Ecology

1.
Kyba, C. C. M. et al. Artificially lit surface of Earth at night increasing in radiance and extent. Sci. Adv. 3, 1–8 (2017).
Google Scholar 
2.
Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
Google Scholar 

3.
Bennie, J., Davies, T. W., Cruse, D. & Gaston, K. J. Ecological effects of artificial light at night on wild plants. J. Ecol. 104, 611–620 (2016).
Google Scholar 

4.
Grenis, K. & Murphy, S. M. Direct and indirect effects of light pollution on the performance of an herbivorous insect. Insect Sci. 26, 770–776 (2019).
PubMed  Google Scholar 

5.
Bennie, J., Davies, T. W., Cruse, D., Inger, R. & Gaston, K. J. Cascading effects of artificial light at night: Resource-mediated control of herbivores in a grassland ecosystem. Phil. Trans. R. Soc. B 370, 1–9 (2015).
Google Scholar 

6.
Bennie, J., Gaston, K. J., Davies, T. W., Cruse, D. & Inger, R. Artificial light at night causes top-­down and bottom-up trophic effects on invertebrate populations. J. Appl. Ecol. 55, 2698–2706 (2018).
CAS  Google Scholar 

7.
Eisenbeis, G. & Hänel, A. Light pollution and the impact of artificial night lighting on insects. Ecol. Cities Towns Comp. Approach https://doi.org/10.1017/CBO9780511609763.016 (2009).
Article  Google Scholar 

8.
Knop, E. et al. Artificial light at night as a new threat to pollination. Nature 548, 206–209 (2017).
ADS  CAS  PubMed  Google Scholar 

9.
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).
ADS  CAS  Google Scholar 

10.
Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Google Scholar 

11.
Banza, P., Belo, A. D. F. & Evans, D. M. The structure and robustness of nocturnal Lepidopteran pollen-transfer networks in a Biodiversity Hotspot. Insect Conserv. Divers. 8, 538–546 (2015).
Google Scholar 

12.
Hahn, M. & Bruhl, C. A. The secret pollinators: An overview of moth pollination with a focus on Europe and North America. Arthropod. Plant. Interact. 10, 21–28 (2016).
Google Scholar 

13.
Macgregor, C. J., Pocock, M. J. O., Fox, R. & Evans, D. M. Pollination by nocturnal Lepidoptera, and the effects of light pollution: A review. Ecol. Entomol. 40, 187–198 (2015).
PubMed  Google Scholar 

14.
van Langevelde, F., Ettema, J. A., Donners, M., WallisDeVries, M. F. & Groenendijk, D. Effect of spectral composition of artificial light on the attraction of moths. Biol. Conserv. 144, 2274–2281 (2011).
Google Scholar 

15.
Van Grunsven, R. H. A., Lham, D., Van Geffen, K. G. & Veenendaal, E. M. Range of attraction of a 6-W moth light trap. Entomol. Exp. Appl. 152, 87–90 (2014).
Google Scholar 

16.
Longcore, T. & Rich, C. Ecological light pollution. Front. Ecol. Environ. 2, 191–198 (2004).
Google Scholar 

17.
Frank, K. D. Impact of outdoor lighting on moths. Int. Astron. Union Colloq. 112, 51 (2016).
Google Scholar 

18.
Van Grunsven, R. H. A. et al. Experimental light at night has a negative long-term impact on macro-moth. Curr. Biol. 30, R694–R695 (2020).
PubMed  Google Scholar 

19.
Van Langevelde, F., Van Grunsven, R. H. A., Veenendaal, E. M. & Fijen, T. P. M. Artificial night lighting inhibits feeding in moths. Biol. Lett. 13, 2–5 (2017).
Google Scholar 

20.
van Geffen, K. G. et al. Artificial light at night inhibits mating in a Geometrid moth. Insect Conserv. Divers. 8, 282–287 (2015).
Google Scholar 

21.
Giavi, S., Blösch, S., Schuster, G. & Knop, E. The darkness defeated: artificial light at night modifies ecosystem functioning beyond the lit area. Sci. Rep. 10, 1–11 (2020).
Google Scholar 

22.
Fatzinger, C. W. Circadian rhythmicity of sex pheromone release by Dioryctria abietella (Lepidoptera: Pyralidae (Phycitinae)) and the effect of a diel light cycle on its precopulatory behavior. Ann. Entomol. Soc. Am. 66, 1147–1153 (1973).
Google Scholar 

23.
Sower, L. L., Shorey, H. H. & Gaston, L. K. Sex pheromones of noctuid moths. XXI. Light:dark cycle regulation and light inhibition of sex pheromone release by females of Trichoplusia ni. Ann. Entomol. Soc. Am. 63, 1090–1092 (1970).
CAS  PubMed  Google Scholar 

24.
Shorey, H. H. & Gaston, L. K. Sex pheromones of noctuid moths. III. Inhibition of male responses to the sex pheromone in Trichoplusia ni (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 57, 775–779 (1964).
Google Scholar 

25.
Donners, M. et al. Colors of attraction: Modeling insect flight to light behavior. J. Exp. Zool. Part A Ecol. Integr. Physiol. 329, 434–440 (2018).
Google Scholar 

26.
Bernasconi, G. et al. Silene as a model system in ecology and evolution. Heredity (Edinb). 103, 5–14 (2009).
CAS  PubMed  Google Scholar 

27.
Labouche, A. M. & Bernasconi, G. Male moths provide pollination benefits in the Silene latifolia–Hadena bicruris nursery pollination system. Funct. Ecol. 24, 534–544 (2010).
Google Scholar 

28.
Biere, A. & Honders, S. C. Coping with third parties in a nursery pollination mutualism: Hadena bicruris avoids oviposition on pathogen-infected, less rewarding Silene latifolia. New Phytol. 169, 719–727 (2006).
PubMed  Google Scholar 

29.
Spoelstra, K. et al. Experimental illumination of natural habitat—An experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition. Phil. Trans. R. Soc. B 370, 20140129 (2015).
PubMed  Google Scholar 

30.
Poot, H. et al. Green light for nocturnally migrating birds. Ecol. Soc. 13, 1–14 (2008).
Google Scholar 

31.
Jalas, J. & Suominen, J. Atlas Florae Europaeae: Distribution of Vascular Plants in Europe Vol. 3 (Cambridge University Press, Cambridge, 1988).
Google Scholar 

32.
Karrenberg, S. & Favre, A. Genetic and ecological differentiation in the hybridizing campions Silene dioica and S. latifolia. Evolution N. Y. 62, 763–773 (2008).
Google Scholar 

33.
Elzinga, J. A., Turin, H., van Damme, J. M. M. & Biere, A. Plant population size and isolation affect herbivory of Silene latifolia by the specialist herbivore Hadena bicruris and parasitism of the herbivore by parasitoids. Oecologia 144, 416–426 (2005).
ADS  PubMed  Google Scholar 

34.
WinSEEDLE Pro 2019a (Regent Instruments Inc., 2018).

35.
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar 

36.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 

37.
Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.1.3. https://CRAN.R-project.org/package=emmeans (2019). More

1.
Manzanedo, R. D., HilleRisLambers, J., Rademacher, T. T. & Pederson, N. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01306-x (2020).
2.
Shestakova, T. A. et al. Proc. Natl Acad. Sci. USA 113, 662–667 (2016).
CAS  Article  Google Scholar 

3.
Pugh, T. A. M. et al. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).
CAS  Article  Google Scholar 

4.
Di Cecco, G. J. & Gouhier, T. C. Sci. Rep. 8, 14850 (2018).
Article  Google Scholar 

5.
Shestakova, T. A., Gutiérrez, E., Valeriano, C., Lapshina, E. & Voltas, J. Agric. For. Meteorol. 268, 318–330 (2019).
Article  Google Scholar 

6.
Hubau, W. et al. Nature 579, 80–87 (2020).
CAS  Article  Google Scholar 

7.
Adams, M. A., Buckley, T. N. & Turnbull, T. L. Nat. Clim. Change 10, 466–471 (2020).
CAS  Article  Google Scholar 

8.
Brienen R. J. W., Schöngart J. & Zuidema, P. A. in Tropical Tree Physiology Tree Physiology Vol. 6 (eds Goldstein, G. & Santiago L. S.) 439–461 (Springer, 2016).

9.
Hansen, B. B., Grøtan, V., Herfindal, I. & Lee, A. M. Ecography https://doi.org/10.1111/ecog.04962 (2020).

10.
Koenig, W. D. & Liebhold, A. M. Nat. Clim. Change 6, 614–617 (2016).
Article  Google Scholar  More

Experiments with robotic fish
We developed, and employed, a bio-mimetic robotic fish platform (Fig. 1, Supplementary Figs. 1–4 and Movie 1 and 2) in order to experimentally evaluate the costs and benefits of swimming together. We constructed two identical robotic fish, 45 cm in length and 800 g in mass. Each has three sequential servo-motors controlling corresponding joints, covered in a soft, waterproof, rubber skin. In addition, the stiffness of the rubber caudal fin decreases towards the tip33 (Supplementary Fig. 1). The motion of the servomotors is controlled using a bio-inspired controller called a central pattern generator (CPG)34,35 resulting in the kinematics that mimic normal real fish body undulations when swimming36 (see Supplementary Fig. 2 and Note 1). Here, due to the complexity of the problem (as discussed above) we consider hydrodynamic interactions between pairs of fish. We note that this is biologically meaningful as swimming in pairs is both the most common configuration found in natural fish populations7,10,37,38, and it has been found that even in schools fish tend to swim close to only a single neighbour7,37.
Fig. 1: The robotic fish platform employed to investigate hydrodynamic benefits of schooling.

a The reverse Karman vortices shedding by the robotic fish with dye flow visualisation. b Schematic view of the setup that allows setting various spatiotemporal differences between two robotic fish swimming in a flow tank (Front-back distance D ∈ [0.22, 1] BL (body length), Left-right distance G ∈ [0.27, 0.33] BL and Phase difference Φ ∈ [0, 2π]). A laser generator was used to visualise the hydrodynamic interactions (see Supplementary Note 1). c The phase difference Φ is evaluated by the difference between the undulation phase of the two robots. Undulation phase is evaluated based on the lateral position Lt of the tail tip. d Power cost (absolute value on the left y-axis, and relative value compared to the average power cost on the right y-axis), is shown as a function of the phase difference at D = 0.33 BL and G = 0.27 BL. Error bars are standard error of the mean.

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To evaluate the energetics of swimming together we conducted experiments on our pair of robotic fish in a flow tank (test area: 0.4-m-wide, 1-m-long and 0.45-m-deep; Fig. 1b and Supplementary Fig. 3). In order to conduct such an assessment we first measured the speed of our robots when freely swimming alone (we did so in a large tank 2-m-wide, 3-m-long and 0.4-m-deep). We then set the flow speed within our flow tank to this free-swimming speed (0.245 ms−1) allowing us to ensure the conditions in the flow tank are similar to those of the free-swimming robot. Unlike in the solitary free-swimming condition, to have precise control of spatial relationships in the flow tank we suspended each robotic fish by attaching a thin aluminium vertical bar to the back of each robot, which was then attached to a step motor above the flow tank (Supplementary Fig. 3 and Movie 2). To establish whether the robotic fish connected with a thin bar has similar hydrodynamics compared to when free swimming, we measured the net force (of the drag and thrust generated by the fish body in the front-back direction) acting on the robot in the flow tank. The measured net force over a full cycle (body undulation) was found to be zero; thus the bar is not measurably impacting the hydrodynamics of our robot fish in the front-back direction as they swim in the flow tank (Supplementary Fig. 5).
To further validate the utility of the platform, we also compared the power consumption of our robots swimming side-by-side, for different relative phase differences Φ, with equivalent measurements made with a simple 2D computational fluid dynamics (CFD) model of the same scenario (Supplementary Note 2). In both cases (see Supplementary Fig. 6a, c for robotic experiments and CFD simulations, respectively) we find that there exists an approximately sinusoidal relationship between power costs and phase difference which is defined as Φ = ϕleader − ϕfollower (Fig. 1c, d). Due to the 2D nature of the simulation, as well as many other inevitable differences between simulations and real world mechanics, the absolute power costs are different from those measured for the robots, but nevertheless the results from these two approaches are broadly comparable and produce qualitatively similar relative power distributions when varying the phase difference between the leader and follower. These results indicate that our robotic fish are both an efficient (making estimates of swimming costs is far quicker with our robotic platform than it is with CFD simulations) and effective (in that they capture the essential hydrodynamic interactions as well as naturally incorporate 3D factors) platform for generating testable hypotheses regarding hydrodynamic interactions in pairs of fish.
We subsequently utilise our robots to directly measure the energy costs associated with swimming together as a function of relative position (front-back distance D from 0.22 to 1 body length (BL) in increments of 0.022 BL and left-right distance G from 0.27 to 0.33 BL in increments of 0.022 BL) while also varying the phase relationships (phase difference, Φ) of the body undulations exhibited by the robots (the phase of the follower’s tailbeat ϕfollower relative to that of the leader’s ϕleader, Fig. 1c).
By conducting 10,080 trials (~120 h of data), we obtain a detailed mapping of the power costs relative to swimming alone associated with these factors (Fig. 2a). Such a mapping allows us to predict how real fish, that continuously change relative positions6,8, should correspondingly continuously adjust their phase relationship in order to maintain hydrodynamic benefits. To quantify the costs we determine the energy required to undulate the tail of each robot allowing us to define, and calculate, a dimensionless relative power coefficient as:

$$eta =frac{({P}_{1}^{{rm{Water}}}-{P}^{{rm{Air}}})-({P}_{2}^{{rm{Water}}}-{P}^{{rm{Air}}})}{{P}_{1}^{{rm{Water}}}-{P}^{{rm{Air}}}}=frac{{P}_{1}^{{rm{Water}}}-{P}_{2}^{{rm{Water}}}}{{P}_{1}^{{rm{Water}}}-{P}^{{rm{Air}}}},$$
(1)

where η is the relative power coefficient, PAir, ({P}_{1}^{{rm{Water}}}) and ({P}_{2}^{{rm{Water}}}) are the power costs of the robotic fish swimming in the air (an approximation of the dissipated power cost due to mechanical friction, resistance, etc. within the robot that are not related to interacting with the water), alone in water, or in a paired context in the water, respectively. ({P}_{1}^{{rm{Water}}}-{P}^{{rm{Air}}}) and ({P}_{2}^{{rm{Water}}}-{P}^{{rm{Air}}}) therefore represent the power costs due to hydrodynamics while swimming alone, and in a pair, respectively (see Methods section). Correspondingly, the coefficient η compares the energy cost of fish swimming in pairs to swimming alone. Positive values (blue in Fig. 2a) and negative values (red in Fig. 2a) respectively represent energy saving and energy cost relative to swimming alone. The difference between the maximum energy saving and maximum energy cost for the robots is ~13.4%.
Fig. 2: Robotic fish save energy by vortex phase matching (VPM).

a Relative power coefficient η shown as a function of the phase difference between the leader and the follower Φ and front-back distance D at left–right distance G = 0.31 BL. The dashed line (also in b) shows the functional relationship described in Eq. (2) that determines the theoretical phase relationship that maximally saves energy (Methods section). ({Phi }_{0}^{* }) is the optimal initial phase difference (fitted to the data points of maximum energy saving, as shown in b). The points marked by red square, blue circle and blue square indicate example cases depicted on panels c–e. b Location of maximal energy saving in the robotic trials. Point size and darkness denote the number of occurrences of each phase difference value at each front-back distance. c–e An illustration of important spatial configurations for vortex phase matching. Energy cost is related to how the follower moves its body relative to the direction of the induced flow of the vortices, in the opposite direction with Φ0 = ({Phi }_{0}^{* })+π (c) or in the same direction with Φ0 = ({Phi }_{0}^{* }) (d, e). Followers interact with the induced flow of vortices with the same body phase at any front-back distance (within the range of hydrodynamic interactions), termed vortex phase matching. (d, e; Φ0 = ({Phi }_{0}^{* }) describes the hydrodynamic interaction resulting in energy saving, see description in the text). As the front-back distance changes, the followers must dynamically adopt phase difference Φ, with respect to that of the leader.

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Our results indicate that there exists a relatively simple linear relationship between front-back distance and relative phase difference of the follower that minimises the power cost of swimming (as indicated by the dashed lines in Fig. 2a, b, the theoretical basis of which we will discuss below). This suggests that a follower can minimise energetic expenditure (and avoid substantial possible energetic costs) by continuously adopting a unique phase difference Φ that varies linearly as a function of front-back distance D (see Fig. 2b for example), even as that distance changes. We find that while left-right distance G does alter energy expenditure, this effect is minimal when compared to front-back positioning, and has little effect on the above relationship (Supplementary Figs. 7 and 8) in the range explored here.
Although we know fish generate reverse Kármán vortices at the Reynolds number (Re = Lu/ν ≈ 105, where L is the fish body length, u is the swimming or flow speed and ν is the kinematic viscosity) in our experiments39 (Supplementary Fig. 9 and Movie 1), turbulence will dominate over longer distances18. In accordance with this, we see a relatively fast decay in the benefits of swimming together as a function of D (e.g., D  > 0.7 BL, Supplementary Fig. 10), a feature we also expect to be apparent in natural fish schools (where it would likely be exacerbated by what would almost always be less-laminar flow conditions). Therefore, we expect, based on our results, that hydrodynamic interactions are dominated by short-distance vortex-body interactions (with D   2 BL), and it thus cannot benefit from neighbour-generated vortices. We also chose this method since isolating the fish would likely induce stress responses that could confound our results. To evaluate body kinematics in the presence of vortices we analysed the body undulations of the follower when in close proximity (within 0.4 BL), where hydrodynamic effects will be strongest (Supplementary Fig. 25). We find that in the vicinity of vortices, fish exhibit a higher tailbeat amplitude and lower tailbeat frequency (Supplementary Fig. 26), which indicates less power consumption48.
To further test if fish can save energy by adopting VPM with the typical vortex-body hydrodynamic interactions (Φ0 = −0.2π), we compared an estimation of the power consumption under different hydrodynamic interactions. Since the hydrodynamic interactions are mainly determined by the initial phase difference Φ0 (see above), we analysed performance in the full possible range from −π to π (see Supplementary Fig. 25 for the detailed method). We define relative energy saving when fish exhibit higher tailbeat amplitudes A (Fig. 4a) and lower tailbeat frequencies f (Fig. 4b) than average48, and find that the range is Φ0∈ [−0.5π, 0.5π] (the shaded area in Fig. 4). Figure 4 also shows that while fish adopting Φ0 ≈ 0 will save the most energy, those exhibiting Φ0 = −0.2π, as in our experiments, will save almost the same amount (thus they are very close to optimal in this respect).
Fig. 4: Relative energetic benefits to a follower in real fish pairs.

a, b Energy cost analysis was conducted by calculating the difference in amplitude A (a) and frequency f (b) at Φ0 and the same measurements with the opposite phase Φ0 + π (written as A+π and f+π respectively) as a function of initial phase difference Φ0 (Supplementary Fig. 25 and Note 4). Data are pooled from all pairs when the follower’s front-back positions are not >0.4 BL distance (where the hydrodynamic interactions are expected to be the strongest). The hatched areas show the energy saving zone of Φ0. The dashed line denotes ({Phi }_{0}^{* }), the most typically observed initial phase difference exhibited by our fish. (Average amplitude is 0.09 BL, average frequency is 2.3 Hz).

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Fish in our experiments (Fig. 3c at D = 0 BL) spent 59% of their time swimming with phase relationships (Φ0∈ [−0.5π, 0.5π]) that save energy, and the remaining 41% that imposes some (relative) energetic costs. However, because the energy cost has a sinusoidal relationship to the phase difference (Fig. 1d) simply calculating the percentage of time in each regime (in which there is either a benefit or a cost, regardless of the magnitude of each) is insufficient. By combining the frequencies (occurrences) of each phase difference Φ observed in Fig. 3c and the sinusoidal shape of the power cost as a function of Φ (Fig. 1d and Supplementary Fig. 6b, d), we can estimate that by behaving as they do, fish (in our flow conditions) save (by accumulating all benefits and extra costs; where a random behaviour would give 0) an overall 15% of the total possible (which would be achieved by perfectly adopting the optimal phase to the neighbour-generated vortices at all the time, Supplementary Fig. 27). It is possible that if fish are exposed to more challenging, stronger flow regimes (here we employed those of typical swimming), that this percentage will increase. However we would never expect fish motion to be completely dominated by a need to save energy as they must also move in ways as to obtain salient social and asocial information from their visual, olfactory, acoustic and hydrodynamic environment, such as to better detect food52, environmental gradients44 and threats16. Nevertheless, kinematic analysis suggests that they adopt VPM in a way that results in energy savings (dashed line in Fig. 4).
In summary, our bio-mimetic robots provided an effective platform with which we could explore the energetic consequences of swimming together in pairs and revealed that followers could benefit from neighbour-generated flows if they adjust their relative tailbeat phase difference linearly as a function of front-back distance, a strategy we term vortex phase matching. A model based on fundamental hydrodynamic principles, informed by our flow visualisations, was able to account for our results. Together, this suggests that the observed energetic benefit occurs when a follower’s tail movement coincides with the induced flow generated by the leader. Finally, experiments with real fish demonstrated that followers indeed employ vortex phase matching and kinematic analysis of their body undulations suggests that they do so, at least in part, to save energy. By providing evidence that fish do exploit hydrodynamic interactions, we gain an understanding of important costs and benefits (and thus the selection pressures) that impact social behaviour. In addition, our findings provide a simple, and robust, strategy that can enhance the collective swimming efficiency of fish-like underwater vehicles. More

1.
Porter, J. R. et al. in Climate Change 2014: Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects 485–534 (IPCC, Cambridge University Press, 2015).
2.
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
ADS  CAS  Article  Google Scholar 

3.
Parent, B. et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. Proc. Natl Acad. Sci. USA 115, 10642–10647 (2018).
CAS  Article  Google Scholar 

4.
Müller, C., Bondeau, A., Popp, A., Waha, K. & Fader, M. Climate Change Impacts on Agricultural Yields (World Development Report, Background Note, 2010).

5.
Liu, Z. et al. Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010. Reg. Environ. Change 15, 919–929 (2015).
Article  Google Scholar 

6.
Meng, Q. et al. The benefits of recent warming for maize production in high latitude China. Clim. Change 122, 341–349 (2014).

7.
Siebert, S. & Ewert, F. Spatio-temporal patterns of phenological development in Germany in relation to temperature and day length. Agric. For. Meteorol. 152, 44–57 (2012).
ADS  Article  Google Scholar 

8.
Zhu, P. et al. The important but weakening maize yield benefit of grain filling prolongation in the US Midwest. Glob. Chang. Biol. 24, 4718–4730 (2018).
ADS  Article  Google Scholar 

9.
Burke, M. & Emerick, K. Adaptation to climate change: evidence from US agriculture. Am. Econ. J. Econ. Policy 8, 106–140 (2016).

10.
Lobell, D. B. Climate change adaptation in crop production: beware of illusions. Glob. Food Sec. 3, 72–76 (2014).
Article  Google Scholar 

11.
Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353, aad9837 (2016).

12.
McFadden, J., Smith, D., Wechsler, S. & Wallander, S. Development, Adoption, and Management of Drought-Tolerant Corn in the United States (US Department of Agriculture, Economic Research Service, 2019).

13.
Gaffney, J. et al. Industry-scale evaluation of maize hybrids selected for increased yield in drought-stress conditions of the US Corn Belt. Crop Sci. 55, 1608–1618 (2015).
Article  Google Scholar 

14.
Cooper, M., Gho, C., Leafgren, R., Tang, T. & Messina, C. Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J. Exp Bot. 65, 6191–6204 (2014).

15.
Goodwin, B. K. & Piggott, N. E. Has technology increased agricultural yield risk? Evidence from the crop insurance Biotech Endorsement. Am. J. Agric. Econ. https://doi.org/10.1002/ajae.12087 (2020).

16.
Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).
ADS  CAS  Article  Google Scholar 

17.
Assefa, Y. et al. Analysis of long term study indicates both agronomic optimal plant density and increase maize yield per plant contributed to yield gain. Sci. Rep. 8, 1–11 (2018).
ADS  Article  Google Scholar 

18.
Leakey, A. D. B. Rising atmospheric carbon dioxide concentration and the future of C4 crops for food and fuel. Proc. R. Soc. B Biol. Sci. 276, 2333–2343 (2009).
CAS  Article  Google Scholar 

19.
Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants 2, 1–8 (2016).
Article  Google Scholar 

20.
Jin, Z., Ainsworth, E. A., Leakey, A. D. B. & Lobell, D. B. Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest. Glob. Chang. Biol. 24, e522–e533 (2018).

21.
Mills, G. et al. Tropospheric ozone assessment report: present-day tropospheric ozone distribution and trends relevant to vegetation. Elementa (Wash. DC) 6, 47 (2018).
Google Scholar 

22.
Mills, G. et al. Ozone pollution will compromise efforts to increase global wheat production. Glob. Chang. Biol. 24, 3560–3574 (2018).

23.
McGrath, J. M. et al. An analysis of ozone damage to historical maize and soybean yields in the United States. Proc. Natl Acad. Sci. USA 112, 14390–14395 (2015).

24.
Quinton, J. N., Govers, G., Van Oost, K. & Bardgett, R. D. The impact of agricultural soil erosion on biogeochemical cycling. Nat. Geosci. 3, 311–314 (2010).
ADS  CAS  Article  Google Scholar 

25.
Barreca, A., Clay, K., Deschenes, O., Greenstone, M. & Shapiro, J. S. Adapting to climate change: the remarkable decline in the US temperature–mortality relationship over the twentieth century. J. Polit. Econ. 124, 105–159 (2016).
Article  Google Scholar 

26.
Roberts, M. J. & Schlenker, W. in The Economics of Climate Change: Adaptations Past and Present (ed. Steckel, R. H.) 225–251 (University of Chicago Press, 2011).

27.
Sakurai, G., Iizumi, T. & Yokozawa, M. Varying temporal and spatial effects of climate on maize and soybean affect yield prediction. Clim. Res 49, 143–154 (2011).
Article  Google Scholar 

28.
Hawkins, E. et al. Increasing influence of heat stress on French maize yields from the 1960s to the 2030s. Glob. Chang. Biol 19, 937–947 (2013).
ADS  Article  Google Scholar 

29.
Wang, E., Cresswell, H., Xu, J. & Jiang, Q. Capacity of soils to buffer impact of climate variability and value of seasonal forecasts. Agric. For. Meteorol. 149, 38–50 (2009).
ADS  Article  Google Scholar 

30.
He, D. & Wang, E. On the relation between soil water holding capacity and dryland crop productivity. Geoderma 353, 11–24 (2019).
ADS  Article  Google Scholar 

31.
Wong, M. T. F. & Asseng, S. Determining the causes of spatial and temporal variability of wheat yields at sub-field scale using a new method of upscaling a crop model. Plant Soil 283, 203–215 (2006).

32.
Gridded Soil Survey Geographic (gSSURGO) Database User Guide 85 (National Resource Conservation Service, 2014).

33.
Ficklin, D. L. & Novick, K. A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. 122, 2061–2079 (2017).
Article  Google Scholar 

34.
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).
ADS  CAS  Article  Google Scholar 

35.
Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).
CAS  Article  Google Scholar 

36.
Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).

37.
Lobell, D. B. & Asner, G. P. Climate and management contributions to recent trends in US agricultural yields. Science 299, 1032 (2003).
CAS  Article  Google Scholar 

38.
Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Chang. 3, 497–501 (2013).
ADS  Article  Google Scholar 

39.
Jin, Z. et al. The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2. Glob. Chang. Biol. 23, 2687–2704 (2017).

40.
Kucharik, C. J. A multidecadal trend of earlier corn planting in the central USA. Agron. J. 98, 1544–1550 (2006).
Article  Google Scholar 

41.
Wade, T., Claassen, R. & Wallander, S. Conservation-Practice Adoption Rates Vary Widely by Crop and Region EIB-147, 40 (US Department of Agriculture, Economic Research Service, 2015).

42.
Jin, Z., Azzari, G. & Lobell, D. B. Improving the accuracy of satellite-based high-resolution yield estimation: a test of multiple scalable approaches. Agric. For. Meteorol. 247, 207–220 (2017).
ADS  Article  Google Scholar 

43.
Lobell, D. B., Thau, D., Seifert, C., Engle, E. & Little, B. A scalable satellite-based crop yield mapper. Remote Sens. Environ. 164, 324–333 (2015).
ADS  Article  Google Scholar 

44.
Urban, D. W., Roberts, M. J., Schlenker, W. & Lobell, D. B. The effects of extremely wet planting conditions on maize and soybean yields. Clim. Change 130, 1–14 (2015).
Article  Google Scholar 

45.
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Chang. Biol. 25, 2325–2337 (2019).

46.
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Chang. Biol. 22, 3112–3126 (2016).

47.
Woodard, J. D. & Verteramo-Chiu, L. J. Efficiency impacts of utilizing soil data in the pricing of the federal crop insurance program. Am. J. Agric. Econ. 99, 757–772 (2017).
Article  Google Scholar 

48.
Wechsler, S. J., McFadden, J. R. & Smith, D. J. What do farmers’ weed control decisions imply about glyphosate resistance? Evidence from surveys of US corn fields. Pest Manag. Sci. 74, 1143–1154 (2018).
CAS  Article  Google Scholar 

49.
DeLucia, E. H. et al. Are we approaching a water ceiling to maize yields in the United States? Ecosphere 10, e02773 (2019).

50.
Cooper, M., Gho, C., Leafgren, R., Tang, T. & Messina, C. Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J. Exp. Bot. 65, 6191–6194 (2014).
CAS  Article  Google Scholar 

51.
Adoption of Genetically Engineered Crops in the US (US Department of Agriculture, 2019); https://www.ers.usda.gov/data-products/adoption-of-genetically-engineered-crops-in-the-us/.

52.
Klümper, W. & Qaim, M. A meta-analysis of the impacts of genetically modified crops. PLoS ONE 9, e111629 (2014).

53.
McFadden, J. R. Yield Maps, Soil Maps, and Technical Efficiency: Evidence from US Corn Fields (Agricultural and Applied Economics Association, 2017); https://doi.org/10.22004/ag.econ.258120

54.
Duvick, D. N. in Variability in Grain Yields: Implications for Agricultural Research and Policy in Developing Countries (eds J. R. Anderson and P. B. R. Hazel) 147–156 (Johns Hopkins University Press, 1989).

55.
Daly, C., Halbleib, M. & Smith, J. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).
Article  Google Scholar 

56.
Boryan, C., Yang, Z., Mueller, R. & Craig, M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer program. Geocarto Int. 26, 341–358 (2011).
Article  Google Scholar 

57.
Wang, S., Di Tommaso, S., Deines, J. & Lobell, D. B. Mapping Twenty Years of Corn and Soybean Across the US Midwest Using the Landsat Archive. Sci. Data 7, 307 (2020).

58.
Johnson, D. M. An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141, 116–128 (2014).
ADS  Article  Google Scholar 

59.
Dobrowski, S. Z. et al. The climate velocity of the contiguous United States during the 20th century. Glob. Chang. Biol. 19, 241–251 (2013).

60.
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

61.
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013). More

1.
Waugh, C. A., Nichols, P. D., Noad, M. C. & Bengtson Nash, S. M. Lipid and fatty acid profiles of migrating Southern Hemisphere humpback whales Megaptera novaeangliae. Mar. Ecol. Prog. Ser. 471, 271–281 (2012).
ADS  CAS  Article  Google Scholar 
2.
Chittleborough, R. G. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (Borowski). Mar. Freshw. Res. 16, 33–128 (1965).
Article  Google Scholar 

3.
Kawamura, A. A Review of food of balaenopterid whales. Sci. Rep. Whales Res. Inst. 32, 155–197 (1980).
Google Scholar 

4.
Danilewicz, D., Tavares, M., Moreno, I. B., Ott, P. H. & Trigo, C. C. Evidence of feeding by the humpback whale (Megaptera novaeangliae) in mid-latitude waters of the western South Atlantic. Mar. Biodivers. Rec. 2, 1–3 (2009).
Article  Google Scholar 

5.
Pinto de sa Alves, L. C. et al. Record of feeding by humpback whales (Megaptera novaeangliae) in tropical waters off Brazil. Mar. Mammal Sci. 25, 416–419 (2009).
Article  Google Scholar 

6.
Stamation, K. A., Croft, D. B., Shaughnessy, P. D. & Waples, K. A. Observations of humpback whales (Megaptera novaeangliae) feeding during their southward migration along the coast of Southeastern New South Wales, Australia: Identification of a possible supplemental feeding ground. Aquat. Mamm. 33, 165–174 (2007).
Article  Google Scholar 

7.
Owen, K. et al. Potential energy gain by whales outside of the Antarctic: Prey preferences and consumption rates of migrating humpback whales (Megaptera novaeangliae). Polar Biol. 40, 277–289 (2017).
Article  Google Scholar 

8.
Eisenmann, P. et al. Isotopic evidence of a wide spectrum of feeding strategies in southern hemisphere humpback whale baleen records. PLoS One 11, e0156698 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Bengtson Nash, S. M. et al. Signals from the south; humpback whales carry messages of Antarctic sea-ice ecosystem variability. Glob. Chang. Biol. 24, 1500–1510 (2018).
ADS  PubMed  Article  PubMed Central  Google Scholar 

10.
IWC. Report of the workshop on the comprehensive assessment of southern hemisphere humpback whales. J. Cetacean Res. Manag. (Spec Issue) 3, 1–50 (2011).
Google Scholar 

11.
Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating east Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244 (2015).
ADS  Article  Google Scholar 

12.
Gales, N. et al. Satellite tracking of southbound East Australian humpback whales (Megaptera novaeangliae): Challenging the feast or famine model for migrating whales. J. Cetacean Res. Manag. 61 (2009).

13.
Falk-Petersen, S., Hagen, W., Kattner, G., Clarke, A. & Sargent, J. Lipids, trophic relationships, and biodiversity in Arctic and Antarctic krill. Can. J. Fish. Aquat. Sci. 57, 178–191 (2000).
CAS  Article  Google Scholar 

14.
Clarke, A. Lipid Content and Composition of Antarctic Krill, Euphausia Superba Dana. J. Crustac. Biol. 4, 285–294 (1984).
CAS  Article  Google Scholar 

15.
Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: A primer on analysis and interpretation. Mar. Mammal Sci. 22, 759–801 (2006).
Article  Google Scholar 

16.
Cook, H. W. Fatty acid desaturation and chain elongation in eucaryotes. In Biochemistry of Lipids, Lipoproteins and Membranes (eds Vance, D. E. & Vance, J.) 141–169 (Elsevier, New York, 1991).
Google Scholar 

17.
Guang, Y., Li, C. & Yanqing, W. Fatty acid composition of Euphausia superba, Thysanoessa macrura and Euphausia crystallorophias collected from Prydz Bay, Antarctica. J. Ocean Univ. China 15, 297–302 (2016).
Article  CAS  Google Scholar 

18.
Hagen, W. & Kattner, G. Lipid metabolism of the Antarctic euphausiid Thysanoessa macrura and its ecological implications. Limnol. Oceanogr. 43, 1894–1901 (1998).
ADS  CAS  Article  Google Scholar 

19.
Mayzaud, P., Boutoute, M. & Alonzo, F. Lipid composition of the euphausiids Euphausia vallentini and Thysanoessa macrura during summer in the Southern Indian Ocean. Antarct. Sci. 15, 463–475 (2003).
ADS  Article  Google Scholar 

20.
O’Brien, C., Virtue, P., Kawaguchi, S. & Nichols, P. D. Aspects of krill growth and condition during late winter-early spring off East Antarctica (110–130°E). Deep. Res. Part II 58, 1211–1221 (2011).
Article  CAS  Google Scholar 

21.
Phleger, C. F., Nichols, P. D. & Virtue, P. Lipids and trophodynamics of Antarctic zooplankton. Comp. Biochem. Physiol. Part B 120, 311–323 (1998).
Article  Google Scholar 

22.
Stübing, D. & Hagen, W. Fatty acid biomarker ratios-suitable trophic indicators in Antarctic euphausiids?. Polar Biol. 26, 774–782 (2003).
Article  Google Scholar 

23.
Phleger, C. F., Nelson, M. M., Mooney, B. D. & Nichols, P. D. Interannual and between species comparison of the lipids, fatty acids and sterols of Antarctic krill from the US AMLR Elephant Island survey area. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 131, 733–747 (2002).
PubMed  Article  Google Scholar 

24.
Varisco, M., Crovetto, C., Colombo, J., Vinuesa, J. & Risso, S. Proximate composition and nutritional quality of the meat of the squat lobster Munida gregaria (Fabricius 1973). J. Aquat. Food Prod. Technol. 29, 229–237 (2020).
CAS  Article  Google Scholar 

25.
Phillips, K. L., Nichols, P. D. & Jackson, G. D. Size-related dietary changes observed in the squid Moroteuthis ingens at the Falkland Islands: Stomach contents and fatty-acid analyses. Polar Biol. 26, 474–485 (2003).
Article  Google Scholar 

26.
Virtue, P. Lipids in Euphausia superba. PhD thesis. (University of Tasmania, 1995).

27.
Baylis, A. M. M., Hamer, D. J. & Nichols, P. D. Assessing the use of milk fatty acids to infer the diet of the Australian sea lion (Neophoca cinerea). Wildl. Res. 36, 169–176 (2009).
CAS  Article  Google Scholar 

28.
Nichols, P. D., Virtue, P., Mooney, B. D., Elliott, N. G. & Yearsley, G. K. Seafood the good food: The oil (fat) content and composition of Australian commercial fishes, shellfishes and crustaceans (CSIRO Div. of Marine Research//Fisheries Research & Development Corporation, 1998).

29.
Borobia, M., Gearing, P. J., Simard, Y., Gearing, J. N. & Béland, P. Blubber fatty acids of finback and humpback whales from the Gulf of St. Lawrence. Mar. Biol. 122, 341–353 (1995).
CAS  Article  Google Scholar 

30.
Bengtson Nash, S. M., Waugh, C. A. & Schlabach, M. Metabolic concentration of lipid soluble organochlorine burdens in the blubber of southern hemisphere humpback whales through migration and fasting. Environ. Sci. Technol. 47, 9404–9413 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Lockyer, C. Body weights of some species of large whales. ICES J. Mar. Sci. 36, 259–273 (1976).
Article  Google Scholar 

32.
Castrillon, J. & Bengtson Nash, S. Evaluating cetacean body condition: A review of traditional approaches and new developments. Ecol. Evol. 1–19 (2020).

33.
Kershaw, J. L., Hall, A. J., Brownlow, A., Ramp, C. A. & Miller, P. J. O. Assessing cetacean body condition: Is total lipid content in blubber biopsies a useful monitoring tool?. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 271–282 (2019).
Article  Google Scholar 

34.
Christiansen, F. et al. Variation in outer blubber lipid concentrations does not reflect morphological body condition in humpback whales. J. Exp. Biol. 223, jeb213769 (2020).
PubMed  Article  PubMed Central  Google Scholar 

35.
Christiansen, F. et al. Response to: Lipid content of whale blubber cannot be measured using biopsies. J. Exp. Biol. 223, 1–2 (2020).
Google Scholar 

36.
Arts, M. T., Brett, M. T. & Kainz, M. J. Lipids in Aquatic Ecosystems (Springer, New York, 2009). https://doi.org/10.1007/978-0-387-89366-2.
Google Scholar 

37.
Ackman, R. G., Hingley, J. H., Eaton, C. A., Sipos, J. C. & Mitchell, E. D. Blubber fat deposition in mysticeti whales. Can. J. Zool. 53, 1332–1339 (1975).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Olsen, E. & Grahl-Nielsen, O. Blubber fatty acids of minke whales: Stratification, population identification and relation to diet. Mar. Biol. 142, 13–24 (2003).
CAS  Article  Google Scholar 

39.
Iverson, S. J. Blubber. In Encyclopedia of Marine Mammals 115–120 (Elsevier Ltd, 2009). https://doi.org/10.1016/B978-0-12-373553-9.00032-8.

40.
Noren, D. P. & Mangel, M. Energy reserve allocation in fasting Northern Elephant Seal Pups: Inter-relationships between body condition and fasting duration. Funct. Ecol. 18, 233–242 (2004).
Article  Google Scholar 

41.
Grahl-Nielsen, O., Krakstad, J. O., Nøttestad, L. & Axelsen, B. E. Dusky dolphins Lagenorhynchus obscurus and Cape fur seals Arctocephalus pusillus pusillus: Fatty acid composition of their blubber and prey species. Afr. J. Mar. Sci. https://doi.org/10.2989/1814232x.2010.501556 (2010).
Article  Google Scholar 

42.
Guerrero, A. I. et al. Vertical fatty acid composition in the blubber of leopard seals and the implications for dietary analysis. J. Exp. Mar. Bio. Ecol. 478, 54–61 (2016).
CAS  Article  Google Scholar 

43.
Ruchonnet, D., Boutoute, M., Guinet, C. & Mayzaud, P. Fatty acid composition of Mediterranean fin whale Balaenoptera physalus blubber with respect to body heterogeneity and trophic interaction. Mar. Ecol. Prog. Ser. 311, 165–174 (2006).
ADS  CAS  Article  Google Scholar 

44.
Strandberg, U. et al. Stratification, composition, and function of marine mammal blubber: The ecology of fatty acids in marine mammals. Physiol. Biochem. Zool. 81, 473–485 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Koopman, H. N., Iverson, S. J. & Read, A. J. High concentrations of isovaleric acid in the fats of odontocetes: Variation and patterns of accumulation in blubber vs. stability in the melon. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 173, 247–261 (2003).
CAS  Article  Google Scholar 

46.
Herman, D. P. et al. Feeding ecology of eastern North Pacific killer whales Orcinus orca from fatty acid, stable isotope, and organochlorind analyses of blubber biopsies. Mar. Ecol. Prog. Ser. 302, 275–291 (2005).
ADS  Article  Google Scholar 

47.
Virtue, P., Nichols, P. D., Nicol, S., McMinn, A. & Sikes, E. L. The lipid composition of Euphausia superba Dana in relation to the nutritional value of Phaeocystis pouchetii (Hariot) Lagerheim. Antarct. Sci. 5, 169–177 (1993).
ADS  Article  Google Scholar 

48.
Stübing, D., Hagen, W. & Schmidt, K. On the use of lipid biomarkers in marine food web analyses: An experimental case study on the Antarctic krill. Euphausia superba. Limnol. Oceanogr. 48, 1685–1700 (2003).
ADS  Article  Google Scholar 

49.
Falk-Petersen, S., Hopkins, C. E. & Sargent, J. R. Trophic relationships in the pelagic, Arctic food web. in Trophic relationships in marine environments (eds Barnes, M. & Gibson, R. N.) 315-333 (Aberdeen University Press, 1990).

50.
Auel, H., Harjes, M., da Rocha, R., Stübing, D. & Hagen, W. Lipid biomarkers indicate different ecological niches and trophic relationships of the Arctic hyperiid amphipods Themisto abyssorum and T. libellula. Polar Biol. 25, 374–383 (2002).
Article  Google Scholar 

51.
Scott, C., Kwasniewski, S., Falk-Petersen, S. & Sargent, J. Species differences, origins and functions of fatty alcohols and fatty acids in the wax esters and phospholipids of Calanus hyperboreus, C. glacialis and C. finmarchicus from Arctic waters. Mar. Ecol. Prog. Ser. 235, 127–134 (2002).
ADS  CAS  Article  Google Scholar 

52.
Dalsgaard, J., John, M. S., Kattner, G., Müller-Navarra, D. & Hagen, W. Fatty acid trophic markers in the pelagic marine environment. Adv. Mar. Biol. 46, 225–340 (2003).
PubMed  Article  Google Scholar 

53.
Graeve, M., Kattner, G. & Hagen, W. Diet-induced changes in the fatty acid composition of Arctic herbivorous copepods: Experimental evidence of trophic markers. J. Exp. Mar. Biol. Ecol. 182, 97–110 (1994).
CAS  Article  Google Scholar 

54.
Iverson, S. J., Field, C., Don Bowen, W. & Blanchard, W. Quantitative fatty acid signature analysis: A new method of estimating predator diets. Ecol. Monogr. 74, 211–235 (2004).
Article  Google Scholar 

55.
Fleming, A. H., Clark, C. T., Calambokidis, J. & Barlow, J. Humpback whale diets respond to variance in ocean climate and ecosystem conditions in the California Current. Glob. Chang. Biol. 22, 1214–1224 (2016).
ADS  PubMed  Article  Google Scholar 

56.
Ericson, J. A. et al. Seasonal and interannual variations in the fatty acid composition of adult Euphausia superba Dana, 1850 (Euphausiacea) samples derived from the Scotia Sea krill fishery. J. Crustac. Biol. 38, 673–681 (2018).
Google Scholar 

57.
Reiss, C. S., Walsh, J. & Goebel, M. E. Winter preconditioning determines feeding ecology of Euphausia superba in the Antarctic Peninsula. Mar. Ecol. Prog. Ser. 519, 89–101 (2015).
ADS  CAS  Article  Google Scholar 

58.
Cleary, A., Durbin, E. & Casas, M. Feeding by Antarctic krill Euphausia superba in the West Antarctic Peninsula: Differences between fjords and open waters. Mar. Ecol. Prog. Ser. 595, 39–54 (2018).
ADS  CAS  Article  Google Scholar 

59.
Schmidt, K. & Atkinson, A. Feeding and food processing in Antarctic krill (Euphausia superba Dana). In Biology and Ecology of Antarctic Krill 175–224 (Springer, 2016).

60.
Hagen, W., Kattner, G., Terbrüggen, A. & Van Vleet, E. S. Lipid metabolism of the antarctic krill Euphausia superba and its ecological implications. Mar. Biol. 139, 95–104 (2001).
CAS  Article  Google Scholar 

61.
Cripps, G. C., Watkins, J. L., Hill, H. J. & Atkinson, A. Fatty acid content of Antarctic krill Euphausia superba at South Georgia related to regional populations and variations in diet. Mar. Ecol. Prog. Ser. 181, 177–188 (1999).
ADS  CAS  Article  Google Scholar 

62.
Lambertsen, R., Baker, C., Weinrich, M. & Modi, W. An improved whale biopsy system designed for multidisciplinary research. In Nondestructive biomarkers in vertebrates 219–244 (Lewis Publishers, 1994).

63.
Waugh, C. A., Nichols, P. D., Schlabach, M., Noad, M. & Bengtson Nash, S. M. Vertical distribution of lipids, fatty acids and organochlorine contaminants in the blubber of southern hemisphere humpback whales (Megaptera novaeangliae). Mar. Environ. Res. 94, 24–31 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Druskat, A., Ghosh, R., Castrillon, J. & Bengtson Nash, S. M. Sex ratios of migrating southern hemisphere humpback whales: A new sentinel parameter of ecosystem health. Mar. Environ. Res. 151, 1–7 (2019).
Article  CAS  Google Scholar 

65.
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).
CAS  PubMed  Article  Google Scholar 

66.
Couturier, L. I. E. et al. State of art and best practices for fatty acid analysis in aquatic sciences. ICES J. Mar. Sci. fsaa121, 1–21 (2020).
Google Scholar 

67.
Volkman, J. K. & Nichols, P. D. Applications of thin-layer chromatography-flame ionization detection to the analysis for lipids and pollutants in marine and environmental samples. J. Planar Chromatogr. Mod. TLC 4, 19–26 (1991).
CAS  Google Scholar 

68.
Alhazzaa, R., Bridle, A. R., Nichols, P. D. & Carter, C. G. Up-regulated desaturase and elongase gene expression promoted accumulation of polyunsaturated fatty acid (PUFA) but not long-chain PUFA in Lates calcarifer, a tropical euryhaline fish, fed a stearidonic acid- and γ-linoleic acid-enriched diet. J. Agric. Food Chem. 59, 8423–8434 (2011).
CAS  PubMed  Article  Google Scholar 

69.
Bode, M. et al. Feeding strategies of tropical and subtropical calanoid copepods throughout the eastern Atlantic Ocean – Latitudinal and bathymetric aspects. Prog. Oceanogr. 138, 268–282 (2015).
ADS  Article  Google Scholar 

70.
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111 (2006).
Article  Google Scholar 

71.
Flores, H. et al. Impact of climate change on Antarctic krill. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).
ADS  Article  Google Scholar 

72.
Turner, J., Hosking, J. S., Bracegirdle, T. J., Marshall, G. J. & Phillips, T. Recent changes in Antarctic Sea Ice. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 73 (2015).

73.
Holland, M. M., Landrum, L., Kostov, Y. & Marshall, J. Sensitivity of Antarctic sea ice to the Southern Annular Mode in coupled climate models. Clim. Dyn. 49, 1813–1831 (2017).
Article  Google Scholar 

74.
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: From CMIP3 to CMIP5. Clim. Dyn. 42, 1999–2018 (2014).
Article  Google Scholar 

75.
O’Carroll, A. G. et al. Observational needs of sea surface temperature. Front. Mar. Sci. 6 (2019).

76.
Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).
ADS  CAS  Article  Google Scholar 

77.
Kattner, G., Hagen, W., Falk-Petersen, S., Sargent, J. R. & Henderson, R. J. Antarctic krill Thysanoessa macrura fills a major gap in marine lipogenic pathways. Mar. Ecol. Prog. Ser. 134, 295–298 (1996).
ADS  Article  Google Scholar  More

1.
Kamenova, S. et al. in Networks of Invasion: A Synthesis of Concepts Vol. 56 Adv. Ecol. Res. (eds Bohan, D. A., Dumbrell, A. J., & Massol, F.) 85–182 (2017).
2.
Kumschick, S. et al. Ecological impacts of alien species: quantification, scope, caveats, and recommendations. Bioscience 65, 55–63. https://doi.org/10.1093/biosci/biu193 (2014).
Article  Google Scholar 

3.
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife—threats to biodiversity and human health. Science 287, 443–449. https://doi.org/10.1126/science.287.5452.443 (2000).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. https://doi.org/10.1038/ncomms12986 (2016).
Article  PubMed  PubMed Central  Google Scholar 

5.
Martin, L. B., Coon, C. A. C., Liebl, A. L. & Schrey, A. W. Surveillance for microbes and range expansion in house sparrows. Proc. R. Soc. Lond. B 281, 20132690. https://doi.org/10.1098/rspb.2013.2690 (2013).
CAS  Article  Google Scholar 

6.
Lindstrom, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl. Acad. Sci. USA 110, 13452–13456. https://doi.org/10.1073/pnas.1303157110 (2013).
ADS  Article  PubMed  Google Scholar 

7.
Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135. https://doi.org/10.1016/j.tree.2005.10.012 (2006).
Article  PubMed  Google Scholar 

8.
Hufbauer, R. A. et al. Anthropogenically induced adaptation to invade (AIAI): contemporary adaptation to human-altered habitats within the native range can promote invasions. Evol. Appl. 5, 89–101. https://doi.org/10.1111/j.1752-4571.2011.00211.x (2012).
Article  PubMed  Google Scholar 

9.
Colautti, R. I. & Lau, J. A. Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Mol. Ecol. 24, 1999–2017. https://doi.org/10.1111/mec.13162 (2015).
Article  PubMed  Google Scholar 

10.
Chuang, A. & Peterson, C. R. Expanding population edges: theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512. https://doi.org/10.1111/gcb.13107 (2016).
ADS  Article  Google Scholar 

11.
Selechnik, D., Rollins, L. A., Brown, G. P., Kelehear, C. & Shine, R. The things they carried: the pathogenic effects of old and new parasites following the intercontinental invasion of the Australian cane toad (Rhinella marina). Int. J. Parasitol. 6, 375–385. https://doi.org/10.1016/j.ijppaw.2016.12.001 (2017).
CAS  Article  Google Scholar 

12.
Torchin, M. E., Lafferty, K. D., Dobson, A. P., McKenzie, V. J. & Kuris, A. M. Introduced species and their missing parasites. Nature 421, 628–630. https://doi.org/10.1038/nature01346 (2003).
ADS  CAS  Article  PubMed  Google Scholar 

13.
Torchin, M. E. & Mitchell, C. E. Parasites, pathogens, and invasions by plants and animals. Front. Ecol. Environ. 2, 183–190. https://doi.org/10.2307/3868313 (2004).
Article  Google Scholar 

14.
Dunn, A. M. in Advances in Parasitology, Vol 68: Natural History of Host-Parasite Interactions Vol. 68 (ed Webster, J. P.) 161–184 (2009).

15.
O’Brien, V. A. et al. An enzootic vector-borne virus is amplified at epizootic levels by an invasive avian host. Proc. R. Soc. Lond. B 278, 239–246 (2011).
Google Scholar 

16.
Perkins, S. E., White, T. A., Pascoe, E. L. & Gillingham, E. L. Parasite community dynamics in an invasive vole—from focal introduction to wave front. Int. J. Parasitol. 6, 412–419. https://doi.org/10.1016/j.ijppaw.2017.07.005 (2017).
Article  Google Scholar 

17.
Yang, C. C. et al. Loss of microbial (pathogen) infections associated with recent invasions of the red imported fire ant Solenopsis invicta. Biol. Invasions 12, 3307–3318. https://doi.org/10.1007/s10530-010-9724-9 (2010).
Article  Google Scholar 

18.
Blossey, B. & Notzold, R. Evolution of increased competitive ability in invasive nonindigenous plants—a hypothesis. J. Ecol. 83, 887–889. https://doi.org/10.2307/2261425 (1995).
Article  Google Scholar 

19.
Lee, K. A. & Klasing, K. C. A role for immunology in invasion biology. Trends Ecol. Evol. 19, 523–529. https://doi.org/10.1016/j.tree.2004.07.012 (2004).
Article  PubMed  Google Scholar 

20.
Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46, 1000–1015. https://doi.org/10.1093/icb/icl049 (2006).
CAS  Article  PubMed  Google Scholar 

21.
Cornet, S., Brouat, C., Diagne, C. A. & Charbonnel, N. EcoImmunology and bioinvasion: revisiting the EICA hypotheses. Evol. Appl. 9, 952–962. https://doi.org/10.1111/eva.12406 (2016).
Article  PubMed  PubMed Central  Google Scholar 

22.
Martin, L. B., Alam, J. L., Imboma, T. & Liebl, A. L. Variation in inflammation as a correlate of range expansion in Kenyan house sparrows. Oecologia 164, 339–347. https://doi.org/10.1007/s00442-010-1654-9 (2010).
ADS  Article  PubMed  Google Scholar 

23.
Bernardi, G., Azzurro, E., Golani, D. & Miller, M. R. Genomic signatures of rapid adaptive evolution in the bluespotted cornetfish, a Mediterranean Lessepsian invader. Mol. Ecol. 25, 3384–3396. https://doi.org/10.1111/mec.13682 (2016).
CAS  Article  PubMed  Google Scholar 

24.
Vera, M., Diez-del-Molino, D. & Garcia-Marin, J. L. Genomic survey provides insights into the evolutionary changes that occurred during European expansion of the invasive mosquitofish (Gambusia holbrooki). Mol. Ecol. 25, 1089–1105. https://doi.org/10.1111/mec.13545 (2016).
CAS  Article  PubMed  Google Scholar 

25.
Hodgins, K. A., Lai, Z., Nurkowski, K., Huang, J. & Rieseberg, L. H. The molecular basis of invasiveness: differences in gene expression of native and introduced common ragweed (Ambrosia artemisiifolia) in stressful and benign environments. Mol. Ecol. 22, 2496–2510. https://doi.org/10.1111/mec.12179 (2013).
CAS  Article  PubMed  Google Scholar 

26.
White, T. A., Perkins, S. E., Heckel, G. & Searle, J. B. Adaptive evolution during an ongoing range expansion: the invasive bank vole (Myodes glareolus) in Ireland. Mol. Ecol. 22, 2971–2985. https://doi.org/10.1111/mec.12343 (2013).
CAS  Article  PubMed  Google Scholar 

27.
Todd, E. V., Black, M. A. & Gemmell, N. J. The power and promise of RNA-seq in ecology and evolution. Mol. Ecol. 25, 1224–1241. https://doi.org/10.1111/mec.13526 (2016).
CAS  Article  PubMed  Google Scholar 

28.
Alvarez, M., Schrey, A. W. & Richards, C. L. Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?. Mol. Ecol. 24, 710–725. https://doi.org/10.1111/mec.13055 (2015).
CAS  Article  PubMed  Google Scholar 

29.
Rius, M. & Darling, J. A. How important is intraspecific genetic admixture to the success of colonising populations?. Trends Ecol. Evol. 29, 233–242. https://doi.org/10.1016/j.tree.2014.02.003 (2014).
Article  PubMed  Google Scholar 

30.
Fraser, B. A., Ramnarine, I. W. & Neff, B. D. Temporal variation at the Mhc class IIB in wild populations of the guppy (Poecilia reticulata). Evolution 64, 2086–2096. https://doi.org/10.1111/j.1558-5646.2010.00965.x (2010).
Article  PubMed  Google Scholar 

31.
Oleksiak, M. F., Churchill, G. A. & Crawford, D. L. Variation in gene expression within and among natural populations. Nat. Genet. 32, 261–266. https://doi.org/10.1038/ng983 (2002).
CAS  Article  PubMed  Google Scholar 

32.
Whitehead, A. & Crawford, D. L. Variation within and among species in gene expression: raw material for evolution. Mol. Ecol. 15, 1197–1211. https://doi.org/10.1111/j.1365-294X.2006.02868.x (2006).
CAS  Article  PubMed  Google Scholar 

33.
Whitehead, A. & Crawford, D. L. Neutral and adaptive variation in gene expression. Proc. Natl. Acad. Sci. USA 103, 5425–5430. https://doi.org/10.1073/pnas.0507648103 (2006).
ADS  CAS  Article  PubMed  Google Scholar 

34.
Rollins, L. A., Richardson, M. F. & Shine, R. A genetic perspective on rapid evolution in cane toads (Rhinella marina). Mol. Ecol. 24, 2264–2276. https://doi.org/10.1111/mec.13184 (2015).
Article  PubMed  Google Scholar 

35.
Vogel, H. J., Schmidtberg, H. & Vilcinskas, A. Comparative transcriptomics in three ladybird species supports a role for immunity in invasion biology. Dev. Comp. Immunol. 67, 452–456. https://doi.org/10.1016/j.dci.2016.09.015 (2017).
CAS  Article  PubMed  Google Scholar 

36.
Selechnik, D., Richardson, M. F., Shine, R., Brown, G. P. & Rollins, L. A. Immune and environment-driven gene expression during invasion: an eco-immunological application of RNA-Seq. Ecol. Evol. 9, 6708–6721. https://doi.org/10.1002/ece3.5249 (2019).
Article  PubMed  PubMed Central  Google Scholar 

37.
Aplin, K. P. et al. Multiple geographic origins of commensalism and complex dispersal history of black rats. PLoS ONE 6, e26357. https://doi.org/10.1371/journal.pone.0026357 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Bonhomme, F. et al. Genetic differentiation of the house mouse around the Mediterranean basin: matrilineal footprints of early and late colonization. Proc. R. Soc. Lond. B 278, 1034–1043. https://doi.org/10.1098/rspb.2010.1228 (2011).
Article  Google Scholar 

39.
Dalecky, A. et al. Range expansion of the invasive house mouse Mus musculus domesticus in Senegal, Western Africa: a three decades synthesis of trapping data, 1983–2014. Mammal Rev. 45, 176–190. https://doi.org/10.1111/mam.12043 (2015).
Article  Google Scholar 

40.
Konecny, A. et al. Invasion genetics of the introduced black rat (Rattus rattus) in Senegal, West Africa. Mol. Ecol. 22, 286–300. https://doi.org/10.1111/mec.12112 (2013).
Article  PubMed  Google Scholar 

41.
Lippens, C. et al. Genetic structure and invasion history of the house mouse (Mus musculus domesticus) in Senegal, West Africa: a legacy of colonial and contemporary times. Heredity 119, 64–75. https://doi.org/10.1038/hdy.2017.18 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Diagne, C. A. et al. Contemporary variations of immune responsiveness during range expansion of two invasive rodents in Senegal. Oikos 126, 435–446. https://doi.org/10.1111/oik.03470 (2017).
CAS  Article  Google Scholar 

43.
Diagne, C. A. et al. Ecological and sanitary impacts of bacterial communities associated to biological invasions in African commensal rodent communities. Nat. Sci. Rep. 7, 14995. https://doi.org/10.1038/s41598-017-14880-1 (2017).
ADS  CAS  Article  Google Scholar 

44.
Diagne, C. A. et al. Parasites and invasions: changes in gastrointestinal helminth assemblages in invasive and native rodents in Senegal. Int. J. Parasitol. 46, 857–869. https://doi.org/10.1016/j.ijpara.2016.07.007 (2016).
Article  PubMed  Google Scholar 

45.
Frank, S. A. Immune response to parasitic attack: evolution of a pulsed character. J. Theor. Biol. 219, 281–290. https://doi.org/10.1006/jtbi.2002.3122 (2002).
MathSciNet  CAS  Article  PubMed  Google Scholar 

46.
Brown, G. P., Shilton, C., Phillips, B. L. & Shine, R. Invasion, stress, and spinal arthritis in cane toads. Proc. Natl. Acad. Sci. USA 104, 17698–17700. https://doi.org/10.1073/pnas.0705057104 (2007).
ADS  Article  PubMed  Google Scholar 

47.
Sikes, R. S. & Gannon, W. L. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253. https://doi.org/10.1644/10-MAMM-F-355.1 (2011).
Article  Google Scholar 

48.
Granjon, L. & Duplantier, J. M. Les rongeurs de l’Afrique sahélo-soudanienne (Publications scientifiques du Muséum, 2009).

49.
Nussey, D. H., Watt, K., Pilkington, J. G., Zamoyska, R. & McNeilly, T. N. Age-related variation in immunity in a wild mammal population. Aging Cell 11, 178–180. https://doi.org/10.1111/j.1474-9726.2011.00771.x (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Schurch, N. J. et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?. RNA 22, 839–851. https://doi.org/10.1261/rna.053959.115 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. https://doi.org/10.1186/gb-2013-14-4-r36 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Langmead, B. & Salzberg, S. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. https://doi.org/10.1038/nmeth.1923 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Robins, J. H. et al. Dating of divergences within the Rattus genus phylogeny using whole mitochondrial genomes. Mol. Phylogenet. Evol. 49, 460–466. https://doi.org/10.1016/j.ympev.2008.08.001 (2008).
CAS  Article  PubMed  Google Scholar 

54.
Anders, S., Pyl, T. P. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. https://doi.org/10.1101/002824 (2015).
CAS  Article  Google Scholar 

55.
Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80. https://doi.org/10.1186/gb-2004-5-10-r80 (2004).
Article  PubMed  PubMed Central  Google Scholar 

56.
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47. https://doi.org/10.1093/nar/gkv007 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Simon, A. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106. https://doi.org/10.1186/gb-2010-11-10-r106 (2010).
CAS  Article  Google Scholar 

59.
Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucl. Acids Res. 45, D362–D368. https://doi.org/10.1093/nar/gkw937 (2017).
CAS  Article  PubMed  Google Scholar 

60.
Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucl. Acids Res. 43, D447-452. https://doi.org/10.1093/nar/gku1003 (2015).
CAS  Article  PubMed  Google Scholar 

61.
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800. https://doi.org/10.1371/journal.pone.0021800 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

62.
Merrick, M. J. & Koprowski, J. L. Altered natal dispersal at the range periphery: the role of behavior, resources, and maternal condition. Ecol. Evol. 7, 58–72. https://doi.org/10.1002/ece3.2612 (2017).
Article  PubMed  Google Scholar 

63.
DeBiasse, M. B. & Kelly, M. W. Plastic and evolved responses to global change: what can we learn from comparative transcriptomics?. J. Heredity 107, 71–81. https://doi.org/10.1093/jhered/esv073 (2016).
Article  Google Scholar 

64.
Whitehead, A., Triant, D. A., Champlin, D. & Nacci, D. Comparative transcriptomics implicates mechanisms of evolved pollution tolerance in a killifish population. Mol. Ecol. 19, 5186–5203. https://doi.org/10.1111/j.1365-294X.2010.04829.x (2010).
CAS  Article  PubMed  Google Scholar 

65.
Barrett, S. C. H. Foundations of invasion genetics: the Baker and Stebbins legacy. Mol. Ecol. 24, 1927–1941. https://doi.org/10.1111/mec.13014 (2015).
Article  PubMed  Google Scholar 

66.
Dlugosch, K. M., Anderson, S. R., Braasch, J., Cang, F. A. & Gillette, H. D. The devil is in the details: genetic variation in introduced populations and its contributions to invasion. Mol. Ecol. 24, 2095–2111. https://doi.org/10.1111/mec.13183 (2015).
Article  PubMed  Google Scholar 

67.
Llewellyn, D., Thompson, M. B., Brown, G. P., Phillips, B. L. & Shine, R. Reduced investment in immune function in invasion-front populations of the cane toad (Rhinella marina) in Australia. Biol. Invasions 14, 999–1008. https://doi.org/10.1007/s10530-011-0135-3 (2012).
Article  Google Scholar 

68.
Brown, G. P., Phillips, B. L., Dubey, S. & Shine, R. Invader immunology: invasion history alters immune system function in cane toads (Rhinella marina) in tropical Australia. Ecol. Lett. 18, 57–65. https://doi.org/10.1111/ele.12390 (2015).
Article  PubMed  Google Scholar 

69.
Dlugosch, K. M. & Parker, I. M. Invading populations of an ornamental shrub show rapid life history evolution despite genetic bottlenecks. Ecol. Lett. 11, 701–709. https://doi.org/10.1111/j.1461-0248.2008.01181.x (2008).
Article  PubMed  Google Scholar 

70.
Chevin, L. M. & Lande, R. Adaptation to marginal habitats by evolution of increased phenotypic plasticity. J. Evol. Biol. 24, 1462–1476. https://doi.org/10.1111/j.1420-9101.2011.02279.x (2011).
Article  PubMed  Google Scholar 

71.
Ninot, O. Vie de relations, organisation de l’espace et développement en Afrique de l’Ouest : la région de Tambacounda au Sénégal. Ph.D. thesis, Rouen University (2003).

72.
Merle, N. S., Noe, R., Halbwachs-Mecarelli, L., Fremeaux-Bacchi, V. & Roumenina, L. T. Complement system part II: role in immunity. Front. Immunol. 6, 257. https://doi.org/10.3389/fimmu.2015.00257 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

73.
Bao, J. et al. Serpin functions in host-pathogen interactions. PeerJ 6, e4557. https://doi.org/10.7717/peerj.4557 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

74.
Mangan, M. S. J., Kaiserman, D. & Bird, P. I. The role of serpins in vertebrate immunity. Tissue Antigens 72, 1–10. https://doi.org/10.1111/j.1399-0039.2008.01059.x (2008).
CAS  Article  PubMed  Google Scholar 

75.
Wang, W., Qu, Q. & Chen, J. Identification, expression analysis, and antibacterial activity of Apolipoprotein A-I from amphioxus (Branchiostoma belcheri). Comp. Biochem. Physiol. 238, 110329. https://doi.org/10.1016/j.cbpb.2019.110329 (2019).
CAS  Article  Google Scholar 

76.
Huntoon, K. M. et al. The acute phase protein haptoglobin regulates host immunity. J. Leukoc. Biol. 84, 170–181. https://doi.org/10.1189/jlb.0208100 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Burger, D. & Dayer, J. M. High-density lipoprotein-associated apolipoprotein AI: the missing link between infection and chronic inflammation?. Autoimmun. Rev. 1, 111–117. https://doi.org/10.1016/S1568-9972(01)00018-0 (2002).
CAS  Article  PubMed  Google Scholar 

78.
Sorci, G. & Faivre, B. Inflammation and oxidative stress in vertebrate host-parasite systems. Philos. Trans. R. Soc. B 364, 71–83. https://doi.org/10.1098/rstb.2008.0151 (2009).
Article  Google Scholar 

79.
McKay, D. M. The beneficial helminth parasite?. Parasitology 132, 1–12. https://doi.org/10.1017/S003118200500884X (2006).
CAS  Article  PubMed  Google Scholar 

80.
Robertson, S., Bradley, J. E. & MacColl, A. D. Measuring the immune system of the three-spined stickleback—investigating natural variation by quantifying immune expression in the laboratory and the wild. Mol. Ecol. Res. 16, 701–713. https://doi.org/10.1111/1755-0998.12497 (2016).
CAS  Article  Google Scholar 

81.
Tengholm, A. & Gylfe, E. cAMP signalling in insulin and glucagon secretion. Diabetes Obes. Metabol. 19, 42–53. https://doi.org/10.1111/dom.12993 (2017).
CAS  Article  Google Scholar 

82.
Jones, B. J., Tan, T. & Bloom, S. R. Minireview: glucagon in stress and energy homeostasis. Endocrinology 153, 1049–1054. https://doi.org/10.1210/en.2011-1979 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

83.
Sih, A., Cote, J., Evans, M. R., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289. https://doi.org/10.1111/j.1461-0248.2011.01731.x (2012).
Article  PubMed  Google Scholar 

84.
Bengston, S. E. et al. Genomic tools for behavioural ecologists to understand repeatable individual differences in behaviour. Nat. Ecol. Evol. 2, 944–955. https://doi.org/10.1038/s41559-017-0411-4 (2018).
Article  PubMed  Google Scholar 

85.
Martin, L. B. et al. Costs of immunity and their role in the range expansion of the house sparrow in Kenya. J. Exp. Biol. 220, 2228–2235. https://doi.org/10.1242/jeb.154716 (2017).
Article  PubMed  Google Scholar 

86.
Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429. https://doi.org/10.1126/science.1193954 (2011).
ADS  CAS  Article  PubMed  Google Scholar  More

1.
Piperno, D. & Pearsall, D. M. The Origins of Agriculture in the Lowland Neotropics (Academic Press, 1998).
2.
Newell-McGloughlin, M. Nutritionally improved agricultural crops. Plant Physiol. 147, 939–953 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

3.
Green, R. E., Cornell, S. J., Scharlemann, J. P. W. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005).
CAS  PubMed  Google Scholar 

4.
Meyer, R. S., DuVal, A. E. & Jensen, H. R. Patterns and processes in crop domestication: an historical review and quantitative analysis of 203 global food crops. New Phytol. 196, 29–48 (2012).
PubMed  Google Scholar 

5.
Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).
CAS  PubMed  Google Scholar 

6.
Milla, R., Osborne, C. P., Turcotte, M. M. & Violle, C. Plant domestication through an ecological lens. Trends Ecol. Evol. 30, 463–469 (2015).
PubMed  Google Scholar 

7.
Evans L. T. Crop Evolution, Adaptation and Yield (Cambridge Univ. Press, 1993)

8.
Turcotte, M. M., Turley, N. E. & Johnson, M. T. J. The impact of domestication on resistance to two generalist herbivores across 29 independent domestication events. New Phytol. 204, 671–681 (2014).
PubMed  Google Scholar 

9.
Chomicki, G. & Renner, S. S. Farming by ants remodels nutrient uptake in epiphytes. New Phytol. 223, 2011–2023 (2019).
PubMed  Google Scholar 

10.
Mueller, U. G., Gerardo, N. M., Aanen, D. K., Six, D. L. & Schultz, T. R. The evolution of agriculture in insects. Annu. Rev. Ecol. Evol. Systemat. 36, 563–595 (2005).
Google Scholar 

11.
Aanen, D. K. et al. The evolution of fungus-growing termites and their mutualistic fungal symbionts. Proc. Natl Acad. Sci. USA 99, 14887–14892 (2002).
CAS  PubMed  Google Scholar 

12.
Mehdiabadi, N. J. & Schultz, T. R. Natural history and phylogeny of the fungus-farming ants (Hymenoptera: Formicidae: Myrmicinae: Attini). Myrmecol. News 13, 37–55 (2009).
Google Scholar 

13.
Mueller, U. G., Scott, J. J., Ishak, H. D., Cooper, M. & Rodrigues, A. Monoculture of leafcutter ant gardens. PLoS ONE 9, e12668 (2010).
Google Scholar 

14.
Schultz, T. R. & Brady, S. G. Major evolutionary transitions in ant agriculture. Proc. Natl Acad. Sci. USA 105, 5435–5440 (2008).
CAS  PubMed  Google Scholar 

15.
Kooij, P. W., Aanen, D. K., Schiøtt, M. & Boomsma, J. J. Evolutionary advanced ant farmers rear polyploid fungal crops. J. Evol. Biol. 28, 1911–1924 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Shik, J. Z. et al. Metabolism and the rise of fungus cultivation by ants. Am. Nat. 184, 364–373 (2014).
PubMed  Google Scholar 

17.
De Fine Licht, H. H. et al. Laccase detoxification mediates the nutritional alliance between leaf-cutting ants and fungus-garden symbionts. Proc. Natl Acad. Sci. USA 110, 583–587 (2012).
PubMed  Google Scholar 

18.
Fernández-Marín, H. et al. Functional role of phenylacetic acid from metapleural gland secretions in controlling fungal pathogens in evolutionarily derived leaf-cutting ants. Proc. R. Soc. B 282, 20150212 (2015).
PubMed  Google Scholar 

19.
Fernández-Marín, H. et al. Dynamic disease management in Trachymyrmex fungus-growing ants (Attini: Formicidae). Am. Nat. 181, 571–582 (2013).
PubMed  Google Scholar 

20.
Currie, C. R., Mueller, U. G. & Malloch, D. The agricultural pathology of ant fungus gardens. Proc. Natl Acad. Sci. USA 96, 7998–8002 (1999).
CAS  PubMed  Google Scholar 

21.
Nygaard, S. et al. Reciprocal genomic evolution in the ant–fungus agricultural symbiosis. Nat. Commun. 7, 12233 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Branstetter, M. G. et al. Dry habitats were crucibles of domestication in the evolution of agriculture in ants. Proc. R. Soc. B 284, 20170095 (2017).
PubMed  Google Scholar 

23.
Li, H. et al. Convergent evolution of complex structures for ant–bacterial defensive symbiosis in fungus-farming ants. Proc. Natl Acad. Sci. USA 115, 10720–10725 (2018).
CAS  PubMed  Google Scholar 

24.
Hölldobler, B. & Wilson, E. O. The Leafcutter Ants: Civilization by Instinct (W. W. Norton & Company, 2010).

25.
Mueller, U. G. et al. Evolution of cold-tolerant fungal symbionts permits winter fungiculture by leafcutter ants at the northern frontier of a tropical ant–fungus symbiosis. Proc. Natl Acad. Sci. USA 108, 4053–4056 (2011).
CAS  PubMed  Google Scholar 

26.
Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton Univ. Press, 2012).

27.
Raubenheimer, D. Toward a quantitative nutritional ecology: the right‐angled mixture triangle. Ecol. Monogr. 81, 407–427 (2011).
Google Scholar 

28.
Sperfeld, E. et al. Bridging ecological stoichiometry and nutritional geometry with homeostasis concepts and integrative models of organism nutrition. Funct. Ecol. 31, 286–296 (2017).
Google Scholar 

29.
Shik, J. Z. & Dussutour, A. Nutritional dimensions of invasive success. Trends Ecol. Evol. 35, 691–703 (2020).
PubMed  Google Scholar 

30.
Shik, J. Z. et al. Nutrition mediates the expression of cultivar–farmer conflict in a fungus-growing ant. Proc. Natl Acad. Sci. USA 113, 10121–10126 (2016).
CAS  PubMed  Google Scholar 

31.
Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J. & Raubenheimer, D. The multidimensional nutritional niche. Trends Ecol. Evol. 31, 355–365 (2016).
PubMed  Google Scholar 

32.
Masiulionis, V. E. et al. A Brazilian population of the asexual fungus-growing ant Mycocepurus smithii (Formicidae, Myrmicinae, Attini) cultivates fungal symbionts with gongylidia-like structures. PLoS ONE 9, e103800 (2014).
PubMed  PubMed Central  Google Scholar 

33.
Vo, T. L., Mikheyev, A. S. & Mueller, U. G. Free-living fungal symbionts (Lepiotaceae) of fungus-growing ants (Attini: Formicidae). Mycologia 101, 206–210 (2009).
CAS  PubMed  Google Scholar 

34.
Schultz, T. R. et al. The most relictual fungus-farming ant species cultivates the most recently evolved and highly domesticated fungal symbiont species. Am. Nat. 185, 693–703 (2015).
PubMed  Google Scholar 

35.
Solomon, S. E. et al. The molecular phylogenetics of Trachymyrmex Forel ants and their fungal cultivars provide insights into the origin and coevolutionary history of ‘higher-attine’ ant agriculture. Syst. Entomol. 44, 939–956 (2019).
Google Scholar 

36.
Quinlan, R. J. & Cherrett, J. M. The role of fungus in the diet of the leaf-cutting ant Atta cephalotes (L.). Ecol. Entomol. 4, 151–160 (1979).
Google Scholar 

37.
Schiøtt, M., de Fin Licht, H. H., Lange, L. & Boomsma, J. J. Towards a molecular understanding of symbiont function: identification of a fungal gene for the degradation of xylan in the fungus gardens of leaf cutting ants. BMC Biol. 8, 40 (2008).
Google Scholar 

38.
De Fine Licht, H. H., Boomsma, J. J. & Tunlid, A. Symbiotic adaptations in the fungal cultivar of leaf-cutting ants. Nat. Commun. 5, 5675 (2014).
CAS  PubMed  Google Scholar 

39.
De Fine Licht, H. H. & Boomsma, J. J. Forage collection, substrate preparation, and diet composition in fungus-growing ants. Ecol. Ent. 35, 259–269 (2010).
Google Scholar 

40.
Sapountzis, P., Zhukova, M., Shik, J. Z., Schiøtt, M. & Boomsma, J. J. Reconstructing the symbiotic functions of intestinal Mollicutes in fungus-growing ants. eLife 7, e39209 (2018).
PubMed  PubMed Central  Google Scholar 

41.
Seal, J. N. & Tschinkel, W. R. Colony productivity of the fungus-gardening ant Trachymyrmex septentrionalis (Hymenoptera: Formicidae) in a Florida pine forest. Ann. Ent. Soc. Am. 99, 673–682 (2006).
Google Scholar 

42.
Wirth, R., Beyschlag, W., Ryel, R. J. & Hölldobler, B. Annual foraging of the leaf-cutting ant Atta colombica in a semideciduous rain forest in Panama. J. Trop. Ecol. 13, 741–757 (1997).
Google Scholar 

43.
Cazin, J. Jr., Wiemer, D. F. & Howard, J. J. Isolation, growth characteristics, and long-term storage of fungi cultivated by attine ants. Appl. Environ. Microbiol. 55, 1346–1350 (1989).
CAS  PubMed  PubMed Central  Google Scholar 

44.
Mueller, U. G., Schultz, T. R., Currie, C. R., Adams, R. M. M. & Malloch, D. The origin of the attine ant–fungus mutualism. Quart. Rev. Biol. 76, 169–197 (2001).
CAS  PubMed  Google Scholar 

45.
De Fine Licht, H. H., Schiøtt, M., Mueller, U. G. & Boomsma, J. J. Evolutionary transitions in enzyme activity of ant fungus gardens. Evolution 64, 2055–2069 (2010).
CAS  PubMed  Google Scholar 

46.
Chapela, I. H., Rehner, S. A., Schultz, T. R. & Mueller, U. G. Evolutionary history of the symbiosis between fungus-growing ants and their fungi. Science 266, 1691–1694 (1994).
CAS  PubMed  Google Scholar 

47.
Mikheyev, A. S., Mueller, U. G. & Boomsma, J. J. Population genetic signatures of diffuse co-evolution between leaf-cutting ants and their cultivar fungi. Mol. Ecol. 16, 209–216 (2007).
CAS  PubMed  Google Scholar 

48.
De Fine Licht, H. H. & Boomsma, J. J. Variable interaction specificity and symbiont performance in Panamanian Trachymyrmex and Sericomyrmex fungus-growing ants. BMC Evol. Biol. 14, 244 (2014).
PubMed  PubMed Central  Google Scholar 

49.
Howe, J., Schiøtt, M. & Boomsma, J. J. Horizontal partner exchange does not preclude stable mutualism in fungus-growing ants. Behav. Ecol. 30, 372–382 (2018).
Google Scholar 

50.
Cornejo, F. H., Varela, A. & Wright, S. J. Tropical forest litter decomposition under seasonal drought: nutrient release, fungi and bacteria. Oikos 70, 183–190 (1994).
Google Scholar 

51.
Wilson, E. O. Caste and division of labor in leaf-cutter ants (Hymenoptera: Formicidae: Atta). II. The ergonomic optimization of leaf cutting. Behav. Ecol. Sociobiol. 7, 157–165 (1980).
Google Scholar 

52.
Roces, F. & Hölldobler, B. Use of stridulation in foraging leaf-cutting ants: mechanical support during cutting or short-range recruitment signal? Behav. Ecol. Sociobiol. 39, 293–299 (1996).
Google Scholar 

53.
Kleineidam, C., Romani, R., Tautz, J. & Isidoro, N. Ultrastructure and physiology of the CO2 sensitive sensillum ampullaceum in the leaf-cutting ant Atta sexdens. Arthropod Struct. Dev. 29, 43–55 (2000).
CAS  PubMed  Google Scholar 

54.
Sapountzis, P., Nash, D. R., Schiøtt, M. & Boomsma, J. J. The evolution of abdominal microbiomes in fungus-growing ants. Mol. Ecol. 28, 879–899 (2019).
PubMed  Google Scholar 

55.
Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123 (2009).
PubMed  Google Scholar 

56.
Mummert, A. E., Esche, E., Robinson, J. & Armelagos, G. J. Stature and robusticity during the agricultural transition: evidence from the bioarchaeological record. Econ. Hum. Biol. 9, 284–301 (2011).
PubMed  Google Scholar 

57.
Fuller, D. Q. et al. The domestication process and domestication rate in rice: spikelet bases from the Lower Yangtze. Science 323, 1607–1610 (2009).
CAS  PubMed  Google Scholar 

58.
Sauer, C. O. Agricultural Origins and Dispersals (American Geographical Society, 1952).

59.
Nuotclà, J. A., Biedermann, P. H. W. & Taborsky, M. Pathogen defence is a potential driver of social evolution in ambrosia beetles. Proc. R. Soc. B 286, 20192332 (2019).
PubMed  Google Scholar 

60.
Nychka, D., Furrer, R. & Sain, S. Fields: Tools for spatial data. R package version 8.2-1 https://cran.r-project.org/web/packages/fields/index.html (2015).

61.
R Core Development Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

62.
Kay, A. D., Shik, J. Z., Van Alst, A., Miller, K. A. & Kaspari, M. Diet composition does not affect ant colony tempo. Funct. Ecol. 26, 317–323 (2011).
Google Scholar 

63.
Felton, A. M. et al. Nutritional ecology of Ateles chamek in lowland Bolivia: how macronutrient balancing influences food choices. Int. J. Primatol. 30, 675–696 (2009).
Google Scholar 

64.
Kellner, K., Fernández-Marín, H., Ishak, H. D., Linksvayer, T. A. & Mueller, U. G. Co-evolutionary patterns and diversification of ant–fungus associations in the asexual fungus-farming ant Mycocepurus smithii in Panama. J. Evol. Biol. 26, 1353–1362 (2013).
CAS  PubMed  Google Scholar 

65.
Butler, I. A., Siletti, K., Oxley, P. R. & Kronauer, D. J. C. Conserved microsatellites in ants enable population genetic and colony pedigree studies across a wide range of species. PLoS ONE 9, e107334 (2014).
PubMed  PubMed Central  Google Scholar 

66.
Ratnasingham, S. & Hebert, P. D. N. BOLD: the Barcode of Life Data System (www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Dussutour, A., Latty, T., Beekman, M. & Simpson, S. J. Amoeboid organism solves complex nutritional challenges. Proc. Natl Acad. Sci. USA 107, 4607–4611 (2010).
CAS  PubMed  Google Scholar 

68.
Dussutour, A. & Simpson, S. J. Description of a simple synthetic diet for studying nutritional responses in ants. Insect. Soc. 55, 329–333 (2008).
Google Scholar 

69.
Dussutour, A. & Simpson, S. J. Communal nutrition in ants. Curr. Biol. 19, 740–744 (2009).
CAS  PubMed  Google Scholar 

70.
Warbrick-Smith, J., Raubenheimer, D., Simpson, S. J. & Behmer, S. T. Three hundred and fifty generations of extreme food specialisation: testing predictions of nutritional ecology. Entomol. Exp. Appl. 132, 65–75 (2009).
Google Scholar  More

Impact of Bd endemism in amphibian communities in Flanders
In a first study, Bd prevalence was determined across our study area (Flanders, Belgium). We sampled 1483 amphibians belonging to 62 populations in 2015–2016 (Supplementary Fig. 1). To detect the presence of Bd, we collected swabs from the superficial skin surface of metamorphosed animals or the mouthparts of larval anurans. To study potential co-existence of Bd in the study region with small populations of a susceptible species (where negative effects are expected to be most obvious), in a second study, we sampled five breeding sites of midwife toads in Flanders for 4 consecutive years (Supplementary Table 2). In these breeding sites, larvae were counted once a year and their mouthparts were sampled for the presence of Bd. In a third field study, we selected 26 ponds across our study area containing at least a population of alpine newt (Ichthyosaura alpestris), being the European urodele most likely infected by Bd. Ponds were sampled with funnel traps three or four times (depending on the presence of water) with a 1-month interval (March–June 2019). An envisaged 30 newts per sampling per pond were swabbed for the presence of Bd (Supplementary Table 3), weighed to the nearest 0.1 g and the snout-vent length measured to the nearest mm. As an estimate of body condition, we used the scaled mass index (SMI)45, which adjusts the mass of all individuals to that which they would have obtained if they had the same body size. SMI was calculated using the equation of the linear regression of log‐body mass on log‐snout-vent length estimated by type 2 (standardized major axis; SMA) regression45. Eleven outliers were present (i.e. |standardized residual| >3). These observations were not used for deriving SMI relationships (as per Peig and Green45). The regression slope was 2.87, and average snout-vent length was 42.7 mm. We thus calculated the SMI as (body mass × (42.7/snout-vent length)2.87). The average number of individuals caught per fyke per pond was used as proxy for newt density. To test whether trends in newt density (i.e. average number of newts per fyke) differed between Bd positive and Bd negative ponds, a generalized linear mixed model (GLMM) was used specifying newt density as the dependent variable and the interaction between time (month) and Bd status (positive versus negative) as independent variables. Trends in newt density were better approximated by a quadratic relationship compared to a linear trend (delta AIC = 5.73). To test whether trends in SMI differed between Bd positive versus Bd negative newts, a GLMM was implemented using SMI as the dependent variable and time (month), Bd status (positive versus negative), newt sex (male versus female) and newt density (see above) as independent variables. The initial model contained all two-way interactions between the independent variables. For both GLMMs, pond was implemented as a random factor, and a Gaussian error structure was specified (model residuals were normally distributed, Shapiro–Wilk W  > 0.90). A frequentist approach was adopted whereby initial models were reduced in a stepwise manner, by excluding the variable with the highest P value until only P 90% purity). All conditions were analysed with Bd spores originating from BdJEL423, BdBE1-BdBE10 and they were tested in fourfold (biological replicates n = 4). Total RNA (1 μg) was reverse transcribed to cDNA with the iScript cDNA synthesis kit (Bio-Rad). The housekeeping genes α-centractin, APRT, TUB and Ctsyn1 were included as reference genes59. The list of genes and sequences of the primers used for quantitative PCR analysis can be found in Supplementary Table 10. Real-time quantitative PCR reactions were run in triplicate (technical replicates n = 3) and the reactions were performed in 10 μl volumes using the iQ SYBR Green Supermix (Bio-Rad). The experimental protocol for PCR (40 cycles) was performed on a CFX384 RT-PCR cycler (Bio-Rad) and data were analysed using the Bio-Rad CFX manager 3.1. The results are shown as fold changes of mRNA expression relative to the mRNA expression levels in fresh spores. Fold changes were calculated using the cycle threshold (ΔΔCT) method, and they were analysed in SPSS version 25 (SPSS Inc., Chicago, IL, USA). Multiple comparisons were assessed by a Kruskal–Wallis analysis, followed by pairwise Mann–Whitney U-tests adjusted for multiple testing with a Benjamini–Hochberg correction60, setting an adjusted P value of 0.05 as significant. Correlation between the relative expression of each isolate to BdJEL423 for CRN-like genes of fresh spores exposed to midwife toad skin and colonisation capacity from the individuals from the multi-isolate A. obstetricans infection trial was assessed by Spearman’s rank correlation in R54.
In vitro infection of A6 cells
Here, we compared the invasive capacity between hypervirulent and low-virulence local BdGPL isolates using a cell culture model. The Xenopus laevis kidney epithelial cell line A6 (ATCC-CCL 102) was grown in 75 cm2 cell culture flasks and maintained in complete growth medium (74% NCTC 109 medium, 15% distilled water, 10% fetal bovine serum (FBS) and 1% of a 10,000 U ml−1 penicillin-streptomycin solution (P/S)) and the cells were incubated at 26 °C and 5% CO2 until they reached confluence. Using trypsin, the cells were detached, washed with 70% Hanks’ Balanced Salt Solution without Ca2+, Mg2+ (HBSS−) by centrifugation for 5 min at 1500 rpm and resuspended in the appropriate cell culture medium for invasion assays, which were performed as described in Verbrugghe et al.61. To assess the germ tube formation, A6 cells were stained with 3 µM CellTrackerTM Green CMFDA, seeded (105 cells per well) in 24-well tissue culture plates containing collagen-coated glass coverslips and they were allowed to attach for 2 h at 20 °C and 5% CO2. After washing three times with 70% HBSS+, they were inoculated with Bd zoospores in invasion medium, at a MOI of 1:10. Two hours p.i., the cells were washed three times with 70% HBSS+ and the invasion medium was replaced by staining medium. Four hours p.i., the infected cells were washed three times with HBSS+ and they were incubated with Calcofluor White stain (1 µg ml−1 in 70% HBSS+) for 10 min. After washing three times with 70% HBSS+, the cells were fixed, mounted and analysed using fluorescence microscopy. To assess the invasive growth, A6 cells were seeded and inoculated with Bd zoospores as described above. Two days p.i., the infected cells were stained with 3 µM CellTrackerTM Green CMFDA, washed three times with 70% HBSS+ and they were incubated with Calcofluor White stain (10 µg ml−1 in 70% HBSS+) for 10 min. After washing three times with HBSS+, the infected cells were fixed, permeabilized for 2 min with 0.1% triton and incubated for 60 min with a polyclonal antibody against Bd (1/1000), which was obtained by immunizing rabbits with Bd-antigen62. After washing three times with 70% HBSS+, the samples were incubated with a goat anti-rabbit Alexa Fluor 568 (1/500). After an incubation of 1 h, the samples were washed three times with 70% HBSS+, mounted and analysed using fluorescence microscopy and Leica Application Suite (LAS) software X. The Alexa Fluor 568 targeting Bd and Calcofluor White stainings are used in concert to assess the ability of Bd to penetrate the host cell. Calcofluor White is not internalized by A6 cells, whereas the Alexa Fluor 568 targeting Bd staining was applied after permeabilisation of the host cells. As such, intracellular Bd will only be targeted by the Alexa Fluor 568 and extracellular Bd bodies will be bound with both the Alexa Fluor 568 and Calcofluor White stain. Overlay pictures were made with ImageJ 1.52d software. To assess the in vitro infection dynamics of different BdGPL isolates, three independent in vitro experiments were conducted with every condition being tested in triplicate, with similar results.
Whole-genome sequence analysis
WGS read data were downloaded from NCBI Bioproject PRJNA413876 (SRA sample accession numbers: BdBE1; SRA: SRS2757215, BdBE3; SRA: SRS2757203, BdBE4; SRA: SRS2757202, BdBE5; SRA: SRS2757217, BdJEL423; SRA: SRS2757141). Reads were aligned to the reference BdJEL423 assembly (BioProject PRJNA13653) using BWA mem version 0.7.1763 and the aligned bam files were processed using Picard tools version 2.21.1 (http://picard.sourceforge.net/) AddOrReplace, MarkDuplicates, SortSam, CreateSequenceDictionary and ReorderSam. Variants were called using GATK v4.1.4.064 HaplotypeCaller, the output GVCFs were combined using CombineGVCFs, genotyped using GenotypeGVCFs, separated into SNP and Indel variants for filtering using SelectVariants and filtered using VariantFiltration with filters QD  60.0 || MQ  More