Philippa Kaur

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).CAS 
Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Muir, P. R. et al. Conclusions of low extinction risk for most species of reef-building corals are premature. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01659-5 (2022).Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 884–895 (2013).Article 

Google Scholar 
Zvuloni, A., Artzy-Randrup, Y., Stone, L., van Woesik, R. & Loya, Y. Ecological size–frequency distributions: how to prevent and correct biases in spatial sampling. Limnol. Oceanogr. Methods 6, 144–153 (2008).Article 

Google Scholar  More

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Richards, Z. T., van Oppen, M. J., Wallace, C. C., Willis, B. L. & Miller, D. J. Some rare Indo-Pacific coral species are probable hybrids. PLoS ONE 3, e3240 (2008).Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 84–895 (2013).Article 

Google Scholar 
Hoeksema, B. W. & Cairns, S. World List of Scleractinia (accessed 6 May 2021); http://www.marinespecies.org/scleractiniaHughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 493–496 (2018).Article 

Google Scholar 
Thurba, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 575927 (2020).Article 

Google Scholar 
Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef building corals: shall the deep inherit the reef? Proc. R. Soc. B 284, 20171551 (2017).Article 

Google Scholar 
van Woesik, R., Sakai, K., Ganase, A. & Loya, Y. Revisiting the winners and the losers a decade after coral bleaching. Mar. Ecol. Prog. Ser. 434, 67–76 (2011).Article 

Google Scholar 
Chen, Y.-H., Shertzer, K. W. & Viehman, T. S. Spatio-temporal dynamics of the threatened elkhorn coral Acropora palmata: implications for conservation. Divers. Distrib. 26, 1582–1597 (2020).Article 

Google Scholar 
Sheppard, C., Sheppard, A. & Fenner, D. Coral mass mortalities in the Chagos Archipelago over 40 years: regional species and assemblage extinctions and indications of positive feedbacks. Mar. Poll. Bull. 154, 111075 (2020).CAS 
Article 

Google Scholar 
DeVantier, L. & Turak, E. Species richness and relative abundance of reef-building corals in the Indo-West Pacific. Diversity 9, 25 (2017).Article 

Google Scholar 
Wallace, C. C. Staghorn Corals of the World (CSIRO, 1999).Benzoni, F., Stefani, F., Pichon, M. & Galli, P. The name game: morpho-molecular species boundaries in the genus Psammocora (Cnidaria, Scleractinia). Zool. J. Linn. Soc. 160, 421–456 (2010).Article 

Google Scholar 
Richards, Z. T., Berry, O. & van Oppen, M. J. H. Cryptic genetic divergence within threatened species of Acropora coral from the Indian and Pacific Oceans. Conserv. Genet. 17, 577–591 (2016).Article 

Google Scholar 
Sheets, E. A., Warner, P. A. & Palumbi, S. R. Accurate population genetic measurements require cryptic species identification in corals. Coral Reefs 37, 549–563 (2018).Article 

Google Scholar 
Bongaerts, P. et al. Morphological stasis masks ecologically divergent coral species on tropical reefs. Curr. Biol. 31, 2286–2298 (2021).CAS 
Article 

Google Scholar 
Frankham, R. Effective population size/adult population size ratios in wildlife: a review. Genet. Res. 89, 491–503 (2008).Article 

Google Scholar  More

Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 

Google Scholar 
Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).
Google Scholar 
Vitasse, Y. et al. Assessing the effects of climate change on the phenology of European temperate trees. Agr. Forest Meteorol. 151, 969–980 (2011).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 

Google Scholar 
Wang, H. et al. Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling. Nat. Commun. 11, 4945 (2020).CAS 

Google Scholar 
Myneni, R. C. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 

Google Scholar 
Piao, S. et al. Leaf onset in the Northern Hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Phil. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
White, A., Cannell, M. G. R. & Friend, A. D. The high-latitude terrestrial carbon sink: a model analysis. Glob. Change Biol. 6, 227–245 (2000).
Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–53 (2008).CAS 

Google Scholar 
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Google Scholar 
Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 11, 5176–5187 (2018).
Google Scholar 
Fu, Y. H. et al. Increased heat requirement for leaf flushing in temperate woody species over 1980–2012: effects of chilling, precipitation and insolation. Glob. Change Biol. 21, 2687–2697 (2015).
Google Scholar 
Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Climatic Change 94, 105–121 (2009).
Google Scholar 
Peñuelas, J. et al. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytol. 161, 837–846 (2004).
Google Scholar 
Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand? Glob. Change Biol. 26, 3336–3355 (2020).
Google Scholar 
Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).CAS 

Google Scholar 
Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).
Google Scholar 
Qian, W., Fu, J. & Yan, Z. Decrease of light rain events in summer associated with a warming environment in China during 1961–2005. Geophys. Res. Lett. 34, L11705 (2007).
Google Scholar 
Sun, Y., Solomon, S., Dai, A. & Portmann, R. W. How often will it rain? J. Clim. 20, 4801–4818 (2007).
Google Scholar 
Chou, C. et al. Mechanisms for global warming impacts on precipitation frequency and intensity. J. Clim. 13, 3291–3306 (2012).
Google Scholar 
Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).
Google Scholar 
Fowler, M. D., Kooperman, G. J., Randerson, J. T. & Pritchard, M. S. The effect of plant physiological responses to rising CO2 on global streamflow. Nat. Clim. Change 9, 873–879 (2019).CAS 

Google Scholar 
Belnap, J., Phillips, S. L. & Miller, M. E. Response of desert biological soil crusts to alterations in precipitation frequency. Oecologia 141, 306–316 (2004).
Google Scholar 
Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).
Google Scholar 
Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
De Boeck, H. J., Dreesen, F. E., Janssens, I. A. & Nijs, I. Climatic characteristics of heat waves and their simulation in plant experiments. Glob. Change Biol. 16, 1992–2000 (2010).
Google Scholar 
Shen, M. et al. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).
Google Scholar 
Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).
Google Scholar 
Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).
Google Scholar 
Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004).
Google Scholar 
White, M. A., Thornton, P. E. & Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles 11, 217–234 (1997).CAS 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
Google Scholar 
Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).
Google Scholar 
Schwartz, M. D., Betancourt, J. L. & Weltzin, J. F. From Caprio’s lilacs to the USA National Phenology Network. Front. Ecol. Environ. 10, 324–327 (2012).
Google Scholar 
Wu, C. et al. Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Glob. Ecol. Biogeogr. 22, 994–1006 (2013).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).
Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agr. Forest Meteorol. 291, 108077 (2020).
Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).
Google Scholar 
Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agr. Forest Meteorol. 189, 71–80 (2014).
Google Scholar 
Wu, C. et al. Widespread decline in winds delayed autumn foliar senescence over high latitudes. Proc. Natl Acad. Sci. USA 118, e2015821118 (2021).CAS 

Google Scholar 
Elmore, A. J. et al. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 
New, M., Hulme, M. & Jones, P. D. Representing twentieth‐century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).
Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).
Google Scholar 
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).
Google Scholar 
Vicenteserrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).
Google Scholar 
Barr, A. G. et al. Inter‐annual variability in the leaf area index of a boreal aspen–hazelnut forest in relation to net ecosystem production. Agr. Forest Meteorol. 126, 237–255 (2004).
Google Scholar 
Chen, J., Chen, W., Liu, J., Cihlar, J. & Gray, S. Annual carbon balance of Canada’s forests during 1895–1996. Glob. Biogeochem. Cycles 14, 839–849 (2000).CAS 

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

Analytical targetsTwo species of undulation motion rays with different pectoral fin shapes bred in KAIYUKAN were analyzed: sharpnose stingray Dasyatis acutirostra and pitted stingray Dasyatis matsubarai (Fig. 1a,b). Blender 2.7925 was used to construct stingray models from pictures26,27 as accurately as possible; Blender is a free and open-source 3D creation suite used to make realistic characters for movies, etc. Detailed information on how to construct models using Blender is provided in our previous paper28. To focus on the effects of pectoral fin movements, we did not consider the body’s shape as in the previous studies12,29. The height and disk width (WD) of all models were set to 0.01 m and 0.44 m, respectively, considering the previous studies30,31. The disk length of each model was determined from WD, referring to the aspect ratio of the rays’ photographs26,27; the disk length (LD) of D. acutirostra and D. matsubarai are 0.348 m and 0.344 m, respectively.Figure 1Analytical targets and description of motion. (a) Analytical model of D. acutirostra. (b) Analytical model of D. matsubarai. (c) Description of motion, (d) the relationship between any two points on the surface before and after the deformation.Full size imageMotionThe motion was given to satisfy the following equations:$$z = left{ {begin{array}{*{20}l} {A;sin left( {omega left( {t – kTleft( {frac{{angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} }}{{All;angl{text{e}}}} – theta } right)} right)h_{1} h_{2} } right.} hfill & {left( {10^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 170^{{text{o}}} } right)} hfill \ {A;sin left( {omega left( {t – kTleft( {frac{{350^{{text{o}}} – left( {angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} } right)}}{{All;angl{text{e}}}}} right)} right)h_{1} h_{2} } right.} hfill & {left( {190^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 350^{{text{o}}} } right)} hfill \ end{array} } right.$$
(1)
$$begin{array}{c}{h}_{1}=a{r}_{i}^{3}+b{r}_{i}^{2}+c{r}_{i}end{array}$$
(2)
$$h_{2} = left{ {begin{array}{*{20}l} {dleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)^{2} + eleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} hfill & {left( {10^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 170^{ circ } } right)} hfill \ {dleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)^{2} + eleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)} hfill & {left( {190^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 350^{ circ } } right)} hfill \ end{array} } right.$$
(3)
$$begin{array}{c}{left({r}_{i}-{r}_{i-1}right)}^{2}+{left({z}_{i}-{z}_{i-1}right)}^{2}={left({r}_{i}^{mathrm{^{prime}}}-{r}_{i-1}^{mathrm{^{prime}}}right)}^{2}+{left({z}_{i}^{mathrm{^{prime}}}-{z}_{i-1}^{mathrm{^{prime}}}right)}^{2}end{array}$$
(4)
$$begin{array}{c}angleleft({x}_{i},{y}_{i}right)= angleleft({x}_{i}^{^{prime}},{y}_{i}^{^{prime}}right).end{array}$$
(5)
Equation (1) represents the amount of movement of the model surface in the z-axis direction, where (A) is the amplitude of the pectoral fin tip, (omega) is the angular velocity, (t) is time, (k) is the wavenumber, (T) is the period, angle(({x}_{i},{y}_{i})) is the angle made by the line connecting the center of rotation and any point (({x}_{i},{y}_{i})) on the model surface with the x-axis, Allangle is the range where the motion is given (160°), and (theta) is the phase difference between the movements of the right and left pectoral fins (Fig. 1c). ({h}_{1}) is the weighting from the center to the radial direction: it is necessary to set the amplitude at the ray’s center to zero and increase the amplitude toward the pectoral fin tip (a = 119.786, b = -7.957, c = 0.498). ({h}_{2}) is the weighting in the circumferential direction: it is necessary to increase the amplitude from the anterior to the tip of the pectoral fin and decrease the amplitude from the tip of the pectoral fin to the posterior (d = − 1.563 × 10–4, e = 0.025). Equation (4) is the condition in which the distance between two neighboring points in the same radial direction is equal before and after the movement (Fig. 1d). (r) is the distance between the center of rotation and any point (({x}_{i},{y}_{i})), defined as (sqrt{{x}_{i}^{2}+{y}_{i}^{2}}). Equation (5) defines angle(({x}_{i},{y}_{i})) as being constant before and after the move (Fig. 1c). The variables after the move are marked with ‘. Variables used in the analysis are A = 0.089 m, T = 0.499, k = 1.270, and ω = 12.599 rad/s. Videos of the created motion from the front and the side are shown in the “Supplement” (Supplement Movies 3, 4).Analytical conditionsAnalysis cases were conducted with eight conditions: two types of pectoral fin shape (Fig. 1a,b) and four types of phase difference (0 (T), 0.25 (T), 0.5 (T), and 0.75 (T)). These conditions were set for investigating the effects of phase differences between left and right pectoral fin movements on swimming and how these effects vary with pectoral fin shape.Numerical methodsA CFD simulation of the ray models in the water flow was performed using OPENFOAM, an open-source finite volume method CFD toolbox32, to calculate the forces acting on the rays in each axial direction and the moment around each axis. The governing equations were the continuity equation and the three-dimensional incompressible Reynolds-averaged Navier–Stokes equation, expressed by:$$begin{array}{*{20}c} {nabla cdot u = 0} \ end{array}$$
(6)
$$begin{array}{*{20}c} {frac{partial u}{{partial {text{t}}}} + nabla cdot left( {uu} right) = – nabla p + nabla cdot left( {vnabla u} right) + nabla cdot left[ {nu left{ {left( {nabla u} right)^{T} – frac{1}{3}nabla cdot uI} right}} right], } \ end{array}$$
(7)
where (u) is the velocity vector, t is the time, p is the static pressure divided by the reference density, (nu) is the kinematic viscosity, and I is the unit tensor. The Reynolds number was defined regarding the previous studies3 as:$$begin{array}{c}{R}_{e}=frac{U{L}_{D}}{nu },end{array}$$
(8)
where (U) (/ms) is the given flow speed3 (1.5 × LD/ms), ({L}_{D}) (m) is the length of the ray models, and (nu) is the kinematic viscosity of water at 20 °C (1.0 × 10–6 m2/s). The Reynolds number in this study is 1.8 × 105; considering this, we used the k–ω shear stress turbulence model33,34. The k–ω shear stress turbulence model is a type of Reynolds-averaged Navier–Stokes equation (RANS) turbulence model that is widely used to calculate for the fish swimming flow35,36,37. The overset grid method was used in this study; it is a generic implementation of overset meshes. For both static and dynamic cases, cell-to-cell mapping between multiple, disconnected mesh regions is employed to generate a composite domain38,39. This method permits complex mesh motions and interactions without the penalties associated with deforming meshes. The process is described in detail by Noack40. The calculation volume was 5.4 WD in length, 5.4 WD in height, and 5.4 WD in width (Fig. 2a,b). A hexahedral volume mesh was created using the snappyHexMesh of OPENFOAM. The fluid region was divided into two parts: the overset region and the background region (Fig. 2a,b). The overset region moves and transforms to match the motion of the ray and was made with fine meshes around the analysis target and coarse meshes in the outlying areas; a one-layer boundary layer mesh was created around the analysis target. The overset region shape is an ellipsoid (Fig. 2a,b). The minimum mesh volume is 7.3 × 10–10 (m3), and the maximum mesh volume is 2.6 × 10–2 (m3). The total number of meshes was 9.0 × 105. At the outlet boundary, the average static relative pressure was set to 0 Pa. The surfaces of the fish model were formed into non-slip surfaces.Figure 2Meshes for CFD simulation and differences in force between different meshes. (a) Meshes at the coronal plane of the whole fluid region. (b) Frontal cross-section of the fluid region at the green line in (a). The red region is the overset region. (c,d) Comparison of the instantaneous drag coefficient and the moment coefficient around the y-axis of D. matsubarai between the coarse, fine, and dense mesh.Full size imageThe drag coefficient ({C}_{D}left(tright)), the lateral force coefficient ({C}_{l}left(tright)), the lift coefficient ({C}_{L}left(tright)), the moment coefficient around the x-axis ({C}_{mx}left(tright)), the moment coefficient around the y-axis ({C}_{my}left(tright)) and the moment coefficient around the z-axis ({C}_{mz}left(tright)) were calculated as:$$begin{array}{c}{C}_{D}left(tright)=frac{Dleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(9)
$$begin{array}{c}{C}_{l}left(tright)=frac{lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(10)
$$begin{array}{c}{C}_{L}left(tright)=frac{Lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(11)
$$begin{array}{c}{C}_{mx}left(tright)=frac{{M}_{psi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(12)
$$begin{array}{c}{C}_{my}left(tright)=frac{{M}_{phi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(13)
$$begin{array}{c}{c}_{mz}left(tright)=frac{{M}_{theta }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}},end{array}$$
(14)
where (Dleft(tright)) is the calculated drag, (lleft(tright)) is the calculated lateral force, (Lleft(tright)) is the calculated lift, ({M}_{psi }left(tright)) is the calculated moment around the x-axis, ({M}_{phi }left(tright)) is the calculated moment around the y-axis, ({M}_{theta }left(tright)) is the calculated moment around the z-axis, and (rho) (kg/m3) is the density of water at 20 °C (998 kg/m3). As shown in a previous study41. the propulsive efficiency (eta) is defined as the ratio of output power ({P}_{o}) to input power ({P}_{e}) which can be written as:$$begin{array}{c}{P}_{o}left(tright)=frac{1}{T}{int }_{0}^{T}Dleft(tright)Udtend{array}$$
(15)
$$begin{array}{c}{P}_{e}left(tright)=frac{1}{T}{int }_{0}^{T}left[Dleft(tright)dot{x}left(tright)+lleft(tright)dot{y}left(tright)+Lleft(tright)dot{z}left(tright)right]dtend{array}$$
(16)
$$begin{array}{c}eta =frac{{P}_{o}}{{P}_{e}}.end{array}$$
(17)
An in-house program calculated the forces acting on rays in each axial direction and the moment around each axis. The numerical method’s validity and reliability were verified by comparing previous experimental and numerical analytical studies of heaving and pitching on airfoil naca001341. A high degree of similarity to previous studies was confirmed; the mean difference in the propulsive efficiency from the previous study of analysis was 5%, and the difference from the previous study of the experiment was 9%. Detailed information such as mesh, length, and velocity, of this analysis method’s verification is provided in the “Supplement”.A grid sensitivity study was conducted using three meshes: coarse, fine, and dense. The coarse mesh has 8.1 × 105 elements, the fine mesh has 9.0 × 105 elements, and the dense mesh has 9.9 × 105 elements. The analysis was conducted using a condition with no phase difference of D. matsubarai. As shown in Fig. 2c,d, the drag coefficient and the moment coefficient around the y-axis are almost the same when the mesh is fine and when the mesh is dense. The mean drag and propulsive efficiency error of fine and coarse meshes are 2.7% and 3.5%, respectively. The fine mesh was used in all simulation cases considering accuracy. We used the same meshes for all cases. More

Let (y_{itk}) denote the WCR count observed for trap i in week t in year k, and assume it to follow a Poisson distribution with parameter (mu _{itk})$$begin{aligned} y_{itk} | mu _{itk}, sim Poisson(mu _{itk}) end{aligned}$$
(1)
The intensity parameter (mu _{itk}) represents the rate of emergence for a given time period. Instead of allowing it to depend purely on time t, a phenological variable of growing degree days (GDD) is used, as warmer temperatures are required for WCR development25,26,27,28. GDDs reflect the heat accumulation and are defined as an integral of warmth above a base temperature after a given start date:$$begin{aligned} GDD = int (T(t)-T_{base})dt. end{aligned}$$
(2)
The above integral can be approximated by$$begin{aligned} GDD = max left( frac{T_{max} – T_{min}}{2} – T_{base}, 0 right) . end{aligned}$$
(3)
Here (T_{min}) is the minimum daily temperature, (T_{max}) is the maximum daily temperature, and (T_{base}) is a set base temperature. In this study, the base temperature was set to (10,^{circ })C, and the starting date was the beginning of April, which marks the start of the growing season in Austria.The rate of cumulative emergence of the WCR beetle can be described by a Gompertz function. The Gompertz function is a sigmoidal function which describes growth as being slowest at the beginning and the end of a given period and is defined as$$begin{aligned} f(z_t) = alpha exp (-beta exp (-gamma z_t)). end{aligned}$$
(4)
where (alpha) is the upper asymptote, (beta) is a relative starting value, (gamma) is a growth rate coefficient which affects the slope, and (z_t) are the cumulative growing degree days. In this study, one can consider the asymptote as proxy to the saturation level of WCR population growth. Lower values of (beta) suggest an earlier first emergence in the season, while lower values of (gamma) indicate a longer emergence period. To investigate whether there is an association between climate variables and the emergence dynamics, the Gompertz curve parameters were assumed to linearly depend on climate covariates. In this regression modelling framework, a spatially correlated residual structure can be added in either (alpha), (beta), and/or (gamma) if there is evidence to do so.To reflect the nature of the emergence dynamics and to preserve the shape of the increasing Gompertz curve, the parameters of the model were restricted to positive values such that (alpha >0), (beta >0), and (gamma >0). The time at inflection or period of highest growth can be obtained by solving Eq. (4) for the value of t at which the concavity of the function changes. The time at inflection is described as:$$begin{aligned} T_z^* = frac{log (beta )}{gamma } end{aligned}$$
(5)
The Gompertz function describes cumulative emergence. Thus to describe the marginal emergence rate, the derivative of the Gompertz function can be used instead. Consequently, as the WCR trapping data consisted of weekly counts, the rate of emergence (mu _{itk}) is better described by the log of the derivative of the Gompertz function$$begin{aligned} log (mu _{itk}) = log (alpha _{ik}) + log (gamma _{ik}) + log (beta _{ik}) + gamma _i z_{itk} – beta _{ik} exp (-gamma z_{itk}). end{aligned}$$
(6)
The parameters (alpha _{ik}), (beta _{ik}) and (gamma _{ik}) are site and year specific such that:$$begin{aligned}&alpha _{ik} sim N(mu _{alpha _{ik}}, tau _{alpha }) end{aligned}$$
(7)
$$begin{aligned}&gamma _{ik} sim N(mu _{gamma _{ik}}, tau _{gamma }) end{aligned}$$
(8)
$$begin{aligned}&beta _{ik} sim N(mu _{beta _{ik}}, tau _{beta }). end{aligned}$$
(9)
Here, (tau _{alpha }), (tau _{beta }), and (tau _{gamma }) are the precision (inverse variance) parameters of the prior distributions for (alpha), (beta) and (gamma) respectively. Moreover, the means of the distributions (mu _{alpha _{ik}}), (mu _{beta _{ik}}), and (mu _{gamma _{ik}}) can be expressed as functions of known covariates:$$begin{aligned} mu _{alpha _{ik}}= & {} a_{0} + {mathbf {w}}^T X_{alpha _{ik}}, end{aligned}$$
(10)
$$begin{aligned} mu _{beta _{ik}}= & {} b_{0}, end{aligned}$$
(11)
$$begin{aligned} mu _{gamma _{ik}}= & {} g_{0} + {mathbf {u}}^T X_{gamma _{ik}}. end{aligned}$$
(12)
Here (a_{0}) is the intercept, ({mathbf {w}}) is a vector of the regression coefficients, and (X_{alpha _{ik}}) are the location and year specific covariates. The predictors used in the regression of (mu _{alpha _{ik}}) are the average winter temperature, the precipitation sum during winter, the year, the percentage of the agricultural area per Austrian municipality used for cultivating maize crops (maize), and the corresponding centred coordinates of the trap locations; x, y, and their functions (x^2), (y^2), and xy. The parameter (g_{0}) is the intercept for the regression of (mu _{gamma _{ik}}), and u is the corresponding regression coefficient. The predictor used for (mu _{gamma _ik}) is the average yearly spring temperature.The intercepts and regression coefficients ((mathbf {w}) and (mathbf {u})) were given non-informative normal priors N(0, 0.01). The precision parameters (tau _{alpha }), (tau _{beta }) and (tau _{gamma }) were assigned prior distributions Gamma(0.01, 0.01).The model was fitted using WinBUGS through the R2WinBUGS package in R29,30,31. The model was run for 20000 iterations, with a burn-in of 10000 iterations, and a thinning rate of five. Convergence was determined by visual assessments of trace plots and marginal posterior densities. More

Denlinger, D. L. Dormancy in tropical insects. Annu. Rev. Entomol. 31, 239–264. https://doi.org/10.1146/annurev.en.31.010186.001323 (1986).CAS 
Article 
PubMed 

Google Scholar 
Moreau, R. E. The breeding seasons of African birds—1. Land birds. Ibis 92, 223–267. https://doi.org/10.1111/j.1474-919X.1950.tb01750.x (1950).Article 

Google Scholar 
Fogden, M. P. L. Seasonality and population dynamics of equatorial forest birds in Sarawak. Ibis 114, 307–343. https://doi.org/10.1111/j.1474-919X.1972.tb00831.x (1972).Article 

Google Scholar 
Karr, J. R. Resource availability, and community diversity in tropical bird communities. Am. Nat. 110, 973–994. https://doi.org/10.1086/283121 (1976).Article 

Google Scholar 
Oppel, S. et al. The effects of rainfall on different components of seasonal fecundity in a tropical forest passerine. Ibis 155, 464–475. https://doi.org/10.1111/ibi.12052 (2013).Article 

Google Scholar 
Shaw, P. Rainfall, leafing phenology and sunrise time as potential Zeitgeber for the bimodal, dry season laying pattern of an African rain forest tit (Parus fasciiventer). J. Ornithol. 158, 263–275. https://doi.org/10.1007/s10336-016-1395-6 (2017).Article 

Google Scholar 
Withers, P. C. & Cooper, C. E. Metabolic depression: A historical perspective. In Aestivation: Molecular and Physiological Aspects, Progress in Molecular and Subcellular Biology (eds Navas, C. A. & Carvalho, J. E.) 1–23 (Springer-Verlag, 2010).
Google Scholar 
Fletcher, B. S., Pappas, S. & Kapatos, E. Changes in ovaries of olive fly (Dacus-oleae-(Gmelin)) during summer, and their relationship to temperature, humidity and fruit availability. Ecol. Entomol. 3, 99–107. https://doi.org/10.1111/j.1365-2311.1978.tb00908.x (1978).Article 

Google Scholar 
Braby, M. F. Reproductive seasonality in tropical satyrine butterflies—Strategies for the dry season. Ecol. Entomol. 20, 5–17. https://doi.org/10.1111/j.1365-2311.1995.tb00423.x (1995).Article 

Google Scholar 
Goehring, L. & Oberhauser, K. S. Effects of photoperiod, temperature, and host plant age on induction of reproductive diapause and development time in Danaus plexippus. Ecol. Entomol. 27, 674–685. https://doi.org/10.1046/j.1365-2311.2002.00454.x (2002).Article 

Google Scholar 
Valera, F., Casas-Crivillé, A. & Hoi, H. Interspecific parasite exchange in a mixed colony of birds. J. Parasitol. 89, 245–250. https://doi.org/10.1645/0022-3395(2003)089[0245:IPEIAM]2.0.CO;2 (2003).Article 
PubMed 

Google Scholar 
Grimaldi, D. The bird flies, genus Carnus: Species revision, generic relationships, and a fossil Meoneura in amber (Diptera: Carnidae). Am. Mus. Novit. 3190, 1–30 (1997).MathSciNet 

Google Scholar 
Valera, F., Casas-Crivillé, A. & Calero-Torralbo, M. A. Prolonged diapause in the ectoparasite Carnus hemapterus (Diptera: Cyclorrapha, Acalyptratae)—How frequent is it in parasites?. Parasitology 133, 179–186. https://doi.org/10.1017/S0031182006009899 (2006).CAS 
Article 
PubMed 

Google Scholar 
Sabrosky, C. W., Bennett, G. F. & Whitworth, T. L. Bird blow flies (Protocalliphora) in North America (Diptera: Calliphoridae), with notes on the Palearctic species. https://library.si.edu/digital-library/book/birdblowfliespro00sabr (Smithsonian Institute Press, 1989).Dodge, H. R. & Aitken, T. H. G. Philornis flies from Trinidad (Diptera: Muscidae). J. Kansas Entomol. Soc. 41, 134–154 (1968).
Google Scholar 
Couri, M. S. Notes and descriptions of Philornis flies (Diptera, Muscidae, Cyrtoneurinininae). Rev. Bras. Entomol. 28, 473–490 (1984).
Google Scholar 
Couri, M. S. Myiasis caused by obligatory parasites. Ia. Philornis Meinert (Muscidae). In Myiasis in Man and Animals in the Neotropical Region (eds Guimaraes, J. H. & Papavero, N.) 44–70 (Editora Pleiade, 1999).
Google Scholar 
Silvestri, L., Antoniazzi, L. R., Couri, M. S., Monje, L. D. & Beldomenico, P. M. First record of the avian ectoparasite Philornis downsi Dodge & Aitken, 1968 (Diptera: Muscidae) in Argentina. Syst. Parasitol. 80, 137–140. https://doi.org/10.1007/s11230-011-9314-y (2011).CAS 
Article 
PubMed 

Google Scholar 
Bulgarella, M. et al. Philornis downsi, an avian nest parasite invasive to the Galápagos Islands, in mainland Ecuador. Ann. Entomol. Soc. Am. 108, 242–250. https://doi.org/10.1093/aesa/sav026 (2015).Article 

Google Scholar 
Kleindorfer, S. & Dudaniec, R. Y. Host-parasite ecology, behavior and genetics: a review of the introduced fly parasite Philornis downsi and its Darwin finch hosts. BMC Zool. 1, 1. https://doi.org/10.1186/s40850-016-0003-9 (2016).Article 

Google Scholar 
Fessl, B., Heimpel, G. E. & Causton, C. E. Invasion of an avian nest parasite, Philornis downsi, to the Galapagos Islands: Colonization history, adaptations to novel ecosystems, and conservation challenges. In Disease Ecology: Social and Ecological Interactions in the Galapagos Islands (ed. Parker, P. G.) 213–266 (Springer, 2018). https://doi.org/10.1007/978-3-319-65909-1_9DO.Chapter 

Google Scholar 
McNew, S. M. & Clayton, D. H. Alien invasion: biology of Philornis flies highlighting Philornis downsi, an introduced parasite of Galapagos birds. Annu. Rev. Entomol. 63, 369–387. https://doi.org/10.1146/annurev-ento-020117-043103 (2018).CAS 
Article 
PubMed 

Google Scholar 
Anchundia, D. & Fessl, B. The conservation status of the Galapagos Martin, Progne modesta: Assessment of historical records and results of recent surveys. Bird Conserv. Int. 31, 129–138. https://doi.org/10.1017/S095927092000009X (2021).Article 

Google Scholar 
Coloma, A., Anchundia, D., Piedrahita, P., Pike, C. & Fessl, B. Observations on the nesting of the Galapagos Dove, Zenaida galapagoensis, in Galapagos, Ecuador. Galapagos Res. 69, 34–38 (2020).
Google Scholar 
Lack, D. Darwin’s Finches (Cambridge University Press, 1947).
Google Scholar 
Grant, P. R. Ecology and Evolution of Darwin’s Finches (Princeton University Press, 1986).
Google Scholar 
Bulgarella, M., Quiroga, M. A. & Heimpel, G. E. Additive negative effects of Philornis nest parasitism and small and declining Neotropical bird populations. Bird Conserv. Int. 29, 339–360. https://doi.org/10.1017/S0959270918000291 (2019).Article 

Google Scholar 
Causton, C. E. et al. Population dynamics of an invasive bird parasite, Philornis downsi (Diptera: Muscidae), in the Galapagos Islands. PLoS ONE 14(10), e0224125. https://doi.org/10.1371/journal.pone.0224125 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hayes, E. J. & Wall, R. Age-grading adult insects: A review of techniques. Physiol. Entomol. 24, 1–10. https://doi.org/10.1046/j.1365-3032.1999.00104.x (1999).Article 

Google Scholar 
Lahuatte, P. F., Lincango, M. P., Heimpel, G. E. & Causton, C. E. Rearing larvae of the avian nest parasite, Philornis downsi (Diptera: Muscidae), on chicken blood-based diets. J. Insect Sci. 16, 84. https://doi.org/10.1093/jisesa/iew064 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Moon, R. D. & Krafsur, E. S. Pterin quantity and gonotrophic stage as indicators of age in Musca autumnalis (Diptera: Muscidae). J. Med. Entomol. 32, 673–684. https://doi.org/10.1093/jmedent/32.5.673 (1995).CAS 
Article 
PubMed 

Google Scholar 
Butler, S. M. et al. Characterization of age and cuticular hydrocarbon variation in mating pairs of house fly, Musca domestica, collected in the field. Med. Vet. Entomol. 23, 426–442. https://doi.org/10.1111/j.1365-2915.2009.00831.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Mail, T. S. & Lehane, M. J. Characterisation of pigments in the head capsule of the adult stablefly Stomoxys calcitrans. Entomol. Exp. Appl. 46, 125–131. https://doi.org/10.1111/j.1570-7458.1988.tb01102.x (1988).Article 

Google Scholar 
Trueman, M. & D’Ozouville, N. Characterizing the Galapagos terrestrial climate in the face of global climate change. Galapagos Res. 67, 26–37 (2010).
Google Scholar 
Stramma, L. et al. Observed El Niño conditions in the eastern tropical Pacific in October 2015. Ocean Sci. 12, 861–873. https://doi.org/10.5194/os-12-861-2016 (2016).ADS 
Article 

Google Scholar 
Martin, N. J. et al. Seasonal and ENSO influences on the stable isotopic composition of Galapagos precipitation. J. Geophys. Res-Atmos. 123, 261–275. https://doi.org/10.1002/2017JD027380 (2018).ADS 
Article 

Google Scholar 
Grant, P. R., Grant, B. R., Keller, L. F. & Petren, K. Effects of El Niño events on Darwin’s finch productivity. Ecology 81, 2442–2457. https://doi.org/10.2307/177466 (2000).Article 

Google Scholar 
Sage, R. et al. Environmentally cued hatching in the bird parasite Philornis downsi (Diptera: Muscidae). Entomol. Exp. Appl. 166, 752–760. https://doi.org/10.1111/eea.12721 (2018).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B 73, 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x (2011).MathSciNet 
Article 
MATH 

Google Scholar 
Lack, D. Breeding seasons in the Galapagos. Ibis 92, 268–278. https://doi.org/10.1111/j.1474-919X.1950.tb01751.x (1950).Article 

Google Scholar 
Grant, P. R. & Boag, P. T. Rainfall on the Galapagos and the demography of Darwin’s Finches. Auk 97, 227–244 (1980).Article 

Google Scholar 
Peck, S. B. Beetles of the Galápagos Islands, Ecuador: Evolution, Ecology, and Diversity (Insecta: Coleoptera) (NRC Research Press, 2006).
Google Scholar 
Grant, P. R. & Grant, B. R. The breeding and feeding characteristics of Darwin’s Finches on Isla Genovesa, Galapagos. Ecol. Monogr. 50, 381–410. https://doi.org/10.2307/2937257 (1980).Article 

Google Scholar 
Boag, P. T. & Grant, P. R. Darwin Finches (Geospiza) on Isla Daphne Major, Galapagos—Breeding and feeding ecology in a climatically variable environment. Ecol. Monogr. 54, 463–489. https://doi.org/10.2307/1942596 (1984).Article 

Google Scholar 
Schluter, D. Feeding correlates of breeding and social organization in two Galapagos Finches. Auk 101, 59–68. https://doi.org/10.1093/auk/101.1.59 (1984).Article 

Google Scholar 
Hau, M., Wikelski, M., Gwinner, H. & Gwinner, E. 2004 Timing of reproduction in a Darwin’s finch: Temporal opportunism under spatial constraints. Oikos 106, 489–500 (2004).Article 

Google Scholar 
Pike, C. L. et al. Behavior of the avian parasite Philornis downsi (Diptera: Muscidae) in and near host nests in the Galapagos Islands. J. Insect Behav. https://doi.org/10.1007/s10905-021-09789-7 (2021).Article 

Google Scholar 
Heleno, R. H., Olesen, J. M., Nogales, M., Vargas, P. & Traveset, A. Seed dispersal network in the Galapagos and the consequences of alien plant invasions. Proc. R. Soc. B. 280, 20122112. https://doi.org/10.1098/rspb.2012.2112 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Traveset, A., Chamorro, S., Olesen, J. M. & Heleno, R. Space, time and aliens: charting the dynamic structure of Galapagos pollination networks. AoB PLANTS 7, plv068. https://doi.org/10.1093/aobpla/plv068 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ervin, S. Nesting behavior of the large-billed flycatcher on Isla Santa Cruz. Noticias de Galapagos 51, 17–20 (1992).
Google Scholar 
Lincango, P. et al. Interactions between the avian parasite, Philornis downsi (Diptera: Muscidae) and the Galapagos Flycatcher, Myiarchus magnirostris Gould (Passeriformes: Tyrannidae). J. Wildl. Dis. 51, 907–910. https://doi.org/10.7589/2015-01-025 (2015).CAS 
Article 
PubMed 

Google Scholar 
Dudaniec, R. Y., Gardner, M. G., Donnellan, S. & Kleindorfer, S. Genetic variation in the invasive parasite, Philornis downsi (Diptera: Muscidae) on the Galapagos archipelago. BMC Ecol. 8, 13. https://doi.org/10.1186/1472-6785-8-13 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cimadom, A. et al. Darwin’s finches treat their feathers with a natural repellent. Sci. Rep. 6, 34559. https://doi.org/10.1038/srep34559 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Common, L. K., Dudaniec, R. Y., Colombelli-Negrel, D. & Kleindorfer, S. Taxonomic shifts in Philornis larval behavior and rapid changes in Philornis downsi Dodge & Aitken (Diptera: Muscidae), an invasive avian parasite on the Galapagos Islands. InTech Open https://doi.org/10.5772/intechopen.88854 (2019).Article 

Google Scholar 
Common, L. K., O’Connor, J. A., Dudaniec, R. Y., Peters, K. J. & Kleindorfer, S. Evidence for rapid downward fecundity selection in an ectoparasite (Philornis downsi) with earlier host mortality in Darwin’s finches. J. Evol. Biol. 33, 524–533. https://doi.org/10.1111/jeb.13588 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Dieckhoff, C., Theobald, J. C., Waeckers, F. L. & Heimpel, G. E. Egg load dynamics and the risk of egg and time limitation experienced by an aphid parasitoid in the field. Ecol. Evol. 4, 1739–1750. https://doi.org/10.1002/ece3.1023 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Papaj, D. R. Ovarian dynamics and host use. Annu. Rev. Entomol. 45, 423–448. https://doi.org/10.1146/annurev.ento.45.1.423 (2000).CAS 
Article 
PubMed 

Google Scholar 
Barton Browne, L., van Gerwen, A. C. M. & Williams, K. L. Oocyte resorption during ovarian development in the blowfly Lucilia cuprina. J. Insect Physiol. 25, 147–153. https://doi.org/10.1016/0022-1910(79)90093-3 (1979).Article 

Google Scholar 
Venkatesh, K. & Morrison, P. E. Some aspects of oogenesis in the stable fly Stomoxys calcitrans (Diptera, Muscidae). J. Insect Physiol. 26, 711–715. https://doi.org/10.1016/0022-1910(80)90045-1 (1980).Article 

Google Scholar 
Spradbery, J. P. & Schweizer, G. Oosorption during ovarian development in the screw-worm fly, Chrysomya bezziana. Entomol. Exp. Appl. 30, 209–214. https://doi.org/10.1111/j.1570-7458.1981.tb03102.x (1981).Article 

Google Scholar 
Curry, R. L. & Grant, P. R. Demography of the cooperatively breeding Galapagos mockingbird, Nesomimus parvulus, in a climatically variable environment. J. Anim. Ecol. 58, 441–463. https://doi.org/10.2307/4841 (1989).Article 

Google Scholar 
Calero-Torralbo, M. A. & Valera, F. Synchronization of host-parasite cycles by means of diapause: Host influence and parasite response to involuntary host shifting. Parasitol. 135, 1343–1352. https://doi.org/10.1017/S0031182008004885 (2008).CAS 
Article 

Google Scholar 
Larimore, R. W. Synchrony of cliff swallow nesting and development of the tick Ixodes baergi. Southwest. Nat. 32, 121–126 (1987).Article 

Google Scholar 
Bulgarella, M. & Heimpel, G. E. Host range and community structure of bird parasites in the genus Philornis (Diptera: Muscidae) on the Island of Trinidad. Ecol. Evol. 5, 3695–3703. https://doi.org/10.1002/ece3.1621 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Common, L. K. et al. Avian vampire fly (Philornis downsi) mortality differs across Darwin’s finch host species. Sci. Rep. 11, 15832. https://doi.org/10.1038/s41598-021-94996-7 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mendonca, E. & Couri, M. S. New associations between Philornis Meinert (Diptera, Muscidae) and Thamnophilidae (Aves, Passeriformes). Revta. Bras. Zool. 16, 1223–1225 (1999).Article 

Google Scholar 
Di Giacomo, A. G. Aves de la Reserva El Bagual. Historia natural y paisaje de la Reserva El Bagual, provincia de Formosa, Argentina. Inventario de la fauna de vertebrados y de la flora vascular de un área del Chaco Húmedo (eds. Di Giacomo, A. G. & Krapovickas, S. F.). Temas de Naturaleza y Conservación 4, 203–465 (Aves Argentinas/AOP, 2005).Koop, J. A. H., Causton, C. E., Bulgarella, M., Cooper, E. & Heimpel, G. E. Population structure of a nest parasite of Darwin’s finches within its native and invasive ranges. Conserv. Genet. 22, 11–22. https://doi.org/10.1007/s10592-020-01315-0 (2021).Article 

Google Scholar 
Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241. https://doi.org/10.1111/j.1461-0248.2004.00684.x (2004).Article 

Google Scholar  More

Dall, S. R. X. & Johnstone, R. A. Managing uncertainty: Information and insurance under the risk of starvation. Philos. Trans. R. Soc. Lond. B 357, 1519–1526 (2002).
Article 

Google Scholar 
Balogh, A. C. V., Gamberale-Stille, G. & Leimar, O. Learning and the mimicry spectrum: from quasi-Bates to super-Müller. Anim. Behav. 76, 1591–1599 (2008).Article 

Google Scholar 
Barnett, C. A., Bateson, M. & Rowe, C. Better the devil you know: Avian predators find variation in prey toxicity aversive. Biol. Lett. 10, 20140533 (2014).Article 

Google Scholar 
Ruxton, G. D., Allen, W. L., Sherratt, T. N. & Speed, M. P. Avoiding Attack: The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry 2nd edn. (Oxford University Press, 2018).Book 

Google Scholar 
Sherratt, T. N. State-dependent risk-taking by predators in systems with defended prey. Oikos 103, 93–100 (2003).Article 

Google Scholar 
Sherratt, T. N., Speed, M. S. & Ruxton, G. D. Natural selection on unpalatable species imposed by state-dependent foraging behaviour. J. Theor. Biol. 228, 217–226 (2004).MathSciNet 
Article 
ADS 

Google Scholar 
Gamberale-Stille, G. & Guilford, T. Automimicry destabilizes aposematism: Predator sample-and-reject behaviour may provide a solution. Proc. R. Soc. Lond. B 271, 2621–2625 (2004).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Avian predators taste-reject aposematic prey on the basis of their chemical defence. Biol. Lett. 2, 348–350 (2006).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Automimic frequency influences the foraging decisions of avian predators on aposematic prey. Anim. Behav. 74, 1563–1572 (2007).Article 

Google Scholar 
Brower, J. V. Z. Experimental studies of mimicry. IV. The reactions of starlings to different proportions of models and mimics. Am. Nat. 94, 271–282 (1960).Article 

Google Scholar 
Huheey, J. E. Studies in warning coloration and mimicry VIII. Further evidence for a frequency-dependent model of predation. J. Herpetol. 14, 223–230 (1980).Avery, M. L. Application of mimicry theory to bird damage control. J. Wildl. Manag. 49, 1116–1121 (1985).Article 

Google Scholar 
Nonacs, P. Foraging in a dynamic mimicry complex. Am. Nat. 126, 165–180 (1985).Article 

Google Scholar 
Rowland, H. M., Ihalainen, E., Lindström, L., Mappes, J. & Speed, M. P. Co-mimics have a mutualistic relationship despite unequal defences. Nature 448, 64–67 (2007).CAS 
Article 
ADS 

Google Scholar 
Skelhorn, J. & Rowe, C. Predators’ toxin burdens influence their strategic decisions to eat toxic prey. Curr. Biol. 17, 1479–1483 (2007).CAS 
Article 

Google Scholar 
Jones, R. S., Davis, S. C. & Speed, M. P. Defence cheats can degrade protection of chemically defended prey. Ethology 119, 52–57 (2013).Article 

Google Scholar 
Guilford, T. “Go-slow” signalling and the problem of automimicry. J. Theor. Biol. 170, 311–316 (1994).Article 
ADS 

Google Scholar 
Skelhorn, J. & Rowe, C. Taste-rejection by predators and the evolution of unpalatability in prey. Behav. Ecol. Sociobiol. 60, 550–555 (2006).Article 

Google Scholar 
Chatelain, M., Halpin, C. G. & Rowe, C. Ambient temperature influences birds’ decisions to eat toxic prey. Anim. Behav. 86, 733–740 (2013).CAS 
Article 

Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).Article 
ADS 

Google Scholar 
Yamazaki, Y., Pagani-Núñez, E., Sota, T. & Barnett C. R. A. The truth is in the detail: predators attack aposematic prey less intensely than other prey types. Biol. J. Linn. Soc. 131, 332–343 (2020).Valkonnen, J. K. et al. Variation in predator species abundance can cause variable selection pressure on warning signalling prey. Ecol. Evol. 2, 1971–1976 (2011).Article 

Google Scholar 
Nokelainen, O., Valkonen, J., Lindstedt, C. & Mappes, J. Changes in predator community structure shifts the efficacy of two warning signals in Arctiid moths. J. Anim. Ecol. 83, 598–605 (2014).Article 

Google Scholar 
Bibby, C. J., Burgess, N. D., Hill, D. A. &. Mustoe S. H. Bird Census Techniques (2nd Edition). (Academic Press, London, 2000).Tsujimoto, D., Lin, C.-H., Kurihara, N. & Barnett, C. R. A. Citizen science in the class-room: the consistency of student collected data and its value in ecological hypothesis testing. Ornithological Sci. 18, 39–47 (2019).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Software 67, 1–48 (2015).Article 

Google Scholar 
Rainey, C. Dealing with separation in logistic regression models. Polit. Anal. 24, 339–355 (2016).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Meth. Ecol. Evol. 4, 133–142 (2012).Article 

Google Scholar 
Hothorn, T,. Bretz, F. & Westfall, P. Simultaneous Inference in General Parametric Models. Biometrical. J. 50, 346–363 (2008).Paradis, E. & Schliep, K. Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Barnett, C. R. A., Ringhofer, M. & Suzuki, T. N. Differences in predatory behavior among three bird species when attacking chemically defended and undefended prey. J. Ethol. 39, 29–37 (2021).Article 

Google Scholar 
Carroll, J. & Sherratt, T. N. A direct comparison of the effectiveness of two anti-predator strategies under field conditions. J. Zool. 291, 279–285 (2013).Article 

Google Scholar 
Krebs, C. J. Ecological Methodology (2nd Edition). (Benjamin/Cummings, Menlo Park, CA, 1999).Oksanen, J. vegan: Community Ecology Package. (2020).R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. URL http://www.Rproject.org (2017).Marples, N. M., Speed, M. P. & Thomas, R. J. An individual-based profitability spectrum for understanding interactions between predators and their prey. Biol. J. Linn. Soc. 125, 1–13 (2018).Article 

Google Scholar 
Boyden, T. C. Butterfly palatability and mimicry: experiments with anolis lizards. Evolution 30, 73–81 (1976).Article 

Google Scholar 
Järvi, T., Sillén-Tullberg, B. & Wiklund, C. The cost of being aposematic. An experimental study of predation on larvae of Papilio machaon by the Great Tit Parus major. Oikos 36, 267–272 (1981).Wiklund, C. & Järvi, T. Survival of distasteful insects after being attacked by naïve birds: a reappraisal of aposematic coloration evolving through individual selection. Evolution 36, 998–1002 (1982).Article 

Google Scholar 
Pinheiro, C. E. G. & Campos, V. C. Do rufous-tailed jacamars (Galbula ruficauda) play with aposematic butterflies. Ornitol. Neotrop. 24, 1–3 (2013).
Google Scholar 
Halpin, C. G. & Rowe, C. The effect of distastefulness and conspicuous coloration on post-attack rejection behaviour of predators and survival of prey. Biol. J. Linn. Soc. 120, 236–244 (2017).
Google Scholar 
Sillén-Tullberg, B. Higher survival of an aposematic than of a cryptic form of a distasteful bug. Oecologia 67, 411–415 (1985).Article 
ADS 

Google Scholar 
Fisher, R. A. The Genetical Theory of Natural Selection (Clarenden Press, 1930).Book 

Google Scholar 
Chai, P. Field observations and feeding experiments on the responses of rufous-tailed jacamars butterflies in a tropical rainforest. Biol. J. Linn. Soc. 29, 161–189 (1986).Article 

Google Scholar 
Wang, L.-Y., Huang, W.-S., Tang, H.-C., Huang, L.-C. & Lin, C.-P. Too hard to swallow: A secret secondary defence of an aposematic insect. J. Exp. Biol. 221, jeb172486 (2018).PubMed 

Google Scholar 
Summers, K., Speed, M. P., Blount, J. D. & Stuckert, A. M. M. Are aposematic signals honest? A review. J. Evol. Biol. 28, 1583–1599 (2015).CAS 
Article 

Google Scholar 
Holen, Ø. H. Disentangling taste and toxicity in aposematic prey. Proc. R. Soc. B 280, 20122588 (2013).Article 

Google Scholar 
Speed, M. P. & Franks, D. W. Antagonistic evolution in an aposematic predator-prey system. Evolution 68, 2996–3007 (2014).Article 

Google Scholar  More

Study subjects and measurement of skin physiological parametersWe analyzed skin microbiome and mycobiome from cheeks and foreheads of healthy younger (19–28 years old, Y-group) and older (60–63 years old, O-group) Korean women who were free from cutaneous disorders (Table 1 and Supplementary Table S1). All 61 subjects had been living in Seoul, Korea, for more than 3 years with normal skin conditions. We preferentially selected those who had sebum secretion greater than 30 arbitrary units and moisture greater than 50 arbitrary units in both groups. Among the measurements of moisture content, pH, sebum content, and transepidermal water loss (TEWL), only sebum and TEWL decreased significantly in the O-group compared to the Y-group in the cheeks (P = 2.25e−06, Wilcoxon rank-sum test; P = 0.019, Welch two-sample t test) and forehead (P = 1.33e−06, Wilcoxon rank-sum test; P = 0.003, Welch two-sample t test). Whereas no significant differences were found in the average values for moisture (cheeks: Y-group, 59.9; O-group, 56.6; forehead: Y-group, 61.1; O-group, 58.7) and pH (cheeks: Y-group, 6.0; O-group, 5.8; forehead: Y-group, 6.0; O-group, 5.6) between the two age groups.Table 1 Characteristics of subjects for aged related skin microbiome and mycobiome study.Full size tableComparisons in cheek and forehead microbiome and mycobiome between the two age groupsWe analyzed bacterial communities from 27 Y-group samples (cheeks, n = 13; forehead, n = 14) and 24 O-group samples (cheeks, n = 12; forehead, n = 12) and fungal communities from 28 Y-group samples (cheeks, n = 15; forehead, n = 13) and 32 O-group samples (cheeks, n = 16; forehead, n = 16), except for samples that were eliminated from the Illumina Mi-Seq sequencing due to low sequence reads (bacteria,  3. 0) (Fig. 4). Pathways belonging to the metabolism category were dominant in each age group. In the cheek of the Y-group, pathways involved in energy metabolism by bacteria, such as glycolysis/gluconeogenesis, citrate cycle, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-alanine metabolism, and thiamine metabolism, were predominant, whereas in the cheek of the O-group, degradation-related pathways, such as fatty acid degradation, synthesis and degradation of ketone bodies, benzoate degradation, and chloroalkane and chloroalkene degradation, were predominant. In the forehead of the Y-group, glycolysis/gluconeogenesis, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-glutamine and d-glutamate metabolism, d-alanine metabolism, and thiamine metabolism pathway were significantly more abundant, whereas in the forehead of the O-group, fatty acid degradation, synthesis and degradation of ketone bodies, valine/leucine and isoleucine degradation, and limonene/pinene degradation pathway were significantly more abundant.Figure 4Heat map for significantly different predicted functional pathways on (a) cheeks and (b) foreheads of Korean women by age based on LEfSe analysis (LDA score  > 3.0).Full size imageThe metabolism pathway for biotin, a water-soluble vitamin that is effective for skin health and essential for keratin production15, was more prevalent in the cheek and forehead of the Y-group. Interestingly, the metabolism pathway for lipoic acid, which is known to possess beneficial effects against skin aging and is used widely in cosmetic and dermatological products16,17, was significantly higher in the foreheads of the Y-group. We tracked the specific ASVs possessing these pathways, in both biotin metabolism and lipoic acid metabolism, Cutibacterium sp. (ASV2136 and ASV2130) and Staphylococcus sp. (ASV3008) were predicted to have the top three relative abundances in KOs. The relative abundances in biotin metabolism and lipoic acid metabolism of Cutibacterium sp. (ASV2136) were 24.9% and 26.1%, respectively. The relative abundances for each pathway for Staphylococcus sp. (ASV3008) were 10.2% and 18.7%, and for Cutibacterium sp. (ASV2130), they were 9.3% and 10.0%, respectively. We confirmed these two pathways in the genome of skin bacteria, C. acnes (Supplementary Fig. S2). These additional analyses support the reliability of the function in the skin environment of Cutibacterium. Interestingly, from the LEfSe result, Cutibacterium sp. (ASV2136) had a significantly higher abundance in the cheek and forehead microbiome of the Y-group. The pathway of biosynthesis of lipopolysaccharide, also known as bacterial endotoxins, showed higher abundance in the cheek and forehead microbiome of the O-group. The ASVs that contribute to inferring the LPS biosynthesis pathway were identified as Paraburkholderia sp. (ASV5030) and B. vesicularis (ASV4155). Also, pathways related to antibiotic biosynthesis (biosynthesis of vancomycin group antibiotics) and bacterial motility (bacterial chemotaxis and flagellar assembly; both belonging to the cellular processes category) were prominent in the cheek and forehead of the O-group. PICRUSt2 analysis implied that, regardless of skin site differences, the potential functions of the microbial community that compose the skin microbiome were similar according to age.Network analysis on cheek and forehead microbiome and mycobiomeWe performed SParse InversE Covariance estimation for Ecological Association Inference (SPIEC-EASI) analysis to evaluate the overall network of the skin microbes. The results of network density (D) on 81 cheek and 87 forehead ASVs, calculated using the ratio of the number of edges, showed higher network density in the skin microbiome of the Y-group (D = 0.015 and D = 0.001, in cheek and forehead, respectively) than the O-group (D = 0.007 and D = 0.007, respectively) (Fig. 5). To examine network correlation between bacteria and fungi, network density for Bacteria–Fungi (DBF) was calculated by the actual number of edges and a potential number of edges in a correlation ([bacterial nodes × fungal nodes]/2). We confirmed higher network density in the cheek of the Y-group (DBF = 0.008) than the O-group (DBF = 0) and edges of the major bacterial and fungal taxa, such as Staphylococcus sp. (ASV3008)—M. sympodialis (ASV500) and Roseomonas sp. (ASV4088)—M. restricta (ASV482), were observed in the cheek of the Y-group. In the forehead, edges of Methylobacterium sp. (ASV4314)—M. globosa (ASV454), Methylobacterium sp. (ASV4314)—Zygosaccharomyces rouxii (ASV208), and Venionella sp. (ASV3575)—M. sympodialis (ASV500) were observed in the Y-group, and edges of Cutibacterium sp. (ASV2107)—M. globosa (ASV461), Staphylococcus sp. (ASV3024)—M. arunalokei (ASV446), and Methylobacterium (ASV4314)—M. dermatis (ASV448) were observed in the O-group (DBF = 0.004). We found a network between bacteria and fungi with different kingdom levels in the skin microbiome, and especially, we confirmed that different genus or species level microbe was involved in the microbial network according to skin location and Y-, O-group.Figure 5Network analysis of the ASVs on (a) cheeks and (b) forehead of Korean women. Each node represents the ASV and the size of the node is based on relative abundance of each ASV. Color markings indicate the major taxa except for unidentified bacteria or fungi. Shapes represent the level of kingdom, Bacteria (bold) and Fungi (dotted line). The ASVs were selected for bacterial ASVs found in more than half of all samples on the cheeks and forehead, respectively, and for the fungal ASVs with a relative abundance of more than 0.1% in each of the cheeks and forehead samples. The D value is network density calculated using the ratio of the number of edges.Full size image More