Philippa Kaur

EDITORIAL
08 December 2021

The UN must get on with appointing its new science board

The decision to appoint a board of advisors is welcome — and urgent, given the twin challenges of COVID and climate change.

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UN secretary-general António Guterres announced plans for a new science board in September, but is yet to release further details.Credit: Juancho Torres/Anadolu Agency/Getty

Scientists helped to create the United Nations system. Today, people look to UN agencies — such as the UN Environment Programme or the World Health Organization — for reliable data and evidence on, say, climate change or the pandemic. And yet, shockingly, the UN leader’s office has not had a department for science advice for most of its 76-year history. That is about to change.UN secretary-general António Guterres is planning to appoint a board of scientific advisers, reporting to his office. The decision was announced in September in Our Common Agenda (see go.nature.com/3y1g3hp), which lays out the organization’s vision for the next 25 years, but few other details have been released.Representatives of the scientific community are excited about the potential for science to have a position at the centre of the UN, but are rightly anxious for rapid action, given the twin challenges of COVID-19 and climate change, which should be urgent priorities for the board. The International Science Council (ISC), the Paris-based non-governmental body representing many of the world’s scientists, recommended such a board in its own report on science and the intergovernmental system, published last week (see go.nature.com/3rjdjos). Council president Peter Gluckman, former chief science adviser to New Zealand’s prime minister, has written to Guterres to say the ISC is ready to help.
COP26 didn’t solve everything — but researchers must stay engaged
But it’s been more than two months since the announcement, and the UN has not yet revealed the names of the board members. Nature spoke to a number of serving and former UN science advisers who said they know little about the UN chief’s plans. So far, there are no terms of reference and there is no timeline.Nature understands that the idea is still being developed, and that Guterres is leaning towards creating a board that would draw on UN agencies’ existing science networks. Guterres is also aware of the need to take into account that both the UN and the world have changed since the last such board was put in place. All the same, the UN chief needs to end the suspense and set out his plans. Time is of the essence.Guterres’s predecessor, Ban Ki-moon, had a science advisory board between 2014 and 2016. Its members were tasked with providing advice to the secretary-general on science, technology and innovation for sustainable development. But COVID-19 and climate change have pushed science much higher up the international agenda. Moreover, global challenges are worsening — the pandemic has put back progress towards the UN’s flagship Sustainable Development Goals (SDGs), a plan to end poverty and achieve sustainability by 2030. There is now widespread recognition that science has an important part to play in addressing these and other challenges.
How science can put the Sustainable Development Goals back on track
Research underpins almost everything we know about the nature of the virus SARS-CoV-2 and the disease it causes. All countries have access to similar sets of findings, but many are coming to different decisions on how to act on those data — for example, when to mandate mask-wearing or introduce travel restrictions. The UN’s central office needs advice that takes this socio-cultural-political dimension of science into account. It needs advice from experts who study how science is applied and perceived by different constituencies and in different regions.Science advice from the heart of the UN system could also help with another problem highlighted by the pandemic — how to reinvigorate the idea that it is essential for countries to cooperate on solving global problems.Climate change is one example. Advice given by the Intergovernmental Panel on Climate Change (IPCC) is being read and applied in most countries, albeit to varying degrees. But climate is also an area in which states are at odds. Despite Guterres’s calls for solidarity, there were times during last month’s climate conference in Glasgow when the atmosphere was combative. Science advisers could help the secretary-general’s office to find innovative ways to encourage cooperation between countries in efforts to meet the targets of the 2015 Paris climate agreement.
Reset Sustainable Development Goals for a pandemic world
The SDGs are also, to some extent, impeded by competition within the UN system. To tackle climate change, manage land and forests, and protect biodiversity, researchers and policymakers need to work collegially. But the UN’s scientific bodies, such as the IPCC, are set up along disciplinary lines with their own objectives, work programmes and rules, all guided by their own institutional histories. The IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), for example, have only begun to collaborate in the past few years .Independence will be key for an advisory role to be credible. Guterres needs to consider an organizational architecture through which UN agencies are represented, and funding could come from outside the UN. But all of those involved would have to accept that their contributions were for common goals — not to promote their own organization’s interests.Leadership matters, as do communication and support. Guterres should ensure that his scientific advisers are chosen carefully to represent individuals from diverse disciplines and across career stages, and to ensure good representation from low-income countries. The board needs to be well staffed and have a direct line to his office. And it will need a decent budget. Guterres should quickly publish the terms of reference so that the research community has time to provide input and critique.At its most ambitious, a scientific advisory board to the secretary-general could help to break the culture of individualism that beleaguers efforts to reach collective, global goals, and bring some coherence to the current marketplace of disciplines, ideas and outcomes. This will be a monumental task, requiring significant resources and the will to change. But if the advisers succeed, there will also be valuable lessons for the practice of science, which, as we know all too well, still largely rewards individual effort.

Nature 600, 189-190 (2021)
doi: https://doi.org/10.1038/d41586-021-03615-y

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Large-scale, long-term field data from the GBR Marine ParkThe field data for CoTS, hard coral cover (here referred to as coral cover) and coral reef fish were obtained from the Australian Institute of Marine Science’s (AIMS) Long-Term Monitoring Programme (LTMP), while fisheries retained catch data were supplied by the Queensland Department of Agriculture and Fisheries (QDAF). The LTMP has been surveying CoTS populations and coral cover at reefs across the length and breadth of the GBR Marine Park since 198350 and has quantified the status and trend of benthic and reef fish assemblages since 1995. Specific examination of the effectiveness of zoning within the GBR Marine Park has also been undertaken24. The surveyed reefs are located within zones open to fishing (i.e. General Use, Habitat Protection and Conservation Park) and zones closed to fishing (i.e. Marine National Park Zones, Preservation and Scientific Research Zones) (Supplementary Table 1). The QDAF fisheries data comprise annual retained catch data from the Coral Reef Fin Fish Fishery including commercial, recreational (including charters) and Indigenous fisheries, as well as the Marine Aquarium Fish Fishery (Supplementary Data 1–3). Monthly catch return logbooks became compulsory for all trawlers and line fisheries on 1 January 198830. Retained catch data from each of these fisheries is collected separately and differently by QDAF (please see details below). Use of these data is by courtesy of the State of Queensland, Australia, through the Department of Agriculture and Fisheries.For both the LTMP and QDAF data, the data sets are chronologically divided into report (LTMP) or financial (QDAF) years, respectively, from 01 July to 30 June. This means that, for instance, the second semester of 2017 belongs to the 2018 report or financial year. Hereafter we will refer to report or financial year as simply year. Below we explain each of these data sets in more detail.LTMP CoTS and coral cover dataLTMP CoTS and coral cover data are available from 1983 to 2020. Both observed CoTS and coral cover data are based on field observations that employ manta tow surveys around the perimeter of each reef following AIMS’ Standard Operational Procedure51. Within this period, manta tows were conducted once per year but not all reefs were sampled every year. Briefly, manta tow surveys are a broad-scale technique that covers large areas of reef quickly and provides an assessment of broad changes in the distribution and abundance of corals and CoTS. During surveys, two boats each tow an observer clockwise and anti-clockwise around reef perimeters in a series of 2-min tows until they meet at the other end of the reef. Each observer records categorical coral cover (Supplementary Table 8) and the number and size of any CoTS observed (Supplementary Table 9) at the end of each 2-min tow51. Manta tow surveys are a non-targeting, rapid assessment method, and therefore it under-samples CoTS individuals that are More

1.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

Google Scholar 
2.Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P. & White, J. W. C. Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years. Nature 488, 70–72 (2012).CAS 
PubMed 
ADS 

Google Scholar 
3.Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 

Google Scholar 
4.Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
5.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).CAS 
PubMed 
ADS 

Google Scholar 
6.Huntzinger, D. N. et al. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions. Sci. Rep. 7, 4765 (2017).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
7.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2383–2385 (2020).
Google Scholar 
8.Sun, Z. et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends. Sci. Total Environ. 668, 696–713 (2019).CAS 
PubMed 
ADS 

Google Scholar 
9.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).ADS 

Google Scholar 
10.Li, W. et al. Recent changes in global photosynthesis and terrestrial ecosystem respiration constrained from multiple observations. Geophys. Res. Lett. 45, 1058–1068 (2018).ADS 

Google Scholar 
11.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).PubMed 
ADS 

Google Scholar 
12.Ehlers, I. et al Detecting long-term metabolic shifts using isotopomers: CO2-driven suppression of photorespiration in C3 plants over the 20th century. Proc. Natl Acad. Sci. USA 112, 15585–15590 (2015).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
13.Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).CAS 
PubMed 
ADS 

Google Scholar 
14.Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).ADS 

Google Scholar 
15.Winkler, A. J., Myneni, R. B. & Brovkin, V. Investigating the applicability of emergent constraints. Earth Syst. Dyn. 10, 501–523 (2019).ADS 

Google Scholar 
16.Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).ADS 

Google Scholar 
17.Keenan, T. F. & Williams, C. A. The terrestrial carbon sink. Annu. Rev. Environ. Resour. 43, 219–243 (2018).
Google Scholar 
18.Ryu, Y., Berry, J. A. & Baldocchi, D. D. What is global photosynthesis? History, uncertainties and opportunities. Remote Sens. Environ. 223, 95–114 (2019).ADS 

Google Scholar 
19.Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun. 10, 95 (2019).ADS 

Google Scholar 
20.Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).PubMed 

Google Scholar 
21.De Kauwe, M. G., Keenan, T. F., Medlyn, B. E., Prentice, I. C. & Terrer, C. Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat Clim. Change 6, 892–893 (2016).ADS 

Google Scholar 
22.Cernusak, L. A. et al Robust response of terrestrial plants to rising CO2. Trends Plant Sci. 24, 578–586 (2019).CAS 
PubMed 

Google Scholar 
23.Piao, S. et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Glob. Change Biol. 19, 2117–2132 (2013).ADS 

Google Scholar 
24.Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Change Biol. 26, 2390–2402 (2020).ADS 

Google Scholar 
25.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

Google Scholar 
26.Zhao, F. et al. Role of CO2, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis. Biogeosciences 13, 5121–5137 (2016).CAS 
ADS 

Google Scholar 
27.Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2018).ADS 

Google Scholar 
28.Running, S. W. & Zhao, M. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm User’s Guide v. 3 (MODIS Land Team, 2015).29.Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, https://doi.org/10.1029/2010JG001566 (2011).30.Zeng, N. et al. Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature 515, 394–397 (2014).CAS 
PubMed 
ADS 

Google Scholar 
31.Long, S. P. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).CAS 

Google Scholar 
32.Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Change Biol. 23, 235–244 (2017).ADS 

Google Scholar 
33.Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).CAS 
ADS 

Google Scholar 
34.Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).ADS 

Google Scholar 
35.Cernusak, L. A. et al. Tropical forest responses to increasing atmospheric CO2: current knowledge and opportunities for future research. Funct. Plant Biol. 40, 531–551 (2013).CAS 
PubMed 

Google Scholar 
36.Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).CAS 
PubMed 

Google Scholar 
37.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

Google Scholar 
38.Yang, J. et al. Low sensitivity of gross primary production to elevated CO2 in a mature eucalypt woodland. Biogeosciences 17, 265–279 (2020).CAS 
ADS 

Google Scholar 
39.McMurtrie, R. E., Comins, H. N., Kirschbaum, M. U. F. & Wang, Y. P. Modifying existing forest growth models to take account of effects of elevated CO2. Aust. J. Bot. 40, 657–677 (1992).CAS 

Google Scholar 
40.Luo, Y., Sims, D. A., Thomas, R. B., Tissue, D. T. & Ball, J. T. Sensitivity of leaf photosynthesis to CO2 concentration is an invariant function for C3 plants: a test with experimental data and global applications. Global Biogeochem. Cycles 10, 209–222 (1996).CAS 
ADS 

Google Scholar 
41.Li, Q. et al. Leaf area index identified as a major source of variability in modeled CO2 fertilization. Biogeosciences 15, 6909–6925 (2018).CAS 
ADS 

Google Scholar 
42.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).CAS 
PubMed 
ADS 

Google Scholar 
43.Zaehle, S. et al. Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate free-air CO2 enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
PubMed Central 

Google Scholar 
45.Stocker, B. D. et al Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019).CAS 
ADS 

Google Scholar 
46.Williamson, M. S. et al Emergent constraints on climate sensitivities. Rev. Mod. Phys. 93, 025004 (2021).MathSciNet 
CAS 
ADS 

Google Scholar 
47.Sanderson, B. et al. On structural errors in emergent constraints. Earth Syst. Dyn. Discuss. https://doi.org/10.5194/esd-2020-85 (2021).48.Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).
Google Scholar 
49.Arora, V. K. et al. Carbon-concentration and carbon-climate feedbacks in CMIP5 earth system models. J. Clim. 26, 5289–5314 (2013).ADS 

Google Scholar 
50.Ballantyne, A. et al. Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Change 7, 148–152 (2017).CAS 
ADS 

Google Scholar 
51.Forkel, M. et al. Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science 351, 696–699 (2016).CAS 
PubMed 
ADS 

Google Scholar 
52.Friedlingstein, P. et al. On the contribution of CO2 fertilization to the missing biospheric sink. Global Biogeochem. Cycles 9, 541–556 (1995).CAS 
ADS 

Google Scholar 
53.Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
PubMed 

Google Scholar 
54.Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 
ADS 

Google Scholar 
55.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).CAS 
ADS 

Google Scholar 
56.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
PubMed 
ADS 

Google Scholar 
57.Ukkola, A. M., Keenan, T. F., Kelley, D. I. & Prentice, I. C. Vegetation plays an important role in mediating future water resources. Environ. Res. Lett. 11, 094022 (2016).ADS 

Google Scholar 
58.Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).CAS 
ADS 

Google Scholar 
59.Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).ADS 

Google Scholar 
60.De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).PubMed 

Google Scholar 
61.Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e0038345 (2012).ADS 

Google Scholar 
62.Smith, N. G. & Keenan, T. F. Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least-cost optimality theory. Glob. Change Biol. 26, 806–834 (2020).
Google Scholar 
63.Lloyd, J. & Farquhar, G. The CO2 dependence of photosynthesis, plant growth responses to elevated atmospheric CO2 concentrations and their interaction with soil nutrient status. I. General principles and forest ecosystems. Funct. Ecol. 10, 4–32 (1996).
Google Scholar 
64.Ehleringer, J. & Björkman, O. Quantum yields for CO2 uptake in C3 and C4 plants: dependence on temperature, CO2, and O2 concentration. Plant Physiol. 59, 86–90 (1997).
Google Scholar 
65.Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, SP. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell Environ. 24, 253–259 (2001).CAS 

Google Scholar 
66.Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 

Google Scholar 
67.Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).CAS 
PubMed 

Google Scholar 
68.Huber, M. L. et al. New international formulation for the viscosity of H2O. J. Phys. Chem. Ref. Data 38, 101–125 (2009).CAS 
ADS 

Google Scholar 
69.Still, C. J., Berry, J. A., Collatz, G. J. & DeFries, R. S. Global distribution of C3 and C4 vegetation: carbon cycle implications. Global Biogeochem. Cycles 17, 6-1–6-14 (2003).ADS 

Google Scholar 
70.Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2. Remote Sens. 5, 927–948 (2013).ADS 

Google Scholar 
71.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 
PubMed 
ADS 

Google Scholar 
72.Gallego-Sala, A. et al. Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain. Clim. Res. 45, 151–162 (2010).
Google Scholar 
73.Veroustraete, F. On the use of a simple deciduous forest model for the interpretation of climate change effects at the level of carbon dynamics. Ecol. Modell. 75–76, 221–237 (1994).
Google Scholar 
74.Jiang, C. & Ryu, Y. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sens. Environ. 186, 528–547 (2016).ADS 

Google Scholar 
75.Zhang, S. et al. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe. Ecol. Modell. 368, 205–232 (2018).CAS 

Google Scholar 
76.Liu, Y., Hejazi, M., Li, H., Zhang, X. & Leng, G. A hydrological emulator for global applications-HE v1.0.0. Geosci. Model Dev. 11, 1077–1092 (2018).ADS 

Google Scholar 
77.Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, aax1396 (2019).ADS 

Google Scholar 
78.Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).CAS 
ADS 

Google Scholar 
79.Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).CAS 
ADS 

Google Scholar 
80.Oleson, K. W. et al. Technical Description of Version 4.0 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).81.Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).CAS 
PubMed 
ADS 

Google Scholar 
82.Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Change Biol. 19, 2893–2906 (2013).ADS 

Google Scholar 
83.Reick, C. H., Raddatz, T., Brovkin, V. & Gayler, V. Representation of natural and anthropogenic land cover change in MPI-ESM. J. Adv. Model Earth Syst. 5, 459–482 (2013).ADS 

Google Scholar 
84.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

Google Scholar 
85.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

Google Scholar 
86.Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 9, 161–185 (2003).ADS 

Google Scholar 
87.Keller, K. M. et al. 20th century changes in carbon isotopes and water-use efficiency: tree-ring-based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).CAS 
ADS 

Google Scholar 
88.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19, GB1015 (2005).ADS 

Google Scholar 
89.Guimberteau, M. et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation. Geosci. Model Dev. 11, 121–163 (2018).CAS 
ADS 

Google Scholar 
90.Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Global Biogeochem. Cycles 19, https://doi.org/10.1029/2004GB002273 (2005).91.Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).
Google Scholar 
92.Fernández-Martínez, M. et al. Atmospheric deposition, CO2, and change in the land carbon sink. Sci. Rep. 7, 9632 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
93.Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74–79 (2012).CAS 
ADS 

Google Scholar 
94.Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
95.Ueyama, M. et al. Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model. Environ. Res. Lett. 15, 084009 (2020).CAS 
ADS 

Google Scholar 
96.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).PubMed 
PubMed Central 

Google Scholar  More

Araújo MS, Perez SI, Magazoni MJC, Petry AC (2014) Body size and allometric shape variation in the molly Poecilia vivipara along a gradient of salinity and predation. BMC Evol Biol 14:251PubMed 

Google Scholar 
Arendt JD (2010) Morphological correlates of sprint swimming speed in five species of spadefoot toad tadpoles: comparison of morphometric methods. J Morphol 271:1044–1052PubMed 

Google Scholar 
Arendt JD, Reznick DN (2008) Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation? Trends Ecol Evol 23:26–32PubMed 

Google Scholar 
Arnett HA, Kinnison MT (2017) Predator-induced phenotypic plasticity of shape and behavior: parallel and unique patterns across sexes and species. Curr Zool 63:369–378PubMed 

Google Scholar 
Arnett HA (2016) Sources of ecologically important trait variation in mosquitofish (Gambusia affinis and Gambusia holbrooki). Thesis, University of MaineArnold SJ (1983) Morphology, performance and fitness. Am Zool 23:347–361
Google Scholar 
Avise JC (1989) Gene trees and organismal histories: a phylogenetic approach to population biology. Evolution 43:1192–1208PubMed 

Google Scholar 
Baldwin BG (1997) Adaptive radiation of the Hawaiian silversword alliance: congruence and conflict of phylogenetic evidence from molecular and non-molecular investigations. In: Givnish TJ, Sytsma KJ (eds.) Molecular evolution and adaptive radiation. Cambridge University Press, Cambridge, UK, p 103–128Belk MC, Tuckfield RC (2010) Changing costs of reproduction: age‐based differences in reproductive allocation and escape performance in a livebearing fish. Oikos 119:163–169
Google Scholar 
Blount ZD, Lenski RE, Losos JB (2018) Contingency and determinism in evolution: replaying life’s tape. Science 362:eaam5979.PubMed 

Google Scholar 
Bryant EH, Meffert LM (1993) The effect of serial founder-flush cycles on quantitative genetic variation in the housefly. Heredity 70:122–129
Google Scholar 
Calsbeek R, Kuchta S (2011) Predator mediated selection and the impact of developmental stage on viability in wood frog tadpoles (Rana sylvatica). BMC Evol Biol 11:353PubMed 

Google Scholar 
Chapuis M-P, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24:621–631CAS 
PubMed 

Google Scholar 
Chenoweth SF, Blows MW (2008) QST meets the G matrix: the dimensionality of adaptive divergence in multiple correlated quantitative traits. Evolution 62:1437–1449PubMed 

Google Scholar 
Constantz GD (1989) Reproductive biology of poeciliid fishes. In: Meffe GK Jr, Snelson FF (eds.) Ecology and evolution of livebearing fishes (Poeciliidae). Prentice Hall, Englewood Cliffs, NJ, p 33–50Cunha RL, Tenorio MJ, Afonso C, Castilho R, Zardoya R (2008) Replaying the tape: recurring biogeographical patterns in Cape Verde Conus after 12 million years. Mol Ecol 17:885–901PubMed 

Google Scholar 
Dale MR, Fortin M-J (2014) Spatial analysis: a guide for ecologists. Cambridge University Press, Cambridge, UKDarwin CE (1859) The origin of species and the descent of man. The Modern Library, New York, NYDay T, Pritchard J, Schluter D (1994) A comparison of two sticklebacks. Evolution 48:1723–1734PubMed 

Google Scholar 
Dayton GH, Saenz D, Baum KA, Langerhans RB, DeWitt TJ (2005) Body shape, burst speed and escape behavior of larval anurans. Oikos 111:582–591
Google Scholar 
DeWitt TJ, Fuentes JI, Ioerger TR, Bishop MP (2021) Rectifying I: three point and continuous fit of the spatial autocorrelation metric, Moran’s I, to ideal form. Landsc Ecol 36:2897–2918
Google Scholar 
DeWitt TJ, Scheiner SM (2004) Phenotypic variation from single genotypes: a primer. In: DeWitt TJ, Scheiner SM (eds.) Phenotypic plasticity: functional and conceptual approaches. Oxford University Press, New York, NY, p 1–9DeWoody J, Avise J (2000) Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. J Fish Biol 56:461–473CAS 

Google Scholar 
Dobzhansky T (1955) A review of some fundamental concepts and problems of population genetics. Cold Spring Harb Symp Quant Biol 20:1–15CAS 
PubMed 

Google Scholar 
Endler JA (1986) Natural selection in the wild. Princeton University Press, Princeton, NJEroukhmanoff F, Hargeby A, Arnberg NN, Hellgren O, Bensch S, Svensson EI (2009) Parallelism and historical contingency during rapid ecotype divergence in an isopod. J Evol Biol 22:1098–1110CAS 
PubMed 

Google Scholar 
Fisher RA (1930) The genetical theory of natural selection. Oxford University Press, Oxford, UKFranssen NR (2011) Anthropogenic habitat alteration induces rapid morphological divergence in a native stream fish. Evol Appl 4:791–804PubMed 

Google Scholar 
Futuyma DJ, Moreno G (1988) The evolution of ecological specialization. Annu Rev Ecol Syst 19:207–233
Google Scholar 
Futuyma DJ (2021) How does phenotypic plasticity fit into evolutionary theory? In: Pfennig DW (ed) Phenotypic plasticity & evolution. CRC Press, Boca Raton, FL, p 349–366Ghalambor CK, Reznick DN, Walker JA (2004) Constraints on adaptive evolution: the functional trade-off between reproduction and fast-start swimming performance in the Trinidadian guppy (Poecilia reticulata). Am Nat 164:38–50PubMed 

Google Scholar 
Givnish TJ, Knox E, Patterson TB, Hapeman JR, Palmer JB, Sytsma KJ (1996) The Hawaiian lobelioids are monophyletic and underwent a rapid initial radiation roughly 15 million years ago. Am J Bot 83:159
Google Scholar 
Gomes JL, Montiero L (2008) Morphological divergence patterns among populations of Poecilia vivipara (Teleostei Poeciliidae): test of an ecomorphological paradigm Biol J Linn Soc 93:799–812
Google Scholar 
Gompel N, Prud’homme B (2009) The causes of repeated genetic evolution. Dev Biol 332:36–47CAS 
PubMed 

Google Scholar 
Grant PR, Grant BR (2014) 40 years of evolution: Darwin’s finches on Daphne Major Island. Princeton University Press, Princeton, NJGreenway R, Barts N, Henpita C, Brown AP, Rodriguez LA, Peña CMR et al. (2020) Convergent evolution of conserved mitochondrial pathways underlies repeated adaptation to extreme environments. Proc Natl Acad Sci USA 117:16424–16430CAS 
PubMed 

Google Scholar 
Hartl DL, Clark AG (1997) Principles of population genetics. Sinauer, Sunderland, MA
Google Scholar 
Haynes JL (1993) Annual reestablishment of mosquitofish populations in Nebraska. Copeia 1993:232–235
Google Scholar 
Holsinger KE, Weir BS (2009) Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat Rev Genet 10:639–650CAS 
PubMed 

Google Scholar 
Ingley SJ, Johnson JB (2016) Divergent natural selection promotes immigrant inviability at early and late stages of evolutionary divergence. Evolution 70:600–616PubMed 

Google Scholar 
Ingley SJ, Billman EJ, Belk MC, Johnson JB (2014) Morphological divergence driven by predation environment within and between species of Brachyrhaphis fishes. PloS One 9:90274
Google Scholar 
James ME, Wilkinson MJ, Bernal DM, Liu H, North HL, Engelstädter J, et al. (2021) Phenotypic and genotypic parallel evolution in parapatric ecotypes of Senecio. Evolution (In press). Online version of record https://doi.org/10.1111/evo.14387Jiang S, Zhu K, Han L, Chen C, Wang M, Wang X (2021) Genetic variation and phylogeographic structure of Laodelphax striatellus in China based on microsatellite markers. J Appl Entomol 145:336–347CAS 

Google Scholar 
Johnson JB, Burt DB, DeWitt TJ (2008) Form, function, fitness-pathways to survival. Evolution 62:1243–1251PubMed 

Google Scholar 
Kobza RM, Trexler JC, Loftus WF, Perry SA (2004) Community structure of fishes inhabiting aquatic refuges in a threatened Karst wetland and its implications for ecosystem management. Biol Conserv 116:153–165
Google Scholar 
Langerhans RB (2009) Morphology, performance, fitness: functional insight into a post-Pleistocene radiation of mosquitofish. Biol Lett 5:488–491PubMed 

Google Scholar 
Langerhans RB, DeWitt TJ (2004) Shared and unique features of evolutionary diversification. Am Nat 164:335–349PubMed 

Google Scholar 
Langerhans RB, Makowicz AM (2009) Shared and unique features of morphological differentiation between predator regimes in Gambusia caymanensis. J Evol Biol 22:2231–2242CAS 
PubMed 

Google Scholar 
Langerhans RB, Layman CA, DeWitt TJ (2005) Male genital size reflects a tradeoff between attracting mates and avoiding predators in two live-bearing fish species. Proc Natl Acad Sci USA 102:7618–7623CAS 
PubMed 

Google Scholar 
Langerhans RB, Gifford ME, Joseph EO (2007) Ecological speciation in Gambusia fishes. Evolution 61:2056–2074CAS 
PubMed 

Google Scholar 
Langerhans RB, Layman CA, Shokrollahi AM, DeWitt TJ (2004) Predator-driven phenotypic diversification in Gambusia affinis. Evolution 58:2305–2318PubMed 

Google Scholar 
Leger EA, Rice KJ (2007) Assessing the speed and predictability of local adaptation in invasive California poppies (Eschscholzia californica). J Evol Biol 20:1090–1103CAS 
PubMed 

Google Scholar 
Levins R (1968) Evolution in changing environments. Princeton University Press, Princeton, NJLoera-Pérez J, Hernández-Stefanoni JL, Chiappa-Carrara X (2020) How do spatial and environmental factors affect the fish community structure in seasonally flooded karst systems? Lat Am J Aquat Res 48:268–279
Google Scholar 
Losos JB, Jackman TR, Larson A, de Queiroz K, Rodrı́guez-Schettino L (1998) Contingency and determinism in replicated adaptive radiations of island lizards. Science 279:2115–2118CAS 
PubMed 

Google Scholar 
Losos JB (2009) Lizards in an evolutionary tree: ecology and adaptive radiation of anoles. University of California Press, Berkeley, CAMaglio VJ, Rosen DE (1969) Changing preference for substrate color by reproductively active mosquitofish, Gambusia affinis (Baird and Girard)(Poeciliidae, Atheriniformes). American Museum novitates; no. 2397Martin RG(1975) Sexual and aggressive behavior, density and social structure in a natural population of mosquitofish, Gambusia affinis holbrooki. Copeia 1975:445–454
Google Scholar 
Maruyama T, Fuerst PA (1985) Population bottlenecks and nonequilibrium models in population genetics. II. Number of alleles in a small population that was formed by a recent bottleneck. Genetics 111:675–689CAS 
PubMed 

Google Scholar 
Matthews WJ, Marsh-Matthews E (2011) An invasive fish species within its native range: community effects and population dynamics of Gambusia affinis in the central United States. Freshw Biol 56:2609–2619
Google Scholar 
Mays JR, DeWitt TJ, Dharampal P, Andrus FT, Findlay RH (2019) Frequent habitat migration, phenotypic plasticity, and vestigial ecophenotypy revealed by isotope-based natal habitat inference in bluegill sunfish, Lepomis macrochirus. Evol Ecol Res 20. Available from https://evolutionary-ecology.com/abstracts/v20/3235.htmlMilano D, Ruzzante DE, Cussac VE, Macchi PJ, Ferriz RA, Barriga JP et al. (2006) Latitudinal and ecological correlates of morphological variation in Galaxias platei (Pisces, Galaxiidae) in Patagonia. Biol J Linn Soc 87:69–82
Google Scholar 
Moen DS, Morlon H, Wiens JJ (2016) Testing convergence versus history: convergence dominates phenotypic evolution for over 150 million years in frogs. Syst Biol 65:146–160PubMed 

Google Scholar 
Moody EK, Lozano-Vilano ML (2018) Predation drives morphological convergence in the Gambusia panuco species group among lotic andlentic habitats. J Evol Biol 31:491–501CAS 
PubMed 

Google Scholar 
Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590CAS 
PubMed 

Google Scholar 
Nepokroeff M, Sytsma KJ (1996) Systematics and patterns of speciation and colonization in Hawaiian Psychotria and relatives based on phylogenetic analysis of ITS sequence data. Am J Bot 83:181–182
Google Scholar 
Nievergelt CM, Libiger O, Schork NJ (2007) Generalized analysis of molecular variance. PLoS Genet 3:e51PubMed 

Google Scholar 
Oke KB, Rolshausen G, LeBlond C, Hendry AP (2017) How parallel is parallel evolution? A comparative analysis in fishes. Am Nat 190:1–16PubMed 

Google Scholar 
Ord TJ, Summers TC (2015) Repeated evolution and the impact of evolutionary history on adaptation. BMC Evol Biol 15:1–12
Google Scholar 
Pandey P, Ramegowda V, Senthil-Kumar M (2015) Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci 6:723PubMed 

Google Scholar 
Pazmino SD, Kent MI, Ward AJ (2020) Locomotion and habituation to novel experimental environments in a social fish species. Behaviour 1:1–17
Google Scholar 
Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295
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 Hered 90:502–503
Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 

Google Scholar 
Purcell KM, Lance SL, Jones KLStockwell CA (2011) Ten novel microsatellite markers for the western mosquitofish Gambusia affinis Conserv Genet Resour 3:361–363
Google Scholar 
Putman AI, Carbone I (2014) Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol Evol 4:4399–4428PubMed 

Google Scholar 
Pyke GH (2005) A review of the biology of Gambusia affinis and G. holbrooki. Rev Fish Biol Fish 15:339–365
Google Scholar 
Reed DH, Frankham R (2001) How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55:1095–1103CAS 
PubMed 

Google Scholar 
Reznick DN, Shaw FH, Rodd FH, Shaw RG (1997) Evaluation of the rate of evolution in natural populations of guppies (Poecilia reticulata). Science 275:1934–1937CAS 
PubMed 

Google Scholar 
Richards TJ, Walter GM, McGuigan K, Ortiz-Barrientos D (2016) Divergent natural selection drives the evolution of reproductive isolation in an Australian wildflower Evolution 70:1993–2003PubMed 

Google Scholar 
Rivera G (2008) Ecomorphological variation in shell shape of the freshwater turtle Pseudemys concinna inhabiting different aquatic flow regimes. Integr Comp Biol 48:769–787PubMed 

Google Scholar 
Robinson BW, Wilson DS (1996) Genetic variation and phenotypic plasticity in a trophically polymorphic population of pumpkinseed sunfish (Lepomis gibbosus). Evol Ecol 10:631–652
Google Scholar 
Ruehl CB, DeWitt TJ (2005) Trophic plasticity and fine-grained resource variation in populations of western mosquitofish, Gambusia affinis. Evol Ecol Res 7:801–819
Google Scholar 
Ruehl CB, Shervette V, DeWitt TJ (2011) Replicated shape variation between simple and complex habitats in two estuarine fishes. Biol J Linn Soc 103:147–158
Google Scholar 
Santi F, Petry AC, Plath M, Riesch R (2020) Phenotypic differentiation in a heterogeneous environment: morphological and life-history responses to ecological gradients in a livebearing fish. J Zool 310:10–23
Google Scholar 
Schluter D, Clifford EA, Nemethy M, McKinnon JS (2004) Parallel evolution and inheritance of quantitative traits. Am Nat 163:809–822PubMed 

Google Scholar 
Schluter D (2000) The ecology of adaptive radiation. Oxford University Press, Oxford, UKSharpe DM, Langerhans RB, Low-Décarie E, Chapman LJ (2015) Little evidence for morphological change in a resilient endemic species following the introduction of a novel predator. J Evol Biol 28:2054–2067CAS 
PubMed 

Google Scholar 
Slatkin M (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:457–462CAS 
PubMed 

Google Scholar 
Spencer CC, Chlan CA, Neigel JE, Scribner KT, Wooten MC, Leberg PL (1999) Polymorphic microsatellite markers in the western mosquitofish, Gambusia affinis. Mol Ecol 8:157–168CAS 
PubMed 

Google Scholar 
Spitze K (1993) Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics 135:367–374CAS 
PubMed 

Google Scholar 
Thibault RE, Schultz RJ (1978) Reproductive adaptations among viviparous fishes (Cyprinodontiformes: Poeciliidae). Evolution 32:320–333PubMed 

Google Scholar 
Tobler M, DeWitt TJ, Schlupp I, García de León FJ, Herrmann R, Feulner PG et al. (2008) Toxic hydrogen sulfide and dark caves: phenotypic and genetic divergence across two abiotic environmental gradients in Poecilia mexicana. Evolution 62:2643–2659PubMed 

Google Scholar 
Tobler M, Palacios M, Chapman LJ, Mitrofanov I, Bierbach D, Plath M et al. (2011) Evolution in extreme environments: replicated phenotypic differentiation in livebearing fish inhabiting sulfidic springs. Evolution 65:2213–2228PubMed 

Google Scholar 
Van Oosterhout C, Weetman D, Hutchinson WF (2006) Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol Ecol Notes 6:255–256
Google Scholar 
Vázquez-Domínguez E, Hernández-Valdés A, Rojas-Santoyo A, Zambrano L (2009) Contrasting genetic structure in two codistributed freshwater fish species of highly seasonal systems. Rev Mex Biodivers 80:181–192
Google Scholar 
Via S, Lande R (1985) Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution 39:505–522PubMed 

Google Scholar 
Waddington CH (1957) The strategy of the genes. Allen & Unwin, London
Google Scholar 
Walker JA (1997) Ecological morphology of lacustrine threespine stickleback Gasterosteus aculeatus L. (Gasterosteidae) body shape. Biol J Linn Soc 61:3–50
Google Scholar 
Walker JA, Bell MA (2000) Net evolutionary trajectories of body shape evolution within a microgeographic radiation of threespine sticklebacks (Gasterosteus aculeatus). J Zool 252:293–302
Google Scholar 
Wang X, Zorraquino V, Kim M, Tsoukalas A, Tagkopoulos I (2018) Predicting the evolution of Escherichia coli by a data-driven approach. Nat Commun 9:1–12
Google Scholar 
Ward RD, Woodwark M, Skibinski DOF (1994) A comparison of genetic diversity levels in marine, freshwater, and anadromous fishes. J Fish Biol 44:213–232
Google Scholar 
Waters JM, McCulloch GA (2021) Reinventing the wheel? Reassessing the roles of gene flow, sorting and convergence in repeated evolution. Mol Ecol 30:4162–4172PubMed 

Google Scholar 
Zambrano L, Vázquez-Domínguez E, García-Bedoya D, Loftus WF, Trexler JC (2006) Fish community structure in freshwater karstic water bodies of the Sian Ka’an Reserve in the Yucatan peninsula, Mexico. Ichthyol Explor Freshw 17:193–206Zane L, Nelson WS, Jones AG, Avise JC (1999) Microsatellite assessment of multiple paternity in natural populations of a live-bearing fish, Gambusia holbrooki. J Evol Biol 12:61–69
Google Scholar  More

1.Jarau, S. & Hrncir, M. Food Exploitation by Social Insects: Ecological, Behavioral, and Theoretical Approaches. (CRC Press, 2009).2.Detrain, C. & Deneubourg, J.-L. Collective decision-making and foraging patterns in ants and honeybees. Adv. Insect Physiol. 35, 123–173 (2008) (Elsevier).
Google Scholar 
3.Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, 1990).
Google Scholar 
4.Beckers, R., Deneubourg, J.-L. & Goss, S. Modulation of trail laying in the ant Lasius niger (Hymenoptera: Formicidae) and its role in the collective selection of a food source. J. Insect Behav. 6, 751–759 (1993).
Google Scholar 
5.Detrain, C. & Prieur, J. Sensitivity and feeding efficiency of the black garden ant Lasius niger to sugar resources. J. Insect Physiol. 64, 74–80 (2014).CAS 
PubMed 

Google Scholar 
6.Jackson, D. E. & Châline, N. Modulation of pheromone trail strength with food quality in Pharaoh’s ant, Monomorium pharaonic. Animal Behav. 74, 463–470 (2007).
Google Scholar 
7.Sumpter, D. J. T. & Beekman, M. From nonlinearity to optimality: Pheromone trail foraging by ants. Anim. Behav. 66, 273–280 (2003).
Google Scholar 
8.Cerdá, X., Angulo, E., Boulay, R. & Lenoir, A. Individual and collective foraging decisions: A field study of worker recruitment in the gypsy ant Aphaenogaster senilis. Behav. Ecol. Sociobiol. 63, 551–562 (2009).
Google Scholar 
9.Detrain, C. & Deneubourg, J.-L. Scavenging by Pheidole pallidula key for understanding decision-making systems in ants. Anim. Behav. 53, 537–547 (1997).
Google Scholar 
10.Mailleux, A.-C., Deneubourg, J. L. & Detrain, C. How do ants assess food volume?. Anim. Behav. 59, 1061–1069 (2000).CAS 
PubMed 

Google Scholar 
11.Breed, M. D., Fewell, J. H., Moore, A. J. & Williams, K. R. Graded recruitment in a ponerine ant. Behav. Ecol. Sociobiol. 20, 407–411 (1987).
Google Scholar 
12.Cammaerts, M.-C. & Cammaerts, R. Food recruitment strategies of the ants Myrmica sabuleti and Myrmica ruginodis. Behav. Proc. 5, 251–270 (1980).CAS 

Google Scholar 
13.Portha, S., Deneubourg, J.-L. & Detrain, C. Self-organized asymmetries in ant foraging: A functional response to food type and colony needs. Behav. Ecol. 13, 776–781 (2002).
Google Scholar 
14.Devigne, C. & Detrain, C. How does food distance influence foraging in the ant Lasius niger: The importance of home-range marking. Insect. Soc. 53, 46–55 (2006).
Google Scholar 
15.Fewell, J. H. Directional fidelity as a foraging constraint in the western harvester ant, Pogonomyrmex occidentalis. Oecologia 82, 45–51 (1990).ADS 
PubMed 

Google Scholar 
16.Howard, D. F. & Tschinkel, W. R. The effect of colony size and starvation on food flow in the fire ant, Solenopsis invicta (Hymenoptera: Formicidae). Behav. Ecol. Sociobiol. 7, 293–300 (1980).
Google Scholar 
17.Mailleux, A.-C., Devigne, C., Deneubourg, J.-L. & Detrain, C. Impact of starvation on Lasius niger’ exploration. Ethology 116, 248–256 (2010).
Google Scholar 
18.Portha, S., Deneubourg, J.-L. & Detrain, C. How food type and brood influence foraging decisions of Lasius niger scouts. Anim. Behav. 68, 115–122 (2004).
Google Scholar 
19.Deneubourg, J.-L., Goss, S., Pasteels, J. M. & Beckers, R. Collective decision making through food recruitment. Insectes Soc. 37, 258–267 (1990).
Google Scholar 
20.Czaczkes, T. J., Salmane, A. K., Klampfleuthner, F. A. M. & Heinze, J. Private information alone can trigger trapping of ant colonies in local feeding optima. J. Exp. Biol. 219, 744–751 (2016).PubMed 

Google Scholar 
21.Collett, T. S. & Collett, M. Memory use in insect visual navigation. Nat. Rev. Neurosci. 3, 542–552 (2002).CAS 
PubMed 
MATH 

Google Scholar 
22.Azevedo, D. L. O., Medeiros, J. C. & Araújo, A. Adjustments in the time, distance and direction of foraging in Dinoponera quadriceps Workers. J. Insect. Behav. 27, 177–191 (2014).
Google Scholar 
23.Beverly, B. D., McLendon, H., Nacu, S., Holmes, S. & Gordon, D. M. How site fidelity leads to individual differences in the foraging activity of harvester ants. Behav. Ecol. 20, 633–638 (2009).
Google Scholar 
24.Fourcassié, V. & Traniello, J. F. A. Food searching behaviour in the ant Formica schaufussi (Hymenoptera, Formicidae): Response of naive foragers to protein and carbohydrate food. Anim. Behav. 48, 69–79 (1994).
Google Scholar 
25.Aron, S., Beckers, R., Deneubourg, J. L. & Pasteels, J. M. Memory and chemical communication in the orientation of two mass-recruiting ant species. Ins. Soc. 40, 369–380 (1993).
Google Scholar 
26.Lehue, M., Detrain, C. & Collignon, B. Nest entrances, spatial fidelity, and foraging patterns in the red ant Myrmica rubra: A field and theoretical study. Insects 11, 317 (2020).PubMed Central 

Google Scholar 
27.Bolek, S., Wittlinger, M. & Wolf, H. What counts for ants? How return behaviour and food search of Cataglyphis ants are modified by variations in food quantity and experience. J. Exp. Biol. 215, 3218–3222 (2012).PubMed 

Google Scholar 
28.Detrain, C., Natan, C. & Deneubourg, J. L. The influence of the physical environment on the self-organised foraging patterns of ants. Naturwissenschaften 88, 171–174 (2001).ADS 
CAS 
PubMed 

Google Scholar 
29.Traniello, J. F. A., Fujita, M. S. & Bowen, R. V. Ant foraging behavior: Ambient temperature influences prey selection. Behav. Ecol. Sociobiol. 15, 65–68 (1984).
Google Scholar 
30.Nonacs, P. & Dill, L. M. Mortality risk versus food quality trade-offs in ants: Patch use over time. Ecol. Entomol. 16, 73–80 (1991).
Google Scholar 
31.Tanner, C. J. Individual experience-based foraging can generate community territorial structure for competing ant species. Behav. Ecol. Sociobiol. 63, 591–603 (2009).
Google Scholar 
32.Brown, M. J. F. & Gordon, D. M. How resources and encounters affect the distribution of foraging activity in a seed-harvesting ant. Behav. Ecol. Sociobiol. 47, 195–203 (2000).
Google Scholar 
33.Fourcassié, V., Schmitt, T. & Detrain, C. Impact of interference competition on exploration and food exploitation in the ant Lasius niger. Psyche J. Entomol. 2012, 1–8 (2012).
Google Scholar 
34.Mehdiabadi, N. & Gilbert, L. Colony-level impacts of parasitoid flies on fire ants. Proceedings of the Royal Society of London. Series B: Biological Sciences. 269, 1695–1699 (2002).35.Feener, D. H. Competition between ant species: Outcome controlled by parasitic flies. Science 214, 815–817 (1981).ADS 
PubMed 

Google Scholar 
36.Schmid-Hempel, P. Parasites in Social Insects (Princeton University Press, 1998).
Google Scholar 
37.Cremer, S., Armitage, S. A. O. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, 693–702 (2007).
Google Scholar 
38.Zhukovskaya, M., Yanagawa, A. & Forschler, B. Grooming behavior as a mechanism of insect disease defense. Insects 4, 609–630 (2013).PubMed 
PubMed Central 

Google Scholar 
39.Ortius-Lechner, D., Maile, R., Morgan, E. D. & Boomsma, J. J. Metapleural gland secretion of the leaf-cutter ant Acromyrmex octospinosus: New compounds and their functional significance. J. Chem. Ecol. 26, 1667–1683 (2000).CAS 

Google Scholar 
40.Ballari, S., Farji-Brener, A. G. & Tadey, M. Waste management in the leaf-cutting ant Acromyrmex lobicornis: Division of labour, aggressive behaviour, and location of external refuse dumps. J. Insect Behav. 20, 87–98 (2007).
Google Scholar 
41.Leclerc, J.-B. & Detrain, C. Impact of colony size on survival and sanitary strategies in fungus-infected ant colonies. Behav. Ecol. Sociobiol. 72, 1–10 (2018).42.Pereira, H., Jossart, M. & Detrain, C. Waste management by ants: The enhancing role of larvae. Anim. Behav. 168, 187–198 (2020).
Google Scholar 
43.Diez, L., Urbain, L., Lejeune, P. & Detrain, C. Emergency measures: Adaptive response to pathogen intrusion in the ant nest. Behav. Proc. 116, 80–86 (2015).
Google Scholar 
44.López-Riquelme, G. O. & Fanjul-Moles, M. L. The funeral ways of social insects. Social strategies for corpse disposal. Trends Entomol. 9, 71–129 (2013).45.Heinze, J. & Walter, B. Moribund ants leave their nests to die in social isolation. Curr. Biol. 20, 249–252 (2010).CAS 
PubMed 

Google Scholar 
46.Leclerc, J.-B. & Detrain, C. Loss of attraction for social cues leads to fungal-infected Myrmica rubra ants withdrawing from the nest. Anim. Behav. 129, 133–141 (2017).
Google Scholar 
47.Fouks, B. & Lattorff, H. M. G. Recognition and avoidance of contaminated flowers by foraging bumblebees (Bombus terrestris). PLoS ONE 6, e26328 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Pereira, H. & Detrain, C. Pathogen avoidance and prey discrimination in ants. R. Soc. Open Sci. 7, 191705 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
49.Pereira, H. & Detrain, C. Prophylactic avoidance of hazardous prey by the ant host Myrmica rubra. Insects 11, 444 (2020).PubMed Central 

Google Scholar 
50.Tranter, C., LeFevre, L., Evison, S. E. F. & Hughes, W. O. H. Threat detection: Contextual recognition and response to parasites by ants. ISBE 26, 396–405 (2015).
Google Scholar 
51.Marikovsky, P. I. On some features of behavior of the ants Formica rufa L. infected with fungous disease. Insectes Soc. 9, 173–179 (1962).
Google Scholar 
52.Lehue, M., Detrain, C. & Collignon, B. Nest entrances, spatial fidelity, and foraging patterns in the red ant Myrmica rubra: A field and theoretical study. Insects 11, 317 (2020).PubMed Central 

Google Scholar 
53.Diez, L., Deneubourg, J.-L., Hoebeke, L. & Detrain, C. Orientation in corpse-carrying ants: Memory or chemical cues?. Anim. Behav. 81, 1171–1176 (2011).
Google Scholar 
54.Brütsch, T., Felden, A., Reber, A. & Chapuisat, M. Ant queens (Hymenoptera: Formicidae) are attracted to fungal pathogens during the initial stage of colony founding. Myrmecol. News 20, 71–76 (2014).
Google Scholar 
55.Pontieri, L., Vojvodic, S., Graham, R., Pedersen, J. S. & Linksvayer, T. A. Ant colonies prefer infected over uninfected nest sites. PLoS ONE 9, e111961 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
56.Leclerc, J.-B., Silva, J. P. & Detrain, C. Impact of soil contamination on the growth and shape of ant nests. R. Soc. Open Sci. 5, 180267 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
57.Loreto, R. G. & Hughes, D. P. Disease dynamics in ants. Adv. Genet. 94, 287–306 (2016) (Elsevier).CAS 
PubMed 

Google Scholar 
58.Angelone, S. & Bidochka, M. J. Diversity and abundance of entomopathogenic fungi at ant colonies. J. Invertebr. Pathol. 156, 73–76 (2018).PubMed 

Google Scholar 
59.Evans, H. C., Groden, E. & Bischoff, J. F. New fungal pathogens of the red ant, Myrmica rubra, from the UK and implications for ant invasions in the USA. J. Funbiol. 114, 451–466 (2010).CAS 

Google Scholar 
60.Bos, N., Kankaanpää-Kukkonen, V., Freitak, D., Stucki, D. & Sundström, L. Comparison of twelve ant species and their susceptibility to fungal infection. Insects 10, 271 (2019).PubMed Central 

Google Scholar 
61.Theis, F. J., Ugelvig, L. V., Marr, C. & Cremer, S. Opposing effects of allogrooming on disease transmission in ant societies. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140108–20140108 (2015).
Google Scholar 
62.Okuno, M., Tsuji, K., Sato, H. & Fujisaki, K. Plasticity of grooming behavior against entomopathogenic fungus Metarhizium anisopliae in the ant Lasius japonicus. J. Ethol. 30, 23–27 (2012).
Google Scholar 
63.Pereira, R. M. & Stimac, J. L. Transmission of Beauveria bassiana within nests of Solenopsis invicta (Hymenoptera: Formicidae) in the laboratory. Environ. Entomol. 21, 1427–1432 (1992).
Google Scholar 
64.Hughes, W. O. H., Thomsen, L., Eilenberg, J. & Boomsma, J. J. Diversity of entomopathogenic fungi near leaf-cutting ant nests in a Neotropical forest, with particular reference to Metarhizium anisopliae var. anisopliae. J. Invertebr. Pathol. 85, 46–53 (2004).CAS 
PubMed 

Google Scholar 
65.Novak, S. & Cremer, S. Fungal disease dynamics in insect societies: Optimal killing rates and the ambivalent effect of high social interaction rates. J. Theor. Biol. 372, 54–64 (2015).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
66.Qiu, H., Lu, L., Shi, Q. & He, Y. Fungus exposed Solenopsis invicta ants benefit from grooming. J. Insect. Behav. 27, 678–691 (2014).
Google Scholar 
67.Reber, A., Purcell, J., Buechel, S. D., Buri, P. & Chapuisat, M. The expression and impact of antifungal grooming in ants. J. Evol. Biol. 24, 954–964 (2011).CAS 
PubMed 

Google Scholar 
68.Sadd, B. M. & Schmid-Hempel, P. Insect immunity shows specificity in protection upon secondary pathogen exposure. Curr. Biol. 16, 1206–1210 (2006).CAS 
PubMed 

Google Scholar 
69.Traniello, J. F. A., Rosengaus, R. B. & Savoie, K. The development of immunity in a social insect: Evidence for the group facilitation of disease resistance. Proc. Natl. Acad. Sci. 99, 6838–6842 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Konrad, M. et al. Social transfer of pathogenic fungus promotes active immunisation in ant colonies. PLoS Biol. 10, e1001300 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Ugelvig, L. V. & Cremer, S. Social prophylaxis: Group interaction promotes collective immunity in ant colonies. Curr. Biol. 17, 1967–1971 (2007).CAS 
PubMed 

Google Scholar 
72.Bordoni, A. et al. No evidence of queen immunisation despite transgenerational immunisation in Crematogaster scutellaris ants. J. Insect Physiol. 120, 103998 (2020).CAS 
PubMed 

Google Scholar 
73.Reber, A. & Chapuisat, M. No evidence for immune priming in ants exposed to a fungal pathogen. PLoS ONE 7, e35372 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Groden, E., Drummond, F. A., Garnas, J. & Franceour, A. Distribution of an invasive ant, Myrmica rubra (Hymenoptera: Formicidae), Maine. J. Econ. Entomol. 98, 1774–1784 (2005).PubMed 

Google Scholar 
75.Radchenko, A. & Elmes, G. W. Myrmica Ants (Hymenoptera: Formicidae) of the Old World (Natura optima dux Foundation, 2010).
Google Scholar 
76.Hänel, H. The life cycle of the insect pathogenic fungus Metarhizium anisopliae in the termite Nasutitermes exitiosus. Mycopathologia 80, 137–145 (1982).
Google Scholar 
77.Leclerc, J.-B. & Detrain, C. Ants detect but do not discriminate diseased workers within their nest. Sci. Nat. 103, 1–12 (2016).78.Lacey, L. A. Manual of Techniques in Invertebrate Pathology (Academic Press imprint of Elsevier Science, 2012).
Google Scholar 
79.R Core Team. R: A Language and Environment for Statistical Computing. (2020).80.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
81.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).MATH 

Google Scholar 
82.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
83.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Soft. 82, 1–26. https://doi.org/10.18637/JSS.V082.I13 (2017).84.Lenth, R. Emmeans: Estimated marginal means, aka least-squares means. R-package version 1.4.8. https://CRAN.R-project.org/package=emmeans (2020).85.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 

Google Scholar 
86.Therneau, T. A Package for Survival Analysis in R. R-package version 3.2-3. https://CRAN.R-project.org/package=survival (2020) More

1.Melbourne, B. A. & Hastings, A. Highly variable spread rates in replicated biological invasions: Fundamental limits to predictability. Science 325, 1536–1539 (2009).ADS 
CAS 
PubMed 

Google Scholar 
2.Lewis, M. A., Petrovskii, S. V. & Potts, J. R. The Mathematics Behind Biological Invasions (Springer, 2016).MATH 

Google Scholar 
3.Phillips, B. L. Evolutionary processes make invasion speed difficult to predict. Biol. Invasions 17, 1949–1960 (2015).
Google Scholar 
4.Peischl, S., Kirkpatrick, M. & Excoffier, L. Expansion load and the evolutionary dynamics of a species range. Am. Nat. 185, E81–E93 (2015).PubMed 

Google Scholar 
5.Burton, O. J., Travis, J. M. J. & Phillips, B. L. Trade-offs and the evolution of life-histories during range expansion. Ecol. Lett. 13, 1210–1220 (2010).PubMed 

Google Scholar 
6.Phillips, B. L. & Perkins, T. A. Spatial sorting as the spatial analogue of natural selection. Theor. Ecol. 12, 155–163 (2019).
Google Scholar 
7.Deforet, M., Carmona-Fontaine, C., Korolev, K. S. & Xavier, J. B. Evolution at the edge of expanding populations. Am. Nat. 194, 291–305 (2019).PubMed 
PubMed Central 

Google Scholar 
8.Travis, J. M. J. & Dytham, C. Dispersal evolution during invasions. Evol. Ecol. Res. 4, 1119–1129 (2002).
Google Scholar 
9.Bouin, E. et al. Invasion fronts with variable motility: Phenotype selection, spatial sorting and wave acceleration. C. R. Math. 350, 761–766 (2012).MathSciNet 
MATH 

Google Scholar 
10.Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than through time. Proc. Natl. Acad. Sci. USA 108, 5708–5711 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Williams, J. L., Kendall, B. E. & Levine, J. M. Rapid evolution accelerates plant population spread in fragmented experimental landscapes. Science 353, 482–485 (2016).ADS 
CAS 
PubMed 

Google Scholar 
12.Weiss-Lehman, C., Hufbauer, R. A. & Melbourne, B. A. Rapid trait evolution drives increased speed and variance in experimental range expansions. Nat. Commun. 8, 14303 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Ochocki, B. M. & Miller, T. E. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 14315 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. The cane toad’s (Chaunus [Bufo] marinus) increasing ability to invade Australia is revealed by a dynamically updated range model. Proc. R. Soc. B 274, 1413–1419 (2007).PubMed 
PubMed Central 

Google Scholar 
15.Phillips, B. L., Brown, G. P. & Shine, R. Evolutionarily accelerated invasions: The rate of dispersal evolves upwards during the range advance of cane toads. J. Evol. Biol. 23, 2595–2601 (2010).CAS 
PubMed 

Google Scholar 
16.Chuang, A. & Peterson, C. R. Expanding population edges: Theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).ADS 

Google Scholar 
17.Phillips, B. L., Brown, G. P., Travis, J. M. & Shine, R. Reid’s paradox revisited: The evolution of dispersal kernels during range expansion. Am. Nat. 172, S34–S48 (2008).PubMed 

Google Scholar 
18.Alford, R. A., Brown, G. P., Schwarzkopf, L., Phillips, B. L. & Shine, R. Comparisons through time and space suggest rapid evolution of dispersal behaviour in an invasive species. Wildl. Res. 36, 23–28 (2009).
Google Scholar 
19.Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl. Acad. Sci. USA 110, 13452–13456 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Brown, G. P., Phillips, B. L. & Shine, R. The straight and narrow path: The evolution of straight-line dispersal at a cane toad invasion front. Proc. R. Soc. B 281, 20141385 (2014).PubMed 
PubMed Central 

Google Scholar 
21.DeVore, J., Ducatez, S. & Shine, R. Spatial ecology of cane toads (Rhinella marina) in their native range: A study from French Guiana. Sci. Rep. 11, 11817 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Brattstrom, B. H. Homing in the giant toad, Bufo marinus. Herpetologica 18, 176–180 (1962).
Google Scholar 
23.Zug, G. R. & Zug, P. B. The marine toad Bufo marinus: A natural history resumé of native populations. Smithson. Contrib. Zool. 284, 1–58 (1979).
Google Scholar 
24.Bayliss, P. The ecology of post-metamorphic Bufo marinus in central Amazonian savanna, Brazil. Unpublished Ph.D. thesis (The University of Queensland, 1995).25.Turvey, N. Cane Toads: A Tale of Sugar, Politics and Flawed Science (Sydney University Press, 2013).
Google Scholar 
26.Carpenter, C. C. & Gillingham, J. C. Water hole fidelity in the marine toad, Bufo marinus. J. Herpetol. 21, 158–161 (1987).
Google Scholar 
27.Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: Spatial ecology and habitat use of invasive cane toads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).PubMed 
PubMed Central 

Google Scholar 
28.Hastings, A. Can spatial variation alone lead to selection for dispersal? Theor. Popul. Biol. 24, 244–251 (1983).MATH 

Google Scholar 
29.Möbius, W., et al. The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions. Preprint at http://arxiv.org/abs/1910.05332 (2019).30.Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).PubMed 

Google Scholar 
31.Macgregor, L. F., Greenlees, M., de Bruyn, M. & Shine, R. An invasion in slow motion: The spread of invasive cane toads (Rhinella marina) into cooler climates in southern Australia. Biol. Invasions 23(11), 3565–3581 (2021).
Google Scholar 
32.Perkins, A. T., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).PubMed 

Google Scholar 
33.Seabrook, W. Range expansion of the introduced cane toad Bufo marinus in New South Wales. Aust. Zool. 27, 58–62 (1991).
Google Scholar 
34.Kearney, M. R. et al. Modelling species distributions without using species distributions: The cane toad in Australia under current and future climates. Ecography 31, 423–434 (2008).
Google Scholar 
35.McCann, S. M., Kosmala, G. K., Greenlees, M. J. & Shine, R. Physiological plasticity in a successful invader: Rapid acclimation to cold occurs only in cool-climate populations of cane toads (Rhinella marina). Conserv. Physiol. 6, cox072 (2018).PubMed 
PubMed Central 

Google Scholar 
36.Schwarzkopf, L. & Alford, R. A. Nomadic movement in tropical toads. Oikos 96, 492–506 (2002).
Google Scholar 
37.Seebacher, F. & Alford, R. A. Movement and microhabitat use of a terrestrial amphibian (Bufo marinus) on a tropical island: Seasonal variation and environmental correlates. J. Herpetol. 33, 208–214 (1999).
Google Scholar 
38.Phillips, B. L., Brown, G. P., Greenlees, M., Webb, J. K. & Shine, R. Rapid expansion of the cane toad (Bufo marinus) invasion front in tropical Australia. Austral Ecol. 32, 169–176 (2007).
Google Scholar 
39.Tingley, R. & Shine, R. Desiccation risk drives the spatial ecology of an invasive anuran (Rhinella marina) in the Australian semi-desert. PLoS ONE 6, e25979 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Brown, G. P., Phillips, B. L., Webb, J. K. & Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 133, 88–94 (2006).
Google Scholar 
41.Pettit, L. J., Greenlees, M. J. & Shine, R. Is the enhanced dispersal rate seen at invasion fronts a behaviourally plastic response to encountering novel ecological conditions? Biol. Lett. 12, 20160539 (2016).PubMed 
PubMed Central 

Google Scholar 
42.Jessop, T. S. et al. Exploring mechanisms and origins of reduced dispersal in island Komodo Dragons. Proc. R. Soc. B 285, 20181829 (2018).PubMed 
PubMed Central 

Google Scholar 
43.Mayr, E. Animal Species and Evolution (Harvard University Press, 1963).
Google Scholar 
44.Duckworth, R. A. The role of behavior in evolution: A search for mechanism. Evol. Ecol. 23, 513–531 (2009).
Google Scholar 
45.Muñoz, M. M. & Losos, J. B. Thermoregulatory behavior simultaneously promotes and forestalls evolution in a tropical lizard. Am. Nat. 191, E15–E26 (2017).PubMed 

Google Scholar 
46.Carroll, S. P. et al. And the beak shall inherit–evolution in response to invasion. Ecol. Lett. 8, 944–951 (2005).PubMed 

Google Scholar 
47.Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).ADS 
CAS 
PubMed 

Google Scholar 
48.Acevedo, A. A., Lampo, M. & Cipriani, R. The cane or marine toad, Rhinella marina (Anura, Bufonidae): Two genetically and morphologically distinct species. Zootaxa 4103, 574–586 (2016).PubMed 

Google Scholar 
49.Reilly, S. M. et al. Conquering the world in leaps and bounds: Hopping locomotion in toads is actually bounding. Funct. Ecol. 29, 1308–1316 (2015).
Google Scholar 
50.Griffis-Kyle, K. L., Kyle, S. & Jungels, J. Use of breeding sites by arid-land toads in rangelands: Landscape-level factors. Southwest. Nat. 56, 251–255 (2011).
Google Scholar 
51.Sinsch, U. Movement ecology of amphibians: From individual migratory behaviour to spatially structured populations in heterogeneous landscapes. Can. J. Zool. 92, 491–502 (2014).
Google Scholar 
52.Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: A review of pond-breeding amphibians. Q. Rev. Biol. 95, 1–36 (2020).
Google Scholar 
53.Child, T., Phillips, B. L., Brown, G. P. & Shine, R. The spatial ecology of cane toads (Bufo marinus) in tropical Australia: Why do metamorph toads stay near the water? Austral Ecol. 33, 630–640 (2008).
Google Scholar 
54.Pettit, L., Ducatez, S., DeVore, J. L., Ward-Fear, G. & Shine, R. Diurnal activity in cane toads (Rhinella marina) is geographically widespread. Sci. Rep. 10, 5723 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Shine, R., Ward-Fear, G. & Brown, G. P. A famous failure: Why were cane toads an ineffective biocontrol in Australia? Conserv. Sci. Pract. 2, e296 (2020).
Google Scholar 
56.Shine, R., Everitt, C., Woods, D. & Pearson, D. J. An evaluation of methods used to cull invasive cane toads in tropical Australia. J. Pest Sci. 91, 1081–1091 (2018).
Google Scholar 
57.Silvester, R., Greenlees, M., Shine, R. & Oldroyd, B. Behavioural tactics used by invasive cane toads (Rhinella marina) to exploit apiaries in Australia. Austral Ecol. 44, 237–244 (2019).
Google Scholar 
58.Finnerty, P. B., Shine, R. & Brown, G. P. The costs of parasite infection: Effects of removing lungworms on performance, growth and survival of free-ranging cane toads. Funct. Ecol. 32, 402–415 (2018).
Google Scholar 
59.Pettit, L., Greenlees, M. & Shine, R. The impact of transportation and translocation on dispersal behaviour in the invasive cane toad. Oecologia 184, 411–422 (2017).ADS 
PubMed 

Google Scholar 
60.Kraus, F. Alien Reptiles and Amphibians: A Scientific Compendium and Analysis (Springer, 2008).
Google Scholar 
61.McCann, S., Greenlees, M. J. & Shine, R. On the fringe of the invasion: The ecological impact of cane toads in marginally suitable habitats. Biol. Invasions 19, 2729–2737 (2017).
Google Scholar 
62.S. Kaiser et al., unpubl. Data.63.Finnerty, P., Shine, R. & Brown, G. P. Survival of the faeces: Does a nematode lungworm adaptively manipulate the behaviour of its cane toad host? Ecol. Evol. 8, 4606–4618 (2018).PubMed 
PubMed Central 

Google Scholar 
64.Brown, G. P., Kelehear, C., Pizzatto, L. & Shine, R. The impact of lungworm parasites on rates of dispersal of their anuran host, the invasive cane toad. Biol. Invasions 18, 103–114 (2016).
Google Scholar 
65.G. Ward-Fear et al., unpubl. Data. More

1.Walther, G. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
PubMed 

Google Scholar 
2.Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A (Cambridge University Press, 2014).
Google Scholar 
3.Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems. A global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).CAS 

Google Scholar 
4.Cañedo-Argüelles, M., Kefford, B. & Schäfer, R. Salt in freshwaters: Causes, effects and prospects—introduction to the theme issue. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
5.Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front. Ecol. Environ. 15, 84–90 (2017).
Google Scholar 
6.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 
PubMed 

Google Scholar 
7.Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES (2019).8.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
CAS 
PubMed 

Google Scholar 
9.Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).ADS 
CAS 
PubMed 

Google Scholar 
10.DeWitt, T. J., Sih, A. & Wilson, D. S. Costs and limits of phenotypic plasticity. Trends Ecol. Evol. 13, 77–81 (1998).CAS 
PubMed 

Google Scholar 
11.Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Climate change and evolution: Disentangling environmental and genetic responses. Mol. Ecol. 17, 167–178 (2008).CAS 
PubMed 

Google Scholar 
12.Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: The role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
13.Salamin, N., Wüest, R. O., Lavergne, S., Thuiller, W. & Pearman, P. B. Assessing rapid evolution in a changing environment. Trends Ecol. Evol. 25, 692–698 (2010).PubMed 

Google Scholar 
14.Hairston, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. & Fox, J. A. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8, 1114–1127 (2005).
Google Scholar 
15.Govaert, L., Pantel, J. H. & De Meester, L. Eco-evolutionary partitioning metrics: Assessing the importance of ecological and evolutionary contributions to population and community change. Ecol. Lett. 19, 839–853 (2016).PubMed 

Google Scholar 
16.Diamond, S. E. & Martin, R. A. The interplay between plasticity and evolution in response to human-induced environmental change. F1000Research 5, 1–10 (2016).
Google Scholar 
17.Barraclough, T. G. How do species interactions affect evolutionary dynamics across whole communities?. Annu. Rev. Ecol. Evol. Syst. 46, 25–48 (2015).
Google Scholar 
18.De Meester, L. et al. Analysing eco-evolutionary dynamics—The challenging complexity of the real world. Funct. Ecol. 33, 43–59 (2019).
Google Scholar 
19.Kleynhans, E. J., Otto, S. P., Reich, P. B. & Vellend, M. Adaptation to elevated CO2 in different biodiversity contexts. Nat. Commun. 7, 20 (2016).
Google Scholar 
20.Walther, G. R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
Google Scholar 
21.Kooyers, N. J., James, B. & Blackman, B. K. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient. Evolution (N.Y.) 71, 1205–1221 (2017).CAS 

Google Scholar 
22.Lawrence, D. et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 10, 20 (2012).
Google Scholar 
23.terHorst, C. P., Lennon, J. T. & Lau, J. A. The relative importance of rapid evolution for plant-microbe interactions depends on ecological context. Proc. R. Soc. B Biol. Sci. 281, 20 (2014).
Google Scholar 
24.Lau, J. A., Shaw, R. G., Reich, P. B. & Tiffin, P. Indirect effects drive evolutionary responses to global change. New Phytol. 201, 335–343 (2014).CAS 
PubMed 

Google Scholar 
25.Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).ADS 
CAS 
PubMed 

Google Scholar 
26.Hart, S. P., Turcotte, M. M. & Levine, J. M. Effects of rapid evolution on species coexistence. Proc. Natl. Acad. Sci. 116, 2112–2117 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Grainger, T. N., Rudman, S. M., Schmidt, P. & Levine, J. M. Competitive history shapes rapid evolution in a seasonal climate. Proc. Natl. Acad. Sci. 118, e22015772118 (2021).
Google Scholar 
28.McGrady-Steed, J., Harris, P. M. & Morin, P. J. Biodiversity regulates ecosystem predictability. Nature 390, 162–165 (1997).ADS 
CAS 

Google Scholar 
29.Altermatt, F. et al. Big answers from small worlds: A user’s guide for protist microcosms as a model system in ecology and evolution. Methods Ecol. Evol. 6, 218–231 (2015).
Google Scholar 
30.Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl. Acad. Sci. USA 115, 6506–6511 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Stoecker, D. & Pierson, J. Predation on protozoa: Its importance to zooplankton revisited. J. Plankton Res. 41, 367–373 (2019).
Google Scholar 
32.Berninger, U.-G., Finlay, B. J. & Kuuppo-Leinikki, P. Protozoan control of bacterial abundances in freshwater. Limnol. Oceanogr. 36, 139–147 (1991).ADS 

Google Scholar 
33.Williams, W. D. Anthropogenic salinisation of inland waters. Hydrobiologia 466, 329–337 (2001).
Google Scholar 
34.Herbert, E. R. et al. A global perspective on wetland salinization: Ecological consequences of a growing threat to freshwater wetlands. Ecosphere 6, 1–43 (2015).
Google Scholar 
35.Neubauer, S. C. & Craft, C. B. Global change and tidal freshwater wetlands: Scenarios and impacts. Tidal Freshw. Wetl. 20, 20 (2009).
Google Scholar 
36.Osmond, M. M. & de Mazancourt, C. How competition affects evolutionary rescue. Philos. Trans. R. Soc. B Biol. Sci. 368, 20 (2013).
Google Scholar 
37.terHorst, C. P. et al. Evolution in a community context: Trait responses to multiple species interactions. Am. Nat. 191, 368–380 (2018).
Google Scholar 
38.Donelson, J. M. et al. Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
39.Vanvelk, H., Govaert, L., van den Berg, E. M., Brans, K. I. & De Meester, L. Interspecific differences, plastic, and evolutionary responses to a heat wave in three co-occurring Daphnia species. Limnol. Oceanogr. 20, 1–20. https://doi.org/10.1002/lno.11675 (2020).Article 

Google Scholar 
40.Svensson, F., Norberg, J. & Snoeijs, P. Diatom cell size, Coloniality and motility: Trade-Offs between temperature, Salinity and nutrient supply with climate change. PLoS One 9, 25 (2014).
Google Scholar 
41.Karp-Boss, L. & Boss, E. The elongated, the squat and the spherical: Selective pressures for phytoplankton shape. In Aquatic Microbial Ecology and Biogeochemistry: A Dual Perspective (eds Glibert, P. & Kana, T.) 25–34 (Springer, 2016).
Google Scholar 
42.Finley, H. E. Toleration of fresh water Protozoa to increased salinity. Ecology 11, 337–347 (1930).
Google Scholar 
43.Chen, H. & Jiang, J. G. Osmotic responses of Dunaliella to the changes of salinity. J. Cell. Physiol. 219, 251–258 (2009).CAS 
PubMed 

Google Scholar 
44.Shetty, P., Gitau, M. M. & Maróti, G. Salinity stress responses and adaptation mechanisms in eukaryotic green microalgae. Cells 8, 1–16 (2019).
Google Scholar 
45.terHorst, C. P. Evolution in response to direct and indirect ecological effects in pitcher plant inquiline communities. Am. Nat. 176, 675–685 (2010).PubMed 

Google Scholar 
46.Stoks, R., Govaert, L., Pauwels, K., Jansen, B. & De Meester, L. Resurrecting complexity: The interplay of plasticity and rapid evolution in the multiple trait response to strong changes in predation pressure in the water flea Daphnia magna. Ecol. Lett. 19, 180–190 (2016).PubMed 

Google Scholar 
47.Hendry, A. P. Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. J. Hered. 107, 25–41 (2016).PubMed 

Google Scholar 
48.Henn, J. J. et al. Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change. Front. Plant Sci. 9, 1–11 (2018).ADS 

Google Scholar 
49.Johansson, J. Evolutionary responses to environmental changes:How does competition affect adaptation?. Evolution (N. Y.) 62, 421–435 (2008).
Google Scholar 
50.Li, S. J. et al. Microbial communities evolve faster in extreme environments. Sci. Rep. 4, 1–9 (2014).
Google Scholar 
51.Terhorst, C. P. Experimental evolution of protozoan traits in response to interspecific competition. J. Evol. Biol. 24, 36–46 (2011).CAS 
PubMed 

Google Scholar 
52.Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. Inferring species interactions in ecological communities: A comparison of methods at different levels of complexity. Methods Ecol. Evol. 6, 895–906 (2015).
Google Scholar 
53.Lorts, C. M. & Lasky, J. R. Competition × drought interactions change phenotypic plasticity and the direction of selection on Arabidopsis traits. New Phytol. 227, 1060–1072 (2020).CAS 
PubMed 

Google Scholar 
54.Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. Bioscience 50, 217–226 (2000).
Google Scholar 
55.Klironomos, J. H. et al. Abrupt rise in atmospheric CO2 overestimates community response in a model plant-soil system. Nature 433, 621–624 (2005).ADS 
CAS 
PubMed 

Google Scholar 
56.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
57.Finlay, B. J., Esteban, G. F., Olmo, J. L. & Tyler, P. A. Global distribution of free-living microbial species. Ecography (Cop.) 22, 138–144 (1999).
Google Scholar 
58.Fox, J. W. & McGrady-Steed, J. Stability and complexity in model ecosystems. J. Anim. Ecol. 71, 749–756 (2002).
Google Scholar 
59.Haddad, N. M. et al. Species’ traits predict the effects of disturbance and productivity on diversity. Ecol. Lett. 11, 348–356 (2008).PubMed 

Google Scholar 
60.Fronhofer, E. A. & Altermatt, F. Eco-evolutionary feedbacks during experimental range expansions. Nat. Commun. 6, 1–9 (2015).
Google Scholar 
61.Sonneborn, T. M. Chapter 12 methods in paramecium research. Methods Cell Biol. 4, 241–339 (1970).
Google Scholar 
62.Berger, H. & Foissner, W. Illustrated guide and ecological notes to ciliate species (Protozoa, Ciliophora) in running waters, lakes, and sewage plants. Handb. Angew. Limnol. Grundlagen-Gewässerbelastung-Restaurierung-Aquatische ökotoxikologie-Bewertung-Gewässerschutz 20, 1–60 (2014).
Google Scholar 
63.Cassidy-Hanley, D. M. Tetrahymena in the laboratory: Strain resources, methods for culture, maintenance, and storage. Methods Cell Biol. 109, 237–276 (2012).PubMed 
PubMed Central 

Google Scholar 
64.Sonzogni, W. C., Richardson, W., Rodgers, P. & Monteith, T. J. Chloride pollution of the Great Lakes. Water Pollut. Control Fed. 55, 513–521 (1983).CAS 

Google Scholar 
65.Lind, L. et al. Salty fertile lakes: How salinization and eutrophication alter the structure of freshwater communities. Ecosphere 9, 25 (2018).ADS 

Google Scholar 
66.Falconer, D. S. Introduction to Quantitative Genetics (Longman Group Ltd, 1981).
Google Scholar 
67.Pennekamp, F., Schtickzelle, N. & Petchey, O. L. BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes. Ecol. Evol. 5, 2584–2595 (2015).PubMed 
PubMed Central 

Google Scholar 
68.Pennekamp, F. et al. Dynamic species classification of microorganisms across time, abiotic and biotic environments—a sliding window approach. PLoS One 12, e0176682 (2017).PubMed 
PubMed Central 

Google Scholar 
69.Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version 2.0-6. Retrieved in July 7. (2014).70.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
71.Barton, K. MuMIn: Multi-Model Inference, Version 1.43.6. 1–75 (2019).72.Fronhofer, E. A., Gut, S. & Altermatt, F. Evolution of density-dependent movement during experimental range expansions. J. Evol. Biol. 30, 2165–2176 (2017).CAS 
PubMed 

Google Scholar 
73.Ellner, S. P., Geber, M. A. & Hairston, N. G. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614 (2011).PubMed 

Google Scholar 
74.Govaert, L. Eco-evolutionary partitioning metrics: A practical guide for biologists. Belgian J. Zool. 148, 167–202 (2018).
Google Scholar  More

1.Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: Region-wide declines in architectural complexity. Proc. R. Soc. B Biol. Sci. 276, 3019–3025 (2009).
Google Scholar 
2.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 

Google Scholar 
3.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
PubMed 

Google Scholar 
4.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 

Google Scholar 
5.Loya, Y. et al. Coral bleaching: The winners and the losers. Ecol. Lett. 4, 122–131 (2001).
Google Scholar 
6.Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).
Google Scholar 
7.Depczynski, M. et al. Bleaching, coral mortality and subsequent survivorship on a West Australian fringing reef. Coral Reefs 32, 233–238 (2013).ADS 

Google Scholar 
8.Edmunds, P. J. Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Sci. Rep. 8, 16615. https://doi.org/10.1038/s41598-018-34686-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Richmond, R. H., Tisthammer, K. H. & Spies, N. P. The effects of anthropogenic stressors on reproduction and recruitment of corals and reef organisms. Front. Mar. Sci. 5, 266. https://doi.org/10.3389/fmars.2018.00226 (2018).Article 

Google Scholar 
10.Oliver, E. C. J. et al. Marine heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).PubMed 

Google Scholar 
11.Rinkevich, B. The contribution of photosynthetic products to coral reproduction. Mar. Biol. 101, 259–263 (1989).CAS 

Google Scholar 
12.Lesser, M. P. Using energetic budgets to assess the effects of environmental stress on corals: Are we measuring the right things?. Coral Reefs 32, 25–33 (2013).ADS 

Google Scholar 
13.Muscatine, L., McCloskey, L. & Marian, R. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
CAS 

Google Scholar 
14.Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).ADS 

Google Scholar 
15.Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl. Acad. Sci. USA 118, e2022653118. https://doi.org/10.1073/pnas.2022653118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 

Google Scholar 
17.Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B 282, 20151887. https://doi.org/10.1098/rspb.2015.1997 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Leuzinger, S., Willis, B. L. & Anthony, K. R. N. Energy allocation in a reef coral under varying resource availability. Mar. Biol. 159, 177–186 (2012).
Google Scholar 
19.Oren, U., Benayahu, Y., Lubinevsky, H. & Loya, Y. Colony integration during regeneration in the stony coral Favia favus. Ecology 82, 802–813 (2001).
Google Scholar 
20.Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876 (2019).ADS 

Google Scholar 
21.Ward, S., Harrison, P. & Hoegh-Guldberg, O. Coral bleaching reduces reproduction of scleractinian corals and increases susceptibility to future stress. Proc. 9th Int. Coral Reef Symp. 1123–1128 (2002).22.Levitan, D. R., Boudreau, W., Jara, J. & Knowlton, N. Long-term reduced spawning in Orbicella coral species due to temperature stress. Mar. Ecol. Prog. Ser. 515, 1–10 (2014).ADS 

Google Scholar 
23.Johnston, E. C., Counsell, C. W. W., Sale, T. L., Burgess, S. C. & Toonen, R. J. The legacy of stress: Coral bleaching impacts reproduction years later. Funct. Ecol. 34, 2315–2325 (2020).
Google Scholar 
24.Szmant, A. M. & Gassman, N. J. The effects of prolonged ‘bleaching’ on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8, 217–224 (1990).ADS 

Google Scholar 
25.Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: energetics and reproduction of a reef coral with heat tolerant Symbiodinium Type-D. J. Mar. Biol. 2011, 185890. https://doi.org/10.1155/2011/185890 (2011).Article 

Google Scholar 
26.Figueiredo, J. et al. Ontogenetic change in the lipid and fatty acid composition of scleractinian coral larvae. Coral Reefs 31, 613–619 (2012).ADS 

Google Scholar 
27.Hagedorn, M. et al. Potential bleaching effects on coral reproduction. Reprod. Fertil. Dev. 28, 1061–1071 (2016).CAS 

Google Scholar 
28.Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239 (2001).
Google Scholar 
29.Howells, E. J. et al. Species-specific trends in the reproductive output of corals across environmental gradients and bleaching histories. Mar. Pollut. Bull. 105, 532–539 (2016).CAS 
PubMed 

Google Scholar 
30.Godoy, L. et al. Southwestern Atlantic reef-building corals Mussismilia spp. are able to spawn while fully bleached. Mar. Biol. 168, 15. https://doi.org/10.1007/s00227-021-03824-z (2021).CAS 
Article 

Google Scholar 
31.Veron, J. E. Acropora hyacinthus. in Corals of the World, vol. 1–3. (ed. Veron, J. E.) 404–405 (Australian Institute of Marine Sciences, 2000).32.Pratchett, M. S., McCowan, D., Maynard, J. A. & Heron, S. F. Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French polynesia. PLoS ONE 8, e70443. https://doi.org/10.1371/journal.pone.0070443 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Speare, K. E., Adam, T. C., Winslow, E. M., Lenihan, H. S. & Burkepile, D. E. Size-dependent mortality of corals during marine heatwave erodes recovery capacity of a coral reef. Glob. Change Biol. https://doi.org/10.1111/gcb.16000 (2021). Article 

Google Scholar 
34.Holbrook, S. J. et al. Recruitment drives spatial variation in recovery rates of resilient coral reefs. Sci. Rep. 8, 7338. https://doi.org/10.1038/s41598-018-25414-8 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Carroll, A., Harrison, P. & Adjeroud, M. Sexual reproduction of Acropora reef corals at Moorea, French polynesia. Coral Reefs 25, 93–97 (2006).ADS 

Google Scholar 
36.Tsounis, G. et al. Anthropogenic effects on reproductive effort and allocation of energy reserves in the Mediterranean octocoral Paramuricea clavata. Mar. Ecol. Prog. Ser. 449, 161–172 (2012).ADS 

Google Scholar 
37.Wall, C. B., Ritson-Williams, R., Popp, B. N. & Gates, R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Jung, E. M. U., Stat, M., Thomas, L., Koziol, A. & Schoepf, V. Coral host physiology and symbiont dynamics associated with differential recovery from mass bleaching in an extreme, macro-tidal reef environment in northwest Australia. Coral Reefs 40, 893–905 (2021).
Google Scholar 
39.Tremblay, P., Gori, A., Maguer, J. F., Hoogenboom, M. & Ferrier-Pagès, C. Heterotrophy promotes the re-establishment of photosynthate translocation in a symbiotic coral after heat stress. Sci. Rep. 6, 38112. https://doi.org/10.1038/srep38112 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Baumann, J., Grottoli, A. G., Hughes, A. D. & Matsui, Y. Photoautotrophic and heterotrophic carbon in bleached and non-bleached coral lipid acquisition and storage. J. Exp. Mar. Bio. Ecol. 461, 469–478 (2014).CAS 

Google Scholar 
41.Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Chang. Biol. 20, 3823–3833 (2014).ADS 
PubMed 

Google Scholar 
42.Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Rapid declines in metabolism explain extended coral larval longevity. Coral Reefs 32, 539–549 (2013).ADS 

Google Scholar 
43.Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. II. Biochemical changes in adults and their eggs. Coral Reefs 19, 240–246 (2001).
Google Scholar 
44.Harii, S., Nadaoka, K., Yamamoto, M. & Iwao, K. Temporal changes in settlement, lipid content and lipid composition of larvae of the spawning hermatypic coral Acropora tenuis. Mar. Ecol. Prog. Ser. 346, 89–96 (2007).ADS 
CAS 

Google Scholar 
45.Wallace, C. C. Reproduction, recruitment and fragmentation in nine sympatric species of the coral genus Acropora. Mar. Biol. 88, 217–233 (1985).
Google Scholar 
46.Ziegler, R. & Ibrahim, M. M. Formation of lipid reserves in fat body and eggs of the yellow fever mosquito, Aedes aegypti. J. Insect Physiol. 47, 623–627 (2001).CAS 
PubMed 

Google Scholar 
47.Baliña, S., Temperoni, B., Greco, L. S. L. & Tropea, C. Losing reproduction: effect of high temperature on female biochemical composition and egg quality in a freshwater crustacean with direct development, the red cherry shrimp, Neocaridina davidi (Decapoda, Atyidae). Biol. Bull. 234, 139–151 (2018).PubMed 

Google Scholar 
48.Levitan, D. R. The relationship between egg size and fertilization success in broadcast-spawning marine invertebrates. Integr. Comp. Biol. 46, 298–311 (2006).PubMed 

Google Scholar 
49.Caballes, C. F., Pratchett, M. S., Kerr, A. M. & Rivera-Posada, J. A. The role of maternal nutrition on oocyte size and quality, with respect to early larval development in the coral-eating starfish, Acanthaster planci. PLoS ONE 11, e0158007. https://doi.org/10.1371/journal.pone.0158007 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 4, 160017. https://doi.org/10.1038/sdata.2016.17 (2017).Article 

Google Scholar 
51.Foster, T. & Gilmour, J. Egg size and fecundity of biannually spawning corals at Scott Reef. Sci. Rep. 10, 12313. https://doi.org/10.1038/s41598-020-68289-4 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Harriott, V. J. Reproductive ecology of four scleratinian species at Lizard Island, Great Barrier Reef. Coral Reefs 2, 9–18 (1983).ADS 

Google Scholar 
53.Vargas-Ángel, B., Colley, S. B., Hoke, S. M. & Thomas, J. D. The reproductive seasonality and gametogenic cycle of Acropora cervicornis off Broward County, Florida, USA. Coral Reefs 25, 110–122 (2006).ADS 

Google Scholar 
54.Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: comparative studies of reef-building corals. Ecology 77, 950–963 (1996).
Google Scholar 
55.Brandt, M. E. The effect of species and colony size on the bleaching response of reef-building corals in the Florida Keys during the 2005 mass bleaching event. Coral Reefs 28, 911–924 (2009).ADS 

Google Scholar 
56.Sakai, K., Singh, T. & Iguchi, A. Bleaching and post-bleaching mortality of Acropora corals on a heat-susceptible reef in 2016. PeerJ 2019, e8138. https://doi.org/10.7717/peerj.8138 (2019).Article 

Google Scholar 
57.Nozawa, Y. & Lin, C. H. Effects of colony size and polyp position on polyp fecundity in the scleractinian coral genus Acropora. Coral Reefs 33, 1057–1066 (2014).ADS 

Google Scholar 
58.Álvarez-Noriega, M. et al. Fecundity and the demographic strategies of coral morphologies. Ecology 97, 3485–3493 (2016).PubMed 

Google Scholar 
59.Bena, C. & Van Woesik, R. The impact of two bleaching events on the survival of small coral colonies (Okinawa, Japan). Bull. Mar. Sci. 75, 115–125 (2004).
Google Scholar 
60.Shenkar, N., Fine, M. & Loya, Y. Size matters: Bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).ADS 

Google Scholar 
61.Hughes, T. P. et al. Global warming impairs stock–recruitment dynamics of corals. Nature 568, 387–390 (2019).ADS 
CAS 
PubMed 

Google Scholar 
62.McClanahan, T. R., Maina, J., Moothien-Pillay, R. & Baker, A. C. Effects of geography, taxa, water flow, and temperature variation on coral bleaching intensity in Mauritius. Mar. Ecol. Prog. Ser. 298, 131–142 (2005).ADS 

Google Scholar 
63.Hoogenboom, M. O. et al. Environmental drivers of variation in bleaching severity of Acropora species during an extreme thermal anomaly. Front. Mar. Sci. 4, 376. https://doi.org/10.3389/fmars.2017.00376 (2017).Article 

Google Scholar 
64.Schoepf, V. et al. Thermally variable, macrotidal reef habitats promote rapid recovery from mass coral bleaching. Front. Mar. Sci. 7, 245. https://doi.org/10.3389/fmars.2020.00245 (2020).Article 

Google Scholar 
65.Golbuu, Y. et al. Palau’s coral reefs show differential habitat recovery following the 1998-bleaching event. Coral Reefs 26, 319–332 (2007).
Google Scholar 
66.van Woesik, R. et al. Climate-change refugia in the sheltered bays of Palau: Analogs of future reefs. Ecol. Evol. 2, 2474–2484 (2012).PubMed 
PubMed Central 

Google Scholar 
67.Penin, L., Adjeroud, M., Schrimm, M. & Lenihan, H. S. High spatial variability in coral bleaching around Moorea (French Polynesia): Patterns across locations and water depths. C. R. Biol. 330, 171–181 (2007).PubMed 

Google Scholar 
68.Penin, L., Vidal-Dupiol, J. & Adjeroud, M. Response of coral assemblages to thermal stress: Are bleaching intensity and spatial patterns consistent between events?. Environ. Monit. Assess. 185, 5031–5042 (2013).PubMed 

Google Scholar 
69.Brown, B. E., Downs, C. A., Dunne, R. P. & Gibb, S. W. Exploring the basis of thermotolerance in the reef coral Goniastrea aspera. Mar. Ecol. Prog. Ser. 242, 119–129 (2002).ADS 

Google Scholar 
70.Kenkel, C. D. et al. Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments. Mol. Ecol. 22, 4335–4348 (2013).CAS 
PubMed 

Google Scholar 
71.Burt, J. A. & Bauman, A. G. Suppressed coral settlement following mass bleaching in the southern Persian/Arabian Gulf. Aquat. Ecosyst. Heal. Manag. 23, 166–174 (2020).
Google Scholar 
72.Shlesinger, T. & Loya, Y. Breakdown in spawning synchrony: A silent threat to coral persistence. Science 365, 1002–1007 (2019).ADS 
CAS 
PubMed 

Google Scholar 
73.Edmunds, P., Gates, R. & Gleason, D. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989 (2001).
Google Scholar 
74.Edmunds, P. J. Spatiotemporal variation in coral recruitment and its association with seawater temperature. Limnol. Oceanogr. 66, 1394–1408 (2021).ADS 

Google Scholar 
75.Bouwmeester, J. et al. Latitudinal variation in monthly-scale reproductive synchrony among Acropora coral assemblages in the Indo-Pacific. Coral Reefs 40, 1411–1418 (2021).
Google Scholar 
76.Edmunds, P. J. MCR LTER: Coral reef: Long-term population and community dynamics: Corals, ongoing since 2005. knb-lter-mcr.4.38. 10.6073/pasta/10ee808a046cb63c0b8e3bc3c9799806 (2020).77.Claar, D. C. & Baum, J. K. Timing matters: Survey timing during extended heat stress can influence perceptions of coral susceptibility to bleaching. Coral Reefs 38, 559–565 (2019).ADS 

Google Scholar 
78.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Leichter, J., Seydel, K. & Gotschalk, C. MCR LTER: Coral reef: Benthic water temperature, ongoing since 2005. knb-lter-mcr.1035.13. 10.6073/pasta/2087a33cdd16986352bed443fecc7fd7 (2020).80.Bradford, M. A rapid and sensitive method for the quantification of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 

Google Scholar 
81.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1955).
Google Scholar 
82.Masuko, T. et al. Carbohydrate analysis by a phenol-sulfuric acid method in microplate format. Anal. Biochem. 339, 69–72 (2005).CAS 
PubMed 

Google Scholar 
83.Stimson, J. & Kinzie, R. A. The temporal pattern and rate of release of zooxanthellae from the reef coral Pocillopora damicornis (Linnaeus) under nitrogen-enrichment and control conditions. J. Exp. Mar. Bio. Ecol. 153, 63–74 (1991).
Google Scholar 
84.Szmant-Froelich, A., Rhetter, M. & Riggs, L. Sexual reproduction of Favis fragum (ESPER): lunar patterns of gametogenesis, embryogenesis and planulation in Puerto Rico. Bull. Mar. Sci. 37, 880–892 (1985).
Google Scholar  More