Ecology

1.Voss, R. S. & Emmons, L. H. Mammalian diversity in Neotropical lowland rainforests: A preliminary assessment. Bull. Am. Museum Nat. Hist. 230, 1–115 (1996).
Google Scholar 
2.Barnosky, A. D. et al. Variable impact of late-Quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl. Acad. Sci. U. S. A. 113, 856–861 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Croft, D. A., Engelman, R. K., Dolgushina, T. & Wesley, G. Diversity and disparity of sparassodonts (Metatheria) reveal non-analogue nature of ancient South American mammalian carnivore guilds. Proc. R. Soc. B 285, 20172012 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fariña, R. A. Trophic relationships among Lujanian mammals. Evol. Theory 11, 125–134 (1996).
Google Scholar 
5.Fariña, R. A. & Blanco, R. E. Megatherium the Stabber. Proc. R. Soc. B Biol. Sci. 263, 1725–1729 (2006).ADS 

Google Scholar 
6.Tejada-Lara, J. V. et al. Body mass predicts isotope enrichment in herbivorous mammals. Proc. R. Soc. B Biol. Sci. 285, 20181020 (2018).Article 
CAS 

Google Scholar 
7.de Muizon, C. & McDonald, H. G. An aquatic sloth from the Pliocene of Peru. Nature 375, 224–227 (1995).ADS 
Article 

Google Scholar 
8.Croft, D. A. The middle Miocene (Laventan) Quebrada Honda Fauna, southern Bolivia and a description of its notoungulates. Palaeontology 50, 277–303 (2007).Article 

Google Scholar 
9.Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440 (2011).Article 

Google Scholar 
10.Lee-Thorp, J. J., Sealy, J. J. C. & van der Merwe, N. J. N. Stable carbon isotope ratio differences between bone collagen and bone apatite, and their relationship to diet. J. Archaeol. Sci. 32, 1459–1470 (1989).
Google Scholar 
11.Clementz, M. T., Fox-Dobbs, K., Wheatley, P. V., Koch, P. L. & Doak, D. F. Revisiting old bones: Coupled carbon isotope analysis of bioapatite and collagen as an ecological and palaeoecological tool. Geol. J. 44, 605–620 (2009).CAS 
Article 

Google Scholar 
12.Tejada, J. V. et al. Comparative isotope ecology of western Amazonian rainforest mammals. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.2007440117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16, 153–162 (2001).CAS 
PubMed 
Article 

Google Scholar 
14.McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, 1–26 (2016).Article 

Google Scholar 
15.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

Google Scholar 
16.Chikaraishi, Y., Ogawa, N. O., Doi, H. & Ohkouchi, N. 15N/14N ratios of amino acids as a tool for studying terrestrial food webs: A case study of terrestrial insects (bees, wasps, and hornets ). Ecol. Res. 26, 835–844 (2011).Article 

Google Scholar 
17.Popp, B. N. et al. Insight into the trophic ecology of yellowfin tuna, Thunnus albacares, from compound- specific nitrogen isotope analysis of proteinaceous amino acids. In Stable Isotopes as Indicators of Ecological Change (eds Dawson, T. E. & Siegwolf, R. T. W.) 173–190 (Elsevier Inc., 2007).
Google Scholar 
18.Naito, Y. I., Honch, N. V., Chikaraishi, Y., Ohkouchi, N. & Yoneda, M. Quantitative evaluation of marine protein contribution in ancient diets based on nitrogen isotope ratios of individual amino acids in bone collagen: An investigation at the Kitakogane Jomon Site. Am. J. Phys. Anthropol. 143, 31–40 (2010).PubMed 
Article 

Google Scholar 
19.O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Chikaraishi, Y., Ogawa, N. O. & Ohkouchi, N. Further evaluation of the trophic level estimation based on nitrogen isotopic composition of amino acids. In Earth, Life, and Isotopes (eds Ohkouchi, N. et al.) 37–51 (Kyoto Universy Press, 2010).
Google Scholar 
21.Steffan, S. A. et al. Trophic hierarchies illuminated via amino acid isotopic analysis. PLoS ONE 8, 1–10 (2013).Article 
CAS 

Google Scholar 
22.Chikaraishi, Y., Kashiyama, Y., Ogawa, N. O., Kitazato, H. & Ohkouchi, N. Metabolic control of nitrogen isotope composition of amino acids in macroalgae and gastropods: Implications for aquatic food web studies. Mar. Ecol. Prog. Ser. 342, 85–90 (2007).ADS 
CAS 
Article 

Google Scholar 
23.Naito, Y. I. et al. Ecological niche of Neanderthals from Spy Cave revealed by nitrogen isotopes of individual amino acids in collagen. J. Hum. Evol. 93, 82–90 (2016).PubMed 
Article 

Google Scholar 
24.Nielsen, J. M., Popp, B. N. & Winder, M. Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia https://doi.org/10.1007/s00442-015-3305-7 (2015).Article 
PubMed 

Google Scholar 
25.Décima, M., Landry, M. R. & Popp, B. N. Environmental perturbation effects on baseline δ15N values and zooplankton trophic flexibility in the southern California current ecosystem. Limnol. Oceanogr. 58, 624–634 (2013).ADS 
Article 
CAS 

Google Scholar 
26.Jarman, C. L. et al. Diet of the prehistoric population of Rapa Nui (Easter Island, Chile) shows environmental adaptation and resilience. Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.23273 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Kendall, I. P. et al. Compound-specific δ15N values express differences in amino acid metabolism in plants of varying lignin content. Phytochemistry 161, 130–138 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Ramirez, M. D., Besser, A. C., Newsome, S. D. & McMahon, K. W. Meta-analysis of primary producer amino acid δ15N values and their influence on trophic position estimation. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13678 (2021).Article 

Google Scholar 
29.Hebert, C. E., Popp, B. N., Fernie, K. J., Rattner, B. A. & Wallsgrove, N. Amino acid specific stable nitrogen isotope values in avian tissues: Insights from captive American kestrels and wild herring gulls. Environ. Sci. Technol. 50, 12928–12937 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. 7, 740–750 (2009).CAS 
Article 

Google Scholar 
31.Steffan, S. A. et al. Microbes are trophic analogs of animals. Proc. Natl. Acad. Sci. 112, 15119–15124 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kendall, I. P., Lee, M. R. F. & Evershed, R. P. The effect of trophic level on individual amino acid δ15N values in a terrestrial ruminant food web. Sci. Technol. Archaeol. Res. 3, 135–145 (2017).
Google Scholar 
33.Matthews, C. J. D., Ruiz-Cooley, R. I., Pomerleau, C. & Ferguson, S. H. Amino acid δ15N underestimation of cetacean trophic positions highlights limited understanding of isotopic fractionation in higher marine consumers. Ecol. Evol. 10, 3450–3462 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Styring, A. K., Sealy, J. C. & Evershed, R. P. Resolving the bulk δ15N values of ancient human and animal bone collagen via compound-specific nitrogen isotope analysis of constituent amino acids. Geochim. Cosmochim. Acta 74, 241–251 (2010).ADS 
CAS 
Article 

Google Scholar 
35.Lorrain, A. et al. Nitrogen and carbon isotope values of individual amino acids: A tool to study foraging ecology of penguins in the Southern Ocean. Mar. Ecol. Prog. Ser. 391, 293–306 (2009).ADS 
CAS 
Article 

Google Scholar 
36.Lorrain, A. et al. Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans. Deep. Res. Part II Top. Stud. Oceanogr. 113, 188–198 (2015).ADS 
CAS 
Article 

Google Scholar 
37.Hartman, G. Are elevated δ15N values in herbivores in hot and arid environments caused by diet or animal physiology?. Funct. Ecol. 25, 122–131 (2011).Article 

Google Scholar 
38.Hartman, G. & Danin, A. Isotopic values of plants in relation to water availability in the Eastern Mediterranean region. Oecologia 162, 837–852 (2010).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Hansen, R. M. Shasta ground sloth food habits, Rampart Cave, Arizona. Paleobiology 4, 302–319 (1978).Article 

Google Scholar 
40.McDonald, H. G. & Morgan, G. S. Ground Sloths of New Mexico. Foss. Rec. 3 New. Mex. Museum Nat. Hist. Sci. Bull. 53, 652–663 (2011).
Google Scholar 
41.Poinar, H. N. Molecular coproscopy: Dung and diet of the extinct ground sloth Nothrotheriops shastensis. Science 281, 402–406 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Clack, A. A., MacPhee, R. D. E. & Poinar, H. N. Mylodon darwinii DNA sequences from ancient fecal hair shafts. Ann. Anat. 194, 26–30 (2012).CAS 
PubMed 
Article 

Google Scholar 
43.Höss, M., Dilling, A., Currant, A. & Pääbo, S. Molecular phylogeny of the extinct ground sloth Mylodon darwinii. Proc. Natl. Acad. Sci. U. S. A. 93, 181–185 (1996).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Moore, D. M. Post-glacial vegetation in the South Patagonian territory of the giant ground sloth, Mylodon. Bot. J. Linn. Soc. 77, 177–202 (1978).Article 

Google Scholar 
45.Bargo, M. S., Toledo, N. & Vizcaino, S. F. Muzzle of South American Pleistocene ground sloths (Xenarthra, Tardigrada). J. Morphol. 267, 248–263 (2006).PubMed 
Article 

Google Scholar 
46.Rasmussen, M. et al. Response to comment by Goldberg et al. on ‘DNA from Pre-Clovis human coprolites in Oregon, North America’. Science 325, 148 (2009).ADS 
CAS 
Article 

Google Scholar 
47.Janis, C. M. Correlations between craniodental anatomy and feeding in ungulates: Reciprocal illumination between living and fossil taxa. In Functional Morphology in Vertebrate Paleontology (ed. Thomason, J.) 76–98 (Cambridge U Press, 1995).
Google Scholar 
48.Clauss, M., Nunn, C., Fritz, J. & Hummel, J. Evidence for a tradeoff between retention time and chewing efficiency in large mammalian herbivores. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 154, 376–382 (2009).PubMed 
Article 
CAS 

Google Scholar 
49.Vizcaino, S. F., Bargo, M. S. & Cassini, G. H. Dental occlusal surface area in relation to body mass, food habits and other biologic features in fossil xenarthrans. Ameghiniana 43, 11–26 (2006).
Google Scholar 
50.McNab, B. K. Energetics, population biology, and distribution of xenarthrans, living and extinct. In The Ecology of Arboreal Folivores 219–232 (Smithsonian Press, 1985).51.Davis, L. B. & Birkbak, R. C. On the transfer of energy in layers of fur. Biophys. J. 14, 249–268 (1974).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Clauss, M. et al. The maximum attainable body size of herbivorous mammals: Morphophysiological constraints on foregut, and adaptations of hindgut fermenters. Oecologia 136, 14–27 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Fariña, R. A., Czerwonogora, A. & Di Giacomo, M. Splendid oddness: Revisiting the curious trophic relationships of South American Pleistocene mammals and their abundance. An. Acad. Bras. Cienc. 86, 311–331 (2014).PubMed 
Article 

Google Scholar 
54.Zhu, D. et al. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum. Nat. Ecol. Evol. 2, 640–649 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, I. S. Mammoth steppe: A high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).ADS 
Article 

Google Scholar 
56.Hannides, C. C. S., Popp, B. N., Landry, M. R. & Graham, B. S. Quantification of zooplankton trophic position in the North Pacific Subtropical Gyre using stable nitrogen isotopes. Limnol. Oceanogr. 54, 50–61 (2009).ADS 
CAS 
Article 

Google Scholar  More

1.Oerke, E.-C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).Article 

Google Scholar 
2.R4P Network. Trends and challenges in pesticide resistance detection. Trends Plant Sci. 21, 834–853 (2016).3.Heap, I. M. The international herbicide-resistant weed database. http://www.weedscience.org/Home.aspx (2021).4.Délye, C., Jasieniuk, M. & Le Corre, V. Deciphering the evolution of herbicide resistance in weeds. Trends Genet. 29, 649–658 (2013).PubMed 
Article 
CAS 

Google Scholar 
5.Gaines, T. A. et al. Mechanisms of evolved herbicide resistance. J. Biol. Chem. 295, 10307–10330 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Murphy, B. P. & Tranel, P. J. Target-site mutations conferring herbicide resistance. Plants 8, 382 (2019).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
7.Beckie, H. J. & Tardif, F. J. Herbicide cross resistance in weeds. Crop Prot. 35, 15–28 (2012).CAS 
Article 

Google Scholar 
8.Han, H. et al. Cytochrome P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to herbicides across at least five modes of action. Plant J. 105, 79–92 (2021).CAS 
PubMed 
Article 

Google Scholar 
9.Kreiner, J. M. et al. Multiple modes of convergent adaptation in the spread of glyphosate-resistant Amaranthus tuberculatus. Proc. Natl. Acad. Sci. 116, 21076–21084 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Milani, A. et al. Population structure and evolution of resistance to acetolactate synthase (ALS)-inhibitors in Amaranthus tuberculatus in Italy. Pest Manag. Sci. 77, 2971–2980 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Clements, D. R. et al. Adaptability of plants invading North American cropland. Agric. Ecosyst. Environ. 104, 379–398 (2004).Article 

Google Scholar 
12.Essl, F. et al. Biological flora of the British Isles: Ambrosia artemisiifolia. J. Ecol. 103, 1069–1098 (2015).Article 

Google Scholar 
13.Cowbrough, M. J., Brown, R. B. & Tardif, F. J. Impact of common ragweed (Ambrosia artemisiifolia) aggregation on economic thresholds in soybean. Weed Sci. 51, 947–954 (2003).CAS 
Article 

Google Scholar 
14.Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L. & King, R. P. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42, 103–109 (1994).Article 

Google Scholar 
15.Bassett, I. J. & Crompton, C. W. The biology of Canadian weeds: Ambrosia artemisiifolia L. and A. psilostachya DC. Can. J. Plant Sci. 55, 463–476 (1975).16.Chauvel, B., Dessaint, F., Cardinal-Legrand, C. & Bretagnolle, F. The historical spread of Ambrosia artemisiifolia L. France from herbarium records. J. Biogeogr. 33, 665–673 (2006).Article 

Google Scholar 
17.Sala, C. A., Bulos, M., Altieri, E. & Ramos, M. L. Genetics and breeding of herbicide tolerance in sunflower. Helia 35, 57–69 (2012).Article 

Google Scholar 
18.Yu, Q. & Powles, S. B. Resistance to AHAS inhibitor herbicides: Current understanding. Pest Manag. Sci. 70, 1340–1350 (2014).CAS 
PubMed 
Article 

Google Scholar 
19.Tranel, P. J., Wright, T. R. & Heap, I. M. ALS mutations from resistant weeds. http://www.weedscience.com (2021).20.Patzoldt, W. L., Tranel, P. J., Alexander, A. L. & Schmitzer, P. R. A common ragweed population resistant to cloransulam-methyl. Weed Sci. 49, 485–490 (2001).CAS 
Article 

Google Scholar 
21.Rousonelos, S. L., Lee, R. M., Moreira, M. S., VanGessel, M. J. & Tranel, P. J. Characterization of a common ragweed (Ambrosia artemisiifolia) population resistant to ALS- and PPO-inhibiting herbicides. Weed Sci. 60, 335–344 (2012).CAS 
Article 

Google Scholar 
22.Zheng, D., Patzoldt, W. L. & Tranel, P. J. Association of the W574L ALS substitution with resistance to cloransulam and imazamox in common ragweed (Ambrosia artemisiifolia). Weed Sci. 53, 424–430 (2005).CAS 
Article 

Google Scholar 
23.Van Wely, A. C. et al. Glyphosate and acetolactate synthase inhibitor resistant common ragweed (Ambrosia artemisiifolia L.) in southwestern Ontario. Can. J. Plant Sci. 95, 335–338 (2015)24.Marsan-Pelletier, F., Vanasse, A., Simard, M.-J. & Cuerrier, M.-E. Survey of imazethapyr-resistant common ragweed (Ambrosia artemisiifolia L.) in Quebec. Phytoprotection 99, 36–44 (2019).25.Owen, M. D. & Zelaya, I. A. Herbicide-resistant crops and weed resistance to herbicides. Pest Manag. Sci. 61, 301–311 (2005).CAS 
PubMed 
Article 

Google Scholar 
26.Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325 (2008).CAS 
PubMed 
Article 

Google Scholar 
27.Barnes, E. R., Knezevic, S. Z., Sikkema, P. H., Lindquist, J. L. & Jhala, A. J. Control of glyphosate-resistant common ragweed (Ambrosia artemisiifolia L.) in glufosinate-resistant soybean [Glycine max (L.) Merr]. Front. Plant Sci. 8, 1455 (2017).28.Tranel, P. J. & Wright, T. R. Resistance of weeds to ALS-inhibiting herbicides: What have we learned?. Weed Sci. 50, 700–712 (2002).CAS 
Article 

Google Scholar 
29.Li, J., Li, M., Gao, X. & Fang, F. A novel amino acid substitution Trp574Arg in acetolactate synthase (ALS) confers broad resistance to ALS-inhibiting herbicides in crabgrass (Digitaria sanguinalis). Pest Manag. Sci. 73, 2538–2543 (2017).CAS 
PubMed 
Article 

Google Scholar 
30.Duggleby, R. G., Pang, S. S., Yu, H. & Guddat, L. W. Systematic characterization of mutations in yeast acetohydroxyacid synthase. Interpretation of herbicide-resistance data. Eur. J. Biochem. 270, 2895–2904 (2003).31.Jung, S.-M. et al. Amino acid residues conferring herbicide resistance in tobacco acetohydroxyacid synthase. Biochem. J. 383, 53–61 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Owen, M. J., Walsh, M. J., Llewellyn, R. S. & Powles, S. B. Widespread occurrence of multiple herbicide resistance in Western Australian annual ryegrass (Lolium rigidum) populations. Aust. J. Agric. Res. 58, 711–718 (2007).CAS 
Article 

Google Scholar 
33.Owen, M. J., Martinez, N. J. & Powles, S. B. Multiple herbicide-resistant Lolium rigidum (annual ryegrass) now dominates across the Western Australian grain belt. Weed Res. 54, 314–324 (2014).CAS 
Article 

Google Scholar 
34.Délye, C. Nucleotide variability at the acetyl coenzyme A carboxylase gene and the signature of herbicide selection in the grass weed Alopecurus myosuroides (Huds.). Mol. Biol. Evol. 21, 884–892 (2004).35.Délye, C., Clément, J. A. J., Pernin, F., Chauvel, B. & Le Corre, V. High gene flow promotes the genetic homogeneity of arable weed populations at the landscape level. Basic Appl. Ecol. 11, 504–512 (2010).Article 

Google Scholar 
36.Délye, C., Pernin, F. & Scarabel, L. Evolution and diversity of the mechanisms endowing resistance to herbicides inhibiting acetolactate-synthase (ALS) in corn poppy (Papaver rhoeas L.). Plant Sci. 180, 333–342 (2011).37.Sudheesh, M. An analysis of polygenic herbicide resistance evolution and its management based on a population genetics approach. Basic Appl. Ecol. 16, 104–111 (2015).Article 

Google Scholar 
38.Bullock, J. M. Assessing and controlling the spread and the effects of common ragweed in Europe. Report, Contractor: Natural environment research Council UK (2012).39.Yu, Q., Nelson, J. K., Zheng, M. Q., Jackson, J. & Powles, S. B. Molecular characterisation of resistance to ALS-inhibiting herbicides in Hordeum leporinum biotypes. Pest Manag. Sci. 63, 918–927 (2007).CAS 
PubMed 
Article 

Google Scholar 
40.Simard, M.-J., Laforest, M., Soufiane, B., Benoit, D. L. & Tardif, F. Linuron resistant common ragweed (Ambrosia artemisiifolia) populations in Quebec carrot fields: presence and distribution of target-site and non-target site resistant biotypes. Can. J. Plant Sci. 98, 345–352 (2017).
Google Scholar 
41.Ganie, Z., Jugulam, M., Varanasi, V. & Jhala, A. J. Investigating mechanism of glyphosate resistance in a common ragweed (Ambrosia artemisiifolia L.) biotype from Nebraska. Can. J. Plant Sci. (2017). https://doi.org/10.1139/CJPS-2017-0036.42.Duhoux, A., Carrère, S., Duhoux, A. & Délye, C. Transcriptional markers enable identification of rye-grass (Lolium sp.) plants with non-target-site-based resistance to herbicides inhibiting acetolactate-synthase. Plant Sci. 257, 22–36 (2017).43.Gardin, J. A. C., Gouzy, J., Carrère, S. & Délye, C. ALOMYbase, a resource to investigate non-target-site-based resistance to herbicides inhibiting acetolactate-synthase (ALS) in the major grass weed Alopecurus myosuroides (black-grass). BMC Genomics 16, 590 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Torra, J. et al. Target-site and non-target-site resistance mechanisms confer multiple and cross- resistance to ALS and ACCase inhibiting herbicides in Lolium rigidum from Spain. Front. Plant Sci. 12, 625138 (2021).45.Manley, B. S., Hatzios, K. K. & Wilson, H. P. Absorption, translocation, and metabolism of chlorimuron and nicosulfuron in imidazolinone-resistant and susceptible smooth pigweed (Amaranthus hybridus). Weed Technol. 13, 759–764 (1999).CAS 
Article 

Google Scholar 
46.Jeffers, G. M., O’Donovan, J. T. & Hall, L. M. Wild mustard (Brassica kaber) resistance to ethametsulfuron but not to other herbicides. Weed Technol. 10, 847–850 (1996).CAS 
Article 

Google Scholar 
47.Veldhuis, L. J., Hall, L. M., O’Donovan, J. T., Dyer, W. & Hall, J. C. Metabolism-based resistance of a wild mustard (Sinapis arvensis L.) biotype to ethametsulfuron-methyl. J. Agric. Food Chem. 48, 2986–2990 (2000).48.Scarabel, L., Pernin, F. & Délye, C. Occurrence, genetic control and evolution of non-target-site based resistance to herbicides inhibiting acetolactate synthase (ALS) in the dicot weed Papaver rhoeas. Plant Sci. 238, 158–169 (2015).CAS 
PubMed 
Article 

Google Scholar 
49.Nakka, S., Thompson, C. R., Peterson, D. E. & Jugulam, M. Target site-based and non-target site based resistance to ALS Inhibitors in Palmer Amaranth (Amaranthus palmeri). Weed Sci. 65, 681–689 (2017).Article 

Google Scholar 
50.Meyer, L. et al. New gSSR and EST-SSR markers reveal high genetic diversity in the invasive plant Ambrosia artemisiifolia L. and can be transferred to other invasive Ambrosia species. PLOS ONE 12, e0176197 (2017).51.Van Boheemen, L. A. et al. Multiple introductions, admixture and bridgehead invasion characterize the introduction history of Ambrosia artemisiifolia in Europe and Australia. Mol. Ecol. 26, 5421–5434 (2017).PubMed 
Article 

Google Scholar 
52.Délye, C. et al. Harnessing the power of next-generation sequencing technologies to the purpose of high-throughput pesticide resistance diagnosis. Pest Manag. Sci. 76, 543–552 (2020).PubMed 
Article 
CAS 

Google Scholar 
53.Délye, C., Matéjicek, A. & Gasquez, J. PCR-based detection of resistance to acetyl-CoA carboxylase-inhibiting herbicides in black-grass (Alopecurus myosuroides Huds) and ryegrass (Lolium rigidum Gaud). Pest Manag. Sci. 58, 474–478 (2002).PubMed 
Article 
CAS 

Google Scholar 
54.Duggleby, R. G., McCourt, J. A. & Guddat, L. W. Structure and mechanism of inhibition of plant acetohydroxyacid synthase. Plant Physiol. Biochem. 46, 309–324 (2008).CAS 
PubMed 
Article 

Google Scholar 
55.Leigh, J. W. & Bryant, D. POPART: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
56.Neff, M. M., Neff, J. D., Chory, J. & Pepper, A. E. dCAPS, a simple technique for the genetic analysis of single nucleotide polymorphisms: Experimental applications in Arabidopsis thaliana genetics. Plant J. 14, 387–392 (1998).CAS 
PubMed 
Article 

Google Scholar 
57.Délye, C. & Boucansaud, K. A molecular assay for the proactive detection of target site-based resistance to herbicides inhibiting acetolactate synthase in Alopecurus myosuroides. Weed Res. 48, 97–101 (2008).Article 

Google Scholar 
58.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2ddCT method. Methods 25, 402–408 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).CAS 
PubMed 
Article 

Google Scholar  More

In this study, the HFLUX model was coupled with the SCEM-UA algorithm for analyzing the uncertainties of the model inputs. The specific procedures started with selecting the inputs of the HFLUX model. With the linked HFLUX and SCEM-UA model and implementation of an iteration scheme, the uncertainty of each of the selected inputs was obtained based on the ranges (minimum and maximum values) of the input data/parameters and the Latin hypercube sampling. The simulations were then compared against the observed data to evaluate the performance of the SCEM-UA algorithm. These steps are depicted in Fig. 1.Figure 1Flowchart for the uncertainty analysis.Full size imageRiver water temperatures simulated by the HFLUX modelRiver water temperature affects the water quality and the ecosystem health, and hence control of river water temperature is important to mitigation of its adverse effects1. The HFLUX model was used to simulate the streamflow temperatures at different locations and times. The model is highly flexible in terms of choosing the solution methods for solving the governing equations and selecting the energy budget terms such as shortwave solar radiation, latent heat flux, and sensible heat transfer flux. The model input data include the initial spatial and temporal temperature conditions, stream geometry data, discharge data, and meteorological data8. The water balance and energy balance equations are respectively given by8:$$frac{partial A}{{partial t}} + frac{partial Q}{{partial x}} = mathop qnolimits_{L}$$
(1)
$$frac{{partial left( {Amathop Tnolimits_{w} } right)}}{partial t} + frac{{partial left( {Qmathop Tnolimits_{w} } right)}}{partial x} = mathop qnolimits_{L} mathop Tnolimits_{L} + R$$
(2)
$$R = frac{{Bmathop varphi nolimits_{total} }}{{mathop rho nolimits_{w} mathop Cnolimits_{w} }}$$
(3)
where A is the cross section area of the stream (m2), x is the distance along the stream (m), t is the time (s), Q is the discharge of the stream (m3/s), qL is the lateral inflow per unit stream length (m2/s), Tw is the stream temperature ((^circ C)), TL is the temperature of the lateral inflow ((^circ C)), R is the energy flux (source or sink) per unit stream length ((^circ C) m2/s), B is the width of the stream (m), (mathop varphi nolimits_{total}) is the total energy flux to the stream per surface area (W/m2), (mathop rho nolimits_{w}) is the density of water (kg/m3), and (mathop Cnolimits_{w}) is the specific heat of water (J/kg (^circ C)). Equation (3) is based on a thermal datum of 0 (^circ C) and the impact on the absolute value of the advective heat flux term. In Eq. (2), if qL is negative, the first term on the right-hand side of the equation becomes a loss of qLTw. Also, dispersive heat transport that is omitted in Eq. 2 is negligible when the longitudinal change in water temperature is small in comparison to the temporal changes8.SCEM-UA algorithmThe SCEM-UA algorithm provides posterior distribution functions for the model parameters and input data by generating an initial sample from the parameter space. First, the indicators of n, q, and s that are respectively dimension (the number of investigate inputs), number of complexes (the population to be divided), and population (the number of sample points) are determined for the algorithm. Then, the algorithm searches the sampling points in the feasible space and sorts the points according to the density. The algorithm determines the sequence and complexes based on those points. The sequence is the first q points of the population and complexes are a collection of m points from the population. Note that m = s/q. In the next step, the points of each complex are sorted based on the density, which can be mathematically expressed as20:$$left{ {begin{array}{*{20}c} {mathop alpha nolimits^{k} le T,,,,,,,,,mathop theta nolimits^{t + 1} = Nleft( {mathop theta nolimits^{t} ,,mathop Cnolimits_{n}^{2} mathop Sigma nolimits^{k} } right)} \ {mathop alpha nolimits^{k} > T,,,,,,,,mathop theta nolimits^{t + 1} = Nleft( {mathop mu nolimits^{k} ,,mathop Cnolimits_{n}^{2} mathop Sigma nolimits^{k} } right)} \ end{array} } right.$$
(4)
where k = 1,2,…,q, α is the ratio of the mean posterior density of the m points of complexes to the mean posterior density of the last m generated points of sequences, (theta) is the points of complexes, ({c}_{n}=frac{2.4}{sqrt{n}}) , (T={10}^{6}), (mu) is the mean, and ∑ denotes the covariance. To investigate the new points created by the algorithm, the points of complexes are replaced by20:$$left{ {begin{array}{*{20}l} {Omega ge Zquad replace,best,member,of,mathop Cnolimits^{k} ,with,mathop theta nolimits^{t + 1} } \ {Omega < Zquad mathop theta nolimits^{t + 1} = mathop theta nolimits^{t} ,,,,,,,,,,,,,,,,,,,,,} \ end{array} } right.$$ (5) where (mathop Cnolimits^{k}) is the Kth complex, Z is drawn from the uniform distribution in the range of 0–1, and Ω is calculated by20:$$Omega = frac{{Pleft( {left. {mathop theta nolimits^{t + 1} } right|y} right)}}{{Pleft( {left. {mathop theta nolimits^{t} } right|y} right)}}$$ (6) where (Pleft( {left. {mathop theta nolimits^{t + 1} } right|y} right)) and (Pleft( {left. {mathop theta nolimits^{t} } right|y} right)) are the posterior probability distributions for (mathop theta nolimits^{t + 1}) and (mathop theta nolimits^{t}), respectively. Then, the algorithm examines the following condition for each complex. If it is rejected, the algorithm replaces the worst member ({c}^{k})(the point with the lowest density) with ({theta }^{t+1}) 20.$$mathop Gamma nolimits^{k} le T,,and,,Pleft( {{{mathop theta nolimits^{t + 1} } mathord{left/ {vphantom {{mathop theta nolimits^{t + 1} } y}} right. kern-nulldelimiterspace} y}} right) < ,Pleft( {{{mathop Cnolimits_{m}^{k} } mathord{left/ {vphantom {{mathop Cnolimits_{m}^{k} } y}} right. kern-nulldelimiterspace} y}} right)$$ (7) where ({Gamma }^{k}) is the ratio of the posterior density of the best (the point with the highest density) to the posterior density of the worst member of ({c}^{k}). The last step is to examine (beta) and L. Note that (beta) = 1 and L = m/10. If (beta < L), (beta = beta + 1) and the algorithm returns to sort complex points. Otherwise, the algorithm examines the Gelman and Rubin convergence6, and eventually provides the posterior distribution functions20. The value of the Gelman and Rubin convergence should be less than 1.2. The Gelman and Rubin convergence is examined by:$$R = sqrt {frac{g - 1}{g} + frac{q + 1}{{q.g}}frac{B}{W}}$$ (8) where g is the number of iterations within each sequence, B is the variance between the q sequence means, and W is the average of the q within-sequence variances for the parameter under consideration20.Study AREAMeadowbrook Creek was selected to test the methods proposed in this study8. The creek flows through the City of Syracuse in New York. Thus, this catchment consists of high residential and industrial land covers, which contribute runoff to the main channel. The creek is about 4 km long. A portion of this creek (475 m long) was selected for the modeling for a period of June 13–19, 2012 in this study. The upstream boundary condition in the HFLUX model was set based on the water temperature of the creek observed at the upstream station8. The uncertainty of the model inputs was examined at three selected points as shown in Fig. 2. Note that the input values at these three points had greater relative changes than the changes at other locations, which provided the possibility to improve the evaluation of the algorithm performance. In addition, these three locations had the same sampling of the selected input data. During the simulation period, the streamflow velocity varied within a range of 0.06–0.63 (m/s). The daily temperature changed between 8.9 and 28.2 °C. The relative humidity, used to calculate the total energy flux to the stream per surface area, changed from 36 to 93%. The creek bed mainly consisted of clay, cobbles, sand, and gravel materials. The basic statistics of the data/variables used in the HFLUX model are presented in Table 1. Figure 2 shows the study area, the creek, and the three selected points for analysis.Figure 2Study area and the locations of three evaluation sections (the gray enlarged map shows the State of New York), the map in this Figure is created by Google Earth 7.0.2.8415 (https://google.com/earth/versions).Full size imageTable 1 Basic statistics of the data/variables used in the HFLUX model.Full size tableEthical approvalAll authors accept all ethical approvals.Consent to participateAll authors consent to participate.Consent to publishAll authors consent to publish. More

1.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science https://doi.org/10.1126/science.aax3100 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Daszak, P., Cunningham, A. A. & Hyatt, A. D. Anthropogenic environmental change and the emergence of infectious diseases in wildlife. Acta Trop. https://doi.org/10.1016/S0001-706X(00)00179-0 (2001).Article 
PubMed 

Google Scholar 
3.Brearley, G. et al. Wildlife disease prevalence in human-modified landscapes. Biol. Rev. https://doi.org/10.1111/brv.12009 (2013).Article 
PubMed 

Google Scholar 
4.Magouras, I. et al. Emerging zoonotic diseases: Should we rethink the animal–human interface?. Front. Vet. Sci. https://doi.org/10.3389/fvets.2020.582743 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
5.American Veterinary Medical Association. One Health: A New Professional Imperative. One Health Initiative Task Force: Final Report. (2008).6.VandeWoude, S. et al. Parallel pandemics illustrate the need for One Health solutions. EcoEvoRxiv (2021).7.Köndgen, S. et al. Pandemic human viruses cause decline of endangered great apes. Curr. Biol. 18, 260–264 (2008).Article 

Google Scholar 
8.Sharp, P. M., Plenderleith, L. J. & Hahn, B. H. Ape origins of human malaria. Annu. Rev. Microbiol. https://doi.org/10.1146/annurev-micro-020518-115628 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Liu, W. et al. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature https://doi.org/10.1038/nature09442 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Keele, B. F. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science (80-). 313, 523–526 (2006).ADS 
CAS 
Article 

Google Scholar 
11.Calvignac-Spencer, S., Leendertz, S. A. J., Gillespie, T. R. & Leendertz, F. H. Wild great apes as sentinels and sources of infectious disease. Clin. Microbiol. Infect. https://doi.org/10.1111/j.1469-0691.2012.03816.x (2012).Article 
PubMed 

Google Scholar 
12.Ryan, S. J. & Walsh, P. D. Consequences of non-intervention for infectious disease in African great apes. PLoS One 6, e29030 (2011).ADS 
CAS 
Article 

Google Scholar 
13.Bermejo, M. et al. Ebola outbreak killed 5000 gorillas. Science 314, 1564 (2006).ADS 
CAS 
Article 

Google Scholar 
14.Walsh, P. D. et al. Catastrophic ape decline in western equatorial Africa. Nature 422, 611–614 (2003).ADS 
CAS 
Article 

Google Scholar 
15.Thompson, M. E. et al. Risk factors for respiratory illness in a community of wild chimpanzees (Pan troglodytes schweinfurthii). R. Soc. Open Sci. https://doi.org/10.1098/rsos.180840 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Williams, J. M. et al. Causes of death in the Kasekela chimpanzees of Gombe National Park, Tanzania. Am. J. Primatol. https://doi.org/10.1002/ajp.20573 (2008).Article 
PubMed 

Google Scholar 
17.Negrey, J. D. et al. Simultaneous outbreaks of respiratory disease in wild chimpanzees caused by distinct viruses of human origin. Emerg. Microbes Infect. https://doi.org/10.1080/22221751.2018.1563456 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Scully, E. J. et al. Lethal respiratory disease associated with human rhinovirus C in wild Chimpanzees, Uganda, 2013. Emerg. Infect. Dis. https://doi.org/10.3201/eid2402.170778 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Smith, K. F., Acevedo-Whitehouse, K. & Pedersen, A. B. The role of infectious diseases in biological conservation. Anim. Conserv. https://doi.org/10.1111/j.1469-1795.2008.00228.x (2009).Article 

Google Scholar 
20.Capps, B. & Lederman, Z. One health, vaccines and ebola: The opportunities for shared benefits. J. Agric. Environ. Ethics 28, 1011–1032 (2015).Article 

Google Scholar 
21.Leendertz, S. A. J. et al. Ebola in great apes—current knowledge, possibilities for vaccination, and implications for conservation and human health. Mamm. Rev. https://doi.org/10.1111/mam.12082 (2017).Article 

Google Scholar 
22.Bull, C. M., Godfrey, S. S. & Gordon, D. M. Social networks and the spread of Salmonella in a sleepy lizard population. Mol. Ecol. 21, 4386–4392 (2012).CAS 
Article 

Google Scholar 
23.Vanderwaal, K. L., Atwill, E. R., Isbell, L. A. & McCowan, B. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis). J. Anim. Ecol. https://doi.org/10.1111/1365-2656.12137 (2014).Article 
PubMed 

Google Scholar 
24.Silk, M. J. et al. Using social network measures in wildlife disease ecology, epidemiology, and management. Bioscience 67, 245–257 (2017).Article 

Google Scholar 
25.Craft, M. E. Infectious disease transmission and contact networks in wildlife and livestock. Philos. Trans. R Soc. Lond. Ser. B Biol. Sci. 370, 1–12 (2015).Article 

Google Scholar 
26.Craft, M. E. & Caillaud, D. Network models: An underutilized tool in wildlife epidemiology?. Interdiscip. Perspect. Infect. Dis. 2011, 676949 (2011).Article 

Google Scholar 
27.Rushmore, J. et al. Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk. J. Anim. Ecol. 82, 976–986 (2013).Article 

Google Scholar 
28.Sandel, A. A. et al. Social network predicts exposure to respiratory infection in a wild chimpanzee group. EcoHealth https://doi.org/10.1007/s10393-020-01507-7 (2021).Article 
PubMed Central 

Google Scholar 
29.Rushmore, J. et al. Network-based vaccination improves prospects for disease control in wild chimpanzees. J. R. Soc. Interface https://doi.org/10.1098/rsif.2014.0349 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Sah, P., Leu, S. T., Cross, P. C., Hudson, P. J. & Bansal, S. Unraveling the disease consequences and mechanisms of modular structure in animal social networks. Proc. Natl. Acad. Sci. 114, 4165–4170 (2017).CAS 
Article 

Google Scholar 
31.Robbins, M. M. et al. Extreme conservation leads to recovery of the virunga mountain gorillas. PLoS One https://doi.org/10.1371/journal.pone.0019788 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Granjon, A. C. et al. Estimating abundance and growth rates in a wild mountain gorilla population. Anim. Conserv. https://doi.org/10.1111/acv.12559 (2020).Article 

Google Scholar 
33.Weber, A., Kalema-Zikusoka, G. & Stevens, N. J. Lack of rule-adherence during mountain gorilla tourism encounters in Bwindi Impenetrable National Park, Uganda, places gorillas at risk from human disease. Front. Public Health. https://doi.org/10.3389/fpubh.2020.00001 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Woodford, M. H., Butynski, T. M. & Karesh, W. B. Habituating the great apes: The disease risks. Oryx 36, 153–160 (2002).Article 

Google Scholar 
35.Spelman, L. H. et al. Respiratory disease in mountain gorillas (gorilla beringei beringei) in rwanda, 1990–2010: Outbreaks, clinical course, and medical management. J. Zoo Wildl. Med. https://doi.org/10.1638/2013-0014R.1 (2013).Article 
PubMed 

Google Scholar 
36.Nutter, F. B., Whittier, C., Cranfield, M. R. & Lowenstine, L. J. Examining causes of death for mountain gorillas (Gorilla beringei beringei and G.b. undecided) from 1968–2004: An aid to conservation programs. In Proceedings of the Wildlife Disease Association International Conference. June 26-July 1, 2005, Cairns, Australia 200–201 (2005).37.Palacios, G. et al. Human metapneumovirus infection in wild mountain gorillas, Rwanda. Emerg. Infect. Dis. https://doi.org/10.3201/eid1704.100883 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Mazet, J. A. K. et al. Human respiratory syncytial virus detected in Mountain Gorilla respiratory outbreaks. EcoHealth https://doi.org/10.1007/s10393-020-01506-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Szentiks, C. A., Köndgen, S., Silinski, S., Speck, S. & Leendertz, F. H. Lethal pneumonia in a captive juvenile chimpanzee (Pan troglodytes) due to human-transmitted human respiratory syncytial virus (HRSV) and infection with Streptococcus pneumoniae. J. Med. Primatol. https://doi.org/10.1111/j.1600-0684.2009.00346.x (2009).Article 
PubMed 

Google Scholar 
40.Grützmacher, K. S. et al. Codetection of respiratory syncytial virus in habituated wild western lowland gorillas and humans during a respiratory disease outbreak. EcoHealth https://doi.org/10.1007/s10393-016-1144-6 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
41.Gryseels, S. et al. Risk of human-to-wildlife transmission of SARS-CoV-2. Mamm. Rev. https://doi.org/10.1111/mam.12225 (2021).Article 

Google Scholar 
42.Melin, A. D., Janiak, M. C., Marrone, F., Arora, P. S. & Higham, J. P. Comparative ACE2 variation and primate COVID-19 risk. Commun. Biol. https://doi.org/10.1038/s42003-020-01370-w (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.2010146117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Caillaud, D. et al. Violent encounters between social units hinder the growth of a high-density mountain gorilla population. Sci. Adv. https://doi.org/10.1126/SCIADV.ABA0724 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Caillaud, D. et al. Gorilla susceptibility to Ebola virus: The cost of sociality. Curr. Biol. 16, 489–491 (2006).Article 

Google Scholar 
46.Reagan, K. J., McGeady, M. L. & Crowell, R. L. Persistence of human rhinovirus infectivity under diverse environmental conditions. Appl. Environ. Microbiol. https://doi.org/10.1128/aem.41.3.618-620.1981 (1981).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Danon, L. et al. Networks and the epidemiology of infectious disease. Interdiscip. Perspect. Infect. Dis. 2011, 1–28 (2011).Article 

Google Scholar 
48.Salazar, M. F. M., Waldner, C., Stookey, J. & Bollinger, T. K. Infectious disease and grouping patterns in mule deer. PLoS One https://doi.org/10.1371/journal.pone.0150830 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Weber, N. et al. Badger social networks correlate with tuberculosis infection. Curr. Biol. https://doi.org/10.1016/j.cub.2013.09.011 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
50.VanderWaal, K. L., Enns, E. A., Picasso, C., Packer, C. & Craft, M. E. Evaluating empirical contact networks as potential transmission pathways for infectious diseases. J. R. Soc. Interface https://doi.org/10.1098/rsif.2016.0166 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Lambert, L. & Culley, F. J. Innate immunity to respiratory infection in early life. Front. Immunol. https://doi.org/10.3389/fimmu.2017.01570 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
52.Jackson, G. G. et al. Susceptibility and immunity to common upper respiratory viral infections—the common cold. Ann. Intern. Med. https://doi.org/10.7326/0003-4819-53-4-719 (1960).Article 
PubMed 

Google Scholar 
53.Kurvers, R. H. J. M., Krause, J., Croft, D. P., Wilson, A. D. M. & Wolf, M. The evolutionary and ecological consequences of animal social networks: Emerging issues. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2014.04.002 (2014).Article 
PubMed 

Google Scholar 
54.Casimir, M. J. An analysis of gorilla nesting sites of the Mt. Kahuzi Region (Zaire). Folia Primatol. 32, 290–308 (1979).Article 

Google Scholar 
55.van Hamme, G., Svensson, M. S., Morcatty, T. Q., Nekaris, K.A.-I. & Nijman, V. Keep your distance: Using social media to evaluate the risk of disease transmission in gorilla ecotourism. People Nat. https://doi.org/10.1002/pan3.10187 (2021).Article 

Google Scholar 
56.Leendertz, F. H. & Kalema-Zikusoka, G. Vaccinate in biodiversity hotspots to protect people and wildlife from each other. Nature https://doi.org/10.1038/d41586-021-00690-z (2021).Article 
PubMed 

Google Scholar 
57.Porter, A. et al. Behavioral responses around conspecific corpses in adult eastern gorillas (Gorilla beringei spp.). PeerJ https://doi.org/10.7717/peerj.6655 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Albers, P. C. H. & De Vries, H. Elo-rating as a tool in the sequential estimation of dominance strengths. Anim. Behav. https://doi.org/10.1006/anbe.2000.1571 (2001).Article 

Google Scholar 
59.Neumann, C. et al. Assessing dominance hierarchies: Validation and advantages of progressive evaluation with Elo-rating. Anim. Behav. https://doi.org/10.1016/j.anbehav.2011.07.016 (2011).Article 

Google Scholar 
60.Neumann, C. & Lars, K. EloRating: Animal dominance hierarchies by Elo rating. R Package Version 0.43. https://rdrr.io/cran/EloRating/ (2014).61.Wright, E. et al. Male body size, dominance rank and strategic use of aggression in a group-living mammal. Anim. Behav. https://doi.org/10.1016/j.anbehav.2019.03.011 (2019).Article 

Google Scholar 
62.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst. 1695(5), 1–9 (2006).
Google Scholar 
63.Wood, S. & Scheipl, F. gamm4: Generalized additive mixed models using ‘mgcv’ and ‘lme4′. R Package Version 0.2-6. https://CRAN.R-project.org/package=gamm4 (2020).64.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. https://doi.org/10.1111/j.1467-9868.2010.00749.x (2011).MathSciNet 
Article 
MATH 

Google Scholar 
65.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
66.VanderWaal, K. L. k-test. GitHub Repository. https://github.com/kvanderwaal/k-test (2017).67.Calenge, C. The package ‘adehabitat’ for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. https://doi.org/10.1016/j.ecolmodel.2006.03.017 (2006).Article 

Google Scholar  More

1.Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science (80-.) 318, 1737–1742 (2007).ADS 
CAS 
Article 

Google Scholar 
2.Roberts, M., Hanley, N., Williams, S. & Cresswell, W. Terrestrial degradation impacts on coral reef health: Evidence from the Caribbean. Ocean Coast. Manag. 149, 52–68 (2017).Article 

Google Scholar 
3.Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl. Acad. Sci. 115, 1754–1759 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Ries, J. B. Skeletal mineralogy in a high-CO2 world. J. Exp. Mar. Biol. Ecol. 403, 54–64 (2011).CAS 
Article 

Google Scholar 
5.Erez, J., Reynaud, S., Silverman, J., Schneider, K. & Allemand, D. Coral calcification under ocean acidification and global change. In Coral Reefs: An Ecosystem in Transition (2011). https://doi.org/10.1007/978-94-007-0114-4_10.6.Dove, S. G. et al. Future reef decalcification under a business-as-usual CO2 emission scenario. Proc. Natl. Acad. Sci. 110, 15342–15347 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Cooper, T. F., De’ath, G., Fabricius, K. E. & Lough, J. M. Declining coral calcification in massive Porites in two nearshore regions of the northern Great Barrier Reef. Glob. Chang. Biol. 14, 529–538 (2008).ADS 
Article 

Google Scholar 
8.Cooper, T. F., O’Leary, R. A. & Lough, J. M. Growth of Western Australian corals in the Anthropocene. Science (80-.) 335, 593–596 (2012).ADS 
CAS 
Article 

Google Scholar 
9.Teixidó, N. et al. Ocean acidification causes variable trait-shifts in a coral species. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15372 (2020).Article 
PubMed 

Google Scholar 
10.Pandolfi, J. M. Incorporating uncertainty in predicting the future response of coral reefs to climate change. Annu. Rev. Ecol. Evol. Syst. 46, 281–303 (2015).Article 

Google Scholar 
11.Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 
Article 

Google Scholar 
12.Jokiel, P. L. et al. Ocean acidification and calcifying reef organisms: A mesocosm investigation. Coral Reefs 27, 473–483 (2008).ADS 
Article 

Google Scholar 
13.Fantazzini, P. et al. Gains and losses of coral skeletal porosity changes with ocean acidification acclimation. Nat. Commun. 6, 7785 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Wittmann, A. C. & Pörtner, H.-O. Sensitivities of extant animal taxa to ocean acidification. Nat. Clim. Chang. 3, 995–1001 (2013).ADS 
CAS 
Article 

Google Scholar 
15.Fabricius, K. E. et al. Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations. Nat. Clim. Chang. 1, 165–169 (2011).ADS 
CAS 
Article 

Google Scholar 
16.Riebesell, U. Acid test for marine biodiversity. Nature 454, 46–47 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
17.Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Johnson, V. R., Russell, B. D., Fabricius, K. E., Brownlee, C. & Hall-Spencer, J. M. Temperate and tropical brown macroalgae thrive, despite decalcification, along natural CO2 gradients. Glob. Chang. Biol. https://doi.org/10.1111/j.1365-2486.2012.02716.x (2012).Article 
PubMed 

Google Scholar 
19.Prada, F. et al. Ocean warming and acidification synergistically increase coral mortality. Sci. Rep. 7, 1–10 (2017).ADS 
MathSciNet 
Article 
CAS 

Google Scholar 
20.Inoue, S., Kayanne, H., Yamamoto, S. & Kurihara, H. Spatial community shift from hard to soft corals in acidified water. Nat. Clim. Chang. 3, 683–687 (2013).ADS 
CAS 
Article 

Google Scholar 
21.Crook, E. D., Cohen, A. L., Rebolledo-Vieyra, M., Hernandez, L. & Paytan, A. Reduced calcification and lack of acclimatization by coral colonies growing in areas of persistent natural acidification. Proc. Natl. Acad. Sci. 110, 11044–11049 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Teixidó, N. et al. Functional biodiversity loss along natural CO2 gradients. Nat. Commun. 9, 5149 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Strahl, J. et al. Physiological and ecological performance differs in four coral taxa at a volcanic carbon dioxide seep. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 184, 179–186 (2015).CAS 
Article 

Google Scholar 
24.Fabricius, K. E., De’ath, G., Noonan, S. & Uthicke, S. Ecological effects of ocean acidification and habitat complexity on reef-associated macroinvertebrate communities. Proc. R. Soc. B Biol. Sci. 281, 20132479 (2014).CAS 
Article 

Google Scholar 
25.Fabricius, K. E., Noonan, S. H. C., Abrego, D., Harrington, L. & De’ath, G. Low recruitment due to altered settlement substrata as primary constraint for coral communities under ocean acidification. Proc. R. Soc. B Biol. Sci. 284, 20171536 (2017).Article 
CAS 

Google Scholar 
26.Siahainenia, L., Tuhumury, S. F., Uneputty, P. A. & Tuhumury, N. C. Survival and growth of transplanted coral reef in lagoon ecosystem of Ihamahu, Central Maluku, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 339, 012003 (2019).Article 

Google Scholar 
27.Horwitz, R., Hoogenboom, M. O. & Fine, M. Spatial competition dynamics between reef corals under ocean acidification. Sci. Rep. 7, 40288 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Noonan, S. H. C., Fabricius, K. E. & Humphrey, C. Symbiodinium community composition in scleractinian corals is not affected by life-long exposure to elevated carbon dioxide. PLoS ONE 8, e63985 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Caroselli, E. et al. Environmental implications of skeletal micro-density and porosity variation in two scleractinian corals. Zoology 114, 255–264 (2011).PubMed 
Article 

Google Scholar 
30.Reggi, M. et al. Biomineralization in Mediterranean corals: The role of the intraskeletal organic matrix. Cryst. Growth Des. 14, 4310–4320 (2014).CAS 
Article 

Google Scholar 
31.Goffredo, S. et al. The skeletal organic matrix from Mediterranean coral Balanophyllia Europaea influences calcium carbonate precipitation. PLoS ONE 6, e22338 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Goffredo, S. et al. Biomineralization control related to population density under ocean acidification. Nat. Clim. Chang. 4, 593–597 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Borgia, G. C., Brown, R. J. S. & Fantazzini, P. Uniform-penalty inversion of multiexponential decay data. J. Magn. Reson. 132, 65–77 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
34.Bortolotti, F., Brown, R. & Fantazzini, P. UpenWin: A Software for Inversion of Multiexponential Decay Data (Windows System Alma Mater Studiorum—Università di Bologna, 2012).
Google Scholar 
35.Fantazzini, P. et al. A time-domain nuclear magnetic resonance study of Mediterranean scleractinian corals reveals skeletal-porosity sensitivity to environmental changes. Environ. Sci. Technol. 47, 12679–12686 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Coronado, I., Fine, M., Bosellini, F. R. & Stolarski, J. Impact of ocean acidification on crystallographic vital effect of the coral skeleton. Nat. Commun. 10, 2896 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Pokroy, B., Fitch, A. & Zolotoyabko, E. The microstructure of biogenic calcite: A view by high-resolution synchrotron powder diffraction. Adv. Mater. 18, 2363–2368 (2006).CAS 
Article 

Google Scholar 
38.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to software and statistical methods. In Plymouth (2008).39.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018). ISBN 3-900051-07-0. http://www.R-project.org.40.Toby, B. H. & Von Dreele, R. B. GSAS-II: The genesis of a modern open-source all purpose crystallography software package. J. Appl. Crystallogr. 46, 544–549 (2013).CAS 
Article 

Google Scholar 
41.Jiang, H. G., Rühle, M. & Lavernia, E. J. On the applicability of the x-ray diffraction line profile analysis in extracting grain size and microstrain in nanocrystalline materials. J. Mater. Res. 14, 549–559 (1999).ADS 
CAS 
Article 

Google Scholar 
42.Vercelloni, J. et al. Forecasting intensifying disturbance effects on coral reefs. Glob. Chang. Biol. 26, 2785–2797 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Guo, W. et al. Ocean acidification has impacted coral growth on the Great Barrier Reef. Geophys. Res. Lett. 47, 1–9 (2020).
Google Scholar 
44.Tambutté, E. et al. Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nat. Commun. 6, 7368 (2015).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Schneider, K. & Erez, J. The effect of carbonate chemistry on calcification and photosynthesis in the hermatypic coral Acropora eurystoma. Limnol. Oceanogr. 51, 1284–1293 (2006).ADS 
CAS 
Article 

Google Scholar 
46.Martinez, A. et al. Species-specific calcification response of Caribbean corals after 2-year transplantation to a low aragonite saturation submarine spring. Proc. Biol. Sci. 286, 20190572 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Comeau, S. et al. Resistance to ocean acidification in coral reef taxa is not gained by acclimatization. Nat. Clim. Chang. 9, 477–483 (2019).ADS 
CAS 
Article 

Google Scholar 
48.McCulloch, M. et al. Resilience of cold-water scleractinian corals to ocean acidification: Boron isotopic systematics of pH and saturation state up-regulation. Geochim. Cosmochim. Acta 87, 21–34 (2012).ADS 
CAS 
Article 

Google Scholar 
49.Movilla, J. et al. Differential response of two Mediterranean cold-water coral species to ocean acidification. Coral Reefs 33, 675–686 (2014).ADS 
Article 

Google Scholar 
50.Kurihara, H., Takahashi, A., Reyes-Bermudez, A. & Hidaka, M. Intraspecific variation in the response of the scleractinian coral Acropora digitifera to ocean acidification. Mar. Biol. 165, 38 (2018).Article 

Google Scholar 
51.Barnes, D. J. & Devereux, M. J. Variations in skeletal architecture associated with density banding in the hard coral Porites. J. Exp. Mar. Biol. Ecol. 121, 37–54 (1988).Article 

Google Scholar 
52.Bucher, D. J., Harriott, V. J. & Roberts, L. G. Skeletal micro-density, porosity and bulk density of acroporid corals. J. Exp. Mar. Biol. Ecol. 228, 117–136 (1998).Article 

Google Scholar 
53.Mass, T. et al. Amorphous calcium carbonate particles form coral skeletons. Proc. Natl. Acad. Sci. 114, E7670–E7678 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Vidal-Dupiol, J. et al. Genes related to ion-transport and energy production are upregulated in response to CO2-driven pH decrease in corals: New insights from transcriptome analysis. PLoS ONE 8, e58652 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Suggett, D. J. et al. Light availability determines susceptibility of reef building corals to ocean acidification. Coral Reefs 32, 327–337 (2013).ADS 
Article 

Google Scholar 
56.Vogel, N., Meyer, F., Wild, C. & Uthicke, S. Decreased light availability can amplify negative impacts of ocean acidification on calcifying coral reef organisms. Mar. Ecol. Prog. Ser. 521, 49–61 (2015).ADS 
CAS 
Article 

Google Scholar 
57.Tanaka, Y. et al. Nutrient availability affects the response of juvenile corals and the endosymbionts to ocean acidification. Limnol. Oceanogr. 59, 1468–1476 (2014).ADS 
CAS 
Article 

Google Scholar 
58.Towle, E. K., Enochs, I. C. & Langdon, C. Threatened Caribbean coral is able to mitigate the adverse effects of ocean acidification on calcification by increasing feeding rate. PLoS ONE 10, e0123394 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Stolarski, J., Przeniosło, R., Mazur, M. & Brunelli, M. High-resolution synchrotron radiation studies on natural and thermally annealed scleractinian coral biominerals. J. Appl. Crystallogr. 40, 2–9 (2007).CAS 
Article 

Google Scholar 
60.Maslen, E. N., Streltsov, V. A., Streltsova, N. R. & Ishizawa, N. Electron density and optical anisotropy in rhombohedral carbonates. III. Synchrotron X-ray studies of CaCO3, MgCO3 and MnCO3. Acta Crystallogr. Sect. B Struct. Sci. 51, 929–939 (1995).Article 

Google Scholar 
61.Wall, M. et al. Linking internal carbonate chemistry regulation and calcification in corals growing at a Mediterranean CO2 vent. Front. Mar. Sci. 6, 699 (2019).Article 

Google Scholar 
62.Wickham, H. ggplot2 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-24277-4.Book 
MATH 

Google Scholar  More

1.Nasi, R., Taber, A. & van Vliet, N. Empty forests, empty stomachs? Wild meat and livelihoods in the Congo and Amazon Basins. Int. For. Rev. 13, 355–368. https://doi.org/10.1505/146554811798293872 (2011).Article 

Google Scholar 
2.van Vliet, N. “Bushmear crisis” and “Cultural imperialism” in wildlife management? Taking value orientations into account for a more sustainable and culturally acceptable wildmeat sector. Front. Ecol. Evol. 6, 112. https://doi.org/10.3389/fevo.2018.00112 (2018).ADS 
Article 

Google Scholar 
3.Nunes, A. V., Peres, C. A., Constantino, P. A. L., Santos, B. A. & Fischer, E. Irreplaceable socioeconomic value of wild meat extraction to local food security in rural Amazonia. Biol. Conserv. 236, 171–179. https://doi.org/10.1016/j.biocon.2019.05.010 (2019).Article 

Google Scholar 
4.Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. PNAS 113, 892–897. https://doi.org/10.1073/pnas.1516525113 (2016).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
5.Brodie, J. F. Carbon costs and bushmeat benefits of hunting in tropical forests. Ecol. Econ. 152, 22–26. https://doi.org/10.1016/j.ecolecon.2018.05.028 (2018).Article 

Google Scholar 
6.Wright, I. J. et al. Relationships among ecologically important dimensions of plant trait variation in seven neotropical forests. Ann. Bot. 99, 1003–1015. https://doi.org/10.1093/aob/mcl066 (2007).Article 
PubMed 

Google Scholar 
7.Bunker, D. E. et al. Species loss and aboveground carbon storage in a tropical forest. Science 310, 1029–1031. https://doi.org/10.1126/science.1117682 (2005).ADS 
Article 
PubMed 
CAS 

Google Scholar 
8.Harrison, R. D. et al. Consequences of defaunation for a tropica tree community. Ecol. Lett. 16, 687–694. https://doi.org/10.1111/ele.12102 (2013).Article 
PubMed 

Google Scholar 
9.Bello, C. et al. Defaunation affects carbon storage in tropical forests. Sci. Adv. 1, e1501105. https://doi.org/10.1126/sciadv.1501105 (2015).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
10.Sarti, F. M. et al. Beyond protein intake: Bushmeat as source of micronutrients in the Amazon. Ecol. Soc. 20, 22 (2015).Article 

Google Scholar 
11.Goelden, C. D. et al. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. PNAS 108, 19653–19656. https://doi.org/10.1073/pnas.1112586108 (2011).ADS 
Article 

Google Scholar 
12.Fa, J. E. et al. Disentangling the relative effects of bushmeat availability on human nutrition in central Africa. Sci. Rep. 5, 8168. https://doi.org/10.1038/srep08168 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
13.Peres, C. A. Conservation in sustainable-use tropical forest reserves. Conserv. Biol. 25(1124–1129), 2011. https://doi.org/10.1111/j.1523-1739.2011.01770.x (2011).Article 

Google Scholar 
14.Ohl-Schacherer, J. et al. The sustainability of subsistence hunting by Matsigenka native communities in Manu National Park, Peru. Conserv. Biol. 21, 1174–1185. https://doi.org/10.1111/j.1523-1739.2007.00759.x (2007).Article 
PubMed 

Google Scholar 
15.Constantino, P. A. L. et al. Indigenous collaborative research for wildlife management in Amazonia: The case of the Kaxinawá, Acre, Brazil. Biol. Conserv. 141, 2718–2729. https://doi.org/10.1016/j.biocon.2008.08.008 (2008).Article 

Google Scholar 
16.Weinbaum, K. Z., Brashares, J. S., Golden, C. D. & Getz, W. M. Searching for sustainability: Are assessments of wildlife harvests behind the times?. Ecol. Lett. 16, 99–111. https://doi.org/10.1111/ele.12008 (2013).Article 
PubMed 

Google Scholar 
17.Novaro, A. J., Redford, K. H. & Bodmer, R. E. Effect of hunting in source-sink systems in the Neotropics. Conserv. Biol. 14, 713–721. https://doi.org/10.1046/j.1523-1739.2000.98452.x (2000).Article 

Google Scholar 
18.Constantino, P. A. C., Benchimol, M. & Antunes, A. P. Designing indigenous lands in Amazonia: Securing indigenous rights and wildlife conservation through hunting management. Land Use Policy 77, 652–660. https://doi.org/10.1016/j.landusepol.2018.06.016 (2018).Article 

Google Scholar 
19.Kaimowitz, D. & Angelsen, A. Will livestock intensification help save Latin America’s tropical forests?. J. Sustain. For. 27, 6–24. https://doi.org/10.1080/10549810802225168 (2008).Article 

Google Scholar 
20.Curtis, P. G., Slat, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111. https://doi.org/10.1126/science.aau3445 (2018).ADS 
Article 
PubMed 
CAS 

Google Scholar 
21.De Sy, V. et al. Land use patterns and related carbon losses following deforestation in South America. Environ. Res. Lett. 10, 124004. https://doi.org/10.1088/1748-9326/10/12/124004 (2015).ADS 
Article 

Google Scholar 
22.Hosonuma, N. et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 7, 044009. https://doi.org/10.1088/1748-9326/7/4/044009 (2012).ADS 
Article 

Google Scholar 
23.Herrero, M. et al. Livestock and the environment—What have we learned in the past decade?. Annu. Rev. Environ. Resour. 40, 177–202. https://doi.org/10.1146/annurev-environ-031113-093503 (2015).Article 

Google Scholar 
24.Hong, C. et al. Global and regional drivers of land-use emissions in 1961–2017. Nature 589, 554–561. https://doi.org/10.6084/m9.figshare.12248735 (2021).ADS 
Article 
PubMed 
CAS 

Google Scholar 
25.Steinfeld, H. et al. Livestock’s Long Shadow (FAO, 2006).
Google Scholar 
26.United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights (ST/ESA/SER.A/423) (2019).27.IPCC Climate Change 2014: Synthesis Report (eds. Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).28.Wolf, C., Ripple, W. J., Levi, T. & Peres, C. A. Eating plants and planting forests for the climate. Glob. Chang. Biol. 25, 3995–3995. https://doi.org/10.1111/gcb.14835 (2019).ADS 
Article 
PubMed 

Google Scholar 
29.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993. https://doi.org/10.1126/science.1201609 (2011).ADS 
Article 
PubMed 
CAS 

Google Scholar 
30.Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821. https://doi.org/10.1126/sciadv.1600821 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Maxwell, S. L. et al. Degradation and forgone removals increase the carbon imáct of intact forest loss by 626%. Sci. Adv. 5, eaax2546. https://doi.org/10.1126/sciadv.aax2546 (2019).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
32.Walker, W. S. et al. The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. PNAS 117, 3015–3025. https://doi.org/10.1073/pnas.1913321117 (2020).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
33.Angelsen, A. et al. Environmental income and rural livelihoods: A global-comparative analysis. World Dev. 64, 12–28. https://doi.org/10.1016/j.worlddev.2014.03.006 (2010).Article 

Google Scholar 
34.UNFCCC. Adoption of the Paris Agreement-Draft Decision-/CP.21 (United Nations Framework Convention on Climate Change, 2015).
Google Scholar 
35.Hinsley, A., Entwistle, A. & Pio, D. V. Does the long-term success of REDD+ also depend on biodiversity?. Oryx 49, 216–221. https://doi.org/10.1017/S0030605314000507 (2015).Article 

Google Scholar 
36.Krause, T. & Nielsen, M. R. Not seeing the forest for the trees: The oversight of defaunation in REDD+ and global forest governance. Forests 10, 344. https://doi.org/10.3390/f10040344 (2019).Article 

Google Scholar 
37.Nardoto, G. B. et al. Frozen chicken for wild fish: Nutritional transition in the Brazilian Amazon region determined by carbon and nitrogen stable isotope ratios in fingernails. Am. J. Hum. Biol. 23, 642–650. https://doi.org/10.1002/ajhb.21192 (2011).Article 
PubMed 

Google Scholar 
38.Farrel, D. The Role of Poultry in Human Nutrition. Poultry Development Review (FAO, 2013).
Google Scholar 
39.Poulsen, J. R., Clark, C. J. & Mavah, G. Wildlife management in a logging concession in Northern Congo: Can livelihoods be maintained through sustainable hunting? In Bushmeat and Livelihoods (eds Davies, G. & Brown, D.) 140–157 (Blackwell Publishing, 2007).
Google Scholar 
40.Nunes, A. V., Guariento, R. D., Santos, B. A. & Fischer, E. Wild meat sharing among non-indigenous people in the Southwestern Amazon. Behv. Ecol. Sociobiol. 73, 26. https://doi.org/10.1007/s00265-018-2628-x (2019).Article 

Google Scholar 
41.WHO/FAO/UNU Protein and Amino Acid Requirements in Human Nutrition; Report of a joint WHO/FAO/UNU Expert Consultation, WHO Tech Rep Ser no. 935 (WHO, 2007).42.FAO. FAOSTAT Agri-Environmental Indicators, Emissions Intensities. http://www.fao.org/faostat/en/#data/EI (2019).43.Opio, C. et al. Greenhouse Gas Emissions from Ruminant Supply Chains—A Global Life Cycle Assessment (Food and Agriculture Organization of the United Nations (FAO), 2013).
Google Scholar 
44.Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992. https://doi.org/10.1126/science.aaq0216 (2018).ADS 
Article 
PubMed 
CAS 

Google Scholar 
45.ICAO. International Civil Aviation Organization. https://www.icao.int/environmental-protection/Carbonoffset/Pages/default.aspx (2016).46.Searchinger, T. D. et al. Assessing the efficiency of changes in land use for mitigating climate change. Nature 564, 249–253. https://doi.org/10.1038/s41586-018-0757-z (2018).ADS 
Article 
PubMed 
CAS 

Google Scholar 
47.Ministério do Meio Ambiente (MMA). Programa áreas protegidas da Amazônia ARPA-Fase II (2010).48.Arensberg, W. W. Critical Ecosystem Partnership Fund Mid-Term Review (Critical Ecosystem Partnership Fund, 2003).49.Sistema Integrado de Planejamento e Orçamento (SIOP). Cadastro de Ações. Apoio à conservação Ambiental e à Erradicação da Extrema Pobreza Bolsa Verde (Secretaria de Orçamento Federal, Ministério do Planejamento, Orçamento e Gestão, 2014).50.World Bank. State and Trends of Carbon Pricing (World Bank, 2020). https://doi.org/10.1596/978-1-4648-1586-7.51.NASA (National Aeronautics and Space Administration). NASA Administrator Statement on Moon to Mars Initiative, fy 2021 Budget. https://www.nasa.gov/press-release/nasa-administrator-statement-on-moon-to-mars-initiative-fy-2021-budget.52.Peres, C. A. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian forest vertebrates. Conserv. Biol. 15, 1490–1505. https://doi.org/10.1046/j.1523-1739.2001.01089.x (2001).Article 

Google Scholar 
53.Griscom, B. W. et al. Natural climate solutions. PNAS 114, 11645–11650. https://doi.org/10.1073/pnas.1710465114 (2017).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
54.Reid, H., Faulkner, L. & Weiser, A. in IIED Climate Change Working Paper (eds. Fisher, S. & Reid, H.) 3–67 (2013).55.Munang, R., Andrews, J., Alverson, K. & Mebratu, D. Harnessing ecosystem-based adaptation to address the social dimensions of climate change. Environ.: Sci. Policy Sustain. Dev. 56, 18–24. https://doi.org/10.1080/00139157.2014.861676 (2013).Article 

Google Scholar 
56.Woroniecki, S. Enabling environments? Examining social co-benefits of ecosystem-based adaptation to climate change in Sri Lanka. Sustainability 11, 772. https://doi.org/10.3390/su11030772 (2019).Article 

Google Scholar 
57.Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 375, 20190120. https://doi.org/10.1098/rstb.2019.0120 (2020).Article 

Google Scholar 
58.Wilkie, D. S., Wieland, M. & Poulsen, J. R. Unsustainable vs. sustainable hunting for food in Gabon: Modeling short- and long- term gains and losses. Front. Ecol. Evol. 7, 357. https://doi.org/10.3389/fevo.2019.00357 (2019).Article 

Google Scholar 
59.Booth, H. et al. Assessing the impact of regulations on the use and trade of wildlife: An operational framework, with a case study on manta rays. Glob. Ecol. Conserv. 22, e00953 (2020).Article 

Google Scholar 
60.Dickman, A. et al. Trophy hunting bans imperil biodiversity. Science 365(6456), 874. https://doi.org/10.1126/science.aaz0735 (2019).ADS 
Article 
PubMed 
CAS 

Google Scholar 
61.Marrocoli, S. et al. Using wildlife indicators to facilitate wildlife monitoring in hunter-self monitoring schemes. Ecol. Indic. 105, 254–263. https://doi.org/10.1016/j.ecolind.2019.05.050 (2019).Article 

Google Scholar 
62.van Vliet, N. et al. Frameworks regulating hunting for meat in tropical countries leave the sectos in the limbo. Front. Ecol. Evol. 7, 1–7. https://doi.org/10.3389/fevo.2019.00280 (2019).Article 

Google Scholar 
63.Ronchail, J. et al. Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the equatorial Pacific and the tropical Atlantic oceans. Int. J. Climatol. 22, 1663–1686. https://doi.org/10.1002/joc.815 (2002).Article 

Google Scholar 
64.CSC. Climate Change Scenarios for the Congo Basin (Climate Service Centre Report No. 11, 2013).65.Akkermans, T., Thiery, W. & Lipzig, N. P. M. V. The regional climate impact of a realistic future deforestation scenario in the Congo Basin. J. Clim. 27, 2714–2734. https://doi.org/10.1175/JCLI-D-D13-00361.1 (2014).ADS 
Article 

Google Scholar 
66.Siebert, A. Hydroclimate extrems in Africa: Variability, observations and modeled projectios. Geography 8, 351–367. https://doi.org/10.1111/gec3.12136 (2014).Article 

Google Scholar 
67.Feldpausch, T. R. et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9, 3381–3403. https://doi.org/10.5194/bg-9-3381-2012 (2012).ADS 
Article 

Google Scholar 
68.Hansen, M. C. et al. High- resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).ADS 
Article 
PubMed 
CAS 

Google Scholar 
69.Mayaux, P. et al. Tropical forest cover change in the 1990s and options for future monitoring. Philos. Trans. R. Soc. B 360, 373–384. https://doi.org/10.1098/rstb.2004.1590 (2005).Article 

Google Scholar 
70.Zelazowski, P., Malhi, Y., Huntingford, C., Sitch, S. & Fisher, J. B. Changes in the potential distribution of humid tropical forests on a warmer planet. Philos. Trans. Soc. A 369, 137–160. https://doi.org/10.1098/rsta.2010.0238 (2011).ADS 
Article 

Google Scholar 
71.Nkem, J., Idinoba, M., Brockhaus, M., Kalame, F. & Tas, A. Adaptation to Climate Change in Africa: Synergies with Biodiversity and Forest (CIFOR, 2008).
Google Scholar 
72.Ganzhorn, J. U., Lowry, P. P., Schatz, G. E. & Sommer, S. The biodiversity of Madagascar: One of the world’s hottest hotspots on its way out. Oryx 35, 346–348. https://doi.org/10.1046/j.1365-3008.2001.00201.x (2001).Article 

Google Scholar 
73.Kingdon, J. East African Mammals Vol. IIIA (Academic Press, 1977).
Google Scholar 
74.Dunning, J. B. CRC Handbook of Avian Body Masses 2nd edn. (CRC, 2008).
Google Scholar 
75.Rushton, J. et al. How important is bushmeat consumption in South America: Now and in the future?. Odi Wildl. Policy Brief. 11, 1–4 (2005).
Google Scholar 
76.Redford, K. H. & Robinson, J. G. The game of choice: Patterns of Indian and colonist hunting in the Neotropics. Am. Anthropol. 89, 650–667. https://doi.org/10.1525/aa.1987.89.3.02a00070 (1987).Article 

Google Scholar 
77.Ojasti, J. Wildlife Utilization in Latin America: Current Situation and Prospects for Sustainable Management (FAO, 1996).
Google Scholar 
78.Wilson, E. D., Fisher, K. H. & Garcia, P. A. Principles of Nutrition (Wiley, 1979).
Google Scholar 
79.Human energy requirements. Report of a Joint FAO/WHO/UNU Expert Consultation (2014).80.Soriano-Santos, J. in Handbook of Poultry Science and Technology (ed. Guerrero-Lagarreta, I.) 467–489 (2009).81.Eggleston, H. S. et al. (eds) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme (IPCC, 2006).
Google Scholar 
82.Carbon Pricing Leadership Coalition (CPLC). Report of the High-Level Commission on Carbon Prices (World Bank Group, 2017).
Google Scholar 
83.Annual Report. Ending Poverty, Investing in Opportunity (World Bank Group, 2019).
Google Scholar 
84.Avitabile, M. V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Chang. Biol. 22, 1406–1420. https://doi.org/10.1111/gcb.13139 (2016).ADS 
Article 
PubMed 

Google Scholar  More

1.Beardall, J. et al. Allometry and stoichiometry of unicellular, colonial and multicellular phytoplankton. New Phytol. https://doi.org/10.1111/j.1469-8137.2008.02660.x (2009).Article 
PubMed 

Google Scholar 
2.Adrian, R. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. https://doi.org/10.4319/lo.2009.54.6_part_2.2283 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Williamson, C. E., Saros, J. E. & Schindler, D. W. Sentinels of change. Science (N. Y.) https://doi.org/10.1126/science.1169443 (2009).Article 

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

Google Scholar 
5.Monier, A. et al. Oceanographic structure drives the assembly processes of microbial eukaryotic communities. ISME J. https://doi.org/10.1038/ismej.2014.197 (2015).Article 
PubMed 

Google Scholar 
6.Paerl, H. W. & Huisman, J. Blooms like it hot. Science (N. Y.) https://doi.org/10.1126/science.1155398 (2008).Article 

Google Scholar 
7.Wells, M. L. et al. Harmful algal blooms and climate change: Learning from the past and present to forecast the future. Harmful Algae https://doi.org/10.1016/j.hal.2015.07.009 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Smith, J. et al. A decade and a half of Pseudo-nitzschia spp. and domoic acid along the coast of southern California. Harmful Algae https://doi.org/10.1016/j.hal.2018.07.007 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
9.McCabe, R. M. et al. An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. https://doi.org/10.1002/2016GL070023 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Ekstrom, J. A., Moore, S. K. & Klinger, T. Examining harmful algal blooms through a disaster risk management lens: A case study of the 2015 U.S. West Coast domoic acid event. Harmful Algae https://doi.org/10.1016/j.hal.2020.101740 (2020).Article 
PubMed 

Google Scholar 
11.Kudela, R. M. & Chavez, F. P. The impact of coastal runoff on ocean color during an El Niño year in Central California. Deep Sea Res. Part II Topical Stud. Oceanogr. https://doi.org/10.1016/j.dsr2.2004.04.002 (2004).Article 

Google Scholar 
12.Kudela, R. M., Lane, J. Q. & Cochlan, W. P. The potential role of anthropogenically derived nitrogen in the growth of harmful algae in California, USA. Harmful Algae https://doi.org/10.1016/j.hal.2008.08.019 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Fischer, A. M., Ryan, J. P., Levesque, C. & Welschmeyer, N. Characterizing estuarine plume discharge into the coastal ocean using fatty acid biomarkers and pigment analysis. Mar. Environ. Res. https://doi.org/10.1016/j.marenvres.2014.04.006 (2014).Article 
PubMed 

Google Scholar 
14.Ryan, J. P. et al. Causality of an extreme harmful algal bloom in Monterey Bay, California, during the 2014–2016 northeast Pacific warm anomaly. Geophys. Res. Lett. https://doi.org/10.1002/2017GL072637 (2017).Article 

Google Scholar 
15.Van Meter, K. J., Basu, N. B. & Van Cappellen, P. Two centuries of nitrogen dynamics: Legacy sources and sinks in the Mississippi and Susquehanna River Basins. Global Biogeochem. Cycles https://doi.org/10.1002/2016GB005498 (2017).Article 

Google Scholar 
16.Conley, D. J. et al. Ecology – Controlling eutrophication: Nitrogen and phosphorus. Science https://doi.org/10.1126/science.1167755 (2009).Article 
PubMed 

Google Scholar 
17.Sinha, E., Michalak, A. M. & Balaji, V. Eutrophication will increase during the 21st century as a result of precipitation changes. Science https://doi.org/10.1126/science.aan2409 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Howard, M. D. A. et al. Anthropogenic nutrient sources rival natural sources on small scales in the coastal waters of the Southern California Bight. Limnol. Oceanogr. https://doi.org/10.4319/lo.2014.59.1.0285 (2014).Article 

Google Scholar 
19.Harvey, E. L. et al. A bacterial quorum-sensing precursor induces mortality in the marine coccolithophore, Emiliania huxleyi. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.00059 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Sison-Mangus, M. P., Jiang, S., Tran, K. N. & Kudela, R. M. Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota. ISME J. https://doi.org/10.1038/ismej.2013.138 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Skerratt, J. H., Bowman, J. P., Hallegraeff, G., James, S. & Nichols, P. D. Algicidal bacteria associated with blooms of a toxic dinoflagellate in a temperate Australian estuary. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps244001 (2002).Article 

Google Scholar 
22.Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature https://doi.org/10.1038/nature14488 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps010257 (1983).Article 

Google Scholar 
24.Platt, T. Concepts in biological oceanography: An interdisciplinary primer (P. A. Jumars). Limnol. Oceanogr. https://doi.org/10.4319/lo.1993.38.8.1842 (1993).Article 

Google Scholar 
25.Larsson, U. & Hagström, A. Phytoplankton exudate release as an energy source for the growth of pelagic bacteria. Mar. Biol. https://doi.org/10.1007/BF00398133 (1979).Article 

Google Scholar 
26.Bidle, K. D. & Azam, F. Accelerated dissolution of diatom silica by marine bacterial assemblages. Nature https://doi.org/10.1038/17351 (1999).Article 

Google Scholar 
27.Ammerman, J. W. & Azam, F. Bacterial 5’-nucleotidase in aquatic ecosystems: A novel mechanism of phosphorus regeneration. Science https://doi.org/10.1126/science.227.4692.1338 (1985).Article 
PubMed 

Google Scholar 
28.Kazamia, E. et al. Mutualistic interactions between vitamin B12-dependent algae and heterotrophic bacteria exhibit regulation. Environ. Microbiol. https://doi.org/10.1111/j.1462-2920.2012.02733.x (2012).Article 
PubMed 

Google Scholar 
29.Sison-Mangus, M. P., Jiang, S., Kudela, R. M. & Mehic, S. Phytoplankton-associated bacterial community composition and succession during toxic diatom bloom and non-bloom events. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.01433 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Anderson, C. R. et al. Scaling up from regional case studies to a global harmful algal bloom observing system. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.250 (2019).Article 

Google Scholar 
31.McGillicuddy, D. J. et al. GEOHAB modelling: Linking Observations to Predictions: A Workshop Report (Galway, Ireland, 2011).32.Song, W., Dolan, J. M., Cline, D. & Xiong, G. Learning-based algal bloom event recognition for oceanographic decision support system using remote sensing data. Remote Sens. https://doi.org/10.3390/rs71013564 (2015).Article 

Google Scholar 
33.Kwon, Y. S. et al. Developing data-driven models for quantifying Cochlodinium polykrikoides using the geostationary ocean color imager (GOCI). Int. J. Remote Sens. https://doi.org/10.1080/01431161.2017.1381354 (2018).Article 

Google Scholar 
34.Asnaghi, V. et al. A novel application of an adaptable modeling approach to the management of toxic microalgal bloom events in coastal areas. Harmful Algae https://doi.org/10.1016/j.hal.2017.02.003 (2017).Article 
PubMed 

Google Scholar 
35.Valbi, E. et al. A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea. Sci. Rep. https://doi.org/10.1038/s41598-019-40664-w (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
36.El Hourany, R. et al. Phytoplankton diversity in the mediterranean sea from satellite data using self-organizing maps. J. Geophys. Res. Oceans 124, 5827–5843 (2019).Article 
ADS 

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

Google Scholar 
38.Ascioti, F. A., Beltrami, E., Carroll, T. O. & Wirick, C. Is there chaos in plankton dynamics?. J. Plankton Res. 15, 603–617 (1993).Article 

Google Scholar 
39.Basu, S., Kumbier, K., Brown, J. B. & Yu, B. Iterative random forests to discover predictive and stable high-order interactions. Proc. Natl. Acad. Sci. U.S.A. https://doi.org/10.1073/pnas.1711236115 (2018).Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
40.Breiman, L. Random forests. Mach. Learn. https://doi.org/10.1023/A:1010933404324 (2001).Article 
MATH 

Google Scholar 
41.Witten, I. H., Cunningham, S., Holmes, G., McQueen, R. J. & Smith, L. A. Practical machine learning and its potential application to problems in agriculture. In Proceedings of New Zealand Computer Conference (1993).42.Lee, J. & Sison-Mangus, M. A Bayesian semiparametric regression model for joint analysis of microbiome data. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00522 (2018).Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
43.Hirsch, R. M., Moyer, D. L. & Archfield, S. A. Weighted regressions on time, discharge, and season (WRTDS), with an application to chesapeake bay river inputs. J. Am. Water Resour. Assoc. https://doi.org/10.1111/j.1752-1688.2010.00482.x (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Shuler, K., Sison-Mangus, M. & Lee, J. Bayesian sparse multivariate regression with asymmetric nonlocal priors for microbiome data analysis. Bayesian Anal. https://doi.org/10.1214/19-ba1164 (2019).Article 
MATH 

Google Scholar 
45.Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science https://doi.org/10.1126/science.1218344 (2012).Article 
PubMed 

Google Scholar 
46.Klindworth, A. et al. Diversity and activity of marine bacterioplankton during a diatom bloom in the North Sea assessed by total RNA and pyrotag sequencing. Mar. Genom. https://doi.org/10.1016/j.margen.2014.08.007 (2014).Article 

Google Scholar 
47.Delmont, T. O., Hammar, K. M., Ducklow, H. W., Yager, P. L. & Post, A. F. Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya. Front. Microbiol. https://doi.org/10.3389/fmicb.2014.00646 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Delmont, T. O., Murat Eren, A., Vineis, J. H. & Post, A. F. Genome reconstructions indicate the partitioning of ecological functions inside a phytoplankton bloom in the Amundsen Sea, Antarctica. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01090 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Kempnich, M. W. & Sison-Mangus, M. P. Presence and abundance of bacteria with the Type VI secretion system in a coastal environment and in the global oceans. PLoS ONE 15, e0244217 (2020).CAS 
Article 

Google Scholar 
50.Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8, (2019).51.Palarea-Albaladejo, J. & Martín-Fernández, J. A. ZCompositions – R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 143, 85–96 (2015).CAS 
Article 

Google Scholar 
52.van den Boogaart, K. G. & Tolosana-Delgado, R. ‘compositions’: A unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).Article 
ADS 

Google Scholar 
53.Heisler, J. et al. Eutrophication and harmful algal blooms: A scientific consensus. Harmful Algae https://doi.org/10.1016/j.hal.2008.08.006 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Howarth, R. W. & Marino, R. Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades. Limnol. Oceanogr. https://doi.org/10.4319/lo.2006.51.1_part_2.0364 (2006).Article 

Google Scholar 
55.Hamasaki, K. Variability in toxicity of the dinoflagellate Alexandrium tamarense isolated from Hiroshima Bay, Western Japan, as a reflection of changing environmental conditions. J. Plankton Res. https://doi.org/10.1093/plankt/23.3.271 (2001).Article 

Google Scholar 
56.Leong, S. C. Y., Murata, A., Nagashima, Y. & Taguchi, S. Variability in toxicity of the dinoflagellate Alexandrium tamarense in response to different nitrogen sources and concentrations. Toxicon https://doi.org/10.1016/j.toxicon.2004.01.015 (2004).Article 
PubMed 

Google Scholar 
57.Howard, M. D. A., Cochlan, W. P., Ladizinsky, N. & Kudela, R. M. Nitrogenous preference of toxigenic Pseudo-nitzschia australis (Bacillariophyceae) from field and laboratory experiments. Harmful Algae https://doi.org/10.1016/j.hal.2006.06.003 (2007).Article 

Google Scholar 
58.Lane, J. Q., Raimondi, P. T. & Kudela, R. M. Development of a logistic regression model for the prediction of toxigenic pseudo-nitzschia blooms in monterey bay, California. Mar. Ecol. Progr. Ser. https://doi.org/10.3354/meps07999 (2009).Article 

Google Scholar 
59.Lecher, A. L. et al. Nutrient loading through submarine groundwater discharge and phytoplankton growth in Monterey bay, CA. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5b00909 (2015).Article 
PubMed 

Google Scholar 
60.Bakun, A. Coastal Upwelling Indices, West Coast of North America, 1946–71. (1972).61.Jacox, M. G., Edwards, C. A., Hazen, E. L. & Bograd, S. J. Coastal upwelling revisited: Ekman, Bakun, and improved upwelling indices for the U.S. West coast. J. Geophys. Res. Oceans 123, 7332–7350 (2018).Article 
ADS 

Google Scholar 
62.Sawyer, A. H., David, C. H. & Famiglietti, J. S. Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities. Science https://doi.org/10.1126/science.aag1058 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Sawyer, A. H., Michael, H. A. & Schroth, A. W. From soil to sea: The role of groundwater in coastal critical zone processes. Wiley Interdiscip. Rev. Water https://doi.org/10.1002/wat2.1157 (2016).Article 

Google Scholar 
64.Garneau, M. È. et al. Examination of the seasonal dynamics of the toxic dinoflagellate Alexandrium catenella at Redondo Beach, California, by quantitative PCR. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.06174-11 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Schiff, K. C., Allen, M. J., Zeng, E. Y. & Bay, S. M. Southern California. Seas Millenn. Environ. Eval. https://doi.org/10.1097/00006205-197605000-00010 (2000).Article 

Google Scholar 
66.Nelson, N. G. et al. Revealing biotic and abiotic controls of harmful algal blooms in a shallow subtropical lake through statistical machine learning. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.7b05884 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
67.Pohlner, M. et al. The majority of active Rhodobacteraceae in marine sediments belong to uncultured genera: A molecular approach to link their distribution to environmental conditions. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.00659 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
68.Wagner-Döbler, I. & Biebl, H. Environmental biology of the marine roseobacter lineage. Annu. Rev. Microbiol. https://doi.org/10.1146/annurev.micro.60.080805.142115 (2006).Article 
PubMed 

Google Scholar 
69.Elifantz, H., Horn, G., Ayon, M., Cohen, Y. & Minz, D. Rhodobacteraceae are the key members of the microbial community of the initial biofilm formed in Eastern Mediterranean coastal seawater. FEMS Microbiol. Ecol. https://doi.org/10.1111/1574-6941.12122 (2013).Article 
PubMed 

Google Scholar 
70.Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. https://doi.org/10.1038/nmicrobiol.2016.5 (2016).Article 
PubMed 

Google Scholar 
71.Williams, T. J. et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ. Microbiol. https://doi.org/10.1111/1462-2920.12017 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Tully, B. J., Sachdeva, R., Heidelberg, K. B. & Heidelberg, J. F. Comparative genomics of planktonic Flavobacteriaceae from the Gulf of Maine using metagenomic data. Microbiome https://doi.org/10.1186/2049-2618-2-34 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
73.Kirchman, D. L. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol. Ecol. https://doi.org/10.1016/S0168-6496(01)00206-9 (2002).Article 
PubMed 

Google Scholar 
74.Pinhassi, J. et al. Changes in bacterioplankton composition under different phytoplankton regimens. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.70.11.6753-6766.2004 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro3326 (2014).Article 
PubMed 

Google Scholar 
76.Zhou, J. et al. Microbial community structure and associations during a marine dinoflagellate bloom. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01201 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
77.Buchan, A., González, J. M. & Moran, M. A. Overview of the marine Roseobacter lineage. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.71.10.5665-5677.2005 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
78.Rajapitamahuni, S., Bachani, P., Sardar, R. K. & Mishra, S. Co-cultivation of siderophore-producing bacteria Idiomarina loihiensis RS14 with Chlorella variabilis ATCC 12198, evaluation of micro-algal growth, lipid, and protein content under iron starvation. J. Appl. Phycol. https://doi.org/10.1007/s10811-018-1591-2 (2019).Article 

Google Scholar  More

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