Philippa Kaur

1.van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).PubMed 
PubMed Central 

Google Scholar 
2.Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

Google Scholar 
6.IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES secretariat, Bonn, Germany, 2019).7.Smith, H. F. & Sullivan, C. A. Ecosystem services within agricultural landscapes—farmers’ perceptions. Ecol. Econ. 98, 72–80 (2014).Article 

Google Scholar 
8.Barnes, A. D. et al. Biodiversity enhances the multitrophic control of arthropod herbivory. Sci. Adv. 6, eabb6603 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).PubMed 
PubMed Central 
Article 

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

Google Scholar 
11.Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
12.Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Economic value of biological control in integrated pest management of managed plant systems. Annu. Rev. Entomol. 60, 621–645 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Frishkoff, L. O. et al. Loss of avian phylogenetic diversity in neotropical agricultural systems. Science 345, 1343–1346 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Mendenhall, C. D., Karp, D. S., Meyer, C. F. J., Hadly, E. A. & Daily, G. C. Predicting biodiversity change and averting collapse in agricultural landscapes. Nature 509, 213–217 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 115, E7863–E7870 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Redlich, S., Martin, E. A. & Steffan-Dewenter, I. Landscape-level crop diversity benefits biological pest control. J. Appl. Ecol. 55, 2419–2428 (2018).Article 

Google Scholar 
18.Muneret, L. et al. Evidence that organic farming promotes pest control. Nat. Sustain. 1, 361–368 (2018).Article 

Google Scholar 
19.Roubos, C. R., Rodriguez-Saona, C. & Isaacs, R. Mitigating the effects of insecticides on arthropod biological control at field and landscape scales. Biol. Control 75, 28–38 (2014).CAS 
Article 

Google Scholar 
20.Roschewitz, I., Hucker, M., Tscharntke, T. & Thies, C. The influence of landscape context and farming practices on parasitism of cereal aphids. Agric. Ecosyst. Environ. 108, 218–227 (2005).Article 

Google Scholar 
21.Frago, E., Pujadevillar, J., Guara, M. & Selfa, J. Hyperparasitism and seasonal patterns of parasitism as potential causes of low top-down control in Euproctis chrysorrhoea L. (Lymantriidae). Biol. Control 60, 123–131 (2012).Article 

Google Scholar 
22.Rosenheim, J. A., Kaya, H. K., Ehler, L. E., Marois, J. J. & Jaffee, B. A. Intraguild predation among biological-control agents: theory and evidence. Biol. Control 5, 303–335 (1995).Article 

Google Scholar 
23.Brobyn, P. J., Clark, S. J. & Wilding, N. The effect of fungus infection of Metopolophium dirhodum [Hom.: Aphididae] on the oviposition behaviour of the aphid parasitoid Aphidius rhopalosiphi [Hym.: Aphidiidae]. Entomophaga 33, 333–338 (1988).Article 

Google Scholar 
24.Tscharntke, T. et al. Conservation biological control and enemy diversity on a landscape scale. Biol. Control 43, 294–309 (2007).Article 

Google Scholar 
25.Rand, T. A., van Veen, F. J. F. & Tscharntke, T. Landscape complexity differentially benefits generalized fourth, over specialized third, trophic level natural enemies. Ecography 35, 97–104 (2012).Article 

Google Scholar 
26.Zhao, Z. H., Hui, C., He, D. H. & Li, B. L. Effects of agricultural intensification on ability of natural enemies to control aphids. Sci. Rep. 5, 8024 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Vollhardt, I. M. G., Tscharntke, T., Wäckers, F. L., Bianchi, F. J. J. A. & Thies, C. Diversity of cereal aphid parasitoids in simple and complex landscapes. Agric. Ecosyst. Environ. 126, 289–292 (2008).Article 

Google Scholar 
28.Tomanović, Z. et al. Regional tritrophic relationship patterns of five aphid parasitoid species (Hymenoptera: Braconidae: Aphidiinae) in agroecosystem-dominated landscapes of southeastern Europe. J. Econ. Entomol. 102, 836–854 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Kaartinen, R. & Roslin, T. Shrinking by numbers: landscape context affects the species composition but not the quantitative structure of local food webs. J. Anim. Ecol. 80, 622–631 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Wang, S. & Brose, U. Biodiversity and ecosystem functioning in food webs: the vertical diversity hypothesis. Ecol. Lett. 21, 9–20 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Garzke, J., Connor, S. J., Sommer, U. & O’Connor, M. I. Trophic interactions modify the temperature dependence of community biomass and ecosystem function. PLoS Biol. 17, e2006806 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Pocock, M. J. O. et al. The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management. Adv. Ecol. Res. 54, 41–85 (2016).Article 

Google Scholar 
33.Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).Article 

Google Scholar 
34.Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).Article 

Google Scholar 
35.Gilbert, A. J. Connectance indicates the robustness of food webs when subjected to species loss. Ecol. Indic. 9, 72–80 (2009).Article 

Google Scholar 
36.Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180–183 (2000).CAS 
PubMed 
Article 

Google Scholar 
37.Galiana, N., Hawkins, B. A. & Montoya, J. M. The geographical variation of network structure is scale dependent: understanding the biotic specialization of host–parasitoid networks. Ecography 42, 1175–1187 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Banašek-Richter, C., Cattin, M.-F. & Bersier, L.-F. Sampling effects and the robustness of quantitative and qualitative food-web descriptors. J. Theor. Biol. 226, 23–32 (2004).PubMed 
Article 

Google Scholar 
39.Varennes, Y. D., Boyer, S. & Wratten, S. D. Un-nesting DNA Russian dolls—the potential for constructing food webs using residual DNA in empty aphid mummies. Mol. Ecol. 23, 3925–3933 (2014).CAS 
PubMed 
Article 

Google Scholar 
40.Zhu, Y. L. et al. A molecular detection approach for a cotton aphid-parasitoid complex in northern China. Sci. Rep. 9, 15836 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Staniczenko, P. P. A. et al. Predicting the effect of habitat modification on networks of interacting species. Nat. Commun. 8, 792 (2018).Article 
CAS 

Google Scholar 
42.Thies, C. & Tscharntke, T. In Biocontrol-Based Integrated Management of Oilseed Rape Pests (ed. Williams, I.H.). (Springer Netherlands, 2010).43.Tylianakis, J. M., Tscharntke, T. & Lewis, O. T. Habitat modification alters the structure of tropical host-parasitoid food webs. Nature 445, 202–205 (2007).CAS 
Article 

Google Scholar 
44.Grass, I., Jauker, B., Steffandewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Gagic, V. et al. Food web structure and biocontrol in a four-trophic level system across a landscape complexity gradient. Proc. Roy. Soc. B. 278, 2946–2953 (2011).Article 

Google Scholar 
46.Lundgren, J. G. & Fausti, S. W. Trading biodiversity for pest problems. Sci. Adv. 1, e1500558 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Zhou, K. et al. Effects of land use and insecticides on natural enemies of aphids in cotton: first evidence from smallholder agriculture in the North China Plain. Agric. Ecosyst. Environ. 183, 176–184 (2014).Article 

Google Scholar 
48.Zhang, Z. Q. The natural enemies of Aphis gossypii Glover (Hom., Aphididae) in China. J. Appl. Entomol. 114, 251–262 (2009).Article 

Google Scholar 
49.Gagic, V. et al. Agricultural intensification and cereal aphid–parasitoid–hyperparasitoid food webs: network complexity, temporal variability and parasitism rates. Oecologia 170, 1099–1109 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Vollhardt, I. M. G. et al. Influence of plant fertilisation on cereal aphid-primary parasitoid-secondary parasitoid networks in simple and complex landscapes. Agric. Ecosyst. Environ. 281, 47–55 (2019).CAS 
Article 

Google Scholar 
51.Sullivan, D. J. & Völkl, W. Hyperparasitism: multitrophic ecology and behavior. Annu. Rev. Entomol. 44, 291–315 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Dainese, M., Montecchiari, S., Sitzia, T., Sigura, M. & Marini, L. High cover of hedgerows in the landscape supports multiple ecosystem services in Mediterranean cereal fields. J. Appl. Ecol. 54, 380–388 (2016).Article 

Google Scholar 
53.Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45, 175–201 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Thies, C., Roschewitz, I. & Tscharntke, T. The landscape context of cereal aphid-parasitoid interactions. Proc. Roy. Soc. B. 272, 203–210 (2005).Article 

Google Scholar 
55.Plećaš, M. et al. Landscape composition and configuration influence cereal aphid–parasitoid–hyperparasitoid interactions and biological control differentially across years. Agric. Ecosyst. Environ. 183, 1–10 (2014).Article 

Google Scholar 
56.Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Lichtenberg, E. M. et al. A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Glob. Change Biol. 23, 4946–4957 (2017).Article 

Google Scholar 
58.Osorio, S., Arnan, X., Bassols, E., Vicens, N. & Bosch, J. Local and landscape effects in a host-parasitoid interaction network along a forest-cropland gradient. Ecol. Appl. 25, 1869–1879 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Dunne, J., Williams, R. & Martinez, N. Network topology and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
60.Montoya, J. M., Rodríguez, M. A. & Hawkins, B. A. Food web complexity and higher-level ecosystem services. Ecol. Lett. 6, 587–593 (2003).Article 

Google Scholar 
61.Hawkins, B. A. Parasitoid-host food webs and donor control. Oikos 65, 159–162 (1992).Article 

Google Scholar 
62.Yeakel, J. D. et al. Diverse interactions and ecosystem engineering can stabilize community assembly. Nat. Commun. 11, 3307 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Poisot, T., Mouquet, N. & Gravel, D. Trophic complementarity drives the biodiversity-ecosystem functioning relationship in food webs. Ecol. Lett. 16, 853–861 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
64.White, L., O’Connor, N. E., Yang, Q., Emmerson, M. C. & Donohue, I. Individual species provide multifaceted contributions to the stability of ecosystems. Nat. Ecol. Evol. 4, 1594–1601 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Ho, H.-C., Tylianakis, J. M. & Pawar, S. Behaviour moderates the impacts of food-web structure on species coexistence. Ecol. Lett. 24, 298–309 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Holland, J. M. et al. Agri-environment scheme enhancing ecosystem services: A demonstration of improved biological control in cereal crops. Agric. Ecosyst. Environ. 155, 147–152 (2012).Article 

Google Scholar 
67.Batary, P., Dicks, L., Kleijn, D. & Sutherland, W. The role of agri-environment schemes in conservation and environmental management: European Agri-Environment Schemes. Conserv. Biol. 29, 1006–1016 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.McGarigal, K., Cushman, S., Maile, N. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html (2012).69.Liu, B. et al. Secondary crops and non-crop habitats within landscapes enhance the abundance and diversity of generalist predators. Agric. Ecosyst. Environ. 258, 30–39 (2018).Article 

Google Scholar 
70.Lu, Y. H., Qi, F. J. & Zhang, Y. J. Integrated Management of Diseases and Insect Pests in Cotton (Golden Shield Press, Beijing 2010).71.Shannon, C. E., Weaver, W., Blahut, R. E. & Hajek, B. The Mathematical Theory of Communications (University of Illinois Press, Urbana, 1949).72.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
Article 

Google Scholar 
73.R Development Core Team. R: A language and environment for statistical computing, Version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org (2020).74.Dormann, C. F., Fründ, J. & Gruber, B. Package ‘bipartite’: Visualising bipartite networks and calculating some (ecological) indices. (2019).75.Huang, H. Y., Zhou, L., Chen, J. & Wei, T. Y. ggcor: Extended tools for correlation analysis and visualization. R package version 0.9.7. (2020).76.Oksanen, J. et al. vegan: community ecology package. R. package version 2, 5–6 (2020).
Google Scholar 
77.Kassambara, A. & Fabian, M. factoextra: Extract and Visualize the Results of Multivariate Data analyses. R package version 1.0.7. (2020).78.Akaike, H. An information criterion (AIC). Math. Sci. 14, 5–9 (1976).
Google Scholar 
79.Burnham, K. P. & Anderson, D. R. Multimodel inference understanding AIC and BIC in model selection. Sociol. Method. Res. 33, 261–304 (2004).Article 

Google Scholar 
80.Cardinale, B. J. et al. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third Edition. (Thousand Oaks CA: Sage., 2011).82.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 
83.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.43.17. (2020).84.Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
85.Tylianakis, J. M. & Binzer, A. Effects of global environmental changes on parasitoid–host food webs and biological control. Biol. Control 75, 77–86 (2014).Article 

Google Scholar 
86.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
87.Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
88.Yang, F. et al. The data for “Species diversity and food web structure jointly shape natural biological control in agricultural landscapes”. Dryad, Dataset https://doi.org/10.5061/dryad.pc866t1kz (2021).Article 

Google Scholar  More

To defend against predators, insects often modify their morphology, flexibly, to enhance survival and reproductive advantages. Here, we report that predation risks from either isolated predator or predator odor cues, induce a higher proportion of nymphs to developed into long-winged females among the parent generation, as well as among F1 generation offspring. Surprisingly, these previously threatened long-winged adults survived better when attacked by a predator owing to the enhanced agility level gained from risk experience. The long wing, and increased agility level, provide adaptive benefits for SBPHs to escape from predation and so are able to go on to reproduce.SBPHs responded more strongly to the caged predators (visual + odor risk cues) and predator odor cues, than just the visual cue of the predator. Different risk cues can elicit different levels of responses in prey33,34,35,36. For example, in the case of the Colorado potato beetle, volatile odor cues from the predator stronger reduced the beetle feeding on plants than predator visual and tactile cues35. But a visual cue has been shown to be crucial for insect pollinators detecting and avoiding flowers with predators37. Insect herbivores frequently communicate via chemical odors33,38. Exploiting the odor cue to perceive the presence of predators should have advantages, because the odor cue can be sensed from a long distance and penetrate the blocking effect of foliage or canopy structure39, enabling the prior detection of risks and the preparation of antipredation behaviors.In densely planted rice paddies, the active foraging behavior of rove beetle may serve as a selective pressure favouring the development in SBPHs of a chemical instead of visual pathway to detect the approach of a rove beetle. However, in the F1 generation, the influence of a predator odor cue on the proportion of winged forms was weaker than that of caged predators, indicating the combined effects of odor and visual cues might be stronger than only an odor cue, suggesting that visual cues cannot be ignored. In our experiments, sealed predator cadavers may have weakened the visual cue of the rove beetles, because the lack of motion did not fully represent the normal visual cue.SBPHs frequently exhibit wing plasticity in response to population density and food quality28,29. When nymph density is higher, or food has deteriorated, a higher proportion of macropters will arise28,29. The development of the winged form is thought to be a strategy for SBPHs to emigrate from inhospitable environments. However, we assumed, predation risks could also induce the occurrence of the winged form, because long wings might enable SBPHs to escape from predation. As expected, the results presented here show that a higher proportion of long-winged females and their offspring arose when nymphs or adults were previously exposed to predation risk, demonstrating that SBPHs can express morphologically plastic defenses in response to prior predation risk. Additionally, the higher proportion of wing forms was not only due to the increasing number of winged females (see Fig. 1, the number of winged females in “caged rove beetle” treatment was lower), but also the increasing proportion of winged females among female groups (the decreasing numbers and proportions of wingless females, Fig. 1). To date, similar patterns have only been shown in pea aphids, in which when predation risk (foot prints from lady beetles) is higher during the parent generation, a higher proportion of winged morphs arise in the offspring40,41. In our experiments, we tested the risk effects passing from nymphs to adults and from parents to their offspring with combined risk cues, an odor cue or a visual cue, which better reveals the capacity for flexible defense strategies within SBPHs and the nature of predation risks in the perpetual ‘arms race’ against predation. This is the first example of how insects can express both within- generational and transgenerational morphological plasticity as a defense strategy in response to prior predator threat, and we suggest that this phenomenon is likely to occur more widely.However, SBPHs do not only face a single lethal pressure from their environment as we discussed above. Nymph density, food quality, even the temperatures or photoperiods may play or interplay roles in the induction of wing plasticity in SBPHs28,29. In these situations, the responses of SBPHs may differ from present results, or opposite results can occur. As an example, the growth rates of snails vary depending on snail densities, food supply and the strength of predation risks. Growth rates were higher when snails were reared on high nutrients and in low densities, but decreased steeply as the predation risk increased. Conversely, the growth rate was lower at high densities and with high predation risk, but increased as nutrient availability increased42. As for SBPHs, the proportion of winged adults may be higher if we reared in higher densities combined with high predation risk, or may be lower if the nutrient condition of the rice plants increases (for example, higher fertilizer inputs benefit the development of planthoppers43) and predators are removed. This hypothesis needs to be tested. Further, the rice plant phenotypes (resistant or sensitive phenotypes) are important to the development of planthoppers or leafhoppers44,45,46, and tests of the interactive effects of plant phenotype, plant quality/quantity, nymph density and predation risk on the wing plasticity of SBPHs should provide insights into the evolution of insects within changing environments.Induced transgenerational defense plasticity as shown in SBPHs may be common in many organisms20,47. It allows parents to transfer their risk experience to offspring and promotes their evolutionary fitness20. When SBPH nymphs are exposed to predation risk, they are likely to develop into long-winged females, because it is an advantageous form for them in the current risk environment. However, such predation risk is variable in time and space, and SBPH parents cannot predict when or whether the predators will disappear. Thus, an appropriate strategy to enhance the survival rate of offspring in an unpredictable environment is to continue producing a higher proportion of long-winged forms. Within-generational and transgenerational plasticity of defense should be a successful adaptive defense strategy for SBPHs, given that rove beetle and other groups of predators such as predatory spiders are abundant all around the year in rice paddies.The higher mortality of SBPH nymphs when they experience predation risk, has been broadly addressed before24,48,49. Reduced food intake during risk periods may contribute to this poorer survival outcome, because insects are likely to alter their feeding behavior50,51, or shift from a high-risk host to a safer, but nutritionally inferior, one52, when they detect the presence of predators. However, we did not observe an apparent behavior change in threatened nymphs in our experiments, even those going on to be macropters, compared to the non-threatened ones. For example, changing feeding location, non-feeding related motility, an increase in jump frequency, etc. did not occur in threatened nymphs. Thus, behavior plasticity seems not to be invoked to explain this phenomenon. However, considering the food consumption of sap-sucking SBPHs is difficult to determine, experiments employing electrical penetration graph (EPG) techniques should be conducted to quantify the amounts of sap consumption during risk periods53. This will help to explain whether the higher mortality is due to a change of feeding behavior (less food intake). Furthermore, some obscure internal physiological plasticity may also cause the higher mortality of SBPH nymphs at risk. For example, increased oxidative damage and decreased assimilation efficiency during the risk period may weaken the survival success of SBPH nymphs. Unfortunately, few studies have verified this assumption, although it has been shown that different assimilation efficiencies may arise under predation risk17, or oxidative damage may be induced by predation risk resulting in a slower growth rate54 and decreased escape performance55.SBPHs exhibit sexual differences in both with- and trans- generational morphological plasticity in relation to defense, i.e., threatened nymphs/parents are more likely to develop into long-winged females, due to the different vulnerability of females and males to predation. This predation difference is particularly acute between short-winged females and males, given that the proportion of short-winged females is lower than that seen in control settings (Fig. 1), and we assume the level of vulnerability may depend on their body size and reproductive role. The body sizes of short-winged females are larger than those of long-winged females or males, causing them to be more vulnerable to predation because they are more highly preferred targets for predator. Also, the short-winged female needs to stay and deposit eggs in the bare rice stem, which increases the time window of exposure to predators while, by contrast, long-winged males are slim and are not required to lay eggs, and so should be not be heavily predated. It follows that short-winged females should be more vulnerable to predation than long-winged females or males. Hence, in SHPBs, increasing the proportion of long-wing females in a population creates greater opportunities to migrate to predator-free habitats for reproduction, while at the same time reducing their vulnerability to predation. We hypothesize that the sexual difference in responses should be adaptive, and might be inheritable if predation pressure frequently favors the long-winged forms among populations over multiple generations.Results presented here also show that previously threatened long-winged offspring survived better than previosuly non-threatened ones when attacked by P. fuscipes. Studies suggest prey-altered morphology in response to predation risks should confer a survival advantage (fitness gained), i.e., a better-developed defensive structure13,24, or refuge in having a larger size that increase survival success57. However, wings themselves are without protective functions for SBPHs, as seen in pea aphids41. Thus, we setup behavioral experiments to reveal how threatened long-winged adults may increase their survival when attacked by a predator. Results show threatened long- winged offspring (but not parents) are more active, and respond more quickly, than unthreatened ones, i.e., a higher number of attacks are needed for P. fuscipes to capture a previously threatened long-winged offspring than one that has not been threatened before. We suggest the increased agility level is not because of the long wing itself, but due to the enhanced muscle strength in the legs of long-winged adults, because in our observation, long-winged adults avoid attack mainly by jumping but not by flight, probably because a jump needs less reaction time than flight.We only observed transgenerational plasticity of induced behavioral defense in SBPHs. This generational difference (within- and trans-generational) in behavioral defense in SBPHs may reflect potential carry-over effects from parents. To our knowledge, the generational difference in defense has rarely been shown in insects, though in pea aphids a fluctuating expression of transgenerational defensive traits (long wing) over generations when predation risk was present or absent has been reported58. We also expect there will be cumulative effects59 accumulated by SBPHs from the parent generation to the F1 generation. However, we are not certain whether these effects exist in our experiments. To determine this, experiments examining defensive traits across multigeneration should be conducted.However, if predation risk increases the number of agile, long-winged SBPH adults, which are of benefit in respect of dispersal, migration, and thus spreading rice viruses, the application of P. fuscipes in biological control appears ultimately to weaken the control effectiveness. Also, a study with field experiments found that predatory ladybugs increase the number of dispersed aphid nymphs, especially in plants with lower resistance. However, surprising results show that the higher number of dispersed aphid nymphs will not necessarily translate into population growth because dispersed aphids are weak (less food intake) and more easily predated by predators60. Thus, the benefits of anti-predator defense in aphids will, over time, translate into negative developmental costs that suppress the aphid population. As for SBPHs, threatened long-winged females perform well in dispersal and defense, but worse in development and reproduction. Recent experiments reveal that previously threatened long-winged females have a longevity that is three days shorter, and produces about 60 fewer eggs per female, than non-threatened long-winged females (unpublished data). Consequently, these negative effects would eventually translate into lower population growth rates within SBPHs. Thus, the introduction of the predation risk from P. fuscipes to control SBPHs is workable, since field experiments in controlling western flower thrips and grasshoppers by exposure to predation risk have been successful49,61, and the main purpose of biological control is to suppress the pest population beneath the relevant economic threshold, and reduce plant mass loss without necessarily eliminating the pest altogether.This study advances the importance of predation risk on the induction of flexible anti-predation defenses in insect parents and their offspring, uncovers the mediating mechanisms, shows how this anti-predation defense expresses differently between sexes, and further explores the adaptation significance of these defense traits for insects exposed to unpredictable environments. These findings should prove important for predicting SBPH migration or dispersal, conducting effective pest control, and better understanding prey-predator interactions. However, future work should examine the effects of predation risks from other groups of predators or parasites on the physiological and behavioral plasticity of SBPHs. More

1.Dell, J. et al. Interaction diversity maintains resiliency in a frequently disturbed ecosystem. Front. Ecol. Evol. 7, 145 (2019).Article 

Google Scholar 
2.White, P. S. & Pickett, S. T. A. In The Ecology of Natural Disturbance and Patch Dynamics (eds S. T. A. Pickett & P. S. White) 3–13 (Academic Press, 1985).3.Newman, E. A. Disturbance ecology in the anthropocene. Front. Ecol. Evolut. https://doi.org/10.3389/fevo.2019.00147 (2019).Article 

Google Scholar 
4.Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Yuan, Z., Jiao, F., Li, Y. & Kallenbach, R. L. Anthropogenic disturbances are key to maintaining the biodiversity of grasslands. Sci. Rep. 6, 22132 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Hughes, A. R., Byrnes, J. E., Kimbro, D. L. & Stachowicz, J. J. Reciprocal relationships and potential feedbacks between biodiversity and disturbance. Ecol. Lett. 10, 849–864. https://doi.org/10.1111/j.1461-0248.2007.01075.x (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Nat. 111, 1119–1144 (1977).Article 

Google Scholar 
8.Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Fox, J. W. The intermediate disturbance hypothesis should be abandoned. Trends Ecol. Evol. 28, 86–92. https://doi.org/10.1016/j.tree.2012.08.014 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Sheil, D. & Burslem, D. F. Disturbing hypotheses in tropical forests. Trends Ecol. Evol. 18, 18–26 (2003).Article 

Google Scholar 
11.Teixidó, N., Garrabou, J., Gutt, J. & Arntz, W. E. Recovery in Antarctic benthos after iceberg disturbance: Trends in benthic composition, abundance and growth forms. Mar. Ecol. Prog. Ser. 278, 1–16. https://doi.org/10.3354/meps278001 (2004).ADS 
Article 

Google Scholar 
12.Teixidó, N., Garrabou, J., Gutt, J. & Arntz, W. Iceberg disturbance and successional spatial patterns: the case of the shelf Antarctic benthic communities. Ecosystems 10, 143–158 (2007).Article 

Google Scholar 
13.Johst, K., Gutt, J., Wissel, C. & Grimm, V. Diversity and disturbances in the Antarctic megabenthos: Feasible versus theoretical disturbance ranges. Ecosystems 9, 1145–1155 (2006).Article 

Google Scholar 
14.Mackey, R. L. & Currie, D. J. The diversity-disturbance relationship: Is it generally strong and peaked?. Ecology 82, 3479–3492. https://doi.org/10.1890/0012-9658(2001) (2001).Article 

Google Scholar 
15.Huston, M. A. Disturbance, productivity, and species diversity: Empiricism vs. logic in ecological theory. Ecology 95, 2382–2396. https://doi.org/10.1890/13-1397.1 (2014).Article 

Google Scholar 
16.Smale, D. A., Brown, K. M., Barnes, D. K., Fraser, K. P. & Clarke, A. Ice scour disturbance in Antarctic waters. Science 321, 371. https://doi.org/10.1126/science.1158647 (2008).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Griffiths, H. J., Danis, B. & Clarke, A. Quantifying Antarctic marine biodiversity: The SCAR-MarBIN data portal. Deep Sea Res. Part II 58, 18–29. https://doi.org/10.1016/j.dsr2.2010.10.008 (2011).ADS 
Article 

Google Scholar 
18.Grange, L. J. & Smith, C. R. Megafaunal communities in rapidly warming fjords along the West Antarctic Peninsula: Hotspots of abundance and beta diversity. PLoS ONE 8, e77917 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Gutt, J., Griffiths, H. J. & Jones, C. D. Circumpolar overview and spatial heterogeneity of Antarctic macrobenthic communities. Mar. Biodivers. 43, 481–487. https://doi.org/10.1007/s12526-013-0152-9 (2013).Article 

Google Scholar 
20.Potthoff, M., Johst, K. & Gutt, J. How to survive as a pioneer species in the Antarctic benthos: Minimum dispersal distance as a function of lifetime and disturbance. Polar Biol. 29, 543–551 (2006).Article 

Google Scholar 
21.Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84, 203–244 (2014).Article 

Google Scholar 
22.Peck, L. S., Brockington, S., Vanhove, S. & Beghyn, M. Community recovery following catastrophic iceberg impacts in a soft-sediment shallow-water site at Signy Island, Antarctica. Mar. Ecol Progr. Ser. 186, 1–8 (1999).ADS 
Article 

Google Scholar 
23.Lee, H., Vanhove, S., Peck, L. & Vincx, M. Recolonisation of meiofauna after catastrophic iceberg scouring in shallow Antarctic sediments. Polar Biol. 24, 918–925. https://doi.org/10.1007/s003000100300 (2001).Article 

Google Scholar 
24.Armstrong, T. World Meteorological Organization. WMO sea-ice nomenclature. Terminology, codes and illustrated glossary. Edition 1970. Geneva, Secretariat of the World Meteorological Organization, 1970. [ix], 147 p. [including 175 photos]+ corrigenda slip. (WMO/OMM/BMO, No. 259, TP. 145.). J. Glaciol. 11, 148–149 (1972).25.Robinson, B. J., Barnes, D. K. & Morley, S. A. Disturbance, dispersal and marine assemblage structure: A case study from the nearshore Southern Ocean. Mar. Environ. Res. 160, 105025 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Gutt, J., Starmans, A. & Dieckmann, G. Impact of iceberg scouring on polar benthic habitats. Mar. Ecol. Prog. Ser. 137, 311–316 (1996).ADS 
Article 

Google Scholar 
27.Barnes, D. K. A. & Conlan, K. E. Disturbance, colonization and development of Antarctic benthic communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 11–38. https://doi.org/10.1098/rstb.2006.1951 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Smale, D. A. Ecological traits of benthic assemblages in shallow Antarctic waters: Does ice scour disturbance select for small, mobile, secondary consumers with high dispersal potential?. Polar Biol. 31, 1225–1231. https://doi.org/10.1007/s00300-008-0461-9 (2008).Article 

Google Scholar 
29.Barnes, D. K. A. The influence of ice on polar nearshore benthos. J. Mar. Biol. Assoc. U.K. 79, 401–407 (1999).Article 

Google Scholar 
30.Gutt, J. On the direct impact of ice on marine benthic communities, a review. Polar Biol. 24, 553–564 (2001).Article 

Google Scholar 
31.Barnes, D. K. A. & Tarling, G. A. Polar oceans in a changing climate. Curr. Biol. 27, R454–R460. https://doi.org/10.1016/j.cub.2017.01.045 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Barnes, D. K. A., Fleming, A., Sands, C. J., Quartino, M. L. & Deregibus, D. Icebergs, sea ice, blue carbon and Antarctic climate feedbacks. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170176. https://doi.org/10.1098/rsta.2017.0176 (2018).ADS 
Article 

Google Scholar 
33.Cook, A. J., Fox, A. J., Vaughan, D. G. & Ferrigno, J. G. Retreating glacier fronts on the Antarctic Peninsula over the past half-century. Science 308, 541–544. https://doi.org/10.1126/science.1104235 (2015).ADS 
CAS 
Article 

Google Scholar 
34.Cook, A. et al. Ocean forcing of glacier retreat in the western Antarctic Peninsula. Science 353, 283–286 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Clarke, A. et al. Climate change and the marine ecosystem of the western Antarctic Peninsula. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 149–166. https://doi.org/10.1098/rstb.2006.1958 (2007).Article 
PubMed 

Google Scholar 
36.Turner, J. & Comiso, J. Solve Antarctica’s sea-ice puzzle. Nat. News 547, 275 (2017).CAS 
Article 

Google Scholar 
37.Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. https://doi.org/10.1029/2005GL024042 (2005).Article 

Google Scholar 
38.Barnes, D. K. A. & Souster, T. Reduced survival of Antarctic benthos linked to climate-induced iceberg scouring. Nat. Clim. Chang. 1, 365–368. https://doi.org/10.1038/nclimate1232 (2011).ADS 
Article 

Google Scholar 
39.Parkinson, C. L. Global sea ice coverage from satellite data: Annual cycle and 35-yr trends. J. Clim. 27, 9377–9382. https://doi.org/10.1175/jcli-d-14-00605.1 (2014).ADS 
Article 

Google Scholar 
40.Rogers, A. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Morley, S. A. et al. Global drivers on Southern Ocean ecosystems: Changing physical environments and anthropogenic pressures in an Earth system. Front. Mar. Sci. 7, 1097 (2020).Article 

Google Scholar 
42.Barnes, D. K. et al. Blue carbon gains from glacial retreat along Antarctic fjords: What should we expect?. Glob. Change Biol. 26, 2750–2755 (2020).ADS 
Article 

Google Scholar 
43.Barnes D. K. A. Blue carbon on polar and subpolar seabeds. In Carbon capture, utilization and sequestration (InTech, 2018). https://doi.org/10.5772/intechopen.78237.44.Bowler, D. et al. The geography of the Anthropocene differs between the land and the sea. bioRxiv https://doi.org/10.1101/432880 (2019).Article 

Google Scholar 
45.Arntz, W., Brey, T. & Gallardo, V. Antarctic zoobenthos. Oceanogr. Mar. Biol. 32, 241–304 (1994).
Google Scholar 
46.Clarke, A. Marine benthic populations in Antarctica: Patterns and processes. Antarct. Res. Ser. 70, 373–388 (1996).Article 

Google Scholar 
47.Fillinger, L., Janussen, D., Lundälv, T. & Richter, C. Rapid glass sponge expansion after climate-induced Antarctic ice shelf collapse. Curr. Biol. 23, 1330–1334 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Clarke, A., Meredith, M. P., Wallace, M. I., Brandon, M. A. & Thomas, D. N. Seasonal and interannual variability in temperature, chlorophyll and macronutrients in northern Marguerite Bay, Antarctica. Deep Sea Res. Part II 55, 1988–2006. https://doi.org/10.1016/j.dsr2.2008.04.035 (2008).ADS 
Article 

Google Scholar 
49.Barnes, D. K. A. Iceberg killing fields limit huge potential for benthic blue carbon in Antarctic shallows. Glob. Chang. Biol. 23, 2649–2659. https://doi.org/10.1111/gcb.13523 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Pinkerton, M., Bradford-Grieve, J., Bowden, D. & Cummings, V. Benthos: Trophic modelling of the Ross Sea. Support. Docum. CCAMLR Sci. 17, 1–31 (2010).
Google Scholar 
51.Pielou, E. Shannon’s formula as a measurement of species diversity: It’s use and disuse. Am. Nat. 100, 463–465 (1966).Article 

Google Scholar 
52.Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 1, 42–58 (1943).Article 

Google Scholar 
53.Everitt, B. & Skrondal, A. The Cambridge Dictionary of Statistics Vol. 106 (Cambridge University Press, Cambridge, 2002).MATH 

Google Scholar 
54.Smale, D. A., Barnes, D. K. A. & Fraser, K. P. P. The influence of ice scour on benthic communities at three contrasting sites at Adelaide Island, Antarctica. Aust. Ecol. 32, 878–888. https://doi.org/10.1111/j.1442-9993.2007.01776.x (2007).Article 

Google Scholar 
55.Peck, L. S., Convey, P. & Barnes, D. K. A. Environmental constraints on life histories in Antarctic ecosystems: Tempos, timings and predictability. Biol. Rev. 81, 75–109. https://doi.org/10.1017/s1464793105006871 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Waller, C., Worland, M., Convey, P. & Barnes, D. Ecophysiological strategies of Antarctic intertidal invertebrates faced with freezing stress. Polar Biol. 29, 1077–1083 (2006).Article 

Google Scholar 
57.Barnes, D. K. A. Polar zoobenthos blue carbon storage increases with sea ice losses, because across-shelf growth gains from longer algal blooms outweigh ice scour mortality in the shallows. Glob. Chang Biol. 23, 5083–5091. https://doi.org/10.1111/gcb.13772 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Smith, C. R., Mincks, S. & DeMaster, D. J. A synthesis of bentho-pelagic coupling on the Antarctic shelf: Food banks, ecosystem inertia and global climate change. Deep Sea Res. Part II 53, 875–894 (2006).ADS 
Article 

Google Scholar 
59.Jansen, J. et al. Abundance and richness of key Antarctic seafloor fauna correlates with modelled food availability. Nat. Ecol. Evolut. 2, 71–80 (2018).Article 

Google Scholar 
60.Henley, S. F. et al. Changing biogeochemistry of the Southern Ocean and its ecosystem implications. Front. Mar. Sci. 7, 581 (2020).Article 

Google Scholar 
61.Marshall, G. J. et al. Causes of exceptional atmospheric circulation changes in the Southern Hemisphere. Geophys. Res. Lett. 31, 14 (2004).Article 

Google Scholar 
62.Ashton, G. V., Morley, S. A., Barnes, D. K., Clark, M. S. & Peck, L. S. Warming by 1 C drives species and assemblage level responses in Antarctica’s marine shallows. Curr. Biol. 27, 2698-2705e2693 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Riesgo, A. et al. Some like it fat: Comparative ultrastructure of the embryo in two demosponges of the genus Mycale (order poecilosclerida) from Antarctica and the Caribbean. PLoS ONE 10, e0118805 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Toszogyova, A. & Storch, D. Global diversity patterns are modulated by temporal fluctuations in primary productivity. Glob. Ecol. Biogeogr. 28, 1827–1838 (2019).Article 

Google Scholar 
65.Clark, G. F. et al. Light-driven tipping points in polar ecosystems. Glob. Change Biol. 19, 3749–3761 (2013).ADS 
Article 

Google Scholar 
66.Brockington, S., Clarke, A. & Chapman, A. Seasonality of feeding and nutritional status during the austral winter in the Antarctic sea urchin Sterechinus neumayeri. Mar. Biol. 139, 127–138 (2001).Article 

Google Scholar 
67.Fratt, D. B. & Dearborn, J. Feeding biology of the Antarctic brittle star Ophionotus victoriae (Echinodermata: Ophiuroidea). Polar Biol. 3, 127–139 (1984).Article 

Google Scholar 
68.Sahade, R., Tatián, M. & Esnal, G. B. Reproductive ecology of the ascidian Cnemidocarpa verrucosa at Potter Cove, South Shetland Islands, Antarctica. Mar. Ecol. Progr. Ser. 272, 131–140 (2004).ADS 
Article 

Google Scholar 
69.Dayton, P. K. et al. Recruitment, growth and mortality of an Antarctic hexactinellid sponge, Anoxycalyx joubini. PLoS ONE 8, e56939 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Vacchi, M., Cattaneo-Vietti, R., Chiantore, M. & Dalù, M. Predator-prey relationship between the nototheniid fish Trematomus bernacchii and the Antarctic scallop Adamussium colbecki at Terra Nova Bay (Ross Sea). Antarct. Sci. 12, 64–68 (2000).ADS 
Article 

Google Scholar 
71.Sheil, D. & Burslem, D. F. Defining and defending Connell’s intermediate disturbance hypothesis: a response to Fox. Trends Ecol. Evol. 28, 571–572. https://doi.org/10.1016/j.tree.2013.07.006 (2013).Article 
PubMed 
PubMed Central 

Google Scholar  More

A. Strain sampling and isolationBradyrhizobium is a commonly occurring genus in soil [21]. Closely related Bradyrhizobium diazoefficiens (previously Bradyrhizobium japonicum) strains were isolated from soil, as previously described [20, 22]. In brief, Bradyrhizobium isolates that formed symbiotic associations with a foundational legume species in the censused region, Acacia acuminata, were isolated from soil sampled along a large region spanning ~300,000 km2 in South West Australia, a globally significant biodiversity hotspot [23]. In total 60 soil samples were collected from twenty sites (3 soil samples per site; Supplementary Fig. S1) and 380 isolates were sequenced (19 isolates per site, 5 or 6 isolates per soil sample, each isolate re-plated from a single colony at least 2 times). Host A. acuminata legume plants were inoculated with field soil in controlled chamber conditions and isolates were cultured on Mannitol Yeast agar plates from root nodules (see [20, 22] for details). A total of 374 strains were included in this study after removing 5 contaminated samples and one sample that was a different Bradyrhizobium species; non- Bradyrhizobium diazoefficiens sample removal was determined from 16S rRNA sequences extracted from draft genome assemblies (Method C) using RNAmmer [24].B. Environmental variation among sampled sitesIn this study, I focus on environmental factors (temperature, rainfall, soil pH and salinity) previously identified to impact either rhizobia growth performance, functional fitness or persistence in soil [25,26,27,28] and where a directionality of rhizobial stress response could be attributed with respect to environmental variation present in the sampled region (i.e. stress occurs at high temperatures, low rainfall, high acidity and high salinity). Each environmental factor was standardised to a mean of 0 and a standard deviation of 1, and pH and rainfall scales were reversed to standardise stress responses directions so that low stress is at low values and high stress is at high values for all factors (Supplementary Fig. S2). Additionally, salinity was transformed using a log transformation (log(x + 0.01) to account for some zeroes) prior to standardisation.C. Isolate sequencing and pangenome annotationIllumina short reads (150 bp paired-end) were obtained and draft genome assemblies were generated using Unicycler from a previous study [29]. Resulting assemblies were of good assembly quality (99.2% of all strains had >95.0% genome completeness score according to BUSCO [30]; Table S1; assembled using reads that contained nominal 0.016 ± 0.00524% non-prokaryotic DNA content across all 374 isolates, according to Kraken classification [31]). Protein coding regions (CDS regions) were identified using Prokka [32] and assembled into a draft pangenome using ROARY [33], which produced a matrix of counts of orthologous gene clusters (i.e. here cluster refers to a set of protein-coding sequences containing all orthologous variants from all the different strains, grouped together and designated as a single putative gene). Gene clusters that occurred in 99% of strains were designated as ‘core genes’ and used to calculate the ‘efficiency of selection’ [34, 35] (measured as dN/dS, Method G.2) and population divergence measured as Fixation Index ‘Fst’, Method H) across each environmental stress factor. The identified gene clusters were then annotated using eggNOG-mapper V2 [36] and the strain by gene cluster matrix was reaggregated using the Seed ortholog ID returned by eggNOG-mapper as the protein identity. Out of the total 2,744,533 CDS regions identified in the full sample of 374 strains, eggNOG-mapper was able to assign 2,612,345 of them to 91,230 unique Seed orthologs. These 91,230 protein coding genes constituted the final protein dataset for subsequent analyses.D. Calculation and statistical analysis of gene richness and pangenome diversity along the stress gradientGene richness was calculated as the total number of unique seed orthologues for each strain (i.e. genome). Any singleton genes that occurred in only a single strain, as well as ‘core’ genes that occurred in every strain (for symmetry, and because these are equally uninformative with respect to variation between strains) were removed, leaving 74,089 genes in this analysis. Gene richness (being count data) was modelled on a negative binomial distribution (glmer.nb function) as a function of each of the four environmental stressors as predictors using the lme4 package in R [37], also accounting for hierarchical structure in the data by including site and soil sample as random effects.To rule out potentially spurious effects of assembly quality (i.e. missed gene annotations due to incomplete draft genomes) on key findings, I confirmed no significant association between gene richness and genome completeness (r = 0.042, p = 0.4224, Fig. S3).Finally, pangenome diversity was calculated as the total number of unique genes that occurred in each soil sample (since multiple strains were isolated from a single soil sample). Pangenome diversity was modelled the same as gene richness, except here soil sample was not included as a random effect.E. Calculation of network and duplication traits for each geneI used the seed orthologue identifier from eggNOG-mapper annotations to query matching genes within StringDB ([38]; https://string-db.org/), which collects information on protein-protein interactions. Out of 91,230 query seed orthologues, 73,126 (~80%) returned a match in STRING. For matching seed orthologue hits, a network was created by connecting any proteins that were annotated as having pairwise interactions in the STRING database using the igraph package in R [39]. Two vertex-based network metrics were calculated for each gene: betweenness centrality, which measures a genes tendency to connect other genes in the gene network, and mean cosine similarity, which is a measure of how much a gene’s links to other genes are similar to other genes.Betweenness centrality was calculated using igraph (functional betweenness). For mean cosine similarity, a pairwise cosine similarity was first calculated between all genes. To do this, the igraph network object was converted into a (naturally sparse yet large) adjacency matrix and the cosSparse function in qlcMatrix in R [40] was used to calculate cosine similarity between all pairs of genes. To obtain an overall cosine similarity trait value for each gene, the average pairwise cosine similarity to all other genes in the network was calculated.Finally, gene duplication level was calculated for each gene as one additional measure of ‘redundancy’, by calculating the average number of gene duplicates found within the same strain. Duplicates were identified as CDS regions with the same Seed orthologue ID.F. Gene distribution modelsTo determine how gene traits predict accessory genome distributions patterns along the stress gradients, I first calculated a model-based metric (hereafter and more specifically a standardised coefficient, ‘z-score’) of the relative tendency of each gene to be found in different soil samples across the four stress gradients (heat, salinity, acidity, and aridity). This was achieved by modelling each gene’s presence or absence in a strain as a function of the four stress gradients, with site and soil sample as a random effect, using a binomial model in lme4 (the structure of the model being the same as the gene richness model, only the response is different). To reduce computational overhead, these models were only run for the set of genes that were used in the gene richness analysis (e.g. after removing singletons and core genes), and which had matching network traits calculated (e.g. they occurred in the STRING database; n = 64,867 genes). Distribution models were run in tandem for each gene using the manyany function in the R package mvabund [41]. Standardised coefficients, or z-scores (coefficient/standard error) indicating the change in the probability of occurrence for each gene across each of the stress gradients were extracted. More negative coefficients correspond to genes that are more likely to be absent in high stress (and vice versa for positive coefficients).To determine how network and duplication traits influence the distribution of genes across the stress gradient, I performed a subsequent linear regression model where the gene’s z-scores was the response and gene traits as predictors. The environmental stress type (i.e. acidity, aridity, heat and salinity) was included as a categorical predictor, and the interaction between stress category and gene function traits were used to infer the influence of gene function traits on gene distributions in a given stress type (see Supplementary Methods S1 for z-score transformation).G. Quantifying molecular signals of natural selection on accessory and core genesTo examine molecular signatures of selection in accessory and core genes, I calculated dN/dS for a subsample of the total pool (n=74,089 genes), which estimates the efficiency of selection [34, 35]. Two major questions relevant to dN/dS that are addressed here require a different gene subsampling approach:(1) Do variable environmental stress responses lead to different dN/dS patterns among accessory genes?Here, I subsampled accessory genes (total accessory gene pool across 374 strains, 74,089) to generate and compare dN/dS among 3 categorical groups, each representing a different level of stress response based on their z-scores (accessory genes that either strongly increase, decrease or have no change in occurrence as stress increases; n = 1000 genes/category; see Supplementary Methods S2 for subsample stratification details).For each gene, sequences were aligned using codon-aware alignment tool, MACSE v2 [42]. dN/dS was estimated by codon within each gene using Genomegamap’s Bayesian model-based approach [43], which is a phylogeny-free method optimised for within bacterial species dN/dS calculation (see Supplementary Methods S3 for dN/dS calculation/summarisation; S9 for xml configuration). The proportion of codons with dN/dS that were credibly less than 1 (purifying selection) and those credibly greater than 1 (positive selection) were analysed, with respect to the genes’ occurrence response to stress. Specifically, I modelled the proportion of codons with dN/dS  1 was overall too low to analyse in this way, so the binary outcome (a gene with any codons with dN/dS  > 1 or not) was modelled using a binomial response model with the response categories as predictors (see Supplementary Methods S4 for details of both models).(2) Does dN/dS among microbial populations vary across environmental stress?Here, I compared the average change in dN/dS in core genes present across all environments at the population level (i.e. all isolates from one soil sample), which is used here to measure the change in the efficiency of selection across each stress gradient. Core genes were used since they occur in all soil samples, allowing a consistent set and sample size of genes to be used in the population-level dN/dS calculation. This contrasts with the previous section, which quantifies gene-level dN/dS on extant accessory genes that intrinsically have variable presence or absence across soil samples. For computational feasibility, 500 core genes were subsampled (total core 1015 genes) and, for each gene, individual strain variants were collated into a single fasta file based on soil sample membership, such that dN/dS could be calculated separately for each gene within each soil sample (i.e. 60 soil samples × 500 genes = 30,000 fasta files). Each fasta file was then aligned in MACSE and dN/dS calculated using the same methodology for accessory genes (Supplementary Method S3). This enabled the average dN/dS in a sample to be associated with soil-sample level environmental stress variables. Specifically, I modelled the mean proportion of codons with dN/dS  1 due to overall rarity of positive selection (average proportion of genes where at least 1 codon with dN/dS  > 1 was ~0.006). This low level of positive selection is expected for core genes which tend to be under strong selective constraint.H. Calculation and analysis of Fixation index (Fst) along stress gradientsUsing the core genome alignment (all SNPs among 1015 core genes) generated previously with ROARY, I computed pairwise environmentally-stratified Fst. Each soil sample (n = 60) was first placed into one of 5 bins based on their distances in total environmental stress space (using all four stress gradients), with the overall goal of generating roughly evenly sized bins such that the environmental similarity of stress was greater within bins than between (see Supplementary Methods S6 and Fig. S4 for clustering algorithm details). Next, fasta alignments were converted to binary SNPs using the adegenet package. Pairwise Fst between all strains (originating from a particular soil sample) within a single bin was calculated using StAMPP in R [44]. For each strain pair, the average of the two stress gradient values was assigned.The relationship between pairwise Fst and the average stress value was evaluated using a linear regression model with each of the four stress values as predictors. Since the analysis uses pairwise data (thus violating standard independence assumptions), the significance of the relationship was determined using a permutation test (see Supplementary Methods S7 for details).I. Chromosomal structure analysis of gene loss patternsTo gain insight into structural variation and test for regional hotspots in gene loss along the chromosome, I mapped each gene’s stress response (i.e. probability of loss or gain indicated by each genes z-score) onto a completed Bradyrhizobium genome (strain ‘36_1’ from the same set of 374 strains (Genbank CP067102.1; [45]). Putative CDS positions on the complete genome were determined using Prokka and annotated with SEED orthologue ID’s using eggNOG-mapper. Matching accessory genes derived from the full set of 374 incomplete draft genomes (n = 74,089) were mapped to positions on the complete genome (6274 matches). The magnitude of gene loss or gain (as measured by their standardised z-scores for each environment from the gene distribution models; see Method F) was then modelled across the genome using a one-dimensional spatial smoothing model. This model was implemented in R INLA [46] (www.r-inla.org), and models a response in a one-dimensional space using a Matern covariance-based random effect. The method uses an integrated nested Laplace approximation to a Bayesian posterior distribution, with a cyclical coordinate system to accommodate circular genomes. The model accounts for spatial non-independence among sites and produces a continuous posterior distribution of average z-score predictions along the entire genome, which was then used to visualise potential ‘hotspots’ of gene loss or gain. The modelling procedure was repeated, instead with gene network traits, such that model predictions of similarity and betweenness could be visualised on the reference chromosome. More

State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, ChinaZhilin Yuan, Huanshen Wei & Long PengResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, ChinaZhilin Yuan, Xinyu Wang, Huanshen Wei & Long PengFungal Genomics Laboratory (FungiG), College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, ChinaIrina S. Druzhinina & Feng CaiDepartment of Food Science, University of Massachusetts, Amherst, MA, USAJohn G. GibbonsState Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhenhui ZhongDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USAZhenhui ZhongDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, BelgiumYves Van de PeerVIB Center for Plant Systems Biology, Ghent, BelgiumYves Van de PeerCentre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South AfricaYves Van de PeerAdaptive Symbiotic Technologies, University of Washington, Seattle, WA, USARussell J. RodriguezKey Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization at College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhongjian LiuState Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, ChinaQi Wu & Guohui ShiKey Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, ChinaJieyu WangBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, ChinaFrancis M. MartinUniversité de Lorraine, INRAE, UMR Interactions Arbres/Micro-Organismes, Centre INRAE Grand Est Nancy, Champenoux, FranceFrancis M. Martin More

1.Zarzycki, K. Paprotniki i rośliny kwiatowe (rośliny naczyniowe). In: Flora i Fauna Pienin. (ed. Razowski J). Monogr. Pienińskie 1, 75–79 (2000).
Google Scholar 
2.Środoń, W. Pieniny w historii szaty roślinnej Podhala [Pieniny in the history of plant cover in Podhale region]. In : K. Zarzycki (ed.). Przyroda Pienin w obliczu zmian [The nature of the Pieniny Mts (West Carpathians) in face of the coming changes]. Stud. Nat. 30B, 115–126 (1982).
Google Scholar 
3.Deptuła, C. Nad rekonstrukcją dziejów regionu czartoryskiego w XIII I XIV wieku [On the reconstruction of the history of the Czorsztyn region from the 13th to 16th centuries]. Pieniny—Człowiek Przyroda 5, 21–35 (1997) (in Polish with English summary).
Google Scholar 
4.Kierś, M. (ed.) Wołosi: Nomadzi Bałkanów (Uniwersytet Jagielloński, 2013).
Google Scholar 
5.Oravcová, M. & Krupa, E. Pedigree analysis of the former Valachian sheep. Slovak. J. Anim. Sci. 44, 6–12 (2011).
Google Scholar 
6.Wace, A.J.B. & Thompson, M.S. The Nomads of the Balkans. Vol. 6 (Methuen & Co., 1914). https://archive.org/stream/nomadsofbalkansa00wace#page/n9/mode/2up. Accessed 28 June 2021.7.Stachurska-Swakoń, A. Phytogeographical aspects of grasses occuring in tall-herb vegetation in the Carpathians. in Grasses in Poland and Elsewhere (ed. Frey, L.). 39–47. (W. Szafer Institute of Botany, Polish Academy of Sciences, 2009).8.Stachurska-Swakoń, A. Syntaxonomical revision of the communities with Rumex alpinus L. in the Carpathians. Phytocoenologia 39, 217–234. https://doi.org/10.1127/0340-269X/2009/0039-0217 (2009).Article 

Google Scholar 
9.Ralska-Jasiewiczowa, M., Nalepka, D. & Goslar, T. Some problems of forest transformation at the transition to the oligocratic/Homo sapiens phase of the Holocene interglacial in northern lowlands of central Europe. Veg. Hist. Archaeobot. 12, 233–247. https://doi.org/10.1007/s00334-003-0021-8 (2003).Article 

Google Scholar 
10.Pawłowski, B., Pawłowska, S. & Zarzycki, K. Zespoły roślinne kośnych łąk północnej części Tatr i Podtatrza. Fragm. Flor. Geobot. Pol. 6(2), 95–222 (1960).
Google Scholar 
11.Korzeniak, J. 6520* Mountain Yellow Trisetum and Bent-Grass Hay Meadows 55–67 (Methodological guide. GIOŚ, 2013).
Google Scholar 
12.Wróbel, I. Pasterstwo w regionie pienińskim [Sheep farming in the Pieniny region]. Pieniny Człowiek Przyroda 5, 43–52 (1997) (in Polish with English summary).
Google Scholar 
13.Kostrakiewicz-Gierałt, K., Palic, C. C., Stachurska-Swakoń, A., Nedeff, V. & Sandu, I. The causes of disappearance of sward lily Gladiolus imbricatus L from natural stands—Synthesis of current state of knowledge. Int. J. Conserv. Sci. 9, 821–834 (2018).
Google Scholar 
14.Wróbel, I. Szata roślinna Pienińskiego Parku Narodowego – podsumowanie Planu Ochrony na lata 2001–2020 [Plant cover of the Pieniny National Park – summing up the Protection Plan for the years 2001–2020]. Pieniny Człowiek Przyroda 8, 63–69 (2003).
Google Scholar 
15.Kubíková, P. & Zeidler, M. Habitat demands and population characteristics of the rare plant species Gladiolus imbricatus L. in the Frenštát region (NE Moravia, the Czech Republic). Čas. Slez. Muz. Opava 60(A), 154–164 (2011).
Google Scholar 
16.Mirek, Z., Piękoś-Mirkowa, H., Zając, A. & Zając, M. Flowering Plants and Pteridophytes of Poland, a Checklist (W. Szafer Institute of Botany, Polish Academy of Sciences, 2002).
Google Scholar 
17.Hamilton, A. P. The European Gladioli. Quart. Bull. Alp. Gard. Soc. 44, 140–146 (1976).
Google Scholar 
18.Kornaś, J. M. & Medwecka-Kornaś, A. Zespoły roślinne Gorców. I. Naturalne i na wpół naturalne zespoły nieleśne. Fragm. Flor. Geobot. Polon. 13(2), 167–316 (1967).
Google Scholar 
19.Ascherson, P. & Engler, A. Beiträge zur Flora Westgaliziens und der Central-Karpaten. Osterr. Bot. Z. 15, 273–285. https://doi.org/10.1007/BF01623075 (1865).Article 

Google Scholar 
20.Wołoszczak, E. Zapiski botaniczne z Karpat Sądeckich. Spraw. Komis. Fizjogr. AU 30, 174–206 (1895).
Google Scholar 
21.Zapałowicz, H. Conspectus Florae Galiciae Criticus Vol. 1 (Nakł. Akad. Umiej., 1906).
Google Scholar 
22.Piękoś-Mirkowa, H. & Mirek, Z. Flora Polski. Rośliny Chronione (Oficyna Wydawnicza Multico, 2006).
Google Scholar 
23.Dembicz, I. et al. New locality of Trollius europaeus L. and Gladiolus imbricatus L. near Sochocin by Płońsk (Central Poland). Opole Sci. Soc. Nat. J. 44, 36–46 (2011).
Google Scholar 
24.Kropač, Z. & Mochnacký, S. Contribution to the segetal communities of Slovakia, Thaiszia. J. Bot. 19, 145–211 (2009).
Google Scholar 
25.Mirek, Z., Nikel, A. & Wilk, Ł. Ozdoba łąk reglowych. Tatry 4(50), 50–51 (2014).
Google Scholar 
26.Kołos, A. A new locality of Gladiolus imbricatus (Iridaceae) in the North Podlasie Lowland. Fragm. Florist. Geobot. Polon. 22(2), 390–395 (2015).
Google Scholar 
27.Falkowski, M. Nowe stanowisko Gladiolus imbricatus (Iridaceae) w dolinie środkowej Wisły. Fragm. Florist. Geobot. Polon. 9, 369–370 (2002).
Google Scholar 
28.Nowak, A. & Antonin, A. Interesujące stanowiska Gladiolus imbricatus (Iridaceae) w Bramie Morawskiej. Fragm. Florist. Geobot. Polon. 13(1), 17–22 (2006).
Google Scholar 
29.Stepansky, A., Kovalski, I. & Perl-Treves, R. Interspecific classification of melons (Cucumis melo L.) in view of their phenotypic and molecular variation. Plant Syst. Evol. 271, 313–332. https://doi.org/10.1007/BF00984373 (1999).Article 

Google Scholar 
30.Gupta, M., Chyi, Y.-S., Romero-Sverson, J. & Owen. J.L. Amplification of DNA markers from evolutionarily diverse genomes using single primers of simple-sequence repeats. Theor. Appl. Genet. 89, 998–1006. https://doi.org/10.1007/BF00224530 (1994).31.Sutkowska, A., Pasierbiński, A., Warzecha, T., Mandal, A. & Mitka, J. Refugial pattern of Bromus erectus in Central Europe based on ISSR fingerprinting. Acta Biol. Cracov. Ser. Bot. 55(2), 107–119. https://doi.org/10.2478/abcsb-2013-0026 (2013).Article 

Google Scholar 
32.Bonin, A. et al. How to track and assess genotyping errors in population genetic studies. Mol. Ecol. 3, 3261–3273. https://doi.org/10.1111/j.1365-294X.2004.02346.x (2004).CAS 
Article 

Google Scholar 
33.Vekemans, X. AFLP-surv 1.0: A Program for Genetic Diversity Analysis with AFLP (and RAPD) Population Data. https://ebe.ulb.ac.be/ebe/AFLP-SURV.html (Laboratoire de Génétique et d’Ecologie Végétales, Université Libre de Bruxelles, 2002).34.Yeh, F., Yang, R. & Boyle, T. POPGENE Version 1.32. Microsoft-Based Freeware for Population Genetic Analysis. https://www.softpedia.com/get/Science-CAD/Popgene-Population-Genetic-Analysis.shtml (Molecular Biology and Biotechnology Center, University of Alberta, 1999).35.Schönswetter, P. & Tribsch, A. Vicariance and dispersal in the Alpine perennial Bupleurum stellatum L (Apiaceae). Taxon 54, 725–732. https://doi.org/10.2307/25065429 (2005).Article 

Google Scholar 
36.Ehrich, D. AFLPdat: A collection of r functions for convenient handling of AFLP data. Mol. Ecol. Notes 6, 603–604. https://doi.org/10.1111/j.1471-8286.2006.01380.x. https://mybiosoftware.com/tag/aflpdat (2006).37.Paun, O., Schönswetter, P. & Winkler, M., Intrabiodiv Consortium & Tribsch, A. Historical divergence versus contemporary gene flow: Evolutionary history of the calcicole Ranunculus alpestris group (Ranunculaceae) in the European Alps and the Carpathians. Mol. Ecol. 17, 4263–4275. https://doi.org/10.1111/j.1365-294x.2008.03908.x (2008).38.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491. https://doi.org/10.1093/genetics/131.2.479 (1992).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Biol. 1, 47–50. http://cmpg.unibe.ch/software/arlequin3/. https://doi.org/10.1177/117693430500100003 (2005).40.Lynch, M. & Milligan, B. Analysis of population-genetic structure using RAPD markers. Mol. Ecol. 3, 91–99. http://cmpg.unibe.ch/software/arlequin3/. https://doi.org/10.1111/j.1365-294x.1994.tb00109.x (1994).41.Saitou, N. & Nei, M. The neighbour-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. https://doi.org/10.1093/oxfordjournals.molbev.a040454 (1987).CAS 
Article 
PubMed 

Google Scholar 
42.Makarenkov, V. T-Rex: Reconstructing and visualizing phylogenetic trees and reticulation networks. Bioinformatics 17, 664–668. http://www.fas.umontreal.ca/biol/casgrain/en/labo/t-rex. https://doi.org/10.1093/bioinformatics/17.7.664 (2001).43.Makarenkov, V. & Legendre, P. The fitting of a tree metric to a given dissimilarity with the weighted least squares criterion. J. Classif. 16, 3–26. https://doi.org/10.1007/s003579900040 (1999).Article 

Google Scholar 
44.Felsenstein, J. Phylip (Phylogeny Inference Package) Version 3.6. https://evolution.genetics.washington.edu/phylip.html (University of Washington, 2005).45.Nei, M. & Li, W. H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. USA 76, 5269–5273. https://doi.org/10.1073/pnas.76.10.5269 (1979).ADS 
CAS 
Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
46.Kruskal, J. B. Nonmetric multidimensional scaling: A new numerical method. Psychometrika 29, 115–129 (1964).MathSciNet 
Article 

Google Scholar 
47.Rohlf, F. J. NTSYS-pc. Numerical Taxonomy and Multivariate Analysis, Version 2.1. https://ntsyspc.software.informer.com/ (Exeter Software, 2002).48.Pritchard, J. K, Stephens, M. & Donelly P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. http://web.stanford.edu/group/pritchardlab/structure.html (2000).49.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578. https://doi.org/10.1111/j.1471-8286.2007.01758.x (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
Article 
PubMed 

Google Scholar 
51.Nordborg, M., Hu, T. T., Ishino, Y., Jhaveri, J. & Toomajian, C. The pattern of polymorphism in Arabidopsis thaliana. PLOS Biol. 3(7), e196. https://doi.org/10.1371/journal.pbio.0030196 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Dybova-Jachowicz, S. & Sadowska, A. (eds) Palinologia (Inst. Botaniki im. W. Szafera, Polska Akademia Nauk, 2003).
Google Scholar 
53.Cieślak, E., Szczepaniak, M., Kamiński, R. & Heine, W. Stan zachowania krytycznie zagrożonego gatunku Gladiolus paluster (Iridaceae) w Polsce – Analiza zmienności genetycznej osobników w uprawie Ogrodu Botanicznego Uniwersytetu Wrocławskiego w kontekście prowadzonych działań ochronnych. Fragm. Florist. Geobot. Polon. 21(1), 49–66 (2014).
Google Scholar 
54.Kutlunina, N., Permyakova, M. & Belyaev, A. Genetic diversity and reproductive traits in triploid and tetraploid populations of Gladiolus tenuis (Iridaceae). Plant Syst. Evol. 303, 1–10. https://doi.org/10.1007/s00606-016-1347-x (2017).Article 

Google Scholar 
55.Sutkowska, A., Pasierbiński, A., Warzecha, T. & Mitka, J. Multiple cryptic refugia of forest grass Bromus benekenii in Europe as revealed by ISSR fingerprinting and species distribution modelling. Plant Syst. Evol. 300, 1437–1452. https://doi.org/10.2478/abcsb-2013-0026 (2014).Article 

Google Scholar 
56.Gajewski, Z, Boroń, P, Lenart-Boroń, A, Nowak, B., Sitek, E. & Mitka, J. Conservation of Primula farinosa in Poland with respect to the genetic structure of populations. Acta Soc. Bot. Pol. 87(2), 3577 (2018). https://doi.org/10.5586/asbp.3577.Article 

Google Scholar 
57.Stojak, J., McDevitt, A. D., Herman, J. S., Searle, J. B. & Wójcik, J. M. Post-glacial colonization of eastern Europe from the Carpathian refugium: evidence from mitochondrial DNA of the common vole Microtus arvalis. Biol. J. Linn. Soc. 115, 927–939. https://doi.org/10.1111/bij.1253541 (2015).Article 

Google Scholar 
58.Szczepaniak, M. & Cieślak, E. Low level of genetic variation within Melica transsilvanica populations from the Kraków-Częstochowa Upland and the Pieniny Mts revealed by AFLPs analysis. Acta Soc. Bot. Pol. 76(4), 321–331. https://doi.org/10.5586/asbp.2007.036 (2007).Article 

Google Scholar 
59.Bennett, K. D. & Provan, J. What do we mean by ‘refugia’?. Quatern. Sci. Rev. 27, 27–28. https://doi.org/10.1016/j.quascirev.2008.08.019 (2008).Article 

Google Scholar 
60.Petit, R. J. et al. Glacial refugia: Hotspots but not melting pots of genetic diversity. Science 300(5625), 1563–1565. https://doi.org/10.1126/science.1083264 (2003).ADS 
CAS 
Article 

Google Scholar 
61.Brus, R. Growing evidence for the existence of glacial refugia of European beech (Fagus sylvatica L.) in the south-eastern Alps and north-western Dinaric Alps. Periodicum Biol. 112, 239–246 (2010).
Google Scholar 
62.Jŏgar, Ü. & Moora, M. Reintroduction of a rare plant (Gladiolus imbricatus) population to a river floodplain—How important is meadow management?. Restor. Ecol. 16, 382–385. https://doi.org/10.1111/j.1526-100X.2008.00435.x (2008).Article 

Google Scholar 
63.Mitka, J., Boroń, P., Wróblewska, A. & Bąba, W. AFLP analysis reveals intraspecific phylogenetic relationships and population genetic structure of two species of Aconitum in Central Europe. Acta Soc. Bot. Pol. 84(2), 267–276. https://doi.org/10.5586/asbp.2015.012 (2015).CAS 
Article 

Google Scholar 
64.Biernacka, M. Dawne oraz współczesne formy organizacji pasterstwa w Bieszczadach. Etnogr. Polska 6, 41–61. http://webcache.googleusercontent.com/search?q=cache:JDjzqMdApxIJ:cyfrowaetnografia.pl/Content/454+&cd=1&hl=pl&ct=clnk&gl=pl (1962).65.Stachurska-Swakoń, A., Cieślak, E. & Ronikier, M. Phylogeography of subalpine tall-herb species in Central Europe: the case of Cicerbita alpina. Preslia 84, 121–140. https://doi.org/10.1111/j.1095-8339.2012.01323.x (2012).Article 

Google Scholar  More

When introducing ARs as a fisheries management tool to Senegal, the Japanese management had the mindset of Japanese stakeholders, i.e., introducing fishing rights. However, after discussions with Senegalese stakeholders, it was decided that no-take areas would be delineated around ARs because the establishment of a strong fishing rights regime was not socially acceptable to the Senegalese fishing community. Japanese governance is based on the acceptance and respect of fishers towards individual, private AR concessions. In contrast, fishers in Senegal, and more widely in West Africa, are characterized by high mobility, particularly in the context of climate change and overexploitation18,19. Consequently, respect for local management regulations is lower, with open access being generally assumed. The basic concept of implementing a no-take area on the AR was not easily accepted by fishers. The immersion of AR concrete blocks was set as a top priority by managers at the expense of more complex socio-economic considerations, such as consciousness-raising activities and self-sustaining participative monitoring of the AR.The clear contradiction between the ecological knowledge of fishers and their behavior was explained by the well-known effects of open access resources on individual behavior. This phenomenon was also observed in our mathematical model. The processes in the mathematical model are in accordance with those perceived by the fishers, so that the results are also those expected by fisher’s local ecological knowledge. It is interesting to notice that the theoretical results presented here are the mathematical solutions of the model at equilibrium between fishing effort and fish population growth, i.e. after an oscillation period. It is obvious that short-term effect of fishing on the AR is always to increase the catch, but many fishers did perceive the longer-term effect of decreasing catches. The potential negative effect of the AR on catch when there is high fish attraction combined with high fishing pressure on the AR might explain the reluctance of a part of the fishers community to AR deployment (Fig. 2). In particular, the model illustrates that the AR attraction effect strongly determines the impact of the management. In general, fish attraction is the most immediate effect perceived after AR deployment11, as was true for our study16. Though the AR volume was relatively small (70 m3), the empty space between the higher blocks also contributes approximately 280 to 570 m3 of good habitat/refuge for schooling fish; therefore, it is actually difficult to accurately describe the volume that affects fish. Thus, it is difficult to say whether this AR is below or above the forecasted optimal volume in absence of fishing (120m3 with model parameters). The existence of an optimal volume for AR was also suggested by field studies as a trade off between food supply and refuge20, in line with our results. For management purposes, it is interesting to determine whether the AR is above or below this optimal level because if the volume is too small, the model predicted that any level of fishing on the AR would, in the long term, decrease the catch in the considered area. On the other hand, if the volume is above the optimal level, a small fishing effort on the AR could be authorized and would increase the total catch in the area.Field observation showed that the fish attraction effect was strong16 but precise estimation of this parameter cannot be inferred, as this would need, ideally, individual fish trajectories. Future field research on the attraction effect may permit estimating the AR attraction parameters. The model sensitivity test showed that the stronger the attraction parameter, the better the impact of the AR for the fisheries in case of no or small fishing effort on the AR (Fig. 3). But at the same time, the attraction is a strong incentive for fishers to fish on the AR, and the predicted benefit for fisheries in the fishing area rapidly vanishes when fishing effort on AR increases. This in turn provides further incentive for fishers to fish the AR, challenging the surveillance capacity. If fish attractiveness is strong and too many fishers fish on the AR, catch in the area will be concentrated on the AR, while the adjacent fishing area will be depleted, with catch levels lower than those prior to AR deployment.Specifically, in the context of generalized overfishing in Senegal21, deciding not to fish on the AR represents significant individual loss, despite being recognized as beneficial, globally22. It has been argued that this situation would rarely occur in small-scale fisheries, due to existing arrangements between individuals23. However, in the context of the highly mobile Senegalese artisanal fishing fleet and its overcapacity, as soon as the AR in Yenne was no longer subject to surveillance, it rapidly attracted fishers from other villages. Also, pre-existing arrangements between fishers might be overruled when new ARs are created, changing the structure of existing fishing grounds.At the time of the survey, the surveillance system set up by the co-management entities was not operational in our case study, because it was dependent on temporally limited external financing. These limitations are typical of short-term projects that focus on a single restricted area for a pre-determined duration, usually up to two years (e.g., NGOs, World Bank). Local fishers perceptions were globally in line with the model prediction that this AR fails to improve fisheries yield when surveillance is not in place to ensure AR regulations are observed, despite effective fish attraction and production existing in the AR.The model predicted that enhanced production on ARs could not keep pace with unrestricted access, which might be particularly true in Senegal where fishing effort rapidly reorganizes itself according to local yields24. Enhanced production due to the AR largely increases the catch if the fishing pressure on the AR remains null or very low, but it has no effect on the catch for higher fishing pressures on the AR (Fig. 3). These results were stable even if fish population growth, fish catchability, mobility and economic parameters could modulate the predicted amplitude of the catch and AR optimal volume. These results are consistent with existing theoretical studies of the impact of fisher movement to high production areas in and around MPAs25. Taking into account several species and their interactions (predation, competition) would lead to a very complex ecosystem model specific to the area (e.g. 26), with necessarily more assumptions. This model would necessarily be more difficult to share with fishers and other stakeholders. Both to simplify model structure and facilitate communication of results to stakeholders, we assumed in our model that the balance of entries exits and is in equilibrium, so that the migratory species did not affect the long-term equilibrium between fishing effort and fish abundance.The design of ARs could be adjusted to reduce the effect of illegal fishing by passively preventing both industrial and artisanal fishing activity. Complex structures are more effective for fish production and attraction27. We showed that, although production might have a limited effect on total catch, attraction can largely increase AR efficiency (total catch) if the rate of illegal fishing rate is very low or absent. Complex structures protect fish more effectively from small scale fishing gear28, including divers (Pers. Comm., Mamadou Sarr, Ouakam fishers committee). Thus, ARs should be appropriately designed to help mitigate potential issues28. Such designs might be more costly, and do not exclude the need for surveillance, but would enhance fisheries management, especially when surveillance cannot capture low levels of illegal fishing.Finally, if socio-economic and governance conditions are not met, well-intentioned AR projects will likely disturb the existing equilibrium among fishers that have different levels of access to the AR. Poor governance of marine resources has previously been described in West Africa, particularly in Senegal29, as has the failure of AR projects in a number of other developing countries9, which further deteriorate fishers trust and management plans efficiency30. In order to avoid that, NGO and governmental agencies driving ARs projects must consider that AR management induces collective costs before providing potentially collective gains. Thus, co-management that involves governmental institutions and fisher communities is required. Future management and adaptation plans for fishers, particularly in developing countries, should, therefore, focus efforts on raising long-term awareness of actors in both government institutions and fishing communities. At the level of institutional or development partners, long-term management costs should be included in the set-up of AR projects. For example, the local fishers committee of Yenne recently reported the establishment of a collective ship chandler whose profits are used to finance AR surveillance during the daytime. Subsequently, fishers noted an improvement in catches around the AR, even though illegal fishing likely continues on the AR at night (Pers. Comm. chair of local fishers committee). These observations support model predictions that low levels of illegal fishing might not disturb the positive impact of the AR. Alternatively surveillance effort could be supported by the community if benefits were managed according to ancestral traditions. Indeed, “no take area” regime on the AR would be in line with some past West African tribal laws, applied before the colonization era, which set marine area where fishing activities were restricted for occasional community celebrations. Collective processes where fishers and other stakeholders can design temporary no-take zones around the AR could increase fishers trust and compliance to the rules, fostering a positive socio-ecological feedback loop30.Hybridization of local and scientific knowledge, through the integration of natural sciences and social sciences, is key point for governance setting31,32,33. Indeed, the communication of the resulting hybrid knowledge in specific events gathering local stakeholders helps strengthen fisheries co-management for the establishment of surveillance and regulatory frameworks. This phenomenon was experienced during the public restitution of the present study with the community, fishers, children’s from local schools and governmental stakeholders. Science popularization of the study results was in French and local language (Wolof) retransmitted on national news (available at https://www.youtube.com/watch?v=yQqFU2P4XZU). Posters were exposed during the event, including pictures of local fishers interviewed and statements reflecting their own perception of how the artificial reef interacts with ecological processes and fisheries dynamics. Straightaway, stakeholders and local promoters of AR publicly expressed their concern and willingness to prioritize the setting up an efficient AR surveillance independent from external resources prior to increase AR deployments. Knowledge hybridization could produce more specific models that could be used for warning and advice, for example by considering potential impacts of ARs on species compositions3,34,35, environmental parameters36, and cascade effects on the trophic food web37. However this approach would need to be adapted to local social-ecological governance, which might require dedicated political-anthropological studies (see concept of adaptive co-management32).In summary, best practices should involve all stakeholders, consider local specificities, such as site configuration, governance, ecosystem, availability of ad hoc human and financial resources for AR surveillance, and define AR volume and design accordingly to these parameters. Thus, if plans exist to deploy ARs at large scales we recommend that legislation is strengthened, with detailed Environmental and social Impact Assessments38 to implement ARs, including considerations of long-term governance. More

Bagworm walking method using a ladder-like silk footholdWhen bagworms are reared in a plastic or glass cage, they walk not only on the floor but also on the walls or ceiling using only their three pairs of thoracic legs. The method by which they achieve this was clarified by placing a bagworm on black paper. Where the bagworm had walked, a ladder-like silk trace was observed on the black paper (Fig. 2a). Scanning electron microscopy (SEM) observation of one of the steps (or rungs) of the ladder-like trace revealed that each step was made up of a zigzag pattern of silk threads (Fig. 2b). Further magnified SEM observations revealed that the folded parts of the zigzag-spun thread were glued selectively to the substrate with adhesive whereas the remaining straight parts (hereafter, termed ‘bridges’ or ‘bridge threads’) were unglued (Fig. 2c–e).Figure 2Architecture of the ladder-like foothold. (a) A typical ladder-like foothold constructed by a bagworm on black paper, (b) an enlarged image showing one of the steps in the foothold and (c) a scanning electron microscopy image of the step shown in (b). The unglued bridge threads and a glued turn in the step shown in (c) are magnified in (d) and (e), respectively. (f) An enlarged image of four continuous steps in the foothold shown in (a). The neighbouring steps are connected via a single thread indicated by the arrows. (g) A schematic depiction of the basic architecture of the foothold; blue lines and green circles correspond to the silk thread and glued parts, respectively. (h) A photograph of a bagworm constructing a foothold on a transparent plastic board.Full size imageNotably, the steps of the foothold were not independent but rather always connected with neighbouring steps via a single thread (Fig. 2f). The overall basic construction of the foothold is schematically depicted in Fig. 2g. We found that the foothold was constructed in one continuous movement and always made of a single thread regardless of walking distance or time; therefore, a continuous thread exceeding a length of 100 m could be collected from one foothold14. We also observed bagworm climbing behaviour on a transparent plastic board, which clarified the important role of the silk trace as a foothold (Fig. 2h). During this behaviour, the bagworm used its sickle claws (Fig. 1e) to hook its second and third pairs of thoracic legs onto the first and second newest steps, respectively, and constructed the next step by spinning silk with a zigzag motion of the head and the skilful use of the first pair of thoracic legs. When the bagworm advanced one step, it always first shifted its third pair of thoracic legs to the next step before then shifting its second pair of thoracic legs to the newest step to avoid overloading this step, which may not yet be fully adhered to the surface (see Supplementary Movie S1). Because of this construction method, the interval distance between neighbouring steps is automatically determined by the interval between the thoracic legs. By repeating this process, the bagworm can advance forward slowly but steadily. This walking method was commonly observed on a horizontal floor surface, vertical wall, or horizontal ceiling. Although we have mainly described and shown observations from E. variegate here, with the exception of Supplementary Fig. S4 and Movie S1, we also observed instances of walking behaviour in other species, namely Eumeta minuscula, Mahasena aurea, Nipponopsyche fuscescens and Bambalina sp. (for a movie on E. minuscula walking behaviour, wherein it climbs a vertical wall, see Supplementary Movie S2). For at least 100 individuals of these bagworm species, we observed essentially identical walking behaviour to that described in the present study without exceptions for locomotion on substrates with slippery surfaces.Based on our observations, we asked the following question: how do bagworms selectively glue the folded parts of the foothold onto the substrate? Real-time observation of the tip of the spinneret (i.e. the spigot) through a transparent plastic board during the construction of the foothold revealed that adhesive was selectively discharged to attach the folded parts to the substrate; this process could be distinguished from the continuous spinning of the silk thread (for a movie showing construction behaviour, see Supplementary Movie S3). Figure 3a–g shows a time-sequence of foothold construction with enlarged images in the vicinity of the spinneret provided, whereas Fig. 3h depicts a schematic trace of the construction process. It was clearly noted that the bagworm discharged the adhesive only at the folded parts (shown in Fig. 3a–c,e,f; termed the ‘glued turn’) and not at the straight bridge parts (shown in Fig. 3d,g; termed the ‘unglued bridge thread’). From these time-sequence observations, we concluded that the bagworm controls the discharge of adhesive in an ‘on and off’ manner as necessary (essentially the same construction behaviours were confirmed for at least 20 individuals).Figure 3Foothold construction. (a–g) (left side) Time-sequence images taken during foothold construction and (right side) enlarged images of the vicinity of the spinneret (corresponding to the yellow rectangular area in each left-side image). The time-sequence images correspond to the parts of the schematic trace of foothold construction depicted by the red line in (h). In each right-side image and the schematic trace, the part of silk thread at which the adhesive was discharged is traced with a light-blue line. Green arrows in the right-side images show the direction of travel of the spinneret.Full size imagePassages of fibroin brins and adhesiveWe next investigated the spinning mechanism that enables continuous spinning of silk thread together with the selective discharge of adhesive via a single spigot. To this end, we observed the morphology of the bagworm from the silk gland to the spigot. Figure 4a shows the area in the vicinity of the spinneret, dissected and isolated from an E. variegata bagworm, which included a pair of silk glands and plural adhesive glands. As we previously reported21, the exterior shape of the silk gland in E. variegata (see Supplementary Fig. S1) is almost the same shape as that in the silkworm Bombyx mori and it is subdivided into three parts: the anterior (ASG), middle (MSG) and posterior (PSG) silk glands. We also previously confirmed that fibroin heavy chain (h-fib), fibroin light chain (l-fib) and fiboinhexamerin genes are expressed dominantly in the PSG, while sericin is expressed in the MSG, which strongly suggests that division-selective production of each protein exists in E. variegata (as has been shown in B. mori22). Figure 4b shows a magnified image of the spinneret including the end of the ASG. Beyond the pair of ASGs, which are merged into a common tube, a silk press and spinning tube appear before the spigot. This basic passage of silk fibroin from the ASG to the spigot is essentially the same as the passage observed in B. mori23. However, more detailed morphological observations of the inner structure of the passage revealed several obvious differences between E. variegata and B. mori.Figure 4Structural examination of the passages of fibroin brins and adhesive. (a) An optical microscope image of the area in the vicinity of a spinneret isolated from a female bagworm in the final instar stage. Indicated by arrows is a pair of silk glands (SG), one of the adhesive glands (ADG) and the spinneret (SP). (b) An optical microscope image of the passage including the (1) end of the anterior SGs (ASGs), (2) common tube, (3) silk press, (4) spinning tube and (5) spigot. (c–j) Optical microscope images showing cross-sections of the passage of fibroin brins obtained from the corresponding positions (c–j) in image (b). To focus on the fibroin brins and its passage, the surrounding outer part was removed so that a pair of fibroin brins was revealed in each image (except for image (c), which shows only one side of the ASG). Unmagnified images of (f–j), including the outer part, are shown in Supplementary Fig. S2. (k–n) 3D X-ray CT images of the spinneret: (k) overview, (l) cross-sectional top view, (m) cross-sectional side view and (n) passage of the fibroin brins and corresponding cross-sectional images at various positions. In the cross-sectional side view (m), the sheath and core parts are coloured blue and pink, respectively. (o) Image of the tip of a spigot from which adhesive is overflowing and a silk thread is emerging.Full size imageCross-sectional images along the spinneret are shown in Fig. 4c–j; these focus on the silk brins and their passage (unmagnified versions of the images in Fig. 4f–j are shown in Supplementary Fig. S2). The fibroin brins have an approximately round cross-sectional shape at the end of the ASG (Fig. 4c) and are merged at a common tube, which deforms their round shape slightly (Fig. 4d). The fibroin brins seem to be coated with a thin layer of sericin after the MSG, similar to B. mori; however, we omit the presence of the sericin layer here for convenience. The paired brins are gradually pressed between the ventral and dorsal hard cuticle plates at the silk press, and a gradual diameter decrease and shape deformation follows (Fig. 4e,f). At the exit of the silk press, each brin becomes elliptic and the diameter in the major axis decreases. Interestingly, the elliptical shape and 1.7-axial ratio for the major and minor axes of the fibroin brin cross-section in bagworm silk, which we previously reported14, are already determined at this stage in the silk press; afterwards, the diameter decreases without any change in the axial ratio of the elliptical cross-section. Notably, the two elliptical fibroin brins are aligned side-by-side so that their major axes are in line horizontally (to resemble a figure of ‘∞’) at the spinning press, and these are followed by the spinning tube (Fig. 4e–h). However, the alignment is twisted by 90° in one direction (to resemble a figure of ‘8’) before the brins are spun from the spigot (Fig. 4i,j).We found that the spinning tube was surrounded by a hard exoskeleton. Using 3D-X-ray CT observations, we produced clear images of the exterior and interior morphologies of the spinning tube enveloped by exoskeleton (Fig. 4k–m; the exterior shape observed from the dorsal-, ventral- and lateral-sides by optical microscopy is provided in Supplementary Fig. S3). The spigot was not cut perpendicularly to the spinning tube but rather with a slope of around 20°; consequently, it was elliptic. X-ray CT clearly showed the core-sheath structure of the spinneret and a wide expanse of sheath parts (Fig. 4m) between the exterior shell and interior spinning tube (Fig. 4l,m). Using optical microscope observations of the cross-sections, we found that at least three pairs of adhesive ducts were running in the sheath space (Supplementary Fig. S2E). Therefore, while the silk brins pass through the central narrow spinning tube, the plural adhesive ducts pass through the outer space independently of the silk thread. Finally, the adhesive enters a ladle-like reservoir located at the spigot and is released together with the silk thread (Fig. 4o). The presence of definitive routes connecting the adhesive passage and the spigot were not clearly observed in our X-ray CT images, probably due to the small structural scale relative to the space resolution used in our analysis (i.e. 0.31 μm). We speculate that the adhesive merges into the spigot via a fine, porous sponge-like structure, and we indicate assumed routes in Fig. 4l,m. X-ray CT observations also revealed a sophisticated structural design involving gradual twists in the silk brins by 90° from ‘∞’ to ‘8’ (Fig. 4n and Supplementary Movie S4). Essentially identical spinneret structures were observed by X-ray CT images for all of eight observed individuals from the third to final instars of E. variegata. More