Ecology

Our experimental data reveals a scaling law between the mass of a layer along the vertical coordinate, M(z), and the weight that it supports, W(z), namely: (W(z) sim M(z)^a) with (a approx 1.5). To better understand the physical mechanism that yields this scaling law, we derive the force balance equation of a layer of the swarm and solve for W(z). We then equate the analytical expression for W(z) with the experimentally determined scaling law, (W(z) sim M(z)^a), to connect the swarm mass distribution to the exponent a and formulate the expressions for M(z) and W(z) in terms of a. We then consider a dimensional analysis of the strength of each layer of the swarm, S, or the maximum weight that it can support before the grip of the bees on one another breaks. As will be described in detail below, we find that (S sim M^{1.5}), which is close to the experimentally determined (a = 1.53). Deviation from this value increases the fraction of maximum strength exerted by different parts of the swarm.Force balance model of the weight distribution in the swarmWe assume that the swarm is at quasi-equilibrium (the shape does not change although individual bees may move), that all of the bees in each layer contribute equally to supporting the weight of the bees underneath that layer, that the layer thickness is very small, and that the swarm is radially symmetrical about the z-axis. We use a cylindrical coordinate system with a vertical coordinate z, as shown in Fig. 1e, and we consider layers of the swarm along the z-axis of thickness dz. Variables labeled with a tilde, as in (tilde{W}(z)), represent analytically derived expressions; variables without a tilde, as in W(z), represent values determined with power law fits to experimental data.We begin our analysis by applying the force balance principle to each layer of a swarm. As shown by the free body diagram in Fig. 1f, the force with which each layer of bees has to grasp the layer above it is equal to the weight of that layer and all of the layers underneath it: (tilde{F} = tilde{W}(z)). We express (tilde{W}(z)) using the force balance equation (a continuous version of the discrete definition in Eq. (5).):$$begin{aligned} tilde{W}(z) = g int _z^L tilde{M}(z) dz, end{aligned}$$
(8)
where the mass of bees per layer is (tilde{M}(z)), the swarm length is L, and g is the gravitational constant. Inspired by our experimental observation that the mass of the layers near the base is highest and the mass of the layers at the tip of the swarm is lowest in Fig. 3a, we model (tilde{M}(z)) as a monotonically decreasing function of z. To keep the units consistent, we normalize the z coordiante by the length of the swarm:$$begin{aligned} tilde{M}(z) = c left( 1-frac{z}{L}right) ^{tilde{b}}, end{aligned}$$
(9)
where the c factor in this expression ensures that the units of the mass per layer are mass/length, and (tilde{b}) is an unknown exponent. Choosing this function form allows us to easily integrate the expression for (tilde{W}(z)) when we substitute (tilde{M}(z)) into it, set this force balance derivation for (tilde{W}(z)) equal to the experimentally determined expression (W(z) = C M(z)^a), and compare the exponents a and (tilde{b}).To solve the expression for (tilde{W}(z)), we substitute the expression for (tilde{M}(z)), Eq. (9), into Eq. (8) and integrate. We then express (tilde{b}) in terms of the experimentally determined a by equating this expression for (tilde{W}(z)) to the scaling law we observe in our experiments, Eq. (7), (W(z) = C tilde{M}(z)^a). The exponent in the expression for (tilde{M(z)}), Eq. (9), is$$begin{aligned} tilde{b} = frac{1}{a-1}. end{aligned}$$
(10)
The weight supported by each layer is then:$$begin{aligned} tilde{W}(z) = cLg left( 1 – frac{1}{a}right) left( 1-frac{z}{L}right) ^{frac{a}{a-1}}. end{aligned}$$
(11)
Next, we test how well our force balance model predicts the data by comparing the predicted value of (tilde{b}) using the force balance to the value of b calculated using experimental fits. We first separate the expression for the layer mass, Eq. (9) into the product of the layer area, (tilde{A}(z)) and the layer density, (tilde{rho }(z)):$$begin{aligned} tilde{M}(z) sim tilde{A}(z) tilde{rho }(z). end{aligned}$$
(12)
To simplify our analysis, we model (tilde{A}(z)) and (tilde{rho }(z)) with a similar monotonically decreasing function to that in Eq. (9):$$begin{aligned} tilde{A}(z) = c_1 left( 1-frac{z}{L}right) ^{tilde{b}_1}, end{aligned}$$
(13)
and$$begin{aligned} tilde{rho }(z) =c_2 left( 1-frac{z}{L}right) ^{tilde{b}_2} end{aligned}$$
(14)
we can then separately measure the effect of the changes in area and density on the exponent in the mass per layer expression in Eq. (9), (tilde{b} = tilde{b}_1 + tilde{b}_2).We first calculate (tilde{b}) using the expression derived from the force balance, Eq. (10), and our experimental result for a, which yields (tilde{b} = 2 pm 0.47). Second, we calculate b by separately calculating power law fits to the data for A(z) in Fig. 2e according to Eq. (13) and (rho (z)) in Fig. 2d according to Eq. (14), which yields (b_1 = 1.38 pm 0.2) and (b_2 = 0.51 pm 0.09). Thus, (b = b_1 + b_2 = 1.89 pm 0.25). See Supplementary Fig. S5(a–c) for log-log plots of M(z), A(z) and (rho (z)), and Supplementary Fig. S5(d–f) for plots of the resulting b, (b_1), and (b_2).We calculate the deviation of (tilde{b}) from b, (frac{tilde{b} – b}{tilde{b}} = 0.03 pm 0.11), and plot the deviation of b from (tilde{b}) in Supplementary Fig. S5(g) as a comparison for the individual CT scans. The values of b and (tilde{b}) being on the same order of magnitude validates the model and allows us to compare (tilde{W}(z)) to a maximum strength of each layer, which we find with dimensional analysis in the following section.Strength of a swarm layer and individual beesThe strength of the layer, (tilde{S}(z)), or the maximum weight that it could support, can be greater than or equal to (tilde{W}(z)): (tilde{S}(z) ge tilde{W}(z)). If the weight of the bees underneath a layer were to exceed its strength (tilde{S}(z)), the layer would not be able to support the weight of those bees, and the swarm would break apart. We perform a dimensional analysis on the strength of each layer to find the relationship between the mass of a layer and its maximum strength, (tilde{S}(z) sim tilde{M}(z)^{alpha }). Force is proportional to mass, which is proprtional to volume, or a length cubed, so a layer’s strength scales with length cubed, (tilde{S}(z) propto L^3). The mass of each layer, with units of mass/length, is proportional to an area, or a length squared, so (tilde{M}(z)) scales with length squared, (tilde{M}(z) propto L^2). Thus, (alpha) must be 1.5 for (tilde{S}(z) sim tilde{M}(z)^{alpha }) to be dimensionally correct. This is similar to the relationship between weightifting capacity and body weight in Ref.16.Estimating (tilde{W}(z)/tilde{S}(z)) gives a measure of how much of its maximum strength each layer uses to hold up the rest of the swarm:$$begin{aligned} frac{tilde{W}(z)}{tilde{S}(z)} sim left( 1-frac{1}{a}right) left( 1-frac{z}{L}right) ^frac{2a-3}{2a-2} end{aligned}$$
(15)
The average number of bees that a bee in a swarm layer supports, (tilde{F}_{bee}(z)), is equal to the mass of bees supported by a layer divided by the sum of the mass of bees in a layer of bees that has the thickness of the length of a bee, (l approx 1.5), as a continuous version of the discrete equation in Eq. (6):$$begin{aligned} tilde{F}_{bee}(z) =frac{int _z^L tilde{M}(z) dz}{int _z^{z+l} tilde{M}(z) dz}. end{aligned}$$
(16)
After integrating, we get an expression for (tilde{F}_{bee} (z)):$$begin{aligned} tilde{F}_{bee}(z)= frac{left( 1-frac{z}{L}right) ^{frac{a}{a-1}}}{left( 1-frac{z}{L}right) ^{frac{a}{a-1}} – left( 1-frac{z + l}{L}right) ^{frac{a}{a-1}}}. end{aligned}$$
(17)
We use the expression for (frac{tilde{W}(z)}{tilde{S}(z)}), Eq. (15), and (tilde{F}_{bee}(z)), Eq. (17), in the next section to evaluate how the force distribution in the swarm would change for swarms with different values of a.Effect of a on the mass of each layer, the fraction of its maximum stregnth it uses, and the average force per beeWe now consider the effect of varying a on the mass and force distribution inside the swarm. To visualize the effect of a on the distribution of bees, we plot the mass per layer of a 1000-g, 12.5 cm long swarm, (tilde{M}(z)) vs. z/L, with (a = 1.5, 1.01, 1000), and (-0.2) in Fig. 3c and the corresponding average force per bee, (F_{bee}(z)) vs. z/L in Fig. 3d. These values of a are example values for the four possible cases of mass distribution in the swarm. We then evaluate how these values of a affect the fraction of maximum strength each layer uses to support the layers underneath it using Eq. (15).If (a approx alpha), as we found in our experiments, layers with higher mass near the attachment surface support the less massive layers under them, as in the solid black line in Fig. 3c. Correspondingly, Fig. 3d shows (tilde{F}_{bee}(z=0) approx 3) at the top of the swarm, and decreases towards the tip. The strength of each layer and the weight it supports are proportional to one another, (tilde{W}(z)/tilde{S}(z) sim 1/3), meaning that the fraction of maximum strength used by a layer is the same for all z. If (1< a < alpha), the swarm approaches one massive layer of bees, as in the dashed purple line in Fig. 3c. The dimensional analysis results in a very small fraction of the total strength used by this layer, (tilde{W}(z)/tilde{S}(z) rightarrow 0 (1-frac{z}{L})^{-infty }). The force supported by each bee in Fig. 3d shows (tilde{F}_{bee}(z) = 1) for the entire swarm, meaning that each bee only supports its own weight. This configuration would either require packing a large number of bees into one very dense or one very wide layer. A swarm with one very dense layer at the top would compress all of the bees; a swarm with one very wide layer would require a large surface area, which would put the swarm in danger from predators and changes in weather. Thus, despite a potentially lower fraction of strength used by the largest layer of bees, this configuration would put the swarm in danger by requiring a large surface area.For values of (a > alpha), as (a rightarrow infty), all the layers of the swarm have the same mass, as in the dash-dot red line in Fig. 3c. The force per bee in Fig. 3d shows (tilde{F}_{bee}(z=0) approx 8) at the top of the swarm, 2.5 times that of the (a = alpha) configuration. In this configuration, the top layers use a higher percentage of their available strength than the lower layers, (tilde{W}(z)/tilde{S}(z) rightarrow (1-frac{z}{L})). Thus, for large swarms, the bees that support the swarm would be under more strain, and the swarm would be more likely to break under external perturbation.Finally, (a < 0) ((0 le a le 1) results in negative values for (tilde{W}(z))) would suggest that the top layers of the swarm have a lower mass than the bottom layers, as in the dotted orange line in Fig. 3c. This is not a realistic range of values for a, but we include it here as a demonstration of a potential mass distribution with the largest layers being on the bottom of the swarm. This configuration would put even more strain on the layers of bees at the top of the swarm, as smaller layers near the attachment surface have a smaller maximum strength. As (a rightarrow 0) on the (a < 0) side, (tilde{W}(z)/tilde{S}(z) rightarrow infty (1-z/L)^{1.5}), and bees in the top layers use a much greater fraction of their strength than bees in the bottom layers. Accordingly, the mean force per bee in Fig. 3d exceeds the maximum bee grip strength of 35 bee weights, and the swarm could not support itself in this configuration.The swarm configuration with (a approx 1.5) uses the full strength of each layer and puts a lower strain on the bees than most other values of a, and avoids weight distributions that could expose a large number of bees to external danger. More

Roelfsema, C., Phinn, S., Jupiter, S., Comley, J. & Albert, S. Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis. Int. J. Remote Sens. 34, 6367–6388 (2013).Article 

Google Scholar 
Soares, M. O., Tavares, T. C. L. & Carneiro, P. Mesophotic ecosystems: Distribution, impacts and conservation in the South Atlantic. Divers. Distrib. 25(2), 255–268 (2019).
Google Scholar 
Leão, Z. M. A. N. et al. Brazilian coral reefs in a period of global change: A synthesis. Braz. J. Oceanogr. 64, 97–116 (2016).Article 

Google Scholar 
Leão, Z. M. A. N., Kikuchi, R. K. P. & Oliveira, M. D. M. The coral reef province of Brazil. World Seas: An Environmental Evaluation Volume I: Europe, the Americas and West Africa vol. 1 (Elsevier Ltd., 2018).Collette, B. B. & Rützler, K. Reef fishes over sponge bottoms off the mouth of the Amazon River. in Proceedings of Third International Coral Reef Symposium (ed. Taylor, D. L.) vol. 1 305–310 (Rosenstiel School of Marine and Atmospheric Science, 1977).Cordeiro, R. T. S., Neves, B. M., Rosa-Filho, J. S. & Pérez, C. D. Mesophotic coral ecosystems occur offshore and north of the Amazon River. Bull. Mar. Sci. 91, 491–510 (2015).Article 

Google Scholar 
Moura, R. L. et al. An extensive reef system at the Amazon River mouth. Sci. Adv. 2, e1501252 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Francini-Filho, R. B. et al. Perspectives on the Great Amazon Reef: Extension, biodiversity, and threats. Front Mar Sci 5, 1–5 (2018).ADS 
Article 

Google Scholar 
de Mahiques, M. M. et al. Insights on the evolution of the living Great Amazon Reef System, equatorial West Atlantic. Sci. Rep. 9, 1–8 (2019).Article 

Google Scholar 
Vale, N. F. et al. Distribution, morphology and composition of mesophotic ‘reefs’ on the Amazon Continental Margin. Mar. Geol. 447, 106779 (2022).ADS 
Article 

Google Scholar 
Moura, R. L. et al. Tropical rhodolith beds are a major and belittled reef fish habitat. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Rocha, L. A. Patterns of distribution and processes of speciation in Brazilian reef fishes. J. Biogeogr. 30, 1161–1171 (2003).Article 

Google Scholar 
Floeter, S. R. et al. Atlantic reef fish biogeography and evolution. J. Biogeogr. 31, 22–47 (2008).
Google Scholar 
Vale, N. F. et al. Structure and composition of rhodoliths from the Amazon River mouth, Brazil. J. S. Am. Earth Sci. 84, 149–159 (2018).Article 

Google Scholar 
IMaRS/USF, IRD, UNEP/WCMC, The WorldFish Center & WRI. Global Coral Reefs composite dataset compiled from multiple sources for use in the Reefs at Risk Revisited project incorporating products from the Millennium Coral Reef Mapping Project. Preprint at (2011).Soares, M. O. et al. Challenges and perspectives for the Brazilian semi-arid coast under global environmental changes. Perspect. Ecol. Conserv. 19, 267–278 (2021).
Google Scholar 
Castro, C. B. & Pires, D. O. Brazilian coral reefs: What we already know and what is still missing. Bull. Mar. Sci. 69, 357–371 (2001).
Google Scholar 
Leão, Z., Kikuchi, R. & Testa, V. Corals and coral reefs of Brazil. in Latin American Coral Reefs (ed. Cortés, J.) 9–52 (Elsevier Science Inc., 2003). https://doi.org/10.1016/B978-044451388-5/50003-5.Laborel-Deguen, F., Castro, C. B., Nunes, F. D. & Pires, D. O. Recifes brasileiros: o legado de Laborel. (Museu Nacional, 2019).Carneiro, P. et al. Marine hardbottom environments in the beaches of Ceará state, equatorial coast of Brazil. Arquivos de Ciências do Mar 54, 120–153 (2021).Carneiro, P. B. M. et al. Structure, growth and CaCO3 production in a shallow rhodolith bed from a highly energetic siliciclastic-carbonate coast in the equatorial SW Atlantic Ocean. Mar. Environ. Res. 166, 105280 (2021).CAS 
PubMed 
Article 

Google Scholar 
Testa, V., Bosence, D. W. J. & Universita, C. Physical and biological controls on the formation of carbonate and siliciclastic bedforms on the north-east Brazilian shelf. Sedimentology 46, 279–301 (1999).ADS 
Article 

Google Scholar 
Carneiro, P. & Morais, J. O. de. Carbonate sediment production in the equatorial continental shelf of South America: Quantifying Halimeda incrassata (Chlorophyta) contributions. J. S. Am. Earth Sci. 72, 1–6 (2016).Milliman, J. D. Role of Calcareous Algae in Atlantic Continental Margin Sedimentation. in Fossil algae: recent results and developments (ed. Flügel, E.) 232–247 (Springer, 1977). https://doi.org/10.1007/978-3-642-66516-5_26.Knoppers, B., Ekau, W. & Figueiredo, A. G. The coast and shelf of east and northeast Brazil and material transport. Geo-Mar. Lett. 19, 171–178 (1999).ADS 
Article 

Google Scholar 
Vital, H. The north and northeast Brazilian tropical shelves. in Continental shelves of the world: their evolution during the lasta glacio-eustatic cycle (eds. Chiocci, F. L. & Chivas, A. R.) 35–46 (Geological Society, 2014).Soares, M. de O. et al. Brazilian marine animal forests: A new world to discover in the southwestern Atlantic. Mar. Anim. For. 1–38. https://doi.org/10.1007/978-3-319-17001-5_51-1 (2016).Soares, M. O. et al. Impacts of a changing environment on marginal coral reefs in the Tropical Southwestern Atlantic Ocean. Coast. Manag. 210, 105692 (2021).
Google Scholar 
Santos, C. L. A., Vital, H., Amaro, V. E. & de Kikuchi, R. K. P. Mapping of the submerged reefs in the coast of the Rio Grande do Norte, near Brazil: Macau to Maracajau. Revista Brasileira de Geofisica 25, 27–36 (2007).Article 

Google Scholar 
Neto, I. C., Córdoba, V. C. & Vital, H. Morfologia, microfaciologia e diagênese de beachrocks costa-afora adjacentes à costa norte do Rio Grande do Norte, brasil. Geociências 32, 471–490 (2013).
Google Scholar 
Gomes, M. P. et al. The investigation of a mixed carbonate-siliciclastic shelf, NE Brazil: Side-scan sonar imagery, underwater photography, and surface-sediment data. Ital. J. Geosci. 134, 9–22 (2015).Article 

Google Scholar 
Soares, M. O., Rossi, S., Martins, F. A. S. & Carneiro, P. The forgotten reefs: Benthic assemblage coverage on a sandstone reef (Tropical South-western Atlantic). J. Mar. Biol. Assoc. U.K. 97(8), 1585–1592. https://doi.org/10.1017/S0025315416000965 (2017).Article 

Google Scholar 
Morais, J. O., Ximenes Neto, A. R., Pessoa, P. R. S. & Souza, L. P. Morphological and sedimentary patterns of a semi-arid shelf, Northeast Brazil. Geo-Ma. Lett. 40, 835–842. https://doi.org/10.1007/s00367-019-00587-x (2019).Cordeiro, R. T., Neves, B. M., Kitahara, M. v., Arantes, R. C. & Perez, C. D. First assessment on Southwestern Atlantic equatorial deep-sea coral communities. Deep-Sea Res. Part I Oceanogr. Res. Papers 163, 103344 (2020).Freitas, J. E. P. & Lotufo, T. M. C. Reef fish assemblage and zoogeographic affinities of a scarcely known region of the western equatorial Atlantic. J. Mar. Biol. Assoc. U.K. 95, 623–633 (2015).Article 

Google Scholar 
Soares, M. O., Davis, M., Paiva, C. C. de & Carneiro, P. Mesophotic ecosystems: Coral and fish assemblages in a tropical marginal reef (northeastern Brazil). Mar. Biodivers. 1–6 (2016). https://doi.org/10.1007/s12526-016-0615-x.Carneiro, P. B. M., Sátiro, I., COE, C. M. & Mendonça, K. V. Valoração ambiental do Parque Estadual Marinho da Pedra da Risca do Meio, Ceará, Brasil. Arquivo de Ciências do Mar 50, 25–41 (2017).Gomes, M. P., Vital, H. & Droxler, A. W. Terraces, reefs, and valleys along the Brazil northeast outer shelf: Deglacial sea-level archives?. Geo-Mar. Lett. 40, 699–711. https://doi.org/10.1007/s00367-020-00666-4 (2020).ADS 
CAS 
Article 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 
Article 

Google Scholar 
Raitsos, D. E. et al. Sensing coral reef connectivity pathways from space. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
Silveira, I. C. A., Miranda, L. B. & Brown, W. S. On the origins of the North Brazil Current. J. Geophys. Res. 99, 22501–22512 (1994).ADS 
Article 

Google Scholar 
Dias, F. J. da S., Castro, B. M. & Lacerda, L. D. Tidal and low-frequency currents off the Jaguaribe River estuary (4° S, 37° 4′ W), northeastern Brazil. Ocean Dynamics 68, 967–985 (2018).Wellington, G. M. & Victor, B. C. Planktonic larval duration of one hundred species of Pacific and Atlantic damselfishes (Pomacentridae). Mar. Biol. 101, 557–567 (1989).Article 

Google Scholar 
Victor, B. C. Duration of the planktonic larval stage of one hundred species of Pacific and Atlantic wrasses (family Labridae). Mar. Biol. 90, 317–326 (1986).Article 

Google Scholar 
Endo, C. A. K., Gherardi, D. F. M., Pezzi, L. P. & Lima, L. N. Low connectivity compromises the conservation of reef fishes by marine protected areas in the tropical South Atlantic. Sci. Rep. 9, 1–11 (2019).Article 

Google Scholar 
Gomes, M. P. et al. Nature and condition of outer shelf habitats on the drowned Açu Reef, Northeast Brazil. in Seafloor Geomorphology as Benthic Habitat 571–585 (Elsevier, 2020). https://doi.org/10.1016/b978-0-12-814960-7.00034-8.Neto, I. C., Córdoba, V. C. & Vital, H. Petrografia de beachrock em zona costa afora adjacente ao litoral norte do Rio Grande do Norte Brasil. Quat. Environ. Geosci. 2, 12–18 (2010).
Google Scholar 
Gomes, M. P., Vital, H., Bezerra, F. H. R., de Castro, D. L. & Macedo, J. W. de P. The interplay between structural inheritance and morphology in the Equatorial Continental Shelf of Brazil. Mar. Geol. 355, 150–161 (2014).Rovira, D. P. T., Gomes, M. P. & Longo, G. O. Underwater valley at the continental shelf structures benthic and fish assemblages of biogenic reefs. Estuar. Coast. Shelf Sci. 224, 245–252 (2019).ADS 
Article 

Google Scholar 
Tosetto, E. G., Bertrand, A., Neumann-Leitão, S. & Nogueira Júnior, M. The Amazon River plume, a barrier to animal dispersal in the Western Tropical Atlantic. Sci. Rep. 12, 537 (2022).ADS 
Article 

Google Scholar 
Cord, I. et al. Brazilian marine biogeography: A multi-taxa approach for outlining sectorization. Mar. Biol. 169, 61 (2022).Article 

Google Scholar 
Moalic, Y. et al. Biogeography revisited with network theory: Retracing the history of hydrothermal vent communities. Syst. Biol. 61, 127 (2012).PubMed 
Article 

Google Scholar 
López-Pérez, A. et al. The coral communities of the Islas Marias archipelago, Mexico: Structure and biogeographic relevance to the Eastern Pacific. Mar. Ecol. 37, 679–690 (2016).ADS 
Article 

Google Scholar 
Cordeiro, C. A. M. M. et al. Conservation status of the southernmost reef of the Amazon Reef System: The Parcel de Manuel Luís. Coral Reefs 40, 165–185 (2021).Article 

Google Scholar 
Segal, B. & Castro, C. B. Coral community structure and sedimentation at different distances from the coast of the Abrolhos Bank Brazil. Braz. J. Oceanogr. 59, 119–129 (2011).Article 

Google Scholar 
Aued, A. W. et al. Large-scale patterns of benthic marine communities in the Brazilian Province. PLoS ONE 13, e0198452 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Soares, M. O. et al. Marginal Reefs in the Anthropocene: They Are Not Noah’s Ark. in Perspectives on the Marine Animal Forests of the World (eds. Rossi, S. & Bramanti, L.) 87–128 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-57054-5_4.Perry, C. T. & Larcombe, P. Marginal and non-reef-building coral environments. Coral Reefs 22, 427–432 (2003).Article 

Google Scholar 
Riegl, B. & Piller, W. E. Coral frameworks revisited – reefs and coral carpets in the northern Red Sea. Coral Reefs 18, 241–253 (1999).Article 

Google Scholar 
Rodríguez-Martínez, R. E., Jordán-Garza, A. G., Maldonado, M. A. & Blanchon, P. Controls on coral-ground development along the Northern Mesoamerican Reef Tract. PLoS ONE 6, e28461 (2011).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lotufo, T. M. et al. Sessile epifauna of Ceará´s shelf – high dominance of sponges. in 7th International Sponge Symposium – Biodiversity, Innovation, Sustainability 123–123 (Museu Nacional – UFRJ, 2006).Fonseca, V. P., Pennino, M. G., de Nóbrega, M. F., Oliveira, J. E. L. & de Figueiredo Mendes, L. Identifying fish diversity hot-spots in data-poor situations. Mar. Environ. Res. 129, 365–373 (2017).Olavo, G., Costa, P. A. S., Martins, A. S. & Ferreira, B. P. Shelf-edge reefs as priority areas for conservation of reef fish diversity in the tropical Atlantic. Aquat. Conserv. Mar. Freshwat. Ecosyst. 21, 199–209 (2011).Article 

Google Scholar 
Eduardo, L. N. et al. Identifying key habitat and spatial patterns of fish biodiversity in the tropical Brazilian continental shelf. Cont. Shelf Res. 166, 108–118 (2018).ADS 
Article 

Google Scholar 
Carneiro, P. B. de M. et al. Structure, growth and CaCO3 production in a shallow rhodolith bed from a highly energetic siliciclastic-carbonate coast in the equatorial SW Atlantic Ocean. Mar. Environ. Res. 166, 105280 (2021).Costa, A. C. P., Garcia, T. M., Paiva, B. P., Ximenes Neto, A. R. & Soares, M. de O. Seagrass and rhodolith beds are important seascapes for the development of fish eggs and larvae in tropical coastal areas. Mar. Environ. Res. 161, 105064 (2020).Testa, V. & Bosence, D. W. J. Carbonate-siliciclastic sedimentation on a high-energy, ocean-facing, tropical ramp, NE Brazil. in Carbonate Ramps (eds. Wright, V. P. & Burchette, T. P.) 55–71 (The Geological Society, 1998).Ximenes Neto, A. R., de Morais, J. O. & Ciarlini, C. Modern and relict sedimentary systems of the semi-arid continental shelf in NE Brazil. J. S. Am. Earth Sci. 84, 56–68 (2018).CAS 
Article 

Google Scholar 
Ximenes Neto, A. R., Morais, J. O. de, Paula, L. F. S. de & Pinheiro, L. de S. Transgressive deposits and morphological patterns in the equatorial Atlantic shallow shelf (Northeast Brazil). Region. Stud. Mar. Sci. 24, 212–224 (2018).Sponaugle, S., Lee, T., Kourafalou, V. & Pinkard, D. Florida Current frontal eddies and the settlement of coral reef fishes. Limnol. Oceanogr. 50, 1033–1048 (2005).ADS 
Article 

Google Scholar 
Cruz, R. et al. Large-scale oceanic circulation and larval recruitment of the spiny lobster Panulirus argus (Latreille, 1804). Crustaceana 88, 298–323 (2015).Article 

Google Scholar 
Luiz, O. J. et al. Ecological traits influencing range expansion across large oceanic dispersal barriers: Insights from tropical Atlantic reef fishes. Proc. R. Soc. B Biol. Sci. 279, 1033–1040 (2012).Article 

Google Scholar 
Romero-Torres, M., Treml, E. A., Blanchon, P., Acosta, A. & Paz-García, D. A. The Eastern Tropical Pacific coral population connectivity and the role of the Eastern Pacific Barrier. Sci. Rep. 8, 1–13 (2018).CAS 
Article 

Google Scholar 
Leal, C. v. et al. Integrative taxonomy of Amazon Reefs’ Arenosclera spp.: A new clade in the Haplosclerida (Demospongiae). Front. Mar. Sci. 4, 291 (2017).Peluso, L. et al. Contemporary and historical oceanographic processes explain genetic connectivity in a Southwestern Atlantic coral. Sci. Rep. 8, 1–12 (2018).CAS 
Article 

Google Scholar 
Targino, A. K. G. & Gomes, P. B. Distribution of sea anemones in the Southwest Atlantic: Biogeographical patterns and environmental drivers. Mar. Biodivers. 50, 1–17 (2020).Article 

Google Scholar 
Barroso, C. X., Lotufo, T. M. da C. & Matthews-Cascon, H. Biogeography of Brazilian prosobranch gastropods and their Atlantic relationships. J. Biogeogr. 43, 2477–2488 (2016).Pinheiro, H. T. et al. South-western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean. Divers. Distrib. 24, 951–965 (2018).Article 

Google Scholar 
Medeiros, A. P. M. et al. Deep reefs are not refugium for shallow-water fish communities in the southwestern Atlantic. Ecol. Evol. 11, 4413–4427 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Sammon, J. W. A nonlinear mapping for data structure analysis. IEEE Trans. Comput. C–18, 401–409 (1969).Prim, R. C. Shortest connection networks and some generalizations. Bell Syst. Tech. J. 36, 1389–1401 (1957).ADS 
Article 

Google Scholar  More

Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 1–8 (2020).Article 

Google Scholar 
Sumaila, U. R. et al. Benefits of the paris agreement to ocean life, economies, and people. Sci. Adv. 5, 1–10 (2019).Article 

Google Scholar 
Beaudreau, A. H. et al. Thirty years of change and the future of Alaskan fisheries: Shifts in fishing participation and diversification in response to environmental, regulatory and economic pressures. Fish Fish. 20, 601–619 (2019).
Google Scholar 
Finkbeiner, E. M. The role of diversification in dynamic small-scale fisheries: Lessons from Baja California Sur. Mexico. Glob. Environ. Chang. 32, 139–152 (2015).Article 

Google Scholar 
Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

Google Scholar 
IPCC. Climate Change 2007: Synthesis Report. Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. (2007).Johnson, J. E. & Welch, D. J. Climate change implications for Torres Strait fisheries: Assessing vulnerability to inform adaptation. Clim. Change 135, 611–624 (2016).ADS 
Article 

Google Scholar 
IPCC. Annex I: Glossary. in IPCC special report on the ocean and cryosphere in a changing climate e [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)] 677–702 (Cambridge University Press, 2019). https://doi.org/10.1017/9781009157964.010Cheung, W. W. L., Watson, R., Morato, T., Pitcher, T. J. & Pauly, D. Intrinsic vulnerability in the global fish catch. Mar. Ecol. Prog. Ser. 333, 1–12 (2007).ADS 
Article 

Google Scholar 
Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing down marine food webs. Science 80(279), 860 (1998).ADS 
Article 

Google Scholar 
Lam, V. W. Y., Cheung, W. W. L., Reygondeau, G. & Rashid Sumaila, U. Projected change in global fisheries revenues under climate change. Sci. Rep. 6(6), 13 (2016).
Google Scholar 
Heck, N. et al. Fisheries at risk: Vulnerability of fisheries to climate change (Nat. Conserv. Tech. Rep, 2020).
Google Scholar 
Allison, E. H. et al. Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish. 10, 173–196 (2009).Article 

Google Scholar 
DuFour, M. R. et al. Portfolio theory as a management tool to guide conservation and restoration of multi-stock fish populations. Ecosphere 6(12), 1 (2015).Article 

Google Scholar 
Kasperski, S. & Holland, D. S. Income diversification and risk for fishermen. Proc. Natl. Acad. Sci. U. S. A. 110, 2076–2081 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bahri, T. et al. Adaptive management of fisheries in response to climate change. FAO Fisheries and Aquaculture Technical Paper 667, (FAO, 2021).Barker, M. J. & Schluessel, V. Managing global shark fisheries: Suggestions for prioritizing management strategies. Aquat. Conserv. Mar. Freshw. Ecosyst. 15, 325–347 (2005).Article 

Google Scholar 
Fletcher, W. J. F. & Fletcher, W. J. The application of qualitative risk assessment methodology to prioritize issues for fisheries management. ICES J. Mar. Sci. 62, 1576–1587 (2005).Article 

Google Scholar 
Cheung, W. W. L. The future of fishes and fisheries in the changing oceans. J. Fish Biol. 92, 790–803 (2018).CAS 
PubMed 
Article 

Google Scholar 
Cinner, J. E. et al. Evaluating social and ecological vulnerability of coral reef fisheries to climate change. PLoS ONE 8(9), e74321 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Colburn, L. L. et al. Indicators of climate change and social vulnerability in fishing dependent communities along the Eastern and Gulf Coasts of the United States. Mar. Policy 74, 323–333 (2016).Article 

Google Scholar 
Pinnegar, J. K. et al. Assessing vulnerability and adaptive capacity of the fisheries sector in Dominica: Long-term climate change and catastrophic hurricanes. ICES J. Mar. Sci. 76, 1353–1367 (2019).
Google Scholar 
Aragão, G. M. et al. The importance of regional differences in vulnerability to climate change for demersal fisheries. ICES J. Mar. Sci. 1, 1–13 (2021).
Google Scholar 
Payne, M. R., Kudahl, M., Engelhard, G. H., Peck, M. A. & Pinnegar, J. K. Climate risk to European fisheries and coastal communities. Proc. Natl. Acad. Sci. U. S. A. 118, e2018086118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baptista, V., Silva, P. L., Relvas, P., Teodósio, M. A. & Leitão, F. Sea surface temperature variability along the Portuguese coast since 1950. Int. J. Climatol. 38, 1145–1160 (2018).Article 

Google Scholar 
Leitão, F. et al. (2019) A 60-year time series analyses of the upwelling along the Portuguese coast. Water 11(11), 1285 (2019).Article 

Google Scholar 
Leitão, F., Relvas, P., Cánovas, F., Baptista, V. & Teodósio, A. Northerly wind trends along the Portuguese marine coast since 1950. Theor. Appl. Climatol. 137(1), 19 (2018).
Google Scholar 
Bueno-Pardo, J. et al. Trends and drivers of marine fish landings in Portugal since its entrance in the European Union. ICES J. Mar. Sci. 77, 988–1001 (2020).Article 

Google Scholar 
Leitão, F., Maharaj, R. R., Vieira, V. M. N. C. S., Teodósio, A. & Cheung, W. W. L. The effect of regional sea surface temperature rise on fisheries along the Portuguese Iberian Atlantic coast. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1351–1359 (2018).Article 

Google Scholar 
Leitão, F., Alms, V. & Erzini, K. A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries. J. Mar. Syst. 139, 128–138 (2014).Article 

Google Scholar 
Ullah, H., Leitão, F., Baptista, V. & Chícharo, L. An analysis of the impacts of climatic variability and hydrology on the coastal fisheries, Engraulis encrasicolus and Sepia officinalis, of Portugal. Ecohydrol. Hydrobiol. 12, 337–352 (2012).Article 

Google Scholar 
EUMOFA. The EU Fish Market – Highlights the EU in the world market supply consumption import-export landings in the EU aquaculture (2021) https://doi.org/10.2771/563899DGPM. Relatório de Monitorização da Estratégia Nacional para o Mar 2013–2020, Documento de Suporte às Políticas do Mar. (2020).Almeida, C., Karadzic, V. & Vaz, S. The seafood market in Portugal: Driving forces and consequences. Mar. Policy 61, 87–94 (2015).Article 

Google Scholar 
Pita, C. & Gaspar, M. (2020) Small-Scale Fisheries in Portugal: Current Situation, Challenges and Opportunities for the Future. In Small-Scale Fisheries in Europe: Status, Resilience and Governance. Springer, Cham 283–305https://doi.org/10.1007/978-3-030-37371-9_14Baeta, F., José Costa, M. & Cabral, H. Changes in the trophic level of Portuguese landings and fish market price variation in the last decades. Fish. Res. 97, 216–222 (2009).Article 

Google Scholar 
Leitão, F. Landing profiles of Portuguese fisheries: Assessing the state of stocks. Fish. Manag. Ecol. 22, 152–163 (2015).Article 

Google Scholar 
Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

Google Scholar 
Bueno-Pardo, J. et al. Climate change vulnerability assessment of the main marine commercial fish and invertebrates of Portugal. Sci. Rep. 11, 2958 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Szynaka, M. J., Erzini, K., Gonçalves, J. M. S. & Campos, A. Identifying métiers using landings profiles: An octopus-driven multi-gear coastal fleet. J. Mar. Sci. Eng. 9, 1022 (2021).Article 

Google Scholar 
Gamito, R., Teixeira, C. M., Costa, M. J. & Cabral, H. N. Climate-induced changes in fish landings of different fleet components of Portuguese fisheries. Reg. Environ. Chang. 13, 413–421 (2013).Article 

Google Scholar 
Leitão, F., Baptista, V., Zeller, D. & Erzini, K. Reconstructed catches and trends for mainland Portugal fisheries between 1938 and 2009: Implications for sustainability, domestic fish supply and imports. Fish. Res. 155, 33–50 (2014).Article 

Google Scholar 
Teixeira, C. M. et al. Trends in landings of fish species potentially affected by climate change in Portuguese fisheries. Reg. Environ. Chang. 14, 657–669 (2014).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant graphics for data analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 3–900051–07–0 (2020).Zuur, A. F., Fryer, R. J., Jolliffe, I. T., Dekker, R. & Beukema, J. J. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14, 665–685 (2003).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Smith, G. M. (2007) Analysing Ecological Data. https://doi.org/10.1007/978-0-387-45972-1Anderson, M., Gorley, R. & Clarke, K. PERMANOVA for PRIMER: Guide to software and statistical methods. (PRIMER-E Ltd., 2008).Heppell, S. S., Heppell, S. a, Read, A. J. & Crowder, L. B. Effects of fishing on long-lived marine organisms. In Marine conservation biology: The science of maintaining the sea’s biodiversity (eds. Norse, E. & Crowder, L.) 211–231 (Island Press, 2005).Maynou, F. et al. Estimating trends of population decline in long-lived marine species in the Mediterranean sea based on fishers’ perceptions. PLoS ONE 6, e21818 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rolland, V., Barbraud, C. & Weimerskirch, H. Combined effects of fisheries and climate on a migratory long-lived marine predator. J. Appl. Ecol. 45, 4–13 (2008).Article 

Google Scholar 
Alves, L. M. F., Correia, J. P. S., Lemos, M. F. L., Novais, S. C. & Cabral, H. Assessment of trends in the Portuguese elasmobranch commercial landings over three decades (1986–2017). Fish. Res. 230, 105648 (2020).Article 

Google Scholar 
Correia, J. P., Morgado, F., Erzini, K. & Soares, A. M. V. M. Elasmobranch landings for the Portuguese commercial fishery from 1986 to 2009. Arquipel. Life Mar. Sci. 33, 81–109 (2016).
Google Scholar 
Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
PubMed 
Article 

Google Scholar 
Pinnegar, J. K. & Engelhard, G. H. The ‘shifting baseline’ phenomenon: A global perspective. Rev. Fish Biol. Fish. 18, 1–16 (2008).Article 

Google Scholar 
Moura, T. et al. Assessing spatio-temporal changes in marine communities along the Portuguese continental shelf and upper slope based on 25 years of bottom trawl surveys. Mar. Environ. Res. 160, 105044 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Martins, M. M., Skagen, D., Marques, V., Zwolinski, J. & Silva, A. Changes in the abundance and spatial distribution of the Atlantic chub mackerel (Scomber colias) in the pelagic ecosystem and fisheries off Portugal. Sci. Mar. 77, 551–563 (2013).Article 

Google Scholar 
Bordalo-Machado, P. & Figueiredo, I. The fishery for black scabbardfish (Aphanopus carbo Lowe, 1839) in the Portuguese continental slope. Rev. Fish Biol. Fish. 19, 49–67 (2009).Article 

Google Scholar 
Gordo, L. S. Black scabbardfish (Aphanopus carbo Lowe, 1839) in the southern Northeast Atlantic: Considerations on its fishery. Sci. Mar. 73, 11–16 (2009).Article 

Google Scholar 
Campos, A., Fonseca, P., Fonseca, T. & Parente, J. Definition of fleet components in the Portuguese bottom trawl fishery. Fish. Res. 83, 185–191 (2007).Article 

Google Scholar 
Bueno-Pardo, J. et al. Deep-sea crustacean trawling fisheries in Portugal: Quantification of effort and assessment of landings per unit effort using a Vessel Monitoring System (VMS). Sci. Rep. 7, 1–10 (2017).ADS 
Article 

Google Scholar 
Gamito, R., Pita, C., Teixeira, C., Costa, M. J. & Cabral, H. N. Trends in landings and vulnerability to climate change in different fleet components in the Portuguese coast. Fish. Res. 181, 93–101 (2016).Article 

Google Scholar 
García-Seoane, E., Marques, V., Silva, A. & Angélico, M. M. Spatial and temporal variation in pelagic community of the western and southern Iberian Atlantic waters. Estuar. Coast. Shelf Sci. 221, 147–155 (2019).ADS 
Article 

Google Scholar 
Vinagre, C., Duarte, F., Cabral, H. & Jose, M. Impact of climate warming upon the fish assemblages of the Portuguese coast under different scenarios. Reg. Environ. Change 11(4), 779. https://doi.org/10.1007/s10113-011-0215-z (2011).Article 

Google Scholar 
Goulart, P., Veiga, F. J. & Grilo, C. The evolution of fisheries in Portugal: A methodological reappraisal with insights from economics. Fish. Res. 199, 76–80 (2018).Article 

Google Scholar 
Pita, C., Pereira, J., Lourenço, S., Sonderblohm, C. & Pierce, G. J. (2015) The Traditional Small-Scale Octopus Fishery in Portugal: Framing Its Governability. 117–132. https://doi.org/10.1007/978-3-319-17034-3_7Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

Google Scholar 
Moreno, A. et al. Essential habitats for pre-recruit Octopus vulgaris along the Portuguese coast. Fish. Res. 152, 74–85 (2014).ADS 
Article 

Google Scholar 
Sbrana, M. et al. Spatiotemporal abundance pattern of deep-water rose shrimp, parapenaeus longirostris, and Norway lobster, nephrops norvegicus, in european mediterranean waters. Sci. Mar. 83, 71–80 (2019).Article 

Google Scholar 
Quattrocchi, F., Fiorentino, F., Lauria, V. & Garofalo, G. The increasing temperature as driving force for spatial distribution patterns of Parapenaeus longirostris (Lucas 1846) in the Strait of Sicily (Central Mediterranean Sea). J. Sea Res. 158, 101871 (2020).Article 

Google Scholar 
Colloca, F., Mastrantonio, G., Lasinio, G. J., Ligas, A. & Sartor, P. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138, 29–39 (2014).Article 

Google Scholar 
Woods, P. J. et al. (2021) A review of adaptation options in fisheries management to support resilience and transition under socio-ecological change. ICES J. Mar. Sci. fsab146Gonzalez-Mon, B. et al. Spatial diversification as a mechanism to adapt to environmental changes in small-scale fisheries. Environ. Sci. Policy 116, 246–257 (2021).Article 

Google Scholar 
Garza-Gil, M. D., Torralba-Cano, J. & Varela-Lafuente, M. M. Evaluating the economic effects of climate change on the European sardine fishery. Reg. Environ. Chang. 11, 87–95 (2011).Article 

Google Scholar 
Borges, M. F., Santos, A. M. P., Crato, N., Mendes, H. & Mota, B. Sardine regime shifts off Portugal: A time series analysis of catches and wind conditions. Sci. Mar. 67, 235–244 (2003).Article 

Google Scholar 
Garrido, S. et al. Temperature and food-mediated variability of European Atlantic sardine recruitment. Prog. Oceanogr. 159, 267–275 (2017).ADS 
Article 

Google Scholar 
ICES. Report of the working group on southern horse mackerel, anchovy and sardine (WGHANSA). (2018).Szalaj, D. et al. Food-web dynamics in the Portuguese continental shelf ecosystem between 1986 and 2017: Unravelling drivers of sardine decline. Estuar. Coast. Shelf Sci. 251, 107259 (2021).Article 

Google Scholar 
Feijó, D. et al. Catch and yield trends of the Portuguese purse seine fishery (2006–2018). Front. Mar. Sci. https://doi.org/10.3389/conf.fmars.2019.08.00013 (2019).Article 

Google Scholar 
Schickele, A., Francour, P. & Raybaud, V. European cephalopods distribution under climate-change scenarios. Sci. Rep. 11, 3930 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Purcell, S. W., Crona, B. I., Lalavanua, W. & Eriksson, H. Distribution of economic returns in small-scale fisheries for international markets: A value-chain analysis. Mar. Policy 86, 9–16 (2017).Article 

Google Scholar 
Thiao, D., Leport, J., Ndiaye, B. & Mbaye, A. Need for adaptive solutions to food vulnerability induced by fish scarcity and unaffordability in Senegal. Aquat. Living Resour. 31, 25 (2018).Article 

Google Scholar 
Education, A. & Variability, H. Cardoso, C., Lourenço, H., Costa, S., Gonçalves, S. & Leonor Nunes, M. Survey Into the Seafood Consumption Preferences and Patterns in the Portuguese Population. J. Food Prod. Mark. 22, 421–435 (2016).Article 

Google Scholar 
Holsten, A. & Kropp, J. P. An integrated and transferable climate change vulnerability assessment for regional application. Nat. Hazards 64, 1977–1999 (2012).Article 

Google Scholar 
Umweltbundesamt guidelines for climate impact and vulnerability assessments recommendations of the interministerial working group on adaptation to climate change of the German federal government for our environment. More

Hume, J. B. et al. Managing native and non-native sea lamprey (Petromyzon marinus) through anthropogenic change: A prospective assessment of key threats and uncertainties. J. Great Lakes Res. 47, S704–S722 (2021).Article 

Google Scholar 
Siefkes, M. J. Use of physiological knowledge to control the invasive sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes. Conserv. Physiol. 5, 1–18 (2017).Article 

Google Scholar 
Hunn, J. B. & Youngs, W. D. Role of physical barriers in the control of Sea Lamprey (Petrorn yzon marinus). Can. J. Fish. Aquat. Sci. 37, 2118–2122 (1980).Article 

Google Scholar 
Christie, M. R., Sepúlveda, M. S. & Dunlop, E. S. Rapid resistance to pesticide control is predicted to evolve in an invasive fish. Sci. Rep. 9, 18157. https://doi.org/10.1038/s41598-019-54260-5 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cline, T. J. et al. Climate impacts on landlocked sea lamprey: Implications for host-parasite interactions and invasive species management. Ecosphere 5(6), 68. https://doi.org/10.1890/ES14-00059.1 (2014).Article 

Google Scholar 
Lennox, R. J. et al. Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change. Glob. Change Biol. 26, 1118–1137. https://doi.org/10.1111/gcb.14957 (2020).ADS 
Article 

Google Scholar 
Siefkes, M. J., Johnson, N. S. & Muir, A. M. A renewed philosophy about supplemental sea lamprey controls. J. Great Lakes Res. 47, S742–S752 (2021).Article 

Google Scholar 
Fissette, S. D. et al. Progress towards integrating an understanding of chemical ecology into sea lamprey control. J. Great Lakes Res. 47, S660–S672 (2021).CAS 
Article 

Google Scholar 
Miehls, S. et al. The future of barriers and trapping methods in the sea lamprey (Petromyzon marinus) control program in the Laurentian Great Lakes. Rev. Fish Biol. Fish. 30, 1–24 (2020).Article 

Google Scholar 
Imre, I., Di Rocco, R. T., Belanger, C. F., Brown, G. E. & Johnson, N. S. The behavioural response of adult Petromyzon marinus to damage-released alarm and predator cues. J. Fish Biol. 84, 1490–1502 (2014).CAS 
PubMed 
Article 

Google Scholar 
Kats, L. B. & Dill, L. M. The scent of death: chemosensory assessment of predation risk by prey animals. Ecoscience 5, 361–394 (1998).Article 

Google Scholar 
Wisenden, B. D. Olfactory assessment of predation risk in the aquatic environment. Philos. Trans. R. Soc. B Biol. Sci. 355, 1205–1208 (2000).Wisenden, B. D., Chivers, D. P., Brown, G. E. & Smith, R. J. The role of experience in risk assessment: Avoidance of areas chemically labelled with fathead minnow alarm pheromone by conspecifics and heterospecifics. Ecoscience 2, 116–122 (1995).Article 

Google Scholar 
Bairos-Novak, K. R., Ferrari, M. C. O. & Chivers, D. P. A novel alarm signal in aquatic prey: Familiar minnows coordinate group defences against predators through chemical disturbance cues. J. Anim. Ecol. 88, 1281–1290 (2019).PubMed 
Article 

Google Scholar 
Chivers, D. P. & Smith, R. J. F. Chemical alarm signalling in aquatic predator-prey systems: A review and prospectus. Ecoscience 5, 338–352 (1998).Article 

Google Scholar 
Ferrari, M. C. O., Wisenden, B. D. & Chivers, D. P. Chemical ecology of predator–prey interactions in aquatic ecosystems: A review and prospectus. Can. J. Zool. 88, 698–724 (2010).Article 

Google Scholar 
Lawrence, B. J. & Smith, R. J. F. Behavioral response of solitary fathead minnows, Pimephales promelas, to alarm substance. J. Chem. Ecol. 3, 209–219 (1989).Article 

Google Scholar 
Bals, J. D. & Wagner, C. M. Behavioral responses of sea lamprey (Petromyzon marinus) to a putative alarm cue derived from conspecific and heterospecific sources. Behaviour 149, 901–923 (2012).Article 

Google Scholar 
Hume, J. B. & Wagner, C. M. A death in the family: Sea lamprey (Petromyzon marinus) avoidance of confamilial alarm cues diminishes with phylogenetic distance. Ecol. Evol. 8, 3751–3762 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Wagner, C. M., Stroud, E. M. & Meckley, T. D. A deathly odor suggests a new sustainable tool for controlling a costly invasive species. Can. J. Fish. Aquat. Sci. 68, 1157–1160 (2011).Article 

Google Scholar 
Byford, G. J., Wagner, C. M., Hume, J. B. & Moser, M. L. Do native pacific lamprey and invasive sea lamprey share an alarm cue? Implications for use of a natural repellent to guide imperiled pacific lamprey into fishways. North Am. J. Fish. Manag. 36, 1090–1096 (2016).Article 

Google Scholar 
Wagner, C. M., Kierczynski, K. E., Hume, J. B. & Luhring, T. M. Exposure to a putative alarm cue reduces downstream drift in larval sea lamprey Petromyzon marinus in the laboratory. J. Fish Biol. 89, 1897–1904 (2016).CAS 
PubMed 
Article 

Google Scholar 
Di Rocco, R. T., Johnson, N. S., Brege, L., Imre, I. & Brown, G. E. Sea lamprey avoid areas scented with conspecific tissue extract in Michigan streams. Fish. Manag. Ecol. 23, 548–560 (2016).Article 

Google Scholar 
Hume, J. B., Luhring, T. M. & Wagner, C. M. Push, pull, or push–pull? An alarm cue better guides sea lamprey towards capture devices than a mating pheromone during the reproductive migration. Biol. Invasions 22, 2129–2142 (2020).Article 

Google Scholar 
Hume, J. B. et al. Application of a putative alarm cue hastens the arrival of invasive sea lamprey (Petromyzon marinus) at a trapping location. Can. J. Fish. Aquat. Sci. 72, 1799–1806 (2015).CAS 
Article 

Google Scholar 
Blumstein, D. T. Habituation and sensitization: New thoughts about old ideas. Anim. Behav. 120, 255–262 (2016).Article 

Google Scholar 
Greggor, A. L., Berger-Tal, O. & Blumstein, D. T. the rules of attraction: The necessary role of animal cognition in explaining conservation failures and successes. Ann. Rev. Ecol. Evol. Syst. 51, 483–503 (2020).Article 

Google Scholar 
Imre, I., Di Rocco, R. T., McClure, H., Johnson, N. S. & Brown, G. E. Migratory-stage sea lamprey Petromyzon marinus stop responding to conspecific damage-released alarm cues after 4 h of continuous exposure in laboratory conditions. J. Fish Biol. 90, 1297–1304 (2017).CAS 
PubMed 
Article 

Google Scholar 
Wagner, C. M., Bals, J. D., Hanson, M. E. & Scott, A. M. Attenuation and recovery of an avoidance response to a chemical antipredator cue in an invasive fish: implications for use as a repellent in conservation. Cons. Phys. 10, 1–12 (2022).CAS 

Google Scholar 
Hussain, A. et al. High-affinity olfactory receptor for the death-associated odor cadaverine. Proc. Natl. Acad. Sci. U. S. A. 110, 19579–19584 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yao, M. et al. The ancient chemistry of avoiding risks of predation and disease. Evol. Biol. 36, 267–281 (2009).Article 

Google Scholar 
Wisman, A. & Shrira, I. The smell of death: Evidence that putrescine elicits threat management. Front. Psychol. 6, 1–11 (2015).Article 

Google Scholar 
Oliveira, T. A. et al. Death-associated odors induce stress in zebrafish. Horm. Behav. 65, 340–344 (2014).PubMed 
Article 

Google Scholar 
Pinel, J. P. J., Gorzalka, B. B. & Ladak, F. Cadaverine and Putrescine Initiate the Burial of Dead Conspecifics by Rats. Physiol. Behav. 27, 819–824 (1981).CAS 
PubMed 
Article 

Google Scholar 
Prounis, G. S. & Shields, W. M. Necrophobic behavior in small mammals. Behav. Processes 94, 41–44 (2013).PubMed 
Article 

Google Scholar 
Sun, Q., Haynes, K. F. & Zhou, X. Dynamic changes in death cues modulate risks and rewards of corpse management in a social insect. Funct. Ecol. 31, 697–706 (2017).Article 

Google Scholar 
Heale, V. R., Petersen, K. & Vanderwolf, C. H. Effect of colchicine-induced cell loss in the dentate gyms and Ammon’s horn on the olfactory control of feeding in rats. Brain. Res. J. 712, 213–220 (1996).CAS 
Article 

Google Scholar 
Rolen, S. H., Sorensen, P. W., Mattson, D. & Caprio, J. Polyamines as olfactory stimuli in the goldfish Carassius auratus. J. Exp. Biol. 206, 1683–1696 (2003).CAS 
PubMed 
Article 

Google Scholar 
Dissanayake, A. A., Wagner, C. M. & Nair, M. G. Nitrogenous compounds characterized in the deterrent skin extract of migratory adult sea lamprey from the Great Lakes region. PLoS ONE 14(5), e0217417. https://doi.org/10.1371/journal.pone.0168609 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cooke, M., Leeves, N. & White, C. Time profile of putrescine, cadaverine, indole and skatole in human saliva. Arch. Oral Biol. 9969, 323–327 (2003).Article 

Google Scholar 
Tilden, J. An account of a singular property of lamprey eels. Mem. Amer. Acad. Sci. 46, 335–336 (1809).
Google Scholar 
Di Rocco, R. T., Belanger, C. F., Imre, I., Brown, G. E. & Johnson, N. S. Daytime avoidance of chemosensory alarm cues by adult sea lamprey (Petromyzon marinus). Can. J. Fish. Aquat. Sci. 830, 824–830 (2014).Article 

Google Scholar 
Imre, I., Di Rocco, R. T., Brown, G. E. & Johnson, N. S. Habituation of adult sea lamprey repeatedly exposed to damage-released alarm and predator cues. Environ. Biol. Fishes 99, 613–620 (2016).Article 

Google Scholar 
Ferrari, M. C. O., Messier, F. & Chivers, D. P. Degradation of chemical alarm cues under natural conditions: Risk assessment by larval woodfrogs. Chemoecology 17, 263–266 (2008).Article 

Google Scholar 
Brown, G. E., Rive, A. C., Ferrari, M. C. O. & Chivers, D. P. The dynamic nature of antipredator behavior: Prey fish integrate threat-sensitive antipredator responses within background levels of predation risk. Behav. Ecol. Sociobiol. 61, 9–16 (2006).Article 

Google Scholar 
McCann, E. L., Johnson, N. S., Hrodey, P. J. & Pangle, K. L. Characterization of sea lamprey stream entry using dual-frequency identification sonar. Trans. Am. Fish. Soc. 147, 514–524 (2018).Article 

Google Scholar 
Binder, T. R. & McDonald, D. G. Is there a role for vision in the behaviour of sea lampreys (Petromyzon marinus) during their upstream spawning migration?. Can. J. Fish. Aquat. Sci. 64, 1403–1412 (2007).Article 

Google Scholar 
Wagner, C. M., Jones, M. L., Twohey, M. B. & Sorensen, P. W. A field test verifies that pheromones can be useful for sea lamprey (Petromyzon marinus) control in the Great Lakes. Can. J. Fish. Aquat. Sci. 63, 475–479 (2006).CAS 
Article 

Google Scholar 
Wagner, C. M., Twohey, M. B. & Fine, J. M. Conspecific cueing in the sea lamprey: Do reproductive migrations consistently follow the most intense larval odour?. Anim. Behav. 78, 593–599 (2009).Article 

Google Scholar 
Boulêtreau, S. et al. High predation of native sea lamprey during spawning migration. Sci. Rep. 10, 6122. https://doi.org/10.1038/s41598-020-62916-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sjöberg, K. Time-related predator/prey interactions between birds and fish in a northern Swedish river. Oecologia 80, 1–10 (1989).ADS 
PubMed 
Article 

Google Scholar 
Fanselow, M. S., Hoffman, A. N. & Zhuravka, I. Timing and the transition between modes in the defensive behavior system. Behav. Processes 166, 103890. https://doi.org/10.1016/j.beproc.2019.103890 (2019).Fanselow, M. S. & Lester, L. S. A functional behavioristic approach to aversively motivated behavior: Predatory imminence as a determinant of the topography of defensive behavior. In Evolution and Learning (ed. Bolles, R.C. & Beecher, M.D.) 185–211 (Earlbaum, 1988).Dissanayake, A. A., Wagner, C. M. & Nair, M. G. Chemical characterization of lipophilic constituents in the skin of migratory adult sea lamprey from the Great Lakes Region. PLoS ONE 11(12), e0168609. https://doi.org/10.1371/journal.pone.0168609 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dissanayake, A. A., Wagner, C. M. & Nair, M. G. Evaluation of health benefits of sea lamprey (Petromyzon marinus) isolates using in vitro antiinflammatory and antioxidant assays. PLoS ONE 16(11), e0259587. https://doi.org/10.1371/journal.pone.0259587 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
UFR-Committee. Guidelines for the use of fishes in research. Am. Fish. Soc. Symp., Bethesday, Maryland (2013).Association, A. V. M. Guidelines for the Euthanasia of. Animals https://doi.org/10.1016/B978-012088449-0.50009-1 (2013).Article 

Google Scholar 
du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the arrive guidelines 2.0. PLoS Biol. 18, 1–65 (2020).Friard, O. & Gamba, M. BORIS: A free versatile open-source event-logging software for video/ audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
Domenici, P. Context-dependent variability in the components of fish escape response: Integrating locomotor performance and behavior. J. Exp. Biol. 313, 59–79 (2010).
Google Scholar 
Perrault, K., Imre, I. & Brown, G. E. Behavioural response of larval sea lamprey (Petromyzon marinus) in a laboratory environment to potential damage-released chemical alarm cues. Can. J. Zool. 92, 443–447 (2014).Article 

Google Scholar 
Curtis, V., de Barra, M. & Aunger, R. Disgust as an adaptive system for disease avoidance behaviour. Philos. Trans. R. Soc. B Biol. Sci. 366, 389–401 (2011).Fanselow, M. S. The role of learning in threat imminence and defensive behaviors. Curr. Opin. Behav. Sci. 24, 44–49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Helfman, G. S. Threat-sensitive predator avoidance in damselfish-trumpetfish interactions. Behav. Ecol. Sociobiol. 24, 47–58 (1989).Article 

Google Scholar 
Stephenson, J. F., Perkins, S. E. & Cable, J. Transmission risk predicts avoidance of infected conspecifics in Trinidadian guppies. J. Anim. Ecol. 87, 1525–1533 (2018).PubMed 
Article 

Google Scholar 
Sepahi, A. et al. Olfactory sensory neurons mediate ultrarapid antiviral immune responses in a TrkA-dependent manner. Proc. Natl. Acad. Sci. U.S.A. 116, 12428–12436 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Croft, D. P., Edenbrow, M., Darden, S. K. & Cable, J. Effect of gyrodactylid ectoparasites on host behaviour and social network structure in guppies Poecilia reticulata. Behav. Ecol. Sociobiol. 65, 2219–2227 (2011).Article 

Google Scholar 
Luhring, T. M. et al. A semelparous fi sh continues upstream migration when exposed to alarm cue, but adjusts movement speed and timing. Anim. Behav. 121, 41–51 (2016).Article 

Google Scholar 
Laframboise, A. J., Ren, X., Chang, S., Dubuc, R. & Zielinski, B. S. Olfactory sensory neurons in the sea lamprey display polymorphisms. Neurosci. Lett. 414, 277–281 (2007).CAS 
PubMed 
Article 

Google Scholar 
Buchinger, T. J., Siefkes, M. J., Zielinski, B. S., Brant, C. O. & Li, W. Chemical cues and pheromones in the sea lamprey (Petromyzon marinus). Front. Zool. 12, 1–11 (2015).Article 

Google Scholar 
Halgand, F. et al. Defining intact protein primary structures from saliva: A step toward the human proteome project. Anal. Chem. 84, 4383–4395 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mackay, R. N., Wood, T. C. & Moore, P. A. Running away or running to? Do prey make decisions solely based on the landscape of fear or do they also include stimuli from a landscape of safety? J. Exp. Biol. 224, jeb242687. https://doi.org/10.1242/jeb.242687 (2021).Meckley, T. D., Gurarie, E., Miller, J. R. & Michaelwagner, C. How fishes find the shore: Evidence for orientation to bathymetry from the non-homing sea lamprey. Can. J. Fish. Aquat. Sci. 74, 2045–2058 (2017).Article 

Google Scholar 
Hume, J. B., Lucas, M. C., Reinhardt, U., Hrodey, P. J. & Wagner, C. M. Sea lamprey (Petromyzon marinus) transit of a ramp equipped with studded substrate: Implications for fish passage and invasive species control. Ecol. Eng. 155, 1–11 (2020).Article 

Google Scholar 
Ioannou, C. C., Ramnarine, I. W. & Torney, C. J. High-predation habitats affect the social dynamics of collective exploration in a shoaling fish. Sci. Adv. 3, e1602682. https://doi.org/10.1126/sciadv.1602682 (2017).Schaerf, T. M., Dillingham, P. W. & Ward, A. J. W. The effects of external cues on individual and collective behavior of shoaling fish. Sci. Adv. 3, e1603201. https://doi.org/10.1126/SCIADV.ABN2232 (2017).Hoare, D. J., Couzin, I. D., Godin, J. G. J. & Krause, J. Context-dependent group size choice in fish. Anim. Behav. 67, 155–164 (2004).Article 

Google Scholar 
Siefkes, M. J., Winterstein, S. R. & Li, W. Evidence that 3-keto petromyzonol sulphate specifically attracts ovulating female sea lamprey Petromyzon marinus. Anim. Behav. 70, 1037–1045 (2005).Article 

Google Scholar 
Wisenden, B. D. Evidence for incipient alarm signalling in fish. J. Anim. Ecol. 88, 1278–1280 (2019).PubMed 
Article 

Google Scholar 
Petersen, R. S. The role of traditional ecological knowledge in understanding a species and river system at risk: Pacific Lamprey in the Lower Klamath Basin (Oregon State University, 2006).
Google Scholar 
Barton, B. A. Stress in fishes: A diversity of responses with particular reference to changes in. Integ. Comp. Biol. 525, 517–525 (2002).Article 

Google Scholar 
Lawrence, M. J., Godin, J. J. & Cooke, S. J. Comparative Biochemistry and Physiology, Part A Does experimental cortisol elevation mediate risk-taking and antipredator behaviour in a wild teleost fish?. Comp. Biochem. Physiol. Part A 226, 75–82 (2018).CAS 
Article 

Google Scholar 
Conrad, J. L., Weinersmith, K. L., Brodin, T. & Saltz, J. B. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
PubMed 
Article 

Google Scholar 
Sanches, F. H. C., Miyai, C. A., Pinho-Neto, C. F. & Barreto, R. E. Stress responses to chemical alarm cues in Nile tilapia. Physiol. Behav. 149, 8–13 (2015).CAS 
PubMed 
Article 

Google Scholar 
Rehnberg, B. G. & Schreck, C. B. Chemosensory detection of predators by coho salmon (Oncorhynchus kisutch): Behavioral reaction and the physiological stress response1. Can. J. Zool. 65, 481–485 (1987).CAS 
Article 

Google Scholar 
Rehnberg, B. G., Smith, R. J. F. & Sloley, B. D. The reaction of pearl dace (Pisces, Cyprinidae) to alarm substance: Time-course of behavior, brain amines, and stress physiology. Can. J. Zool. 65, 2916–2921 (1987).CAS 
Article 

Google Scholar 
Close, D. A., Yun, S. S., McCormick, S. D., Wildbill, A. J. & Li, W. 11-Deoxycortisol is a corticosteroid hormone in the lamprey. Proc. Natl. Acad. Sci. U. S. A. 107, 13942–13947 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shaughnessy, C. A. & Mccormick, S. D. 11-Deoxycortisol is a stress responsive and gluconeogenic hormone in a jawless vertebrate, the sea lamprey (Petromyzon marinus). J. Exp. Biol. 224, jeb241943. https://doi.org/10.1242/jeb.241943 (2021).Cull, F. et al. Consequences of experimental cortisol manipulations on the thermal biology of the checkered puffer (Sphoeroides testudineus) in laboratory and field environments. J. Therm. Biol. 47, 63–74 (2015).CAS 
PubMed 
Article 

Google Scholar 
Pleizier, N., Wilson, A. D. M., Shultz, A. D. & Cooke, S. J. Puffed and bothered: Personality, performance, and the effects of stress on checkered puffer fish. Physiol. Behav. 152, 68–78 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lawrence, M. J. et al. An experimental evaluation of the role of the stress axis in mediating predator-prey interactions in wild marine fish. Comp. Biochem. Physiol. Part A 207, 21–29 (2017).CAS 
Article 

Google Scholar 
Atema, J., Kingsford, M. J. & Gerlach, G. Larval reef fish could use odour for detection, retention and orientation to reefs. Mar. Ecol. Prog. Ser. 241, 151–160 (2002).ADS 
Article 

Google Scholar 
Gardiner, J. M. & Atema, J. Sharks need the lateral line to locate odor sources: rheotaxis and eddy chemotaxis. J. Exp. Biol. 210, 1925–1934 (2007).PubMed 
Article 

Google Scholar 
Jutfelt, F., Sundin, J., Raby, G. D., Krång, A. S. & Clark, T. D. Two-current choice flumes for testing avoidance and preference in aquatic animals. Methods Ecol. Evol. 8, 379–390 (2017).Article 

Google Scholar 
Moser, M. L., Almeida, P. R., Kemp, P. S. & Sorensen, P. W. Lamprey Spawning Migration in Lampreys: Biology, Conservation and Control. (ed. Docker, M. F.) 215–263 (Springer, 2015).Imre, I., Brown, G. E., Bergstedt, R. A. & Mcdonald, R. Use of chemosensory cues as repellents for sea lamprey: Potential directions for population management. J. Great Lakes Res. 36, 790–793 (2010).CAS 
Article 

Google Scholar 
Merrick, M. J. & Koprowski, J. L. Should we consider individual behavior differences in applied wildlife conservation studies?. Biol. Conserv. 209, 34–44 (2017).Article 

Google Scholar  More

All procedures accorded to administrative provision of animal welfare of the Fisheries Research Education Agency Japan. All statistical tests used in this study are two-sided.Otolith samplesFrom the western North Pacific, age-0 JP sardine were collected from samples taken during acoustic and sub-surface trawl surveys in the offshore Oyashio region conducted during 2006–2010 and 2014–2015. The surveys were conducted by Japan Fisheries Research and Education Agency every autumn since 2005 which aim to estimate the abundance of small pelagic species. The abundance of young-of-the-year sardine in the region in the season, approximately 10–15 cm in standard length (SL), is considered a proxy for the abundance of recruits of the Pacific stock and used to tune the cohort analysis in stock assessment4. As representatives of the young-of-the-year population in the region, 2–6 trawl stations each year that had relatively larger catch-per-unit-effort were selected (Supplementary Fig. 1), and 9–20 individuals were randomly selected from each station for otolith analyses (Supplementary Table 1). Age of fish was initially judged by SL (10–15 cm) and later confirmed by the counts of otolith daily increments.From the eastern North Pacific, archived otoliths of CA sardine captured in cruise surveys and in the pelagic fishery of the Southern California Bight during 1987, 1991–1998, and 2005–2007 were collected. Fish in the size range of 10–16 cm SL were regarded as age-1 individuals born in the previous year, following Takahashi and Checkley56. The number of individuals varied between year classes in the range of 4–20 (Supplementary Table 2).Otolith processing, microstructure and somatic growth analysisSagittal otoliths were cleaned to remove the attached tissue in freshwater and then air-dried. Otoliths of JP sardine were embedded in epoxy resin (Petropoxy 154, Burnham Petrographics LLC) on slide-glass, while those of CA were glued to slide-glass using enamel resin and then ground and polished with sandpaper to expose the core. For some otoliths of CA sardine, the polished surface was coated with additional resin to facilitate identification of the daily increment width. Using an otolith measurement system (RATOC System Engineering Co. Ltd.), the number and location of daily increments were examined along the axis in the postrostrum from the core. Although daily increments were clearly observed until the otolith edge for JP sardine, it was difficult to do this for CA sardine probably because they had experienced winter when otolith growth slowed down. Therefore, the rings were counted as far as possible for CA sardine, which typically resulted in more than 150 counts. The first daily increment was assumed to form after 3 days post hatch (dph) for JP and 8 dph for CA sardine following Takahashi et al.26 and Takahashi and Checkley56. The otolith radius at each age was calculated by adding all the increment widths up to that age. Standard lengths at each age were back-calculated assuming a linear relationship between otolith radius and standard length using the biological intercept method34 as follows:$${SL}_{n}=left({{SL}}_{{catch}}-{{SL}}_{{first}}right)times left({{OR}}_{n}-{{OR}}_{{first}}right)/left({OR}_{catch}-{{OR}}_{{first}}right)+{{SL}}_{{first}}$$
(1)
where SLn is the standard length at age n, SLcatch is the standard length at catch, SLfirst is the standard length at the age of first daily increment deposition fixed at 5.9 mm for JP sardine and 5.5 mm for CA sardine following the previous studies26,56, ORn is the otolith radius at age n, ORfirst is the otolith radius at the age of first daily increment deposition, and ORcatch is the otolith radius at catch. Based on rearing experiments of field collected eggs, Lasker57 showed the SL of CA sardine at 6–8 dph ranged between 3.8 to 6.5 mm, and Matsuoka and Mitani58 showed the total length at 2–4 dph ranged between 4.8 to 6.2 mm, corresponding to 4.7 to 6.1 mm in SL. To deal with these uncertainties regarding the size at the age of first daily increment deposition, we conducted Monte Carlo simulations (10,000 times) to estimate the uncertainties of back-calculated SL, assuming that the initial SLs fall between 3.8 to 6.5 mm for both sardines. Standard deviations of the temporal back-calculated SL at each age were presented as the uncertainty of each SLn estimation, which varied between 0.51 and 0.73 at the end of larval stage (JP: 45 dph, CA: 60 dph), between 0.34 and 0.64 at the end of early juvenile stage (JP: 75 dph, CA: 90 dph) and between 0.20 and 0.53 at the end of late juvenile stage (JP: 105 dph, CA: 120 dph). These values were significantly smaller than the variability of estimated SL among individuals assuming initial sizes of 5.9 and 5.5 mm for JP and CA sardine, respectively (standard deviations: 4.2, 8.1 and 8.3 in JP sardine and 5.5, 9.1 and 10.3 in CA sardine for the end of larval, early juvenile and late juvenile stages, respectively), suggesting that the back-calculated SL is robust to variations of initial size. Nevertheless, the biological intercept method assumes a constant linear relationship between fish and otolith size within individual59, which can vary depending on physiological or environmental conditions60,61. Therefore, to examine the relationships between temperature and growth, we used both otolith growth, which contains fewer assumptions, and back-calculated somatic growth as growth proxies. Since the use of the two proxies did not show remarkable differences in the relationships between temperature and growth (Supplementary Figs. 11, 12), we mainly used the back-calculated SL in the discussion, which has a more direct ecological implication.To more generally test whether growth trajectories are different between the western and eastern boundary current systems, otolith growth data of JP and CA sardines were compared with those of sardines in the east to south and west coasts of South Africa. The biological intercept method to back-calculate standard length could not be used in sardine from South Africa because the size at catch was large, some over 20 cm, and otolith radius and standard length were not linearly correlated for fish of this size. Therefore, the otolith radius and increment width were directly used as proxy for size and growth in this comparison, respectively. For visualisation (Fig. 2a), the means of year class mean otolith radii were estimated for JP and CA sardines. For CA sardine, otolith radii at ages were simply averaged within each year class. For JP sardine, to account for the variation in the number of individuals captured at the same station, otolith radii were first averaged within each station, and the station means were averaged within each year, weighted by catch-per-unit-effort. For South African sardine, data of otolith daily increment widths from hatch to 100 dph of 67 adults captured at six stations on the east to south coast ( >22oE), and 51 individuals captured at six stations on the west coast ( 0.05). Theoretically, the relationship between metabolism and temperature tends to show a linear trend after the metabolic rate is log-transformed79. Thus, we applied “identity (data without transformed)” and “log (data transformed)” links to evaluate if model shows a better linearity with data transformation. Based on AIC, however, the result showed Moto have a better linearity without data transformation (Supplementary Table 7). We, therefore, used “identity” links for the further model selection. Model selection base on AIC was performed for models including temperature, region (JP and CA sardines), life history stages (larvae, early juvenile and late juvenile) and interactions of these factors. The full model including all the interactions had the lowest AIC (Supplementary Table 7). As the diagnostic for the full model showed normality and homogeneity of residuals (Supplementary Fig. 9), we selected this model for interpretation. The CA sardine at the larval stage as the baseline, we found only JP sardine at early and late juvenile stages has relatively higher Moto values, and the temperature-dependent slope is significantly gentler in JP sardine at early and late juvenile stages (Supplementary Table 8).Next, the diversity of Moto across temperature range was assessed to estimate the optimal temperature in each stage. The relationship between the maximum metabolic rate and temperature is known to be parabolic, while that between the standard metabolic rate and temperature is logarithmic28,79. As the highest field metabolic rate would be constrained by maximum metabolic rate and the lowest field metabolic rate would be close to resting metabolic rate43, fish would have the most diverse metabolic performance at the optimal temperature with the widest aerobic scope. Thus, we modelled the highest and lowest Moto values in each 1 °C bin using a polynomial regression and a generalised linear model with Gaussian distribution and a log link for the 95th and 5th percentile values of each bin, respectively (Supplementary Fig. 10). The values of the bin that included less than four values were excluded from the regression analyses to reduce the uncertainty caused by under-sampled temperature bins. The gap between the two regression lines was considered as a proxy for the aerobic scope, and the temperature at which the gap reached the maximum was regarded as the optimal temperature.Statistical analyses for the relationships between temperature and growthTo understand how variation in ambient water temperature affects early life growth of sardines, we compared back-calculated standard length at around the end of the larval stage (hatch–35 mm; JP: 45 dph, CA: 60 dph), the end of the early juvenile stage (35–60 mm; JP: 75 dph, CA: 90 dph), and the end of the late juvenile stage (60–85 mm; JP: 105 dph, CA: 120 dph) and the mean seawater temperature from hatch to the ages. Median of each sampling batch were used as minimal data unit. Pearson’s r and p-values were first calculated for each comparison (Supplementary Table 9). As the relationship between mean temperature and standard length of JP at 75 dph seemed to be dome-shaped rather than linear, we introduced quadratic term of temperature and tested whether the term increased explanatory power using a linear model and stepwise model selection based on AIC. The model selection showed that the full model (Standard length ∼ Temperature2 + Temperature) was the best model, and the coefficients of the quadratic and linear terms were both significant (Supplementary Table 10). To account for these multiple tests, we corrected the p-values of the coefficients of the quadratic term in the linear model for JP sardine at 75 dph and of the Pearson’s r for the rest using the Benjamini-Hochberg procedure with α = 0.05, and selected the null hypotheses that could be rejected (Supplementary Table 9). To compare the temperature that allow maximisation of growth rate and optimal temperature derived from the analysis of Moto for each stage, median somatic growth rate and otolith increment width in each 1 °C bin was calculated together with its 3-window running mean (Supplementary Figs. 11, 12).Statistical analyses for the relationships between sea surface temperature and survival indexTo test whether habitat temperatures during the first 4 months after hatch affect the survival of sardines in the first year of life on a multidecadal scale, satellite-derived sea surface temperature (SST) since 1982 and survival of JP and CA sardines were compared. The log recruitment residuals from Ricker recruitment models (LNRR)13, representing early life survivals taking into account the effect of population density, were calculated based on the stock assessment data for JP and CA sardines as follows:$${LNR}{R}_{t}={ln}({R}_{t}/{S}_{t}) , – , (a+btimes {S}_{t})$$
(6)
where LNRRt is the LNRR at year t, Rt is the recruitment of year-class t, St is the spawning stock biomass in year t, and a and b are the coefficients of linear regression of ln(Rt/St) on St. Pearson’s r between the LNRR and the mean SST values from March to June for JP and from April to July for CA sardine was calculated for each grid points in the western and eastern boundaries of the North Pacific basin, derived from a SST product based on satellite and in situ observations80 (Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed (https://resources.marine.copernicus.eu/product-detail/SST_GLO_SST_L4_REP_OBSERVATIONS_010_011/INFORMATION), accessed on 11th August and 28th October 2021). The correlations were generally negative and positive in the western and eastern regions, respectively (Supplementary Fig 13a, b). In particular, mean SST values in the area where eggs, larvae and juveniles of JP or CA sardines are mainly found in the months26,39,49,56,78,81,82 (dotted areas in Supplementary Fig 13a, b) were compared with LNRR values to test the relationship between habitat temperature and survival in the early life stages (Supplementary Fig 13c). It should be noted that the mean SST values were not significantly correlated with otolith-derived year-class mean temperatures of JP and CA sardines during the larval to late juvenile stages (JP: r = 0.01, p = 0.98, n = 7, CA: r = 0.29, p = 0.38, n = 11), likely due to the short periods analysed, patchy distribution and inter annual variation in larval and juvenile dispersal and migration patterns. Nevertheless, the regions included areas where SST showed weak to significant (p  More

Abram, P. K., Boivin, G., Moiroux, J. & Brodeur, J. Behavioural effects of temperature on ectothermic animals: Unifying thermal physiology and behavioural plasticity. Biol. Rev. 92, 1859–1876 (2017).Article 

Google Scholar 
Horwitz, R. et al. Near-future ocean warming and acidification alter foraging behaviour, locomotion, and metabolic rate in a keystone marine mollusc. Sci. Rep. 10, 5461 (2020).ADS 
Article 

Google Scholar 
Minuti, J. J., Byrne, M., Hemraj, D. A. & Russell, B. D. Capacity of an ecologically key urchin to recover from extreme events: Physiological impacts of heatwaves and the road to recovery. Sci. Total Environ. 785, 147281 (2021).ADS 
CAS 
Article 

Google Scholar 
Angilletta, M. J., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Angilletta Jr., M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis. (Oxford University Press, 2009). https://doi.org/10.1093/acprof:oso/9780198570875.001.1.Mertens, N. L., Russell, B. D. & Connell, S. D. Escaping herbivory: Ocean warming as a refuge for primary producers where consumer metabolism and consumption cannot pursue. Oecologia 179, 1223–1229 (2015).ADS 
Article 

Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 
Article 

Google Scholar 
Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).ADS 
Article 

Google Scholar 
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).Article 

Google Scholar 
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B Biol. Sci. 280, 20122829 (2013).Article 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
Article 

Google Scholar 
Atkinson, J., King, N. G., Wilmes, S. B. & Moore, P. J. Summer and winter marine heatwaves favor an invasive over native seaweeds. J. Phycol. 56, 1591–1600 (2020).CAS 
Article 

Google Scholar 
Hemraj, D. A., Posnett, N. C., Minuti, J. J., Firth, L. B. & Russell, B. D. Survived but not safe: Marine heatwave hinders metabolism in two gastropod survivors. Mar. Environ. Res. 162, 105117 (2020).CAS 
Article 

Google Scholar 
Vinagre, C. et al. Vulnerability to climate warming and acclimation capacity of tropical and temperate coastal organisms. Ecol. Indic. 62, 317–327 (2016).Article 

Google Scholar 
Vinagre, C. et al. Ecological traps in shallow coastal waters—Potential effect of heat-waves in tropical and temperate organisms. PLoS ONE 13, e0192700 (2018).Article 

Google Scholar 
Falkenberg, L. J., Russell, B. D. & Connell, S. D. Future herbivory: The indirect effects of enriched CO2 may rival its direct effects. Mar. Ecol. Prog. Ser. 492, 85–95 (2013).ADS 
CAS 
Article 

Google Scholar 
Lorda, J., Hechinger, R. F., Cooper, S. D., Kuris, A. M. & Lafferty, K. D. Intraguild predation by shore crabs affects mortality, behavior, growth, and densities of California horn snails. Ecosphere 7, e01262 (2016).Article 

Google Scholar 
Falkenberg, L. J., Connell, S. D. & Russell, B. D. Herbivory mediates the expansion of an algal habitat under nutrient and CO2 enrichment. Mar. Ecol. Prog. Ser. 497, 87–92 (2014).ADS 
CAS 
Article 

Google Scholar 
Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B Biol. Sci. 281, 20140846 (2014).Article 

Google Scholar 
Brothers, C. J. & McClintock, J. B. The effects of climate-induced elevated seawater temperature on the covering behavior, righting response, and Aristotle’s lantern reflex of the sea urchin Lytechinus variegatus. J. Exp. Mar. Biol. Ecol. 467, 33–38 (2015).Article 

Google Scholar 
DeWhatley, M. C. & Alexander, J. E. Impacts of elevated water temperatures on righting behavior and survival of two freshwater caenogastropod snails. Mar. Freshw. Behav. Physiol. 51, 251–262 (2018).Article 

Google Scholar 
Sokolova, I. M. & Pörtner, H.-O. Metabolic plasticity and critical temperatures for aerobic scope in a eurythermal marine invertebrate (Littorina saxatilis, Gastropoda: Littorinidae) from different latitudes. J. Exp. Biol. 206, 195–207 (2003).Article 

Google Scholar 
Sokolova, I. M., Frederich, M., Bagwe, R., Lannig, G. & Sukhotin, A. A. Energy homeostasis as an integrative tool for assessing limits of environmental stress tolerance in aquatic invertebrates. Mar. Environ. Res. 79, 1–15 (2012).CAS 
Article 

Google Scholar 
Monaco, C. J., McQuaid, C. D. & Marshall, D. J. Decoupling of behavioural and physiological thermal performance curves in ectothermic animals: a critical adaptive trait. Oecologia 185, 583–593 (2017).ADS 
Article 

Google Scholar 
Anderson, K. M. & Falkenberg, L. J. Variation in thermal performance curves for oxygen consumption and loss of critical behaviors in co-occurring species indicate the potential for ecosystem stability under ocean warming. Mar. Environ. Res. 172, 105487 (2021).CAS 
Article 

Google Scholar 
Lemmnitz, G., Schuppe, H. & Wolff, H. G. Neuromotor bases of the escape behaviour of Nassa Mutabilis. J. Exp. Biol. 143, 493–507 (1989).Article 

Google Scholar 
Poore, A. G. B. et al. Global patterns in the impact of marine herbivores on benthic primary producers. Ecol. Lett. 15, 912–922 (2012).Article 

Google Scholar 
Britton, D. et al. Adjustments in fatty acid composition is a mechanism that can explain resilience to marine heatwaves and future ocean conditions in the habitat-forming seaweed Phyllospora comosa (Labillardière) C. Agardh. Glob. Change Biol. 26, 3512–3524 (2020).ADS 
Article 

Google Scholar 
Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235 (2021).ADS 
CAS 
Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl. Acad. Sci. 111, 5610–5615 (2014).ADS 
CAS 
Article 

Google Scholar 
Pansch, C. et al. Heat waves and their significance for a temperate benthic community: A near-natural experimental approach. Glob. Change Biol. 24, 4357–4367 (2018).ADS 
Article 

Google Scholar 
Nguyen, H. M. et al. Stress memory in seagrasses: First insight into the effects of thermal priming and the role of epigenetic modifications. Front. Plant Sci. 11, 494 (2020).Article 

Google Scholar 
Xu, Y. et al. Impacts of marine heatwaves on pearl oysters are alleviated following repeated exposure. Mar. Pollut. Bull. 173, 112932 (2021).CAS 
Article 

Google Scholar 
Schram, J. B., Schoenrock, K. M., McClintock, J. B., Amsler, C. D. & Angus, R. A. Multiple stressor effects of near-future elevated seawater temperature and decreased pH on righting and escape behaviors of two common Antarctic gastropods. J. Exp. Mar. Biol. Ecol. 457, 90–96 (2014).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienne Austria (2020).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Therneau, T. M. coxme: Mixed Effects Cox Models. R package version 2.2-16. (2020).Therneau, T. M. & Grambsch, P. M. The cox model. In Modeling Survival Data: Extending the Cox Model 39–77 (Springer, 2000).Fox, J. & Weisburg, S. An R Companion to Applied Regression. (Sage, 2011).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.3. (2020). More

Model substancesWe used the sulfoximine insecticide sulfoxaflor, the methoxy-acrylate fungicide Amistar (azoxystrobin 250 g/l, Suspension Concentrate, see supplementary methods, S1) and the glycine herbicide glyphosate (as active substance, RoundUp ProActive or RoundUp FL, see supplementary methods, S1) as model substances. Our choice was justified by their widespread use, regulatory status and systemic uptake in plants. Because of these characteristics, the likelihood of bees being exposed in the field was considered similarly plausible across model substances. Additionally, we are not aware of published evidence of the acute toxicity of these substances across castes and sexes of B. terrestris and O. bicornis.Sulfoxaflor is a relatively novel insecticide55,56,57, developed to replace or complement the use of older chemical classes against which insect pest populations had developed resistance57. However, because of its risks to bees58, its uses have been recently restricted in the EU to indoor growing conditions. As a nicotinic acetylcholine receptor (nAChR) competitive modulator, sulfoxaflor targets the same neural receptor as the bee-harming neonicotinoid insecticides55,56,57. Despite evidence that sulfoxaflor may target the nAChR in a distinct way compared to recently banned neonicotinoids55,56,57, these substances were shown to be similarly lethal in acute exposure laboratory settings for individuals of Apis mellifera, B. terrestris and O. bicornis38. Additionally, sulfoxaflor was shown to reduce reproduction59,60,61 (but not learning62,63) in bumble bees under field-realistic laboratory settings. When applied pre-flowering in a semi-field study design, sulfoxaflor impacted colony growth, colony size and foraging in bumble bees64 but not honey bees65. Azoxystrobin is a broad-spectrum, systemic fungicide, which has been widely used in agriculture since its first marketing authorisation in 199666. Azoxystrobin shows low acute toxicity to honey bees67. Azoxystrobin residues were found in nectar and pollen from treated crops68,69 and subsequently in the bodies of wild bees70. In a semi-field experimental setting, foraging, but not colony growth, was significantly impaired in B. terrestris exposed to Amistar (azoxystrobin 250 g/L SC)64, while no lethal or sublethal effects could be observed in honey bees65 or in O. bicornis71. However, a recent study showed that, when formulated as Amistar this pesticide induced acute mortality in bumble bees at high doses, which was attributed to the dietary toxicity of the co-formulant C16-18 alcohol ethoxylates50.Glyphosate is a broad-spectrum systemic herbicide and the most widely used pesticide in the world72. Products containing glyphosate may be applied to flowering weeds73 and contaminate their pollen and nectar54, thus driving bee contact and oral exposure. Glyphosate showed low lethal hazards in regulatory-ready laboratory74 and semi-field designs when dosed as pure active substance or as MON 52276 (SL formulation containing 360 g glyphosate/L)75. A recent study found ready-to-use consumer products containing glyphosate to be lethally hazardous to bumble bees73. However, this toxicity was attributed to co-formulants, rather than the active substance itself.We characterised the acute oral and contact toxicity to B. terrestris and O. bicornis of sulfoxaflor, azoxystrobin and glyphosate as either pure active substances or formulation (see supplementary material S2 Table S1). Each test was repeated across castes and sexes of these two species. For bumble bees we used workers, males and gynes (i.e., unmated queens), hereby referred to as queens, whereas for O. bicornis we used males and females. Bumble bee experiments were designed following OECD protocols30,31, while O. bicornis was tested following published76 and ring-tested protocols32, as an OECD protocol for this latter species is not yet available.We used a dose response design whenever the test item was found to drive significant mortality in the tested species. In all other cases, a limit test design using a single, high pesticide dose was used. Details on the methods and results of the limit tests are reported in the supplementary materials (S2 and S4).Pesticide treatmentsAll dose response tests were performed with pure sulfoxaflor, while azoxystrobin was tested as a plant protection product (Amistar 250 g a.s./l, SC, Syngenta, UK) in all oral tests, as its solubility in water was insufficient (6.7 mg a.s./L, see EFSA, 2010) to achieve the desired concentrations. Amistar contains co-formulants with hazard classification (54 C16-18 alcohols, ethoxylated  More

Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Angert, A. L., LaDeau, S. L. & Ostfeld, R. S. Climate change and species interactions: ways forward. Ann. N. Y. Acad. Sci. 1297, 1–7 (2013).ADS 
PubMed 
Article 

Google Scholar 
Yang, L. H. & Rudolf, V. H. W. Phenology, ontogeny and the effects of climate change on the timing of species interactions. Ecol. Lett. 13, 1–10 (2010).CAS 
PubMed 
Article 

Google Scholar 
Kersting, D. K. et al. Experimental evidence of the synergistic effects of warming and invasive algae on a temperate reef-builder coral. Sci. Rep. 5, 18635 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, Y. et al. Warming reshaped the microbial hierarchical interactions. Glob. Chang. Biol. 27, 6331–6347 (2021).CAS 
PubMed 
Article 

Google Scholar 
Grainger, T. N., Rego, A. I. & Gilbert, B. Temperature-dependent species interactions shape priority effects and the persistence of unequal competitors. Am. Nat. 191, 197–209 (2018).PubMed 
Article 

Google Scholar 
Ørsted, M., Schou, M. F. & Kristensen, T. N. Biotic and abiotic factors investigated in two Drosophila species: evidence of both negative and positive effects of interactions on performance. Sci. Rep. 7, 40132 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Sniegula, S., Golab, M. J. & Johansson, F. Size-mediated priority and temperature effects on intra-cohort competition and cannibalism in a damselfly. J. Anim. Ecol. 88, 637–648 (2019).PubMed 
Article 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872 (2007).ADS 
Article 

Google Scholar 
Carter, S. K. & Rudolf, V. H. W. Shifts in phenological mean and synchrony interact to shape competitive outcomes. Ecology 100, e02826 (2019).PubMed 
Article 

Google Scholar 
Rudolf, V. H. W. Nonlinear effects of phenological shifts link interannual variation to species interactions. J. Anim. Ecol. 87, 1395–1406 (2018).PubMed 
Article 

Google Scholar 
Rasmussen, N. L., Allen, B. G. V. & Rudolf, V. H. W. Linking phenological shifts to species interactions through size-mediated priority effects. J. Anim. Ecol. 83, 1206–1215 (2014).PubMed 
Article 

Google Scholar 
Bailey, L. D. & Pol, M. van de. Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. J. Anim. Ecol. 85, 85–96 (2016).Walker, R., Wilder, S. M. & González, A. L. Temperature dependency of predation: increased killing rates and prey mass consumption by predators with warming. Ecol. Evol. 10, 9696–9706 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Schulte, P. M. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).PubMed 
Article 

Google Scholar 
Anholt, B. R. Cannibalism and early instar survival in a larval damselfly. Oecologia 99, 60–65 (1994).ADS 
PubMed 
Article 

Google Scholar 
Johansson, F. & Crowley, P. H. Larval cannibalism and population dynamics of dragonflies. in Aquatic insects: challenges to populations (eds. Lancaster, J. & Briers, R. A.) 36–54 (CABI, 2008). doi:https://doi.org/10.1079/9781845933968.0036.Takashina, N. & Fiksen, Ø. Optimal reproductive phenology under size-dependent cannibalism. Ecol. Evol. 10, 4241–4250 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Crumrine, P. W. Body size, temperature, and seasonal differences in size structure influence the occurrence of cannibalism in larvae of the migratory dragonfly, Anax junius. Aquat. Ecol. 44, 761–770 (2010).Article 

Google Scholar 
Op de Beeck, L., Verheyen, J. & Stoks, R. Competition magnifies the impact of a pesticide in a warming world by reducing heat tolerance and increasing autotomy. Environ. Pollut. 233, 226–234 (2018).Enriquez-Urzelai, U., Nicieza, A. G., Montori, A., Llorente, G. A. & Urrutia, M. B. Physiology and acclimation potential are tuned with phenology in larvae of a prolonged breeder amphibian. Oikos 2022, e08566 (2022).Article 

Google Scholar 
Knight, C. M., Parris, M. J. & Gutzke, W. H. N. Influence of priority effects and pond location on invaded larval amphibian communities. Biol. Invasions 11, 1033–1044 (2009).Article 

Google Scholar 
Raczyński, M., Stoks, R., Johansson, F., Bartoń, K. & Sniegula, S. Phenological shifts in a warming world affect physiology and life history in a damselfly. Insects 13, 622 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Murillo-Rincón, A. P., Kolter, N. A., Laurila, A. & Orizaola, G. Intraspecific priority effects modify compensatory responses to changes in hatching phenology in an amphibian. J. Anim. Ecol. 86, 128–135 (2017).PubMed 
Article 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

Google Scholar 
Jermacz, Ł. et al. Continuity of chronic predation risk determines changes in prey physiology. Sci. Rep. 10, 6972 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raczyński, M., Stoks, R., Johansson, F. & Sniegula, S. Size-mediated priority effects are trait-dependent and consistent across latitudes in a damselfly. Oikos 130, 1535–1547 (2021).Article 

Google Scholar 
Peacor, S. D. & Werner, E. E. Predator effects on an assemblage of consumers through induced changes in consumer foraging behavior. Ecology 81, 1998–2010 (2000).Article 

Google Scholar 
Stoks, R., Block, M. D., Meutter, F. V. D. & Johansson, F. Predation cost of rapid growth: behavioural coupling and physiological decoupling. J. Anim. Ecol. 74, 708–715 (2005).Article 

Google Scholar 
Hermann, S. L. & Landis, D. A. Scaling up our understanding of non-consumptive effects in insect systems. Curr. Opin. Insect. Sci. 20, 54–60 (2017).PubMed 
Article 

Google Scholar 
Sniegula, S., Nsanzimana, J. d’Amour & Johansson, F. Predation risk affects egg mortality and carry over effects in the larval stages in damselflies. Freshw. Biol. 64, 778–786 (2019).Preisser, E. L. & Orrock, J. L. The allometry of fear: interspecific relationships between body size and response to predation risk. Ecosphere 3, art77 (2012).Gehr, B. et al. Evidence for nonconsumptive effects from a large predator in an ungulate prey?. Behav. Ecol. 29, 724–735 (2018).Article 

Google Scholar 
Jiménez-Cortés, J. G., Serrano-Meneses, M. A. & Córdoba-Aguilar, A. The effects of food shortage during larval development on adult body size, body mass, physiology and developmental time in a tropical damselfly. J. Insect Physiol. 58, 318–326 (2012).PubMed 
Article 

Google Scholar 
Weissburg, M., Smee, D. L., Ferner, M. C., Schmitz, A. E. O. J. & Bronstein, E. J. L. The sensory ecology of nonconsumptive predator effects. Am. Nat. 184, 141–157 (2014).PubMed 
Article 

Google Scholar 
Zhang, D.-W., Xiao, Z.-J., Zeng, B.-P., Li, K. & Tang, Y.-L. Insect behavior and physiological adaptation mechanisms under starvation stress. Front. Physiol. 10, 163 (2019).PubMed 
PubMed Central 
Article 

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

Google Scholar 
Bell, A. M., Dingemanse, N. J., Hankison, S. J., Langenhof, M. B. W. & Rollins, K. Early exposure to nonlethal predation risk by size-selective predators increases somatic growth and decreases size at adulthood in threespined sticklebacks. J. Evol. Biol. 24, 943–953 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De Block, M. & Stoks, R. Compensatory growth and oxidative stress in a damselfly. Proc. Royal Soc. B 275, 781–785 (2008).Article 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. The trade-off between growth rate and locomotor performance varies with perceived time until breeding. J. Exp. Biol. 213, 3289–3298 (2010).PubMed 
Article 

Google Scholar 
Catalán, A. M. et al. Community-wide consequences of nonconsumptive predator effects on a foundation species. J. Anim. Ecol. 90, 1307–1316 (2021).PubMed 
Article 

Google Scholar 
Preisser, E. L., Bolnick, D. I. & Benard, M. F. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86, 501–509 (2005).Article 

Google Scholar 
Gjoni, V., Basset, A. & Glazier, D. S. Temperature and predator cues interactively affect ontogenetic metabolic scaling of aquatic amphipods. Biol. Lett. 16, 20200267 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller, L. P., Matassa, C. M. & Trussell, G. C. Climate change enhances the negative effects of predation risk on an intermediate consumer. Glob. Chang. Biol. 20, 3834–3844 (2014).ADS 
PubMed 
Article 

Google Scholar 
Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
Article 

Google Scholar 
Lancaster, L. T., Morrison, G. & Fitt, R. N. Life history trade-offs, the intensity of competition, and coexistence in novel and evolving communities under climate change. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 372, 20160046 (2017).Sniegula, S., Janssens, L. & Stoks, R. Integrating multiple stressors across life stages and latitudes: combined and delayed effects of an egg heat wave and larval pesticide exposure in a damselfly. Aquat. Toxicol. 186, 113–122 (2017).CAS 
PubMed 
Article 

Google Scholar 
Stoks, R., Block, M. D., Slos, S., Doorslaer, W. V. & Rolff, J. Time constraints mediate predator-induced plasticity in immune function, condition, and life history. Ecology 87, 809–815 (2006).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Pintanel, P., Tejedo, M., Salinas-Ivanenko, S., Jervis, P. & Merino-Viteri, A. Predators like it hot: thermal mismatch in a predator-prey system across an elevational tropical gradient. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13516 (2021).Article 
PubMed 

Google Scholar 
Stoks, R., Swillen, I. & Block, M. D. Behaviour and physiology shape the growth accelerations associated with predation risk, high temperatures and southern latitudes in Ischnura damselfly larvae. J. Anim. Ecol. 81, 1034–1040 (2012).PubMed 
Article 

Google Scholar 
Wang, Y.-J., Sentis, A., Tüzün, N. & Stoks, R. Thermal evolution ameliorates the long-term plastic effects of warming, temperature fluctuations and heat waves on predator–prey interaction strength. Funct. Ecol. 35, 1538–1549 (2021).Article 

Google Scholar 
Sniegula, S., Golab, M. J. & Johansson, F. Cannibalism and activity rate in larval damselflies increase along a latitudinal gradient as a consequence of time constraints. BMC Evol. Biol. 17, 167 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Gyssels, F. & Stoks, R. Behavioral responses to fish kairomones and autotomy in a damselfly. J. Ethol. 24, 79–83 (2006).Article 

Google Scholar 
McPeek, M. A., Grace, M. & Richardson, J. M. L. Physiological and behavioral responses to predators shape the growth/predation risk trade-off in damselflies. Ecology 82, 1535–1545 (2001).Article 

Google Scholar 
Beermann, J., Boos, K., Gutow, L., Boersma, M. & Peralta, A. C. Combined effects of predator cues and competition define habitat choice and food consumption of amphipod mesograzers. Oecologia 186, 645–654 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Evol. Syst. 2, 369–404 (1971).Article 

Google Scholar 
Dijkstra, K., Schröter, A. & Lewington, R. Field Guide to the Dragonflies of Britain and Europe. Second edition. (Bloomsbury Publishing, 2020).Corbet, P. S., Suhling, F. & Soendgerath, D. Voltinism of Odonata: a review. Int. J. Odonatol. 9, 1–44 (2006).Article 

Google Scholar 
Zwick, P. & Corbet, P. S. Dragonflies: behaviour and ecology of Odonata. (Comstock Publishing Associates, 1999).Fontana-Bria, L., Selfa, J., Tur, C. & Frago, E. Early exposure to predation risk carries over metamorphosis in two distantly related freshwater insects. Ecol. Entomol. 42, 255–262 (2017).Article 

Google Scholar 
Sniegula, S., Raczyński, M., Golab, M. J. & Johansson, F. Effects of predator cues carry over from egg and larval stage to adult life-history traits in a damselfly. Freshw. Sci. 39, 804–811 (2020).Article 

Google Scholar 
Chivers, D. P., Wisenden, B. D. & Smith, R. J. F. Damselfly larvae learn to recognize predators from chemical cues in the predator’s diet. Anim. Behav. 52, 315–320 (1996).Article 

Google Scholar 
Mikolajczuk, P. Stwierdzenie wylotu drugiej generacji tężnicy małej Ischnura pumilio (Charpentier, 1825) i tężnicy wytwornej Ischnura elegans (Vander Linden, 1820) (Odonata: Coenagrionidae) w Polsce środkowo-wschodniej. Odonatrix 1, (2014).De Block, M., Pauwels, K., Van Den Broeck, M., De Meester, L. & Stoks, R. Local genetic adaptation generates latitude-specific effects of warming on predator-prey interactions. Glob. Chang. Biol. 19, 689–696 (2013).ADS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2021).Buskirk, J. V., Krügel, A., Kunz, J., Miss, F. & Stamm, A. The rate of degradation of chemical cues indicating predation risk: an experiment and review. Ethology 120, 942–949 (2014).Article 

Google Scholar 
Hagler, J. R. & Jackson, C. G. Methods for marking insects: current techniques and future prospects. Annu. Rev. Entomol. 46, 511–543 (2001).CAS 
PubMed 
Article 

Google Scholar 
Crumrine, P. W. Size structure and substitutability in an odonate intraguild predation system. Oecologia 145, 132–139 (2005).ADS 
PubMed 
Article 

Google Scholar 
Strobbe, F. & Stoks, R. Life history reaction norms to time constraints in a damselfly: differential effects on size and mass. Biol. J. Linn. Soc. 83, 187–196 (2004).Article 

Google Scholar 
De Block, M., McPeek, M. A. & Stoks, R. Stronger compensatory growth in a permanent-pond Lestes damselfly relative to temporary-pond Lestes. Oikos 117, 245–254 (2008).Article 

Google Scholar 
Marsh, J. B. & Weinstein, D. B. Simple charring method for determination of lipids. J. Lipid Res. 7, 574–576 (1966).CAS 
PubMed 
Article 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 
Article 

Google Scholar 
Stoks, R., Block, M. D. & McPeek, M. A. Physiological costs of compensatory growth in a damselfly. Ecology 87, 1566–1574 (2006).PubMed 
Article 

Google Scholar 
R Development Core Team. R: The R Project for Statistical Computing. Vienna, Austria https://www.r-project.org/ (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Cyrus, A. Z., Swiggs, J., Santidrian Tomillo, P., Paladino, F. V. & Peters, W. S. Cannibalism causes size-dependent intraspecific predation pressure but does not trigger autotomy in the intertidal gastropod Agaronia propatula. J. Molluscan Stud. 81, 388–396 (2015).Jara, F. G. Trophic ontogenetic shifts of the dragonfly Rhionaeschna variegata: the role of larvae as predators and prey in Andean wetland communities. Ann. Limnol. 50, 173–184 (2014).Article 

Google Scholar 
Fréchette, M. & Lefaivre, D. On self-thinning in animals. Oikos 73, 425–428 (1995).Article 

Google Scholar 
Johansson, F., Stoks, R., Rowe, L. & De Block, M. Life history plasticity in a damselfly: effects of combined time and biotic constraints. Ecology 82, 1857–1869 (2001).Article 

Google Scholar 
Mikolajewski, D. J., Conrad, A. & Joop, G. Behaviour and body size: plasticity and genotypic diversity in larval Ischnura elegans as a response to predators (Odonata: Coenagrionidae). Int. J. Odonatol. 18, 31–44 (2015).Article 

Google Scholar 
Antoł, A. & Sniegula, S. Damselfly eggs alter their development rate in the presence of an invasive alien cue but not a native predator cue. Ecol. Evol. 11, 9361–9369 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Hassall, C. & Thompson, D. J. The effects of environmental warming on Odonata: a review. Int. J. Odonatol. 11, 131–153 (2008).Article 

Google Scholar 
Debecker, S. & Stoks, R. Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes. Ecol. Monogr. 89, e01332 (2019).Article 

Google Scholar 
Anderson, T. L. & Semlitsch, R. D. Top predators and habitat complexity alter an intraguild predation module in pond communities. J. Anim. Ecol. 85, 548–558 (2016).PubMed 
Article 

Google Scholar 
Norling, U. Growth, winter preparations and timing of emergence in temperate zone odonata: control by a succession of larval response patterns. Int. J. Odonatol. 24, 1–36 (2021).Article 

Google Scholar 
Abrams, P. A., Leimar, O., Nylin, S. & Wiklund, C. The effect of flexible growth rates on optimal sizes and development times in a seasonal environment. Am. Nat. 147, 381–395 (1996).Article 

Google Scholar 
Arendt, J. D. Adaptive intrinsic growth rates: an integration across taxa. Q. Rev. Biol. 72, 149–177 (1997).Article 

Google Scholar 
Bobrek, R. Odonate phenology recorded in a Central European location in an extremely warm season. Biologia 76, 2957–2964 (2021).Article 

Google Scholar 
Dmitriew, C. M. The evolution of growth trajectories: what limits growth rate?. Biol. Rev. 86, 97–116 (2011).PubMed 
Article 

Google Scholar 
Śniegula, S., Johansson, F. & Nilsson-Örtman, V. Differentiation in developmental rate across geographic regions: a photoperiod driven latitude compensating mechanism?. Oikos 121, 1073–1082 (2012).Article 

Google Scholar 
Angell, C. S. et al. Development time mediates the effect of larval diet on ageing and mating success of male antler flies in the wild. Proc. R. Soc. B 287, 20201876 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Johansson, F., Watts, P. C., Sniegula, S. & Berger, D. Natural selection mediated by seasonal time constraints increases the alignment between evolvability and developmental plasticity. Evolution 75, 464–475 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Nilsson-Örtman, V. & Rowe, L. The evolution of developmental thresholds and reaction norms for age and size at maturity. PNAS 118, (2021).Rohner, P. T. & Moczek, A. P. Evolutionary and plastic variation in larval growth and digestion reveal the complex underpinnings of size and age at maturation in dung beetles. Ecol. Evol. 11, 15098–15110 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Rolff, J., Fellowes, M & Holloway, G. Insect Evolutionary Ecology: Proceedings of the Royal Entomological Society’s 22nd Symposium. (CABI Oxford University Press, 2006).Beukeboom, L. W. Size matters in insects: an introduction. Entomol. Exp. Appl. 166, 2–3 (2018).Article 

Google Scholar 
Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483–492 (1993).Article 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B 280, 20122370 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Burraco, P., Díaz-Paniagua, C. & Gomez-Mestre, I. Different effects of accelerated development and enhanced growth on oxidative stress and telomere shortening in amphibian larvae. Sci. Rep. 7, 7494 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dańko, M. J., Dańko, A., Golab, M. J., Stoks, R. & Sniegula, S. Latitudinal and age-specific patterns of larval mortality in the damselfly Lestes sponsa: Senescence before maturity?. Exp. Gerontol. 95, 107–115 (2017).PubMed 
Article 

Google Scholar 
Kong, J. D., Hoffmann, A. A. & Kearney, M. R. Linking thermal adaptation and life-history theory explains latitudinal patterns of voltinism. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180547 (2019).Śniegula, S., Gołąb, M. J. & Johansson, F. Time constraint effects on phenology and life history synchrony in a damselfly along a latitudinal gradient. Oikos 125, 414–423 (2016).Article 

Google Scholar 
Popova, O. N. & Haritonov, AYu. Disclosure of biotopical groups in the population of the dragonfly Coenagrion armatum (Charpentier, 1840). Contemp. Probl. Ecol. 7, 175–181 (2014).Article 

Google Scholar 
Mikolajewski, D. J., De Block, M. & Stoks, R. The interplay of adult and larval time constraints shapes species differences in larval life history. Ecology 96, 1128–1138 (2015).PubMed 
Article 

Google Scholar 
Wolf, J. B. & Wade, M. J. What are maternal effects (and what are they not)? Philos. Trans. R Soc. Lond. B Biol. Sci. 364, 1107–1115 (2009).Zehnder, C. B., Parris, M. A. & Hunter, M. D. Effects of maternal age and environment on offspring vital rates in the Oleander Aphid (Hemiptera: Aphididae). Environ. Entomol. 36, 910–917 (2007).PubMed 
Article 

Google Scholar 
Hernández, C. M., van Daalen, S. F., Caswell, H., Neubert, M. G. & Gribble, K. E. A demographic and evolutionary analysis of maternal effect senescence. PNAS 117, 16431–16437 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shama, L. N. S., Campero-Paz, M., Wegner, K. M., De Block, M. & Stoks, R. Latitudinal and voltinism compensation shape thermal reaction norms for growth rate. Mol. Ecol. 20, 2929–2941 (2011).PubMed 
Article 

Google Scholar 
Sniegula, S., Golab, M. J., Drobniak, S. M. & Johansson, F. Seasonal time constraints reduce genetic variation in life-history traits along a latitudinal gradient. J. Anim. Ecol. 85, 187–198 (2016).PubMed 
Article 

Google Scholar 
De Block, M. & Stoks, R. Adaptive sex-specific life history plasticity to temperature and photoperiod in a damselfly. J. Evol. Biol. 16, 986–995 (2003).PubMed 
Article 

Google Scholar 
Verberk, W. C. E. P. et al. Shrinking body sizes in response to warming: explanations for the temperature–size rule with special emphasis on the role of oxygen. Biol. Rev. 96, 247–268 (2021).PubMed 
Article 

Google Scholar 
Sheriff, M. J., Peacor, S. D., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: a dearth of evidence. J. Anim. Ecol. 89, 1302–1316 (2020).PubMed 
Article 

Google Scholar 
Wirsing, A. J., Heithaus, M. R., Brown, J. S., Kotler, B. P. & Schmitz, O. J. The context dependence of non-consumptive predator effects. Ecol. Lett 24, 113–129 (2021).PubMed 
Article 

Google Scholar 
McCauley, S. J., Rowe, L. & Fortin, M.-J. The deadly effects of ‘nonlethal’ predators. Ecology 92, 2043–2048 (2011).PubMed 
Article 

Google Scholar 
Palacios, M. del M. & McCormick, M. I. Positive indirect effects of top-predators on the behaviour and survival of juvenile fishes. Oikos 130, 219–230 (2021).Thaler, J. S., McArt, S. H. & Kaplan, I. Compensatory mechanisms for ameliorating the fundamental trade-off between predator avoidance and foraging. PNAS 109, 12075–12080 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Janssens, L., Van Dievel, M. & Stoks, R. Warming reinforces nonconsumptive predator effects on prey growth, physiology, and body stoichiometry. Ecology 96, 3270–3280 (2015).PubMed 
Article 

Google Scholar 
Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
Article 

Google Scholar 
Nation, J. L. Insect Physiology and Biochemistry. (CRC Press, 2011). doi:https://doi.org/10.1201/9781420061789.Rudolf, V. H. W. & Singh, M. Disentangling climate change effects on species interactions: effects of temperature, phenological shifts, and body size. Oecologia 173, 1043–1052 (2013).ADS 
PubMed 
Article 

Google Scholar 
Pfennig, D. W. Effect of predator-prey phylogenetic similarity on the fitness consequences of predation: a trade-off between nutrition and disease?. Am. Nat. 155, 335–345 (2000).PubMed 
Article 

Google Scholar 
Lee, K. P., Simpson, S. J. & Wilson, K. Dietary protein-quality influences melanization and immune function in an insect. Funct. Ecol. 22, 1052–1061 (2008).Article 

Google Scholar 
Wu, Q., Patočka, J. & Kuča, K. Insect Antimicrobial Peptides, a Mini Review. Toxins (Basel) 10, 461 (2018).Bullard, B. et al. The molecular elasticity of the insect flight muscle proteins projectin and kettin. PNAS 103, 4451–4456 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mamat-Noorhidayah, Yazawa, K., Numata, K. & Norma-Rashid, Y. Morphological and mechanical properties of flexible resilin joints on damselfly wings (Rhinocypha spp.). PLoS One 13, e0193147 (2018).Muthukrishnan, S., Merzendorfer, H., Arakane, Y. & Kramer, K. J. 7 – Chitin Metabolism in Insects. in Insect Molecular Biology and Biochemistry (ed. Gilbert, L. I.) 193–235 (Academic Press, 2012). doi:https://doi.org/10.1016/B978-0-12-384747-8.10007-8.Van Dievel, M., Stoks, R. & Janssens, L. Beneficial effects of a heat wave: higher growth and immune components driven by a higher food intake. J. Exp. Biol. 220, 3908–3915 (2017).PubMed 

Google Scholar  More