Philippa Kaur

1.Norton, L. & Simon, R. Tumor size, sensitivity to therapy, and design of treatment schedules. Cancer Treat. Rep. 61, 1307–1317 (1977).CAS 
PubMed 

Google Scholar 
2.Goldie, J. H. & Coldman, A. J. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat. Rep. 63, 1727–1733 (1979).CAS 
PubMed 

Google Scholar 
3.Gatenby, R. A. A change of strategy in the war on cancer. Nature 459, 508–509 (2009).CAS 
Article 

Google Scholar 
4.Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1816 (2017).Article 

Google Scholar 
5.Martin, R. B., Fisher, M. E., Minchin, R. F. & Teo, K. L. Optimal control of tumor size used to maximize survival time when cells are resistant to chemotherapy. Math. Biosci. 110, 201–219 (1992).CAS 
Article 

Google Scholar 
6.Gatenby, R. A., Silva, A. S., Gillies, R. J. & Frieden, B. R. Adaptive therapy. Cancer Res. 69, 4894–4903 (2009).CAS 
Article 

Google Scholar 
7.Gatenby, R. & Brown, J. The evolution and ecology of resistance in cancer therapy. Cold Spring Harb. Perspect. Med. 10, a040972 (2020).CAS 
Article 

Google Scholar 
8.Bourguet, D. et al. Heterogeneity of selection and the evolution of resistance. Trends Ecol. Evol. 28, 110–118 (2013).Article 

Google Scholar 
9.Tabashnik, B. E., Brévault, T. & Carrière, Y. Insect resistance to Bt crops: lessons from the first billion acres. Nat. Biotechnol. 31, 510–521 (2013).CAS 
Article 

Google Scholar 
10.Cunningham, J. J. A call for integrated metastatic management. Nat. Ecol. Evol. 3, 996–998 (2019).Article 

Google Scholar 
11.Bacevic, K. Spatial competition constrains resistance to targeted cancer therapy. Nat. Commun. 8, 1995 (2017).Article 

Google Scholar 
12.Silva, A. S. et al. Evolutionary approaches to prolong progression-free survival in breast cancer. Cancer Res. 72, 6362–6370 (2012).CAS 
Article 

Google Scholar 
13.Enriquez-Navas, P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl. Med. 8, 327ra24 (2016).Article 

Google Scholar 
14.Monro, H. C. & Gaffney, E. A. Modelling chemotherapy resistance in palliation and failed cure. J. Theor. Biol. 257, 292–302 (2009).CAS 
Article 

Google Scholar 
15.Carrère, C. Optimization of an in vitro chemotherapy to avoid resistant tumours. J. Theor. Biol. 413, 24–33 (2017).Article 

Google Scholar 
16.Gallaher, J. A., Enriquez-Navas, P. M., Luddy, K. A., Gatenby, R. A. & Anderson, A. R. A. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies. Cancer Res. 78, 2127–2139 (2018).CAS 
Article 

Google Scholar 
17.Hansen, E., Woods, R. J. & Read, A. F. How to use a chemotherapeutic agent when resistance to it threatens the patient. PLoS Biol. 15, e2001110 (2017).Article 

Google Scholar 
18.Cunningham, J. J., Brown, J. S., Gatenby, R. A. & Staňková, K. Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer. J. Theor. Biol. 459, 67–78 (2018).CAS 
Article 

Google Scholar 
19.West, J., Ma, Y. & Newton, P. K. Capitalizing on competition: an evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment. J. Theor. Biol. 455, 249–260 (2018).Article 

Google Scholar 
20.Pouchol, C., Clairambault, J., Lorz, A. & Trélat, E. Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy. J. Math. Pures Appl. 116, 268–308 (2018).Article 

Google Scholar 
21.Carrère, C. & Zidani, H. Stability and reachability analysis for a controlled heterogeneous population of cells. Optim. Control Appl. Methods 41, 1678–1704 (2020).Article 

Google Scholar 
22.Greene, J. M., Sanchez-Tapia, C. & Sontag, E. D. Mathematical details on a cancer resistance model. Front. Bioeng. Biotechnol. 8, 501 (2020).Article 

Google Scholar 
23.Martin, R. B., Fisher, M. E., Minchin, R. F. & Teo, K. L. Low-intensity combination chemotherapy maximizes host survival time for tumors containing drug-resistant cells. Math. Biosci. 110, 221–252 (1992).CAS 
Article 

Google Scholar 
24.Gerlee, P. The model muddle: in search of tumor growth laws. Cancer Res. 73, 2407–2411 (2013).CAS 
Article 

Google Scholar 
25.Noble, R., Burri, D., Kather, J. N. & Beerenwinkel, N. Spatial structure governs the mode of tumour evolution. Preprint at bioRxiv https://doi.org/10.1101/586735 (2019).26.Hansen, E. & Read, A. F. Cancer therapy: attempt cure or manage drug resistance? Evol. Appl. 13, 1660–1672 (2020).Article 

Google Scholar 
27.Enriquez-Navas, P. M., Wojtkowiak, J. W. & Gatenby, R. A. Application of evolutionary principles to cancer therapy. Cancer Res. 75, 4675–4680 (2015).CAS 
Article 

Google Scholar 
28.Gatenby, R. A. & Brown, J. S. Integrating evolutionary dynamics into cancer therapy. Nat. Rev. Clin. Oncol. 17, 675–686 (2020).Article 

Google Scholar 
29.Strobl, M. A. R. et al. Turnover modulates the need for a cost of resistance in adaptive therapy. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-20-0806 (2020).Article 
PubMed 

Google Scholar 
30.Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).Article 

Google Scholar 
31.Pérez-García, V. M. et al. Universal scaling laws rule explosive growth in human cancers. Nat. Phys. 16, 1232–1237 (2020).Article 

Google Scholar 
32.Greene, J. M., Gevertz, J. L. & Sontag, E. S. Mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment. JCO Clin. Cancer Inform. 3, CCI.18.00087 (2019).PubMed Central 

Google Scholar 
33.Kuosmanen, T. et al. Drug-induced resistance evolution necessitates less aggressive treatment. Preprint at bioRxiv https://doi.org/10.1101/2020.10.07.330134 (2020).34.Fusco, D., Gralka, M., Kayser, J., Anderson, A. & Hallatschek, O. Excess of mutational jackpot events in expanding populations revealed by spatial Luria–Delbrück experiments. Nat. Commun. 7, 12760 (2016).CAS 
Article 

Google Scholar 
35.Mistry, H. B. Evolutionary based adaptive dosing algorithms: beware the cost of cumulative risk. Preprint at bioRxiv https://doi.org/10.1101/2020.06.23.167056 (2020).36.Benzekry, S. et al. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput. Biol. 10, e1003800 (2014).Article 

Google Scholar 
37.Vaghi, C. et al. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Comput. Biol. 16, e1007178 (2020).CAS 
Article 

Google Scholar 
38.Hansen, E., Karslake, J., Woods, R. J., Read, A. F. & Wood, K. B. Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations. PLoS Biol. 18, e3000713 (2020).CAS 
Article 

Google Scholar 
39.Soetaert, K. E. R., Petzoldt, T. & Setzer, R. W. Solving differential equations in R : package deSolve. J. Stat. Softw. 33, 9 (2010). More

1.Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Ballou, J. D. et al. Demographic and genetic management of captive populations. in Wild Mammals in Captivity: Principles and Techniques for Zoo Management (eds. Kleiman, D. G., Thompson, K. V. & Kirk Baer, C.) 219–252 (The University of Chicago Press, 2010).3.Ralls, K. & Ballou, J. D. Captive breeding and reintroduction. in Encyclopedia of Biodiversity (ed. Levin, S. A.) 662–667 (Elsevier Academic Press, 2013). https://doi.org/10.1016/B978-0-12-384719-5.00268-9.4.IUCN. Guidelines on the Use of Ex Situ Management for Species Conservation (2nd ed.). www.iucn.org/about/work/programmes/species/publications/iucn_guidelines_and__policy__statements/ (2014).5.Lacy, R. C. Loss of genetic diversity from managed populations: interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1, 143–158 (1987).Article 

Google Scholar 
6.Lockyear, K. M., MacDonald, S. E., Waddell, W. T. & Goodrowe, K. L. Investigation of captive red wolf ejaculate characteristics in relation to age and inbreeding. Theriogenology 86, 1369–1375 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Frankham, R. Genetic adaptation to captivity in species conservation programs. Mol. Ecol. 17, 325–333 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).Article 

Google Scholar 
9.Robert, A., Couvet, D. & Sarrazin, F. Integration of demography and genetics in population restorations. Écoscience 14, 463–471 (2007).Article 

Google Scholar 
10.Charlesworth, D. & Charlesworth, B. Inbreeding depression and its evolutionary consequences. Annu. Rev. Ecol. Syst. 18, 237–268 (1987).Article 

Google Scholar 
11.McPhee, M. E. & McPhee, N. F. Relaxed selection and environmental change decrease reintroduction success in simulated populations: altered selection in captive populations. Anim. Conserv. 15, 274–282 (2012).Article 

Google Scholar 
12.Ford, M. J. Selection in captivity during supportive breeding may reduce fitness in the wild. Conserv. Biol. 16, 815–825 (2002).Article 

Google Scholar 
13.Stockwell, C. A., Hendry, A. P. & Kinnison, M. T. Contemporary evolution meets conservation biology. Trends Ecol. Evol. 18, 94–101 (2003).Article 

Google Scholar 
14.Robert, A. Captive breeding genetics and reintroduction success. Biol. Conserv. 142, 2915–2922 (2009).Article 

Google Scholar 
15.Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318, 100–103 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. 109, 238–242 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
18.Gordon, S. P., Hendry, A. P. & Reznick, D. N. Predator-induced contemporary evolution, phenotypic plasticity, and the evolution of reaction norms in guppies. Copeia 105, 514–522 (2017).Article 

Google Scholar 
19.Forslund, P. & Pärt, T. Age and reproduction in birds—hypotheses and tests. Trends Ecol. Evol. 10, 374–378 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Smith, J. M. Review lectures on senescence—I. The causes of ageing. Proc. R. Soc. Lond. B Biol. Sci. 157, 115–127 (1962).ADS 
Article 

Google Scholar 
21.Partridge, L. & Barton, N. H. Optimally, mutation and the evolution of ageing. Nature 362, 305–311 (1993).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Langen, K., Bakker, T. C. M., Baldauf, S. A., Shrestha, J. & Thünken, T. Effects of ageing and inbreeding on the reproductive traits in a cichlid fish I: the male perspective. Biol. J. Linn. Soc. 120, 752–761 (2017).Article 

Google Scholar 
24.Kirkwood, T. B. L. Evolution of ageing. Nature 270, 301 (1977).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Benton, C. H. et al. Inbreeding intensifies sex- and age-dependent disease in a wild mammal. J. Anim. Ecol. 87, 1500–1511 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.de Boer, R. A., Eens, M. & Müller, W. Sex-specific effects of inbreeding on reproductive senescence. Proc. R. Soc. B Biol. Sci. 285, 20180231 (2018).Article 

Google Scholar 
27.Promislow, D. E. L. & Tatar, M. Mutation and senescence: where genetics and demography meet. Genetica 102, 299–314 (1998).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl. Acad. Sci. 93, 6140–6145 (1996).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Snoke, M. S. & Promislow, D. E. L. Quantitative genetic tests of recent senescence theory: age-specific mortality and male fertility in Drosophila melanogaster. Heredity 91, 546–556 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Robert, A., Toupance, B., Tremblay, M. & Heyer, E. Impact of inbreeding on fertility in a pre-industrial population. Eur. J. Hum. Genet. 17, 673–681 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Lesobre, L. et al. Conservation genetics of Houbara Bustard (Chlamydotis undulata undulata): population structure and its implications for the reinforcement of wild populations. Conserv. Genet. 11, 1489–1497 (2010).Article 

Google Scholar 
32.Rabier, R., Robert, A., Lacroix, F. & Lesobre, L. Genetic assessment of a conservation breeding program of the houbara bustard (Chlamydotis undulata undulata) in Morocco, based on pedigree and molecular analyses. Zoo Biol. 39, 365–447 (2020).Article 

Google Scholar 
33.Hardouin, L. A., Legagneux, P., Hingrat, Y. & Robert, A. Sex-specific dispersal responses to inbreeding and kinship. Anim. Behav. https://doi.org/10.1016/j.anbehav.2015.04.002 (2015).Article 

Google Scholar 
34.Cornec, C., Robert, A., Rybak, F. & Hingrat, Y. Male vocalizations convey information on kinship and inbreeding in a lekking bird. Ecol. Evol. 9, 4421–4430 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Vuarin, P. et al. No evidence for prezygotic postcopulatory avoidance of kin despite high inbreeding depression. Mol. Ecol. 27, 5252–5262 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Bacon, L., Hingrat, Y. & Robert, A. Evidence of reproductive senescence of released individuals in a reinforced bird population. Biol. Conserv. 215, 288–295 (2017).Article 

Google Scholar 
37.Chantepie, S. et al. Quantitative genetics of the aging of reproductive traits in the houbara bustard. PLoS ONE 10, e0133140 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Preston, B. T., Saint Jalme, M., Hingrat, Y., Lacroix, F. & Sorci, G. Sexually extravagant males age more rapidly. Ecol. Lett. 14, 1017–1024 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Preston, B. T., Saint Jalme, M., Hingrat, Y., Lacroix, F. & Sorci, G. The sperm of aging male bustards retards their offspring’s development. Nat. Commun. 6, 1–9 (2015).Article 
CAS 

Google Scholar 
40.Vuarin, P. et al. Post-copulatory sexual selection allows females to alleviate the fitness costs incurred when mating with senescing males. Proc. R. Soc. B Biol. Sci. 286, 20191675 (2019).Article 

Google Scholar 
41.Chargé, R. et al. Quantitative genetics of sexual display, ejaculate quality and size in a lekking species. J. Anim. Ecol. 82, 399–407 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Chargé, R. et al. Does recognized genetic management in supportive breeding prevent genetic changes in life-history traits?. Evol. Appl. 7, 521–532 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Gaucher, P. et al. Taxonomy of the Houbara Bustard Chlamydotis undulata subspecies considered on the basis of sexual display and genetic divergence. Ibis 138, 273–282 (1996).Article 

Google Scholar 
44.Hingrat, Y., Saint Jalme, M., Chalah, T., Orhant, N. & Lacroix, F. Environmental and social constraints on breeding site selection. Does the exploded-lek and hotspot model apply to the Houbara bustard Chlamydotis undulata undulata?. J. Avian Biol. 39, 393–404 (2008).Article 

Google Scholar 
45.Duursma, D. E., Gallagher, R. V., Price, J. J. & Griffith, S. C. Variation in avian egg shape and nest structure is explained by climatic conditions. Sci. Rep. 8, 1–10 (2018).
Google Scholar 
46.Cucco, M., Grenna, M. & Malacarne, G. Female condition, egg shape and hatchability: a study on the grey partridge. J. Zool. 287, 186–194 (2012).Article 

Google Scholar 
47.Adamou, A.-E. et al. Egg size and shape variation in Rufous Bush Chats Cercotrichas galactotes breeding in date palm plantations: hatching success increases with egg elongation. Avian Biol. Res. 11, 100–107 (2018).Article 

Google Scholar 
48.Goriup, P. D. The world status of the Houbara Bustard Chlamydotis undulata. Bird Conserv. Int. 7, 373–397 (1997).Article 

Google Scholar 
49.BirdLife International. Chlamydotis undulata. The IUCN Red List of Threatened Species 2016: e.T22728245A90341807. (2016) https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22728245A90341807.en.50.Lacroix, F., Seabury, J., Al Bowardi, M. & Renaud, J. The Emirates Center for Wildlife Propagation: developing a comprehensive strategy to secure a self-sustaining population of houbara bustard (Chlamydotis undulata undulata) in Eastern Morocco. Houbara News 5, (2003).
51.Conway, W. Wild and zoo animal interactive management and habitat conservation. Biodivers. Conserv. 4, 573–594 (1995).Article 

Google Scholar 
52.Saint Jalme, M., Gaucher, P. & Paillat, P. Artificial insemination in Houbara bustards (Chlamydotis undulata): influence of the number of spermatozoa and insemination frequency on fertility and ability to hatch. Reproduction 100, 93–103 (1994).CAS 
Article 

Google Scholar 
53.Allendorf, F. W. Delay of adaptation to captive breeding by equalizing family size. Conserv. Biol. 7, 416–419 (1993).Article 

Google Scholar 
54.Percie du Sert, N. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLOS Biol. 18, e3000410 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Vuarin, P. et al. Sperm competition accentuates selection on ejaculate attributes. Biol. Lett. 15, 20180889 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Chalah, T., Seigneurin, F., Blesbois, E. & Brillard, J. P. In vitro comparison of fowl sperm viability in ejaculates frozen by three different techniques and relationship with subsequent fertility in vivo. Cryobiology 39, 185–191 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Hoyt, D. F. Practical methods of estimating volume and fresh weight of bird eggs. Auk 96, 73–77 (1979).
Google Scholar 
58.Wellmann, R. optiSel: Optimum Contribution Selection and Population Genetics. R package version 2.0.2. https://CRAN.R-project.org/package=optiSel (2018).59.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org (2019).60.Princée, F. P. G. Exploring Studbooks for Wildlife Management and Conservation (Springer, Berlin, 2016).
Google Scholar 
61.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal. 9, 378–400 (2017).Article 

Google Scholar 
62.Ludecke, D., Makowski, D. & Waggoner, P. performance: Assessment of Regression Models Performance. R package version 0.3.0. https://CRAN.R-project.org/package=performance (2019).63.Ludecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772. https://doi.org/10.21105/joss.00772 (2018).ADS 
Article 

Google Scholar 
64.Wickham, H. ggplot2: elegant graphics for data analysis (Springer, Berlin, 2009).
Google Scholar 
65.Newton, I. & Rothery, P. Senescence and reproductive value in sparrowhawks. Ecology 78, 1000–1008 (1997).Article 

Google Scholar 
66.Bouwhuis, S., Sheldon, B. C., Verhulst, S. & Charmantier, A. Great tits growing old: selective disappearance and the partitioning of senescence to stages within the breeding cycle. Proc. R. Soc. B Biol. Sci. 276, 2769–2777 (2009).CAS 
Article 

Google Scholar 
67.Angelier, F., Shaffer, S. A., Weimerskirch, H. & Chastel, O. Effect of age, breeding experience and senescence on corticosterone and prolactin levels in a long-lived seabird: the wandering albatross. Gen. Comp. Endocrinol. 149, 1–9 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Angelier, F., Weimerskirch, H., Dano, S. & Chastel, O. Age, experience and reproductive performance in a long-lived bird: a hormonal perspective. Behav. Ecol. Sociobiol. 61, 611–621 (2007).Article 

Google Scholar 
69.Ottinger, M. A. et al. The Japanese quail: a model for studying reproductive aging of hypothalamic systems. Exp. Gerontol. 39, 1679–1693 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Lecomte, V. J. et al. Patterns of aging in the long-lived wandering albatross. Proc. Natl. Acad. Sci. 107, 6370–6375 (2010).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Opatová, P. et al. Inbreeding depression of sperm traits in the zebra finch Taeniopygia guttata. Ecol. Evol. 6, 295–304 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Croquet, C. et al. Linear and curvilinear effects of inbreeding on production traits for Walloon Holstein cows. J. Dairy Sci. 90, 465–471 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Leroy, G. Inbreeding depression in livestock species: review and meta-analysis. Anim. Genet. 45, 618–628 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Ralls, K. et al. Call for a paradigm shift in the genetic management of fragmented populations: genetic management. Conserv. Lett. 11, e12412 (2018).Article 

Google Scholar 
75.Huisman, J., Kruuk, L. E. B., Ellis, P. A., Clutton-Brock, T. & Pemberton, J. M. Inbreeding depression across the lifespan in a wild mammal population. Proc. Natl. Acad. Sci. 113, 3585–3590 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Frankham, R. & Ralls, K. Inbreeding leads to extinction. Nature 392, 441–442 (1998).ADS 
CAS 
Article 

Google Scholar 
77.Armbruster, P. & Reed, D. H. Inbreeding depression in benign and stressful environments. Heredity 95, 235–242 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Robert, A. Negative environmental perturbations may improve species persistence. Proc. R. Soc. B Biol. Sci. 273, 2501–2506 (2006).Article 

Google Scholar 
79.Crnokrak, P. & Roff, D. A. Inbreeding depression in the wild. Heredity 83, 260–270 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Araki, H., Berejikian, B. A., Ford, M. J. & Blouin, M. S. Fitness of hatchery-reared salmonids in the wild. Evol. Appl. 1, 342–355 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Lynch, M. & O’Hely, M. Captive breeding and genetic fitness of natural populations. Conserv. Genet. 2, 363–378 (2001).Article 

Google Scholar 
82.Robert, A., Sarrazin, F., Couvet, D. & Legendre, S. Releasing adults versus young in reintroductions: interactions between demography and genetics. Conserv. Biol. 18, 1078–1087 (2004).Article 

Google Scholar 
83.Roche, E. A., Cuthbert, F. J. & Arnold, T. W. Relative fitness of wild and captive-reared piping plovers: does egg salvage contribute to recovery of the endangered Great Lakes population?. Biol. Conserv. 141, 3079–3088 (2008).Article 

Google Scholar 
84.Ford, N. B. & Seigel, R. A. Phenotypic plasticity in reproductive traits: evidence from a viviparous snake. Ecology 70, 1768–1774 (1989).Article 

Google Scholar 
85.Bacon, L. Etude des paramètres de reproduction et de la dynamique d’une population renforcée d’outardes Houbara nord-africaines (Chlamydotis undulata undulata) au Maroc. (Museum National d’Histoire Naturelle, 2017).86.Robert, A. et al. Defining reintroduction success using IUCN criteria for threatened species: a demographic assessment. Anim. Conserv. 18, 397–406 (2015).Article 

Google Scholar 
87.Bacon, L., Robert, A. & Hingrat, Y. Long lasting breeding performance differences between wild-born and released females in a reinforced North African Houbara bustard (Chlamydotis undulata undulata) population: a matter of release strategy. Biodivers. Conserv. 28, 553–570 (2019).Article 

Google Scholar 
88.Vuarin, P. et al. Paternal age negatively affects sperm production of the progeny. Ecol. Lett. https://doi.org/10.1111/ele.13696 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
89.Keller, L. F., Reid, J. M. & Arcese, P. Testing evolutionary models of senescence in a natural population: age and inbreeding effects on fitness components in song sparrows. Proc. R. Soc. B Biol. Sci. 275, 597–604 (2008).CAS 
Article 

Google Scholar 
90.Reynolds, R. M. et al. Age specificity of inbreeding load in Drosophila melanogaster and implications for the evolution of late-life mortality plateaus. Genetics 177, 587–595 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Tan, C. K. W., Pizzari, T. & Wigby, S. Parental age, gametic age, and inbreeding interact to modulate offspring viability in Drosophila melanogaster. Evolution 67, 3043–3051 (2013).PubMed 
PubMed Central 

Google Scholar 
92.Deubel, W., Bassukas, I. D., Schlereth, W., Lorenz, R. & Hempel, K. Age dependent selection against HPRT deficient T lymphocytes in the HPRT± heterozygous mouse. Mutat. Res. Mol. Mech. Mutagen. 351, 67–77 (1996).CAS 
Article 

Google Scholar 
93.Réale, D. & Festa-Bianchet, M. Predator-induced natural selection on temperament in bighorn ewes. Anim. Behav. 65, 463–470 (2003).Article 

Google Scholar 
94.Coltman, D. W., Pilkington, J. G., Smith, J. A. & Pemberton, J. M. Parasite-mediated selection against Inbred Soay Sheep in a free-living, island population. Evolution 53, 1259 (1999).PubMed 
PubMed Central 

Google Scholar 
95.Wang, J., Hill, W. G., Charlesworth, D. & Charlesworth, B. Dynamics of inbreeding depression due to deleterious mutations in small populations: mutation parameters and inbreeding rate. Genet. Res. 74, 165–178 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Combined culture-dependent and culture-independent approach identifies the prevalent and viable bacterial community members of the human lung post-transplantTo characterize the bacterial community composition of the lung microbiota post-transplant, we performed 16S rRNA gene amplicon sequencing of 234 longitudinal BALF samples from 64 lung transplant recipients collected over a 49-month period (Fig. 1a, Supplementary Table 1). A total of 7164 operational taxonomic units (OTUs) were identified, excluding OTUs contributing to reads in 11 negative control samples32 (see “Methods”, Supplementary Fig. 1a, Supplementary Data 1 and 2). In accordance with previous studies on BALF samples from healthy non-transplant individuals4,5,6,26, we found that Bacteroidetes and Firmicutes followed by Proteobacteria and Actinobacteria are the most abundant phyla in the post-transplant lung (Fig. 1b). Prevalence analysis across all BALF samples showed that the community composition is highly variable with only 22 OTUs shared by ≥50% of the samples (Supplementary Fig. 1b, Supplementary Data 3). However, these 22 OTUs constituted 42% of the total number of rarefied reads, indicating that they are predominant members of the post-transplant lung microbiota (Fig. 1c, Supplementary Fig. 1c, Supplementary Table 2, Supplementary Data 3). They belonged to the genera Prevotella 7, Streptococcus, Veillonella, Neisseria, Alloprevotella, Pseudomonas, Gemella, Granulicatella, Campylobacter, Porphyromonas and Rothia, the majority of which are also prevailing community members in the healthy human lung3,5,7,26, suggesting a considerable overlap in the overall composition of the lung microbiota between the healthy and the transplanted lung.Fig. 1: Combining BALF amplicon sequencing and bacterial culturing to deduce the microbial ecology of deep lung microbiota.a Schematic of the sampling of Bronchoalveolar lavage fluid (BALF) from lung transplant recipients over time (months post-transplant). b Relative abundances (%) of most abundant phyla across BALF samples. Box plots show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). c Prevalence (% samples) vs contribution to total reads across samples for most abundant phyla. Dot color shows different genera and size show total rarefied reads. Gray dashed horizontal line shows prevalence ≥50%. d Scatter plot shows correlation between number of observed OTUs and bacterial counts per BALF sample obtained by quantifying 16S rRNA gene copies with qPCR. Linear regression is shown by the blue line with gray shaded area showing 95% confidence interval (n = 234, two-sided, F(1, 232) = 91.04, P = 2.2 × 10−16), Coefficient of correlation; R2 = 0.28. e Bar chart shows lung taxa (genera; OTU IDs) that contributed ≥75% of total bacterial biomass across samples (n = 234). Venn diagram inset shows overlap (yellow) between the most prevalent (≥50% incidence, light blue) and the most abundant (≥75% total count, red) taxa in the transplanted lung. Bar colors also show the same.Full size imageDifferences in bacterial loads between samples can skew community analyses when based on relative abundance profiling alone. Therefore, we used qPCR to determine the total copies of the 16S rRNA gene as an estimate for bacterial counts, and normalized the abundances of each OTU across the 234 samples (absolute abundance). We found that the bacterial counts vastly differed between samples, ranging between 101 and 106 gene copies per ml of BALF (Supplementary Fig. 1d). The number of observed OTUs increased with decreasing counts (Fig. 1d) suggesting that a large fraction of the OTUs were detected in samples of low bacterial biomass and hence represent either transient or extremely low-abundant community members, or sequencing artefacts and contaminations. In turn, 19 of the 7164 OTUs constituted >75% of the total bacterial biomass detected across the 234 BALF samples (Fig. 1e). This included 11 of the 22 most prevalent OTUs (see above) plus eight OTUs that were detected in only a few samples but at very high abundance (Staphylococcus; OTU_2, Corynebacterium 1; OTU_16 and OTU_24, Anaerococcus; OTU_49 and OTU_234, Haemophilus; OTU_78, Streptococcus; OTU_6768, Peptoniphilus; OTU_63, Supplementary Table 2). It is important to differentiate these opportunistic colonizers from other community members with low incidence, as they reached very high bacterial counts in some samples with potential implications for lung health.To demonstrate the viability of prevalent lung microbiota members and to establish a reference catalogue of bacterial isolates from the human lung for experimental studies, we complemented the amplicon sequencing with a bacterial culturing approach (Supplementary Fig. 2). We cultivated 21 random BALF samples from 18 individuals, on 15 different semi-solid media (both general and selective) in combination with 3 oxygen concentrations; aerobic, 5% CO2, and anaerobic (See “Methods” and Supplementary Table 3), representing 26 different conditions. We cultured fresh BALF immediately upon extraction (within 2 h), as we observed loss in bacterial diversity upon cultivating frozen samples. This resulted in a total of 300 bacterial isolates, representing 5 phyla, 7 classes, 13 orders, and 17 families from which we built an open-access biobank called the Lung Microbiota culture Collection (LuMiCol, Supplementary Data 4, https://github.com/sudu87/Microbial-ecology-of-the-transplanted-human-lung).To examine the extent of overlap between bacteria in LuMiCol and the diversity obtained by amplicon sequencing, we included 16S rRNA gene sequences from 215 isolates that passed our quality filter into the community analysis, which allowed for the retrieval of OTU-isolate matching pairs32 (Methods). We found that 213 isolates matched to 47 OTUs (Fig. 2a, c, Supplementary Data 5), including 17 of the most prevalent and abundant bacteria (Fig. 1e, Supplementary Table 2). As expected, bacteria with high abundance in the amplicon sequencing-based community analysis were isolated more frequently, with Firmicutes revealing the highest isolate diversity (Fig. 2a–c, Supplementary Data 4, 5) and being recovered under the most diverse culturing conditions.Fig. 2: A lung microbiota culture collection (LuMiCol) reveals extended diversity and phenotypic characteristics of the lower airway bacterial community.a Phylogenetic tree of the 47 OTU-isolate matching pairs inferred with FastTree. Branch bootstrap support values (size of dark gray circles) ≥80% are displayed. b Growth characteristics of each OTU-isolate matching pair in three different oxygen conditions (Anaerobic – light brown, 5% CO2-yellow, aerobic-light blue, n = 3). Column with pie charts shows growth on semi-solid agar. Heatmap shows median change in Optical Density (OD) at 600 nm growth in three different liquid media (THY, RPMI, RPMI without glucose) over 3 days. c Cumulative counts of each OTU-isolate matching pair across all BALF samples (gray). d Number of isolates in Lumicol (black) per OTU-isolate matching pair. Taxa are labeled as genus; OTU ID, with an indication of whether they are prevalent (gray rectangle) or opportunistic (magenta rectangle) in the lower airway community. The names of the closest hit in databases: eHOMD and SILVA are used as species descriptor.Full size imageIn summary, our results from the combined culture-dependent and culture-independent approach show that the lung microbiota post-transplant is highly variable in terms of both bacterial load and community composition with many transient and low-abundant bacterial taxa. However, a few community members display relatively high prevalence and/or abundance suggesting that they represent important colonizers of the human lung.LuMiCol informs on the diversity and metabolic preferences of culturable human lung bacteriaWe characterized the culturable community members of the lower respiratory tract contained in LuMiCol by testing a wide range of growth conditions and phenotypic properties (see “Methods”). The majority of the cultured isolates could taxonomically be assigned at the species level based on genotyping of the 16S rRNA gene V1-V5 region. However, the limited taxonomic resolution offered by this method does not allow to discriminate between closely related strains, which can include both pathogenic and non-pathogenic bacteria. Hence for Streptococcus, we additionally tested for type of hemolysis (alpha, beta, or gamma) and resistance to optochin, which differentiates the pathogenic pneumococcus and the non-pathogenic viridans groups (Fig. 2a, Supplementary Fig. 2b, c). This demonstrated that the 16 Streptococcus OTU-isolate pairs belong to the viridans group of streptococci (VS)33. Interestingly, these isolates exhibited the highest genotypic and phenotypic diversity throughout our collection and belonged to five OTUs among the 22 most prevalent community members, with Streptococcus mitis (OTU_11) present in 93.6% of all samples.BALF from healthy individuals contains amino acids, citrate, urate, fatty acids, and antioxidants such as glutathione but no detectable glucose34, which is associated with increased bacterial load and infection35,36,37. To get insights into basic bacterial metabolism, we assessed the growth of all 47 isolates matching an OTU under different oxygen concentrations. We used undefined rich media (Todd-Hewitt Yeast extract) and defined low-complexity liquid media (RPMI 1640), including a glucose-free version to mimic the deep lung environment (see “Methods”). Despite the presence of oxygen in the human lung, the majority of the isolates were either obligate or facultative anaerobes (Fig. 2a), including some of the most prevalent members (Prevotella melaninogenica (OTU_3), Streptococcus mitis (OTU_11), Veillonella atypica (OTU_6) and Granulicatella adiacens (OTU_17). A similar trend was also observed in liquid media under anaerobic conditions, with the exception of the genera Prevotella, Veillonella and Granulicatella. Most streptococci from the human lung grew best in complex liquid media containing glucose under anaerobic conditions, including the most prevalent species in our cohort, S. mitis (OTU_11) (Fig. 2b). However, noticeable exceptions were S. vestibularis (OTU_34), S. oralis (OTU_3427 and OTU_1567), and S. gordonii (OTU_10031), which grew equally well in the presence of oxygen and in low-complexity liquid medium (Fig. 2b). Most Actinobacteria grew best on rich medium in the presence of 5% CO2, with an exception of Actinomyces odontolyticus (OTU_39), which required anaerobic conditions. Some Actinobacteria grew equally well in anaerobic conditions as in the presence of 5% CO2, i.e., Corynebacterium durum (OTU_501), Actinobacteria sp. oral taxon (OTU_328 and OTU_228).The two most predominant opportunistic pathogens in our lung cohort, P. aeruginosa (OTU_1) and S. aureus (OTU_2), grew best in rich liquid medium in the presence of oxygen (Fig. 2c), although these also grew to lower degree under anaerobic conditions. These results indicate that changes in the physicochemical conditions in the lung may favor the growth of these two opportunistic pathogens. In summary, our observations from the bacterial culture collection provide first insights into the phenotypic properties of human lung bacteria and will serve as a basis for future experimental work.Identification of four compositionally distinct pneumotypes post-transplant using machine learning based on ecological metricsTo detect and characterize differences in bacterial community composition between BALF samples from transplant patients, we clustered the samples using an unsupervised machine learning algorithm based on pairwise Bray–Curtis dissimilarity32 (beta diversity, See “Methods”, Supplementary Data 6). This segregated the samples into four partitions around medoids (PAMs) at both phylum and OTU level (Fig. 3a, b, Supplementary Fig. 3a, b). We refer to these clusters as “pneumotypes” PAM1, PAM2, PAM3, and PAM4 (Supplementary Table 4). PAM1 formed the largest cluster consisting of the majority of samples (n = 115) followed by PAM3 (n = 76), PAM2 (n = 19), and PAM4 (n = 24) (Supplementary Data 7). Examination of various diversity measures (Species occurrence, OTU diversity, OTU richness, Fig. 3c–e), distribution of the dominant community members (Fig. 3f), and bacterial counts (16S rRNA gene copies, Fig. 3g) revealed distinctive characteristics between the four pneumotypes.Fig. 3: Bacterial communities of the lung post-transplant fall into four ‘pneumotypes’ with distinct community characteristics.a, b Principal component analysis shows Partition around medoids (PAMs) at phylum and OTU level respectively generated by k-medoid-based unsupervised machine learning using Bray–Curtis dissimilarity (occurrence and abundance). Pneumotypes are color coded: Balanced (red, n = 115), Staphylococcus (green, n = 19), Microbiota-depleted (MD, blue, n = 76), and Pseudomonas (orange, n = 24). c–g Violin plots show distributions of pairwise species occurrence (Sorenson’s index, PERMANOVA, two-sided, F(3, 229) = 8.49, P = 9.9 × 10−5), OTU diversity (Kruskal–Wallis test, χ2 = 89.2, df = 3, two-sided, P = 2.2 × 10−16), OTU richness (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), proportion of most dominant OTUs (Kruskal–Wallis test, χ2 = 94.45, df = 3, two-sided, P = 2.2 × 10−16), and total bacterial counts (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), respectively, across the four pneumotypes. h, i Enrichment analysis of prevalence (green dotted line ≥50%) and absolute abundance across all samples of the 30 most dominant taxa (i.e., OTUs) in PneumotypeBalanced and PneumotypeMD respectively, when each was compared to the other three combined pneumotypes (gray boxes). Differential abundances after enrichment analysis was calculated between each PAM and the other three PAMs combined, using ART-ANOVA. j Heatmap shows relative percentage of taxa (right colored panel) cultured from paired samples of Bronchial aspiration (BA) and Bronchoalveolar lavage fluid (BALF) from each pneumotype (left colored panel). Oropharyngeal flora mainly corresponds to PneumotypeBalanced (i.e., Streptococcus, Prevotella, Veillonella). All box plots including insets show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). Multiple comparison of beta diversity indices was done by pairwise PERMANOVA (adonis) with False Discovery rate (FDR). Post hoc analyses (95% Confidence Interval) were done by using Tukey’s test (ANOVA) or Dunn’s test (Kruskal test) with False Discovery Rate (FDR) or least-squares means (ART-ANOVA) with False Discovery Rate (FDR). * P  More

1.Schaub, S. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 1–11 (2020).Article 

Google Scholar 
2.Tonn, B., Komainda, M. & Isselstein, J. Results from a biodiversity experiment fail to represent economic performance of semi-natural grasslands. Nat. Commun. https://doi.org/10.1038/s41467-021-22309-7 (2021).3.Roscher, C. et al. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 5, 107–121 (2004).Article 

Google Scholar 
4.Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 

Google Scholar 
5.Roscher, C., Schumacher, J., Weisser, W. W., Schmid, B. & Schulze, E. D. Detecting the role of individual species for overyielding in experimental grassland communities composed of potentially dominant species. Oecologia 154, 535–549 (2007).ADS 
Article 

Google Scholar 
6.Deak, A., Hall, M., Sanderson, M. & Archibald, D. Production and nutritive value of grazed simple and complex forage mixtures. Agron. J. 99, 814–821 (2007).Article 

Google Scholar 
7.Sturludóttir, E. et al. Benefits of mixing grasses and legumes for herbage yield and nutritive value in Northern Europe and Canada. Grass Forage Sci. 69, 229–240 (2014).Article 

Google Scholar 
8.Oelmann, Y., Vogel, A., Wegener, F., Weigelt, A. & Scherer-Lorenzen, M. Management intensity modifies plant diversity effects on N yield and mineral N in soil. Soil Sci. Soc. Am. J. 79, 559–568 (2015).ADS 
CAS 
Article 

Google Scholar 
9.Schaub, S., Buchmann, N., Lüscher, A. & Finger, R. Economic benefits from plant species diversity in intensively managed grasslands. Ecol. Econ. 168, 106488 (2020b).Article 

Google Scholar 
10.Trenbath, B. R. Biomass productivity of mixtures. Adv. Agron. 26, 177–210 (1974).Article 

Google Scholar 
11.Binder, S., Isbell, F., Polasky, S., Catford, J. A. & Tilman, D. Grassland biodiversity can pay. Proc. Natl Acad. Sci. USA 115, 3876–3881 (2018).CAS 
Article 

Google Scholar 
12.Weigelt, A., Weisser, W., Buchmann, N. & Scherer‐Lorenzen, M. Biodiversity for multifunctional grasslands: equal productivity in high‐diversity low‐input and low‐diversity high‐input systems. Biogeosciences 6, 1695–1706 (2009).ADS 
CAS 
Article 

Google Scholar 
13.Vogel, A., Scherer-Lorenzen, M. & Weigelt, A. Grassland resistance and resilience after drought depends on management intensity and species richness. PLoS ONE 7, e36992 (2012).ADS 
CAS 
Article 

Google Scholar 
14.Finn, J. A. et al. Ecosystem function enhanced by combining four functional types of plant species in intensively managed grassland mixtures: a 3‐year continental‐scale field experiment. J. Appl. Ecol. 50, 365–375 (2013).Article 

Google Scholar 
15.Jans, F., Kessler, J., Münger, A. & Schlegel, P. in Fütterungsempfehlungen für Wiederkäuer (Grünes Buch) Ch. 7 (Agroscope, 2015).16.FAO (Food and Agriculture Organization of the United Nations), IDF (International Dairy Federation), and IFCN (IFCN Dairy Research Network). World Mapping of Animal Feeding Systems in the Dairy Sector. (FAO, 2014).17.Delaby, L., Peyraud, J. L., Foucher, N. & Michel, G. The effect of two contrasting grazing managements and level of concentrate supplementation on the performance of grazing dairy cows. Anim. Res. 52, 437–460 (2003).Article 

Google Scholar 
18.Leiber, F., Wettstein, H. R. & Kreuzer, M. Is the intrinsic potassium content of forages an important factor in intake regulation of dairy cows? J. Anim. Physiol. Anim. Nutr. 93, 391–399 (2009).CAS 
Article 

Google Scholar 
19.Schaub, S. et al. Data: forage quality and biomass yield of the Management Experiment set up within the Jena Experiment. ETH Zur. Res. Collect. https://doi.org/10.3929/ethz-b-000374100 (2019). More

1.Bayne, B. et al. The proposed dropping of the genus Crassostrea for all Pacific cupped oysters and its replacement by a new genus Magallana: a dissenting view. J. Shellfish Res. 36, 545–547 (2017).Article 

Google Scholar 
2.Mann, R. Some biochemical and physiological aspects of growth and gametogenesis in Crassostrea gigas and Ostrea edulis grown at sustained elevated temperatures. J. Mar. Biol. Assoc. UK 59, 95–110 (1979).CAS 
Article 

Google Scholar 
3.Humphreys, J., Herbert, R. J., Roberts, C. & Fletcher, S. A reappraisal of the history and economics of the Pacific oyster in Britain. Aquaculture 428, 117–124 (2014).Article 

Google Scholar 
4.Ellis, T., Gardiner, R., Gubbins, M., Reese, A. & Smith, D. Aquaculture statistics for the UK, with a focus on England and Wales 2012. Centre for Environment Fisheries & Aquaculture Science (Cefas) Weymouth (2015).5.Herbert, R. J. et al. Ecological impacts of non-native Pacific oysters (Crassostrea gigas) and management measures for protected areas in Europe. Biodivers. Conserv. 25, 2835–2865 (2016).Article 

Google Scholar 
6.Reise, K., Buschbaum, C., Büttger, H., Rick, J. & Wegner, K. M. Invasion trajectory of Pacific oysters in the northern Wadden Sea. Mar. Biol. 164, 68 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Geburzi, J. C. & McCarthy, M. L. How do they do it? Understanding the success of marine invasive species. In YOUMARES 8—Oceans Across Boundaries: Learning from each other, 109–124 (Springer, 2018).8.Herbert, R., Roberts, C., Humphreys, J. & Fletcher, S. The Pacific oyster (Crassostrea gigas) in the UK: Economic, legal and environmental issues associated with its cultivation, wild establishment and exploitation. Report for the Shellfish Association of Great Britain (2012).9.Fabioux, C., Huvet, A., Le Souchu, P., Le Pennec, M. & Pouvreau, S. Temperature and photoperiod drive Crassostrea gigas reproductive internal clock. Aquaculture 250, 458–470 (2005).Article 

Google Scholar 
10.Diederich, S., Nehls, G., Van Beusekom, J. E. & Reise, K. Introduced Pacific oysters (Crassostrea gigas) in the northern Wadden Sea: Invasion accelerated by warm summers?. Helgol. Mar. Res. 59, 97 (2005).ADS 
Article 

Google Scholar 
11.Mills, S.R.A. Population structure and ecology of wild Crassostrea gigas (Thunberg, 1793) on the south coast of England. Ph.D. thesis, University of Southampton (2016).12.Dutertre, M., Beninger, P. G., Barillé, L., Papin, M. & Haure, J. Rising water temperatures, reproduction and recruitment of an invasive oyster, Crassostrea gigas, on the French Atlantic coast. Mar. Environ. Res. 69, 1–9 (2010).CAS 
PubMed 
Article 

Google Scholar 
13.Chávez-Villalba, J. et al. Broodstock conditioning of the oyster Crassostrea gigas: Origin and temperature effect. Aquaculture 214, 115–130 (2002).Article 

Google Scholar 
14.Rico-Villa, B., Pouvreau, S. & Robert, R. Influence of food density and temperature on ingestion, growth and settlement of Pacific oyster larvae, Crassostrea gigas. Aquaculture 287, 395–401 (2009).Article 

Google Scholar 
15.Li, G. & Hedgecock, D. Genetic heterogeneity, detected by PCR-SSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55, 1025–1033 (1998).CAS 
Article 

Google Scholar 
16.Hedge, L. H. & Johnston, E. L. Colonisation of the non-indigenous Pacific oyster Crassostrea gigas determined by predation, size and initial settlement densities. PLoS ONE9 (2014).17.Maurer, D. et al. Reproduction de l’huître creuse dans le Bassin d’Arcachon. Année 2015. Ifremer Report (2016).18.Quayle, D.B. Pacific oyster culture in British Columbia (Department of Fisheries and Oceans, 1988).19.Rico-Villa, B. et al. A flow-through rearing system for ecophysiological studies of Pacific oyster Crassostrea gigas larvae. Aquaculture 282, 54–60 (2008).Article 

Google Scholar 
20.Kheder, R. B., Moal, J. & Robert, R. Impact of temperature on larval development and evolution of physiological indices in Crassostrea gigas. Aquaculture 309, 286–289 (2010).Article 

Google Scholar 
21.Kennedy, V. S. & Breisch, L. L. Maryland’s Oysters: Research and Management Vol. 81 (University of Maryland College Park, Maryland, 1981).
Google Scholar 
22.Helm, M. Cultured aquatic species information programme—Crassostrea gigas. Cultured aquatic species fact sheets. FAO Inland Water Resources and Aquaculture Service (2007).23.Child, A. & Laing, I. Comparative low temperature tolerance of small juvenile European, Ostrea edulis L., and Pacific oysters, Crassostrea gigas Thunberg. Aquacul. Res. 29, 103–113 (1998).Article 

Google Scholar 
24.Strand, A., Waenerlund, A. & Lindegarth, S. High tolerance of the Pacific oyster (Crassostrea gigas, Thunberg) to low temperatures. J. Shellfish Res. 30, 733–735 (2011).Article 

Google Scholar 
25.Rinde, E. et al. Increased spreading potential of the invasive Pacific oyster (Crassostrea gigas) at its northern distribution limit in Europe due to warmer climate. Mar. Freshw. Res. 68, 252–262 (2017).ADS 
Article 

Google Scholar 
26.Wrange, A.-L. et al. Massive settlements of the Pacific oyster, Crassostrea giga, in Scandinavia. Biol. Invasions 12, 1145–1152 (2010).Article 

Google Scholar 
27.Spencer, B., Edwards, D., Kaiser, M. & Richardson, C. Spatfalls of the non-native Pacific oyster, Crassostrea gigas, in British waters. Aquat. Conserv. Mar. Freshw. Ecosyst. 4, 203–217 (1994).Article 

Google Scholar 
28.England, N. Pacific oyster survey of the North East Kent European marine sites. Natural England Commissioned Report NECR016 (2009).29.Smith, I. P., Guy, C. & Donnan, D. Pacific oysters, Crassostrea gigas, established in Scotland. Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 733–742 (2015).Article 

Google Scholar 
30.Cook, E. J. et al. Impacts of climate change on non-native species. Mar. Clim. Change Impact Partnersh. Sci. Rev. 155–166 (2013).31.Cook, E., Beveridge, C., Lamont, P., O’Higgins, T. & Wilding, T. Survey of wild Pacific oyster Crassostrea gigas in Scotland. In Scottish Aquaculture Research Forum Report SARF099 (2014).32.Kochmann, J. Into the wild: documenting and predicting the spread of Pacific oysters (Crassostrea gigas) in Ireland. Ph.D. thesis, University College Dublin (2012).33.Syvret, M., Fitzgerald, A. & Hoare, P. Development of a Pacific oyster aquaculture protocol for the UK: Technical report. Sea Fish Industry Authority, FIFG Project No. 7 (2008).34.d’Auriac, M. B. A. et al. Rapid expansion of the invasive oyster Crassostrea gigas at its northern distribution limit in Europe: Naturally dispersed or introduced? PLoS ONE, 12 (2017).35.Dame, R. F. & Prins, T. C. Bivalve carrying capacity in coastal ecosystems. Aquat. Ecol. 31, 409–421 (1997).Article 

Google Scholar 
36.Leguerrier, D., Niquil, N., Petiau, A. & Bodoy, A. Modeling the impact of oyster culture on a mudflat food web in Marennes-Oléron Bay (France). Mar. Ecol. Prog. Ser. 273, 147–162 (2004).ADS 
Article 

Google Scholar 
37.Forrest, B. M., Keeley, N. B., Hopkins, G. A., Webb, S. C. & Clement, D. M. Bivalve aquaculture in estuaries: Review and synthesis of oyster cultivation effects. Aquaculture 298, 1–15 (2009).Article 

Google Scholar 
38.Ferreira, J. G. et al. Ecological carrying capacity for shellfish aquaculture: Sustainability of naturally occurring filter-feeders and cultivated bivalves. J. Shellfish Res. 37, 709–726 (2018).Article 

Google Scholar 
39.Jordan-Cooley, W. C., Lipcius, R. N., Shaw, L. B., Shen, J. & Shi, J. Bistability in a differential equation model of oyster reef height and sediment accumulation. J. Theor. Biol. 289, 1–11 (2011).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
40.Lipcius, R. N. et al. Modeling quantitative value of habitats for marine and estuarine populations. Front. Mar. Sci. 6, 280 (2019).Article 

Google Scholar 
41.Enríquez-Díaz, M., Pouvreau, S., Chávez-Villalba, J. & Le Pennec, M. Gametogenesis, reproductive investment, and spawning behavior of the Pacific giant oyster Crassostrea gigas: Evidence of an environment-dependent strategy. Aquacult. Int. 17, 491–506 (2009).Article 

Google Scholar 
42.Wood, L. E. et al. Unaided dispersal risk of Magallana gigas into and around the UK: Combining particle tracking modelling and environmental suitability scoring. Biological Invasions, 1–20 (2021).43.Hily, C. Prolifération de l’huître creuse du Pacifique Crassotrea gigas sur les côtes manche-atlantique françaises: bilan, dynamique, conséquences écologiques, économiques et ethnologiques, expériences et scénarios de gestion. Rapport LITEAU, 20 (2009).44.McKnight, W. & Chudleigh, I. J. Pacific oyster Crassostrea gigas control within the inter-tidal zone of the North East Kent Marine Protected Areas, UK. Conserv. Evid. 12, 28–32 (2015).
Google Scholar 
45.Brown, J. & Hartwick, E. A habitat suitability index model for suspended tray culture of the Pacific oyster, Crassostrea gigas Thunberg.. Aquacult. Res. 19, 109–126 (1988).Article 

Google Scholar 
46.Diederich, S. High survival and growth rates of introduced Pacific oysters may cause restrictions on habitat use by native mussels in the Wadden Sea. J. Exp. Mar. Biol. Ecol. 328, 211–227 (2006).Article 

Google Scholar 
47.Moran, A. & Manahan, D. Physiological recovery from prolonged ‘starvation’ in larvae of the Pacific oyster Crassostrea gigas. J. Exp. Mar. Biol. Ecol. 306, 17–36 (2004).CAS 
Article 

Google Scholar 
48.Calvo, G. W., Luckenbach, M. W. & Burreson, E. M. A comparative field study of Crassostrea gigas and Crassostrea virginica in relation to salinity in Virginia. Special Report in Applied Marine Science and Ocean Engineering, 349 (1999).49.Petton, B., Boudry, P., Alunno-Bruscia, M. & Pernet, F. Factors influencing disease-induced mortality of Pacific oysters, Crassostrea gigas. Aquacul. Environ. Interact. 6, 205–222 (2015).Article 

Google Scholar 
50.Li, L. et al. Divergence and plasticity shape adaptive potential of the Pacific oyster. Nat. Ecol. Evol. 2, 1751–1760 (2018).PubMed 
Article 

Google Scholar 
51.Ferreira, J., Duarte, P. & Ball, B. Trophic capacity of Carlingford Lough for oyster culture-analysis by ecological modelling. Aquat. Ecol. 31, 361–378 (1997).Article 

Google Scholar 
52.Cognie, B., Haure, J. & Barillé, L. Spatial distribution in a temperate coastal ecosystem of the wild stock of the farmed oyster Crassostrea gigas (Thunberg). Aquaculture 259, 249–259 (2006).Article 

Google Scholar 
53.Enríquez-Díaz, M., Pouvreau, S., Chávez-Villalba, J. & Le Pennec, M. Gametogenesis, reproductive investment, and spawning behavior of the Pacific giant oyster Crassostrea gigas: evidence of an environment-dependent strategy. Aquacult. Int. 17, 491 (2009).Article 

Google Scholar 
54.Ben-Horin, T. et al. Intensive oyster aquaculture can reduce disease impacts on sympatric wild oysters. Aquacul. Environ. Interact. 10, 557–567 (2018).Article 

Google Scholar 
55.Mailleret, L. & Lemesle, V. A note on semi-discrete modelling in the life sciences. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 367, 4779–4799 (2009).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
56.Powell, E., Klinck, J., Hofmann, E. & Ray, S. Modeling oyster populations. IV: Rates of mortality, population crashes and management. Fish. Bull. 92, 347–373 (1994).
Google Scholar 
57.Wilson, R. A stage-structured oyster population model for reef restoration. Undergraduate Honors Theses Paper, 1403 (2019).58.Guo, X., Hedgecock, D., Hershberger, W. K., Cooper, K. & Jr, S. K. A. Genetic determinants of protandric sex in the Pacific oyster, Crassostrea gigas Thunberg. Evolution 52, 394–402 (1998).59.Morris, D. et al. Cefas coastal temperature network (2016).60.Pouvreau, S. et al. Velyger database: The oyster larvae monitoring French project. SEANOE 10, 41888 (2016).
Google Scholar 
61.Dhoop, T. & Thompson, C. Directional waverider metadata, supplement for QC data download from Realtime Data page. Channel Coastal Observatory (2019).62.Collins, M. et al. Long-term climate change: projections, commitments and irreversibility. In Climate Change 2013-The Physical Science Basis: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1029–1136 (Cambridge University Press, 2013).63.Pastor, D. Reproductive biology of Crassostrea gigas. Ph.D. thesis, University of Southampton (2010).64.Benton, T. G. & Grant, A. Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol. Evol. 14, 467–471 (1999).CAS 
PubMed 
Article 

Google Scholar 
65.Grant, A. & Benton, T. G. Elasticity analysis for density-dependent populations in stochastic environments. Ecology 81, 680–693 (2000).Article 

Google Scholar 
66.Caswell, H. & Gassen, N. S. The sensitivity analysis of population projections. Demogr. Res. 33, 801–840 (2015).Article 

Google Scholar 
67.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2019).68.Soetaert, K., Petzoldt, T. & Setzer, R. W. Solving differential equations in R: Package deSolve. J. Stat. Softw. 33, 1–25 (2010).
Google Scholar 
69.Inkscape Project. Inkscape. More

The experiment underlying the study provides a diversity gradient of 1–60 plant species, established in assemblages randomly chosen from a pool of species typical of Arrhenatheretum grasslands. Recently sown on fertile arable soil and maintained by weeding, this experiment is a highly artificial system that fails to meet the definition of semi-natural grasslands7. Four years after establishment, a management intensity gradient of one to four annual cuts and three fertilization levels was established in subplots randomly assigned to the 1–60-species plots. Data presented in this study were collected in the following year.Intensive management was thus imposed on plant species typical of Arrhenaterethum meadows, a plant community characterized by two annual cuts8. The potential effect size of increased management intensity is thus underestimated by applying the management to a plant community not adapted to it. More importantly, it is unlikely that the species-richness of high-diversity plots could be maintained under increased management intensity over longer periods. In fact, 22% of these subplots managed at very high intensity had to be excluded for missing or insufficient yield after only two years, indicating that their species did not persist under high defoliation frequency and fertilizer levels, even when competitors were excluded by weeding.While the discussion hardly addresses this crucial trade-off between management intensity and plant diversity, Schaub et al.6 do indicate that repeated resowing is likely to be necessary to maintain high diversity under increased management intensities. In contrast to permanent grasslands, whose species composition is shaped by site conditions and management, species selection in (re-)sown grasslands is a conscious choice. To be advantageous, mixtures have to show larger yields than the most productive monoculture, so-called transgressive overyielding. Transgressive overyielding is one of the reasons why mixtures, especially grass-clover mixtures, are frequently used in sown grasslands. A European-scale experiment demonstrated that four-species mixtures showed transgressive overyielding at a wide range of sites under intensive agricultural management9,10. Although Schaub et al.6 generally quantify the diversity effects in comparison to monocultures, they argue that grasslands with the high-diversity characteristic of semi-natural grasslands have benefits not only over monocultures but over low-diversity grasslands, such as the 1–8 species standard mixtures shown in Fig. 6 of their paper. However, their results fail to demonstrate that their high-diversity plots show any transgressive overyielding even over monocultures, not to speak of low-diversity mixtures. As species assemblages of the experiment are randomly drawn from the species pool, monocultures and low-diversity mixtures cannot be expected to include the most productive species or species combinations and thus cannot be used to assess transgressive overyielding. When transgressive overyielding was quantified for one- to eight-species plots of the same experiment under extensive management in 2003, it decreased with species number. While two-species mixtures showed a mean transgressive overyielding of 5%, eight-species mixtures were only 70% as productive as the corresponding best monoculture, on average11.Accordingly, the experimental design fails to capture the real trade-offs faced by grassland managers, either in permanent or in sown grassland. It cannot answer if high levels of diversity and the associated biodiversity benefits can be maintained under intensive management for a longer period than just a few years. Neither can it show a productivity benefit of high-diversity grassland assemblages compared to species-poor mixtures, or even monocultures, when in practice the sown species are deliberately chosen rather than randomly drawn from a species pool. While the underlying biodiversity experiment has made valuable contributions to our fundamental understanding of plant diversity effects on ecosystem functioning, it thus cannot be used to derive direct management recommendations for managed grassland. More

Study areaThis study was conducted using tree census data collected from 119 forest inventory plots (73 tropical, 46 subtropical) situated across a latitudinal range of 7.1°N (Colombia) to 27.8°S (Argentina), a longitudinal range of 79.5° to −63.8° W, and an elevation range of 500–3511 m asl (Fig. 1). The mean annual temperature (MAT) of plots ranged from 7.3 to 23.8 °C (mean = 16.7 ± 4.1 °C; mean ± SD) and mean annual precipitation (MAP) of the plots ranged from 608 to 4313 mm y−1 (mean = 1405.0 ± 623.9 mm y−1) (External Databases 1). The number of plots sampled in each country was: Argentina = 46, Bolivia = 26, Peru = 16, Ecuador = 21, and Colombia = 10 (Fig. 1). The 119 forest plots ranged in size from 0.32 to 1.28 ha and represent a cumulative sample area of 104.4 ha (horizontal areas corrected for slope) that containe more than 63,000 trees with a diameter at breast height (DBH, 1.3 m) ≥10 cm (External Database 1). Ninety-four of the plots (79.0%) were ≥1 ha in size. Neither secondary forests nor plantations were included. However, only seven of the plots (five in Argentina and two in Bolivia) were located in forests >100 km2 in extent41, which suggests that at least the edges and borders of some plots could have experienced some degree of disturbance or degradation. All plots were censused at least twice between 1991 and 2017 (census intervals ranged between 2 and 9 years).In each plot, we tagged, mapped, measured, and collected vouchers of all trees and palms (DBH ≥ 10 cm). DBH was measured 50 cm above buttresses or aerial roots when present (where the stem was cylindrical). During the second or subsequent set of censuses, DBH growth, recruitment, and mortality were recorded. In cases where the recorded DBH growth of the second census was less than −0.1 cm y−1 or greater than 7.5 cm y−1, the DBH of the second census was augmented/reduced in order to match these minimum/maximum values42. To homogenize and validate species names of palms and trees recorded in each country and plot, we submitted the combined list from all plots to the Taxonomic Name Resolution Service (TNRS; http://tnrs.iplantcollaborative.org/) version 3.0. Any species with an unassigned TNRS accepted name or with a taxonomic status of ‘no opinion’, ‘illegitimate’, or ‘invalid’ was manually reviewed. Families and genera were changed in accordance with the new species names. If a full species name was not provided or could not be found, the genus and/or family name from the original file was retained.Aboveground carbon stocksThe aboveground biomass (AGB) of each tree was estimated using the allometric equation proposed by Chave et al43., defined as: AGB = 0.0673 × (WD × DBH2 × H)0.976 where AGB (kg) is the estimated aboveground biomass, DBH (cm) is the diameter of the tree at breast height, H (m) is the estimated total height, and WD (g cm−3) is the stem wood density. To estimate WD, we assigned the WD values available in the literature44 to each species found in each plot. In cases where we could not assign a WD value at the species level, we used the average value at the genus- or family level. For unidentified individuals, we used the average WD value of all other species in the plot. Tree height (H) was estimated (see below) based on the heights measured on a subset of the individual stems in each plot using digital hypsometers or clinometers. The estimated AGB of each tree was then converted to units of aboveground carbon (AGC) by applying a conversion factor of 1 kg AGB = 0.456 kg C45. The AGC per ha was then determined by converting kg to Mg, summing the values for all trees in a plot, and extrapolating or interpolating to a sample area of 1 ha.Estimates of AGB and AGC are highly dependent on tree height. Unfortunately, tree height was difficult or impossible to measure on all stems due to physical and logistical constraints. Therefore, we estimated the height of each stem based on allometric relationships between DBH and tree height that we developed for each plot based on height and DBH measurements taken on a subset of individuals. Although the AGB/AGC estimates are only for trees with DBH ≥ 10, we used trees with DBH ≥ 5 cm to construct the H:DBH models when possible in order to be as comparable as possible with the existing pantropical H:DBH models46. In total, 44,442 trees had their heights measured in the field and were employed to construct the H:DBH models. The percentage of trees with direct field measurements of H (DBH ≥ 5 cm) in each country was: Argentina = 19%, Bolivia = 98%, Peru = 96%, Ecuador = 97%, and Colombia = 46%. In Argentina, 32 of 46 plots did not have any field measurements of H, while all plots in all other countries had field measurements of H for at least a subset of trees.We tested and compared the expected effects of using H:DBH models constructed using the local (plot), country, or pantropical (regional) level data. To select the best model to estimate H from DBH at the plot and country level, we used the function modelHD available in the BIOMASS package for R47. We chose the best allometric model from four candidate models (two log-log polynomial models, the three-parameter Weibull model, and a two-parameter Michaelis-Menten model (Supplementary Table 7)) by selecting the model with the lowest RSE and bias (Supplementary Table 8). At the regional level, we used a pantropical model46. The use of country and pantropical H:DBH allometries underestimates tree heights in the lowlands and overestimates tree heights in highlands, thereby homogenizing AGB estimates along elevational gradients10,48 (Supplementary Figs. 11, 12, 13). Using plot level allometries eliminates this problem. However, in the 32 plots in Argentina where we had no information about tree height, we used the country-level H:DBH model developed with the data available in the remaining 14 plots to estimate the height of each tree, which could have homogenized the AGC estimates along the Argentinian elevational gradient (Supplementary Figs. 11, 12, 13).Aboveground carbon dynamicsThe AGC dynamics of each plot was estimated from the annualized values of AGC mortality, AGC productivity (AGC change due to recruitment + growth), and AGC net change3. The calculations of the separate AGC dynamic components was performed as follows: (i) AGC mortality (Mg ha−1 y−1) = the sum of the AGC of all individuals that died between censuses divided by the time between measurements. (ii) AGC recruitment (Mg C ha−1 y−1) = the sum of the AGC of individuals that recruited into DBH ≥ 10 cm between censuses divided by the time between measurements. However, for each tree recruited (DBH ≥ 10 cm), we subtracted the corresponding AGC associated with a tree of 9.99 cm (i.e. just below the detection limit) in order to avoid overestimations of the overall increase in AGC due to recruitment49. (iii) AGC growth (Mg ha−1 y−1) = the sum of the increase in AGC of all individuals with DBH ≥ 10 cm that survived between censuses divided by the time between censuses. (iv) AGC net change (Mg ha−1 y−1) = the difference between AGC stock in the last census (AGCfinal) and AGC stock in the first census (AGC1) divided by the elapsed time (t; in years) between measurements [(AGC net change = AGCfinal − AGC1)/t]. We recognize that these methods exclude C stored in soils or in belowground tissues9,48; however, quantifying just aboveground C stocks and fluxes provides valuable information about the overall status of these forests as net C sinks or sources.ClimateClimate variables at each plot location were extracted from the CHELSA28 bioclimatic rasters at a resolution of 30-arcsec (~1 km2 at the equator). The climate variables extracted were: Mean Annual Temperature (MAT), Mean Diurnal Range (MDR), Isothermality (Isoth), Temperature Seasonality (TS), Maximum Temperature of Warmest Month (MaxTWarmM), Minimum Temperature of Coldest Month (MinTCM), Temperature Annual Range (TAR), Mean Temperature of Wettest Quarter (MeanTWarmQ), Mean Temperature of Driest Quarter (MeanTDQ), Mean Temperature of Warmest Quarter (MeanTWetQ), Mean Temperature of Coldest Quarter (MeanTCQ), Mean Annual Precipitation (MAP), Precipitation of Wettest Month (PWetM), Precipitation of Driest Month (PDM), Precipitation Seasonality (PS), Precipitation of Wettest Quarter (PWetQ), Precipitation of Driest Quarter (PDQ), Precipitation of Warmest Quarter (PWarmQ), Precipitation of Coldest Quarter (PCQ). We separated all variables associated with temperature (°C) from those associated with precipitation (mm y−1) and applied a Principal Component Analysis (PCA) to the 11 variables associated with temperature (PCAtemp) and a separate PCA to the eight variables associated with precipitation (PCAprec). The first two principal components of both PCAtemp and PCAprec (four PCA axes in total) were selected for use in subsequent analyses. Plot elevations were estimated based on their coordinates and the SRTM 1 ArcSec Global V3 (https://lta.cr.usgs.gov) 30 m resolution digital elevation model (DEM).PCAtemp1 (Supplementary Fig. 1a) explained 53.0% of the total variance of the temperature variables and had high loading from Isothermality and Maximum Temperature of Warmest Month, which was primarily associated with changes in elevation (r = −0.97, p  More

Experimental study and protein domain analysisInsectsHosts and parasitoids were maintained as previously described25. Five Leptopilina heterotoma (Hymenoptera: Figitidae) populations were used for experiments: a population from Japan (Sapporo), two populations from the United Kingdom (1: Whittlesford; 2: Great Shelford) and two populations from Belgium (1: Wilsele; 2: Eupen). Information on collection sites, including GPS coordinates, can be found in25.Determination of host fat contentD. simulans and D. melanogaster hosts were allowed to lay eggs during 24 h in glass flasks containing ~ 50 mL standard medium25. After two days, developing larvae were sieved and ~ 200 were larvae placed in a Drosophila tube containing ~ 10 mL medium. Seven days after egg laying, newly formed pupae were frozen at – 18 °C, after which fat content was determined as described in25, where dry weight before and after neutral fat extraction was used to calculate absolute fat amount (in μg) for each host. The host pupal stage was chosen for estimating fat content, because at this point the host ceases to feed, while the parasitoid starts consuming the entire host36. All data were analysed using R Project version 3.4.360. Fat content of hosts was compared using a one-way ANOVA with host species as fixed factor.Manipulation of host fat contentTo generate leaner D. melanogaster hosts, we adapted our standard food medium25 to contain 100 times less (0.5 g) sugar per litre water. Manipulating sugar content did not alter the structure of the food medium, thus maintaining similar rearing conditions, with the exception of sugar content. Fat content of leaner and fatter D. melanogaster hosts was determined and analysed as described above.Fat synthesis quantification of wasp populationsMated female L. heterotoma were allowed to lay eggs on host fly larvae collected as described above with ad libitum access to honey as a food source until death. Honey consists of sugars and other carbohydrates that readily induce fat synthesis. After three weeks, adult offspring emergence was monitored daily and females were haphazardly placed in experimental treatments. Females were either killed at emergence (to measure teneral lipid reserves) or after feeding for 7 days on honey. Wasps were frozen at − 18 °C after completion of experiments. Fat content was determined as described above for hosts. The ability for fat synthesis was then determined by comparing mean fat levels of recently emerged compared to fed individuals, similar to procedures described in15,25,28. An increase in fat levels after feeding is indicative of active fat synthesis; equal or lower fat levels suggest fat synthesis did not take place. Each population tested on D. melanogaster or D. simulans represented an independent dataset that was analysed separately, as in Visser et al. 201825, because we are interested in the response of each population on each host species. We used T-tests when data was normally distributed and variances equal, log-transformed data for non-normal data, and a Welch’s t-test when variances were unequal. We corrected for multiple testing using Benjamini and Hochberg’s False Discovery Rate61.Fat synthesis quantification using a familial design and GC–MS analysesTo tease apart the effect of wasp genotype and host environment, we used a split-brood design where the offspring of each mother developed on lean D. simulans or fat D. melanogaster hosts in two replicated experiments (experiment 1 and 2). In both experiments, mothers were allowed to lay eggs in ~ 200 2nd to 3rd instar host larvae of one species for four days, after which ~ 200 host larvae of the other species were offered during four days. The order in which host larvae were presented was randomized across families. Following offspring emergence, daughters were allocated into two treatment groups: a control where females were fed a mixture of honey and water (1:2 w/w) or a treatment group fed a mixture of honey and deuterated water (Sigma Aldrich) (1:2 w/w; stable isotope treatment) for 7 days. Samples were prepared for GC–MS as described in 28. Incorporation of up to three deuterium atoms can be detected, but percent incorporation is highest when only 1 deuterium atom is incorporated. As incorporation of a single atom unequivocally demonstrates active fat synthesis, we only analysed percent incorporation (in relation to the parent ion) for the abundance of the m + 1 ion. Percent incorporation was determined for five fatty acids, C16:1 (palmitoleic acid), C16:0 (palmitate), C18:2 (linoleic acid), C18:1 (oleic acid), and C18:0 (stearic acid), and the internal standard C17:0 (margaric acid). Average percent incorporation for C17:0 was 19.4 (i.e. baseline incorporation of naturally occurring deuterium) and all values of the internal standard remained within 3 standard deviations of the mean (i.e. 1.6). Percent incorporation of control samples was subtracted from treatment sample values to correct for background levels of deuterium (i.e. only when more deuterium is incorporated in treatment compared to controls fatty acids are actively being synthesized). For statistical analyses, percent incorporation was first summed for C16:1, C16:0, C18:2, C18:1 and C18:0 to obtain overall incorporation levels, as saturated C16 and C18 fatty acids are direct products of the fatty acid synthesis pathway (that can subsequently be desaturated).Data (presented in Fig. 1) was analysed by means of a linear mixed effects model (GLMM, lme4 package) with host (lean D. simulans and fat D. melanogaster) and experiment (conducted twice) as fixed effect, family nested within population (Japan, United Kingdom 1 and 2, Belgium 1 and 2) as random factor, and percentage of incorporation of stable isotopes as dependent variable (log transformed; n = 138). Non-significant terms (i.e., experiment) were sequentially removed from the model to obtain the minimal adequate model as reported in Table 2. When referring to “families,” we are referring to the comparison of daughters of singly inseminated females, which (in these haplodiploid insects) share 75% of their genome.Identification of functional acc and fas genes in distinct parasitoid speciesTo obtain acc and fas nucleotide sequences for L. clavipes, G. legneri, P. maculata and A. bilineata, we used D. melanogaster mRNA ACC transcript variant A (NM_136498.3 in Genbank) and FASN1-RA (FBtr0077659 in FlyBase) and blasted both sequences against transcripts of each parasitoid (using the blast function available at http://www.parasitoids.labs.vu.nl62,63). Each nucleotide sequence was then entered in the NCBI Conserved Domain database64 to determine the presence of all functional protein domains. All sequences were then translated using the Expasy translate tool (https://web.expasy.org/translate/), where the largest open reading frame was selected for further use and confirming no stop codons were present. Protein sequences were then aligned using MAFFT v. 7 to compare functional amino acid sequences between all species (Supplementary files 1 and 2)65.Simulation studyWe consider the general situation where phenotypic plasticity is only sporadically adaptive and ask the question whether and under what circumstances plasticity can remain functional over long evolutionary time periods when the regulatory processes underlying plasticity are gradually broken down by mutations. We consider a regulatory mechanism that switches on or off a pathway (like fat synthesis) in response to environmental conditions (e.g., host fat content).Fitness considerationsWe assume that the local environment of an individual is characterized by two factors: fat content F and nutrient content N, where nutrients represent sugars and other carbohydrates that can be used to synthesize fat. Nutrients are measured in units corresponding to the amount of fat that can be synthesized from them. We assume that fitness (viability and/or fecundity) is directly proportional to the amount of fat stored by the individual. When fat synthesis is switched off, this amount is equal to F, the amount of fat in the environment. When fat synthesis is switched on, the amount of fat stored is assumed to be (N – c + (1 – k)F). This expression reflects the following assumptions: (i) fat is synthesized from the available nutrients, but this comes at a fitness cost c; (ii) fat can still be absorbed from the environment, but at a reduced rate ((1 – k)). It is adaptive to switch on fat synthesis if (N – c + (1 – k)F) is larger than F, or equivalently if (F < tfrac{1}{k}(N - c)).The right-hand side of this inequality is a straight line, which is illustrated by the blue line in Fig. 4. The three boxes in Fig. 4 illustrate three types of environmental conditions. Red box low-fat environments. Here, (F < tfrac{1}{k}(N - c)) is always satisfied, implying that fat synthesis should be switched on constitutively. Yellow box high-fat environments. Here, (F > tfrac{1}{k}(N – c)), implying that fat synthesis should be switched off constitutively.

Orange box intermediate-fat environments. Here, fat synthesis should be plastic and switched on if for the given environment (N, F) the fat content is below the blue line and switched off otherwise.

Figure 4Environmental conditions encountered by the model organisms. For a given combination of environmental nutrient content N and environmental fat content F, it is adaptive to switch on fat synthesis if (N, F) is below the blue line (corresponding to (F < tfrac{1}{k}(N - c))) and to switch it off otherwise. The three boxes illustrate three types of environment: a low-fat environment (red) where fat synthesis should be switched on constitutively; a high-fat environment (yellow) where fat synthesis should be switched off constitutively; and an intermediate-fat environment (orange) where a plastic switch is selectively favoured.Full size imageThe simulations reported here were all run for the parameters (k = tfrac{1}{2}{text{ and }}c = tfrac{1}{4}). We also investigated many other combinations of these parameters; in all cases, the results were very similar to those reported in Fig. 3.Gene regulatory networks (GRN)In our model, the switching device was implemented by an evolving gene regulatory network (as in van Gestel and Weissing66). The simulations shown in Fig. 3 of the main text are based on the simplest possible network that consists of two receptor nodes (sensing the fat and the nutrient content in the local environment, respectively) and an effector node that switches on fat synthesis if the combined weighted input of the two receptor nodes exceeds a threshold value T and switches it off otherwise. Hence, fat synthesis is switched on if (w_{F} F + w_{N} N > T) (and off otherwise). The GRN is characterized by the weighing factors (w_{F} {text{ and }}w_{N}) and the threshold T. These parameters are transmitted from parents to offspring, and they evolve subject to mutation and selection. We also considered alternative network structures (all with two receptor nodes and one effector node, but with a larger number of evolvable weighing factors67, and obtained very similar results, see below).For the simple GRN described above, the switching device is 100% adaptive when the switch is on (i.e., (w_{F} F + w_{N} N > T)) if (F < tfrac{1}{k}(N - c)) and off otherwise. A simple calculation yields that this is the case if: (w_{N} > 0{, }w_{F} = – k{kern 1pt} w_{N} {text{ and }}T = c{kern 1pt} w_{N}).Evolution of the GRNFor simplicity, we consider an asexual haploid population with discrete, non-overlapping generations and fixed population size (N = 10,000). Each individual has several gene loci, each locus encoding one parameter of the GRN. In case of the simple network described above, there are three gene loci, each with infinitely many alleles. Each individual harbours three alleles, which correspond to the GRN parameters (w_{F} {, }w_{N} {text{ and }}T), and hence determine the functioning of the genetic switch. In the simulations, each individual encounters a randomly chosen environment ((N{, }F)). Based on its (genetically encoded) GRN, the individual decides on whether to switch on or off fat synthesis. If synthesis is switched on, the individual’s fitness is given by (N – c + (1 – k)F); otherwise its fitness is given by F. Subsequently, the individuals produce offspring, where the number of offspring produced is proportional to the amount of fat stored by an individual. Each offspring inherits the genetic parameters of its parent, subject to mutation. With probability μ (per locus) a mutation occurs. In such a case the parental value (in case of a simple network: the parent’s allelic value (w_{F} {, }w_{N} {text{ or }}T)) is changed to a mutated value ((w_{F} { + }delta {, }w_{N} { + }delta {text{ or }}T + delta)), where the mutational step size δ is drawn from a normal distribution with mean zero and standard deviation σ. In the reported simulations, we chose (mu = 0.001) and (sigma = 0.1). The speed of evolution is proportional to (mu cdot sigma^{2}), implying that the rate of change in Fig. 3 (both the decay of plasticity and the rate of regaining adaptive plasticity) are positively related to μ and σ.Preadaptation of the GRNsStarting with a population with randomly initialized alleles for the GRN parameters, we first let the population evolve for 10,000 generations in the intermediate-fat environment (the orange box in Fig. 4). In all replicate simulations, a “perfectly adapted switch” (corresponding to (w_{N} > 0{, }w_{F} = – k{kern 1pt} w_{N} {text{ and }}T = c{kern 1pt} w_{N})) evolved, typically within 1,000 generations. Still, the evolved GRNs differed across replicates, as they evolved different values of (w_{N} > 0). These evolved networks were used to seed the populations in the subsequent “decay” simulations.Evolutionary decay of the GRNsFor the decay experiments reported in Fig. 3 of the main text, we initiated a large number of monomorphic replicate populations with one of the perfectly adapted GRNs from the preadaptation phase. These populations were exposed for an extended period of time (1,000,000 generations) to a high-fat environment (the yellow box in Fig. 4), where all preadapted GRNs switched off fat synthesis. However, in some scenarios, the environmental conditions changed back sporadically (with probability q) to the intermediate-fat environment (the orange box in Fig. 4), where it is adaptive to switch on fat metabolism in 50% of the environmental conditions (when (N, F) is below the blue line in Fig. 4). In Fig. 3, we report on the changing rates (q = 0.0) (no changing back; red), (q = 0.001) (changing back once every 1,000 generations; purple), and (q = 0.01) (changing back once every 100 generations; pink). When such a change occurred, the population was exposed to the intermediate-fat environment for t generations (Fig. 3 is based on t = 3).Throughout the simulation, the performance of the network was monitored every 100 generations as follows: 100 GRNs were chosen at random from the population, and each of these GRNs was exposed to 100 randomly chosen environmental conditions from the intermediate-fat environment (orange box in Fig. 4). From this, we could determine the average percentage of “correct” decisions (where the network should be switched on if and only if (F < tfrac{1}{k}(N - c)). 1.0 means that the GRN is still making 100% adaptive decisions; 0.5 means that the GRN only makes 50% adaptive decision, as would be expected by a random GRN or a GRN that switches the pathway constitutively on or off. This measure for performance in the “old” intermediate-fat environment was determined for 100 replicate simulations per scenario and plotted in Fig. 3 (mean ± standard deviation).Evolving robustness of the GRNsThe simulations in Fig. 3 are representative for all networks and parameters considered. Whenever (q = 0.0), the performance of the regulatory switch eroded in evolutionary time, but typically at a much lower rate in case of the more complex GRNs. Whenever (q = 0.01), the performance of the switch went back to levels above 90% and even above 95% for the more complex GRNs. Even for (q = 0.001), a sustained performance level above 75% was obtained in all cases.Intriguingly, in the last two scenarios the performance level first drops rapidly (from 1.0 to a much lower level, although this drop is less pronounced in the more complex GRNs) and subsequently recovers to reach high levels again. Apparently, the GRNs have evolved a higher level of robustness, a property that seems to be typical for evolving networks8. For the simple GRN studied in Fig. 3, this outcome can be explained as follows. The initial network was characterized by the genetic parameters (w_{N} > 0{, }w_{F} = – k{kern 1pt} w_{N} {text{ and }}T = c{kern 1pt} w_{N}) (see above), where (w_{N}) was typically a small positive number. In the course of evolutionary time, the relation between the three evolving parameters remained approximately the same, but (w_{N}) (and with it the other parameters) evolved to much larger values. This automatically resulted in an increasingly robust network, since mutations with a given step size distribution affect the performance of a network much less when the corresponding parameter is large in absolute value.Costs of plasticityPhenotypically plastic organisms can incur different types of costs68. In our simple model, we only consider the cost of phenotype-environment mismatching, that is, the costs of expressing the ‘wrong’ phenotype in a given environment. When placed in a high-fat environment, the preadapted GRNs in our simulations take the ‘right’ decision to switch off fat metabolism. Accordingly, they do not face any costs of mismatching. Yet, the genetic switch rapidly decays (as indicated in Fig. 3 by the rapid drop in performance when tested in an intermediate-fat environment), due to the accumulation of mutations.It is not unlikely that there are additional fitness costs of plasticity, such as the costs for the production and maintenance of the machinery underlying plasticity68. In the presence of such constitutive costs, plasticity will be selected against when organisms are living in an environment where only one phenotype is optimal (as in the high- and low-fat environments in Fig. 4). This would obviously affect the evolutionary dynamics in Fig. 3, but the size of the effect is difficult to judge, as the constitutive costs of plasticity are notoriously difficult to quantify. In case of the simple switching device considered in our model, we consider the constitutive costs of plasticity as marginal, but these costs might be substantial in other scenarios. More