Philippa Kaur

Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Grace, J., José, J. S., Meir, P., Miranda, H. S. & Montes, R. A. Productivity and carbon fluxes of tropical savannas. J. Biogeogr. 33, 387–400 (2006).
Google Scholar 
Van Der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).ADS 

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

Google Scholar 
Russell-Smith, J. et al. Opportunities and challenges for savanna burning emissions abatement in southern Africa. J. Environ. Manage. 288, 112414 (2021).CAS 
PubMed 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, C. et al. Historical and future global burned area with changing climate and human demography. One Earth 4, 517–530 (2021).
Google Scholar 
Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194–198 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Higgins, S. I. et al. Effects of four decades of fire manipulation on woody vegetation structure in savanna. Ecology 88, 1119–1125 (2007).
Google Scholar 
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).ADS 
CAS 
PubMed 
MATH 

Google Scholar 
Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).ADS 
CAS 

Google Scholar 
Pellegrini, A. F. A., Hedin, L. O., Staver, A. C. & Govender, N. Fire alters ecosystem carbon and nutrients but not plant nutrient stoichiometry or composition in tropical savanna. Ecology 96, 1275–1285 (2015).PubMed 

Google Scholar 
Tilman, D. et al. Fire suppression and ecosystem carbon storage. Ecology 81, 2680–2685 (2000).
Google Scholar 
Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).ADS 

Google Scholar 
de Miranda, S. D. C. et al. Regional variations in biomass distribution in Brazilian savanna woodland. Biotropica 46, 125–138 (2014).
Google Scholar 
Wigley, B. J., Cramer, M. D. & Bond, W. J. Sapling survival in a frequently burnt savanna: mobilisation of carbon reserves in Acacia karroo. Plant Ecol. 203, 1 (2009).
Google Scholar 
Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Staver, A. C., Botha, J. & Hedin, L. Soils and fire jointly determine vegetation structure in an African savanna. New Phytol. 216, 1151–1160 (2017).CAS 
PubMed 

Google Scholar 
Zhou, Y., Wigley, B. J., Case, M. F., Coetsee, C. & Staver, A. C. Rooting depth as a key woody functional trait in savannas. New Phytol. 227, 1350–1361 (2020).PubMed 

Google Scholar 
Govender, N., Trollope, W. S. W., Van, & Wilgen, B. W. The effect of fire season, fire frequency, rainfall and management on fire intensity in savanna vegetation in South Africa. J. Appl. Ecol. 43, 748–758 (2006).
Google Scholar 
Colgan, M. S., Asner, G. P. & Swemmer, T. Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas. Ecol. Appl. 23, 1170–1184 (2013).PubMed 

Google Scholar 
Davies, A. B. & Asner, G. P. Elephants limit aboveground carbon gains in African savannas. Glob. Change Biol. 25, 1368–1382 (2019).ADS 

Google Scholar 
Butnor, J. R. et al. Utility of ground-penetrating radar as a root biomass survey tool in forest systems. Soil Sci. Soc. Am. J. 67, 1607–1615 (2003).ADS 
CAS 

Google Scholar 
Staver, A. C., Wigley-Coetsee, C. & Botha, J. Grazer movements exacerbate grass declines during drought in an African savanna. J. Ecol. 107, 1482–1491 (2019).
Google Scholar 
Ryan, C. M., Williams, M. & Grace, J. Above- and belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica 43, 423–432 (2011).
Google Scholar 
Swezy, D. M. & Agee, J. K. Prescribed-fire effects on fine-root and tree mortality in old-growth ponderosa pine. Can. J. For. Res. 21, 626–634 (1991).
Google Scholar 
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).ADS 
CAS 
PubMed 

Google Scholar 
Coetsee, C., Bond, W. J. & February, E. C. Frequent fire affects soil nitrogen and carbon in an African savanna by changing woody cover. Oecologia 162, 1027–1034 (2010).ADS 
PubMed 

Google Scholar 
Holdo, R. M., Mack, M. C. & Arnold, S. G. Tree canopies explain fire effects on soil nitrogen, phosphorus and carbon in a savanna ecosystem. J. Veg. Sci. 23, 352–360 (2012).
Google Scholar 
Lloyd, J. et al. Contributions of woody and herbaceous vegetation to tropical savanna ecosystem productivity: a quasi-global estimate. Tree Physiol. 28, 451–468 (2008).PubMed 

Google Scholar 
Wigley, B. J., Augustine, D. J., Coetsee, C., Ratnam, J. & Sankaran, M. Grasses continue to trump trees at soil carbon sequestration following herbivore exclusion in a semiarid African savanna. Ecology 101, e03008 (2020).PubMed 

Google Scholar 
Khomo, L., Trumbore, S., Bern, C. R. & Chadwick, O. A. Timescales of carbon turnover in soils with mixed crystalline mineralogies. Soil 3, 17–30 (2017).ADS 
CAS 

Google Scholar 
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant Soil 241, 155–176 (2002).CAS 

Google Scholar 
Abreu, R. C. R. et al. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3, e1701284 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).PubMed 

Google Scholar 
West, T. A., Börner, J. & Fearnside, P. M. Climatic benefits from the 2006–2017 avoided deforestation in Amazonian Brazil. Front. For. Glob. Change 2, 52 (2019).
Google Scholar 
Aleman, J. C., Blarquez, O. & Staver, C. A. Land-use change outweighs projected effects of changing rainfall on tree cover in sub-Saharan Africa. Glob. Change Biol. 22, 3013–3025 (2016).ADS 

Google Scholar 
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).ADS 

Google Scholar 
Ratajczak, Z., Nippert, J. B. & Collins, S. L. Woody encroachment decreases diversity across North American grasslands and savannas. Ecology 93, 697–703 (2012).PubMed 

Google Scholar 
Smit, I. P. & Prins, H. H. Predicting the effects of woody encroachment on mammal communities, grazing biomass and fire frequency in African savannas. PLoS One 10, e0137857 (2015).PubMed 
PubMed Central 

Google Scholar 
Huxman, T. E. et al. Ecohydrological implications of woody plant encroachment. Ecology 86, 308–319 (2005).
Google Scholar 
Hermoso, V., Regos, A., Morán-Ordóñez, A., Duane, A. & Brotons, L. Tree planting: a double-edged sword to fight climate change in an era of megafires. Glob. Change Biol. 27, 3001–3003 (2021).
Google Scholar 
Venter F. A. Classification of Land for Management Planning in the Kruger National Park. PhD thesis, Univ. South Africa (1990).Biggs, R., Biggs, H. C., Dunne, T. T., Govender, N. & Potgieter, A. L. F. Experimental burn plot trial in the Kruger National Park: history, experimental design and suggestions for data analysis. Koedoe 46, 15 (2003).
Google Scholar 
Codron, J. et al. Taxonomic, anatomical, and spatio-temporal variations in the stable carbon and nitrogen isotopic compositions of plants from an African savanna. J. Archaeol. Sci. 32, 1757–1772 (2005).
Google Scholar 
Zhou, Y., Boutton, T. W. & Ben Wu, X. Soil carbon response to woody plant encroachment: importance of spatial heterogeneity and deep soil storage. J. Ecol. 105, 1738–1749 (2017).CAS 

Google Scholar 
Sheldrick B. & Wang C. In Soil Sampling and Methods of Analysis (ed. Carter, M. R.) 499–511 (CRC Press, 1993).Butnor, J. R. et al. Surface-based GPR underestimates below-stump root biomass. Plant Soil 402, 47–62 (2016).CAS 

Google Scholar 
Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hirano, Y. et al. Limiting factors in the detection of tree roots using ground-penetrating radar. Plant Soil 319, 15–24 (2009).CAS 

Google Scholar 
Popescu, S. C. & Wynne, R. H. Seeing the trees in the forest. Photogramm. Eng. Remote Sensing 70, 589–604 (2004).
Google Scholar 
Case, M. F., Wigley-Coetsee, C., Nzima, N., Scogings, P. F. & Staver, A. C. Severe drought limits trees in a semi-arid savanna. Ecology 100, e02842 (2019).PubMed 

Google Scholar 
Beucher S. & Meyer F. In Mathematical Morphology in Image Processing (ed. Dougherty, E. R.) 433–481 (CRC Press, 1993).Nickless, A., Scholes, R. J. & Archibald, S. A method for calculating the variance and confidence intervals for tree biomass estimates obtained from allometric equations. S. Afr. J. Sci. 107, 1–10 (2011).
Google Scholar 
Plowright A. & Roussel J.-R. ForestTools: analyzing remotely sensed forest data. R package version 0.2.1. https://CRAN.R-project.org/package=ForestTools (2020).Hijmans R. J. raster: geographic data analysis and modeling. R package version 3.3-7. https://CRAN.R-project.org/package=raster (2020).Penman J. et al. (eds) Good Practice Guidance for Land Use, Land-Use Change and Forestry (Intergovernmental Panel on Climate Change, 2003).Kuznetsova, A., Brockhoff, P. & Christensen, R. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar  More

Costa, F. et al. Global morbidity and mortality of leptospirosis: A systematic review. PLoS Negl. Trop. Dis. 9, e0003898. https://doi.org/10.1371/journal.pntd.0003898 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Cosson, J.-F. et al. Epidemiology of Leptospira transmitted by rodents in southeast Asia. PLoS Negl. Trop. Dis. 8, e2902. https://doi.org/10.1371/journal.pntd.0002902 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Jonsson, C. B., Figueiredo, L. T. M. & Vapalahti, O. A global perspective on Hantavirus ecology, epidemiology, and disease. Clin. Microbiol. Rev. 23, 412–441. https://doi.org/10.1128/CMR.00062-09 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vaheri, A. et al. Uncovering the mysteries of Hantavirus infections. Nat. Rev. Microbiol. 11, 539–550. https://doi.org/10.1038/nrmicro3066 (2013).CAS 
Article 
PubMed 

Google Scholar 
Peniche Lara, G., Dzul-Rosado, K. R., Zavala Velázquez, J. E. & Zavala-Castro, J. Murine typhus: Clinical and epidemiological aspects. Colomb. Med. (Cali) 43, 175–180 (2012).Article 

Google Scholar 
Pimentel, D., Lach, L., Zuniga, R. & Morrison, D. Environmental and economic costs of nonindigenous species in the United States. Bioscience 50, 53–65. https://doi.org/10.1641/0006-3568(2000)050[0053:EAECON]2.3.CO;2 (2000).Article 

Google Scholar 
Smith, R. & Meyer, A. Rodent control methods: Non-chemical and non-lethal chemical, with special reference to food stores. in Rodent Pests and Their Control (Buckle, A.P., Smith, R. eds.). 2nd edn. 101–122. (CAB International, 2015).Himsworth, C. G., Jardine, C. M., Parsons, K. L., Feng, A. Y. T. & Patrick, D. M. The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses. PLoS ONE 9, e91654. https://doi.org/10.1371/journal.pone.0091654 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mari Saez, A. et al. Rodent control to fight Lassa fever: Evaluation and lessons learned from a 4-year study in Upper Guinea. PLoS Negl. Trop. Dis. 12, e0006829–e0006829. https://doi.org/10.1371/journal.pntd.0006829 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Baldwin, R., Quinn, N., Davis, D. & Engeman, R. Effectiveness of rodenticides for managing invasive roof rats and native deer mice in orchards. Environ. Sci. Pollut. Res. 21, 5795–5802. https://doi.org/10.1007/s11356-014-2525-4 (2014).CAS 
Article 

Google Scholar 
Hadler, M. R. & Buckle, A. P. Forty five years of anticoagulant rodenticides—Past, present and future trends. Proc. Vertebr. Pest Conf. 15, 149–155 (1992).
Google Scholar 
Rost, S. et al. Novel mutations in the VKORC1 gene of wild rats and mice – A response to 50 years of selection pressure by warfarin?. BMC Genet. 10, 4. https://doi.org/10.1186/1471-2156-10-4 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Buckle, A., Prescott, C. & Ward, K. J. Resistance to the first and second generation anticoagulant rodenticides – A new perspective. Proc. Verebr. Pest Conf. 16, 138–144 (1994).
Google Scholar 
Goulois, J., Lambert, V., Legros, L., Benoit, E. & Lattard, V. Adaptative evolution of the Vkorc1 gene in Mus musculus domesticus is influenced by the selective pressure of anticoagulant rodenticides. Ecol. Evol. 7, 2767–2776. https://doi.org/10.1002/ece3.2829 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Meerburg, B. G., van Gent-Pelzer, M. P. E., Schoelitsz, B. & van der Lee, T. A. J. Distribution of anticoagulant rodenticide resistance in Rattus norvegicus in the Netherlands according to Vkorc1 mutations. Pest Manag. Sci. 70, 1761–1766. https://doi.org/10.1002/ps.3809 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lund, M. Rodent resistance to the anticoagulant rodenticides, with particular reference to Denmark. Bull. World Health Organ. 47, 611–618 (1972).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, M. J. et al. Effects of culling on Leptospira interrogans carriage by rats. Emerg. Infect. Dis. 24, 356–360. https://doi.org/10.3201/eid2402.171371 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Greaves, J. H. Resistance to anticoagulant rodenticides. in Rodent Pests and Their Control (Buckle, A.P., Smith, R. eds.). 2nd edn. 187–208. (CAB International, 2015).Lefebvre, S. B., Benoit, E. & Lattard, V. Comparative biology of the resistance to vitamin K antagonists: An overview of the resistance mechanisms in Anticoagulation Therapy (Basaran, O., Biteker, M. eds.). 20–45. (Intech Open, 2016).Grandemange, A. et al. Consequences of the Y139F Vkorc1 mutation on resistance to AVKs: In-vivo investigation in a 7th generation of congenic Y139F strain of rats. Pharmacogenet. Genomics. 19, 742–750. https://doi.org/10.1097/FPC.0b013e32832ee55b (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sadowski, J. A., Esmon, C. T. & Suttie, J. W. Vitamin K-dependent carboxylase. Requirements of the rat liver microsomal enzyme system. J. Biol. Chem. 251, 2770–2776 (1976).CAS 
Article 

Google Scholar 
Mooney, J. et al. VKORC1 sequence variants associated with resistance to anticoagulant rodenticides in Irish populations of Rattus norvegicus and Mus musculus domesticus. Sci. Rep. 8, 4535. https://doi.org/10.1038/s41598-018-22815-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thijssen, H. H. W. Warfarin-based rodenticides: Mode of action and mechanism of resistance. Pestic. Sci. 43, 73–78. https://doi.org/10.1002/ps.2780430112 (1995).CAS 
Article 

Google Scholar 
Bell, R. G. & Caldwell, P. T. Mechanism of warfarin resistance. Warfarin and the metabolism of vitamin K1. Biochemistry 12, 1759–1762. https://doi.org/10.1021/bi00733a015 (1973).CAS 
Article 
PubMed 

Google Scholar 
Pelz, H.-J. et al. The genetic basis of resistance to anticoagulants in rodents. Genetics 170, 1839–1847. https://doi.org/10.1534/genetics.104.040360 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baert, K., Stuyck, J., Breyne, P., Maes, D. & Casaer, J. Distribution of anticoagulant resistance in the brown rat in Belgium. Belg. J. Zool. 142, 39–48 (2012).
Google Scholar 
Prescott, C. V., Buckle, A. P., Gibbings, J. G., Allan, E. N. W. & Stuart, A. M. Anticoagulant resistance in Norway rats (Rattus norvegicus Berk.) in Kent – A VKORC1 single nucleotide polymorphism, tyrosine139phenylalanine, new to the UK. Int. J. Pest Manag. 57, 61–65. https://doi.org/10.1080/09670874.2010.523124 (2010).CAS 
Article 

Google Scholar 
Grandemange, A., Lasseur, R., Longin-Sauvageon, C., Benoit, E. & Berny, P. Distribution of VKORC1 single nucleotide polymorphism in wild Rattus norvegicus in France. Pest Manag. Sci. 66, 270–276. https://doi.org/10.1002/ps.1869 (2009).CAS 
Article 

Google Scholar 
Goulois, J. et al. Evidence of a target resistance to antivitamin K rodenticides in the roof rat Rattus rattus: Identification and characterisation of a novel Y25F mutation in the Vkorc1 gene. Pest Manag. Sci. 72, 544–550. https://doi.org/10.1002/ps.4020 (2015).CAS 
Article 
PubMed 

Google Scholar 
Endepols, S., Klemann, N., Jacob, J. & Buckle, A. P. Resistance tests and field trials with bromadiolone for the control of Norway rats (Rattus norvegicus) on farms in Westphalia, Germany. Pest Manag. Sci. 68, 348–354. https://doi.org/10.1002/ps.2268 (2011).CAS 
Article 
PubMed 

Google Scholar 
Andru, J., Cosson, J.-F., Caliman, J.-P. & Benoit, E. Coumatetralyl resistance of Rattus tanezumi infesting oil palm plantations in Indonesia. Ecotoxicology 22, 377–386. https://doi.org/10.1007/s10646-012-1032-y (2012).CAS 
Article 
PubMed 

Google Scholar 
Department of Statistics Singapore. Population and Population Structure. https://www.singstat.gov.sg/find-data/search-by-theme/population/population-and-population-structure/latest-data (2020).Department of Statistics Singapore. Environment. https://www.singstat.gov.sg/find-data/search-by-theme/society/environment/latest-data (2020).Department of Statistics Singapore. M890531—Licensed Food Establishments (End of Period), Annual. https://www.tablebuilder.singstat.gov.sg/publicfacing/createDataTable.action?refId=14624 (2021).QGIS Development Team. QGIS Geographic Information System. QGIS Association. https://www.qgis.org/en/site/ (2021).Ivanova, N. V., Clare, E. L. & Borisenko, A. V. in DNA Barcodes: Methods and Protocols (John Kress, W. & Erickson, D.L. eds.) 153–182 (Humana Press, 2012).Pagès, M. et al. Revisiting the taxonomy of the Rattini tribe: A phylogeny-based delimitation of species boundaries. BMC Evol. Biol. 10, 184. https://doi.org/10.1186/1471-2148-10-184 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pagès, M. et al. Cytonuclear discordance among Southeast Asian black rats (Rattus rattus complex). Mol. Ecol. 22, 1019–1034. https://doi.org/10.1111/mec.12149 (2013).CAS 
Article 
PubMed 

Google Scholar 
Rungrojn, A. et al. Prevalence and molecular characterization of Rickettsia spp. from wild small mammals in public parks and urban areas of Bangkok metropolitan, Thailand. Trop. Med. Infect. Dis. https://doi.org/10.3390/tropicalmed6040199 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wulandhari, S. A. et al. High prevalence and low diversity of chigger infestation in small mammals found in Bangkok metropolitan parks. Med. Vet. Entomol. 35, 534–546. https://doi.org/10.1111/mve.12531 (2021).CAS 
Article 
PubMed 

Google Scholar 
Cowan, P. E. et al. Vkorc1 sequencing suggests anticoagulant resistance in rats in New Zealand. Pest Manag. Sci. 73, 262–266. https://doi.org/10.1002/ps.4304 (2016).CAS 
Article 
PubMed 

Google Scholar 
Rost, S. et al. Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature 427, 537–541. https://doi.org/10.1038/nature02214 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wong, T. W. et al. Hantavirus infections in humans and commensal rodents in Singapore. Trans. R. Soc. Trop. Med. Hyg. 83, 248–251. https://doi.org/10.1016/0035-9203(89)90666-4 (1989).CAS 
Article 
PubMed 

Google Scholar 
Dubock, A. Pulsed baiting – A new technique for high potency, slow acting rodenticides. Proc. Vertebr. Pest Conf. 10, 123–136 (1982).
Google Scholar 
Garg, N., Singla, N., Jindal, V. & Babbar, B. Studies on bromadiolone resistance in Rattus rattus populations from Punjab, India. Pestic. Biochem. Physiol. 139, 24–31 (2017).CAS 
Article 

Google Scholar 
Song, Y., Lan, Z. & Kohn, M. H. Mitochondrial DNA phylogeography of the Norway rat. PLoS ONE 9, e88425. https://doi.org/10.1371/journal.pone.0088425 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Aplin, K. P. et al. Multiple geographic origins of commensalism and complex dispersal history of black rats. PLoS ONE 6, e26357. https://doi.org/10.1371/journal.pone.0026357 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boyle, C. M. Case of apparent resistance of Rattus norvegicus Berkenhout to anticoagulant poisons. Nature 188, 517. https://doi.org/10.1038/188517a0 (1960).ADS 
Article 

Google Scholar 
Jackson, W. B. & Kaukeinen, D. Resistance of wild Norway rats in North Carolina to warfarin rodenticide. Science 176, 1343 (1972).ADS 
CAS 
Article 

Google Scholar 
Ma, X. et al. Low warfarin resistance frequency in Norway rats in two cities in China after 30 years of usage of anticoagulant rodenticides. Pest Manag. Sci. 74, 2555–2560. https://doi.org/10.1002/ps.5040 (2018).CAS 
Article 
PubMed 

Google Scholar 
Markussen, M. D. K., Heiberg, A.-C., Fredholm, M. & Kristensen, M. Differential expression of cytochrome P450 genes between bromadiolone-resistant and anticoagulant-susceptible Norway rats: A possible role for pharmacokinetics in bromadiolone resistance. Pest Manag. Sci. 64, 239–248. https://doi.org/10.1002/ps.1506 (2008).CAS 
Article 
PubMed 

Google Scholar  More

Badola, H. K. & Aitken, S. Potential biological resources for poverty alleviation in Indian Himalaya. Biodiver. 11(3–4), 8–18 (2010).
Google Scholar 
Pandey, A., Badola, H. K., Rai, S. & Singh, S. P. Timberline structure and woody taxa regeneration towards treeline along latitudinal gradients in Khangchendzonga National Park Eastern Himalaya. PLoS ONE 13(11), e0207762 (2018).PubMed 
PubMed Central 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature https://doi.org/10.1038/nature22899 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hansen, A. J. et al. Global change in forests: Responses of species, communities, and biomes. Bio-Sciences 51, 765–779 (2001).
Google Scholar 
Gooden, B., French, K. O. & Turner, P. Invasion and management of a woody plant, Lantana camara L., alters vegetation diversity within wet sclerophyll forest in southeastern Australia. For. Ecol. Manag. 257(3), 960–967 (2009).
Google Scholar 
Xu, Q. et al. Rapid bamboo invasion (expansion) and its effects on biodiversity and soil processes. Global Ecol. Cons. https://doi.org/10.1016/j.gecco.2019.e00787 (2020).Article 

Google Scholar 
Dhar, U., Rawal, R. S. & Samant, S. S. Structural diversity and representativeness of forest vegetation in a protected area of Kumaun Himalaya, India: Implications for conservation. Biodiver. Cons. 6, 1045–1062 (1997).
Google Scholar 
Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710 (2000).
Google Scholar 
Tomimatsu, H. et al. Consequences of forest fragmentation in an understory plant community: Extensive range expansion of native dwarf bamboo. Plant Species Biol. 26, 3–12 (2011).
Google Scholar 
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 

Google Scholar 
Royo, A. A. & Carson, W. P. On the formation of dense understory layers in forests worldwide: Consequences and implications for forest dynamics, biodiversity, and succession. Can. J. For. Res. 36, 1345–1362 (2006).
Google Scholar 
Royo, A. A., Stout, S. L. & Pierson, T. G. Restoring forest herb communities through landscape-level deer herd reductions: Is recovery limited by legacy effects?. Biol. Cons. 143, 2425–2434 (2010).
Google Scholar 
Taylor, A. H., Jinyan, H. & ShiQiang, Z. Canopy tree development and undergrowth bamboo dynamics in old-growth Abies-Betula forests in southwestern China: A 12-year study. For. Ecol. Manag. 200(1), 347–360 (2004).
Google Scholar 
Zhou, X., Chen, L. & Lin, Q. Effects of chemical foaming agents on the physico-mechanical properties and rheological behavior of bamboo powder-polypropylene foamed composites. Bio Resour. 7(2), 2183–2198 (2012).CAS 

Google Scholar 
Lima, R. A., Rother, D. C., Muler, A. E., Lepsch, I. F. & Rodrigues, R. R. Bamboo overabundance alters forest structure and dynamics in the Atlantic Forest hotspot. Biol. Conserv. 147, 32–39 (2012).
Google Scholar 
Tariyal, K. Bamboo as a successful carbon sequestration substrate in Uttarakhand: A brief analysis. Int. J. Curr. Adv. Res. 5(4), 736–738 (2016).
Google Scholar 
Badoni, A.K., Badola, H. K. & Sharma, S.P. Inter-disciplinary approach towards environmental management: A case study with wild bamboos in Garhwal Himalayas, In: Prakash R (Ed), Editor. Advances in Forestry Research in India, Vol. III, Intl. Book Distrib., Dehradun. pp 261–280 (1989).Bahadur, K. N. Bamboos in the service of man. Biol. Contemp. J. 1(2), 69–72 (1974).
Google Scholar 
Tomar, J. M. S., Hore, D. K. & Annadurai, A. Bamboos and their conservation in North-East India. Indian For. 135(6), 817–824 (2009).
Google Scholar 
Kumar, P. S., Kumari, K. U., Devi, M. P., Choudhary, V. K. & Sangeetha, A. Bamboo shoot as a source of nutraceuticals and bioactive compounds: a review. Indian J. Nat. Proc. Res. 8(1), 32–46 (2016).
Google Scholar 
Pradhan, S., Saha, G. K. & Khan, J. A. Ecology of the red panda Ailurus fulgens in the Singhalila National Park, Darjeeling, India. Biol. Cons. 98(1), 11–18 (2001).
Google Scholar 
Dorji, S., Vernes, K. & Rajaratnam, A. Habitat correlates of the Red Panda in the temperate forests of Bhutan. PLoS ONE 610, 1–11 (2011).
Google Scholar 
Mohan Ram, H. Y. & Tandon, R. Bamboos and rattans—from riches to rags. Proc. Natl. Sci. Acad. India 63(3), 245–267 (1997).
Google Scholar 
Sharma, R., Wahono, J. & Baral, H. Bamboo as an alternative bioenergy crop and powerful ally for land restoration in Indonesia. Sustainability 10, 4367 (2018).
Google Scholar 
Seethalakshmi, K.K. & Kumar, M.S.M. Bamboos of India: A Compendium. Bamboo Information Center, India, Kerala Forest Research Institute, Peechi and International Network for Bamboo and Ratten, Beijing (1998).Sarmah, A., Thomas, S., Goswami, M., Haridashan, K. & Borthakur, S. K. Rattan and bamboo flora of North-East India in a conservation perspective. In Sustainable Management of Forests (eds Arunachalan, A. & Khan, M. L.) 37–45 (International Book Distributors, 2000).
Google Scholar 
Das, M., Bhattacharya, S., Singh, P., Filgueiras, T. S. & Pal, A. Bamboo taxonomy and diversity in the era of molecular markers. Adv. Bot. Res. 47, 225–268 (2008).CAS 

Google Scholar 
Biswas, S. et al. Evidence of stress induced flowering in bamboo and comments on probable biochemical and molecular factors. J. Plant Biochem. Biotechnol. 30(4), 1020–1026 (2021).CAS 

Google Scholar 
Ray, P. K. Gregarious flowering of a common hill bamboo Arundinaria maling. Indian For. 78(2), 89–90 (1952).
Google Scholar 
Taylor, A. H. & Zisheng, Q. Culm dynamics and dry matter production of bamboos in the Wolong and Tangjiahe giant panda reserves, Sichuan, China. J. Appl. Ecol. 24, 419–433 (1987).
Google Scholar 
Okutomi, K., Shinoda, S. & Fukuda, H. Causal analysis of the invasion of broadleaved forest by bamboo in Japan. J. Veg. Sci. 7, 723–728 (1996).
Google Scholar 
Nath, A. J., Das, M. C. & Das, A. K. Gregarious flowering in woody bamboos: Does it mean end of life?. Curr. Sci. 106(1), 12–13 (2014).
Google Scholar 
Silveira, M. Ecological aspects of bamboo-dominated Forest in southwestern Amazonia: An ethnoscience perspective. Ecotropica 5, 213–216 (1999).
Google Scholar 
Song, Q. N. et al. Accessing the impacts of bamboo expansion on NPP and N cycling in evergreen broadleaved forest in subtropical China. Sci. Rep. 7(1), 1–10 (2017).ADS 

Google Scholar 
Rother, D. C., Rodrigues, R. R. & Pizo, M. A. Effects of bamboo stands on seed rain and seed limitation in a rainforest. For. Ecol. Manag. 257, 885–892 (2009).
Google Scholar 
Srivastava, V., Griess, V.C. & Padalia, H. Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas. Ecol Model 385:35–44 (2018).Roy, A., Bhattacharya, S., Ramprakash, M. & Kumar, A. S. Modelling critical patches of connectivity for invasive Maling bamboo (Yushania maling) in Darjeeling Himalayas using graph theoretic approach. Ecol. Model. 329, 77–85 (2016).
Google Scholar 
Stapleton, C. M. A. The morphology of woody bamboos. LinneanSocietySymposium Series 19 251–268 (Academic Press Limited, 1997).
Google Scholar 
Larpkern, P., Mor, S. R. & Totland, Q. Bamboo dominance reduces tree regeneration in a disturbed tropical forest. Oecologia 165(1), 161–168 (2011).ADS 
PubMed 

Google Scholar 
Tao, J. P., Shi, X. P. & Wang, Y. J. Effects of different bamboo densities on understory species diversity and trees regeneration in an Abies faxoniana forest, Southwest China. Sci. Res. Essays 7, 660–668 (2012).
Google Scholar 
Wang, W., Franklin, S. B., Ren, Y. & Ouellette, J. R. Growth of bamboo Fargesiaqinlingensis and regeneration of trees in a mixed hardwood-conifer forest in the Qinling Mountains, China. For. Ecol. Manag. 234(1–3), 107–115 (2006).
Google Scholar 
Gratzer, G., Rai, P. B. & Glatzel, G. The influence of the bamboo Yushaniamicrophylla on regeneration of Abies densa in central Bhutan. Can. J. For. Res. 29, 1518–1527 (1999).
Google Scholar 
Takahashi, K., Uemura, S., Suzuki, J. I. & Hara, T. Effect of understory dwarf bamboo on soil water and the growth of overstory trees in a dense secondary Betula ermanii forest, northern Japan. Ecol. Res. 18(6), 767–774 (2003).
Google Scholar 
Ito, H. & Hino, T. Effects of deer, mice and dwarf bamboo on the emergence, survival and growth of Abieshomolepis (Piceaceae) seedlings. Ecol. Res. 19(2), 217–223 (2004).
Google Scholar 
Tenzin, K. & Rinzin, A. Impact of Livestock Grazing on the Regeneration of Some Major Species of Plants in Conifer Forest (RNR-RC, 2003).
Google Scholar 
Darabant, A., Rai, P. B., Tenzin, K., Roder, W. & Gratzer, G. Cattle grazing facilitates tree regeneration in a conifer forest with palatable bamboo understory. For. Ecol. Manag. 252(1–3), 73–83 (2007).
Google Scholar 
Sinha, S. et al. Effect of altitude and climate in shaping the forest compositions of Singalila National Park in Khangchendzonga Landscape, Eastern Himalaya, India. J. Asia-Pac. Biodiver. 11(2), 267–275 (2018).
Google Scholar 
Zhang, W., Huang, D., Wang, R., Liu, J. & Du, N. Altitudinal patterns of species diversity and phylogenetic diversity across temperate mountain forests of Northern China. PLoS ONE 11(7), e0159995. https://doi.org/10.1371/journal.pone.0159995 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sharma, C. M., Mishra, A. K., Tiwari, O. P., Krishna, R. & Rana, Y. S. Effect of altitudinal gradients on forest structure and composition on ridge tops in Garhwal Himalaya. Energy Ecol. Environ. 2(6), 404–417. https://doi.org/10.1007/s40974-017-0067-6(2016) (2017).Article 

Google Scholar 
Silveira, M. Ecological aspects of bamboo-dominated forests in southwestern Amazonia: An ethnoscience perspective. Ecotropica 5, 213–216 (1999).
Google Scholar 
Franklin, D. C. Vegetation phenology and growth of a facultatively deciduous bamboo in a monsoonal climate. Biotropica 37, 343–350 (2005).
Google Scholar 
Nath, A. N., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Cons. 3, 654–663 (2015).
Google Scholar 
Venkatesh, M. S., Bhatt, B. P., Kumar, K., Majumdar, B. & Singh, K. Soil properties as influenced by some important edible bamboo species in the North Eastern Himalayan region. Indian J. Bamboo Rattan 4(3), 221–230 (2005).
Google Scholar 
ICIMOD, WCD, GBPNIHESD, RECAST Kangchenjunga landscape feasibility assessment report. ICIMOD Working Paper 2017/9. Kathmandu: ICIMOD (2017).Mueller-Dombois, A. & Ellenburg, A. Aims and Methods of Vegetation Ecology 48–50 (John Wiley Sons, 1974).
Google Scholar 
Polunin, O. & Stainton, A. Flowers of the Himalaya 580 (Oxford University Press, 2001).
Google Scholar 
Ghosh, D.K. & Mallick, J.K. Flora of darjeeling himalayas and foothills: Angiosperms. Research Circle, Forest Directorate, Government of West Bengal & Bishen Singh Mahendra Pal Singh (2014).Pradhan, U. C. & Lachungpa, M. L. Sikkim Himalayan Rhododendrons 130 (Primulaceae Books, 1990).
Google Scholar 
de Bello, F., Leps, J. & Sebastia, M. T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecograph 29, 801–810 (2006).
Google Scholar 
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman and Hall, 1990).MATH 

Google Scholar 
Guisan, A., Edwards, T. C. Jr. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).
Google Scholar 
McCullagh, P. & Nelder, J. A. Generalized Linear Models 2nd edn. (Chapman and Hall, 1989).MATH 

Google Scholar 
Gaira, K. S., Dhar, U. & Belwal, O. K. Potential of herbarium records to sequence phenological pattern: A case study of Aconitum heterophyllum in the Himalaya. Biodiver. Cons. 20, 2201–2210 (2011).
Google Scholar  More

The Bayesian VMD Method we developed can classify pulsed signals with similar frequency content in poor SNR files from underwater acoustic recordings. The Method consists of two parts. The first part scans the incoming audio data as segments that potentially contain signals of interest by detecting energy peaks. It then uses the start and end of the energy peaks to isolate those areas of interest from non-signal areas of the audio file. The second part classifies the detected signals into separate categories based on their frequency content. The algorithms of our Detector and Classifier steps are self-developed, but some key components in them were inspired by previous work39,40,41.DetectorThe proposed detector uses full audio files that are 4.5 s long at a sampling rate of 100 kHz. It then finds audio file segments where potential signals of interest exist.For a given audio file, denoted by ({hat{x}}(n)), where (n=1, dots , N), and N is the total number of samples, the Laplacian Differential Operator (LDO) is applied to ({hat{x}}(n)) resulting in an enhanced version of the audio file denoted by y(n), as follows:$$begin{aligned} y(n) = frac{1}{4}frac{partial ^2 {hat{x}}}{partial n^2} end{aligned}$$
(1)
The LDO enhances the transient signals (edge detection) and filters out the low frequencies ((< 10) kHz) which are not needed for Gg and Lo pulsed signal classification. The y(n) is then transformed into a time-frequency representation using Short-time Fourier transform (STFT). The STFT was implemented on 1024 samples with 90% overlap and a 1024-point Hanning window. The magnitude of the STFT matrix s(n, f) is given as ({hat{S}}(n,f)).$$begin{aligned} {hat{S}}(n,f) = begin{bmatrix} |s_{11}| &{} dots &{} |s_{1N}|\ vdots &{} ddots \ |s_{M1}| &{} &{} |s_{MN}| end{bmatrix} end{aligned}$$ (2) Where (N) is the length of the input segment and (M) is the number of frequency bins. The dimensionality of matrix ({hat{S}}(n,f)) is reduced from 2-D to 1-D as follows:$$begin{aligned} S_{d}(n) = sum _{f=1}^{M} {hat{S}}(n,f) end{aligned}$$ (3) The resulting temporal sequence is an accumulated sum of all frequency bins from (begin{aligned} {hat{S}}(n,f) end{aligned}), so scaling is applied, as follows:$$begin{aligned} S_{d}(n) = frac{S_{d}(n)}{max{S_{d}(n)}} end{aligned}$$ (4) After finding (S_{d}(n)) from Eq. (4), the mean of (S_{d}(n)) is subtracted. Then, to determine the boundaries of the acoustic signal, an adaptive threshold is applied. The first step in developing the threshold is to vectorize the matrix ({hat{S}}(n,f)) in column order into a vector called (S_{r}(n)):$$begin{aligned} S_{r}(n) = overrightarrow{{hat{S}}(n,f)} end{aligned}$$ (5) Then, (S_r (n)) is scaled similar to (S_{d}(n)) and is sorted into ascending order, denoted by ({hat{S}}_r(n)). The changing point where the root-mean-square level of the sorted curve ({hat{S}}_r(n)) changes the most is obtained by minimizing Eq. (6)39,40,42$$begin{aligned} J(k) = sum _{i=1}^{k-1} Delta ({hat{S}}_{r,i}; chi ([{hat{S}}_{r,1} dots {hat{S}}_{r,k-1}])) + sum _{i=k}^{N} Delta ({hat{S}}_{r,i}; chi ([{hat{S}}_{r,k} dots {hat{S}}_{r,N}])) end{aligned}$$ (6) where (k) and N are the index of the changing point and the length of the sorted curve ({hat{S}}_r (n)), respectively, and$$begin{aligned} sum _{i=u}^{v} Delta ({hat{S}}_{r,i}; chi ([{hat{S}}_{r,u} dots {hat{S}}_{r,v}])) = (u-v+1)log left( frac{1}{u-v+1}sum _{n=u}^{v}{hat{S}}_{r,n},^{2}right) end{aligned}$$ (7) The threshold, (lambda), is the value of ({hat{S}}_r (k)) which equals the noise floor estimation, and can be represented as follows:$$begin{aligned} begin{aligned} {mathcal {H}}_{0}: S_d(n) < lambda \ {mathcal {H}}_{1}: S_d(n) ge lambda end{aligned} end{aligned}$$ (8) where ({mathcal {H}}_0) and ({mathcal {H}}_1) are the hypothesis that the activity was below or above the threshold, respectively. The calculated threshold can vary for each file, thus making it adaptable if ambient noise conditions change between files. The threshold (lambda) is then projected onto the temporal sequence (S_{d}(n)) to extract the boundaries of the regions of the acoustic signal that comprised the detected energy peak. The start and end points of each acoustic signal are determined as the first and last points that are greater than (lambda) in amplitude.The boundaries of the detected segments are scaled by the sampling rate to obtain start and end times which will be used to extract the audio file segments from the original data file in the classification step. Figure 4 illustrates the layout of the the proposed detector.Figure 4Block diagram of the proposed detector.Full size imageClassifierOnce segments with energy peaks were identified, they were scanned by the team’s bioacoustics expert, and any segments confirmed to contain only Gg or Lo signals were sifted out for use in testing the accuracy of the Bayesian VMD Method classifier.In this paper, the metric weight was defined for classification purposes. The weight for a parameter (varvec{theta _i}) given its measurement (varvec{y_i}) is defined as$$begin{aligned} w(varvec{theta _i} mid varvec{y_i}) = P_{varvec{Theta mid Y}}(varvec{theta _i} mid varvec{y_i}) * varvec{p_i} end{aligned}$$ (9) where (varvec{theta _i}) is the probability density function (PDF) of (varvec{y_i}), (varvec{y_i}) is one measurement in the measurement vector (varvec{y}), (P_{varvec{Theta mid Y}}(varvec{theta _i} mid varvec{y_i})) is the posterior probability of the parameter (varvec{theta _i}) given the measurement (varvec{y_i}), and (varvec{p_i}) is the scaled prominence value of (varvec{y_i}).When a detected audio file segment is fed into the Bayesian VMD classifier, the classification process starts with a feature extraction step. During this step, peak and notch frequencies and their prominence values were obtained from the VMD-Hilbert spectrum of the segment. The prominence measures how much a peak stands out due to its intrinsic height or how much a notch stands out due to its depth and its location relative to surrounding peaks or notches. In general, peaks that are taller and more isolated have a higher “prominence” (p) than peaks that are shorter or surrounded by other peaks.In the feature extraction step, VMD decomposed the input audio segment into a set of IMFs. The HHT was then applied to all IMFs to create a Hilbert spectrum with a frequency resolution of 50 Hz. The Hilbert spectrum is a matrix, (H(n,f)) that contains the instantaneous energies, (h(n,f)).$$begin{aligned} H(n,f) = begin{bmatrix} h_{11} &{}dots &{} h_{1R} \ vdots &{} ddots \ h_{Q1} &{} &{} h_{QR} end{bmatrix} end{aligned}$$ (10) where r is the length of the input segment and q is the number of frequency bins in (H).The matrix (H (n,f)) is then converted from a 2-D array to a 1-D spectral representation by summing all instantaneous energy values in each frequency bin, as follows:$$begin{aligned} H(f) = sum _{n=1}^{R} H(n,f) end{aligned}$$ (11) The energy summation sequence was converted to a base-10 logarithmic scale and then smoothed by passing through a 17-point median filter and an 11-point moving average filter for the purpose of easily extracting features. All peaks and notches in the sequence whose prominence values exceeded the threshold of 0.5 were located, and their frequency values and prominence values were then stored as extracted features from the input signal (see Fig. 5).Figure 5Example of locating peak and notch frequencies and how prominent they are compared to other peaks and notches. The wave form in (a) is the smoothed energy summation sequence from the Hilbert spectrum of the Lo signal in Fig. 1. Subplot (b) is a flipped version of the energy summation sequence for the convenience of extracting notch frequencies and their prominence values. The length of the red line represents the prominence value of a peak or notch.Full size imageFor testing the effectiveness of the VMD feature extractor, a second set of features were extracted from the FFT-based power spectrum using the same input signals with the Welch’s algorithm. The FFT-based spectrum was calculated on 2048 samples with 50% overlap and a 2048-point Hanning window with 48.82 Hz frequency resolution. The power spectral density sequence was then converted to dB and went through a 21-point median filter and a 15-point moving average filter. Feature extraction followed the same strategies as in VMD feature extractor except using a prominence threshold of 2 dB.Next, the measured features, frequencies (Hz) of the peaks and notches (henceforth referred to as “measured peaks and notches”), were matched with the probability distribution functions (PDFs) of peaks and notches (henceforth referred to as “parameter peaks and notches”) from Soldevilla et al. (2008). The matching between measured and parameter peaks and notches was done in preparation of weight calculations, and it was implemented for both Gg and Lo. There are four Gaussian PDFs for parameter peaks and three for parameter notches for each species in Soldevilla et al. (2008) (Table 2). A 95% confidence interval of a Gaussian PDF was used here as a frequency range defined as 1.96 standard deviations to the left and right of its mean value. When measured peaks and notches were matched to parameter peaks and notches, only the peak or notch that fell within a 95% confidence interval were kept. Any peaks or notches outside the 95% confidence intervals were discarded.Because there are overlaps between the 95% confidence intervals of 22.4 kHz and 25.5 kHz parameter peaks of Gg and between 33.7 kHz and 37.3 kHz parameter peaks of Lo (see Table 2), it is likely that some measured peaks will fall in the overlapping areas. In this paper, the maximum a posterior (MAP) estimation41 was used to determine which PDF results in the measured peak in an overlapping area. For a measured peak that falls into an overlapping area, two parameter peaks’ PDFs are plugged in the MAP estimation equation sequentially, and then the measured peak will be matched with the PDF that maximizes the posterior probability of it given the measured peak.After the preliminary match, if more than one measured peak or notch remains in any one PDF confidence interval, the measured peak and notch with the highest prominence value is selected as the real measured peak or notch of this PDF, and the redundant ones are discarded. Finally, all remaining peak prominence values and notch prominence values were scaled to be between 0 and 1, respectively.Once peak and notch matching and selection was finished, Bayesian weights were calculated to select the most likely species. From Bayes’s rule, the posterior probability of a parameter given its measurement is proportional to the product of the likelihood function of the measurement given the parameter and the prior probability of the parameter41, as shown in Eq. (12).$$begin{aligned} P_{varvec{Theta mid Y}}(varvec{theta _i} mid varvec{y_i}) propto f_{varvec{Y mid Theta }}(varvec{y_i} mid varvec{theta _i}) P_{varvec{Theta }}(varvec{theta _i}) end{aligned}$$ (12) therefore, substitution of the posterior probability in Eq. (9) yields$$begin{aligned} w(varvec{theta _i} mid varvec{y_i}) = f_{varvec{Y mid Theta }}(varvec{y_i} mid varvec{theta _i}) *P_{varvec{Theta }}(varvec{theta _i}) * varvec{p_i} end{aligned}$$ (13) Figure 6Example of feature matching. The top plots show a set of measured peaks and notches matched with both Gg’s PDFs (a) and Lo’s PDFs (b) parameter peaks and notches like in Fig. 5 during the feature matching and selection step. Middle plots show how closely to the parameter PDFs that the measured peaks match either Gg (c) or Lo (d) and their weight calculations. The width of each PDF represents its 95% confidence interval, and the ordinate represents the weight value. Subplots (e) and (f) show the same weight calculations for notches. The final weight value is the summation of all weight values of peaks and notches matched with Gg or Lo.Full size imageWith all PDFs and a priori probabilities from Soldevilla et al. (2008), the weight value in terms of Gg and Lo given a set of measurements, (varvec{y}), was obtained by Eqs. (13) and (14)$$begin{aligned} w(Gg mid varvec{y}) = sum _{forall i} w(varvec{theta _i} mid varvec{y_i}) qquad w(Lo mid varvec{y}) = sum _{forall j} w(varvec{theta _j} mid varvec{y_j}) end{aligned}$$ (14) where (varvec{y_i}) and (varvec{y_j}) are the remaining measured peaks and notches that were matched with Gg’s PDFs and Lo’s PDFs after the matching and matching step. The feature matching and selection results and the weight calculation process are shown in Fig. 6.The last step was a comparison between weight values in terms of Gg and Lo. If (w(Lo mid varvec{y}) > w(Gg mid varvec{y})), the signal was labeled an Lo signal; otherwise, it was labeled a Gg signal. The classifier is illustrated in Fig. 7. The weight values are significant to three digits because weights are normally smaller than 1.000 and three significant digits was sufficient for comparing all calculated weight values for these audio files. In the case that the weight comparison is equal to three significant digits (even though this never happened in these 174 signals), the Bayesian VMD algorithm will automatically classify the input as a Gg signal given that the highest precision (85.91%) by the Bayesian VMD Method was achieved on Gg.Figure 7Block diagram of the Bayesian VMD Method classifier.Full size image More

EDITORIAL
16 March 2022

Are there limits to economic growth? It’s time to call time on a 50-year argument

Researchers must try to resolve a dispute on the best way to use and care for Earth’s resources.

Twitter

Facebook

Email

Download PDF

Lead author Donella Meadows wrote that the book The Limits to Growth “was written not to predict doom but to challenge people to find ways of living that are consistent with the laws of the planet”.Credit: Alamy

Fifty years ago this month, the System Dynamics group at the Massachusetts Institute of Technology in Cambridge had a stark message for the world: continued economic and population growth would deplete Earth’s resources and lead to global economic collapse by 2070. This finding was from their 200-page book The Limits to Growth, one of the first modelling studies to forecast the environmental and social impacts of industrialization.For its time, this was a shocking forecast, and it did not go down well. Nature called the study “another whiff of doomsday” (see Nature 236, 47–49; 1972). It was near-heresy, even in research circles, to suggest that some of the foundations of industrial civilization — mining coal, making steel, drilling for oil and spraying crops with fertilizers — might cause lasting damage. Research leaders accepted that industry pollutes air and water, but considered such damage reversible. Those trained in a pre-computing age were also sceptical of modelling, and advocated that technology would come to the planet’s rescue. Zoologist Solly Zuckerman, a former chief scientific adviser to the UK government, said: “Whatever computers may say about the future, there is nothing in the past which gives any credence whatever to the view that human ingenuity cannot in time circumvent material human difficulties.”But the study’s lead author, Donella Meadows, and her colleagues stood firm, pointing out that ecological and economic stability would be possible if action were taken early. Limits was instrumental to the creation of the United Nations Environment Programme, also in 1972. Overall, more than 30 million copies of the book have been sold.
The value of biodiversity is not the same as its price
But the debates haven’t stopped. Although there’s now a consensus that human activities have irreversible environmental effects, researchers disagree on the solutions — especially if that involves curbing economic growth. That disagreement is impeding action. It’s time for researchers to end their debate. The world needs them to focus on the greater goals of stopping catastrophic environmental destruction and improving well-being.Researchers such as Johan Rockström at the Potsdam Institute for Climate Impact Research in Germany advocate that economies can grow without making the planet unliveable. They point to evidence, notably from the Nordic nations, that economies can continue to grow even as carbon emissions start to come down. This shows that what’s needed is much faster adoption of technology — such as renewable energy. A parallel research movement, known as ‘post-growth’ or ‘degrowth’, says that the world needs to abandon the idea that economies must keep growing — because growth itself is harmful. Its proponents include Kate Raworth, an economist at the University of Oxford, UK, and author of the 2017 book Doughnut Economics, which has inspired its own global movement.Economic growth is typically measured by gross domestic product (GDP). This composite index uses consumer spending, as well as business and government investment, to arrive at a figure for a country’s economic output. Governments have entire departments devoted to ensuring that GDP always points upwards. And that is a problem, say post-growth researchers: when faced with a choice between two policies (one more green than the other), governments are likely to opt for whichever is the quicker in boosting growth to bolster GDP, and that might often be the option that causes more pollution.
G20’s US$14-trillion economic stimulus reneges on emissions pledges
A report published last week by the World Health Organization (see go.nature.com/3j9xcpi) says that if policymakers didn’t have a “pathological obsession with GDP”, they would spend more on making health care affordable for every citizen. Health spending does not contribute to GDP in the same way that, for example, military spending does, say the authors, led by economist Mariana Mazzucato at University College London.Both communities must do more to talk to each other, instead of at each other. It won’t be easy, but appreciation for the same literature could be a starting point. After all, Limits inspired both the green-growth and post-growth communities, and both were similarly influenced by the first study on planetary boundaries (J. Rockström et al. Nature 461, 472–475; 2009), which attempted to define limits for the biophysical processes that determine Earth’s capacity for self-regulation.Opportunities for cooperation are imminent. At the end of January, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services announced a big study into the causes of biodiversity loss, including the role of economic systems. More than 100 authors from 40 countries and different fields will spend two years assessing the literature. They will recommend “transformative change to the systems leading us to catastrophe”, says study co-chair, political scientist Arun Agrawal at the University of Michigan in Ann Arbor.Another opportunity is an upcoming revision of the rules for what is measured in GDP. These will be agreed by countries’ chief statisticians and organized through the UN, and are due to be finalized in 2025. For the first time, the statisticians are asking how sustainability and well-being could be more closely aligned to GDP. Both post-growth and green-growth advocates have valuable perspectives.Research can be territorial — new communities emerge sometimes because of disagreements in fields. But green-growth and post-growth scientists need to see the bigger picture. Right now, both are articulating different visions to policymakers, and there is a risk this will delay action. In 1972, there was still time to debate, and less urgency to act. Now, the world is running out of time.

Nature 603, 361 (2022)
doi: https://doi.org/10.1038/d41586-022-00723-1

Related Articles

G20’s US$14-trillion economic stimulus reneges on emissions pledges

Get the Sustainable Development Goals back on track

The value of biodiversity is not the same as its price

At 50, the UN Environment Programme must lead again

Growing support for valuing ecosystems will help conserve the planet

Climate tipping points — too risky to bet against

Subjects

Economics

Biodiversity

Environmental sciences

Policy

Latest on:

Economics

G20’s US$14-trillion economic stimulus reneges on emissions pledges
Comment 02 MAR 22

Chile: new cabinet is rich in scientists and women
Correspondence 01 MAR 22

Gender pay gap closes after salary information goes public
Research Highlight 16 FEB 22

Biodiversity

Africa: sequence 100,000 species to safeguard biodiversity
Comment 15 MAR 22

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

Apply Singapore Index on Cities’ Biodiversity at scale
Correspondence 22 FEB 22

Environmental sciences

Landmark treaty on plastic pollution must put scientific evidence front and centre
Editorial 08 MAR 22

Madagascar’s biggest mine achieves striking conservation success
Research Highlight 07 MAR 22

An engineer advances fire-management laws in Colombia
Career Q&A 23 FEB 22

Jobs

Team Manager (Genetics, Ecology, Evolution and Conservation)

Springer Nature
London, Greater London, United Kingdom

Postdoctoral position in drug-microbiome interactions

University of Ottawa (uOttawa)
Ottawa, Ontario, Canada

Postdoctoral position in NMR-based structural biology

Umeå University (UmU)
Umeå, Sweden

Associate Professor in Natural Sciences

University of Southampton
Southampton, United Kingdom More

Department of Zoology, University of Cambridge, Cambridge, UKYucheng Wang, Bianca De Sanctis, Ana Prohaska, Daniel Money & Eske WillerslevLundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, DenmarkYucheng Wang, Mikkel Winther Pedersen, Fernando Racimo, Antonio Fernandez-Guerra, Alexandra Rouillard, Anthony H. Ruter, Hugh McColl, Nicolaj Krog Larsen, James Haile, Lasse Vinner, Thorfinn Sand Korneliussen, Jialu Cao, David J. Meltzer, Kurt H. Kjær & Eske WillerslevThe Arctic University Museum of Norway, UiT— The Arctic University of Norway, Tromsø, NorwayInger Greve Alsos, Eric Coissac, Marie Kristine Føreid Merkel, Youri Lammers & Galina GusarovaDepartment of Genetics, University of Cambridge, Cambridge, UKBianca De Sanctis & Richard DurbinUniversité Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, FranceEric CoissacCenter for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, DenmarkHannah Lois Owens, Carsten Rahbek & David Nogues BravoDepartment of Geosciences, UiT—The Arctic University of Norway, Tromsø, NorwayAlexandra RouillardUniversité Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, FranceAdriana AlbertiGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université Evry, Université Paris-Saclay, Evry, FranceAdriana Alberti, France Denoeud & Patrick WinckerInstitute of Earth Sciences, St Petersburg State University, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovArctic and Antarctic Research Institute, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovSchool of Geography and Environmental Science, University of Southampton, Southampton, UKMary E. EdwardsAlaska Quaternary Center, University of Alaska Fairbanks, Fairbanks, AK, USAMary E. EdwardsCentre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, Faculté de Médecine Purpan, Toulouse, FranceLudovic OrlandoNational Research University, Higher School of Economics, Moscow, RussiaThorfinn Sand KorneliussenDepartment of Geography and Environment, University of Hawaii, Honolulu, HI, USADavid W. BeilmanDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkAnders A. BjørkCarlsberg Research Laboratory, Copenhagen, DenmarkChristoph Dockter & Birgitte SkadhaugeCenter for Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USAJulie EsdaleFaculty of Biology, St Petersburg State University, St Petersburg, RussiaGalina GusarovaDepartment of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen, DenmarkKristian K. KjeldsenDepartment of Earth Science, University of Bergen, Bergen, NorwayJan Mangerud & John Inge SvendsenBjerknes Centre for Climate Research, Bergen, NorwayJan Mangerud & John Inge SvendsenUS National Park Service, Gates of the Arctic National Park and Preserve, Fairbanks, AK, USAJeffrey T. RasicZoological Institute, , Russian Academy of Sciences, St Petersburg, RussiaAlexei TikhonovResource and Environmental Research Center, Chinese Academy of Fishery Sciences, Beijing, ChinaYingchun XingCollege of Plant Science, Jilin University, Changchun, ChinaYubin ZhangDepartment of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, CanadaDuane G. FroeseCenter for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, DenmarkCarsten RahbekSchool of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UKPhilip B. Holden & Neil R. EdwardsDepartment of Anthropology, Southern Methodist University, Dallas, TX, USADavid J. MeltzerDepartment of Geology, Quaternary Sciences, Lund University, Lund, SwedenPer MöllerWellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UKEske WillerslevMARUM, University of Bremen, Bremen, GermanyEske Willerslev More

Our research determined noise to share a potentially important negative relationship with native bird richness and abundance and appears to be the most limiting factor in green space occupancy by native bird species, more so than the type and amount of vegetation present in urban green spaces, and more so than urbanization itself, represented as building height and cover surrounding green spaces. Thus, noise is potentially acting as an invisible source of habitat degradation, limiting the bird species capable of inhabiting an area, regardless of whether the appropriate vegetative conditions exist.As predicted, native urban avoiders reached their maximum abundances in PAR, which, given their high vegetation cover and large size, act as patches of natural habitat in cities. Native urban utilizers tended to be found in more suburban areas, and urban dwellers, both native and exotic, were detected in green spaces of all noise levels. All exotic bird species were urban dwellers, referring to their high tolerance to urbanization5,25, thus reaching the high abundances observed, particularly in SGS.SGS possessed higher average noise levels and greater exotic bird abundance than PAR, which presented significantly higher numbers of native bird richness and abundance. The potential influence of noise on native bird species first becomes evident when we consider that native bird abundance tended to rise above the generally high abundance of exotic birds when average noise levels in green spaces reached below 52 dB (it should be noted that, according to the Chilean Noise Norm No. 146, the maximum allowable noise levels generated by fixed sources in residential areas of Santiago is 55 dB during the day, 7 a.m.–9 p.m.). The negative relations between noise and urban avoider, urban utilizer, and urban dweller species richness and abundance further indicate how noise may be regulating the native bird species present in green spaces, affecting urban avoider richness the most and urban dweller richness the least, while influencing the abundance of all native bird species rather similarly. Meanwhile, building height surrounding green spaces negatively influenced native urban avoider and urban dweller richness and abundance, with the greatest influence on urban dweller abundance, yet all native birds were less likely to be detected in green spaces surrounded by buildings over 10 m tall on average.The importance of vegetation for native bird communities also cannot be denied, given that native birds reached higher abundances than exotic birds when vegetation cover reached an average NDVI value greater than 0.5. Results from this study thus suggest that exotic birds begin to replace native birds in terms of abundance as noise levels rise in urban green spaces, vegetation cover decreases, and building height surrounding green spaces increases, with native urban avoider species being the least tolerant to the influences of urbanization, and, consequently, the first to disappear when noise levels and building height become too great. The observed negative relationship between native bird species richness and maximum noise levels, and the positive relationship with vegetation cover, are comparable to results seen in other Neotropical cities24,26, yet our results indicate that the relationships between these variables and bird abundance are stronger. This may indicate how bird abundance fluctuates in green spaces as some birds temporarily leave during noisy events or become quieter and more cryptic under noisy conditions26, while noise also negatively influences bird species richness by filtering the species that can inhabit areas of varying noise levels.Detection probability models found native bird detectability to mostly increase with vegetation cover and tree cover in urban green spaces, except for the common diuca finch, whose detectability decreased with rising tree cover. Some of the bird species that displayed the lowest detection probabilities, such as the picui ground dove and fire-eyed diucon (Xolmis pyrope), are not frequently found in cities and possess vocalizations that are unlikely to be heard well in high-noise areas due to their low frequencies, making them more easily masked by the anthrophony, characterized by its low frequency and high intensity31. Consequently, birds whose vocalizations are similar in frequency and amplitude to the anthrophony were more commonly or exclusively found in green spaces that registered low noise levels, their detectability also decreasing with rising noise, as was the case with the fire-eyed diucon.Urban green space occupancy by native bird species was mainly influenced by average maximum noise levels recorded in green spaces. Of the modeled native species, the long-tailed meadowlark and the picui ground dove, an urban avoider and an urban utilizer species respectively, were the species most sensitive to noise, their probability of occupying green spaces with average maximum noise levels over 55 dB decreasing rapidly and approaching zero when over 65 dB. Meanwhile, the austral thrush, an urban dweller species, was by far the most tolerant to noise of the native birds, its presence probability just beginning to decrease when average maximum noise levels reached over 73 dB in green spaces. The differing tendencies of urban avoiders, urban utilizers, and urban dwellers to occupy green spaces of varying noise levels is thus evident, with native urban dweller species more likely to occupy higher noise urban green spaces than urban avoiders and utilizers, seemingly more adapted to the high noise levels that come with inhabiting a busy city. Nonetheless, although native urban dwellers displayed greater noise tolerances than urban avoiders and utilizers, their presence in city parks can also be expected to diminish if noise levels become too high, which for the most tolerant of the native birds, means reaching an average maximum level of 73 dB or more, but 55 dB or more for less tolerant species.No relation was found between vegetation cover and noise, and some of the highest noise levels were recorded in PAR. This suggests that PAR, often considered to be quiet and peaceful areas to escape the busyness of city life, can reach noise levels as high as those recorded in SGS, reducing the quality of the greatest sources of natural habitat for birds and other wildlife in cities.The results from this study regarding the influence of noise on bird communities support previous studies indicating that birds may be excluded from suitable habitats on account of the acoustic conditions of the local environment12,15. Despite abundant vegetation in PAR and some SGS, certain bird species, particularly urban avoiders and utilizers, were less likely to occupy areas that presented high noise levels. However, it is important to consider other potential influencing factors, such as predators (e.g., dogs and cats) and food availability, both of which could be linked to pedestrians and could therefore also increase noise levels in green spaces. Furthermore, in an effort to focus on the influence of anthropogenic variables on urban birds (i.e., urban morphology, noise, and vegetation type and cover), this study did not consider the size of urban green spaces as a variable in occupancy modeling, but as the results of this study suggest and others in Latin America have shown23,32, green space size is likely an influencing factor that should be considered in future studies. Another variable worth considering would be road coverage, which undoubtedly plays a role in noise levels, particularly for SGS.Measures to control the COVID-19 pandemic have significantly reduced noise levels in major cities worldwide33,34,35. Noise reduction in the San Francisco Bay Area, characterized by a Mediterranean climate like Santiago, resulted in songbirds rapidly occupying newly available acoustic niches within urban soundscapes and maximizing communication through higher performance songs35. Consequently, native bird species not commonly found in high-noise areas, mainly urban avoider and utilizer species, may now be found in greater abundance at the community level in urban green spaces where they had been scarce or non-existent during this study, conducted pre-pandemic. Furthermore, if average noise levels dropped below 52 dB in Santiago green spaces due to region-wide shut-down measures, native birds may reach higher abundances than exotic birds. The negative effects of urban noise on bird communities are extensive, yet recent research indicating birds’ rapid adaptability and improved vocal performance when noise levels are significantly lowered provides hope. Native bird species susceptible to noise may stand a chance despite growing urbanization, if noise levels in urban green spaces are regulated.Rapid urban expansion in Latin America places natural ecosystems at great risk, reducing or altogether eliminating natural habitats for native birds and other wildlife, making urban green spaces necessary for their persistence, especially in biodiversity hotspots like central Chile. As this study illustrates, noise associated with urbanization plays a significant role in influencing green space occupancy by native bird species, and, quite possibly, other animal species dependent on acoustic signaling (e.g., amphibians and mammals). Given the recreational role of urban green spaces in cities, noise regulation within these areas should be considered, while also considering how city morphology may impact bird communities. This study exemplifies how, in addition to noise, the size of urban green spaces and the vegetation cover in them, particularly tree cover, are vital aspects to consider in city planning in order to preserve native bird communities in urban systems. Large urban parks held significantly richer bird communities than small green spaces, with greater native bird richness and abundance. Therefore, it is imperative that science and city planning collaborate to develop cities with networks of large green spaces with abundant tree cover, surrounded by smaller urban morphology, where noise is regulated and maintained at tolerable levels for native birds. There is a clear need to move towards biophilic city planning to harmonize urban growth and the protection and expansion of networks of green areas that generate habitat for birds that, in turn, provide important ecosystem services to cities. More

Barrett, R. D. H., Rogers, S. M. & Schluter, D. Environment specific pleiotropy facilitates divergence at the ectodysplasin locus in threespine stickleback. Evolution 63, 2831–2837 (2009).PubMed 

Google Scholar 
Johnston, S. E. et al. Life history trade-offs at a single locus maintain sexually selected genetic variation. Nature 502, 93–95 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Christie, M. R., McNickle, G. G., French, R. A. & Blouin, M. S. Life history variation is maintained by fitness trade-offs and negative frequency-dependent selection. Proc. Natl Acad. Sci. 115, 4441–4446 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zajitschek, F. & Connallon, T. Antagonistic pleiotropy in species with separate sexes, and the maintenance of genetic variation in life-history traits and fitness. Evolution 72, 1306–1316 (2018).PubMed 

Google Scholar 
Mérot, C., Llaurens, V., Normandeau, E., Bernatchez, L. & Wellenreuther, M. Balancing selection via life-history trade-offs maintains an inversion polymorphism in a seaweed fly. Nat. Commun. 11, 1–11 (2020).Bonduriansky, R. & Chenoweth, S. F. Intralocus sexual conflict. Trends Ecol. Evol. 24, 280–288 (2009).PubMed 

Google Scholar 
Chippindale, A. K., Gibson, J. R. & Rice, W. R. Negative genetic correlation for adult fitness between sexes reveals ontogenetic conflict in Drosophila. Proc. Natl Acad. Sci. 98, 1671–1675 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Connallon, T. & Clark, A. G. Balancing selection in species with separate sexes: Insights from fisher’s geometric model. Genetics 197, 991–1006 (2014).PubMed 
PubMed Central 

Google Scholar 
Mokkonen, M. et al. Negative frequency-dependent selection of sexually antagonistic alleles in Myodes glareolus. Science 334, 972–974 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Connallon, T. & Matthews, G. Cross‐sex genetic correlations for fitness and fitness components: Connecting theoretical predictions to empirical patterns. Evol. Lett. 3, 254–262 (2019).PubMed 
PubMed Central 

Google Scholar 
Abbott, J., Rios-Cardenas, O. & Morris, M. R. Insights from intralocus tactical conflict: adaptive states, interactions with ecology and population divergence. Oikos 128, 1525–1536 (2019).
Google Scholar 
Morris, M. R., Goedert, D., Abbott, J. K., Robinson, D. M. & Rios-Cardenas, O. Intralocus tactical conflict and the evolution of alternative reproductive tactics. Adv Study Behav. 45, 447–478 (2013).Kim, K. W. et al. A sex-linked supergene controls sperm morphology and swimming speed in a songbird. Nat. Ecol. Evol. 1, 1168–1176 (2017).PubMed 

Google Scholar 
Schwander, T., Libbrecht, R. & Keller, L. Supergenes and complex phenotypes. Curr. Biol. 24, 288–294 (2014).
Google Scholar 
Thompson, M. J. & Jiggins, C. D. Supergenes and their role in evolution. Heredity 113, 1–8 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dobzhansky, T. Genetics of natural populations. XIX. Origin of heterosis through natural selection in populations of Drosophila pseudoobscura. Genetics 35, 288–302 (1950).CAS 
PubMed 
PubMed Central 

Google Scholar 
Küpper, C. et al. A supergene determines highly divergent male reproductive morphs in the ruff. Nat. Genet. 48, 79–83 (2016).PubMed 

Google Scholar 
Lamichhaney, S. et al. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nat. Genet. 48, 84–88 (2016).CAS 
PubMed 

Google Scholar 
Horton, B. M. et al. Estrogen receptor α polymorphism in a species with alternative behavioral phenotypes. Proc. Natl Acad. Sci. 111, 1–6 (2014).
Google Scholar 
Faria, R., Johannesson, K., Butlin, R. K. & Westram, A. M. Evolving inversions. Trends Ecol. Evol. 34, 239–248 (2019).PubMed 

Google Scholar 
Wellenreuther, M. & Bernatchez, L. Eco-evolutionary genomics of chromosomal inversions. Trends Ecol. Evol. 33, 427–440 (2018).PubMed 

Google Scholar 
Knief, U. et al. A sex-chromosome inversion causes strong overdominance for sperm traits that affect siring success. Nat. Ecol. Evol. 1, 1177–1184 (2017).PubMed 

Google Scholar 
Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).Keller, L. & Ross, K. G. Selfish genes: A green beard in the red fire ant. Nature 394, 573–575 (1998).ADS 
CAS 

Google Scholar 
Avril, A., Purcell, J., Béniguel, S. & Chapuisat, M. Maternal effect killing by a supergene controlling ant social organization. Proc. Natl Acad. Sci. 117, 17130–17134 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gilmartin, P. M. & Li, J. Homing in on heterostyly. Heredity 105, 161–162 (2010).CAS 
PubMed 

Google Scholar 
Loveland, J. L., Lank, D. B. & Küpper, C. Gene expression modification by an autosomal inversion associated with three male mating morphs. Front. Genet. https://doi.org/10.3389/fgene.2021.641620 (2021).van Rhijn, J. G. The ruff. (T. & A.D. Poyser, 1991).Giraldo-Deck, L. M. et al. Development of intraspecific size variation in black coucals, white-browed coucals and ruffs from hatching to fledging. J. Avian Biol. 51, 1–14 (2020).
Google Scholar 
Lank, D. B., Farrell, L. L., Burke, T., Piersma, T. & McRae, S. B. A dominant allele controls development into female mimic male and diminutive female ruffs. Biol. Lett. 9, 15–18 (2013).
Google Scholar 
Loveland, J. L. et al. Functional differences in the hypothalamic-pituitary-gonadal axis are associated with alternative reproductive tactics based on an inversion polymorphism. Horm. Behav. 127, 104877 (2021).CAS 
PubMed 

Google Scholar 
Verkuil, Y. I. et al. The interplay between habitat availability and population differentiation: A case study on genetic and morphological structure in an inland wader (Charadriiformes). Biol. J. Linn. Soc. 106, 641–656 (2012).
Google Scholar 
Kirkpatrick, M. & Barton, N. Chromosome inversions, local adaptation and speciation. Genetics 173, 419–434 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Llaurens, V., Whibley, A. & Joron, M. Genetic architecture and balancing selection: the life and death of differentiated variants. Mol. Ecol. 26, 2430–2448 (2017).PubMed 

Google Scholar 
Christians, J. K. Avian egg size: Variation within species and inflexibility within individuals. Biol. Rev. Camb. Philos. Soc. 77, 1–26 (2002).PubMed 

Google Scholar 
Pick, J. L. et al. Artificial selection reveals the energetic expense of producing larger eggs. Front. Zool. 13, 1–10 (2016).
Google Scholar 
Jha, A. R. et al. Whole-genome resequencing of experimental populations reveals polygenic basis of egg-size variation in Drosophila melanogaster. Mol. Biol. Evol. 32, 2616–2632 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Verhoeven, M. A. et al. Variation in egg size of black-tailed godwits. Ardea 107, 291–302 (2019).
Google Scholar 
Birchard, G. F. & Deeming, D. C. Egg allometry: influences of phylogeny and the altricial-precocial continuum. in Nests, eggs, and incubation (eds. Deeming, D. C. & Reynolds, S. J.) 97–112 (Oxford University Press, 2015).Amat, J. A., Fraga, R. M. & Arroyo, G. M. Intraclutch egg-size variation and offspring survival in the Kentish Plover Charadrius alexandrinus. Ibis (Lond. 1859). 143, 17–23 (2001).
Google Scholar 
Rahn, H. & Paganelli, C. V. Relationship of avian egg weight to body weight. Auk 92, 750–765 (1975).
Google Scholar 
Krist, M. Egg size and offspring quality: A meta-analysis in birds. Biol. Rev. 86, 692–716 (2011).PubMed 

Google Scholar 
Blomqvist, D., Johansson, O. C. & Go, F. Parental quality and egg size affect chick survival in a precocial bird, the lapwing Vanellus vanellus. Oecologia 110, 18–24 (1997).ADS 
PubMed 

Google Scholar 
Cabana, G., Frewin, A., Peters, R. H. & Randall, L. The effect of sexual size dimorphism on variations in reproductive effort of birds and mammals. Am. Nat. 120, 17–25 (1982).
Google Scholar 
Weatherhead, P. J. & Teather, K. L. Sexual size dimorphism and egg-size allometry in birds. Evolution 48, 671–678 (1994).PubMed 

Google Scholar 
Teather, K. L. & Weatherhead, P. J. Sex-specific energy requirements of great-tailed grackle (Quiscalus mexicanus). J. Anim. Ecol. 57, 659–668 (1988).
Google Scholar 
Tschirren, B., Postma, E., Gustafsson, L., Groothuis, T. G. G. & Doligez, B. Natural selection acts in opposite ways on correlated hormonal mediators of prenatal maternal effects in a wild bird population. Ecol. Lett. 17, 1310–1315 (2014).PubMed 

Google Scholar 
Hegyi, G. et al. Yolk androstenedione, but not testosterone, predicts offspring fate and reflects parental quality. Behav. Ecol. 22, 29–38 (2011).
Google Scholar 
Berdan, E. L., Blanckaert, A., Butlin, R. K. & Bank, C. Deleterious mutation accumulation and the long-term fate of chromosomal inversions. PLoS Genet. e1009411 https://doi.org/10.1371/journal.pgen.1009411 (2021).Jay, P. et al. Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms. Nat. Genet. 53, 288–293 (2021).CAS 
PubMed 

Google Scholar 
Stolle, E. et al. Degenerative expansion of a young supergene. Mol. Biol. Evol. 36, 553–561 (2018).PubMed Central 

Google Scholar 
Tuttle, E. M. et al. Divergence and functional degradation of a sex chromosome-like supergene. Curr. Biol. 26, 344–350 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stuglik, M. T., Babik, W., Prokop, Z. & Radwan, J. Alternative reproductive tactics and sex-biased gene expression: The study of the bulb mite transcriptome. Ecol. Evol. 4, 623–632 (2014).
Google Scholar 
Gamble, M. M. & Calsbeek, R. G. Intralocus sexual conflict can maintain alternative reproductive tactics. bioRxiv Prepr. 6 (2021).Mank, J. E. Population genetics of sexual conflict in the genomic era. Nat. Rev. Genet. 18, 721–730 (2017).CAS 
PubMed 

Google Scholar 
Jukema, J. & Piersma, T. Permanent female mimics in a lekking shorebird. Biol. Lett. 2, 161–164 (2006).PubMed 
PubMed Central 

Google Scholar 
Lank, D. B. & Smith, C. M. Conditional lekking in ruff (Philomachus pugnax). Behav. Ecol. Sociobiol. 20, 137–145 (1986).
Google Scholar 
Hamburger, V. & Hamilton, H. L. A series of normal stages in the development of the chick embryo. J. Morphol. 88, 49–92 (1951).CAS 
PubMed 

Google Scholar 
von Engelhardt, N. & Groothuis, T. G. G. Maternal Hormones in Avian Eggs. Hormones and Reproduction of Vertebrates – Volume 4. https://doi.org/10.1016/B978-0-12-374929-1.10004-6 (2011).Schielzeth, H. & Bolund, E. Patterns of conspecific brood parasitism in zebra finches. Anim. Behav. 79, 1329–1337 (2010).
Google Scholar 
Colwell, M. A. Egg-laying intervals in shorebirds. Wader Study Gr. Bull. 111, 50–59 (2006).
Google Scholar 
Goymann, W. et al. Testosterone and corticosterone during the breeding cycle of equatorial and European stonechats (Saxicola torquata axillaris and S. t. rubicola). Horm. Behav. 50, 779–785 (2006).CAS 
PubMed 

Google Scholar 
Goymann, W., East, M. L. & Hofer, H. Androgens and the role of female ‘hyperaggressiveness’ in spotted hyenas (Crocuta crocuta). Horm. Behav. 39, 83–92 (2001).CAS 
PubMed 

Google Scholar 
Schwabl, H. Yolk is a source of maternal testosterone for developing birds. Neurobiology 90, 11446–11450 (1993).CAS 

Google Scholar 
Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models. (Cambridge University Press, 2006).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2020).Giraldo-Deck, L. M. et al. Accepted version of paper data and code of manuscript: Intralocus conflicts associated with a supergene. Nature Communications (2022). Edmond Repository https://doi.org/10.17617/3.71.Therneau, T. M. & Grambsch, P. M. The Cox Model. in Modeling Survival Data: Extending the Cox Model (eds. Therneau, T. M. & Grambsch, P. M.) 39–77 (Springer US, 2000). More