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.

Twitter

Facebook

Email

Download PDF

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

Related Articles

COP26 didn’t solve everything — but researchers must stay engaged

Ending Hunger: Science must stop neglecting smallholder farmers

Reset Sustainable Development Goals for a pandemic world

How science can put the Sustainable Development Goals back on track

Subjects

Sustainability

Biodiversity

Climate change

Government

Latest on:

Sustainability

Battery-powered trains offer a cost-effective ride to a cleaner world
Research Highlight 22 NOV 21

All aboard the climate train! Scientists join activists for COP26 trip
News 02 NOV 21

Machine learning enables global solar-panel detection
News & Views 27 OCT 21

Biodiversity

Link knowledge and action networks to tackle disasters
Correspondence 16 NOV 21

COP26 climate pledges: What scientists think so far
News 05 NOV 21

The answer to the biodiversity crisis is not more debt
Editorial 26 OCT 21

Climate change

An IPCC reviewer shares his thoughts on the climate debate
Career Q&A 08 DEC 21

Brazil is in water crisis — it needs a drought plan
Comment 08 DEC 21

Build solar-energy systems to last — save billions
Comment 07 DEC 21

Jobs

Postdoc in Formulation Development for Gene Delivery Therapies

Technical University of Denmark (DTU)
2800 Kgs. Lyngby, Denmark

​​​​​​​Postdoc in Molecular Biology for Gene Delivery Project

Technical University of Denmark (DTU)
2800 Kgs. Lyngby, Denmark

Post-doctoral Research Fellows

Brigham and Women’s Hospital (BWH)
Boston, MA, United States

HPC/Research Computing Engineer

Francis Crick Institute
London, United Kingdom More

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.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

1.Amaja, L. G., Feyssa, D. H. & Gutema, T. M. Assessment of types of damage and causes of Human–wildlife conflict in Gera district, southwestern Ethiopia. J. Ecol. Nat. Environ. 8, 49–54 (2016).Article 

Google Scholar 
2.Decker, D. J., Laube, T. B. & Siemer, W. F. Human–Wildlife Conflict Management: A Practitioner’s Guide (Northeastern Wildlife Damage Management Research and Outreach Cooperative, 2002).
Google Scholar 
3.Habib, A., Nazir, I., Fazili, M. F. & Bhat, B. A. Human–wildlife conflict-causes, consequences and mitigation measures with special reference to Kashmir. J. Zool. Stud. 2, 26–30 (2015).
Google Scholar 
4.Eklund, A., Lopez-Bao, J. V., Tourani, M., Chapron, G. & Frank, J. Author Correction: Limited evidence on the effectiveness of interventions to reduce livestock predation by large carnivores. Sci. Rep. 8, 5770 (2018).ADS 
Article 

Google Scholar 
5.Hussain, S. The status of the snow leopard in Pakistan and its conflict with local farmers. Oryx 37, 26–33 (2003).Article 

Google Scholar 
6.Miller, J. R., Jhala, Y. V. & Schmitz, O. J. Human perceptions mirror realities of carnivore attack risk for livestock: Implications for mitigating human-carnivore conflict. PLoS ONE 11, e0162685 (2016).Article 

Google Scholar 
7.Aryal, P. et al. Human–carnivore conflict: Ecological and economical sustainability of predation on livestock by snow leopard and other carnivores in the Himalaya. Sustain. Sci. 9, 321–329 (2014).Article 

Google Scholar 
8.Khan, B. et al. Pastoralist experience and tolerance of snow leopard, wolf and lynx predation in Karakoram Pamir Mountains. J. Biol. Environ. Sci. 5, 214–229 (2014).
Google Scholar 
9.Jackson, R. M., Ahlborn, G., Gurung, M. & Ale, S. Reducing livestock depredation losses in the Nepalese Himalaya. In Proc. 17th Vertebrate Pest Conference (eds Timm, R. M. & Crabb, A. C.) 241–247 (University of California, 1996).
Google Scholar 
10.Qamar, Q. Z. et al. Human leopard conflict: An emerging issue of common leopard conservation in Machiara National Park, Azad Jammu, and Kashmir, Pakistan. Pak. J. Wildl. 1, 50–56 (2010).
Google Scholar 
11.Atickem, A., Williams, S., Bekele, A. & Thirgood, S. Livestock predation in the Bale Mountains, Ethiopia. Afr. J. Ecol. 48, 1076–1082 (2010).Article 

Google Scholar 
12.Gittleman, J. L., Funk, S. M., Macdonald, D. W. & Wayne, R. K. Carnivore conservation. Cambridge University Press, Cambridge consequences and mitigation measures with special reference to Kashmir. J. Zool. Stud. 2, 26–30 (2001).
Google Scholar 
13.Treves, A. K. & Karanth, K. U. Human–carnivore conflict—Local solutions with global applications (Special section): Introduction. Conserv. Biol. 17, 1489–1490 (2003).Article 

Google Scholar 
14.Li, J., Yin, H., Wang, D., Jiagong, Z. & Lu, Z. Human-snow leopard conflicts in the Sanjiangyuan Region of the Tibetan Plateau. Biol. Conserv. 166, 118–123 (2013).Article 

Google Scholar 
15.McCarthy, T. M. & Chapron, G. Snow Leopard Survival Strategy (IT and SLN, 2003).
Google Scholar 
16.Suryawanshi, K.R. Human carnivore conflicts: Understanding predation ecology and livestock damage by snow leopards. Ph.D. Thesis. Manipal University, India (2013).17.Bocci, A., Lovari, S., Khan, M. Z. & Mori, E. Sympatric snow leopards and Tibetan wolves: coexistence of large carnivores with human-driven potential competition. Eur. J. Wildl. Res. 63, 92 (2017).Article 

Google Scholar 
18.Wang, S. W. & Macdonald, D. Livestock predation by carnivores in Jigme Singye Wangchuck National Park, Bhutan. Biol. Conserv. 129, 558–565 (2006).Article 

Google Scholar 
19.Khan, M. Z., Khan, B., Awan, M. S. & Begum, F. Livestock depredation by large predators and its implications for conservation and livelihoods in the Karakoram Mountains of Pakistan. Oryx 52, 519–525 (2018).Article 

Google Scholar 
20.Ali, H., Younus, M., Din, J. U., Bischof, R. & Nawaz, M. A. Do Marco Polo argali Ovis ammon polii persist in Pakistan?. Oryx 53, 329–333 (2019).Article 

Google Scholar 
21.Dar, N. I., Minhas, R. A., Zaman, Q. & Linkie, M. Predicting the patterns, perceptions, and causes of human-carnivore conflict in and around Machiara National Park, Pakistan. Biol. Conserv. 142, 2076 (2009).Article 

Google Scholar 
22.RC Team. R: A Language and Environment for Statistical Computing (2013).23.Din, J. U. et al. A Tran’s boundary study of spatiotemporal patterns of livestock predation and prey preferences by snow leopard and wolf in the Pamir. Glob. Ecol. Conserv. 20, e00719 (2019).Article 

Google Scholar 
24.Conover, M. R. Resolving Human–Wildlife Conflicts: The Science of Wildlife Damage Management 418 (Lewis Publishers, 2002).
Google Scholar 
25.Graham, K., Beckerman, A. P. & Thirgood, S. Human–predator–prey conflicts: Ecological correlates, prey losses and patterns of management. Biol. Conserv. 122, 159–171 (2005).Article 

Google Scholar 
26.Li, X., Buzzard, P., Chen, Y. & Jiang, X. Patterns of livestock predation by carnivores: Human–wildlife conflict in Northwest Yunnan, China. Environ. Manage. 52, 1334–1340 (2013).ADS 
Article 

Google Scholar 
27.Dar, N. I., Minhas, R. A., Zaman, Q. & Linkie, M. Predicting the patterns, perceptions and causes of human–carnivore conflict in and around Machiara National Park, Pakistan. Biol. Conserv. 142, 2076–2082 (2009).Article 

Google Scholar 
28.Mishra, C., Prins, H. H. T. & van Wieren, S. E. Overstocking in the trans-Himalayan rangelands of India. Environ. Conserv. 28, 279–283 (1997).Article 

Google Scholar 
29.Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera Leo). J. Zool. (Lond.) 267(267), 309–322 (2005).Article 

Google Scholar 
30.Mc Guinness, S. & Taylor, D. Farmers’ perceptions and actions to decrease crop raiding by forest-dwelling primates around a Rwandan Forest fragment. Hum. Dimens. Wildl. 19, 361–372 (2014).Article 

Google Scholar 
31.ICIMOD. Glacial Lakes and Glacial Lake Outburst Floods in Nepal (Gland, 2011).Book 

Google Scholar 
32.Distefano, E. Human–Wildlife Conflict Worldwide: Collection of Case Studies, Analysis of Management Strategies and Good Practices (Food and Agricultural Organization of the United Nations (FAO), 2005).
Google Scholar 
33.Shedayi, A. A., Xu, M., Naseer, I. & Khan, B. Altitudinal gradients of soil and vegetation carbon and nitrogen in a high altitude nature reserve of Karakoram ranges. Springerplus 5, 1–14 (2016).CAS 
Article 

Google Scholar  More

PatientsStool samples from a total of 52 patients with varying stages of CKD were collected in this study: CKD3A (n = 12), CKD3B (n = 11), CKD4 (n = 15), CKD5 (n = 4) and ESRD (n = 10) (Table 1). Patients’ characteristics are summarized in Table 1. Among 52 patients, 31 were reported to have Type 2 diabetes mellitus and 7 patients were reported to have human immunodeficiency virus (HIV) infection. As expected, urine protein creatinine ratio, serum creatinine and blood urea nitrogen level increased with progressing stages of CKD (CKD 3A to ESRD). There was no significant difference in fat, protein, carbohydrates, dietary fiber and calorie intake between CKD patients with different stages (Supplementary Table S1).Table 1 Patients’ characteristics.Full size tableAlpha and beta-diversityRichness and Shannon index were not significantly different between different patient groups, meanwhile the CKD5 group showed a significant decrease in Simpson diversity compared with CKD 3A (FDR  More

1.Flemming HC, Wuertz S. Bacteria and archaea on Earth and their abundance in biofilms. Nat Rev Microbiol. 2019;17:247–60.CAS 
PubMed 

Google Scholar 
2.Wrighton KC, Thomas BC, Sharon I, Miller CS, Castelle CJ, VerBerkmoes NC, et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science. 2012;337:1661–5.CAS 
PubMed 

Google Scholar 
3.Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–8.CAS 
PubMed 

Google Scholar 
4.Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:1–11.
Google Scholar 
5.Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
6.Gleeson T, Befus KM, Jasechko S, Luijendijk E, Cardenas MB. The global volume and distribution of modern groundwater. Nat Geosci. 2016;9:161–7.CAS 

Google Scholar 
7.Akob DM, Küsel K. Where microorganisms meet rocks in the Earth’s Critical Zone. Biogeosciences. 2011;8:3531–43.CAS 

Google Scholar 
8.Griebler C, Lueders T. Microbial biodiversity in groundwater ecosystems. Freshw Biol. 2009;54:649–77.
Google Scholar 
9.Bell E, Lamminmäki T, Alneberg J, Andersson AF, Qian C, Xiong WL, et al. Active sulfur cycling in the terrestrial deep subsurface. ISME J. 2020;14:1260–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Einsiedl F, Mayer B. Hydrodynamic and microbial processes controlling nitrate in a fissured-porous karst aquifer of the Franconian Alb, Southern Germany. Environ Sci Technol. 2006;40:6697–702.CAS 
PubMed 

Google Scholar 
11.Schlesinger WH. On the fate of anthropogenic nitrogen. Proc Natl Acad Sci USA. 2009;106:203–8.CAS 
PubMed 

Google Scholar 
12.McCollom TM, Seewald JS. Serpentinites, hydrogen, and life. Elements. 2013;9:129–34.CAS 

Google Scholar 
13.Emerson JB, Thomas BC, Alvarez W, Banfield JF. Metagenomic analysis of a high carbon dioxide subsurface microbial community populated by chemolithoautotrophs and bacteria and archaea from candidate phyla. Environ Microbiol. 2016;18:1686–703.CAS 
PubMed 

Google Scholar 
14.Probst AJ, Ladd B, Jarett JK, Geller-McGrath DE, Sieber CMK, Emerson JB, et al. Differential depth distribution of microbial function and putative symbionts through sediment- hosted aquifers in the deep terrestrial subsurface. Nat Microbiol. 2018;3:328–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Anantharaman K, Hausmann B, Jungbluth SP, Kantor RS, Lavy A, Warren LA, et al. Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle. ISME J. 2018;12:1715–28.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Wegner CE, Gaspar M, Geesink P, Herrmann M, Marz M, Küsel K. Biogeochemical regimes in shallow aquifers reflect the metabolic coupling of the elements nitrogen, sulfur, and carbon. Appl Environ Microbiol. 2019;85:1–18.
Google Scholar 
17.Herrmann M, Rusznyak A, Akob DM, Schulze I, Opitz S, Totsche KU, et al. Large fractions of CO2-fixing microorganisms in pristine limestone aquifers appear to be involved in the oxidation of reduced sulfur and nitrogen compounds. Appl Environ Microbiol. 2015;81:2384–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Probst AJ, Castelle CJ, Singh A, Brown CT, Anantharaman K, Sharon I, et al. Genomic resolution of a cold subsurface aquifer community provides metabolic insights for novel microbes adapted to high CO2 concentrations. Environ Microbiol. 2017;19:459–74.CAS 
PubMed 

Google Scholar 
19.Jewell TNM, Karaoz U, Brodie EL, Williams KH, Beller HR. Metatranscriptomic evidence of pervasive and diverse chemolithoautotrophy relevant to C, S, N and Fe cycling in a shallow alluvial aquifer. ISME J. 2016;10:2106–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Handley KM, Bartels D, O’Loughlin EJ, Williams KH, Trimble WL, Skinner K, et al. The complete genome sequence for putative H2- and S-oxidizer Candidatus Sulfuricurvum sp., assembled de novo from an aquifer-derived metagenome. Environ Microbiol. 2014;16:3443–62.CAS 
PubMed 

Google Scholar 
21.Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6.CAS 
PubMed 

Google Scholar 
22.von Bergen M, Jehmlich N, Taubert M, Vogt C, Bastida F, Herbst FA, et al. Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology. ISME J. 2013;7:1877–85.
Google Scholar 
23.Taubert M, Vogt C, Wubet T, Kleinsteuber S, Tarkka MT, Harms H, et al. Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing, benzene-degrading microbial consortium. ISME J. 2012;6:2291–301.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Taubert M, Baumann S, von Bergen M, Seifert J. Exploring the limits of robust detection of incorporation of 13C by mass spectrometry in protein-based stable isotope probing (protein-SIP). Anal Bioanal Chem. 2011;401:1975–82.CAS 
PubMed 

Google Scholar 
25.Rimstidt JD, Vaughan DJ. Pyrite oxidation: a state-of-the-art assessment of the reaction mechanism. Geochim Cosmochim Acta. 2003;67:873–80.CAS 

Google Scholar 
26.Schippers A, Jozsa PG, Sand W. Sulfur chemistry in bacterial leaching of pyrite. Appl Environ Microbiol. 1996;62:3424–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Kohlhepp B, Lehmann R, Seeber P, Küsel K, Trumbore SE, Totsche KU. Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate-siliciclastic alternations of the Hainich CZE, central Germany. Hydrol Earth Syst Sci. 2017;21:6091–116.CAS 

Google Scholar 
28.Grimm F, Franz B, Dahl C. Thiosulfate and sulfur oxidation in purple sulfur bacteria. In: Dahl C, Friedrich CG, editors. Microbial Sulfur Metabolism. Berlin, Heidelberg: Springer; 2008. p. 101–16.29.Ghosh W, Dam B. Biochemistry and molecular biology of lithotrophic sulfur oxidation by taxonomically and ecologically diverse Bacteria and Archaea. FEMS Microbiol Rev. 2009;33:999–1043.CAS 
PubMed 

Google Scholar 
30.Kumar S, Herrmann M, Blohm A, Hilke I, Frosch T, Trumbore SE, et al. Thiosulfate- and hydrogen-driven autotrophic denitrification by a microbial consortium enriched from groundwater of an oligotrophic limestone aquifer. FEMS Microbiol Ecol. 2018;94:fiy141.CAS 

Google Scholar 
31.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Core Team; 2019 [cited 2021]; Available from: https://www.R-project.org/.32.Ryabchykov O, Bocklitz T, Ramoji A, Neugebauer U, Foerster M, Kroegel C, et al. Automatization of spike correction in Raman spectra of biological samples. Chemom Intell Lab. 2016;155:1–6.CAS 

Google Scholar 
33.Dörfer T, Bocklitz T, Tarcea N, Schmitt M, Popp J. Checking and improving calibration of Raman spectra using chemometric approaches. Z Phys Chem. 2011;225:753–64.
Google Scholar 
34.Bocklitz TW, Dörfer T, Heinke R, Schmitt M, Popp J. Spectrometer calibration protocol for Raman spectra recorded with different excitation wavelengths. Spectrochim Acta A. 2015;149:544–9.CAS 

Google Scholar 
35.Guo SX, Heinke R, Stöckel S, Rösch P, Bocklitz T, Popp J. Towards an improvement of model transferability for Raman spectroscopy in biological applications. Vib Spectrosc. 2017;91:111–8.CAS 

Google Scholar 
36.Liland KH, Almoy T, Mevik BH. Optimal choice of baseline correction for multivariate calibration of spectra. Appl Spectrosc. 2010;64:1007–16.CAS 
PubMed 

Google Scholar 
37.Taubert M, Stöckel S, Geesink P, Girnus S, Jehmlich N, von Bergen M, et al. Tracking active groundwater microbes with D2O labelling to understand their ecosystem function. Environ Microbiol. 2018;20:369–84.CAS 
PubMed 

Google Scholar 
38.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 

Google Scholar 
39.Seifert J, Taubert M, Jehmlich N, Schmidt F, Völker U, Vogt C, et al. Protein-based stable isotope probing (protein-SIP) in functional metaproteomics. Mass Spectrom Rev. 2012;31:683–97.CAS 
PubMed 

Google Scholar 
40.Taubert M. SIsCA. 2020 [updated 23.10.2020; cited 2021]; Available from: https://github.com/m-taubert/SIsCA.41.MacCoss MJ, Wu CC, Matthews DE, Yates JR. Measurement of the isotope enrichment of stable isotope-labeled proteins using high-resolution mass spectra of peptides. Anal Chem. 2005;77:7646–53.CAS 
PubMed 

Google Scholar 
42.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Google Scholar 
43.Friedrich CG, Rother D, Bardischewsky F, Quentmeier A, Fischer J. Oxidation of reduced inorganic sulfur compounds by bacteria: Emergence of a common mechanism? Appl Environ Microbiol. 2001;67:2873–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Kelly DP, Shergill JK, Lu WP, Wood AP. Oxidative metabolism of inorganic sulfur compounds by bacteria. Antonie Van Leeuwenhoek. 1997;71:95–107.CAS 
PubMed 

Google Scholar 
45.Beller HR, Letain TE, Chakicherla A, Kane SR, Legler TC, Coleman MA. Whole-genome transcriptional analysis of chemolithoautotrophic thiosulfate oxidation by Thiobacillus denitrificans under aerobic versus denitrifying conditions. J Bacteriol. 2006;188:7005–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Beller HR, Chain PSG, Letain TE, Chakicherla A, Larimer FW, Richardson PM, et al. The genome sequence of the obligately chemolithoautotrophic, facultatively anaerobic bacterium Thiobacillus denitfificans. J Bacteriol. 2006;188:1473–88.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.McKinlay JB, Harwood CS. Carbon dioxide fixation as a central redox cofactor recycling mechanism in bacteria. Proc Natl Acad Sci USA. 2010;107:11669–75.CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Tabita FR. Microbial ribulose 1,5-bisphosphate carboxylase/oxygenase: a different perspective. Photosyn Res. 1999;60:1–28.CAS 

Google Scholar 
49.Berg IA. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl Environ Microbiol. 2011;77:1925–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Overholt WA, Trumbore S, Xu X, Bornemann TL, Probst AJ, Krüger M, et al. Rates of primary production in groundwater rival those in oligotrophic marine systems. bioRxiv 2021 [Preprint]. 2021. Available from: https://doi.org/10.1101/2021.10.13.464073.51.Alfreider A, Vogt C, Geiger-Kaiser M, Psenner R. Distribution and diversity of autotrophic bacteria in groundwater systems based on the analysis of RubisCO genotypes. Syst Appl Microbiol. 2009;32:140–50.CAS 
PubMed 

Google Scholar 
52.Herrmann M, Geesink P, Yan L, Lehmann R, Totsche KU, Küsel K. Complex food webs coincide with high genetic potential for chemolithoautotrophy in fractured bedrock groundwater. Water Res. 2020;170:115306.CAS 
PubMed 

Google Scholar 
53.Yan LJ, Herrmann M, Kampe B, Lehmann R, Totsche KU, Küsel K. Environmental selection shapes the formation of near-surface groundwater microbiomes. Water Res. 2020;170:115341.CAS 
PubMed 

Google Scholar 
54.Mattes TE, Alexander AK, Richardson PM, Munk AC, Han CS, Stothard P, et al. The genome of Polaromonas sp. strain JS666: Insights into the evolution of a hydrocarbon- and xenobiotic-degrading bacterium, and features of relevance to biotechnology. Appl Environ Microbiol. 2008;74:6405–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Salinero KK, Keller K, Feil WS, Feil H, Trong S, Di Bartolo G, et al. Metabolic analysis of the soil microbe Dechloromonas aromatica str. RCB: indications of a surprisingly complex life-style and cryptic anaerobic pathways for aromatic degradation. BMC Genomics. 2009;10:1–23.
Google Scholar 
56.Kämpfer P, Schulze R, Jäckel U, Malik KA, Amann R, Spring S. Hydrogenophaga defluvii sp. nov. and Hydrogenophaga atypica sp. nov., isolated from activated sludge. Int J Syst Evol Microbiol. 2005;55:341–4.PubMed 

Google Scholar 
57.Jin CZ, Zhuo Y, Wu XW, Ko SR, Li TH, Jin FJ, et al. Genomic and metabolic insights into denitrification, sulfur oxidation, and multidrug efflux pump mechanisms in the bacterium Rhodoferax sediminis sp. nov. Microorganisms. 2020;8:262.CAS 
PubMed Central 

Google Scholar 
58.Geisel N. Constitutive versus responsive gene expression strategies for growth in changing environments. PLoS ONE. 2011;6:e27033.CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Boden R, Hutt LP, Rae AW. Reclassification of Thiobacillus aquaesulis (Wood & Kelly, 1995) as Annwoodia aquaesulis gen. nov., comb. nov., transfer of Thiobacillus (Beijerinck, 1904) from the Hydrogenophilales to the Nitrosomonadales, proposal of Hydrogenophilalia class. nov within the ‘Proteobacteria’, and four new families within the orders Nitrosomonadales and Rhodocyclales. Int J Syst Evol Microbiol. 2017;67:1191–205.CAS 
PubMed 

Google Scholar 
60.Katayama-Fujimura Y, Tsuzaki N, Hirata A, Kuraishi H. Polyhedral inclusion-bodies (Carboxysomes) in Thiobacillus species with reference to the taxonomy of the genus Thiobacillus. J Gen Appl Microbiol. 1984;30:211–22.CAS 

Google Scholar 
61.Küsel K, Totsche KU, Trumbore SE, Lehmann R, Steinhäuser C, Herrmann M. How deep can surface signals be traced in the Critical Zone? Merging biodiversity with biogeochemistry research in a Central German Muschelkalk landscape. Front Earth Sci. 2016;4:32.
Google Scholar 
62.Roth VN, Lange M, Simon C, Hertkorn N, Bucher S, Goodall T, et al. Persistence of dissolved organic matter explained by molecular changes during its passage through soil. Nat Geosci. 2019;12:755–61.CAS 

Google Scholar 
63.Herrmann M, Wegner CE, Taubert M, Geesink P, Lehmann K, Yan LJ, et al. Predominance of Cand. Patescibacteria in groundwater is caused by their preferential mobilization from soils and flourishing under oligotrophic conditions. Front Microbiol. 2019;10:1407.PubMed 
PubMed Central 

Google Scholar 
64.Gray CM, Monson RK, Fierer N. Emissions of volatile organic compounds during the decomposition of plant litter. J Geophys Res Biogeosci. 2010;115:G03015.
Google Scholar 
65.Benk SA, Yan LJ, Lehmann R, Roth VN, Schwab VF, Totsche KU, et al. Fueling diversity in the subsurface: composition and age of dissolved organic matter in the Critical Zone. Front Earth Sci. 2019;7:296.
Google Scholar 
66.Schwab VF, Nowak ME, Elder CD, Trumbore SE, Xu XM, Gleixner G, et al. 14C-free carbon is a major contributor to cellular biomass in geochemically distinct groundwater of shallow sedimentary bedrock aquifers. Water Resour Res. 2019;55:2104–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Eiler A. Evidence for the ubiquity of mixotrophic bacteria in the upper ocean: Implications and consequences. Appl Environ Microbiol. 2006;72:7431–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Hansson TH, Grossart HP, del Giorgio PA, St-Gelais NF, Beisner BE. Environmental drivers of mixotrophs in boreal lakes. Limnol Oceanogr. 2019;64:1688–705.CAS 

Google Scholar 
69.Perez-Riverol Y, Csordas A, Bai JW, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–D50.CAS 
PubMed 

Google Scholar  More