Philippa Kaur

1.Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).Article 
ADS 

Google Scholar 
2.Pardini, R., Nichols, E. & Püttker, T. Biodiversity response to habitat loss and fragmentation. Encycl. Anthropocene https://doi.org/10.1016/B978-0-12-809665-9.09824-4 (2017).Article 

Google Scholar 
3.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14(2), 101–112. https://doi.org/10.1111/j.1461-0248.2010.01559.x (2011).Article 
PubMed 

Google Scholar 
4.Moilanen, A. & Hanski, I. Metapopulation dynamics: Effects of hábitat quality and landscape structure. Ecology 79, 2503–2515 (1998).Article 

Google Scholar 
5.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34(1), 487–515 (2003).Article 

Google Scholar 
6.Cornelius, C., Awade, M., Candia-Gallardo, C., Sieving, K. E. & Metzger, J. P. Habitat fragmentation drives inter-population variation in dispersal behavior in a Neotropical rainforest bird. Perspect. Ecol. Conserv. 15, 3–9 (2017).
Google Scholar 
7.Xu, Y. et al. Loss of functional connectivity in migration networks induces population decline in migratory birds. Ecol. Appl. 29(7), e01960. https://doi.org/10.1002/eap.1960 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Hens, H., Pakanen, V., Jäkäläniemi, A., Tuomi, J. T. & Kvist, L. Low population viability in small endangered orchid populations: Genetic variation, seedling recruitment and stochasticity. Biol. Cons. 210, 174–183 (2017).Article 

Google Scholar 
9.Silva, J. P. et al. EU protected area network did not prevent a country wide population decline in a threatened grassland bird. PeerJ 6, e4284 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Gaget, E., Fay, R., Audiron, S., Villers, A. & Bretagnolle, V. Long-term decline despite conservation efforts questions Eurasian Stone-curlew population viability in intensive farmlands. Ibis 161, 359–371 (2019).Article 

Google Scholar 
11.van Oosten, H. H. et al. Hatching failure and accumulation of organic pollutants through the terrestrial food web of a declining songbird in Western Europe. Sci. Total Environ. 650, 1547–1553 (2019).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
12.Brambilla, M. et al. Sixty years of habitat decline: impact of land-cover changes in northern Italy on the decreasing ortolan bunting Emberiza hortulana. Reg. Environ. Change 17, 323–333 (2017).Article 

Google Scholar 
13.Heldbjerg, H., Sunde, P. & Fox, A. D. Continuous Population Declines for Specialist Farmland Birds 1987–2014 in Denmark Indicates No Halt in Biodiversity Loss in Agricultural Habitats 278–292 (Bird Conservation International, 2018).
Google Scholar 
14.Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated with the loss of fallowland. Sci. Rep. 9, 9473 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
15.Reif, J. & Vermouzek, Z. Collapse of farmland bird populations in an Eastern European country following its EU accession. Conserv. Lett. https://doi.org/10.1111/conl.12585 (2018).Article 

Google Scholar 
16.Levins, R. Extinction. In: Some mathematical problems in biology. Mathematical Society of America, Providence, R.I. Pages 77–107 (1970).17.Hanski, I. Metapopulation ecology (Oxford University Press, 1999).
Google Scholar 
18.Johnson, M. D. Measuring habitat quality: A review. Condor 109, 489–504 (2007).Article 

Google Scholar 
19.Vögeli, M., Serrano, D., Pacios, F. & Tella, J. L. The relative importance of patch habitat quality and landscape attributes on a declining steppe-bird metapopulation. Biol. Cons. 143, 1057–1067 (2010).Article 

Google Scholar 
20.Traba, J., Sastre, P. & Morales, M. B. Factors determining species richness and composition of steppe bird communities in peninsular Spain: grass-steppe vs. shrub-steppe bird species. In Steppe Ecosystems (eds Morales, M. B. & Traba, J.) (Nova Science, 2013).
Google Scholar 
21.Burfield, I. J. The conservation status of steppic birds in Europe. In Ecology and Conservation of Steppe-Land Birds (eds Bota, G. et al.) 69–102 (Lynx Edicions, 2005).
Google Scholar 
22.Donald, P. F., Sanderson, F. J., Burfield, I. J. & van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agr. Ecosyst. Environ. 116(3–4), 189–196 (2006).Article 

Google Scholar 
23.Burfield, I. & van Bommel, F. Birds in Europe: Population Estimates, Trends and Conservation Status (Birdlife International, 2004).
Google Scholar 
24.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key?. Trends Ecol. Evol. 18(4), 182–188 (2003).Article 

Google Scholar 
25.Santos, T. & Suárez, F. Biogeography and population trends of iberian steppe bird. In Ecology and Conservation of Steppe-Land Birds (eds Bota, G. et al.) 69–102 (Lynx Edicions, 2005).
Google Scholar 
26.Gómez-Catasús, J., Garza, V. & Traba, J. Wind farms affect the occurrence, abundance and population trends of small passerine birds: The case of the Dupont’s Lark. J. Appl. Ecol. 55, 2033–2042 (2018).Article 

Google Scholar 
27.Donald, P. F., Green, R. & Heath, M. F. Agricultural intensification and the collapse of Europe’s farmland bird populations. Proc. R. Soc. Ser. B. 155, 39–43 (2001).
Google Scholar 
28.Brotons, L., Mañosa, S. & Estrada, J. Modelling the effects of irrigation schemes on the distribution of steppe birds in Mediterranean farmland. Biodivers. Conserv. 13, 1039–1058 (2004).Article 

Google Scholar 
29.Madroño, A. et al. (eds) Libro rojo de las aves de España (SEO/BirdLife y Dirección General para la Biodiversidad, 2004).
Google Scholar 
30.Concepción, E. D. & Díaz, M. Medidas agroambientales y conservación de la biodiversidad: Limitaciones y perspectivas de futuro. Ecosistemas 22(1), 44–49. https://doi.org/10.7818/ECOS.2013.22-1.08 (2013).Article 

Google Scholar 
31.Traba, J. Intensificación agrícola y efectos sobre las aves. Revista de la Sociedad Cordobesa de Historia Natural 3, 39–50 (2020).
Google Scholar 
32.Prévosto, B. et al. Impacts of land abandonment on vegetation: Successional pathways in European habitats. Folia Geobot 46, 303–325. https://doi.org/10.1007/s12224-010-9096-z (2011).Article 

Google Scholar 
33.García-Tejero, S., Taboada, A., Tárrega, R. & Salgado, J. M. Land use changes and ground dwelling beetle conservation in extensive grazing dehesa systems of north-west Spain. Biol. Cons. 161, 58–66 (2013).Article 

Google Scholar 
34.Dennis, P. et al. The effects of livestock grazing on foliar arthropods associated with bird diet in upland grasslands of Scotland. J. Appl. Ecol. 45(1), 279–287 (2008).Article 

Google Scholar 
35.BirdLife International. European Red List of Birds (Office for Official Publications of the European Communities, 2015).
Google Scholar 
36.Gómez-Catasús, J. et al. European population trends and current conservation status of an endangered steppe-bird species: the Dupont’s Lark Chersophilus duponti. PeerJ 6, e5627. https://doi.org/10.7717/peerj.5627 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
37.de Juana, E. & Suárez, F. Dupont’s Lark (Chersophilus duponti), version 1.0. In Birds of the World (eds del Hoyo, J. et al.) (Cornell Lab of Ornithology, 2020).
Google Scholar 
38.García, J. T. et al. Genetic and phenotypic variation among geographically isolated populations of the globally threatened Dupont’s Lark Chersophilus duponti. Mol. Phylogenet. Evol. 46(1), 237–251 (2008).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
39.Méndez, M., Tella, J. L. & Godoy, J. A. Restricted gene flow and genetic drift in recently fragmented populations of an endangered steppe bird. Biol. Cons. 144, 2615–2622 (2011).Article 

Google Scholar 
40.Méndez, M., Vögeli, M., Tella, J. L. & Godoy, J. A. Joint effects of population size and isolation on genetic erosion in fragmented populations: Finding fragmentation thresholds for management. Evol. Appl. 7, 506–518 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Garza, V. & Suárez, F. Distribución, población y selección de hábitat de la Alondra de Dupont (Chersophilus duponti) en la Península Ibérica. Ardeola 37, 3–12 (1990).
Google Scholar 
42.Martín-Vivaldi, M., Marín, J. M., Archila, F., López, E. & De Manuel, L. C. Caracterización de una nueva población reproductora de Alondra de Dupont (Chersophilus duponti) (Passeriformes, Alaudidae) en el Sureste Ibérico. Zool. Baetica 10, 185–192 (1999).
Google Scholar 
43.Garza, V. et al. Home range, territoriality and habitat selection by the Dupont’s Lark Chersophilus duponti during the breeding and postbreeding periods. Ardeola 52, 133–146 (2005).
Google Scholar 
44.Seoane, J. et al. Habitat-suitability modelling to assess the effects of land-use changes on Dupont’s Lark Chersophilus duponti: A case study in the Layna Important Bird Area. Biol. Cons. 128, 241–252 (2006).Article 

Google Scholar 
45.Nogués-Bravo, D. & Agirre, A. Patrón y modelos de distribución espacial de la alondra ricotí Chersophilus duponti durante el periodo reproductor en el LIC de Ablitas (Navarra). Ardeola 53, 55–68 (2006).
Google Scholar 
46.García, J. T. et al. Assessing the distribution, habitat, and population size of the threatened Dupont’s Lark Chersophilus duponti in Morocco: Lessons for conservation. Oryx 42, 592–599 (2008).Article 

Google Scholar 
47.Pérez-Granados, C., López-Iborra, G. M. & Seoane, J. A multi-scale analysis of habitat selection in peripheral populations of the endangered Dupont’s Lark Chersophilus duponti. Bird Conserv. Int. 27, 398–413 (2017).Article 

Google Scholar 
48.García-Antón, A., Garza, V., Hernández-Justribó, J. & Traba, J. Factors affecting Dupont’s Lark distribution and range regression in Spain. PLoS ONE 14, e0211549 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.García-Antón, A., Garza, V. & Traba, J. (in press). Connectivity in Spanish metapopulation of Dupont’s Lark may be maintained by dispersal over medium 3 distances and stepping stones. PeerJ.50.Traba, J., Garza, V., García-Antón, A., Gómez-Catasús, J., Zurdo, J., Pérez-Granados, C., Morales, M. B., Oñate, J. J., Herranz, J., Malo, J. Criterios para la gestión y conservación de la población española de alondra ricotí Chersophilus duponti. Fundación Biodiversidad, Ministerio de Agricultura, Alimentación y Medio Ambiente. Madrid. (2019).51.Vögeli, M., Serrano, D., Tella, J. L., Méndez, M. & Godoy, J. A. Sex determination of Dupont´s lark Chersophilus duponti using molecular sexing and discriminant functions. Ardeola 54, 69–79 (2007).
Google Scholar 
52.Suárez, F. et al. Sex-ratios of an endangered lark after controlling for a male-biased sampling. Ardeola 56, 113–118 (2009).
Google Scholar 
53.Garza, V., Suárez, F., Tella, J. L. Alondra de Dupont, Chersophilus duponti. In: Madroño, A., González, C., Atienza, J. C. (eds). Libro Rojo de las Aves de España. Madrid: Dirección General para la Biodiversidad-SEO/BirdLife pp 309–312. (2004).54.Íñigo, A., Garza, V., Tella, J. L., Laiolo, P., Suárez, F., Barov, B. Action Plan for the Dupont’s Lark Chersophilus duponti in the European Union. SEO/Birdlife – BirdLife International –Comisión Europea. (2008).55.Gómez-Catasús, J. et al. Hierarchical habitat-use by an endangered steppe bird in fragmented landscapes is associated with large connected patches and high food availability. Sci Rep 9, 19010 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
56.Reverter, M. et al. Interactions in shrub-steppes: Implications for the maintenance of a threatened bird. Ecosistemas 28, 69–77 (2019).Article 

Google Scholar 
57.Serrano, D. et al. Renewables in Spain threaten biodiversity. Science 370, 1282–1283 (2020).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
58.Pe’er, G. et al. A greener path for the EU common agricultural policy. Science 365(6452), 449–451 (2019).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
59.Bland, L. M., Keith, D. A., Miller, R. M., Murray, N. J. & Rodríguez, J. P. (eds.) Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 1.1. Gland, Switzerland: IUCN. ix + 99pp. (2017).60.Flather, C. H., Hayward, G. D., Beissinger, S. R. & Stephens, P. A. Minimum viable populations: Is there a “magic number” for conservation practitioners?. Trends Ecol. Evol. 26(6), 307–316 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
61.Carvajal, M. A., Alaniz, A. J., Smith-Ramírez, C. & Sieving, K. E. Assessing habitat loss and fragmentation and their effects on population viability of forest specialist birds: Linking biogeographical and population approaches. Divers. Distrib. 24, 820–830 (2018).Article 

Google Scholar 
62.Trask, A. E. et al. Evaluating the efficacy of independent versus simultaneous management strategies to address ecological and genetic threats to population viability. J. Appl. Ecol. 56, 2264–2273 (2019).Article 

Google Scholar 
63.Akçakaya, H. R. & Sjögren-Gulve, P. Population viability analyses in conservation planning: An overview. Ecol. Bull. 48, 9–21 (2000).
Google Scholar 
64.Frankham, R., Ballou, J., Briscoe, D. & McInnes, K. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

Google Scholar 
65.Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132(5), 652–661 (1988).Article 

Google Scholar 
66.Bota, G., Giralt, D. & Guixé, D. L. Alondra Ricotí en Cataluña: evolución histórica de una población en el límite del área de distribución (II Meeting of the Dupont’s Lark Experts Group, 2016).
Google Scholar 
67.Pérez-Granados, C., Bota, G., Giralt, D. & Traba, J. A cost-effective protocol for monitoring birds using autonomous recording units: A case study with a night-time singing passerine. Bird Study 65(3), 338–345 (2018).Article 

Google Scholar 
68.García-Antón, A., Garza, V. & Traba, J. Dispersión de más de 30 km en un macho de primer año de alondra ricotí (Chersophilus duponti) en el Sistema Ibérico. I Workshop Nacional de la Alondra ricotí Chersophilus duponti: Estrategias Futuras. Estación Ornitológica de Padul, Granada. 13 junio (2015).69.Kauhala, K., Helle, P. & Helle, E. Predator control and the density and reproductive success of grouse populations in Finland. Ecography 23, 161–168 (2000).Article 

Google Scholar 
70.Fletcher, K., Aebischer, N. J., Baines, D., Foster, R. & Hoodless, A. N. Changes in breeding success and abundance of ground-nesting moorland birds in relation to the experimental deployment of legal predator control. J. Appl. Ecol. 47, 263–272 (2010).Article 

Google Scholar 
71.Banks, P. B., Dickman, C. R. & Newsome, A. E. Ecological costs of feral predator control: Foxes and rabbits. J. Wildl. Manag. 62(2), 766–772 (1998).Article 

Google Scholar 
72.Bolton, M., Tyler, G., Smith, K. & Bamford, R. The impact of predator control on lapwing Vanellus vanellus breeding success on wet grassland nature reserves. J. Appl. Ecol. 44, 534–544 (2007).Article 

Google Scholar 
73.Walsh, J. C., Wilson, K. A., Benshemesh, J. & Possingham, H. P. Unexpected outcomes of invasive predator control: The importance of evaluating conservation management actions. Anim. Conserv. 15, 319–328 (2012).Article 

Google Scholar 
74.Oro, D., Margalida, A., Carrete, M., Heredia, R. & Donázar, J. A. Testing the goodness of supplementary feeding to enhance population viability in an endangered vulture. PLoS ONE 3(12), e4084 (2008).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
75.Ruffino, L. et al. Reproductive responses of birds to experimental food supplementation: A meta-analysis. Front. Zool. 11, 80 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Cuesta, D., Taboada, A., Calvo, L. & Salgado, J. M. Short- and medium-term effects of experimental nitrogen fertilization on arthropods associated with Calluna vulgaris heathlands in north-west Spain. Environ. Pollut. 152, 394–402 (2008).CAS 
PubMed 
Article 

Google Scholar 
77.Estrada, A., Delgadom, M. P., Arroyo, B., Traba, J. & Morales, M. B. Forecasting large-scale habitat suitability of European bustards under climate change: The role of environmental and geographic variables. PLoS ONE 11(3), e0149810 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Zhang, X., Johnston, E. R., Li, L., Konstantinidis, K. T. & Han, X. Experimental warming reveals positive feedbacks to climate change in the Eurasian Steppe. ISME J. 11, 885–895 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Laiolo, P., Vögeli, M., Serrano, D. & Tella, J. L. Song diversity predicts the viability of fragmented bird populations. PLoS ONE 3(3), e1822 (2008).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
80.Traba, J., García de la Morena, E. L. & Garza, V. Análisis de Viabilidad de Poblaciones como herramienta para el diseño de Parques Eólicos. El caso de las poblaciones de alondra ricotí (Chersophilus duponti) del sur de Soria. I Congreso Ibérico sobre Energía Eólica y Conservación de Fauna. Jerez de la Frontera, Cádiz (2011).81.Suárez, F. & Carriles, E. Análisis de viabilidad poblacional. In: Suárez, F. (ed.) La alondra ricotí (Chersophilus duponti), pp. 319–326. Dirección General para la Biodiversidad. Ministerio de Medio Ambiente y Medio Rural y Marino. Madrid (2010).82.Pérez-Granados, C., López-Iborra, G. M., Garza, V. & Traba, J. Breeding biology of the endangered Dupont’s Lark Chersophilus duponti in two separate Spanish shrub-steppes. Bird Study 64(3), 328–338 (2017).Article 

Google Scholar 
83.Pérez-Granados, C. & López-Iborra, G. M. ¿Por qué la alondra ricotí debe catalogarse como “En peligro de Extinción”?. Quercus 337, 18–25 (2014).
Google Scholar 
84.Pérez-Granados, C. & López-Iborra, G. M. Census of breeding birds and population trends of the Dupont’s Lark Chersophilus duponti in eastern Spain. Ardeola 60, 143–150 (2013).Article 

Google Scholar 
85.Suárez, F. La alondra ricotí (Chersophilus duponti). Dirección General para la Biodiversidad. Ministerio de Medio Ambiente y Medio Rural y Marino Medio Rural y Marino, Madrid. 525 pp (2010).86.Lacy, R. C., & Pollak, J. P. Vortex: A Stochastic Simulation of the Extinction Process. Version 10.2.9. Chicago Zoological Society, Brookfield, Illinois, USA (2017).87.Lacy, R. C. Considering Threats to the Viability of Small Populations Using Individual-Based Models. Ecol. Bull. 48, 39–51 (2000).
Google Scholar 
88.Ogrady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133(1), 42–51 (2006).Article 

Google Scholar 
89.Laiolo, P., Vögeli, M., Serrano, D. & Tella, J. L. Testing acoustic versus physical marking: Two complementary methods for individual-based monitoring of elusive species. J. Avian Biol. 38, 672–681 (2007).Article 

Google Scholar 
90.Vögeli, M., Laiolo, P., Serrano, D. & Tella, J. L. Who are we sampling? Apparent survival differs between methods in a secretive species. Oikos 117(12), 1816–1823 (2008).Article 

Google Scholar 
91.Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.92.Pérez-Granados, C. & López-Iborra, G. M. Baja dispersión adulta y baja tasa de recaptura juvenil de la alondra ricotí (Chersophilus duponti) en el Rincón de Ademuz (Valencia). XX Iberian Ringing Congress (2015).93.Briefer, E., Rybak, F. & Aubin, T. When to be a dear enemy: flexible acoustic relationships of neighbouring skylarks Alauda arvensis. Anim. Behav. 76, 1319–1325 (2008).Article 

Google Scholar 
94.Delius, J. D. A population study of Skylarks Alauda arvensis. Ibis 107, 466–492 (1965).Article 

Google Scholar 
95.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar  More

Isolation and characterization of bean dreg-degrading strainsA 1362-bp amplification fragment of 16S rDNA was obtained by PCR (GenBank accession number MW406939). This sequence was compared with others in the GenBank database, aligning the 16S rDNA sequences with several Geobacillus sp. strains and constructed a phylogenetic tree (Fig. 2a). The phylogenetic tree clearly showed that strain GT-02 belongs to the G.toebii branch and was similar to G.toebii R-32652, G.toebii NBRC 107807, and G.toebii SK-1 with 99.78%, 99.63% and 99.05% similarities, respectively. According to the study described previously, G.toebii was a gram-positive, aerobic rod and motile bacterial26. G.toebii could produce acid from inositol and gas from nitrate. G.toebii could hydrolysis casein and utilize n-alkanes as carbon source27.Figure 2(a) Phylogenetic tree based on 16S rDNA gene sequences from related species of the genus Geobacillus constructed using the neighbour-joining method with 1000 bootstrap replicates. Branch length is indicated at each node. (b) The growth curve of strain GT-02 with temperature. (c) The growth curve of strain GT-02 with pH.Full size imageThe growth characteristics of strain GT-02, such as temperature and pH values, were investigated. The bacterial strain could grow within a range of 40–75 °C and pH 6.50–9.50, and the optimum temperature and pH were 65 °C and 7.50, respectively (Fig. 2b,c). Compared to other G.toebii strains, the maximum growth temperature and pH of strains R-32652 and SK-1 were 70 °C and 9.0026,28, respectively. These results showed that strain GT-02 was more resistant to high temperature and alkalinity. Fermentation temperature above 70 °C could effectively inactivate harmful microorganisms in organic solid waste12. Therefore, the fermentation temperature was set at 70 °C in this study.Changes in the composition of bean dregs during fermentationChanges in GI, TOC and TN of bean dregs during fermentationThe GI is traditionally used to evaluate the phytotoxicity and maturity of organic fertilizer12. As shown in Fig. 3a, both groups of experiments reached the standard of maturity (GI ≥ 85.00%). Therefore, the fermentation was terminated in five days. In the initial stage of fermentation, the GI of CK dropped to 51.85% on day 2, and the GI of T1 dropped to 41.98% on day 1. Phytotoxicity, which is usually caused by various heavy metals and low-molecular-weight substances, such as NH3 and organic acids, can reduce seed germination and inhibit root development29. During fermentation, bean dregs might produce NH3, organic acids and other substances, which could trigger a decrease in the GI. The GI of T1 showed a clear decrease, which was likely due to the production of toxic organic acids and might also explain the decrease in pH observed in T1 (Fig. 3d). Due to the degradation of organic acids, the GI of T1 increased to 95.06% on the third day and continued to increase to more than 100.00%, whereas in CK, the GI only reached 86.42% at the end of the fermentation. These results revealed that the maturity of T1 on day 3 was markedly higher than that of CK on day 5 and thus suggest that G.toebii can significantly enhance the fermentation efficiency by accelerating the maturation process and thus reducing the thermophilic fermentation period from 5 to 3 days.Figure 3Profiles of GI (a), TOC (b), TN (c), pH (d) and EC (e) during the fermentation process of CK and T1. The data represent the means ± standard deviations from three measurements.Full size imageTOC is usually used as an energy source by microorganisms30. The TOC loss in both CK and T1 increased during fermentation (Fig. 3b). The reduction of TOC was mainly caused by the production of carbon dioxide from bacterial respiration. The rate of TOC loss in T1 was higher than that in CK. At the end of the fermentation, the TOC loss of T1 was 11.78% higher than that in CK. Because of the addition of G.toebii, bacterial metabolism in T1 was more active, and organic degradation was faster.The TN loss in both CK and T1 also showed an upward trend (Fig. 3c). The loss of TN was mainly caused by the volatilization of ammonia nitrogen31. The rate of TN loss in T1 increased more than that of CK group. After fermentation (day 5), the TN loss in T1 was 6.83% higher than that of CK. The mineralization in T1 was more active and thus ammonia nitrogen was more, which was easy to cause volatilization. However, the bean dregs in CK were mature on the 5th day, while those in T1 were on the 3rd day. At this time, the TN loss of mature bean dregs in T1 was 5.66% lower than that in CK, which indicated that the bean dregs lost less nitrogen source when they reached the standard of maturity after the addition of G.toebii.Changes in pH and EC of bean dregs during fermentationThe variation in pH observed during fermentation is due to the interaction between inorganic nitrogen and organic acids produced by the decomposition of organic matter32. As shown in Fig. 3d, the pH of CK gradually increased to 8.72 at the end of the fermentation. The ammonification process and the release of free NH3 during organic matter (OM) degradation lead to increases in pH33. The pH of T1 decreased to 5.73 on day 1, which was due to the formation of more organic acids than CK, and then increased to 8.76 on day 2, which was due to acid consumption and ammonia formation. Figure 2c showed that GT-02 could hardly grow when the pH was lower than 6.00, but the heterogeneity of solid fermentation provided a possible living environment for the growth of GT-02. Subsequently, the pH of T1 slowly decreased to 8.10 due to ammonia volatilization or ammonia conversion. These study findings showed that the pH value of the fermentation process was significantly affected by the addition of GT-02. G.toebii can produce abundant high-temperature enzymes, such as amylase, protease, cellulase, xylanase, and mannanase17, which explains why the ammonification process was faster in T1 than in CK and thus the higher pH was found in T1.The EC, which is a measure of the total ion concentration, describes changes in the levels of organic and inorganic ions such as SO42−, Na+, NH4+, K+, Cl−, and NO3− during the fermentation process34. As shown in Fig. 3e, the EC of the two groups increased significantly during fermentation process (P  More

1.Collins, M. et al. Long-term climate change: Projections, commitments and irreversibility. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).2.Kawahata, H. et al. Perspective of the response by marine calcifiers to global warming and ocean acidification –Behavior of corals and foraminifers in the high CO2 world in “hot house”. Prog. Earth Planet Sci. 6, 5 (2019).Article 

Google Scholar 
3.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).Article 

Google Scholar 
4.Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Schiebel, R. Planktic foraminiferal sedimentation and the marine calcite budget. Glob. Biogeochem. Cycles 16, 1065 (2002).ADS 
Article 
CAS 

Google Scholar 
6.Keul, N., Langer, G., de Nooijer, L. J. & Bijma, J. Effect of ocean acidification on the benthic foraminifera Ammonia sp. is caused by a decrease in carbonate ion concentration. Biogeosciences 10, 6185–6198 (2013).ADS 
CAS 
Article 

Google Scholar 
7.Doo, S. S., Fujita, K., Byrne, M. & Uthicke, S. Fate of calcifying tropical symbiont-bearing large benthic Foraminifera: Living sands in a changing ocean. Biol. Bull. 226, 169–186 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Prazeres, M., Uthicke, S. & Pandolfi, J. M. Ocean acidification induces biochemical and morphological changes in the calcification process of large benthic foraminifera. Proc. R. Soc. B 282, 20142782 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Iwasaki, S. et al. Sensitivity of planktic foraminiferal test bulk density to ocean acidification. Sci. Rep. 9, 9803 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Hohenegger, J., Kinoshita, S., Briguglio, A., Eder, W. & Wöger, J. Lunar cycles and rainy seasons drive growth and reproduction in nummulitid foraminifera, important producers of carbonate buildups. Sci. Rep. 9, 8286 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Kinoshita, S. et al. Temperature effects on the shell growth of a larger benthic foraminifer (Sorites orbiculus): Results from culture experiments and micro X-ray computed tomography. Mar. Micropaleontol. 163, 101960 (2021).ADS 
Article 

Google Scholar 
12.Fujita, K. & Fujimura, H. Organic and inorganic carbon production by algal symbiont-bearing foraminifera on northwest Pacific coral-reef flat. J. Foraminifer. Res. 38, 117–126 (2008).Article 

Google Scholar 
13.Raja, R., Saraswati, P. K., Rogers, K. & Iwao, K. Magnesium and strontium compositions of recent symbiont-bearing benthic foraminifera. Mar. Micropaleontol. 58, 31–44 (2005).ADS 
Article 

Google Scholar 
14.Narayan, G. R. et al. Response of large benthic foraminifera to climate and local changes: Implications for future carbonate production. Sedimentology. 12858. https://doi.org/10.1111/sed.12858 (2021).
15.Morse, J. W., Andersson, A. J. & Mackenzie, F. T. Initial responses of carbonate-rich shelf sediments to rising atmospheric pCO2 and “ocean acidification”: Role of high Mg-calcites. Geochim. Cosmochim. Acta 70, 5814–5830 (2006).ADS 
CAS 
Article 

Google Scholar 
16.Fujita, K., Nishi, H. & Saito, T. Population dynamics of Marginopora kudakajimaensis Gudmundsson (Foraminifera: Soritidae) in the Ryukyu Islands, the tropical northwest Pacific. Mar. Micropaleontol. 38, 267–284 (2000).ADS 
Article 

Google Scholar 
17.Kuroyanagi, A., Kawahata, H., Suzuki, A., Fujita, K. & Irie, T. Impacts of ocean acidification on large benthic foraminifers: Results from laboratory experiments. Mar. Micropaleontol. 73, 190–195 (2009).ADS 
Article 

Google Scholar 
18.Barker, S. & Elderfield, H. Foraminiferal calcification response to glacial–interglacial changes in atmospheric CO2. Science 297, 833–836 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Osborne, E. B. et al. Calcification of the planktonic foraminifera Globigerina bulloides and carbonate ion concentration: Results from the Santa Barbara Basin. Paleoceanography 31, 1083–1102 (2016).ADS 
Article 

Google Scholar 
20.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 
21.Schmidt, C., Kucera, M. & Uthicke, S. Combined effects of warming and ocean acidification on coral reef Foraminifera Marginopora vertebralis and Heterostegina depressa. Coral Reefs 33, 805–818 (2014).ADS 
Article 

Google Scholar 
22.Sinutok, S., Hill, R., Kühl, M., Doblin, M. & Ralph, P. Ocean acidification and warming alter photosynthesis and calcification of the symbiont-bearing foraminifera Marginopora vertebralis. Mar. Biol. 161, 2143–2154 (2014).CAS 
Article 

Google Scholar 
23.ter Kuile, B., Erez, J. & Padan, R. Mechanisms for the uptake of inorganic carbon by two species of symbiont-bearing foraminifera. Mar. Biol. 103, 241–251 (1989).Article 

Google Scholar 
24.Nijweide, P. J., Kawilarang-de Haas, E. W. & Wassenaar, A. M. Alkaline phosphatase and calcification, correlated or not?. Metab. Bone Dis. Relat. Res. 3, 61–66 (1981).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Guo, M. K. & Messer, H. H. A comparison of Ca2+-, Mg2+-ATPase and alkaline phosphatase activities of rat incisor pulp. Calc. Tissue Res. 26, 33–38 (1978).CAS 
Article 

Google Scholar 
26.Vogel, N. & Uthicke, S. Calcification and photobiology in symbiont-bearing benthic foraminifera and responses to a high CO2 environment. J. Exp. Mar. Biol. Ecol. 424–425, 15–24 (2012).Article 
CAS 

Google Scholar 
27.Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer, 2017).Book 

Google Scholar 
28.Bassinot, F. C., Mélières, F., Gehlen, M., Levi, C. & Labeyrie, L. Crystallinity of foraminifera shells: A proxy to reconstruct past botto m water CO3= changes?. Geochem. Geophys. Geosyst. 5, Q08D10 (2004).Article 

Google Scholar 
29.Broecker, W. & Clark, E. Shell weights from the South Atlantic. Geochem. Geophys. Geosyst. 5, Q03003 (2004).ADS 
Article 

Google Scholar 
30.Beer, C. J., Schiebel, R. & Wilson, P. A. Testing planktic foraminiferal shell weight as a surface water [CO32−] proxy using plankton net samples. Geology 38, 103–106 (2010).ADS 
CAS 
Article 

Google Scholar 
31.Naik, S. S., Naidu, P. D., Govil, P. & Godad, S. Relationship between weights of planktonic foraminifer shell and surface water CO3= concentration during the Holocene and Last Glacial Period. Mar. Geol. 275, 278–282 (2010).ADS 
CAS 
Article 

Google Scholar 
32.Moy, A. D., Howard, W. R., Bray, S. G. & Trull, T. W. Reduced calcification in modern Southern Ocean planktonic foraminifera. Nat. Geosci. 2, 276–280 (2009).ADS 
CAS 
Article 

Google Scholar 
33.Gonzalez-Mora, B., Sierro, F. J. & Flores, J. A. Controls of shell calcification in planktonic foraminifers. Quat. Sci. Rev. 27, 956–961 (2008).ADS 
Article 

Google Scholar 
34.Marr, J. P. et al. Ecological and temperature controls on Mg/Ca ratios of Globigerina bulloides from the southwest Pacific Ocean. Paleoceanography 26, PA2209 (2011).ADS 
Article 

Google Scholar 
35.de Villiers, S. A 425 ka record of foraminiferal shell weight variability in the western Equatorial Pacific. Paleoceanography 18, 1080 (2003).ADS 

Google Scholar 
36.de Villiers, S. Occupation of an ecological niche as the fundamental control on the shell-weight of calcifying planktonic foraminifera. Mar. Biol. 144, 45–50 (2004).Article 

Google Scholar 
37.Reymond, C. E., Lloyd, A., Kline, D. I., Dove, S. G. & Pandolfi, J. M. Decline in growth of foraminifer Marginopora rossi under eutrophication and ocean acidification scenarios. Glob. Change Biol. 19, 291–302 (2013).ADS 
Article 

Google Scholar 
38.Weinkauf, M. F. G., Moller, T., Koch, M. C. & Kucera, M. Calcification intensity in planktic foraminifera reflects ambient conditions irrespective of environmental stress. Biogeosciences 10, 6639–6655 (2013).ADS 
CAS 
Article 

Google Scholar 
39.Doo, S. S. et al. Amelioration of ocean acidification and warming effects through physiological buffering of a macroalgae. Ecol. Evol. 10, 8465–8475 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Hikami, M. et al. Contrasting calcification responses to ocean acidification between two reef foraminifers harboring different algal symbionts. Geophys. Res. Lett. 38, L19601 (2011).ADS 
Article 
CAS 

Google Scholar 
41.Sanyal, A. et al. Oceanic pH control on the boron isotopic composition of foraminifera: Evidence from culture experiments. Paleoceanography 11, 513–517 (1996).ADS 
Article 

Google Scholar 
42.Anagnostou, E. et al. Changing atmospheric CO2 concentration was the primary driver of early Cenozoic climate. Nature 533, 380–384 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Foster, G. L. & Rae, J. W. B. Reconstructing ocean pH with boron isotopes in foraminifera. Annu. Rev. Earth Planet. Sci. 44, 207–237 (2016).ADS 
CAS 
Article 

Google Scholar 
44.Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 
Article 

Google Scholar 
45.Dove, S. G. et al. Future reef decalcification under a business-as-usual CO2 emission scenario. Proc. Nat. Acad. Sci. 110, 15342–15347 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proc. Nat. Acad. Sci. 118, 2015265118 (2021).Article 
CAS 

Google Scholar 
47.Langer, M. R., Silk, M. T. & Lipps, J. H. Global ocean carbonate and carbon dioxide production: the role of reef foraminifera. J. Foraminifer. Res 27, 271–277 (1997).Article 

Google Scholar 
48.Pierrot, D., Lewis E. D. & Wallace, D.W. MS EXCEL Program Developed for CO2 System Calculations. ORNL/CDIAC-105a. (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, 2006). https://doi.org/10.3334/cdiac/otg.co2sys_xls_cdiac105a.49.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 
Article 

Google Scholar 
50.Bartlett, M. S. Properties of sufficiency and statistical test. Proc. R. Soc. A 160, 268–282 (1937).ADS 
MATH 

Google Scholar  More

Infection-age structured dynamicsFor the description of the dynamics, we follow the customary infection-age structured approach (for details see for instance Refs.4,10,11,12). Explicitly, we consider the infection-age structured dynamics of the number of individuals ({u}_{I}left(t,tau right)) at time (t) who were infected at time (t-tau) given by$$begin{array}{c}frac{partial }{partial t}{u}_{I}left(t,tau right)+frac{partial }{partial tau }{u}_{I}left(t,tau right)=0end{array}$$
(7)
with boundary condition$$begin{array}{c}{u}_{I}left(t,0right)=jleft(tright).end{array}$$
(8)
Here, (tau) is the time elapsed after infection, referred to as infection age, and (jleft(tright)={int }_{0}^{infty }{k}_{I}(t,tau ){u}_{I}left(t,tau right)dtau) is the incidence, with ({k}_{I}(t,tau )) being the rate of secondary transmissions per single primary case.The solution is obtained through the method of characteristics32 as$$begin{array}{c}{u}_{I}left(t,tau right)=jleft(t-tau right)end{array}$$
(9)
for (tge tau) and ({u}_{I}left(t,tau right)=0) for (t1 for countries and for US locations.The daily death counts (Delta {n}_{W}left(tright)={n}_{W}left(tright)-{n}_{W}left(t-1right)) are considered to contain reporting artifacts if they are negative or if they are unrealistically large. This last condition is defined explicitly as larger than 4 times its previous 14-day average value plus 10 deaths, (Delta {n}_{W}left(tright) >10+4times frac{1}{14}left({n}_{W}left(tright)-{n}_{W}left(t-14right)right)), from a non-sparse reporting schedule with at least 2 consecutive non-zero values before and after the time (t), (Delta {n}_{W}left(tright)ne frac{1}{5}left({n}_{W}left(t+2right)-{n}_{W}left(t-3right)right)).Reporting artifacts identified at time (t) are considered to be the result of previous miscounting. The excess or lack of deaths are imputed proportionally to previous death counts. Explicitly, death counts are updated as$$begin{array}{c}{n}_{W}left(t-1-iright)leftarrow {n}_{W}left(t-1-iright)frac{{n}_{W}{left(t-1right)}_{estimated}}{{n}_{W}left(t-1right)}end{array}$$
(32)
with ({n}_{W}{left(t-1right)}_{estimated}={n}_{W}left(tright)-frac{1}{7}left({n}_{W}left(t-1right)-{n}_{W}left(t-8right)right)) for all (ige 0). In this way, (Delta {n}_{W}left(tright)) is assigned its previous seven-day average value.The expected daily deaths, (Delta {n}_{D}(t)), are obtained through a density estimation multiscale functional, ({f}_{de}), as (Delta {n}_{D}(t)={f}_{de}left(Delta {n}_{W}left(tright)right)), which leads to the estimation of the expected cumulative deaths at time (t) as ({n}_{D}left(tright)={n}_{W}left({t}_{0}right)+{sum }_{s={t}_{0}+1}^{t}Delta {n}_{D}(s)). Specifically,$$begin{array}{c}{f}_{de}left(Delta {n}_{W}left(tright)right)=left(1-{r}_{1}right)d{d}_{0}+{r}_{1}left(left(1-{r}_{2}right)d{d}_{1}+{r}_{2}d{d}_{2}right)end{array}$$
(33)
with$$begin{array}{c}{r}_{1} = {e}^{-0.3d{d}_{1}},end{array}$$
(34)
$$begin{array}{c}{r}_{2} = {e}^{-3d{d}_{2}},end{array}$$
(35)
$$begin{array}{c}d{d}_{0}={ma}_{14}left({ma}_{14}left(Delta {n}_{W}left(tright)right)right),end{array}$$
(36)
$$begin{array}{c}d{d}_{1}={rg}_{12}left({ma}_{14}left(Delta {n}_{W}left(tright)right)right),end{array}$$
(37)
$$begin{array}{c}d{d}_{2}={rg}_{48}left({ma}_{14}left(Delta {n}_{W}left(tright)right)right),end{array}$$
(38)
where ({ma}_{14}left(cdot right)) is a centered moving average with window size of 14 days and ({rg}_{sigma }left(cdot right)) is a centered rolling average through a Gaussian window with standard deviation (sigma). The specific value of the window size has been chosen to mitigate weekly reporting effects. The values of the standard deviations of the Gaussian windows have been selected to achieve a smooth representation of the expected death estimation for each country as shown in the bottom panels of Supplementary Fig. S1.Reporting delaysWe consider an average delay of two days between reporting a death and its occurrence. This value is obtained by comparing the daily death counts reported for Spain1 and their actual values33 from February 15 to March 31, 2020. The values of the root-mean-squared deviation between reported and actual deaths shifted by 0, 1, 2, 3, and 4 days are 77.9, 58.4, 38.5, 58.7, and 88.6 deaths respectively.Infection fatality rate ((IFR))The infection fatality rate is computed assuming homogeneous attack rate as$$begin{array}{c}IFR=frac{1}{{sum }_{a}{g}_{a}}{sum }_{a}{IFR}_{a}{g}_{a} ,end{array}$$
(39)
where ({mathrm{IFR}}_{a}) is the previously estimated (IFR) for the age group (a)5 and ({g}_{a}) is the population in the age group (a) as reported by the United Nations for countries18 and the US Census for states19.Clinical parametersWe obtained the values of the average ({tau }_{G}) and standard deviation ({sigma }_{G}) of the generation time from Ref.13, of the averages of the incubation ({tau }_{I}) and symptom onset-to-death ({tau }_{OD}) times from Refs.5,14, and of the average number of days (Delta {t}_{TP}) of positive testing by an infected individual from Refs.15,17. The average time at which an individual tested positive after infection ({tau }_{TP}) was computed as ({tau }_{TP}={tau }_{I}-2+Delta {t}_{TP}/2), where we have assumed that on average an individual started to test positive 2 days before symptom onset. The average seroconversion time after infection ({tau }_{SP}) was estimated as ({tau }_{I}) plus the 7 days of 50% seroconversion after symptom onset reported in Ref.16.Dynamical constraints implementation with discrete timeWe implemented the dynamical constraints to compute the infectious and infected population as outlined in the main text and as detailed in the previous section of this document, using days as time units. Time delays were rounded to days to assign daily values.The first derivative of the cumulative number of deaths is computed as$$begin{array}{c}frac{d{n}_{D}left(tright)}{dt}=Delta {n}_{D}left(tright),end{array}$$
(40)
with (Delta {n}_{D}left(tright)={n}_{D}left(tright)-{n}_{D}(t-1)).The growth rate was computed explicitly from the discrete time series as the centered 7-day difference$$begin{array}{c}{k}_{G}left(tright)=frac{1}{7}left({mathrm{ln}}left(Delta {n}_{D}left(t+4right)+Delta {n}_{D}left(t+3right)right)-{mathrm{ln}}left(Delta {n}_{D}left(t-3right)+Delta {n}_{D}left(t-4right)right)right).end{array}$$
(41)
The 7-day value was chosen to mitigate reporting artifacts.Confidence and credibility intervalsConfidence intervals associated with death counts were computed using bootstrapping with 10,000 realizations34. These confidence intervals were combined with the credibility intervals of the (IFR) in infectious and infected populations assuming independence and additivity on a logarithmic scale.Fold accuracyThe fold accuracy, ({F}_{A}), is explicitly computed as$$begin{array}{c}{mathrm{log}}{F}_{A}=frac{1}{N}{sum }_{i=1}^{N}left|{mathrm{log}}{x}_{i}^{obs}-{mathrm{log}}{x}_{i}^{est}right|,end{array}$$
(42)
where (left|cdot right|) is the absolute value function, ({x}_{i}^{obs}) is the ({i}^{th}) observation, ({x}_{i}^{est}) is its corresponding estimation, and (N) is the total number of observations.Inference and extrapolationBecause of the delay between infections and deaths, inference for the values of the growth rate and infectious populations ends on December 30, 2020 and for the values of the infected populations ends on December 26, 2020. Extrapolation to the current time (January 21, 2021) is carried out assuming the last growth rate computed.Reproduction numberThe quantities ({R}_{t}) and ({k}_{G}left(tright)) are related to each other through the Euler–Lotka equation, ({R}_{t}^{-1}={int }_{0}^{infty }{f}_{GT}left(tau right){e}^{-{k}_{G}left(tright)tau }dtau ,) which considers (jleft(t-tau right)simeq {e}^{-{k}_{G}left(tright)tau }jleft(tright)) in the renewal equation (jleft(tright)={int }_{0}^{infty }{k}_{I}left(t,tau right)jleft(t-tau right)dtau). Generation times can generally be described through a gamma distribution ({f}_{GT}left(tau right)=frac{{beta }^{alpha }}{Gamma left(alpha right)}{tau }^{alpha -1}{e}^{-beta tau }) with (alpha ={tau }_{G}^{2}/{sigma }_{G}^{2}) and (beta ={tau }_{G}/{sigma }_{G}^{2}), which leads to ({R}_{t}={left(1+{k}_{G}(t)/beta right)}^{alpha }) for ({k}_{G}(t) >-beta) and ({R}_{t}=0) for ({k}_{G}left(tright)le -beta). In the case of the exponentially distributed limit ((alpha simeq 1)) or small values of ({k}_{G}(t)/beta), it simplifies to ({R}_{t}=1+{k}_{G}left(tright){tau }_{G}) for ({k}_{G}left(tright) >-1/{tau }_{G}) and ({R}_{t}=0) for ({k}_{G}left(tright)le -1/{tau }_{G}). Global prevalence data were obtained from multiple data sources35,36,37,38,39,40,41,42, as described in Supplementary Table S1. 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.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