Ecology

World Food and Agriculture—Statistical Yearbook 2020 (FAO, 2020).Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).CAS 

Google Scholar 
Hedden, P. The genes of the Green Revolution. Trends Genet. 19, 5–9 (2003).CAS 

Google Scholar 
Hochman, Z., Gobbett, D. L. & Horan, H. Climate trends account for stalled wheat yields in Australia since 1990. Glob. Change Biol. 23, 2071–2081 (2017).
Google Scholar 
Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).
Google Scholar 
Rebetzke, G. J. et al. Genotypic increases in coleoptile length improves stand establishment, vigour and grain yield of deep-sown wheat. Field Crops Res. 100, 10–23 (2007).
Google Scholar 
Gan, Y., Stobbe, E. H. & Moes, J. Relative date of wheat seedling emergence and its impact on grain yield. Crop Sci. 32, 1275–1281 (1992).
Google Scholar 
Rebetzke, G., Ingvordsen, C., Bovill, W., Trethowan, R. & Fletcher, A. in Australian Agriculture in 2020: From Conservation to Automation (eds Pratley, J. & Kirkegaard, J.) 273–288 (Agronomy Australia and Charles Sturt Univ., 2019).Schillinger, W. F., Donaldson, E., Allan, R. E. & Jones, S. S. Winter wheat seedling emergence from deep sowing depths. Agron. J. 90, 582–586 (1998).
Google Scholar 
Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).
Google Scholar 
Richards, R. The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust. J. Agric. Res. 43, 517–527 (1992).
Google Scholar 
Rebetzke, G. et al. Quantitative trait loci on chromosome 4B for coleoptile length and early vigour in wheat (Triticum aestivum L.). Aust. J. Agric. Res. 52, 1221–1234 (2001).CAS 

Google Scholar 
Rebetzke, G., Richards, R., Sirault, X. & Morrison, A. Genetic analysis of coleoptile length and diameter in wheat. Aust. J. Agric. Res. 55, 733–743 (2004).
Google Scholar 
Rebetzke, G. J., Zheng, B. & Chapman, S. C. Do wheat breeders have suitable genetic variation to overcome short coleoptiles and poor establishment in the warmer soils of future climates? Funct. Plant Biol. 43, 961–972 (2016).
Google Scholar 
Rebetzke, G. J. et al. Height reduction and agronomic performance for selected gibberellin-responsive dwarfing genes in bread wheat (Triticum aestivum L.). Field Crops Res. 126, 87–96 (2012).
Google Scholar 
Zhao, Z., Rebetzke, G. J., Zheng, B., Chapman, S. C. & Wang, E. Modelling impact of early vigour on wheat yield in dryland regions. J. Exp. Bot. 70, 2535–2548 (2019).CAS 

Google Scholar 
Brown, H. E. et al. Plant Modelling Framework: software for building and running crop models on the APSIM platform. Environ. Model. Softw. 62, 385–398 (2014).
Google Scholar 
Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350 (2014).
Google Scholar 
Smith, C. J. et al. Using fertiliser to maintain soil inorganic nitrogen can increase dryland wheat yield with little environmental cost. Agric. Ecosyst. Environ. 286, 106644 (2019).CAS 

Google Scholar 
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).
Google Scholar 
Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).
Google Scholar 
Anderson, W. K., Stephens, D. & Siddique, K. H. M. in Innovations in Dryland Agriculture (eds Farooq, M. & Siddique, K. H. M.) 299–319 (Springer International, 2016).Flohr, B. M., Hunt, J. R., Kirkegaard, J. A., Evans, J. R. & Lilley, J. M. Genotype × management strategies to stabilise the flowering time of wheat in the south-eastern Australian wheatbelt. Crop Pasture Sci. 69, 547–560 (2018).
Google Scholar 
Rebetzke, G., Botwright, T., Moore, C., Richards, R. & Condon, A. Genotypic variation in specific leaf area for genetic improvement of early vigour in wheat. Field Crops Res. 88, 179–189 (2004).
Google Scholar 
Richards, R. A. & Lukacs, Z. Seedling vigour in wheat—sources of variation for genetic and agronomic improvement. Aust. J. Agric. Res. 53, 41–50 (2002).CAS 

Google Scholar 
López-Castañeda, C. & Richards, R. A. Variation in temperate cereals in rainfed environments III. Water use and water-use efficiency. Field Crops Res. 39, 85–98 (1994).
Google Scholar 
Zerner, M. C., Rebetzke, G. J. & Gill, G. S. Genotypic stability of weed competitive ability for bread wheat (Triticum aestivum) genotypes in multiple environments. Crop Pasture Sci. 67, 695–702 (2016).
Google Scholar 
Allan, R. E., Vogel, O. A. & Peterson, C. J. Jr Seedling emergence rate of fall-sown wheat and its association with plant height and coleoptile length. Agron. J. 54, 347–350 (1962).
Google Scholar 
Towards a Global Programme on Sustainable Dryland Agriculture (FAO, 2020); https://www.fao.org/3/nd366en/nd366en.pdfAntle, J. M., Cho, S., Tabatabaie, S. H. & Valdivia, R. O. Economic and environmental performance of dryland wheat-based farming systems in a 1.5 C world. Mitig. Adapt. Strateg. Glob. Change 24, 165–180 (2019).
Google Scholar 
Kirkegaard, J. & Hunt, J. Increasing productivity by matching farming system management and genotype in water-limited environments. J. Exp. Bot. 61, 4129–4143 (2010).CAS 

Google Scholar 
Rebetzke, G. J. et al. Agronomic assessment of the durum Rht18 dwarfing gene in bread wheat. Crop Pasture Sci. https://doi.org/10.1071/CP21645 (2022).Bathgate, J. The Influence of Wheat (Triticum aestivum L.) Semi-dwarfing Genes and the Lcol-A1 QTL on the Coleoptile, Seedling Vigour, and Establishment from Deep Sowing. Honours thesis, Charles Sturt Univ. (2021).Brown, H., Huth, N. & Holzworth, D. Crop model improvement in APSIM: using wheat as a case study. Eur. J. Agron. 100, 141–150 (2018).
Google Scholar 
Botwright, T., Rebetzke, G., Condon, T. & Richards, R. The effect of rht genotype and temperature on coleoptile growth and dry matter partitioning in young wheat seedlings. Funct. Plant Biol. 28, 417–423 (2001).
Google Scholar 
Ellis, M. H. et al. The effect of different height reducing genes on the early growth of wheat. Funct. Plant Biol. 31, 583–589 (2004).CAS 

Google Scholar 
Whan, B. The association between coleoptile length and culm length in semidwarf and standard wheats. J. Aust. Inst. Agric. Sci. 42, 194–196 (1976).
Google Scholar 
Whan, B. The emergence of semidwarf and standard wheats, and its association with coleoptile length. Aust. J. Exp. Agric. 16, 411–416 (1976).
Google Scholar 
Bush, M. & Evans, L. Growth and development in tall and dwarf isogenic lines of spring wheat. Field Crops Res. 18, 243–270 (1988).
Google Scholar 
Rebetzke, G. J., Bonnett, D. G. & Ellis, M. H. Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Res. 127, 17–25 (2012).
Google Scholar 
Miralles, D., Calderini, D., Pomar, K. & D’Ambrogio, A. Dwarfing genes and cell dimensions in different organs of wheat. J. Exp. Bot. 49, 1119–1127 (1998).CAS 

Google Scholar 
Radford, B. Effect of constant and fluctuating temperature regimes and seed source on the coleoptile length of tall and semidwarf wheats. Aust. J. Exp. Agric. 27, 113–117 (1987).
Google Scholar 
Botwright, T., Rebetzke, G., Condon, A. & Richards, R. Influence of variety, seed position and seed source on screening for coleoptile length in bread wheat (Triticum aestivum L.). Euphytica 119, 349–356 (2001).
Google Scholar 
Cornish, P. & Hindmarsh, S. Seed size influences the coleoptile length of wheat. Aust. J. Exp. Agric. 28, 521–523 (1988).
Google Scholar 
Zheng, B., Chenu, K. & Doherty, A. The APSIM-Wheat Module (7.5 R3008) (APSIM Initiative, 2015); https://www.apsim.info/wp-content/uploads/2019/09/WheatDocumentation.pdfZadoks, J. C., Chang, T. T. & Konzak, C. F. A decimal code for the growth stages of cereals. Weed Res. 14, 415–421 (1974).
Google Scholar 
Bell, L. W., Lilley, J. M., Hunt, J. R. & Kirkegaard, J. A. Optimising grain yield and grazing potential of crops across Australia’s high-rainfall zone: a simulation analysis. 1. Wheat. Crop Pasture Sci. 66, 332–348 (2015).
Google Scholar 
Flohr, B. M., Hunt, J. R., Kirkegaard, J. A. & Evans, J. R. Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia. Field Crops Res. 209, 108–119 (2017).
Google Scholar 
Chen, C. et al. Spatial patterns of estimated optimal flowering period of wheat across the southwest of Western Australia. Field Crops Res. 247, 107710 (2020).
Google Scholar 
Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).
Google Scholar 
Liu, B. et al. Global wheat production with 1.5 and 2.0 °C above pre-industrial warming. Glob. Change Biol. 25, 1428–1444 (2019).
Google Scholar 
Zhao, Z., Wang, E., Rebetzke, G. J. & Kirkegaard, J. A. Supporting data for ‘Sowing deep to beat the heat using novel genetics adapts wheat to a changing climate’. CSIRO Data Access Portal https://data.csiro.au/collection/csiro:53658 (2022).Holzworth, D. et al. APSIM Next Generation: overcoming challenges in modernising a farming systems model. Environ. Model. Softw. 103, 43–51 (2018).
Google Scholar 
APSIM Initiative. Source code of APSIM Next Generation. GitHub https://github.com/APSIMInitiative/ApsimX (2021). More

Froehner, S., Rizzi, J., Vieira, L. M. & Sanez, J. PAHs in water, sediment and biota in an area with port activities. Arch. Environ. Contam. Toxicol. 75, 236–246. https://doi.org/10.1007/s00244-018-0538-6 (2018).CAS 
Article 
PubMed 

Google Scholar 
Anyanwu, I. N., Sikoki, F. D. & Semple, K. T. Risk assessment of PAHs and N-PAH analogues in sediment cores from the Niger Delta. Mar. Pollut. Bull. 161, 111684. https://doi.org/10.1016/j.marpolbul.2020.111684 (2020).CAS 
Article 
PubMed 

Google Scholar 
Han, B., Cui, D. Y., Liu, A., Li, Q. & Zheng, L. Distribution, sources, and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from Daya Bay, South China. Environ. Sci. Pollut. Res. 28, 25858–25865. https://doi.org/10.1007/s11356-020-11956-w (2021).CAS 
Article 

Google Scholar 
Li, J. W. et al. Polycyclic aromatic hydrocarbons in water, sediment, soil, and plants of the Aojiang River waterway in Wenzhou, China. J. Hazardous Mater. 173, 75–81. https://doi.org/10.1016/j.jhazmat.2009.08.050 (2010).CAS 
Article 

Google Scholar 
Honda, M. & Suzuki, N. Toxicities of polycyclic aromatic hydrocarbons for aquatic animals. Int. J. Environ. Res. Public Health 17, 1363. https://doi.org/10.3390/ijerph17041363 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Lu, G. N., Tao, X. Q., Dang, Z., Yi, X. Y. & Yang, C. Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors. Cent. Eur. J. Chem. 6, 310–318. https://doi.org/10.2478/s11532-008-0010-y (2008).CAS 
Article 

Google Scholar 
Yuan, H. Z., Zhang, E. L., Lin, Q., Wang, R. & Liu, E. F. Sources appointment and ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Erhai Lake, a low-latitude and high-altitude lake in southwest China. Environ. Sci. Pollut. Res. 23, 4430–4441. https://doi.org/10.1007/s11356-015-5626-9 (2016).CAS 
Article 

Google Scholar 
Souza, M. R. R. et al. Concentration, distribution and source apportionment of polycyclic aromatic hydrocarbons (PAH) in Poxim River sediments, Brazil. Mar. Pollut. Bull. 127, 478–483. https://doi.org/10.1016/j.marpolbul.2017.12.045 (2018).CAS 
Article 
PubMed 

Google Scholar 
Davis, E. et al. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in small craft harbor (SCH) surficial sediments in Nova Scotia, Canada. Sci. Total Environ. 691, 528–537. https://doi.org/10.1016/j.scitotenv.2019.07.114 (2019).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Dreyer, A., Radke, M., Turunen, J. & Blodau, C. Long-term change of polycyclic aromatic hydrocarbon deposition to peatlands of eastern Canada. Environ. Sci. Technol. 39, 3918–3924. https://doi.org/10.1021/es0481880 (2005).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Ma, W. L. et al. Polycyclic aromatic hydrocarbons in water, sediment and soil of the Songhua River Basin, China. Environ. Monitoring Assessment 185, 8399–8409. https://doi.org/10.1007/s10661-013-3182-7 (2013).CAS 
Article 

Google Scholar 
Yang, Y. Y. et al. Distributions, compositions, and ecological risk assessment of polycyclic aromatic hydrocarbons and phthalic acid esters in surface sediment of Songhua river, China. Mar. Pollut. Bull. 152, 10923. https://doi.org/10.1016/j.marpolbul.2020.110923 (2020).CAS 
Article 

Google Scholar 
Rahmanpoor, S., Ghafourian, H., Hashtroudi, S. M. & Bastami, K. D. Distribution and sources of polycyclic aromatic hydrocarbons in surface sediments of the Hormuz strait, Persian Gulf. Mar. Pollut. Bull. 78, 224–229. https://doi.org/10.1016/j.marpolbul.2013.10.032 (2014).CAS 
Article 
PubMed 

Google Scholar 
Aghadadashi, V., Mehdinia, A., Bakhtiari, A. R., Mohammadi, J. & Moradi, M. Source, spatial distribution, and toxicity potential of Polycyclic Aromatic Hydrocarbons in sediments from Iran’s environmentally hot zones, the Persian Gulf. Ecotoxicol. Environ. Saf. 173, 514–525. https://doi.org/10.1016/j.ecoenv.2019.02.029 (2019).CAS 
Article 
PubMed 

Google Scholar 
Zhang, L., Zhu, B., Gao, J. H. & Kang, H. Q. Impact of Taihu Lake on city ozone in the Yangtze River Delta. Adv. Atmos. Sci. 34, 226–234. https://doi.org/10.1007/s00376-016-6099-6 (2017).CAS 
Article 

Google Scholar 
Huang, S. B., Qiao, M., Wang, H. & Wang, Z. J. Organchlorinated pesticides in surface sediments of meiliang bay in Taihu Lake, China. J. Environ. Sci. Health Part a-Toxic/Hazardous Substances Environ. Eng. 41, 223–234. https://doi.org/10.1080/10934520500354664 (2006).CAS 
Article 

Google Scholar 
Su, H. L. et al. Distribution characteristics and risk assessments of PAHs in fish from Lake Taihu, China. Hum. Ecol. Risk Assessment 21, 1753–1765. https://doi.org/10.1080/10807039.2014.975003 (2015).CAS 
Article 

Google Scholar 
Wang, W. W., Qu, X. L., Lin, D. H. & Yang, K. Octanol-water partition coefficient (logKow) dependent movement and time lagging of polycyclic aromatic hydrocarbons (PAHs) from emission sources to lake sediments: A case study of Taihu Lake, China. Environ. Pollut. 288, 117709. https://doi.org/10.1016/j.envpol.2021.117709 (2021).CAS 
Article 
PubMed 

Google Scholar 
Peng, X. Z., Zhang, G., Zheng, L. P., Mai, B. X. & Zeng, S. W. The vertical variations of hydrocarbon pollutants and organochlorine pesticide residues in a sediment core in Lake Taihu, East China. Geochem.-Exploration Environ. Anal. 5, 99–104. https://doi.org/10.1144/1467-7873/03-038 (2005).CAS 
Article 

Google Scholar 
Zhang, Y., Lu, Y. & Zhao, W. Y. Spatial distribution of polycyclic aromatic hydrocarbons from Lake Taihu, China. Bull. Environ. Contam. Toxicol. 87, 80–85. https://doi.org/10.1007/s00128-011-0292-1 (2011).CAS 
Article 
PubMed 

Google Scholar 
Dong, Y. B. et al. Polycyclic aromatic hydrocarbons in sediments from typical Algae, Macrophyte Lake Bay and adjoining river of Taihu Lake, China: Distribution, sources, and risk assessment. Water 13, 470. https://doi.org/10.3390/w13040470 (2021).CAS 
Article 

Google Scholar 
Chen, P. & Liang, J. Polycyclic aromatic hydrocarbons in green space soils in Shanghai: Source, distribution, and risk assessment. J. Soils Sediments 21, 967–977. https://doi.org/10.1007/s11368-020-02838-2 (2021).CAS 
Article 

Google Scholar 
Xia, Z. et al. New approaches to reduce sample processing times for the determination of polycyclic aromatic compounds in environmental samples. Chemosphere 274, 129738. https://doi.org/10.1016/j.chemosphere.2021.129738 (2021).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Sun, T., Wang, Y. H., Tian, J. M. & Kong, X. G. Characteristics of PAHs in soils under different land-use types and their associated health risks in the northern Taihu Basin, China. J. Soils Sediments 22, 134–145. https://doi.org/10.1007/s11368-021-03050-6 (2022).CAS 
Article 

Google Scholar 
Abdollahi, S. et al. Contamination levels and spatial distributions of heavy metals and PAHs in surface sediment of Imam Khomeini Port, Persian Gulf, Iran. Mar. Pollut. Bull. 72, 336–345. https://doi.org/10.1016/j.marpolbul.2013.01.025 (2013).CAS 
Article 

Google Scholar 
Santos, E. et al. Polycyclic aromatic hydrocarbons (PAH) in superficial water from a tropical estuarine system: Distribution, seasonal variations, sources and ecological risk assessment. Mar. Pollut. Bull. 127, 352–358. https://doi.org/10.1016/j.marpolbul.2017.12.014 (2018).CAS 
Article 
PubMed 

Google Scholar 
Long, E. R., Field, L. J. & Macdonald, D. D. Predicting toxicity in marine sediments with numerical sediment quality guidelines. Environ. Toxicol. Chem. 17, 714–727. https://doi.org/10.1002/etc.5620170428 (1998).CAS 
Article 

Google Scholar 
Han, B., Liu, A., He, S., Li, Q. & Zheng, L. Composition, content, source, and risk assessment of PAHs in intertidal sediment in Shilaoren Bay, Qingdao, China. Mar. Pollut. Bull. 159, 111499. https://doi.org/10.1016/j.marpolbul.2020.111499 (2020).CAS 
Article 
PubMed 

Google Scholar 
Abba, E. J., Unnikrishnan, S., Kumar, R., Yeole, B. & Chowdhury, Z. Fine aerosol and PAH carcinogenicity estimation in outdoor environment of Mumbai City, India. Int. J. Environ. Health Res. 22, 134–149. https://doi.org/10.1080/09603123.2011.613112 (2012).CAS 
Article 
PubMed 

Google Scholar 
Ji, H., Zhang, D. & Shinohara, R. Size distribution and estimated carcinogenic potential of particulate polycyclic aromatic hydrocarbons collected at a downtown site in Kumamoto, Japan, in Spring. J. Health Sci. 53, 700–707. https://doi.org/10.1248/jhs.53.700 (2007).CAS 
Article 

Google Scholar 
Lei, P., Zhang, H. & Shan, B. Q. Vertical records of sedimentary PAHs and their freely dissolved fractions in porewater profiles from the northern bays of Taihu Lake, Eastern China. RSC Adv. 6, 98835–98844. https://doi.org/10.1039/c6ra11180g (2016).CAS 
Article 
ADS 

Google Scholar 
Li, A. L. et al. Sedimentary archive of Polycyclic Aromatic Hydrocarbons and perylene sources in the northern part of Taihu Lake, China. Environ. Pollut. 246, 198–206. https://doi.org/10.1016/j.envpol.2018.11.112 (2019).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y. et al. Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models. Water Res. 46, 3065–3073. https://doi.org/10.1016/j.watres.2012.03.006 (2012).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Qiao, M., Wang, C. X., Huang, S. B., Wang, D. H. & Wang, Z. J. Composition, sources, and potential toxicological significance of PAHs in the surface sediments of the Meiliang Bay, Taihu Lake, China. Environ. Int. 32, 28–33. https://doi.org/10.1016/j.envint.2005.04.005 (2016).CAS 
Article 

Google Scholar 
Sun, L., Zang, S. Y. & Sun, H. J. Sources and history of PAHs in lake sediments from oil-producing and industrial areas, northeast China. Int. J. Environ. Sci. Technol. 11, 2051–2060. https://doi.org/10.1007/s13762-013-0396-8 (2014).CAS 
Article 

Google Scholar 
Guo, J. Y. et al. Screening level of PAHs in sediment core from Lake Hongfeng, Southwest China. Arch. Environ. Contam. Toxicol. 60, 590–596. https://doi.org/10.1007/s00244-010-9568-4 (2011).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Z. D. et al. A study on PAHs in the surface soil of the region around Qinghai Lake in the Tibet plateau: Evaluation of distribution characteristics, sources and ecological risks. Environ. Res. Commun. 3, 041005. https://doi.org/10.1088/2515-7620/abf3d9 (2021).Article 

Google Scholar 
Li, C. C. et al. Spatial distribution, potential risk assessment, and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Lake Chaohu, China. Environ. Sci. Pollut. Res. 21, 12028–12039. https://doi.org/10.1007/s11356-014-3137-8 (2014).CAS 
Article 

Google Scholar 
Romo-Gomez, C., Monks, S., Pulido-Flores, G. & Gordillo-Martinez, A. J. Determination of polycyclic aromatic hydrocarbons (PAHs) in superficial water and sediment of Lake Tecocomulco, Mexico. Interciencia 35, 905–911 (2010).
Google Scholar 
Yuan, Z. J. et al. Polycyclic aromatic hydrocarbons (PAHs) in urban stream sediments of Suzhou Industrial Park, an emerging eco-industrial park in China: Occurrence, sources and potential risk. Ecotoxicol. Environ. Saf. 214, 112095. https://doi.org/10.1016/10.1016/j.ecoenv.2021.112095 (2021).CAS 
Article 
PubMed 

Google Scholar 
Shen, B. B., Wu, J. L. & Zhao, Z. H. Residues of organochlorine pesticides and polycyclic aromatic hydrocarbons in surface waters, soils and sediments of the Kaidu River catchment, northwest China. Int. J. Environ. Pollut. 63, 104–116. https://doi.org/10.1504/IJEP.2018.10014155 (2018).CAS 
Article 

Google Scholar 
Yan, W., Chi, J. S., Wang, Z. Y., Huang, W. X. & Zhang, G. Spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) in sediments from Daya Bay, South China. Environ. Pollut. 157, 1823–1830. https://doi.org/10.1016/j.envpol.2009.01.023 (2009).CAS 
Article 
PubMed 

Google Scholar 
Arias, A. H. et al. Presence, distribution, and origins of polycyclic aromatic hydrocarbons (PAHs) in sediments from Bahia Blanca estuary, Argentina. Environ. Monitor. Assess. 160, 301–314. https://doi.org/10.1007/s10661-008-0696-5 (2010).CAS 
Article 

Google Scholar 
Yunker, M. B. et al. PAHs in the Fraser River basin: A critical appraisal of PAH ratios as indicators of PAH source and composition. Org. Geochem. 33, 489–515. https://doi.org/10.1016/S0146-6380(02)00002-5 (2002).CAS 
Article 

Google Scholar 
Tobiszewski, M. & Namiesnik, J. PAH diagnostic ratios for the identification of pollution emission sources. Environ. Pollut. 162, 110–119. https://doi.org/10.1016/j.envpol.2011.10.025 (2012).CAS 
Article 
PubMed 

Google Scholar 
Chen, M. H., Li, C. H., Ye, C. & Xu, S. H. Distribution, sources and risk assessment of polycyclic aromatic hydrocarbon in sediments from Zhushan Bay littoral zone, Lake Taihu. J. Environ. Eng. Technol. 4, 199–205 (2014) (in Chinese).
Google Scholar 
Zhao, Q., Yu, Q. & Chen, L. M. Particulate matter and particle-bound polycyclic aromatic hydrocarbons in the Dapu road tunnel in Shanghai. Int. J. Environ. Pollut. 41, 21–37. https://doi.org/10.1504/IJEP.2010.032243 (2010).CAS 
Article 

Google Scholar 
Tian, Y. Z. et al. Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three-way source apportionment approach. Environ. Toxicol. Chem. 33, 1747–1753. https://doi.org/10.1002/etc.2628 (2014).CAS 
Article 
PubMed 

Google Scholar 
Zhang, F. et al. Polycyclic aromatic hydrocarbons (PAHs) and Pb isotopic ratios in a sediment core from Shilianghe Reservoir, eastern China: Implying pollution sources. Appl. Geochem. 66, 140–148. https://doi.org/10.1016/j.apgeochem.2015.12.010 (2016).CAS 
Article 
ADS 

Google Scholar 
Harrison, R. M., Smith, D. J. T. & Luhana, L. Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, UK. Environ. Sci. Technol. 30, 825–832. https://doi.org/10.1021/es950252d (1996).CAS 
Article 
ADS 

Google Scholar 
Simcik, M. F., Eisenreich, S. J. & Lioy, P. J. Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan. Atmos. Environ. 33, 5071–5079. https://doi.org/10.1016/S1352-2310(99)00233-2 (1999).CAS 
Article 
ADS 

Google Scholar 
Yang, J., Xu, W. L. & Cheng, H. Y. Seasonal variations and sources of airborne polycyclic aromatic hydrocarbons (PAHs) in Chengdu, China. Atmosphere 9, 63. https://doi.org/10.3390/atmos9020063 (2018).CAS 
Article 
ADS 

Google Scholar 
Cetin, B. Investigation of PAHs, PCBs and PCNs in soils around a Heavily Industrialized Area in Kocaeli, Turkey: Concentrations, distributions, sources and toxicological effects. Sci. Total Environ. 560, 160–169. https://doi.org/10.1016/j.scitotenv.2016.04.037 (2016).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Ali-Taleshi, M. S., Squizzato, S., Riyahi Bakhtiari, A., Moeinaddini, M. & Masiol, M. Using a hybrid approach to apportion potential source locations contributing to excess cancer risk of PM25-bound PAHs during heating and non-heating periods in a megacity in the Middle East. Environ. Res. 201, 111617. https://doi.org/10.1016/j.envres.2021.111617 (2021).CAS 
Article 
PubMed 

Google Scholar 
Xu, J. et al. Historical trends of concentrations, source contributions and toxicities for PAHs in dated sediment cores from five lakes in western China. Sci. Total Environ. 470, 519–526. https://doi.org/10.1016/j.scitotenv.2013.10.022 (2014).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Wei, H., Liu, G. B., Yong, T. & Qin, Z. Emission of polycyclic aromatic hydrocarbons from different types of motor vehicles’ exhaust. Environ. Earth Sci. 74, 5557–5564. https://doi.org/10.1007/s12665-015-4570-9 (2015).CAS 
Article 

Google Scholar  More

ACIA (2004) Impacts of a Warming Arctic: Arctic Climate Impact Assessment. Cambridge University Press, Cambridge, UK
Google Scholar 
Alsos IG, Ehrich D, Thuiller W, Eidesen PB, Tribsch A, Schonswetter P et al. (2012) Genetic consequences of climate change for northern plants. Proc R Soc B-Biol Sci 279:2042–2051
Google Scholar 
Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA (2016) Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet 17:81–92CAS 
PubMed 
PubMed Central 

Google Scholar 
Archer FI, Adams PE, Schneiders BB (2017) strataG: an R package for manipulating, summarizing and analysing population genetic data. Mol Ecol Resour 17:5–11CAS 
PubMed 

Google Scholar 
Arenas M, Ray N, Currat M, Excoffier L (2011) Consequences of range contractions and range shifts on molecular diversity. Mol Biol Evol 29:207–218PubMed 

Google Scholar 
Beaumont MA (1999) Detecting population expansion and decline using microsatellites. Genetics 153:2013–2029CAS 
PubMed 
PubMed Central 

Google Scholar 
Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statistical Soc Series B 57:289–300BirdLife International (2018). Pagophila eburnea. The IUCN Red List of Threatened Species 2018: e.T22694473A132555020. https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22694473A132555020.en. Downloaded on11 June 2020Boertmann D, Petersen IK, Nielsen HH (2020) Ivory Gull population status in Greenland 2019. Dan Orn Foren Tidsskr 114:141–150
Google Scholar 
Boitard S, Rodríguez W, Jay F, Mona S, Austerlitz F (2016) Inferring population size history from large samples of genome-wide molecular data – an approximate Bayesian computation approach. PLOS Genet 12:1–36
Google Scholar 
Box JE, Colgan WT, Christensen TR, Schmidt NM, Lund M, Parmentier F-JW et al. (2019) Key indicators of Arctic climate change: 1971–2017. Environ Res Lett 14:045010CAS 

Google Scholar 
Braune BM, Mallory ML, Gilchrist HG (2006) Elevated mercury levels in a declining population of ivory gulls in the Canadian Arctic. Mar Pollut Bull 52:978–982CAS 
PubMed 

Google Scholar 
Broquet T, Angelone S, Jaquiéry J, Joly P, Léna JP, Lengagne T et al. (2010) Genetic bottlenecks driven by population disconnection. Conserv Biol 24:1596–1605PubMed 

Google Scholar 
Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026CAS 
PubMed 

Google Scholar 
Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA (2010) The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186:983–995PubMed 
PubMed Central 

Google Scholar 
Collevatti RG, Nabout JC, Diniz-Filho JAF (2011) Range shift and loss of genetic diversity under climate change in Caryocar brasiliense, a Neotropical tree species. Tree Genet Genomes 7:1237–1247
Google Scholar 
Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014CAS 
PubMed 
PubMed Central 

Google Scholar 
Cotto O, Schmid M, Guillaume F (2020) Nemo-age: spatially explicit simulations of eco-evolutionary dynamics in stage-structured populations under changing environments. Methods Ecol Evol 11:1227–1236
Google Scholar 
Cubaynes S, Doherty PF, Schreiber EA, Gimenez O (2011) To breed or not to breed: a seabird’s response to extreme climatic events. Biol Lett 7:303–306PubMed 

Google Scholar 
Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB (1994). Mutational processes of simple-sequence repeat loci in human populations. Proc Natl Acad Sci USA 91: 3166–3170Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014) NeEstimator V2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
PubMed 

Google Scholar 
Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5:435–445CAS 
PubMed 

Google Scholar 
England PR, Cornuet J-M, Berthier P, Tallmon DA, Luikart G (2006) Estimating effective population size from linkage disequilibrium: severe biasin small samples. Conserv Genet 7:303
Google Scholar 
Engler JO, Secondi J, Dawson DA, Elle O, Hochkirch A (2016) Range expansion and retraction along a moving contact zone has no effect on the genetic diversity of two passerine birds. Ecography 39:884–893
Google Scholar 
Environment Canada (2014) Recovery Strategy for the Ivory Gull (Pagophila eburnea) in Canada. Species at Risk Act Recovery Strategy Series. Environment Canada, Ottawa. iv+ 21 ppEstoup A, Angers B (1998) Microsatellites and minisatellites for molecular ecology: theoretical and empirical considerations. In Carvalho GR (ed) Advances in molecular ecology, 55–85. IOS Press, Burke, Virginia, USAEwens WJ (1972) The sampling theory of selectively neutral alleles. Theor Popul Biol 3:87–112CAS 
PubMed 

Google Scholar 
Felsenstein J (1971) Inbreeding and variance effective numbers in populations with overlapping generations. Genetics 168:581–597
Google Scholar 
Fort J, Moe B, Strøm H, Grémillet D, Welcker J, Schultner J et al. (2013) Multicolony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea ice cover. Diversity Distrib 19:1322–1332
Google Scholar 
Fraïsse C, Popovic I, Mazoyer C, Spataro B, Delmotte S, Romiguier J et al. (2021). DILS: Demographic inferences with linked selection by using ABC. Mol Ecol Resourc 21:2629–2644Frankham R (1995) Effective population size/adult population size ratios in wildlife: a review. Genetical Res 66:95–107
Google Scholar 
Frankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv 170:56–63
Google Scholar 
Garnier J, Lewis MA (2016) Expansion under climate change: the genetic consequences. Bull Math Biol 78:2165–2185PubMed 

Google Scholar 
Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–318CAS 
PubMed 

Google Scholar 
Gavrilo M, Martynova D (2017) Conservation of rare species of marine flora and fauna of the Russian Arctic National Park, included in the Red Data Book of the Russian Federation and in the IUCN Red List. Nat Conserv Res 2:10–42
Google Scholar 
Gienapp P, Teplitsky C, Alho JS, Mills JA, Merila J (2008) Climate change and evolution: disentangling environmental and genetic responses. Mol Ecol 17:167–178CAS 
PubMed 

Google Scholar 
Gilchrist HG, Mallory ML (2005) Declines in abundance and distribution of the ivory gull (Pagophila eburnea) in Arctic Canada. Biol Conserv 121:303–309
Google Scholar 
Gilchrist HG, Strøm H, Gavrilo MV, Mosbech A (2008). International ivory gull conservation strategy and action plan. CAFF International Secretariat,Circumpolar Seabird Group (CBird), CAFF Technical Report No. 18Gilg O, Boertmann D, Merkel F, Aebischer A, Sabard B (2009) Status of the endangered ivory gull, Pagophila eburnea, in Greenland. Polar Biol 32:1275–1286
Google Scholar 
Gilg O, Istomina L, Heygster G, Strøm H, Gavrilo M, Mallory ML et al. (2016) Living on the edge of a shrinking habitat: the ivory gull, Pagophila eburnea, an endangered sea-ice specialist. Biol Lett 12:20160277PubMed 
PubMed Central 

Google Scholar 
Gilg O, Kovacs KM, Aars J, Fort J, Gauthier G, Gremillet D et al. (2012) Climate change and the ecology and evolution of Arctic vertebrates. Ann N Y Acad Sci 1249:166–190PubMed 

Google Scholar 
Goutte A, Kriloff M, Weimerskirch H, Chastel O (2011) Why do some adult birds skip breeding? A hormonal investigation in a long-lived bird. Biol Lett 7:790–792PubMed 
PubMed Central 

Google Scholar 
Guillaume F, Rougemont J (2006) Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22:2556–2557CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill WG (1972) Effective size of populations with overlapping generations. Theor Popul Biol 3:278–289CAS 
PubMed 

Google Scholar 
Hoban SM, Mezzavilla M, Gaggiotti OE, Benazzo A, van Oosterhout C, Bertorelle G (2013) High variance in reproductive success generates a false signature of a genetic bottleneck in populations of constant size: a simulation study. BMC Bioinforma 14:309
Google Scholar 
Hoffmann AA, Sgrò CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485CAS 
PubMed 

Google Scholar 
Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82PubMed 

Google Scholar 
Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584PubMed 

Google Scholar 
Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in a finite population. Proc Natl Acad Sci USA 75:2868–2872CAS 
PubMed 
PubMed Central 

Google Scholar 
Laporte V, Charlesworth B (2002) Effective population size and population subdivision in demographically structured populations. Genetics 162:501–519CAS 
PubMed 
PubMed Central 

Google Scholar 
Leblois R, Pudlo P, Néron J, Bertaux F, Reddy Beeravolu C, Vitalis R et al. (2014) Maximum-Likelihood Inference of Population Size Contractions from Microsatellite Data. Mol Biol Evol 31:2805–2823CAS 
PubMed 

Google Scholar 
Li H, Durbin R (2011) Inference of human population history from individual whole-genome sequences. Nature 475:493–496CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Fu Y-X (2015) Exploring population size changes using SNP frequency spectra. Nat Genet 47:555–559CAS 
PubMed 
PubMed Central 

Google Scholar 
Lucia M, Verboven N, Strom H, Miljeteig C, Gavrilo MV, Braune BM et al. (2015) Circumpolar contamination in eggs of the high-arctic ivory gull Pagophila eburnea. Environ Toxicol Chem 34:1552–1561CAS 
PubMed 

Google Scholar 
Luikart G, Cornuet J-M (1998) Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv Biol 12:228–237
Google Scholar 
Mallory ML, Allard KA, Braune BM, Gilchrist HG, Thomas VG (2012) New longevity record for ivory gulls (Pagophila eburnea) and evidence of natal philopatry. Arctic 65:98–101
Google Scholar 
Marandel F, Charrier G, Lamy J-B, Le Cam S, Lorance P, Trenkel VM (2020) Estimating effective population size using RADseq: Effects of SNP selection and sample size. Ecol Evol 10:1929–1937PubMed 
PubMed Central 

Google Scholar 
McInerny GJ, Turner JRG, Wong HY, Travis JMJ, Benton TG (2009) How range shifts induced by climate change affect neutral evolution. Proc R Soc B: Biol Sci 276:1527–1534CAS 

Google Scholar 
McRae L, Deinet S, Gill M, Collen B (2012) Arctic species trend index: tracking trends in Arctic marine populations. CAFF Assessment Series No. 7. Conservation of Arctic Flora and Fauna, Iceland
Google Scholar 
Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykin A, Hollowed A et al. (2020) Polar Regions. In: Pörtner HO, Roberts DC, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska E, Mintenbeck K, Alegría A, Nicolai M, Okem A, Petzold J, Rama B, Weyer NM (eds.) IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Intergovernmental Panel on Climate Change (IPCC)) Geneva, pp 203–320Miljeteig C, Strom H, Gavrilo MV, Volkov A, Jenssen BM, Gabrielsen GW (2009) High levels of contaminants in ivory gull Pagophila eburnea eggs from the Russian and Norwegian Arctic. Environ Sci Technol 43:5521–5528CAS 
PubMed 

Google Scholar 
Miller MP, Haig SM, Mullins TD, Popper KJ, Green M (2012) Evidence for population bottlenecks and subtle genetic structure in the yellow rail. Condor 114:100–112
Google Scholar 
Nadachowska-Brzyska K, Li C, Smeds L, Zhang G, Ellegren H (2015) Temporal dynamics of avian populations during Pleistocene revealed by whole-genome sequences. Curr Biol 25:1375–1380CAS 
PubMed 
PubMed Central 

Google Scholar 
Nunney L (1993) The influence of mating system and overlapping generations on effective population size. Evolution 47:1329–1341PubMed 

Google Scholar 
Nunney L, Elam DR (1994) Estimating the Effective Population Size of Conserved Populations. Conserv Biol 8:175–184
Google Scholar 
Nunziata SO, Weisrock DW (2018) Estimation of contemporary effective population size and population declines using RAD sequence data. Heredity 120:196–207CAS 
PubMed 

Google Scholar 
Nyström V, Angerbjörn A, Dalén L (2006) Genetic consequences of a demographic bottleneck in the Scandinavian arctic fox. Oikos 114:84–94
Google Scholar 
Orive ME (1993) Effective population size in organisms with complex life-histories. Theor Popul Biol 44:316–340CAS 
PubMed 

Google Scholar 
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669
Google Scholar 
Parreira BR, Chikhi L (2015) On some genetic consequences of social structure, mating systems, dispersal, and sampling. Proc Natl Acad Sci USA 112:E3318CAS 
PubMed 
PubMed Central 

Google Scholar 
Parreira B, Quéméré E, Vanpé C, Carvalho I, Chikhi L (2020) Genetic consequences of social structure in the golden-crowned sifaka. Heredity 125:328–339PubMed 
PubMed Central 

Google Scholar 
Peart CR, Tusso S, Pophaly SD, Botero-Castro F, Wu C-C, Aurioles-Gamboa D et al. (2020) Determinants of genetic variation across eco-evolutionary scales in pinnipeds. Nat Ecol Evol 4:1095–1104PubMed 

Google Scholar 
Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Bëer E, Robinson S et al. (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21:3403–3418PubMed 

Google Scholar 
Piry S, Luikart G, Cornuet J-M (1999) Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Heredity 90:502–503
Google Scholar 
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Heredity 86:248–249
Google Scholar 
Rousset F (1999) Genetic Differentiation in Populations with Different Classes of Individuals. Theor Popul Biol 55:297–308CAS 
PubMed 

Google Scholar 
Rousset F (2008) Genepop’007: a complete re-implementation of the Genepop software for windows and linux. Mol Ecol Notes 8:103–1006
Google Scholar 
Rousset F, Beeravolu CR, Leblois R (2018) Likelihood computation and inference of demographic and mutational parameters from population genetic data under coalescent approximations. J de la Société Française de Statistique 159:142–166
Google Scholar 
Rubidge EM, Patton JL, Lim M, Burton AC, Brashares JS, Moritz C (2012) Climate-induced range contraction drives genetic erosion in an alpine mammal. Nat Clim Change 2:285–288
Google Scholar 
Shafer ABA, Gattepaille LM, Stewart REA, Wolf JBW (2015) Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. Mol Ecol 24:328–345PubMed 

Google Scholar 
Spencer NC, Gilchrist HG, Mallory ML (2014) Annual movement patterns of endangered ivory gulls: the importance of sea ice. Plos ONE 9:e115231PubMed 
PubMed Central 

Google Scholar 
Stenhouse IJ, Robertson GJ, Gilchrist HG (2004) Recoveries and survival rates of ivory gulls (Pagophila eburnea) banded in Nunavut, Canada 1971–1999. Waterbirds 27:486–492
Google Scholar 
Storz J, Ramakrishnan U, Alberts S (2002) Genetic effective size of a wild primate population: influence of current and historical demography. Evolution 56:817–29PubMed 

Google Scholar 
Strøm H, Bakken V, Skoglund, Descamps S, Fjeldheim VB, Steen H (2020) Population status and trend of the threatened ivory gull Pagophila eburnea in Svalbard. Endanger Species Res 43:435–445
Google Scholar 
Volkov AE, de Korte J (2000) Breeding ecology of the Ivory Gull (Pagophila eburnea) in Sedov Archipelago, Severnaya Zemlya. Heritage of the Russian Arctic. Research, conservation and international cooperation. Ecopros Publishers, Moscow, p 483–500
Google Scholar 
Waples RS (2016) Life-history traits and effective population size in species with overlapping generations revisited: the importance of adult mortality. Heredity 117:241–250CAS 
PubMed 
PubMed Central 

Google Scholar 
Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780PubMed 
PubMed Central 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evolut Appl 3:244–262
Google Scholar 
Waples RS, Luikart G, Faulkner JR, Tallmon DA (2013) Simple life-history traits explain key effective population size ratios across diverse taxa. Proc R Soc B: Biol Sci 280:20131339
Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson-Natesan EG (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv Genet 6:551–562
Google Scholar 
Wogan GOU, Voelker G, Oatley G, Bowie RCK (2020) Biome stability predicts population structure of a southern African aridland bird species. Ecol Evol 10:4066–4081PubMed 
PubMed Central 

Google Scholar 
Xenikoudakis G, Ersmark E, Tison J-L, Waits L, Kindberg J, Swenson JE et al. (2015) Consequences of a demographic bottleneck on genetic structure and variation in the Scandinavian brown bear. Mol Ecol 24:3441–3454CAS 
PubMed 

Google Scholar 
Yannic G, Broquet T, Strøm H, Aebischer A, Dufresnes C, Gavrilo MV et al. (2016) Genetic and morphological sex identification methods reveal a male-biased sex ratio in the Ivory Gull Pagophila eburnea. J Ornithol 157:861–873
Google Scholar 
Yannic G, Sermier R, Aebischer A, Gavrilo MV, Gilg O, Miljeteig C et al. (2011) Description of microsatellite markers and genotyping performances using feathers and buccal swabs for the ivory gull (Pagophila eburnea). Mol Ecol Resour 11:877–889PubMed 

Google Scholar 
Yannic G, Yearsley J, Sermier R, Dufresnes C, Gilg O, Aebischer A et al. (2016) High connectivity in a long-lived high-Arctic seabird, the ivory gull Pagophila eburnea. Polar Biol 39:221–236
Google Scholar 
Yurkowski DJ, Auger-Méthé M, Mallory ML, Wong SNP, Gilchrist G, Derocher AE et al. (2019) Abundance and species diversity hotspots of tracked marine predators across the North American Arctic. Diversity Distrib 25:328–345
Google Scholar  More

Cape gannet movement trackingThe study took place in the Western Cape, South Africa, where we studied chick-rearing Cape gannets from Malgas Island (33.05° S, 17.93° E) during October–November from 2008 to 2015 (Fig. 1). We caught birds using a pole fitted with a loop and fitted 197 adult Cape gannets (22 in 2008, 16 in 2009, 38 in 2010, 11 in 2011, 29 in 2012, 29 in 2013, 23 in 2014, and 29 in 2015) with GPS-loggers (2008: GPS mass 65 g, i.e., 2.4 % adult body mass, Technosmart, Rom. 2009–2010: GPS mass 45 g, i.e., 1.7% adult body mass, Technosmart, Rom. From 2011: GPS mass 30 g, 1.1% of bird body mass, Catnip Technologies, Hong-Kong). Loggers were attached to the lower back with waterproof Tesa® tape and recorded position at a regular 30-s to 2-min intervals, reinterpolated over 1-min intervals. Devices were recovered after one foraging trip lasting a few hours to one week. Bird handling and tracking using these procedures do not have a measurable impact on foraging behavior19,20. We caught adult birds at-random from the colony, and previous studies showed that this resulted in a well-balanced sex-ratio preventing confounding sex effects21. All experiments were performed under permit from South African National Parks with respect to animal ethics (N° RYAP/AGR/001-2002/V1).Cape gannet movement tactics and behavioral phasesWe identified two movement trip tactics for Cape gannets: After their daytime foraging activities, some birds returned to the colony at night (rest at colony tactic) while others spent all the night at sea (rest at sea tactic). Within the GPS tracks of gannets from these two categories, we discriminated resting, foraging, and commuting phases, with a segmentation-clustering method based on smoothed speed (i.e., speed smoothed over two steps before and after the focal location) and turning angle measured at constant step length. This corresponded to the angle between the focal location, the first location entering a circle of radius equal to the median step length, and the last location inside the circle22. We fitted behavioral identification with the segclust2d package23 for the R software24. See complete details on behavioral classification for Cape gannets tracks in Appendix 1 in Courbin et al.25.Cape fur seal movement tracking and the seascape of fearWe assessed the at-sea spatial distribution of Cape fur seals, a predator of Cape gannet fledglings7 and adults (Supplementary Data 1). We used Argos data collected from 25 lactating female seals before (2003 and 2004) and again concomitantly with gannet tracking (2012 and 2014). Seals were tracked during the same period of the year as gannets (i.e., September to November). Adult females nursing pups were selected at random and captured using a modified hoop net. Once restrained, anesthesia was induced using isoflurane gas delivered via a portable vaporizer (Stinger, Advanced Anesthesia Specialists, Gladesville, New South Wales, Australia). A satellite tag was glued to the guard hairs on the upper back. Individuals were allowed to recover from the anesthesia and resumed normal behavior within 45 min of capture. Throughout the process, the animals’ breathing was closely monitored and their flippers were repeatedly flushed with seawater to prevent hyperthermia. Seals were equipped with Argos satellite transmitters at three colonies (Fig. 1): Kleinsee (29°35’09”S, 16°59’56”E) located ~400 km to the North of the gannet colony (n = 8 seals in 2003 and 2004); Vondeling Island (33°09’11”S, 17°58’57”E), ~12 km away from the gannet colony (n = 12 seals in 2012 and 2014); and Geyser Rock (34°41’19”S, 19°24’49”E) located ~230 km to the South of the gannet colony (n = 5 seals in 2003). Seals at Vondeling Island were equipped with Argos-linked Spot-6 position transmitting tags (Wildlife Computers) following deployment procedures outlined in Kirkman et al.26. Seals at Kleinsee and Geyser Rock were equipped with ST18 and ST20 satellite-linked platform terminal transmitters (Telonics, Mesa, USA), as detailed in Skern-Mauritzen et al.27. Devices collected a well-balanced number of Argos locations during the day (n = 6080 locations) and at night (n = 6501 locations). See full details on seal tracking in Supplementary Table 6. All fieldwork was permitted by the Animal Ethics Committee of the Department of Environmental Affairs and Tourism’s Marine and Coastal Management branch, which at the time was the management authority of South Africa’s marine and coastal environment (Ref: DEAT2006-06-23).We modeled both daytime and nighttime at-sea occurrences of seals for each colony with resource selection functions (RSF)28,29, a proxy of the fear effect for Cape gannets. RSF compared environmental features of seal’s at-sea Argos positions (i.e., further 500 m than the colony) with five times more random locations that captured the breadth of environmental conditions available to seals. We sampled random locations for each individual within the yearly area used by seals from each colony, delineated by the 95% kernel utilization distribution of the Argos locations of all seals of the colony. RSF were fitted with a generalized linear mixed model with a binomial distribution for errors. As environmental variables, we considered bathymetry (m), the slope of the bathymetry (°) and the distance to the colony (km) within the RSF. These variables were not highly correlated (|r| ≤ 0.61) and had low collinearity with a variance inflation factor VIF  More

Alberdi, M. T. A review of Old World hipparionine horses in The Evolution of Perissodactyls (eds. Prothero, D. R. & Schoch, R. M.) 234–261 (Clarendon Press, Oxford University Press, New York, NY ⋅ Oxford, 1989).Sen S. Hipparion Datum and its chronologic evidence in the Mediterranean area in European Neogene Mammal Chronology (eds. Lindsay, E. H., Fahlbusch, V. & Mein, P.) 495–506 (Plenum Press, New York, 1990).Garcés, M., Cabrera, L., Agustí, J. & Parés, J. M. Old World first appearance datum of “Hipparion” horses: Late Miocene large–mammal dispersal and global events. Geology 25(1), 19–22. https://doi.org/10.1130/0091-7613(1997)025%3c0019:OWFADO%3e2.3.CO;2 (1997).ADS 
Article 

Google Scholar 
Woodburne, M. O. A new occurrence of Cormohipparion, with implications for the Old World Hippotherium Datum. J. Vert. Paleont. 25(1), 256–257 (2005).Woodburne, M. O. Phyletic diversification of the Cormohipparion occidentale complex (Mammalia; Perissodactyla, Equidae), Late Miocene, North America, and the origin of the Old World Hippotherium Datum. B. Am. Mus. Nat. Hist. 306, 1–180 (2007).Article 

Google Scholar 
Bernor, R. L., Qiu, Z. & Tobien, H., 1987. Phylogenetic and biogeographic bases for an Old World hipparionine horse geochronology. Proceedings of the VIIIth International Congress of the Regional Committee on Mediterranean Neogene Stratigraphy, Budapest. Ann. Inst. Geol. Publ. Hung. 70, 43–53 (1987).Bernor, R. L., Tobien, H., Hayek, L–A. C. & Mittmann, H. –W. Hippotherium primigenium (Equidae, Mammalia) from the late Miocene of Höwenegg (Hegau, Germany). Andrias 10, 1–230 (1997).Qiu, Z., Huang, W. & Guo, Z. The Chinese hipparionine fossils. Palaeont. Sin. New Ser C 25, 1–250 (1987) ((in Chinese with English summary)).
Google Scholar 
Zouhri, S. & Bensalmia, A. Révision systématique des Hipparion sensu lato (Perissodactyla, Equidae) de l’Ancien Monde. Estud. Geol. 61, 61–99. https://doi.org/10.3989/egeol.05611-243 (2005).Article 

Google Scholar 
Liu, Y. Late Miocene hipparionine fossils from Lantian, Shaanxi Province and phylogenetic analysis on Chinese Hipparionines. PhD thesis (University of Chinese Academy of Sciences, Beijing) (2013).Bernor, R. L., Wang, S., Liu, Y., Chen, Y. & Sun, B. Shanxihippus dermatorhinus (new gen.) with comparisons to Old World hipparions with specialized nasal apparati. Riv. Ital. Paleontol. Stratigr. 124, 361–386 (2018).Bernor, R. L., Boaz, N. T., Omar, C., El-Shawaihdi, M. H. & Rook, L. Sahabi Eurygnathohippus feibeli: Its systematic, stratigraphic, chronologic and biogeographic contexts. Riv. Ital. Paleontol. Stratigr. 126, 561–581 (2020).
Google Scholar 
Liu, T., Li, C. & Zhai, R. Pliocene mammalian fauna of Lantian, Shaangxi. Prof. Pap. Stratigr. Paleont. 7, 149–200 (1978) (in Chinese).Deng, T. et al. Locomotive implication of a Pliocene three–toed horse skeleton from Tibet and its paleo–altimetry significance. Proc. Natl. Acad. Sci. USA 109, 7374–7378. https://doi.org/10.1073/pnas.1201052109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fang, X., Garzione, C., van der Voo, R., Li, J. & Fan, M. Flexural subsidence by 29 Ma on the NE edge of Tibet from the magnetostratigraphy of Linxia Basin China. Earth Planet. Sci. Lett. 210, 545–560 (2003).ADS 
CAS 
Article 

Google Scholar 
Fang, X. et al. Tectonosedimentary evolution model of an intracontinental flexural (foreland) basin for paleoclimatic research. Glob. Planet Change 145, 78–97. https://doi.org/10.1016/j.gloplacha.2016.08.015 (2016).ADS 
Article 

Google Scholar 
Deng, T., Qiu, Z., Wang, B., Wang, X. & Hou, S. Chapter 9: Late Cenozoic Biostratigraphy of the Linxia Basin, Northwestern China in Fossil Mammals of Asia: Neogene Biostratigraphy and Chronology (eds. Wang, X., Flynn, L. J. & Fortelius, M.) 243–273 (Columbia University Press, New York, 2013).Li, Y., Deng, T., Hua, H., Li, Y. & Zhang, Y. Assessment of dental ontogeny in late Miocene hipparionines from the Lamagou fauna of Fugu, Shaanxi Province China. PLoS ONE https://doi.org/10.1371/journal.pone.0175460 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y., Deng, T., Hua, H., Sun, B. & Zhang, Y. Locomotor adaptations of 7.4 Ma Hipparionine fossils from the middle reaches of the Yellow River and their palaeoecological significance. Hist. Biol. 33(7), 927–940 (2021).Bernor, R. L. & Sun, B. Morphology through ontogeny of Chinese Proboscidipparion and Plesiohipparion and observations on their Eurasian and African relatives. Vert. PalAsiat. 53, 73–92 (2015).
Google Scholar 
Woodburne, M. O. & Bernor, R. L. On superspecific groups of some Old World hipparinonine horses. J. Paleontol. 54(6), 1319–1348 (1980).
Google Scholar 
Bernor, R. L., Kaya, F., Kaakinen, A., Saarinen, J. & Fortelius, M. Old World hipparion evolution, biogeography, climatology and ecology. Earth Sci. Rev. 211, 103784 (2021).Article 

Google Scholar 
Bernor, R. L., Göhlich, U. B., Harzhauser, M. & Semprebon, G. M. The Pannonian C hipparions from the Vienna Basin. Palaegeogr. Palaeoclim. Palaeoecol. 476, 28–41. https://doi.org/10.1016/j.palaeo.2017.03.026 (2017).ADS 
Article 

Google Scholar 
Arambourg, C. Vertebres continentaux du Miocene superieur de l’Afrique du Nord. Service Carte Geologie Algerie Paleontologie Memoire, Nouveaux Serie 4, 1–159 (1959).
Google Scholar 
Bernor, R.L. & White, T.D. Systematics and biogeography of “Cormohipparion” africanum, Early Vallesian (MN 9, ca. 10.5 Ma) of Bou Hanifia, Algeria in Papers on Geology, Vertebrate Paleontology, and Biostratigraphy in Honorof Michael O. Woodburne. (ed. Albright, B.), Bull., Mus. of No. Arizona. 65, 635–658 (2009).Bernor, R. L., Scott, R. S., Fortelius, M., Kappelman, J. & Sen, S. Systematics and Evolution of the late Miocene Hipparions from Sinap, Turkey in The Geology and Paleontology of the Miocene Sinap Formation, Turkey (eds. Fortelius, M., Kappelman, J., Sen, S. & Bernor, R. L.) 220–281 (Columbia University Press, New York, 2003).Sack, W. O. The stay–apparatus of the horse’s hindlimb, explained. Equine Pract. 11, 31–35 (1988).
Google Scholar 
MacFadden, B. J. In Fossil Horses: Systematics, Paleobiology, and Evolution of the Family Equidae (Cambridge University Press, 1992).
Google Scholar 
Li, F. & Li D. Latest Miocene Hipparion (Plesiohipparion) of Zanda Basin in Paleontology of the Ngari Area, Tibet (Xizang) (eds. Yang, Z. & Nie, Z.) 186–193 (China University of Geosciences Press, Wuhan, 1990).Thomason, J. J. The functional morphology of the manus in tridactyl equids Merychippus and Mesohippus: Paleontological inferences from neontological models. J. Vert. Paleont. 6, 143–161 (1986).Article 

Google Scholar 
Eisenmann, V. What metapodial morphometry has to say about some Miocene Hipparions in Paleoclimate and Evolution, with Emphasis on Human Origins (eds. Vrba, E. S., Denton, G. H., Partridge, T. C. & Burckle, L. H.) 148–164 (Yale University Press, New Haven, 1995).Deng, T. & Wang, X. Late Miocene Hipparion (Equidae, Mammalia) of eastern Qaidam Basin in Qinghai, China. Vert. PalAsiat. 42(4), 316–333 (2004) (in Chinese with English summary).Wang, X. et al. Vertebrate paleontology, biostratigraphy, geochronology, and paleoenvironment of Qaidam Basin in northern Tibetan Plateau. Palaegeogr. Palaeoclim. Palaeoecol. 254, 363–385 (2007).Article 

Google Scholar 
Zhang, Z. et al. Chapter 6: Mammalian Biochronology of the Late Miocene Bahe Formation in Fossil Mammals of Asia: Neogene Biostratigraphy and Chronology (eds. Wang, X., Flynn, L. J. & Fortelius, M.) 187–202 (Columbia University Press, New York, 2013).Xue, X. X., Zhang, Y. X. & Yue, L. P. Discovery of Hipparion fauna of Laogaochuan and its division of eras, Fugu County Shaanxi. Chin. Sci. Bull. 40, 447–449 (1995).Article 

Google Scholar 
Deng, T. Late Cenozoic environmental changes in the Linxia Basin (Gansu, China) as indicated by cenograms of fossil mammals. Vert. PalAsiat. 47(4), 282–298 (2009).
Google Scholar 
An, Z., Kutzbach, J. E., Prell, W. L. & Porter, S. C. Evolution of Asian monsoons and phased uplift of the Himalaya-Tibetan plateau since Late Miocene times. Nature 411, 62–66. https://doi.org/10.1038/35075035 (2001).ADS 
CAS 
Article 

Google Scholar 
An, Z. et al. Changes of the monsoon–arid environment in China and growth of the Tibetan Plateau since the Miocene. Q. Sci. 26(5), 678–693. https://doi.org/10.3321/j.issn:1001-7410.2006.05.002 (2006).Article 

Google Scholar 
Wang, X. et al. Origin of the Red Earth sequence on the northeastern Tibetan Plateau and its implications for regional aridity since the middle Miocene. Sci. China D Earth Sci. 49(5), 505–517. https://doi.org/10.1007/s11430-006-0505-3 (2006).ADS 
CAS 
Article 

Google Scholar 
Dettman, D. L., Fang, X., Garzione, C. N. & Li, J. Uplift–driven climate change at 12 Ma: A long δ18O record from the NE margin of the Tibetan plateau. Earth Planet. Sci. Lett. 214, 267–277. https://doi.org/10.1016/S0012-821X(03)00383-2 (2003).ADS 
CAS 
Article 

Google Scholar 
Jiang, H. C. & Ding, Z. L. A 20 Ma pollen record of East-Asian summer monsoon evolution from Guyuan, Ningxia China. Palaegeogr. Palaeoclim. Palaeoecol. 265, 30–38. https://doi.org/10.1016/j.palaeo.2008.04.016 (2008).ADS 
Article 

Google Scholar 
Fortelius, M. et al. Late Miocene and Pliocene large land mammals and climatic changes in Eurasia. Palaegeogr. Palaeoclim. Palaeoecol. 238, 219–227. https://doi.org/10.1016/j.palaeo.2006.03.042 (2006).ADS 
Article 

Google Scholar 
Fortelius, M. et al. Evolution of neogene mammals in Eurasia: Environmental forcing and biotic interactions. Annu. Rev. Earth Pl Sci. 42, 579–604 (2014).ADS 
CAS 
Article 

Google Scholar 
Bernor, R. L., Scott, R. S., Fortelius, M., Kappelman, J. & Sen, S. Equidae (Perissodactyla) in The Geology and Paleontology of the Miocene Sinap Formation, Turkey. (eds. Fortelius, M., Kappelman, J., Sen, S. & Bernor, R.L.) 220–281 (Columbia University Press, New York, 2003).Zhang, Z. S., Wang, H. J., Guo, Z. T. & Jiang, D. B. What triggers the transition of palaeoenvironmental patterns in China, the Tibetan Plateau uplift or the Paratethys Sea retreat?. Palaegeogr. Palaeoclim. Palaeoecol. 245, 317–331. https://doi.org/10.1016/j.palaeo.2006.08.003 (2007).ADS 
Article 

Google Scholar 
Böhme, M., Ilg, A. & Winklhofer, M. Late Miocene “washhouse” climate in Europe. Earth Planet. Sci. Lett. 275, 393–401. https://doi.org/10.1016/j.epsl.2008.09.011 (2008).ADS 
CAS 
Article 

Google Scholar 
Janis, C. M., Damuth, J. & Theodor, J. M. Miocene ungulates and terrestrial primary productivity: Where have all the browsers gone?. Proc. Natl. Acad. Sci. USA 97(14), 7899–7904. https://doi.org/10.1073/pnas.97.14.7899 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Janis, C. M., Damuth, J. & Theodor, J. M. The origins and evolution of the North American grassland biome: The story from the hoofed mammals. Palaegeogr. Palaeoclim. Palaeoecol. 177, 183–198. https://doi.org/10.1016/S0031-0182(01)00359-5 (2002).ADS 
Article 

Google Scholar 
Mihlbachler, M. C., Rivals, F., Solounias, N. & Semprebon, G. M. Dietary change and evolution of horses in North America. Science 331, 1178–1181. https://doi.org/10.1126/science.1196166 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hayek, L. C., Bernor, R. L., Solounias, N. & Steigerwald, A. Preliminary studies of hipparionine horse diet as measured by tooth microwear. Ann. Zool. Fenniuci 28, 187–200 (1992).
Google Scholar 
Sisson, S. The anatomy of the domestic animals (Saunders W B Comp, 1953).
Google Scholar 
Budras, K.-D., Sack, W. O. & Röck, S. In Anatomy of the horse. (Schlütersche, Hannover, 2009).Eisenmann, V., Alberdi, M. T., de Giuli, C. & Staesche, U. In Studying Fossil Horses, Vol. I: Methodology. (ed. Brill, E. J.) 1–71 (Leiden, 1988).Deng, T., Hou, S. & Wang, S. Neogene integrative stratigraphy and timescale of China. Sci. China Earth Sci. 62, 310–323 (2019).ADS 
CAS 
Article 

Google Scholar  More

Witman, J. D. & Smit, F. Rapid community change at a tropical upwelling site in the Galapagos Marine Reserve. Biodivers. Conserv. 12, 25–45 (2003).
Google Scholar 
Edgar, G. J., Banks, S., Fariña, J. M., Calvopiña, M. & Martínez, C. Regional biogeography of shallow reef fish and macro-invertebrate communities in the Galapagos archipelago. J. Biogeogr. 31, 1107–1124 (2004).
Google Scholar 
Okey, T. A. et al. A trophic model of a Galápagos subtidal rocky reef for evaluating fisheries and conservation strategies. Ecol. Model. 172, 383–401 (2004).
Google Scholar 
Briggs, J. C. & Bowen, B. W. A realignment of marine biogeographic provinces with particular reference to fish distributions. J. Biogeogr. 39, 12–30 (2012).
Google Scholar 
Salinas de León, P. et al. Largest global shark biomass found in the northern Galápagos Islands of Darwin and Wolf. PeerJ 4, e1911. https://doi.org/10.7717/peerj.1911 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Humann, P. & DeLoach, N. Reef Fish Identification: Galápagos (ed. Humann, P.) (New World Publications, Inc., 2003).McCosker, J. E. & Rosenblatt, R. H. The fishes of the Galápagos archipelago: An update. Proc. Calif. Acad. Sci. 61, 167–195 (2010).
Google Scholar 
Grove, J. S. & Lavenberg, R. J. The Fishes of the Galapagos Islands (Stanford University Press, 1997).Allen, G. & Ross-Robertson, D. Fishes of Tropical Eastern Pacific (University of Hawaii Press, 1994).Ruttenberg, B. I., Haupt, A. J., Chiriboga, A. I. & Warner, R. R. Patterns, causes and consequences of regional variation in the ecology and life history of a reef fish. Oecologia 145, 394–403 (2005).ADS 
PubMed 

Google Scholar 
Bernardi, G. et al. Darwin’s fishes: Phylogeography of Galápagos Islands reef fishes. Bull. Mar. Sci. 90, 533–549 (2014).
Google Scholar 
Banks, S., Vera, M. & Chiriboga, A. Establishing reference points to assess long-term change in zooxanthellate coral communities of the northern Galápagos coral reefs. Galapagos Res. 66, 43–64 (2009).
Google Scholar 
Palacios, D., Bograd, S., Foley, D. & Schwing, F. Oceanographic characteristics of biological hot spots in the North Pacific: A remote sensing perspective. Deep Sea Res Part II Top. Stud. Oceanogr. 53, 250–269 (2006).ADS 

Google Scholar 
Sweet, W. V. et al. Water mass seasonal variability in the Galapagos Archipelago. Deep Sea Res. Part I Oceanogr. Res. Pap. 54, 2023–2035 (2007).ADS 

Google Scholar 
Schaeffer, B. et al. Phytoplankton biomass distribution and identification of productive habitats within the Galapagos Marine Reserve by MODIS, a surface acquisition system, and in-situ measurements. Remote Sens. Environ. 112, 3044–3054 (2008).ADS 

Google Scholar 
Witman, J. D., Brandt, M. & Smith, F. Coupling between subtidal prey and consumers along a mesoscale upwelling gradient in the Galapagos Islands. Ecol. Monogr. 80, 153–177 (2010).
Google Scholar 
Moity, N. Evaluation of no-take zones in the Galápagos marine reserve, zoning plan 2000. Frontiers. 5, 244. https://doi.org/10.3389/fmars.2018.00244 (2018).Article 

Google Scholar 
Lamb, R. W., Smith, F. & Witman, J. D. Consumer mobility predicts impacts of herbivory across an environmental stress gradient. Ecology 101, e02910. https://doi.org/10.1002/ecy.2910 (2020).Article 
PubMed 

Google Scholar 
Edgar, G. J. et al. Conservation of threatened species in the Galapagos Marine Reserve through identification and protection of marine key biodiversity areas. Aquat. Conserv. 18, 955–968 (2008).
Google Scholar 
Carrión-Cortez, J. A., Zárate, P. & Seminoff, J. A. Feeding ecology of the green sea turtle (Chelonia mydas) in the Galapagos Islands. J. Mar. Biol. Assoc. U. K. 90, 1005–1013 (2010).
Google Scholar 
Moity, N., Delgado, B. & Salinas-de-León, P. Correction: Mangroves in the Galapagos islands: Distribution and dynamics. PLoS One 14, e0212440. https://doi.org/10.1371/journal.pone.0212440 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Seitz, R. D., Wennhage, H., Bergström, U., Lipcius, R. N. & Ysebaert, T. Ecological value of coastal habitats for commercially and ecologically important species. ICES J. Mar. Sci. 71, 648–665 (2014).
Google Scholar 
Aguaiza, C. The role of mangrove as nursery habitats for coral reef fish species in the Galapagos Islands. MSc Thesis (University of Queensland, 2016).Llerena-Martillo, Y., Peñaherrera-Palma, C. & Espinoza, E. Fish assemblages in three fringed mangrove bays of Santa Cruz Island, Galapagos Marine Reserve. Rev. Biol. Trop. 66, 674–687 (2018).
Google Scholar 
Fierro-Arcos, D. et al. Mangrove fish assemblages reflect the environmental diversity of the Galapagos Islands. Mar. Ecol. Prog. Ser. 664, 183–205 (2021).ADS 

Google Scholar 
Henseler, C. et al. Coastal habitats and their importance for the diversity of benthic communities: A species-and trait-based approach. Estuar. Coast. Shelf Sci. 226, 106272. https://doi.org/10.1016/j.ecss.2019.106272 (2019).Article 

Google Scholar 
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 

Google Scholar 
Menezes, R. F. et al. Variation in fish community structure, richness, and diversity in 56 Danish lakes with contrasting depth, size, and trophic state: Does the method matter?. Hydrobiologia 710, 47–59 (2013).
Google Scholar 
Hu, M., Wang, C., Liu, Y., Zhang, X. & Jian, S. Fish species composition, distribution and community structure in the lower reaches of Ganjiang River, Jiangxi, China. Sci. Rep. 9, 10100. https://doi.org/10.1038/s41598-019-46600-2 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Clarke, K. R. & Warwick, R. M. Changes in Marine Communities: An Approach to Statistical Analysis and Interpretation, 2nd ed. (PRIMER-E Ltd, Plymouth Marine Laboratory, 2001).Warwick, R. M. & Clarke, K. R. New biodiversity measures reveal a decrease in taxonomic distinctness with increasing stress. Mar. Ecol. Prog. Ser. 129, 301–305 (1995).ADS 

Google Scholar 
Clarke, K. R. & Warwick, R. M. The taxonomic distinctness measure of biodiversity: Weighting of step lengths between hierarchical levels. Mar. Ecol. Prog. Ser. 184, 21–29 (1999).ADS 

Google Scholar 
Nieto-Navarro, J. T., Zetina-Rejón, M. A., Arreguín-Sánchez, F., Palacios-Salgado, D. & Jordán, F. Changes in fish bycatch during the shrimp fishing season along the eastern coast of the mouth of the Gulf of California. J. Appl. Ichthyol. 29, 610–616 (2013).
Google Scholar 
Escobar-Toledo, F., Zetina-Rejón, M. J. & Duarte, L. O. Measuring the spatial and seasonal variability of community structure and diversity of fish by-catch from tropical shrimp trawling in the Colombian Caribbean Sea. Mar. Biol. Res. 11, 528–539 (2015).
Google Scholar 
Herrera-Valdivia, E., López-Martínez, J., Castillo Vargasmachuca, S. & García-Juárez, A. R. Diversidad taxonómica y funcional en la comunidad de peces de la pesca de arrastre de camarón en el norte del Golfo de California, México. Rev. Biol. Trop. 64, 587–602 (2016).PubMed 

Google Scholar 
Heylings, P., Bensted-Smith, R. & Altamirano, M. Zonificación e historia de la Reserva Marina de Galápagos. In Reserva Marina de Galápagos. Línea Base de la Biodiversidad (eds. Danulat, E. & Edgar, G. J.) 10–21 (Fundación Charles Darwin y Servicio Parque Nacional de Galápagos, 2002).Edgar, G. J. et al. Bias in evaluating the effects of marine protected areas: The importance of baseline data for the Galapagos Marine Reserve. Environ. Conserv. 3, 212–218. https://doi.org/10.1017/S0376892904001584 (2004).Article 

Google Scholar 
Jennings, S., Brierley, A. S. & Walker, J. W. The inshore fish assemblages of the Galápagos archipelago. Biol. Conserv. 70, 49–57 (1994).
Google Scholar 
Brito, A., Pérez-Ruzafaga, A. & Bacallado, J. J. Ictiofauna costera de las islas Galápagos: composición y estructura del poblamiento de los fondos rocosos. Res. Cient. Proy. Galápagos TFCM 5, 61 (1997).
Google Scholar 
Bruneel, S. et al. Assessing the drivers behind the structure and diversity of fish assemblages associated with rocky shores in the Galapagos Archipelago. J. Mar. Sci. Eng. 9, 375. https://doi.org/10.3390/jmse9040375 (2021).Article 

Google Scholar 
Wellington, G. M., Strong, A. E. & Merlen, G. Sea surface temperature variation in the Galápagos Archipelago: A comparison between AVHRR nighttime satellite data and in-situ instrumentation (1982–1998). Bull. Mar. Res. 69, 27–42 (2001).
Google Scholar 
Snell, H., Stone, P. & Snell, H. L. A summary of geographical characteristics of the Galapagos Islands. J. Biogeogr. 23, 619–624 (1996).
Google Scholar 
Bustamante, R. H., et al. Outstanding marine features of Galápagos. In A Biodiversity Vision for the Galapagos Islands: An Exercise for Ecoregional Planning (eds. Bensted-Smith, R. & Dinnerstein, E.) 60–71 (WWF, 2002).Airoldi, L. & Beck, M. W. Loss, status and trends for coastal marine habitats of Europe. In Oceanography and Marine Biology: An Annual Review (eds. Gibson, R. N., Atkinson, R. J. A. & Gordon, J. D. M.) vol. 45, 345–405 (Taylor & Francis, 2007).Carr, M. H., Malone, D. P., Hixon, M. A., Holbrook, S. J. & Schmitt, R. J. How Scuba changed our understanding of nature: underwater breakthrough in reef fish ecology. In Research and Discoveries: The Revolution of Science Through Scuba vol. 39, 157–167 (Smithsonian Contributions to the Marine Sciences, 2013).Durkacz, S. Assessing the Oceanographic Conditions and Distribution of Reef Fish Assemblages Throughout the Galápagos Islands Using Underwater Visual Survey Methods. MSc Thesis (Texas A & M University, 2014).Fischer, W. et al. Guía FAO para la identificación de especies para los fines de pesca. Pacífico Centro-Oriental vol. II–III, 648–1652 (FAO, 1995).Clarke, K. R. & Warwick, R. M. A taxonomic distinctness index and its statistical properties. J. Appl. Ecol. 35, 523–531 (1998).
Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Google Scholar 
Rosenberg, A., Binford, T. E., Leathery, S., Hill, R. L. & Bickers, K. Ecosystem approaches to fishery management through essential fish habitat. Bull. Mar. Sci. 66, 535–542 (2000).
Google Scholar 
Aburto-Oropeza, O. & Balart, E. F. Community structure of reef fish in several habitats of a rocky reef in the Gulf of California. Mar. Ecol. 22, 283–305 (2001).ADS 

Google Scholar 
Fulton, C. J., Bellwood, D. R. & Wainwright, P. C. Wave energy and swimming performance shape coral reef fish assemblages. Proc. R. Soc. B 272, 827–832 (2005).CAS 
PubMed 

Google Scholar 
Dominici-Arosemena, A. & Wolff, M. Reef fish community structure in the Tropical Eastern Pacific (Panamá): Living on a relatively stable rocky reef environment. Helgol. Mar. Res. 60, 287–305 (2006).ADS 

Google Scholar 
Villegas-Sánchez, C. A., Abitia-Cárdenas, L. A., Gutiérrez-Sánchez, F. J. & Galván-Magaña, F. Rocky-reef fish assemblages at San José Island, Mexico. Rev. Mex. Biodivers. 80, 169–179 (2009).
Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).
Google Scholar 
Glynn, P. Some physical and biological determinants of coral community structure in the eastern Pacific. Ecol. Monogr. 46, 431–456 (1976).
Google Scholar 
Ramos-Miranda, J. et al. Changes in four complementary facets of fish diversity in a tropical coastal lagoon after 18 years: A functional interpretation. Mar. Ecol. Prog. Ser. 304, 1–13 (2005).ADS 

Google Scholar 
Gristina, M., Bahri, T., Fiorentino, F. & Garofalo, G. Comparison of demersal fish assemblages in three areas of the Strait of Sicily under different trawling pressure. Fish. Res. 81, 60–71 (2006).
Google Scholar 
Pérez-Ruzafa, A. P., Marcos, C. & Bacallado, J. J. Biodiversidad marina en archipiélagos e islas: patrones de riqueza específica y afinidades faunísticas. Vieraea Folia Scientarum Biologicarum Canariensium. 33, 455–476 (2005).
Google Scholar 
Malcolm, H. A., Jordan, A. & Smith, S. D. Biogeographical and cross-shelf patterns of reef fish assemblages in a transition zone. Mar. Biodivers. 40(3), 181–193 (2010).
Google Scholar 
García-Charton, J. A. & Pérez-Ruzafa, A. P. Correlation between habitat structure and a rocky reef fish assemblage in the Southwest Mediterranean. Mar. Ecol. 19(2), 111–128 (1998).ADS 

Google Scholar 
Mumby, P. J. et al. Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature 427, 533–536 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Unsworth, R. K. F. et al. High connectivity of Indo-Pacific seagrass fish assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 353, 213–224 (2008).ADS 

Google Scholar 
Birkeland, C. & Amesbury, S. S. Fish-transect surveys to determine the influence of neighboring habitats on fish community structure in the tropical Pacific. Co-operation for environmental protection in the Pacific. UNEP Reg. Seas Rep. Stud. 97, 195–202 (1988).
Google Scholar 
Thollot, P., Kulbicki, M., & Wantiez, L. Temporal patterns of species composition in three habitats of the St Vincent Bay area (New Caledonia): Coral reefs, soft bottoms and mangroves. In Proceedings International Soc. Reef Studies. 127–137 (1991).Kulbicki, M. Present knowledge of the structure of coral reef fish assemblages in the Pacific. UNEP Reg. Seas Rep. Stud. 147, 31–53 (1992).
Google Scholar 
Cruz-Romero, M., Chávez, E.A., Espino, E. & García, A. Assessment of a snapper complex (Lutjanus spp.) of the eastern tropical Pacific. In Biology, Fisheries and Culture of Tropical Groupers and Snappers (eds. Arreguín-Sánchez, F., Munro, J. L., Balgos, M. C. & Pauly, D.) 324–330 (ICLARM Conf. Proc. 48, 1996).Aguilar-Santana, F. Biología reproductiva de Prionurus laticlavius (Valenciennes, 1846) (Teleostei: Acanthuridae) en la Costa Sudoccidental del Golfo de California, México. PhD Thesis (Instituto Politécnico Nacional, 2020).Hall, S. The Effects of Fishing on Marine Ecosystems and Communities (Blackwell Science Ltd., 1999).Mangi, S. C. & Roberts, C. M. Quantifying the environmental impacts of artisanal fishing gear on Kenya’s coral reef ecosystems. Mar. Pollut. Bull. 52, 1646–1660 (2006).CAS 
PubMed 

Google Scholar 
Rees, M. J., Jordan, A., Price, O. F., Coleman, M. A. & Davis, A. R. Abiotic surrogates for temperate rocky reef biodiversity: Implications for marine protected areas. Divers. Distrib. 20(3), 284–296 (2014).
Google Scholar 
Ferrari, R. et al. Habitat structural complexity metrics improve predictions of fish abundance and distribution. Ecography 41(7), 1077–1091 (2018).
Google Scholar 
Pihl, L. & Wennhage, H. Structure and diversity of fish assemblages on rocky and soft bottom shores on the Swedish west coast. J. Fish Biol. 61, 148–166 (2002).
Google Scholar 
La Mesa, G., Molinari, A., Gambaccini, S. & Tunesi, L. Spatial pattern of coastal fish assemblages in different habitats in North-western Mediterranean. Mar. Ecol. 32, 104–114 (2011).ADS 

Google Scholar 
Kristensen, L. D. et al. Establishment of blue mussel beds to enhance fish habitats. Appl. Ecol. Environ. Res. 13, 783–798 (2015).
Google Scholar 
Bergström, L., Karlsson, M., Bergström, U., Pihl, L. & Kraufvelin, P. Distribution of mesopredatory fish determined by habitat variables in a predator-depleted coastal system. Mar. Biol. 163, 201. https://doi.org/10.1007/s00227-016-2977-9 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Galván-Villa, C. M., Arreola-Robles, J. L., Ríos-Jara, E. & Rodríguez-Zaragoza, F. A. Ensamblajes de peces arrecifales y su relación con el hábitat bentónico de la Isla Isabel, Nayarit, México. Rev. Biol. Mar. Oceanogr. 45, 311–324 (2010).
Google Scholar 
Lunt, J. & Smee, D. L. Turbidity alters estuarine biodiversity and species composition. ICES J. Mar. Sci. 77, 379–387 (2019).
Google Scholar 
Anthony, K. R., Ridd, P. V., Orpin, A. R., Larcombe, P. & Lough, J. Temporal variation of light availability in coastal benthic habitats: Effects of clouds, turbidity, and tides. Limnol. Oceanogr. 49, 2201–2211 (2004).ADS 

Google Scholar 
Helfman, G. S. Patterns of community structure in fishes: Summary and overview’. Environ. Biol. Fishes 3, 129–148 (1978).
Google Scholar 
Helfman, G. S. Fish behaviour by day, night and twilight. In The Behaviour of Teleost Fishes (ed. Pitcher T.J.) (Springer, 1986).Warwick, R. M. & Clarke, K. R. Taxonomic distinctness and environmental assessment. J. Appl. Ecol. 35, 532–543 (1998).
Google Scholar 
Rogers, S. I., Clarke, K. R. & Reynolds, J. D. The taxonomic distinctness of coastal bottom-dwelling fish communities of the North-east Atlantic. J. Anim. Ecol. 68, 769–782 (1999).
Google Scholar 
Robertson, A. I., & Blaber, S. J. M. Plankton, epibenthos and fish communities. In Tropical Mangrove Ecosystems (eds. Robertson, A. I. & Alongi, D. M.) Coastal and Estuarine Studies No. 41, 173–224 (American Geophysical Union, 1992).Koranteng, K. A. Diversity and stability of demersal species assemblages in the Gulf of Guinea. West Afr. J. Appl. Ecol. 2, 49–63 (2001).
Google Scholar 
McCormick, M. I. Comparison of field methods for measuring surface topography and their associations with a tropical reef fish assemblage. Mar. Ecol. Prog. Ser. 112, 87–96 (1994).ADS 

Google Scholar 
Moraes, L. E., Paes, E., Garcia, A., Möller, O. Jr. & Vieira, J. Delayed response of fish abundance to environmental changes: A novel multivariate time-lag approach. Mar. Ecol. Prog. Ser. 456, 159–168 (2012).ADS 

Google Scholar 
Edgar, G. J. et al. El Niño, grazers and fisheries interact to greatly elevate extinction risk for Galapagos marine species. Glob. Change. Biol. 16, 2876–2890 (2010).ADS 

Google Scholar 
Glynn, P. W., Enochs, I. C., Afflerbach, J. A., Brandtneris, V. W. & Serafy, J. E. Eastern Pacific reef fish responses to coral recovery following El Niño disturbances. Mar. Ecol. Prog. Ser. 495, 233–247 (2014).ADS 

Google Scholar  More

Field experiments: collective structuresWe found that self-assembled Eciton hamatum bridges adaptively adjust in response to shifts in the terrain on which they are built. Detailed methods are included in Methods: Field experiments. Briefly, we moved foraging trails onto an apparatus where we could introduce a terrain gap. We repeatedly changed the size of this gap by first incrementally increasing it to 30 mm, by 1 mm every 30 s, and then incrementally contracting it at the same rate (See Fig. 1, Methods: Field experiments, and Supplementary Movie 1). As the size of the gap was expanded (the period before the dotted line in Fig. 2a, b) both the volume and number of ants increased to mean maximum values of 1080 mm3 (standard error, s.e. 84) and 18.9 ants (s.e. 1.6), respectively. Ants typically began forming a bridge when the gap was ~5 mm. As the gap size was decreased (period after dotted line), volume and the number of ants decreased back to zero as ants left the bridge. These broad dynamics across the ten complete trials were similar (Fig. 2a, b, inset panels and Supplementary Figs. 2, 3). Additionally, bridge volume (Fig. 2a) strongly correlated with the number of ants in the bridge (Fig. 2b), indicating that the density of ants per unit volume in these structures is relatively consistent (Pearson correlation coefficients range from 0.88 to 0.98 across the ten trials, see also Supplementary Fig. 4). Bridges broke and quickly reformed in eight of the ten trials; breaks occurred in both experimental phases, and these broken periods were excluded from analyses. Overall, these results show that bridges adjust dynamically to changing terrain geometry, as stretching the bridges caused them to become larger, with more ants, and contracting bridges caused them to become smaller, with fewer ants.Fig. 1: Experimental procedure and data extraction summary.Experiments were conducted on robust E. hamatum foraging trails, which were moved onto the experimental apparatus while it was closed. a Experimental procedure: The size of the gap was increased by 1 mm every 30 s until the gap reached 30 mm (expansion phase), then decreased at the same rate till no gap remained (the contraction phase). b Field setup: Experiments were recorded from both the side and the top, examples of bridges during each phase of the same trial are shown. c Data extraction: Example images and silhouettes from the maximum size bridge (30 mm) of the same trial as the images of 20 mm bridges shown in panel a. The envelopes of the bridges were extracted at a temporal resolution of 1 s; for each focal second, image frames were averaged over 10 s to remove ants walking on the bridge from the extracted envelopes. Envelopes were automatically extracted using hue-saturation-value (HSV) thresholding, with thresholds checked independently for each trial due to lighting differences. Locations of fixed points on the platform were used to re-scale and combine data from the side and top views into a single coordinate system in which 100 pixels = 1 cm. Estimates of bridge volume, mean cross-sectional area, and relative height of the center of mass were recorded from the extracted envelopes as shown. See Methods: Data extraction and Supplementary Note 1 for additional details of the data extraction process, including additional bridge metrics.Full size imageFig. 2: Changes in collective structures in experiments.a, b Volume and group size of self-assembled bridges: a Estimated volume of collective bridge structures over time for one focal trial (main figure) and three other examples (inset). The dotted vertical line indicates the time when the experiment shifted from the expansion phase (increasing gap size) to the contraction phase (decreasing gap size). Gray shading indicates that the bridge was broken or recovering from a break; result metrics may be inaccurate during these periods and they were, therefore, excluded from analyses. b The number of ants in the bridge structure over time for the same focal trial (main figure) and three other examples (inset). c–f Hysteresis: Trials consistently show hysteresis, with bridge status at a particular gap size differing during the expansion and contraction phases, for volume (c), number of ants (d), mean cross-sectional area (e), and tautness, or the height of the center of mass of the bridge from the side view (f; lower values indicate bridge is hanging lower). c–f Panels show result metrics over gap size for the same focal trial as in panels a and b, as well as for three other examples (inset). Points show individual measurements, taken every second, lines are smoothed LOESS (local regression) for the expansion (orange points, dashed orange line) and contraction (green points, solid green line) phases. The area between the smoothed lines (shaded gray) shows the extent of hysteresis. c, e, f) Points are jittered to improve clarity. a–f See Supplementary Figs. 2, 3, 5–8 for all complete trials.Full size imageHowever, these changes were not symmetric—adjustments in the contraction phase were not the inverse of adjustments in the expansion phase. We found consistent hysteresis in several metrics; for a given gap size, bridges were larger and made up of more individuals during the contraction of the gap than the expansion (Fig. 2c, d; t-test for volume: mean extent of hysteresis = 0.43, 95% CI = 0.29 to 0.58, t = 6.7, df = 9, p  More

Model descriptionThe model involves a discrete time, discrete valued stochastic Markov process. Model variables and parameters are given in Tables 1 and 2 respectively. Both time and the number of individuals of each type are constrained to be integer valued. Death and reproductive mutation are stochastic processes derived from sampling from binomial distributions given by the relevant probabilities. All ensemble results give the 100-replicate average for the parameter choices in question.Growth of individuals from species ({S}_{1}) and ({S}_{2}) is proportional to the bio-available level of environmental substances ({R}_{1}) and ({R}_{2}) respectively. At time (t) (where time is in units of biological generations) the change in the number ({N}_{q,j}) of individuals of genotype (j) (non-producer, producer, plastic) within species (q) (({S}_{1}) or ({S}_{2})) can be written as a function of the state of the variables at the previous time-step:$${N}_{q,j}left(t+1right)=left(left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {G}_{q,j}left(tright)-{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)-{delta }_{q,j}left(tright)+{{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)right)cdot left(1-frac{{S}_{q}left(tright)}{K}right)$$
(1)
The leftmost bracket on the right-hand side represents the number of individuals escaping starvation (death due to insufficient environmental substance) at the previous time-step and ({G}_{q,j}left(tright)) is the per capita reproductive growth rate. ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) gives the number of mutant offspring individuals produced during reproduction from parent individuals of genotype (j). ({{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)) represents the number of (j) genotype individuals derived from mutation in parent individuals of other genotypes. ({delta }_{q,j}left(tright)) is the number of individuals of genotype (j) lost to random death, and the rightmost bracket relates the total number ({S}_{q}left(tright)) of individuals of species (q) to carrying capacity (K), which represents limitation of growth by any factor other than the relevant environmental substance, e.g. space. (The steady state population size in all simulations shown is below (K) and limited by the environmental substance influx. The carrying capacity is included in the model for computational reasons and as a “crash preventer” but has no qualitative effect on the results).The total number of individuals ({S}_{q}left(tright)) in species (q) is the sum of the number of individuals of each genotype (producer, non-producer and plastic, as discussed in the main text):$${S}_{q}left(tright)=mathop{sum }limits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)+{N}_{q,{plast}}left(tright)$$
(2)
The genotype-specific reproductive growth rate ({G}_{q,j}left(tright)) (again for genotype (j) within species (q), time (t)), gives the number of offspring individuals produced per parent individual, per time-step. Growth rate is an increasing function of the bio-available level of environmental substance ({R}_{q,{BIOAVAIL}{ABLE}}) (the subscript (q) being identical because species ({S}_{1}) and ({S}_{2}) assimilate substances ({R}_{1}) and ({R}_{2}) respectively). Growth rate also includes a substance-to-biomass conversion efficiency parameter ({f}_{{conv}}) and a genotype-specific per capita term ({G}_{q,j,{PR}}) (number of offspring per parent, per unit environmental substance assimilated, per unit time). In the absence of growth-limitation by environmental substance levels, growth rate is capped at a genotype-specific maximum ({G}_{q,{jMAX}}):$${G}_{q,j}left(tright)={MIN}[{G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}},{G}_{q,{jMAX}}]$$
(3)
$${G}_{q,{non}-{prod},{PR}}={G}_{0}$$
(4)
$${G}_{q,{non}-{prod},{MAX}}={G}_{0}cdot {R}_{{assimMAX}}$$
(5)
({G}_{0}) is the baseline number of offspring, per parent, per unit substance assimilated. ({R}_{{assimMAX}}) is a universal maximum potential number of units of environmental substance that can be assimilated by a single individual per time-step (i.e. representing basic physiological constraints on growth). The producer genotype incurs a per capita reproductive growth rate cost ({kappa }_{{prod}}) relative to the non-producer:$${G}_{q,{prod},{PR}}={G}_{0}cdot (1-{kappa }_{{prod}})$$
(6)
$${G}_{q,{prod},{MAX}}={G}_{0}cdot (1-{kappa }_{{prod}})cdot {R}_{{assimMAX}}$$
(7)
This growth rate formulation is therefore a highly simplified linearization of the Michaelis-Menten kinetics normally used in models of resource and nutrient assimilation.The plastic genotype switches phenotype depending upon the level of environmental substance relative to a fixed threshold ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}), in effect becoming a second non-producer genotype below this threshold and a second producer genotype above it:$${IF}[{R}_{q,{BIOAVAILABLE}}(t)ge {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{prod}}left(tright)$$$${ELSEIF}[{R}_{q,{BIOAVAILA}{BLE}}left(tright) , < , {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{non}-{prod}}left(tright)$$ (8) There is no spatial structure whatsoever, thus access to environmental substance is uniform across individuals. The bioavailable quantity of each environmental substance is simply the total amount ({R}_{q,{NET}}(t)) divided by the total number of individuals assimilating it:$${R}_{q,{BIOAVAILABLE}}left(tright)=frac{{R}_{q,{NET}}(t)}{{S}_{q}left(tright)}$$ (9) We allow the per capita reproductive growth rate to fall below ({G}_{q,j}left(tright)=1), which, if interpreted deterministically at the individual level would correspond to an individual failing to sustain its biomass to the next time-step and thus dying. However, a population-level average ({G}_{q,j}left(tright) , < , 1) is interpretable in terms of a thinning factor that maps between discretized individuals and continuously distributed environmental substance. Thus, a thinning factor of (left(1-{G}_{q,j}left(tright)right)) is used to calculate the total number of individuals dying of starvation ({rho }_{q,j}) (again genotype (j), species (q)). This represents pre-reproduction deaths, corresponding to the difference between the actual population size and the population size that the environmental substance pool is capable of supporting. ({rho }_{q,j}left(tright)) is constrained to be an integer and is zero for ({G}_{q,j}left(tright) , > , 1):$${rho }_{q,j}left(tright)={N}_{q,j}left(tright)cdot {MAX}left[0,left(1-{G}_{q,j}left(tright)right)right]$$
(10)
A subset of offspring are a different genotype from their parent via mutation. For parent genotype (j), the number ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) of mutant offspring with genotype (ne j) is calculated using baseline mutation probability per reproductive event ({mu }_{0}), with the total number of new individuals produced by the parent individuals surviving starvation ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The total number of mutant offspring ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) is thus a binomially distributed random variable with success probability ({mu }_{0}) and number of trials ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The expected value (Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]) is the product of these two numbers:$$ {{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright) sim Bleft({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right),{mu }_{0}right), \ Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]={G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {mu }_{0}$$
(11)
Mutation to genotype (j) from the other genotypes is calculated in exactly the same way using the number and reproductive growth rates of the relevant (other) genotypes. Any particular mutant offspring is randomly allocated to one of the other genotypes with equal probability ({p}_{kto j}=frac{1}{{j}_{{total}}-1}=0.5) (where ({j}_{{total}}=3) is the total number of genotypes per species). The expected number of offspring with genotype (j) produced by mutation within parent offspring of other genotypes (kne j) is therefore:$$E[{{{{{{rm{{Upsilon }}}}}}}}_{q,x , ne , j}left(tright)]={left(mathop{sum }limits_{k=1}^{{k}_{{to}{tal}}}{{{{{{rm{{Upsilon }}}}}}}}_{q,k}left(tright)right)}_{kne j}cdot frac{1}{{j}_{{total}}-1}$$
(12)
Independently of reproduction and assimilation of environmental substance, any given individual has a probability ({delta }_{0}) at each time point of death due to stochastic factors. The genotype/species specific number of such deaths is again a random sample from a binomial distribution, with success probability ({delta }_{0}):$${delta }_{q,j}left(tright) sim Bleft({N}_{q,j}left(tright),{delta }_{0}right),Eleft[{delta }_{q,j}left(tright)right]={N}_{q,j}left(tright)cdot {delta }_{0}$$
(13)
The net quantity of growth-limiting environmental substance at each time-step is given by the difference between total biotic assimilation ({A}_{{R}_{q}}) and the production ({P}_{{R}_{q}}) and abiotic input ({varphi }_{{R}_{q}}) fluxes:$${R}_{q,{NET}}(t+1)={varphi }_{{R}_{q}}(t)+{P}_{{R}_{q}}(t)-{A}_{{R}_{q}}(t)$$
(14)
The abiotic net influx is the sum of two fluxes. First, an input term that is the product of a baseline scaling factor ({{varphi }_{0}}_{{R}_{q}}) and a model forcing (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}) representing the mapping between abiotic-geological and biotic-evolutionary timescales. In practice (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)) was set to either (1) or (0) or (in fluctuation runs) a time-dependent switching between the two. (More sophisticated implementations of (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)), e.g. sinusoidal oscillations and stochastic time dependence, were attempted but made little qualitative difference to the results). Second, an abiotic removal term that scales linearly with the quantity of environmental substance:$${varphi }_{{R}_{q}}(t)={{varphi }_{0}}_{{R}_{q}}cdot frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}left(tright)-frac{{R}_{q,{NET}(t)}}{{R}_{q,{NET}0}}$$
(15)
where ({R}_{q,{NET}0}) is a normalization factor representing the sensitivity of the abiotic efflux to the influx. In the absence of any biota and for (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}=1) the steady state environmental substance level is immediately given by (15) as ({R}_{q,{NET}(t)}={R}_{q,{NET}0}cdot {{varphi }_{0}}_{{R}_{q}}), thus the numerical value of ({R}_{q,{NET}0}) corresponds to the abiotic steady state residence time.Total biotic assimilation ({A}_{R}) of each environmental substance is given by:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$
(16)
The numerator gives the total number of individuals produced as a result of biological assimilation of environmental substance ({R}_{q}) and the denominator is the genotype specific number of individuals produced per unit substance assimilated, dividing through by which therefore converts to total units of substance assimilated by the population as a whole.Net biotic ({P}_{{R}_{q}{NET}}) production of substance ({R}_{q}) by the producer genotype in the other species (p) is calculated equivalently, via the product of the per capita production rate ({P}_{q,{prod}}) and the total number of reproducing individuals:$${P}_{q,{prod}}left(tright)=frac{{MIN}[{G}_{q,{prod}}left(tright)cdot {f}_{{convprod}},{G}_{q,{prodMAX}}]}{{G}_{p,{PRODUCER;PR}}}$$
(17)
$${P}_{{R}_{q}{NET}}left(tright)={P}_{q,{prod}}left(tright)cdot left({N}_{p,{PRODUCER}}left(tright)-{rho }_{p,P{RODUCER}}left(tright)right)$$
(18)
Where ({f}_{{conv},{PROD}}) is the per capita efficiency by which producers convert the environmental substance that they assimilate into the by-product they produce (note that the equivalent conversion efficiency for assimilation ({f}_{{conv}}) already appears in the growth functions of each genotype, therefore does not appear in Eq. (17)).The residence time ({T}_{{R}_{q}})of each environmental substance is given by the net quantity of this substance divided by the influx, to give units of the average number of biological generations a unit of environmental substance spends in the relevant pool before being removed. In those simulations in which the abiotic influx ({varphi }_{{R}_{q}}(t)) was set to zero (i.e. during the shut-off intervals) production ({P}_{{R}_{q}{NE}T}left(tright)) was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]!,{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){& }left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right)!,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{T}_{{R}_{q}}left(tright)=0]$$
(19)
The cycling ratio ({{CR}}_{{R}_{q}}) of each substance is given by the ratio between net biotic assimilation of that substance ({A}_{{R}_{q}}left(tright)) and the abiotic influx of that substance ({varphi }_{{R}_{q}}left(tright)). As with the residence time, when the abiotic influx was zero, the input from biological production was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){{& }}left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right),{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{{CR}}_{{R}_{q}}left(tright)=0]$$
(20)
Deterministic approximation to steady stateAssume that at steady state substance assimilation will reach a maximal state such that the level of environmental substance is limiting to population size. Assume that such a state is below the level ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}) at which the plastic genotype effectively becomes a second non-producer genotype and can thus be subsumed into non-producer frequency, such that (2) becomes ({S}_{q}left(tright)=mathop{sum }nolimits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)). Assume that there are non-zero starvations at each time-step for all genotypes, which implies growth rate ({G}_{q,j}left(tright) , < , 1,ll {G}_{q,{jMAX}},forall j,q), which gives by (3)({G}_{q,j}left(tright)={G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}}). Substituting this into (10), then the first bracketed term in (1), then labeling the post-starvation number of individuals as ({({N}_{q,j}left(tright))}_{{NET}}):$${({N}_{q,j}left(tright))}_{{NET}}=left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)={N}_{q,j}left(tright)cdot left(1-left(1-{G}_{q,j}left(tright)right)right)={N}_{q,j}left(tright)cdot {G}_{q,j}left(tright)$$ (21) Approximate (14) deterministically by a fixed fractional parameter corresponding to the baseline random death rate:$${delta }_{q,j}left(tright)approx {N}_{q,j}left(tright)cdot {delta }_{0}$$ (22) Doing the same for mutation:$${{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)={G}_{q,j}left(tright)cdot {left({N}_{q,j}left(tright)right)}_{{NET}}cdot {mu }_{0}={N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot {mu }_{0}$$ (23) The term in (12) for mutation to (j) from other genotypes simplifies to$${{{{{{rm{{Upsilon }}}}}}}}_{q,x ,ne , j}left(tright)={sum }_{k,=,1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot {left({N}_{q,k}left(tright)right)}_{{NET}}cdot {mu }_{0}}{{j}_{{total}}-1}={N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}$$Substituting Eqs. (21–23) into (1):$${N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,j}left(tright)cdot {delta }_{0}+{N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}=0$$ (24) Noting that by Eqs. (3)–(5) combined with the above assumptions, the growth rate of the producer can be written as:$${G}_{q,{prod}}left(tright)={G}_{q,{cheat}}left(tright)cdot (1-{kappa }_{{prod},q})$$ (25) Writing (24) explicitly for each genotype:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$$${N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Adding:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot left(1-{mu }_{0}right)-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}$$$$+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot {mu }_{0}+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot left(1-{mu }_{0}right)$$$$-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Because the mutation terms cancel:$$left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({{G}_{q,{non}-{prod}}left(tright)}^{2}-{delta }_{0}right)=0$$ (26) Substituting in for the growth rate terms (3–7) gives:$$left({N}_{q,{non}-{prod}}(t)+{N}_{q,{prod}}(t)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({left({G}_{0}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}}right)}^{2}-{delta }_{0}right)=0$$ (27) By (16), (4), (6) and the above, total steady state assimilation of the growth limiting environmental substance by species (q) is:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$$$kern2.4pc=frac{{N}_{q,{non}-{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}}+frac{left({N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}cdot left(1-{kappa }_{{prod}}right)}$$$$=left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {{G}_{0}cdot ({R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}$$ (28) By (16–18), production of this substance by the producer allele in the other species (p , ne , q), assuming the various arguments above simultaneously apply to this species, is:$${P}_{{R}_{q}}left(tright)= frac{{G}_{p,{prod}}left(tright)cdot left({N}_{p,{prod}}left(tright)-{rho }_{p,{PRODUCER}}left(tright)right)}{{G}_{p,{prodPR}}}cdot {f}_{{conv},{PROD}}\ = {N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right) cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}$$ (29) Balance between input and output fluxes of each environmental substance requires ({varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)={A}_{{R}_{q}}left(tright)), meaning that by substituting in ({A}_{{R}_{q}}left(tright)) from (28) it is possible to solve for bioavailable substance level, then substitute in the production flux of this substance derived from the producer allele in the other species (p,ne, q):$${R}_{q,{BIO}{AVAILABLE}}left(tright) =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}\ =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}$$ (30) Substituting this into (27) gives a symmetrical condition for steady state genotype frequencies and substance levels across the system:$$ left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot\ left(frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}-{delta }_{0}right)=0$$ (31) This solution illustrates the intuitive ideas that growth and reproduction balance random death at steady state and that the associated producer frequency is lower than that of the non-producer by a factor of the cost. (This factor is of second order because the growth rate is used both directly and (by (10)) in the calculation of starvations). Because our model is a discrete stochastic process, (31) can be viewed as an approximation to a steady state condition, subject to the above assumptions combined with the continuous generation of producers by mutation at a sufficient rate to preclude their extinction. The key point is that over long timescales in the finite populations with which we deal, organism-level selection unavoidably favors the non-producer, with no possibility for multi-level fecundity selection. The producer’s stable presence is thus attributable to the combination of mutation and cycle-level selection. More