and Kareiva, P. Allee dynamics and the spread of invading organisms. Theoretical Population Biology 43 (1993), 141–158.Google Scholar
[317]
Kot, M., Lewis, M. A., and van den Driessche, P. Dispersal data and the spread of invading organisms. Ecology 77 (1996), 2027–2042.Google Scholar
[318]
Leung, B., Drake, J. M., and Lodge, D. M. Predicting invasions: propagule pressure and the gravity of Allee effects. Ecology 85 (2004), 1651–1660.Google Scholar
[319]
Potapov, A. B., and Lewis, M. A. Allee effect and control of lake system invasion. Bulletin of Mathematical Biology 70 (2008), 1371–1397.Google Scholar
[320]
MacIsaac, H. J. Potential abiotic and biotic impacts of zebra mussels on the inland waters of North America. American Zoologist 36 (1996), 287–299.Google Scholar
[321]
Strayer, D. L., and Malcom, H. M. Long-term demography of a zebra mussel (Dreissena polymorpha) population. Freshwater Biology 51 (2006), 117–130.Google Scholar
[322]
Mantecca, P., Vailati, G., Garibaldi, L., and Bacchetta, R. Depth effects on zebra mussel reproduction. Malacologia 45 (2003), 109–120.Google Scholar
[323]
Schneider, D. W., Stoeckel, J. A., Rehmann, C. R., Douglas Blodgett, K., Sparks, R. E., and Padilla, D. K. A developmental bottleneck in dispersing larvae: implications for spatial population dynamics. Ecology Letters 6 (2003), 352–3 6 0.Google Scholar
[324]
Pachepsky, E., Nisbet, R. M., and Murdoch, W. W. Between discrete and continuous: consumer–resource dynamics with synchronized reproduction. Ecology 89 (2008), 280–288.Google Scholar
[325]
Bertuzzo, E., Azaele, S., Maritan, A., Gatto, M., Rodriguez-Iturbe, I., and Rinaldo, A. On the space-time evolution of a cholera epidemic. Water Resources Research 44 (2008), W01424.Google Scholar
[326]
MacIsaac, H. J., Sprules, W. G., and Leach, J. H. Ingestion of small-bodied zooplankton by zebra mussels (Dreissena polymorpha): can canniblism on larvae influence population dynamics? Canadian Journal of Fisheries and Aquatic Sciences 48 (1991), 2051–2060.Google Scholar
[327]
Keevin, T. M., Yarbrough, R. E., and Miller, A. C. Long-distance dispersal of zebra mussels Dreissena polymorpha attached to hulls of commercial vessels. Journal of Freshwater Ecology 7 (1992), 437–43 7.Google Scholar
[328]
Schneider, D. W., Ellis, C. D., and Cummings, K. S. A transportation model assessment of the risk to native mussel communities from zebra mussel spread. Conservation Biology 12 (1998), 788–800.Google Scholar
[329]
Buchan, L. A. J., and Padilla, D. K. Estimating the probability of long-distance overland dispersal of invading aquatic species. Ecological Applications 9 (1999), 254–263.Google Scholar
[330]
Bossenbroek, J. M., Kraft, C. E., and Nekola, J. C. Prediction of long-distance dispersal using gravity models: zebra mussel invasion of inland lakes. Ecological Applications 11 (2001), 1778–1788.Google Scholar
[331]
Bossenbroek, J. M., Johnson, L. E., Peters, B., and Lodge, D. M. Forecasting the expansion of zebra mussels in the United States. Conservation Biology 21 (2007), 800–810.Google Scholar
[332]
Johnson, L. E., and Carlton, J. T. Post-establishment spread in large-scale invasions: dispersal mechanisms of the zebra mussel Dreissena polymorpha. Ecology 77 (1996), 1686–1690.Google Scholar
[333]
Sprung, M. The other life: an account of present knowledge of the larval phase of dreissena polymorpha. In Zebra Mussels: Biology, Impacts and Control, Nalepa, T. F. and Schloesser, D. W., eds. Lewis, 1993, pp. 39–53.Google Scholar
[334]
Hastings, A., et al. The spatial spread of invasions: new developments in theory. Ecology Letters 8, 1 (2005), 91–101.Google Scholar
[335]
Neubert, M., Kot, M., and Lewis, M. Invasion speed in fluctuating environments. Proceedings of the Royal Society Series B 267 (2000), 1603–1610.Google Scholar
[336]
Andow, D., Kareiva, P., Levin, S., and Okubo, A. Spread of invading organisms. Landscape Ecology 4, 2/3 (1990), 177–188.Google Scholar
[337]
Volpert, V., and Petrovskii, S. Reaction-diffusion waves in biology. Physics of Life Reviews 6, 4 (2009), 267–310.Google Scholar
[338]
Lubina, J., and Levin, S. The spread of a reinvading species: range expansion in the California sea otter. American Naturalist 131, 4 (1988), 526–543.Google Scholar
[339]
Ellner, S., and Schreiber, S. Temporally variable dispersal and demography can accelerate the spread of invading species. Theoretical Population Biology 82 (2012), 283–298.Google Scholar
[341]
Cantrell, R., Cosner, C., and Lou, Y. Evolutionary stability of ideal free dispersal strategies in patchy environments. Journal of Mathematical Biology 65 (2011), 943–9 65.Google Scholar
[342]
Berestycki, H., Nadin, G., Perthame, B., and Ryzhik, L. The non-local fisher-kpp equation: travelling wavesand steady states. Nonlinearity 22 (2009), 2813.Google Scholar
[343]
Coulan, A., and Roquejoffre, J. Transition between linear and exponential propagation in fisher-kpp type reaction-diffusion equations. Communications in Partial Differential Equations 37 (2012), 2029–2049.Google Scholar
[344]
Benichou, O., Calvez, V., Meunier, N., and Voituriez, R. Front acceleration by dynamic selection in fisher population waves. Physical Review Letters 86 (2012), 041908.Google Scholar
[345]
Alfaro, M., Coville, J., and Raoul, G. Travelling waves in a nonlocal equation as a model for a population structured by a space variable and a phenotypic trait. Communications in Partial Differential Equations 38 (2013), 2126–2154.Google Scholar
[346]
Berestycki, N., Mouhot, L., and Raoul, G. Existence of self-accelerating fronts for a non-local reation-diffusion equation. arXiv:1512.00903v2 math.AP (2018).Google Scholar
[347]
Bilton, D., Freeland, J., and Okamura, B. Dispersal in freshwater invertebrates. Annual Review of Ecology and Systematics 32 (2001), 88–93.Google Scholar
[348]
Holmes, E. Are diffusion models too simple? A comparison with telegraph models of invasion. American Naturalist 142 (1993), 779–795.Google Scholar
[349]
With, K., and Christ, T. Critical thresholds in species responses to landscape structure. Ecology 76 (1995), 2446–2459.Google Scholar
[350]
With, K. The landscape ecology of invasive spread. Conservation Biology 16 (2002), 1192–1203.Google Scholar
[351]
Dewhirst, S., and Lutscher, F. Dispersal in heterogeneous habitats: spatial scales and approximate rates of spread. Ecology 90 (2009), 1338–1345.Google Scholar
[352]
Bergelson, J., et al. Rates of weed spread in spatially heterogeneous environments. Ecology 74 (1994), 999–1011.Google Scholar
[353]
Bailey, D., Otten, W., and Gilligan, C. Saprotrophic invasion by the soil-borne fungal plant pathogen Rhizoctonia solani and percolation thresholds. New Phytologist 146 (2000), 535–544.Google Scholar
[354]
Williams, J., et al. The influence of evolution on population spread through patchy landscapes. American Naturalist 188 (2016), 15–26.Google Scholar
[355]
Fronhofer, E., et al. Information use shapes the dynamics of range expansions into environmental gradients. Global Ecology and Biogeography 26 (2017), 400–411.Google Scholar
[356]
Keller, E., and Segel, L. Initiation of slime mold aggregation viewed as an instability. Journal of Theoretical Biology 26 (1970), 399–415.Google Scholar
[357]
Keller, E., and Segel, L. Model for chemotaxis. Journal of Theoretical Biology 30 (1971), 225–234.Google Scholar
[358]
Tindall, M., Maini, P., Porter, S., and Armitage, J. Overview of mathematical approaches used to model bacterial chemotaxis II: Bacterial populations. Bulletin of Mathematical Biology 70 (2008), 1570–1607.Google Scholar
[359]
Giometto, A., Nelson, D., and Murray, A. Physical interactions reduce the power of natural selection in growing yeast colonies. Proceedings of the National Academy of Sciences of the USA 115 (2018), 11448–11453.Google Scholar
[360]
Dornic, I., et al. Integration of Langevin equations with multiplicative noise and the viability of field theories for absorbing phase transitions. Physical Review Letters 94 (2005), 100601.Google Scholar
[361]
Bonachela, J., et al. Patchiness and demographic noise in three ecological examples. Journal of Statistical Physics 148 (2012), 723–739.Google Scholar
[362]
Hallatscheck, O., and Korolev, K. Fisher waves in the strong noise limit. Physical Review Letters 103 (2009), 108103.Google Scholar
[363]
Van Dyck, H., and Baguette, M. Dispersal behavior in fragmented landscapes: routine or special movements. Basic and Applied Ecology 6 (2005), 535–545.Google Scholar
[364]
Borger, L., et al. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecology Letters 11 (2008), 637–650.Google Scholar
[365]
Méndez, V., et al. Speed of reaction-diffusion fronts in spatially heterogeneous media. Physical Review E 68 (2003), 041105.Google Scholar
[366]
Urban, M., et al. A toad more traveled: the hetereogeneous invasion dynamics of cane toads in Australia. American Naturalist 171 (2008), 134–148.Google Scholar
[367]
Mack, R., Simberloff, D., Lonsdale, W., Evans, H., Clout, M., and Bazzaz, F. Biotic invasions: causes, epidemiology, global consequences, and control. Ecological Applications 3 (2000), 689–710.Google Scholar
[368]
Fjellheim, A., Raddum, G., and Barlaup, B. Dispersal, growth and mortality of brown trout (Salmo trutta L.) stocked in a regulated West Norwegian river. Regulated Rivers: Research & Management 10 (1995), 137–145.Google Scholar
[369]
Kahler, T. H., Roni, P., and Quinn, T. Summer movement and growth of juvenile anadromous salmonids in small western Washington streams. Canadian Journal of Fisheries and Aquatic Sciences 58 (2001), 1947–1956.Google Scholar
[370]
Mortensen, E. The population dynamics of young trout (Salmo trutta L.) in a Danish brook. Journal of Fish Biology 10 (1977), 23–33.Google Scholar
[371]
Knouft, J. H., and Spotila, J. Assessment of movements of resident stream brown trout (Salmo trutta L.) among contiguous sections of stream. Ecology of Freshwater Fish 11 (2002), 85–92.Google Scholar
[372]
Jonsson, N. Influence of water flow, water temperature and light on fish migration in rivers. Nordic Journal of Freshwater Research 66 (1991), 2–35.Google Scholar
[373]
Jonsson, B., and Jonsson, N. Ecology of Atlantic Salmon and Brown Trout: Habitat as a Template for Life Histories. Springer, 2011.Google Scholar
[374]
Barquin, J., et al. Assessing the conservation status of alder-ash alluvial forest and Atlantic salmon in the Natura 2000 river network of Cantabria, northern Spain. River Conservation and Management 66 (2012), 193–210.Google Scholar
[375]
Rinaldo, A., Vogel, G., Rigon, R., and Rodriguez-Iturbe, I. Can one gauge the shape of a basin? Water Resources Research 31 (1995), 1119–1127.Google Scholar
[376]
Carraro, L., Bertuzzo, E., Hartikainen, H., Jokkela, J., and Rinaldo, A. Estimating species distribution and abundance in river networks using environmental DNA. Proceedings of the National Academy of Sciences of the USA 115 (2018), 11724–11729.Google Scholar
[377]
Rodriguez-Iturbe, I., and Valdes, J. The geomorphologic structure of hydrologic response. Water Resources Research 15 (1979), 1409–1420.Google Scholar
[378]
Gupta, V., Waymire, E., and Wang, C. A representation of an IUH from geomorphology. Water Resources Research 16 (1980), 885–862.Google Scholar
[379]
Rinaldo, A., Botter, G., Bertuzzo, E., Uccelli, A., Settin, T., and Marani, M. Transport at basin scale. 1. Theoretical framework. Hydrology and Earth System Science 10 (2006), 19–29.Google Scholar
[380]
Shreve, R. Stream lengths and basin areas in topologically random networks. Journal of Geology 77 (1969), 397–414.Google Scholar
[381]
Kirkby, M. Tests of the random model and its application to basin hydrology. Earth Surface Processes and Landforms 1 (1976), 197–2 1 2.Google Scholar
[382]
Rinaldo, A., and Marani, A. Basin scale model of solute transport. Water Resources Research 23, 11 (1987), 2107–2118.Google Scholar
[383]
Bak, P., and Chen, K. Self-organized criticality. Scientific American 46 (1991), 52–61.Google Scholar
[384]
Taberlet, P., Coissac, E., Hajibabaei, M., and Rieseberg, L. H. Environmental DNA. Molecular Ecology 21, 8 (2012), 1789–1793.Google Scholar
[385]
Thomsen, P. F., and Willerslev, E. Environmental DNA – an emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation 183 (2015), 4–18.Google Scholar
[386]
Pace, N. R. A molecular view of microbial diversity and the biosphere. Science 276, 5313 (1997), 734–740.Google Scholar
[387]
Bass, D., Stentiford, G. D., Littlewood, D. T. J., and Hartikainen, H. Diverse applications of environmental DNA methods in parasitology. Trends in Parasitology 31, 10 (2015), 499–513.Google Scholar
[388]
Bohmann, K., et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology and Evolution 29, 6 (2014), 358–367.Google Scholar
[389]
Kelly, R. P., et al. Harnessing DNA to improve environmental management. Science 344, 6191 (2014), 1455–1456.Google Scholar
[390]
Yoccoz, N. G. The future of environmental DNA in ecology. Molecular Ecology 21, 8 (2012), 2031–2038.Google Scholar
[391]
Ficetola, G. F., Miaud, C., Pompanon, F., and Taberlet, P. Species detection using environmental DNA from water samples. Biology Letters 4, 4 (2008), 423–425.Google Scholar
[392]
Jerde, C. L., Mahon, A. R., Chadderton, W. L., and Lodge, D. M. “Sight-unseen” detection of rare aquatic species using environmental DNA. Conservation Letters 4, 2 (2011), 150–157.Google Scholar
[393]
Dejean, T., Valentini, A., Miquel, C., Taberlet, P., Bellemain, E., and Miaud, C. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. Journal of Applied Ecology 49, 4 (2012), 953–959.Google Scholar
[394]
Mächler, E., Deiner, K., Steinmann, P., and Altermatt, F. Utility of environmental DNA for monitoring rare and indicator macroinvertebrate species. Freshwater Science 33, 4 (2014), 1174–1183.Google Scholar
[395]
Takahara, T., Minamoto, T., Yamanaka, H., Doi, H., and Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, 4 (2012), e35868.Google Scholar
[396]
Huver, J. R., Koprivnikar, J., Johnson, P. T. J., and Whyard, S. Development and application of an eDNA method to detect and quantify a pathogenic parasite in aquatic ecosystems. Ecological Applications 25, 4 (2015), 991–1002.Google Scholar
[397]
Olds, B., et al. Estimating species richness using environmental DNA. Ecology and Evolution 6, 12 (2016), 4214–4226.Google Scholar
[398]
Barnes, M., Turner, C., Jerde, C., Renshaw, M., Chadderton, W., and Lodge, D. Environmental conditions influence eDNA persistence in aquatic systems. Environmental Science and Technology 48, 3 (2014), 1819–1827.Google Scholar
[399]
Lance, R. F., et al. Experimental observations on the decay of environmental DNA from bighead and silver carps. Management of Biological Invasions 8, 3 (2017), 343–359.Google Scholar
[400]
Jerde, C. L., et al. Influence of stream bottom substrate on retention and transport of vertebrate environmental DNA. Environmental Science and Technology 50, 16 (2016), 8770–8779.Google Scholar
[401]
Shogren, A. J., et al. Controls on eDNA movement in streams: transport, retention, and resuspension. Scientific Reports 7 (2017), 5065.Google Scholar
[402]
Deiner, K., and Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, 2 (2014), e88786.Google Scholar
[403]
Wilcox, T. M., McKelvey, K. S., Young, M. K., Lowe, W. H., and Schwartz, M. K. Environmental DNA particle size distribution from brook trout (Salvelinus fontinalis). Conservation Genetics Resources 7, 3 (2015), 639–641.Google Scholar
[404]
Klymus, K., Richter, C., Chapman, D., and Paukert, C. Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix. Biological Conservation 183, 1 (2015), 77–84.Google Scholar
[405]
Bylemans, J., Furlan, E., Hardy, C., McGuffie, P., Lintermans, M., and Gleeson, D. An environmental DNA-based method for monitoring spawning activity: a case study, using the endangered macquarie perch (Macquaria australasica). Methods in Ecology and Evolution 8, 5 (2017), 646–655.Google Scholar
[406]
Sansom, B. J., and Sassoubre, L. M. Environmental DNA (eDNA) shedding and decay rates to model freshwater mussel eDNA transport in a river. Environmental Science and Technology 51, 24 (2017), 14244–14253.Google Scholar
[407]
Pfister, L., et al. The rivers are alive: on the potential for diatoms as a tracer of water source and hydrological connectivity. Hydrological Processes 23, 19 (2009), 2841–2845.Google Scholar
[408]
Pilgrim, D. Isochrones of travel time and distribution of flood storage from a tracer study on a small watershed. Water Resources Research 13, 3 (1977), 587–595.Google Scholar
[409]
Burkhardt-Holm, P., et al. Where have all the fish gone? Environmental Science and Technology 39, 21 (2005), 441A–447A.Google Scholar
[410]
Okamura, B., Hartikainen, H., Schmidt-Posthaus, H., and Wahli, T. Life cycle complexity, environmental change and the emerging status of salmonid proliferative kidney disease. Freshwater Biology 56, 4 (2011), 735–753.Google Scholar
[412]
Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning. Springer, 2001.Google Scholar
[413]
Roberts, G., and Rosenthal, J. Examples of adaptive MCMC. Journal of Computational and Graphical Statistics 18, 2 (2009), 349–367.Google Scholar
[414]
Tops, S., and Okamura, B. Infection of bryozoans by Tetracapsuloides bryosalmonae at sites endemic for salmonid proliferative kidney disease. Diseases of Aquatic Organisms 57, 3 (2003), 221–226.Google Scholar
[415]
Heggenes, J., Bagliniare, J. L., and Cunjak, R. A. Spatial niche variability for young Atlantic salmon (Salmo salar) and brown trout (S. trutta) in heterogeneous streams. Ecology of Freshwater Fish 8, 1 (1999), 1–21.Google Scholar
[416]
Carraro, L., Mari, L., Gatto, M., Rinaldo, A., and Bertuzzo, E. Spread of proliferative kidney disease in fish along stream networks: a spatial metacommunity framework. Freshwater Biology 63 (2018), 114–127.Google Scholar
[417]
Anderson, M., and May, R. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, 2008.Google Scholar
[418]
Morens, D., Folkers, G., and Fauci, A. S. The challenge of emerging and re-emerging infectious diseases. Nature 430 (2004), 242–249.Google Scholar
[419]
World Health Organization. Fact sheet no. 115: schistosomiasis. Tech. rep., 2014.Google Scholar
[420]
World Health Organization. Preventing diarrhoea through better water, sanitation and hygiene. Tech. rep., 2014.Google Scholar
[422]
Jones, K. E., et al. Global trends in emerging infectious diseases. Nature 451 (2008), 990–994.Google Scholar
[423]
Daszak, P., Cunningham, A. A., and Hyatt, A. D. Emerging infectious diseases of wildlife – threats to biodiversity and human health. Science 287 (2000), 443–449.Google Scholar
[424]
Heesterbeek, J., and Roberts, M. Mathematical models for microparasites of wildlife. In Ecology of Infectious Diseases in Natural Populations, Grenfell, B. and Dobson, A. P., eds., Cambridge University Press, pp. 90–122.Google Scholar
[425]
Pacini, F. Osservazioni microscopiche e deduzioni patologiche sul cholera asiatico. Gazzetta Medica Italiana Federativa Toscana 4 (1854), 397–401, 405– 4 12.Google Scholar
[426]
Rebaudet, S., Gazin, P., Barrais, R., Moore, S., Rossignol, E., Barthelemy, N., Gaudart, J., Boncy, J., Magloire, R., and Piarroux, R. The dry season in Haiti: a window of opportunity to eliminate cholera. PLoS Currents Outbreaks 1 (2013).Google Scholar
[427]
Gaudart, J., et al. Spatio-temporal dynamics of cholera during the first year of the epidemic in Haiti. PLoS Neglected Tropical Diseases 7 (2013), e2145.Google Scholar
[428]
Rinaldo, A., et al. Reassessment of the 2010–2011 Haiti cholera outbreak and rainfall-driven multiseason projections. Proceedings of the National Academy of Sciences of the USA 109 (2012), 6602–6607.Google Scholar
[429]
Eisenberg, M. C., Kujbida, G., Tuite, A. R., Fisman, D. N., and Tien, J. H. Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches. Epidemics 5 (2013), 197–207.Google Scholar
[430]
Anderson, R. M., and May, R. M. Regulation and stability of host–parasite population interactions: I. Regulatory processes. Journal of Animal Ecology 47 (1978), 219–2 47.Google Scholar
[431]
Hudson, P., Dobson, A., and Newborn, D. Regulation and stability of a free-living host-parasite system: Trichostrongylus tenuis in red grouse. I. Monitoring and parasite reduction experiments. Journal of Animal Ecology 61 (1992), 477–486.Google Scholar
[432]
Dobson, A., and Hudson, P. J. Regulation and stability of a free-living host-parasite system, Trichostrongylus tenuis in red grouse. II: Population models. Journal of Animal Ecology 61 (1992), 487–498.Google Scholar
[433]
Capasso, V., and Paveri-Fontana, S. A mathematical model for the 1973 cholera epidemic in the European Mediterranean region. Revue d’Epidemiologie et de Sante Publique 27 (1979), 121–132.Google Scholar
[434]
Codeço, C. Endemic and epidemic dynamics of cholera: the role of the aquatic reservoir. GBMC Infectious Diseases 1 (2001), 1–12.Google Scholar
[435]
Mari, L., Bertuzzo, E., Finger, F., Casagrandi, R., Gatto, M., and Rinaldo, A. On the predictive ability of mechanistic models for the Haitian cholera epidemic. Journal of the Royal Society Interface 12 (2015), 20140840.Google Scholar
[436]
Mukandavire, Z., Liao, S., Wang, J., Gaff, H., Smith, D. L., and Morris, J. G. Estimating the reproductive numbers for the 2008–2009 cholera outbreaks in Zimbabwe. Proceedings of the National Academy of Sciences of the USA 108, 21 (2011), 8767–8772.Google Scholar
[437]
Gatto, M., et al. Generalized reproduction numbers and the prediction of patterns in waterborne disease. Proceedings of the National Academy of Sciences of the USA 48 (2012), 19703–19708.Google Scholar
[438]
Mari, L., Bertuzzo, E., Righetto, L., Casagrandi, R., Gatto, M., Rodriguez-Iturbe, I., and Rinaldo, A. Modelling cholera epidemics: the role of waterways, human mobility and sanitation. Journal of the Royal Society Interface 9 (2012), 376–388.Google Scholar
[439]
Watts, D., and Strogatz, S. Collective dynamics of small-world networks. Nature 393 (1998), 440–442.Google Scholar
[440]
Pasetto, D., Finger, F., Rinaldo, A., and Bertuzzo, E. Real-time projections of cholera outbreaks through data assimilation and rainfall forecasting. Advances in Water Resources 37 (2017), e1006127.Google Scholar
[441]
Lemaitre, J., Pasetto, D., Perez-Saez, J., Sciarra, C., Wamala, J., and Rinaldo, A. Rainfall as a driver of epidemic cholera: comparative model assessments of the effect of intra-seasonal precipitation events. Acta Tropica XX (2019), 235–243.Google Scholar
[442]
Diekmann, O., Heesterbeek, J. A. P., and Roberts, M. G. The construction of next-generation matrices for compartmental epidemic models. Journal of the Royal Society Interface 7 (2010), 873–885.Google Scholar
[443]
Roberts, M. G., and Heesterbeek, J. A new method for estimating the effort required to control an infectious disease. Proceedings of the Royal Society B 270 (2003), 1359–1364.Google Scholar
[444]
Lopez, L. F., Coutinho, F. A. B., Burattini, M. N., and Massad, E. Threshold conditions for infection persistence in complex host–vectors interactions. CR Biologies 325 (2002), 1073–1084.Google Scholar
[445]
Diekmann, O., Heesterbeek, J., and Metz, J. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology 28, 4 (1990), 365–382.Google Scholar
[446]
Bertuzzo, E., Casagrandi, R., Gatto, M., Rodriguez-Iturbe, I., and Rinaldo, A. On spatially explicit models of cholera epidemics. Journal of the Royal Society Interface 7, 43 (2010), 321–333.Google Scholar
[447]
Mari, L., Casagrandi, R., Bertuzzo, E., Rinaldo, A., and Gatto, M. Floquet theory for seasonal environmental forcing of spatially explicit waterborne epidemics. Theoretical Ecology 7, 4 (2014), 351–365.Google Scholar
[448]
Neubert, M., and Caswell, H. Alternatives to resilience for measuring the responses of ecological systems to perturbations. Ecology 78 (1997), 653–665.Google Scholar
[449]
Bittanti, S., and Colaneri, P. Periodic Systems, Filtering and Control. Springer, 2008.Google Scholar
[450]
Bacaër, N. Approximation of the basic reproduction number R0 for vector-borne diseases with a periodic vector population. Bulletin of Mathematical Biology 69 (2007), 1067–1091.Google Scholar
[451]
Gatto, M., et al. Spatially explicit conditions for waterborne pathogen invasion. American Naturalist 182 (2013), 328–346.Google Scholar
[452]
Mari, L., Casagrandi, R., Rinaldo, A., and Gatto, M. Epidemicity thresholds for water-borne and water-related diseases. Journal of Theoretical Biology 447 (2018), 126–138.Google Scholar
[453]
Mari, L., Casagrandi, R., Bertuzzo, E., Rinaldo, A., and Gatto, M. Conditions for transient epidemics of waterborne disease in spatially explicit systems. Royal Society Open Science 6 (2019), 181517.Google Scholar
[454]
Caswell, H., and Neubert, M. Reactivity and transient dynamics of discrete-time ecological systems. Journal of Difference Equations and Applications 2 (2005), 295–310.Google Scholar
[455]
Heffernan, J., Smith, R., and Wahl, L. Perspectives on the basic reproductive ratio. Journal of the Royal Society Interface 2 (2005), 281–293.Google Scholar
[456]
Hastings, A. Timescales, dynamics, and ecological understanding. Ecology 91 (2010), 3471–3480.Google Scholar
[457]
Neubert, M., Klanjscek, T., and Caswell, H. Reactivity and transient dynamics of predator-prey and food web models. Ecological Modelling 139 (2004), 29–38.Google Scholar
[458]
Neubert, M., Caswell, H., and Murray, J. Transient dynamics and pattern formation: reactivity is necessary for Turing instabilities. Mathematical Biosciences 175, 8 (2002), 1–11.Google Scholar
[459]
Hastings, A. Transients: the key to long-term ecological understanding? Trends in Ecology and Evolution 19 (2004), 39–45.Google Scholar
[460]
Hosack, G., Rossignol, P., and van den Driessche, P. The control of vector-borne disease epidemics. Journal of Theoretical Biology 255 (2008), 16–25.Google Scholar
[461]
Tang, S., and Allesina, S. Reactivity and stability of large ecosystems. Frontiers in Ecology and Evolution 2 (2014), 21–35.Google Scholar
[462]
Suweis, S., Grilli, J., Banavar, J., Allesina, S., and Maritan, A. Effect of localization on the stability of mutualistic ecological networks. Nature Communications 6 (2015), 10179.Google Scholar
[463]
Mari, L., Casagrandi, R., Rinaldo, A., and Gatto, M. A generalized definition of reactivity for ecological systems and the problem of transient species dynamics. Methods in Ecology and Evolution 8, 11 (2017), 1574–1584.Google Scholar
[464]
Tien, J., Shuai, Z. S., Eisenberg, M. C., and van den Driessche, P. Disease invasion on community networks with environmental pathogen movement. Journal of Mathematical Biology 70 (2015), 1065–1092.Google Scholar
[465]
Frerichs, R. R., Keim, P. S., Barrais, R., and Piarroux, R. Nepalese origin of cholera epidemic in Haiti. Clinical Microbiology and Infection 18, 6 (2012), 158–163.Google Scholar
[466]
Pan American Health Organization. Haiti cholera outbreak data. Tech. rep., 2011.Google Scholar
[467]
Tuite, A., Tien, J., Eisenberg, M., Earns, D. J. D., Ma, J., and Fisman, D. N. Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions. Annals of Internal Medicine 154 (2011), 593–601.Google Scholar
[468]
Chao, D. L., Halloran, M. E., and Longini, I. M. Vaccination strategies for epidemic cholera in Haiti with implications for the developing world. Proceedings of the National Academy of Sciences of the USA 108 (2011), 7081–7085.Google Scholar
[469]
Andrews, J. R., and Basu, S. Transmission dynamics and control of cholera in Haiti: an epidemic model. Lancet 377 (2011), 1248–1252.Google Scholar
[470]
Clark, J. A new method for estimating the effort required to control an infectious disease. Science 293 (2001), 657–660.Google Scholar
[471]
Azman, A., and Lessler, J. Reactive vaccination in the presence of disease hotspots. Proceedings of the Royal Society Series B 282, 1 (2014), 1– 1 3.Google Scholar
[472]
Scobie, H. M., et al. Safe water, sanitation, hygiene, and a cholera vaccine. Lancet 387, 1 (2016), 28–29.Google Scholar
[473]
Rebaudet, S., Sudre, B., Faucher, B., and Piarroux, R. Environmental determinants of cholera outbreaks in inland Africa: a systematic review of main transmission foci and propagation routes. Journal of Infectious Diseases 208 (2013), S46–54.Google Scholar
[474]
Centers for Disease Control and Prevention. Defeating cholera: clinical presentation and management for Haiti cholera outbreak. Tech. rep., 2010.Google Scholar
[475]
Dunkle, S., et al. Epidemic cholera in a crowded urban environment, Port-au-Prince, Haiti. Emerging Infectious Diseases 17 (2011), 2143–2146.Google Scholar
[476]
Chin, C., et al. The origin of the Haitian cholera outbreak strain. New England Journal of Medicine 364 (2011), 33–42.Google Scholar
[477]
Colwell, R. R. Global climate and infectious disease: the cholera paradigm. Science 274 (1996), 2025–2031.Google Scholar
[478]
King, A., Ionides, E., Pascual, M., and Bouma, M. Inapparent infections and cholera dynamics. Nature 454 (2008), 877–880.Google Scholar
[479]
Weil, A., et al. Frequency of reexposure to Vibrio cholerae O1 evaluated by subsequent vibriocidal titer rise after an episode of severe cholera in a highly endemic area in Bangladesh. American Journal of Tropical Medicine and Hygiene 87 (2012), 921–926.Google Scholar
[480]
Piarroux, R., et al. Understanding the cholera epidemic, Haiti. Emerging Infectious Diseases 17 (2011), 1161–1168.Google Scholar
[481]
Eubank, S., et al. Modelling disease outbreaks in realistic urban social networks. Nature 180–184 (2004), 429.Google Scholar
[482]
Riley, S. Large-scale spatial-transmission models of infectious disease. Science 316 (2007), 1298–1301.Google Scholar
[483]
Bengtsson, L., et al. Using mobile phone data to predict the spatial spread of cholera. Scientific Reports 5 (2015), 8923.Google Scholar
[484]
Koelle, K., Rodó, X., Pascual, M., Yunus, M., and Mostafa, G. Refractory periods and climate forcing in cholera dynamics. Nature 436 (2005), 696–700.Google Scholar
[485]
Colizza, V., Barrat, A., Barthèlemy, M., and Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the USA 103 (2006), 2015–2020.Google Scholar
[486]
Longini, I. M. Jr. A mathematical model for predicting the geographic spread of new infectious agents. Mathematical Biosciences 90 (1988), 367–383.Google Scholar
[487]
Eggo, R. M., Cauchemez, S., and Ferguson, N. M. Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. Journal of the Royal Society Interface 55 (2010), 233–243.Google Scholar
[488]
Thomas, R. Reproduction rates in multiregion modeling systems for HIV/AIDS. Journal of Regional Science 39 (1999), 359–385.Google Scholar
[489]
Ferrari, M. J., et al. The dynamics of measles in sub-Saharan Africa. Nature 451 (2008), 679–684.Google Scholar
[490]
Merrell, D., et al. Host-induced epidemic spread of the cholera bacterium. Nature 417 (2002), 642–645.Google Scholar
[491]
Alam, A., et al. Hyperinfecttivity of human-passaged V. cholerae can be modeled by growth in the infant mouse. Infection and Immunity 73 (2005), 6674–6679.Google Scholar
[492]
Hartley, D., Morris, J., and Smith, D. Hyperinfectivity: a critical element of Vibrio cholerae to cause epidemics? PLoS Medicine 3 (2006), 63–69.Google Scholar
[493]
Pascual, M., Koelle, K., and Dobson, A. P. Hyperinfectivity in cholera: a new mechanism for an old epidemiological model? PLoS Medicine 3 (2006), 931–9 3 8.Google Scholar
[494]
Bertuzzo, E., Finger, F., Mari, L., Gatto, M., and Rinaldo, A. On the probability of extinction of the Haiti cholera epidemic. Stochastic Environmental Research and Risk Assessment 30 (2016), 2043–2055.Google Scholar
[495]
Pasetto, D., et al. Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew. PLoS Computational Biology 14 (2018), e1006127.Google Scholar
[496]
Vrugt, J., and Robinson, B. Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences of the USA 104 (2007), 708–711.Google Scholar
[497]
ter Braak, C., and Vrugt, J. Differential evolution Markov chain with snooker updater and fewer chains. Statistics and Computing 18 (2008), 435–446.Google Scholar
[498]
Pascual, M., Bouma, M., and Dobson, A. Predicting endemic cholera: the role of climate variability and disease dynamics. Climate Research 36 (2008), 131–140.Google Scholar
[499]
Ali, M., et al. Natural cholera infection-derived immunity in an endemic setting. Journal of Infectious Diseases 204 (2011), 912–918.Google Scholar
[500]
Cash, R., et al. Response of man to infection with V. cholerae 1. Clinical, serologic, and bacteriological response to a known inoculum. Journal of Infectious Diseases 129 (1974), 45–52.Google Scholar
[501]
Longini, I. M. Jr., Yunus, M., Zaman, K., Siddique, A. K., Sack, R. B., and Nizam, A. Epidemic and endemic cholera trends over a 33-year period in Bangladesh. Journal of Infectious Diseases 186 (2002), 246–251.Google Scholar
[503]
Harris, J., et al. Blood group, immunity, and risk of infection with Vibrio cholerae in an area of endemicity. Infection and Immunity 73 (2005), 7422–7427.Google Scholar
[504]
Sack, D., Sack, R., Nair, G., and Siddique, A. K. Cholera. Lancet 377 (2004), 223–233.Google Scholar
[505]
Ivers, L., et al. Five complementary interventions to slow cholera: Haiti. Lancet 376 (2010), 2048–2051.Google Scholar
[506]
Farmer, P. Meeting cholera’s challenge to Haiti and the world: a joint statement on cholera prevention and care. PLoS Neglected Tropical Diseases 5 (2011), e1145.Google Scholar
[507]
Chaignat, C., et al. Cholera in disasters: do vaccines prompt new hopes? Expert Review of Vaccines 7 (2008), 431–435.Google Scholar
[509]
World Health Organization. Fact sheet n. 107 August 2011. Tech. rep., Regional Office for the Eastern Mediterranean, 2014.Google Scholar
[510]
Nelson, E., Harris, J., Morris, J., Calderwood, S., and Camilli, A. Cholera transmission: the host, pathogen and bacteriophage dynamic. Nature Reviews Microbiology 7 (2009), 693– 7 02.Google Scholar
[511]
Harris, J., et al. Immunologic responses to V. cholerae in patients co-infected with intestinal parasites in Bangladesh. PLoS Neglected Tropical Diseases 3 (2009), e403.Google Scholar
[512]
Mwansa, J., et al. Multiply antibiotic-resistant Vibrio cholerae O1 biotype El Tor strains emerge during cholera outbreaks in zambia. Epidemiology & Infection 135 (2007), 847–853.Google Scholar
[513]
Kitaoka, M., Miyata, S. T., Unterweger, D., and Pukatzki, S. Antibiotic resistance mechanisms of Vibrio cholerae micro-epidemiology of urinary schistosomiasis in Zanzibar: local risk factors associated with distribution of infections among schoolchildren and relevance for control. Journal of Medical Microbiology 60 (2011), 397–407.Google Scholar
[514]
Beaber, J., Hochhut, B., and Waldor, M. SOS response promotes horizontal dissemination of antibiotic resistance genes. Nature 427 (2004), 72–74.Google Scholar
[515]
Guerin, E., et al. The SOS response controls integron recombination. Science 324 (2009), 1034–1035.Google Scholar
[516]
Meibom, K., Blokesch, M., Dolganov, N., Wu, C., and Schoolnik, G. Chitin induces natural competence in V. cholerae. Science 310 (2005), 1824–1827.Google Scholar
[517]
Suckow, G., Seitz, P., and Blokesch, M. Quorum sensing contributes to natural transformation of Vibrio cholerae in a species-specific manner. Journal of Bacteriology 193 (2011), 4914–4924.Google Scholar
[518]
Borgeaud, S., Metzger, L., Scrignani, T., and Blokesch, M. The type VI secretion system of V. cholerae fosters horizontal gene transfer. Science 347 (2015), 63–67.Google Scholar
[519]
Kühn, J., et al. Glucose- but not rice-based oral rehydration therapy enhances the production of virulence determinants in the human pathogen V. cholerae. PLoS Neglected Tropical Diseases 8 (2014), e3347.Google Scholar
[520]
Finger, F., et al. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. Proceedings of the National Academy of Sciences of the USA 113 (2016), 6421–6426.Google Scholar
[521]
Diekmann, O., and Heesterbeek, J. A. P. Mathematical Epidemiology of Infectious Diseases. Wiley, 2000Google Scholar
[522]
Barzilay, E., et al. Cholera surveillance during the Haiti epidemic: the first 2 years. New England Journal of Medicine 368 (2013), 599–609.Google Scholar
[523]
Lipp, E., Huq, A., and Colwell, R. Effects of global climate on infectious disease: the cholera model. Clinical Microbiology Reviews 15 (2002), 757–762.Google Scholar
[524]
Islam, M., et al Role of cyanobacteria in the persistence of Vibrio cholerae O139 in saline microcosms. Canadian Journal of Microbiology 50 (2004), 127–131.Google Scholar
[525]
Hill, V., et al. Toxigenic Vibrio cholerae O1 in water and seafood, Haiti. Emerging Infectious Diseases 17 (2011), 2147–2150.Google Scholar
[526]
Azaele, S., Maritan, A., Bertuzzo, E., Rodriguez-Iturbe, I., and Rinaldo, A. Stochastic dynamics of cholera epidemics. Physical Review E 81, 5 (2010), 051901.Google Scholar
[527]
Angulo, J., Yu, H., Langousis, A., Madrid, A., and Christakos, G. Modeling of space-time infectious disease spread under conditions of uncertainty. International Journal of Geographical Information Science 26 (2012), 1751–1772.Google Scholar
[528]
Angulo, J., et al. Spatiotemporal infectious disease modeling: a BME-SIR approach. PLoS ONE 8 (2013), e72168.Google Scholar
[529]
Vrugt, J., et al. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation 10 (2009), 271–288.Google Scholar
[530]
World Health Organization. Fact sheet no. 115: prevention and control of cholera outbreaks: WHO policy and recommendations. Tech. rep., 2010.Google Scholar
[531]
Gillespie, D. Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81 (1977), 2340–2361.Google Scholar
[532]
Zaidi, A. Make plans to eliminate cholera outbreaks. Nature 550 (2017), 28980651.Google Scholar
[533]
Simini, F., González, M. C., Maritan, A., and Barabási, A. L. A universal model for mobility and migration patterns. Nature 484 (2012), 96–100.Google Scholar
[534]
Candia, J., et al. Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical 41 (2008), 224015.Google Scholar
[535]
Lu, X., Bengtsson, L., and Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences of the USA 109 (2012), 11576–11581.Google Scholar
[536]
Perkins, T., et al. Theory and data for simulating fine-scale human movement in an urban environment. Journal of the Royal Society Interface 11 (2014), 20140642.Google Scholar
[537]
Lu, X., Wetter, E., Bharti, N., Tatem, A. J., and Bengtsson, L. Approaching the limit of predictability in human mobility. Scientific Reports 3 (2013), 2923.Google Scholar
[538]
Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., and Buckee, C. Quantifying the impact of human mobility on malaria. Science 6104 (2012), 267–270.Google Scholar
[539]
Wesolowski, A., et al. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Proceedings of the National Academy of Sciences of the USA 112 (2015), 11114–11119.Google Scholar
[540]
Mari, L., et al. Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis. Scientific Reports 7, 1 (2017), 489.Google Scholar
[541]
Panigutti, C., Tizzoni, M., Bajardi, P., Smoreda, Z., and Colizza, V. Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models. Royal Society Open Science 4 (2017), 160950.Google Scholar
[542]
Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., and Buckee, C. O. The impact of biases in mobile phone ownership on estimates of human mobility. Journal of the Royal Society Interface 10, 81 (2013), 20120986.Google Scholar
[543]
Gryseels, B., Polman, K., Clerinx, J., and Kestens, L. Human schistosomiasis. Lancet 368, 9541 (2006), 1106–1118.Google Scholar
[544]
Garba, A., Touré, S., Dembelé, R., Bosque-Oliva, E., and Fenwick, A. Implementation of national schistosomiasis control programmes in West Africa. Trends in Parasitology 22, 7 (2006), 322–6.Google Scholar
[545]
Poda, J.-N., et al. Profil parasitologique de la schistosomose urinaire du complexe hydroagricole du Sourou au Burkina Faso. Société de pathologie exotique 94, 1 (2001), 21–24.Google Scholar
[546]
Poda, J., Traoré, A., and Sondo, B. L’endémie bilharzienne au Burkina Faso. Société de Pathologie Exotique 97, 1 (2004), 47–52.Google Scholar
[547]
Koukounari, A., et al. Schistosoma haematobium infection and morbidity before and after large-scale administration of praziquantel in Burkina Faso. Journal of Infectious Diseases 196, 5 (2007), 659–669.Google Scholar
[548]
Anonymous, . Rapport du suivi-évaluation de vingt deux (22) sites sentinelles pour le contrôle de la schistosomiase et les vers intestinaux. Tech. rep., Programme National de Lutte Contre les Maladies Tropicales Négligées, Ministère de la Santé du Burkina Faso, Ouagadougou (BF), 2013. Unpublished data.Google Scholar
[549]
Bagayan, M., et al. Evolution recente de la schistosomiase au Burkina Faso: cas de 11 regions sanitaires. Unpublished data.Google Scholar
[550]
Gurarie, D., and Seto, E. Y. W. Connectivity sustains disease transmission in environments with low potential for endemicity: modelling schistosomiasis with hydrologic and social connectivities. Journal of the Royal Society Interface 6 (2009), 495–508.Google Scholar
[551]
Bella, H., de C. Marshall, T., Omer, , A, ., and Vaughan, J. Migrant workers and schistosomiasis in the Gezira, Sudan. Transactions of the Royal Society of Tropical Medicine and Hygiene 74, 1 (1980), 36–39.Google Scholar
[552]
Appleton, C., Ngxongo, S., Braack, L., and Le Sueur, D. Schistosoma mansoni in migrants entering South Africa from Moçambique a threat to public health in north-eastern KwaZulu-Natal? South African Medical Journal 86, 4 (1996), 350–353.Google Scholar
[553]
Cetron, M. S., et al. Schistosomiasis in Lake Malawi. Lancet 348, 9037 (1996), 1274–1278.Google Scholar
[554]
Bruun, B., and Aagaard-Hansen, J. The social context of schistosomiasis and its control: an introduction and annotated bibliography. Tech. rep., World Health Organization.Google Scholar
[555]
Kloos, H., Correa-Oliveira, R., dos Reis, D. C., Rodrigues, E. W., Monteiro, L. A. S., and Gazzinelli, A. The role of population movement in the epidemiology and control of schistosomiasis in Brazil: a preliminary typology of population movement. Memórias do Instituto Oswaldo Cruz 105, 4 (2010), 578–586.Google Scholar
[556]
Criscione, C. D., et al. Landscape genetics reveals focal transmission of a human macroparasite. PLoS Neglected Tropical Diseases 4, 4 (2010), e665.Google Scholar
[557]
Macdonald, G. The dynamics of helminth infections, with special reference to schistosomes. Transactions of the Royal Society of Tropical Medicine and Hygiene 59 (1965), 489–506.Google Scholar
[558]
May, R. M. and Anderson, R. Population biology of infectious diseases: Part II. Nature 280 (1979), 455–461.Google Scholar
[559]
Barbour, A. Macdonald’s model and the transmission of bilharzia. Transactions of the Royal Society of Tropical Medicine and Hygiene 72, 1 (1978), 1–6.Google Scholar
[560]
Woolhouse, M. On the application of mathematical models of schistosome transmission dynamics. I. Natural transmission. Acta Tropica 49, 4 (1991), 241–270.Google Scholar
[561]
Hu, H., Gong, P., and Xu, B. Spatially explicit agent-based modelling for schistosomiasis transmission: human-environment interaction simulation and control strategy assessment. Epidemics 2, 2 (2010), 49–65.Google Scholar
[562]
Rollinson, D., et al. Time to set the agenda for schistosomiasis elimination. Acta Tropica 128, 2 (2013), 423–440.Google Scholar
[563]
Basáñez, , et al. A research agenda for helminth diseases of humans: modelling for control and elimination. PLoS Neglected Tropical Diseases 6, 4 (2012), e1548.Google Scholar
[564]
Colley, D., Bustinduy, A., Secor, W., and King, C. Human schistosomiasis. Lancet 383 (2014), 2253–2264.Google Scholar
[565]
Sokolow, S., Lafferty, K., and Kuris, A. Regulation of laboratory populations of snails Biomphalaria and Bulinus spp. by river prawns, Macrobrachium spp. (Decapoda, Palaemonidae): Implications for control of schistosomiasis. Acta Tropica 132 (2014), 64–74.Google Scholar
[566]
Swartz, S., De Leo, G., Wood, C., and Sokolow, S. Infection with schistosome parasites in snails leads to increased predation by prawns: implications for human schistosomiasis control. Journal of Experimental Biology 218 (2015), 3962–3967.Google Scholar
[567]
Perez-Saez, J., Mande, T., Ceperley, N., Bertuzzo, E., Mari, L., Gatto, M., and Rinaldo, A. Hydrology and density feedbacks control the ecology of the intermediate hosts of schistosomiasis across habitats in seasonal climates. Proceedings of the National Academy of Sciences of the USA 113 (2016), 6427–6432.Google Scholar
[568]
Sokolow, S., et al. Reduced transmission of human schistosomiasis after restoration of a native river prawn that preys on the snail intermediate host. Proceedings of the National Academy of Sciences of the USA 112 (2015), 9650–9655.Google Scholar
[569]
Grimes, J., Croll, D., Harrison, W., Utzinger, J., Freeman, M., and Templeton, M. The roles of water, sanitation and hygiene in reducing schistosomiasis: a review. Parasites & Vectors 8 (2015), 156.Google Scholar
[570]
Sokolow, S., et al. Global assessment of schistosomiasis control over the past century shows targeting the snail intermediate host works best. PLoS Neglected Tropical Diseases 10 (2016), e0004794.Google Scholar
[571]
Spear, R. Internal versus external determinants of Schistosoma japonicum transmission in irrigated agricultural villages. Journal of the Royal Society Interface 9 (2012), 272–282.Google Scholar
[572]
Mari, L., Ciddio, M., Casagrandi, R., Perez-Saez, J., Bertuzzo, E., Rinaldo, A., Sokolow, S., De Leo, G., and Gatto, M. Heterogeneity in schistosomiasis transmission dynamics. Journal of Theoretical Biology 432 (2017), 87–99.Google Scholar
[573]
Remais, J. V., Zhong, B., Carlton, E., and Spear, R. Model approaches for estimating the influence of time varying socio-environmental factors on macroparasite transmission in two endemic regions. Epidemics 1 (2009), 213–220.Google Scholar
[574]
Sivakumar, M., and Faustin, G. Agroclimatology of West Africa: Burkina Faso. Information Bulletin 23. International Crops Research Institute for the Semi-Arid Tropics, 1987.Google Scholar
[575]
Poda, J.-N. Distribution spatiale des hôtes intermediaires des schistosomes au Burkina Faso: Facteurs influençant la dynamique des populations de Bulinus truncatus rohlfsi Classin, 1886 et de Bulinus senegalensis Muller, 1781. Ph.D. thesis,Google Scholar
[576]
Poda, J.-N., Sellin, B., Sawadogo, L., and Sanogo, S. Distribution spatiale des mollusques hôtes intermédiaires potentiels des schistosomes et de leurs biotopes au Burkina Faso. OCCGE INFO 101 (1994), 12–19.Google Scholar
[577]
Poda, J.-N., et al. Les schistosomoses au complexe hydroagricole du Sourou au Burkina Faso: situation et modèle de transmission. OCCGE INFO (2006), www.sifee.org/static/uploaded/Files/ressources/actes-des-colloques/bamako/session-2/A_Poda_etal_comm.pdf.Google Scholar
[578]
Zongo, D., Kabré, B., Poda, J.-N., and Dianou, D. Schistosomiasis among farmers and fisherman in the west part of Burkina Faso (west africa). Journal of Biological Sciences 8, 2 (2008), 482–485.Google Scholar
[579]
Zongo, D., Kabre, B. G., Dayeri, D., Savadogo, B., and Poda, J. N. Comparative study of schistosomiasis transmission (urinary and intestinal forms) at ten sites in Burkina Faso (in sub-Saharan Africa). Médecine et Santé Tropicales 22, 3 (2012), 323–9.Google Scholar
[580]
Kpoda, N. W., Sorgho, H., Poda, J.-N., Ouédraogo, J. B., and Kabré, G. B. Endémie bilharzienne à Schistosoma mansoni à la vallée du Kou : caractérisation du système de transmission et impact socioéconomique. Comptes Rendus Biologies 336, 5 (2013), 284–288.Google Scholar
[581]
Cecchi, P. Les petits barrages au Burkina Faso : un vecteur du changement social et de mutations des réalités rurales. Tech. rep., Small Reservoirs Project.Google Scholar
[582]
Kloos, H. Water resources development and schistosomiasis ecology in the Awash Valley, Ethiopia. Social Science & Medicine 20, 6 (1985), 609–625.Google Scholar
[583]
Hunter, J. M. Inherited burden of disease: agricultural dams and the persistence of bloody urine (Schistosomiasis hematobium) in the Upper East Region of Ghana, 1959–1997. Social Science & Medicine 56, 2 (2003), 219–234.Google Scholar
[584]
Poda, J.-N., Sondo, B., and Parent, G. Influence des hydro-aménagements sur la distribution des bilharzioses et de leurs hôtes intermédiaires au Burkina Faso. Cahiers d’Études et de Recherches Francophones / Santé 13 , 1 (2003), 49–53.Google Scholar
[585]
Boelee, E., and Madsen, H. Irrigation and schistosomiasis in Africa: ecological aspects. Tech. rep., International Water Management Institute, Colombo, Sri Lanka, 2006. (IWMI Research Report 99).Google Scholar
[586]
Steinmann, P., Keiser, J., Bos, R., Tanner, M., and Utzinger, J. Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infectious Diseases 6, 7 (2006), 411–425.Google Scholar
[587]
Barbier, B., Yacouba, H., Maïga, A. H., Mahé, G., and Paturel, J.-E. Le retour des grands investissements hydrauliques en Afrique de l’Ouest: les perspectives et les enjeux. Géocarrefour 1-2 (2009), 31–41.Google Scholar
[588]
Cecchi, P., Meunier-Nikiema, A., Moiroux, N., Sanou, B., and Bougaire, F. Why an atlas of lakes and reservoirs in Burkina Faso? Tech. rep. III, Small Reservoirs Project. 1–20 pages.Google Scholar
[589]
Utzinger, J., N’Goran, E. K., Caffrey, C. R., and Keiser, J. From innovation to application: social–ecological context, diagnostics, drugs and integrated control of schistosomiasis. Acta Tropica 120 (2011), S121–S137.Google Scholar
[590]
Südmeier-Rieux, K., Masundire, H., Rizvi, A., and Rietbergen, R. Ecosystems, Livelihoods and Disasters: An Integrated Approach to Disaster Risk Management. IUCN, 2006.Google Scholar
[591]
Abdelhak, S., Sulaiman, J., and Mohd, S. The missing link in understanding and assessing vulnerability to poverty: a conceptual framework. Trends in Applied Sciences Research 7, 4 (2012), 256–272.Google Scholar
[592]
Perez-Saez, , et al. A theoretical analysis of the geography of schistosomiasis in Burkina Faso highlights the roles of human mobility and water resources development in disease transmission. PLoS Neglected Tropical Diseases 9 (2015), e0004127.Google Scholar
[593]
Feng, Z., Eppert, A., Milner, F. A., and Minchella, D. J. Estimation of parameters governing the transmission dynamics of schistosomes. Applied Mathematics Letters 17, 10 (2004), 1105–1112.Google Scholar
[594]
Phillips, S. J., Anderson, R. P., and Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 3 (2006), 231–259.Google Scholar
[595]
Stensgaard, A., et al. Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter? Acta Tropica 128 (2013), 378–390.Google Scholar
[596]
Palchykov, V., Mitrović, M., Jo, H.-H., Saramäki, J., and Pan, R. K. Inferring human mobility using communication patterns. Scientific Reports 4 (2014), 6174.Google Scholar
[597]
Compaoré, G., and Kaboré, I. Gestion urbaine et environnement: l’exemple de Ouagadougou (Burkina Faso). Villes du Sud et environnement 3 (1997), 80–99.Google Scholar
[598]
Kêdowidé, C. M. G., Sedogo, M. P., and Cissé, G. Dynamique spatio temporelle de l’agriculture urbaine à Ouagadougou: Cas du Maraîchage comme une activité montante de stratégie de survie. VertigO 10, 2 (2010).Google Scholar
[599]
Ernould, J. C., Kaman, A., Labbo, R., Couret, D., and Chippaux, J. P. Recent urban growth and urinary schistosomiasis in Niamey, Niger. Tropical Medicine and International Health 5, 6 (2000), 431–437.Google Scholar
[600]
Anderson, R. M., Mercer, J. G., Wilson, R. A., and Carter, N. P. Transmission of Schistosoma mansoni from man to snail: experimental studies of miracidial survival and infectivity in relation to larval age, water temperature, host size and host age. Parasitology 85, 2 (1982), 339–360.Google Scholar
[601]
Rohani, P., Earn, D., and Grenfell, B. Opposite patterns of synchrony in sympatric disease metapopulations. Science 286 (1999), 968–971.Google Scholar
[602]
Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for avifaunas of Southwest Pacific islands. Proceedings of the National Academy of Sciences of the USA 69, 11 (1972), 3199–3203.Google Scholar
[603]
Fenwick, A., Rollinson, D., and Southgate, V. Implementation of human schistosomiasis control: challenges and prospects. Advances in Parasitology 61 (2006), 567–622.Google Scholar
[604]
Utzinger, J., and de Savigny, D. Control of neglected tropical diseases: integrated chemotherapy and beyond. PLoS Medicine 3, 5 (2006), e112.Google Scholar
[605]
Auger, P., Charles, S., Viala, M., and Poggiale, J.-C. Aggregation and emergence in ecological modelling: integration of ecological levels. Ecological Modelling 127, 1 (2000), 11–20.Google Scholar
[606]
Gurarie, D., and King, C. Heterogeneous model of schistosomiasis transmission and long-term control: the combined influence of spatial variation and age-dependent factors on optimal allocation of drug therapy. Parasitology 130 (2005), 49–65.Google Scholar
[607]
Gurarie, D., King, C. H., and Wang, X. A. A new approach to modelling schistosomiasis transmission based on stratified worm burden. Parasitology 137 (2010), 1951–1965.Google Scholar
[608]
Gurarie, D., and King, C. H. Population biology of Schistosoma mating, aggregation, and transmission breakpoints: more reliable model analysis for the end-game in communities at risk. PLoS ONE 9 (2014), e115875.Google Scholar
[609]
Gurarie, D., King, C. H., Yoon, N., and Li, E. Refined stratified-worm-burden models that incorporate specific biological features of human and snail hosts provide better estimates of Schistosoma diagnosis, transmission, and control. Parasites & Vectors 9 (2016), 428–431.Google Scholar
[610]
Ciddio, M., Mari, L., Sokolow, S., De Leo, G., Casagrandi, R., and Gatto, M. The spatial spread of schistosomiasis: a multidimensional network model applied to Saint-Louis region, Senegal. Advances in Water Resources 108 (2017), 406–415.Google Scholar
[611]
Clennon, J. A., King, C. H., Muchiri, E. M., and Kitron, U. Hydrological modelling of snail dispersal patterns in Msambweni, Kenya and potential resurgence of Schistosoma haematobium transmission. Parasitology 134, Pt 5 (2007), 683–93.Google Scholar
[612]
Grimes, J. E. T., Croll, D., Harrison, W. E., Utzinger, J., Freeman, M. C., and Templeton, M. R. The relationship between water, sanitation and schistosomiasis: a systematic review and meta-analysis. PLoS Neglected Tropical Diseases 8, 12 (2014), e3296.Google Scholar
[613]
Gurarie, D., King, C. H., Yoon, N., Alsallaq, R., and Wang, X. Seasonal dynamics of snail populations in coastal Kenya: model calibration and snail control. Advances in Water Resources 108 (2017), 397–405.Google Scholar
[614]
McCreesh, N., and Booth, M. Challenges in predicting the effects of climate change on Schistosoma mansoni and Schistosoma haematobium transmission potential. Trends in Parasitology 29 (2013), 548–555.Google Scholar
[615]
Ferguson, H., and Ball, H. Epidemiological aspects of proliferative kidney disease amongst rainbow trout Salmo gairdneri Richardson in Northern Ireland. Journal of Fish Diseases 2, 3 (1979), 219–225.Google Scholar
[616]
Clifton-Hadley, R. S., Richards, R. H., and Bucke, D. Proliferative kidney disease (PKD) in rainbow trout Salmo gairdneri: further observations on the effects of water temperature. Aquaculture 55, 3 (1986), 165–171.Google Scholar
[617]
Feist, S. W., and Longshaw, M. Phylum Myxozoa. In Fish Diseases and Disorders vol. 1, Woo, P. T. K. ed. CABI Publishing, Wallingford, UK, 230–296.Google Scholar
[618]
Borsuk, M. E., Reichert, P., Peter, A., Schager, E., and Burkhardt-Holm, P. Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network. Ecological Modelling 192, 1–2 (2006), 224–244.Google Scholar
[619]
Hari, R., Livingstone, D. M., Siber, R., Burkhardt-Holm, P., and Güttinger, H. Consequences of climatic change for water temperature and brown trout populations in alpine rivers and streams. Global Change Biology 12, 1 (2006), 10–26.Google Scholar
[620]
Hartikainen, H., and Okamura, B. Ecology and evolution of malacosporean-bryozoan interactions. In Myxozoan Evolution, Ecology and Development, Okamura, B., Gruhl, A., and Bartholomew, J.L., eds. Springer, 2015, pp. 201–216.Google Scholar
[621]
Carraro, L., et al. An epidemiological model for proliferative kidney disease in salmonid populations. Parasites & Vectors 9 (2016), 487.Google Scholar
[622]
Wahli, T., et al. Proliferative kidney disease in Switzerland: current state of knowledge. Journal of Fish Diseases 25, 8 (2002), 491–500.Google Scholar
[623]
Wahli, T., Bernet, D., Steiner, P. A., and Schmidt-Posthaus, H. Geographic distribution of Tetracapsuloides bryosalmonae infected fish in Swiss rivers: an update. Aquatic Sciences 69, 1 (2007), 3–10.Google Scholar
[624]
Carraro, L., et al. An integrated field, laboratory and theoretical study of PKD spread in a Swiss prealpine river. Proceedings of the National Academy of Sciences of the USA 114 (2017), 11992–11997.Google Scholar
[625]
Burkhardt-Holm, P., Peter, A., and Segner, H. Decline of fish catch in Switzerland: Project Fishnet – a balance between analysis and synthesis. Aquatic Sciences 64, 1 (2002), 36–54.Google Scholar
[626]
Federal Office of Topography Swisstopo. Geological map of Switzerland 1:500000. www.geocat.ch/geonetwork/srv/eng/md.viewer#/full_view/ca917a71-dcc9–44b6–8804-823c694be516, 2005.Google Scholar
[627]
Alexander, J. D., Bartholomew, J. L., Wright, K. A., Som, N. A., and Hetrick, N. J. Integrating models to predict distribution of the invertebrate host of myxosporean parasites. Freshwater Science 35, 4 (2016), 1263–1275.Google Scholar
[628]
Fontes, I., Hartikainen, H., Taylor, N., and Okamura, B. Conditional persistence and tolerance characterize endoparasite–colonial host interactions. Parasitology 144, 8 (2017), 1052–1063.Google Scholar
[629]
Keesing, F., et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 7324 (2010), 647–652.Google Scholar
[630]
Penczykowski, R. M., Hall, S. R., Civitello, D. J., and Duffy, M. A. Habitat structure and ecological drivers of disease. Limnology and Oceanography 59, 2 (2014), 340–348.Google Scholar
[631]
Engering, A., Hogerwerf, L., and Slingenbergh, J. Pathogen–host–environment interplay and disease emergence. Emerging Microbes & Infections 2, 2 (2013), e5.Google Scholar
[632]
Ricker, W. E. Stock and recruitment. Journal of the Fish Resources Board of Canada 11, 5 (1954), 559–623.Google Scholar
[633]
Frank, B. M., Gimenez, O., and Baret, P. V. Assessing brown trout (Salmo trutta) spawning movements with multistate capture-recapture models: a case study in a fully controlled Belgian brook. Canadian Journal of Fisheries and Aquatic Sciences 69, 6 (2012), 1091–1104.Google Scholar
[634]
Johnsen, B., and Jenser, A. The Gyrodactylus story in Norway. Aquaculture 98, 1–3 (1991), 289–302.Google Scholar
[635]
Tien, J. H., and Earn, D. J. D. Multiple transmission pathways and disease dynamics in a waterborne pathogen model. Bulletin of Mathematical Biology 72 (2010), 1506–1533.Google Scholar
[636]
Jutla, A. S., Akanda, A. S., Griffiths, J. K., Colwell, R. R., and Islam, S. Warming oceans, phytoplankton and zooplankton blooms, and river discharge: implications for cholera outbreaks. American Journal of Tropical Medicine and Hygiene 85 (2011), 303–308.Google Scholar
[637]
Jutla, A. S., et al. Environmental factors influencing epidemic cholera. American Journal of Tropical Medicine and Hygiene 89 (2013), 597–607.Google Scholar
[638]
Finger, F., et al. Cholera in the Lake Kivu region (DRC): integrating remote sensing and spatially explicit epidemiological modeling. Water Resources Research 50 (2014), 5624–5637.Google Scholar
[639]
Bergquist, R., Yang, G. J., Knopp, S., Utzinger, J., and Tanner, M. Surveillance and response: tools and approaches for the elimination stage of neglected tropical diseases. Acta Tropica 141 (2015), 229–234.Google Scholar
[640]
Ruiz-Moreno, D., Pascual, M., Bouma, M., Dobson, A. P., and Cash, B. Cholera seasonality in Madras (1901–1940): dual role for rainfall in endemic and epidemic regions. EcoHealth 4 (2007), 52–62.Google Scholar
[641]
Hashizume, M., et al. The effect of rainfall on the incidence of cholera in Bangladesh. Epidemiology 19 (2008), 103–110.Google Scholar
[642]
Boelee, E., et al. Options for water storage and rainwater harvesting to improve health and resilience against climate change in Africa. Regional Environmental Change 13 (2013), 509–519.Google Scholar
[643]
Faruque, S. M., et al. Self-limiting nature of seasonal cholera epidemics: role of host-mediated amplification of phage. Proceedings of the National Academy of Sciences of the USA 102 (2005), 6119–6124.Google Scholar
[644]
Pascual, M., Rodó, X., Ellner, S. P., Colwell, R. R., and Bouma, M. J. Cholera dynamics and El Niño Southern Oscillation. Science 289 (2000), 1766–1769.Google Scholar
[645]
Pascual, M., Bouma, M., and Dobson, A. Cholera and climate: revisiting the quantitative evidence. Microbes and Infection 4 (2002), 237–2 4 5.Google Scholar
[646]
Magny, de, et al. Cholera outbreak in Senegal in 2005: was climate a factor? PLoS ONE 7 (2012), e44577.Google Scholar
[647]
Emch, M., Feldacker, C., Islam, M. S., and Ali, M. Seasonality of cholera from 1974 to 2005: a review of global patterns. International Journal of Health Geographics 7 (2008), 31.Google Scholar
[648]
Jutla, A. S., Akanda, A. S., and Islam, S. A framework for predicting endemic cholera using satellite derived environmental determinants. Environmental Modelling & Software 47 (2013), 148–158.Google Scholar
[649]
Reiner, R. C. Jr., King, A. A., Emch, M., Yunus, M., Faruque, A. S., and Pascual, M. Highly localized sensitivity to climate forcing drives endemic cholera in a megacity. Proceedings of the National Academy of Sciences of the USA 109 (2012), 2033–2036.Google Scholar
[650]
Rodo, X., et al. Climate change and infectious diseases: can we meet the needs for better prediction? Climatic Change 118 (2013), 625–640.Google Scholar
[651]
Ramírez, I. J., and Grady, S. C. El Niño, climate, and cholera associations in Piura, Peru, 1991–2001: a wavelet analysis. EcoHealth 13 (2016), 83–99.Google Scholar
[652]
Vezzulli, L., et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proceedings of the National Academy of Sciences of the USA 113 (2016), 5062–5071.Google Scholar
[653]
Baker-Austin, C., Trinanes, J. A., Taylor, N. G. H., Hartnell, R., Siitonen, A., and Martinez-Urtaza, J. Emerging Vibrio risk at high latitudes in response to ocean warming. Nature Climate Change 3 (2013), 73–77.Google Scholar
[654]
Vezzulli, L., Colwell, R., and Pruzzo, C. Ocean warming and spread of pathogenic vibrios in the aquatic environment. Microbial Ecology 65 (2013), 817–825.Google Scholar
[655]
Cash, B. A., Rodó, X., Emch, M., Yunus, M., Faruque, A. S. G., and Pascual, M. Cholera and shigellosis: different epidemiology but similar responses to climate variability. PLoS ONE 9 (2014), e107223.Google Scholar
[656]
Escobar, L. E., et al. A global map of suitability for coastal Vibrio cholerae under current and future climate conditions. Acta Tropica 149 (2015), 202–211.Google Scholar
[657]
Vezzulli, L., Pezzati, E., Brettar, I., Höfle, M., and Pruzzo, C. Effects of global warming on Vibrio ecology. Microbiology Spectrum 3 (2015), 0004–2014.Google Scholar
[658]
Pedersen, U. B., et al. Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate. Geospatial Health 8 (2014), 335–343.Google Scholar
[659]
Wang, W., Dai, J., and Liang, Y. Apropos: factors impacting on progress towards elimination of transmission of Schistosomiasis japonica in China. Parasites & Vectors 7 (2014), 408.Google Scholar
[660]
Stensgaard, A., Booth, M., Nikulin, G., and McCreesh, N. Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa. Geospatial Health 11 (2016), 94–101.Google Scholar
[661]
Zhou, X., et al. Potential impact of climate change on schistosomiasis transmission in China. American Journal of Tropical Medicine and Hygiene 78 (2008), 188–194.Google Scholar
[662]
de Magny, , et al. Environmental signatures associated with cholera epidemics. Proceedings of the National Academy of Sciences of the USA 105 (2008), 17676–17681.Google Scholar
[663]
Rebaudet, S., Sudre, B., Faucher, B., and Piarroux, R. Cholera in coastal Africa: a systematic review of its heterogeneous environmental determinants. Journal of Infectious Diseases 208 (2013), S98–S106.Google Scholar
[664]
Lai, Y., et al. Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: a systematic review and geostatistical analysis. Lancet Infectious Diseases 15 (2015), 927–940.Google Scholar
[665]
McCreesh, N., Nikulin, G., and Booth, M. Predicting the effects of climate change on Schistosoma mansoni transmission in eastern Africa. Parasites & Vectors 8 (2015), 4.Google Scholar
[666]
Hu, Y., et al. Spatial pattern of schistosomiasis in Xingzi, Jiangxi Province, China: the effects of environmental factors. Parasites & Vectors 6 (2013), 214.Google Scholar
[667]
Wu, X. H., Zhang, S. Q., Xu, X. J., and Huang, Y. X. Effect of floods on the transmission of schistosomiasis in the Yangtze River valley, People’s Republic of China. Parasitology International 57 (2008), 271–276.Google Scholar
[668]
Rohr, J., et al. Agrochemicals increase trematode infections in a declining amphibian species. Nature 455 (2008), 1235–1239.Google Scholar
[669]
Ouedraogo, H., et al. Schistosomiasis in school-age children in Burkina Faso after a decade of preventive chemotherapy. Bulletin of the World Health Organization 94, 1 (2016), 37–45.Google Scholar
[670]
Perez-Saez, J., Mande, T., Ceperley, N., and Rinaldo, A. Classification and prediction of river network ephemerality and its relevance for waterborne disease epidemiology. Advances in Water Resources 110 (2017), 263–278.Google Scholar
[671]
Simoonga, C., et al. Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa. Parasitology 136 (2009), 1683–1693.Google Scholar
[672]
Bajardi, P., Poletto, C., Ramasco, J., Tizzoni, M., Colizza, V., and Vespignani, A. Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic. PLoS ONE 6, 1 (2011), 1–8.Google Scholar
[673]
Merler, S., Ajelli, N., Fumanelli, L., Aleta, A., Moreno, Y., and Vespignani, A. Spatiotemporal spread of the 2014 outbreak of ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis. Lancet Infectious Diseases 15 (2015), 204–211.Google Scholar
[674]
Vonghachack, Y., Odermatt, P., Taisayyavong, K., Phounsavath, S., Akkhavong, K., and Sayasone, S. Transmission of Opisthorchis viverrini, Schistosoma mekongi and soil-transmitted helminths on the Mekong islands, Southern Lao PDR. Infectious Diseases of Poverty 131 (2017), 1–15.Google Scholar
[675]
Forrer, A., et al. Spatial distribution of, and risk factors for Opisthorchis viverrini infection in Southern Lao PDR. PLoS Neglected Tropical Diseases 6 (2012), e1481.Google Scholar
[676]
Buerli, C., Harbrecht, H., Odermatt, P., Sayasone, S., and Chitnis, N. Mathematical analysis of the transmission dynamics of the liver fluke, Opisthorchis viverrini. Journal of Theoretical Biology 439 (2018), 181–194.Google Scholar
[677]
Beltrame, L., et al. A mechanistic hydro-epidemiological model of liver fluke risk. Journal of the Royal Society Interface 15 (2018), 20180072.Google Scholar
[678]
Coopersmith, E., Bell, J., Benedict, K., Shriber, J., McCotter, O., and Cosh, M. Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions. GeoHealth 1 (2017), 51–63.Google Scholar
[679]
Bomblies, A., Duchemin, J., and Eltahir, E. Hydrology of malaria: model development and application to a Sahelian village. Water Resources Research 44 (2008), W12445.Google Scholar
[680]
Yamana, T., and Eltahir, E. Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasites & Vectors 6 (2013), 235.Google Scholar
[681]
Whittaker, C., et al. Loa loa: more than meets the eye? Trends in Parasitology 34 (2019), 254–262.Google Scholar
[682]
Verver, S., et al. How can Onchocerciasis elimination in Africa be accelerated? Modeling the impact of increased Ivermectin treatment frequency and complementary vector control. Clinical Infectious Diseases 66 (2018), S267–S274.Google Scholar
[683]
Colebunders, R., et al. From river blindness control to elimination: bridge over troubled water. Infectious Diseases of Poverty 21 (2018), 1–15.Google Scholar
[684]
Basáñez, M., et al. River blindness: mathematical models for control and elimination. Advances in Parasitology 94 (2016), 247–341.Google Scholar
[685]
de Montjoye, Y., Hidalgo, C., Verleysen, M., and Blondel, V. Unique in the crowd: the privacy bounds of human mobility. Scientific Reports 3 (2013), 1376–1 3 80.Google Scholar
[686]
Wesolowski, A., et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Scientific Reports 4 (2014), 5678.Google Scholar
[687]
Liu, Q., Ajelli, M., Aleta, A., Merler, S., Moreno, Y., and Vespignani, A. Measurability of the epidemic reproduction number in data-driven contact networks. Proceedings of the National Academy of Sciences of the USA 115, 50 (2018), 12680–12685.Google Scholar
[688]
Finger, F., et al. The potential impact of case-area targeted interventions in response to cholera outbreaks: a modeling study. PLoS Medicine 15 (2018), e1002509.Google Scholar
[689]
Rebaudet, S., et al. Epidemiological and molecular forensics of cholera recurrence in Haiti. Scientific Reports 9 (2019), 1164–1175.Google Scholar
[690]
Lee, E., Azman, A., Kaminsky, J., and Lessler, J. The projected impact of geographic targeting of oral cholera vaccination in sub-Saharan Africa: a modeling study. PLoS Medicine 16, 12 (2019), e1003003.Google Scholar
[691]
Garbin, S., Alessi Celegon, E., Fanton, S., and Botter, G. Hydrological controls on river network connectivity. Royal Society Open Science 6 (2019), 181428.Google Scholar
[692]
Hoover, C., et al. Modelled effect of prawn aquaculture on poverty alleviation and schistosomiasis control. Nature Sustainability 2 (2019), 611–620.Google Scholar
[693]
Perez-Saez, J., Mande, T., and Rinaldo, A. Space and time predictions of schistosomiasis snail host population dynamics across hydrologic regimes in Burkina Faso. Geospatial Health 14 (2019), 306–313.Google Scholar
[694]
Wood, C., et al. Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission. Proceedings of the National Academy of Sciences of the USA 116, 46 (2019), 23182–23191.Google Scholar
[695]
Rabone, M., et al. Freshwater snails of biomedical importance in the Niger River Valley: evidence of temporal and spatial patterns in abundance, distribution and infection with Schistosoma spp. Parasites & Vectors 12 (2019), 498–518.Google Scholar
[696]
Morel-Journel, T., Rais Assa, C., Meilleret, L., and Vercken, E. It’s all about connections: hubs and invasion in habitat networks. Ecology Letters 22 (2019), 313–321.Google Scholar
[697]
Streyer, D., Fisher, D., Hamilton, S., Malcom, H., Pace, M., and Solomon, C. Long-term variability and density dependence in Hudson River Dreissena populations. Freshwater Biology 65, 3 (2019), 474–489.Google Scholar
[698]
Geba, E., et al. Use of the bivalve Dreissena polymorpha as a biomonitoring tool to reflect the protozoan load in freshwater bodies. Water Research 170 (2020), 115297.Google Scholar
[699]
Sengupta, M., et al. Environmental DNA for improved detection and environmental surveillance of schistosomiasis. Proceedings of the National Academy of Sciences of the USA 116, 18 (2019), 8931–8940.Google Scholar
[700]
Magnabosco, C., et al. The biomass and biodiversity of the continental subsurface. Nature Geosciences 11 (2018), 707–7 11.Google Scholar
[701]
Simkus, D., et al. Variations in microbial carbon sources and cycling in the deep continental subsurface. Geochimica et Cosmochimica Acta 173 (2016), 264–283.Google Scholar
[702]
Besemer, K., Singer, G., Quince, C., Bertuzzo, E., Sloan, W., and Battin, T. Headwaters are critical reservoirs of microbial diversity for fluvial networks. Proceedings of the Royal Society B 280, 1771 (2013), 20131760.Google Scholar
[703]
Rodriguez-Iturbe, I., Caylor, K., and Rinaldo, A. Metabolic principles of river basin organization. Proceedings of the National Academy of Sciences of the USA 108 (2011), 11751–11755.Google Scholar
[704]
Palmer, M., and Ruhi, A. Linkages between flow regime, biota, and ecosystem processes: implications for river restoration. Science 365 (2019), eaaw2087.Google Scholar
[706]
Brown, J., Gilloly, J., Allen, A., Savage, V., and West, G. Toward a metabolic theory of ecology. Ecology 85 (2004), 1171–1789.Google Scholar
[709]
West, G., and Brown, J. Life’s universal scaling laws. Physics Today 57 (2004), 36–42.Google Scholar
[710]
Banavar, J., Maritan, A., and Rinaldo, A. Size and form in efficient transportation networks. Nature 399 (1999), 130–132.Google Scholar
[711]
Maritan, A., Rigon, R., Banavar, J., and Rinaldo, A. Network allometry. Geophysical Research Letters 29 (2002), 1–4.Google Scholar
[712]
Helton, A., Hall, R., and Bertuzzo, E. How network structure affects nitrogen removal by streams. Freshwater Biology 63 (2019), 128–140.Google Scholar
[713]
Wollheim, W., Stewart, R., Aiken, G., Butler, K., Morse, N., and Salisbury, J. Removal of terrestrial DOC in aquatic ecosystems of a temperate river network. Geophysical Research Letters 42 (2015), 6671–6679.Google Scholar
[714]
Hotchkiss, E., and Hall, R. Whole-stream 13 C tracer addition reveals distinct fates of newly fixed carbon. Ecology 92 (2015), 403–416.Google Scholar
[715]
Raymond, P., Saiers, J., and Sobczak, W. Hydrological and biogeochemical controls on watershed dissolved organic matter transport: pulse-shunt concept. Ecology 97 (2016), 5–16.Google Scholar
[716]
Horgby, A., et al. Unexpected large evasion fluxes of carbon dioxide from turbulent streams draining the world’s mountains. Nature Communications 10 (2019), 4888.Google Scholar
[717]
Bernhardt, E., et al. The metabolic regimes of flowing waters. Limnology and Oceanography 63 (2018), S99–S118.Google Scholar
[718]
Koenig, L. E., Helton, A., Savoy, P., Bertuzzo, E., Heffernan, J., Hall, R., and Bernhardt, E. Emergent productivity regimes of river networks. Limnology and Oceanography Letters 4 (2019), 173–181.Google Scholar
[719]
Tsuji, S., Takahara, T., Doi, H., Shibata, N., and Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis – a review of methods for collection, extraction, and detection. Environmental DNA 1, 2 (2019), 99–108.Google Scholar
[720]
Bálint, M., et al. Environmental DNA time series in ecology. Trends in Ecology & Evolution 33 (2018), 945–957.Google Scholar
[721]
Peterson, E., Hanks, E., Hooten, M., Ver Hoef, J., and Fortin, J. Spatially structured statistical network models for landscape genetics. Ecological Monographs 89, 2 (2019), e01355.Google Scholar
[722]
Pilger, T., Gido, K., Propst, D., Whitney, J., and Turner, T. River network architecture, genetic effective size and distributional patterns predict differences in genetic structure across species in a dryland stream fish community. Molecular Ecology 26 (2017), 2687–2697.Google Scholar
[723]
Bullock, J., et al. Human-mediated dispersal and the rewiring of spatial networks. Trends in Ecology & Evolution 33, 12 (2018), 958–970.Google Scholar
[724]
Erös, T. Scaling fish metacommunities in stream networks: synthesis and future research avenues. Community Ecology 18, 1 (2017), 72–86.Google Scholar
[725]
Gonzáles-Ferreira, A., Bertuzzo, E., Barquín, J., Carraro, L., Alonso, C., and Rinaldo, A. Effects of altered river network connectivity on the distribution of Salmo trutta: insights from a metapopulation model. Freshwater Biology 64 (2019), 1877–1895.Google Scholar
[726]
Muneepeerakul, R., Bertuzzo, E., Rinaldo, A., and Rodriguez-Iturbe, I. Evolving biodiversity patterns in changing river networks. Journal of Theoretical Biology 462 (2019), 418–424.Google Scholar
[727]
Maynard Smith, J., and Price, G. The logic of animal conflict. Nature 246 (1973), 15–18.Google Scholar
[728]
Terui, A., Ishiyama, N., Urabe, U., Ono, S., Finlay, J., and Nakamura, F. Metapopulation stability in branching river networks. Proceedings of the National Academy of Sciences of the USA 115 (2018), 5963–5969.Google Scholar
[729]
May, R. Stability and Complexity of Model Ecosystems. Princeton University Press, 1972.Google Scholar
[730]
Ma, C., Shen, Y., Bearup, D., Fagan, W., and Liao, J. Spatial variation in branch size promotes metapopulation persistence in dendritic river networks. Freshwater Biology 65, 3 (2020), 426–434.Google Scholar
[731]
Bylemans, J., Furlan, E. M., Gleeson, D. M., Hardy, C. M., and Duncan, R. P. Does size matter? An experimental evaluation of the relative abundance and decay rates of aquatic environmental DNA. Environmental Science and Technology 52, 11 (2018), 6408–6416.Google Scholar
[732]
Bruneaux, M., et al. Parasite infection and decreased thermal tolerance: impact of proliferative kidney disease on a wild salmonid fish in the context of climate change. Functional Ecology 31 (2017), 216–226.Google Scholar
[733]
Debes, P., Gross, R., and Vasemägi, A. Quantitative genetic variation in, and environmental effects on pathogen resistance and temperature-dependent disease severity in a wild trout. American Naturalist 190 (2017), 244–265.Google Scholar
[734]
Dercole, F., and Rinaldi, S. Dynamical Systems and Their Bifurcations. IEEE-Wiley Press, 2011.Google Scholar
[735]
Meijer, H. G. E., Dercole, F., and Oldeman, B. E. Numerical Bifurcation Analysis. Springer, 2009.Google Scholar
[736]
Maritan, A., Colaiori, F., Flammini, A., Cieplank, M., and Banavar, J. Universality classes of optimal channel networks. Science 272 (1996), 984–986.Google Scholar
[737]
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. Equations of state calculations by fast computing machines. Journal of Chemical Physics 21 (1996), 1087–1092.Google Scholar
[738]
Bak, P. How Nature Works: The Science of Self-Organized Criticality. Springer, 1996.Google Scholar
[739]
Bhattacharjee, S., and Seno, F. A measure of data collapse for scaling. Journal of Physics A Math Gen 34, 33 (2001), 6375–6380.Google Scholar
[740]
Rinaldo, A., et al. Thermodynamics of fractal networks. Physical Review Letters 76 (1996), 3364–3367.Google Scholar