Ecology

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

Google Scholar 
Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971 (2019).CAS 
PubMed 

Google Scholar 
Wagner, D. L. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 

Google Scholar 
Heikkinen, R. K. et al. Assessing the vulnerability of European butterflies to climate change using multiple criteria. Biodivers. Conserv. 19, 695–723 (2010).
Google Scholar 
Montgomery, G. A. et al. Is the insect apocalypse upon us? How to find out. Biol. Conserv. 241, 108327 (2020).
Google Scholar 
Hufnagel, L. & Kocsis, M. Impacts of climate change on Lepidoptera species and communities. Appl. Ecol. Environ. Res. 9, 43–72 (2011).
Google Scholar 
Geyle, H. M. et al. Butterflies on the brink: identifying the Australian butterflies (Lepidoptera) most at risk of extinction. Austral Entomol. 60, 98–110 (2021).
Google Scholar 
Merckx, T., Huertas, B., Basset, Y. & Thomas, J. A global perspective on conserving butterflies and moths and their habitats. Key Topics in Conservation Biology 2, 237–257 (2013).
Google Scholar 
New, T. R. Moths (Insecta: Lepidoptera) and conservation: background and perspective. J. Insect Conserv. 8, 79–94 (2004).
Google Scholar 
Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: Moth biodiversity trends are complex and heterogeneous. Proc. Natl. Acad. Sci. USA 118, e2002549117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Langevelde, F. et al. Declines in moth populations stress the need for conserving dark nights. Glob. Chang. Biol. 24, 925–932 (2018).ADS 
PubMed 

Google Scholar 
Green, K. et al. Australian Bogong moths Agrotis infusa (Lepidoptera: Noctuidae). 1951–2020: decline and crash. Austral Entomol. 60, 66–81 (2021).
Google Scholar 
Sánchez‐Bayo, F. & Wyckhuys, K. A. Further evidence for a global decline of the entomofauna. Austral Entomol. 60, 9–26 (2021).
Google Scholar 
Rönkä, K., Mappes, J., Kaila, L. & Wahlberg, N. Putting Parasemia in its phylogenetic place: a molecular analysis of the subtribe Arctiina (Lepidoptera). Syst. Entomol. 41, 844–853 (2016).
Google Scholar 
Witt, T. J., Speidel, W., Ronkay, G., Ronkay, L. & László, G. M. Subfamilia Arctiinae in Noctuidae Europaeae. Volume 13. Lymantriinae and Arctiinae including phylogeny and check list of the quadrifid Noctuoidea of Europe (eds. Witt, T. J. & Ronkay, L.) 81-216 (Entomological Press, 2011).Dowdy, N. J. et al. A deeper meaning for shallow‐level phylogenomic studies: nested anchored hybrid enrichment offers great promise for resolving the tiger moth tree of life (Lepidoptera: Erebidae: Arctiinae). Syst. Entomol. 45, 874–893 (2020).
Google Scholar 
Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, Noctuoidea). Syst. Entomol. 37, 102–124 (2012).
Google Scholar 
Holloway, J. D. The Moths of Borneo 6: family Arctiidae, subfamilies: Syntominae, Euchromiinae, Arctiinae; Noctuidae misplaced in Arctiidae (Camptoma, Aganinae) (Southdene Sdn. Bhd., 1988).Černý, K. & Pinratana, A. Arctiidae. Moths of Thailand 6, 1–283 (2009).
Google Scholar 
Černý, K. A review of the subfamily Arctiinae (Lepidoptera: Arctiidae) from the Philippines. Entomofauna 32, 29–92 (2011).
Google Scholar 
Bucsek, K. Erebidae, Arctiinae (Lithosiini, Arctiini) of Malay Peninsula – Malaysia (Institut of Zoology SAS, 2012).Bolotov, I. N., Kondakov, A. V. & Spitsyn, V. M. A review of tiger moths (Lepidoptera: Erebidae: Arctiinae: Arctiini) from Flores Island, Lesser Sunda Archipelago, with description of a new species and new subspecies. Ecol. Montenegrina 16, 1–15 (2018).
Google Scholar 
Dubatolov, V. V. New genera and species of Arctiinae from the Afrotropical fauna (Lepidoptera: Arctiidae). Nachr. Entomol. Ver. Apollo 27, 139–152 (2006).
Google Scholar 
Ferro, V. G., Melo, A. S. & Diniz, I. R. Richness of tiger moths (Lepidoptera: Arctiidae) in the Brazilian Cerrado: how much do we know? Zoologia (Curitiba) 27, 725–731 (2010).
Google Scholar 
Schmidt, B. C. A new genus and two new species of arctiine tiger moth (Noctuidae, Arctiinae, Arctiini) from Costa Rica. Zookeys 9, 89–96 (2009).
Google Scholar 
Dubatolov, V. V. Tiger-moths of Eurasia (Lepidoptera, Arctiidae) (Nyctemerini by Rob de Vos and V. V. Dubatolov). Neue Ent. Nachr. 65, 1–106 (2010).
Google Scholar 
Fibiger, M. et al. Lymantriinae and Arctiinae, including phylogeny and check list of the quadrifid Noctuoidea of Europe. Noctuidae Europaeae 13, 1–448 (2011).
Google Scholar 
Koshkin, E. S. Moths (Lepidoptera, Macroheterocera, excluding Geometridae and Noctuidae s.l.) of the Bureinsky State Nature Reserve and adjacent territories (Khabarovsk Krai, Russia) [In Russian]. Amur. Zool. J. 12, 412–435 (2020).
Google Scholar 
Kullberg, J., Filippov, B. Y., Spitsyn, V. M., Zubrij, N. A. & Kozlov, M. V. Moths and butterflies (Insecta: Lepidoptera) of the Russian Arctic islands in the Barents Sea. Polar Biol. 42, 335–346 (2019).
Google Scholar 
Bolotov, I. N. et al. The distribution and biology of Pararctia subnebulosa (Dyar, 1899) (Lepidoptera: Erebidae: Arctiinae), the largest tiger moth species in the High Arctic. Polar Biol. 38, 905–911 (2015).
Google Scholar 
Bolotov, I. N. et al. New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae). Ecol. Montenegrina 39, 121–128 (2021).
Google Scholar 
Bolotov, I. N., Gofarov, M. Y., Kolosova, Y. S. & Frolov, A. A. Occurrence of Borearctia menetriesii (Eversmann, 1846) (Erebidae: Arctiinae) in Northern European Russia: a new locality in a disjunct species range. Nota Lepidopterol. 36, 65–75 (2013).
Google Scholar 
Dubatolov, V. V. Borearctia gen. n., a new genus for the tiger moth Callimorpha menetriesi (Ev.) (Lepidoptera, Arctiidae) [In Russian]. Entomol. Rev. 63, 157–161 (1984).
Google Scholar 
Hori, H. An unrecorded species of the Arctiidae [In Japanese]. Kontyu 1, 86 (1926).
Google Scholar 
Eversmann, E. Lepidoptera quaedam nova in Rossia observata. Bulletin de la Société Impériale des Naturalistes de Moscou 19, 83–88 (1846).
Google Scholar 
Koshkin, E. S. Life history of the rare boreal tiger moth Arctia menetriesii (Eversmann, 1846) (Lepidoptera, Erebidae, Arctiinae) in the Russian Far East. Nota Lepidopterol. 44, 141–151 (2021).
Google Scholar 
Krogerus, H. D. Vorkommen von Callimorpha menetriesi Ev. in Fennoskandien, nebst Beschriebungen der verschiedenen Entwicklungsstadien [In German]. Not. Entomol. 24, 79–86 (1944).
Google Scholar 
Saarenmaa, H. Conservation ecology of Borearctia menetriesii [online]. http://www.bormene.myspecies.info/en (2011-2021).Berlov, O. E. & Bolotov, I. N. Record of Borearctia menetriesii (Eversmann, 1846) (Lepidoptera, Erebidae, Arctiinae) larva on Aconitum rubicundum Fischer (Ranunculaceae) in Eastern Siberia. Nota Lepidopterol. 38, 23–27 (2015).
Google Scholar 
Staudinger, O. & Rebel, H. Catalog der Lepidopteren des palaearctischen Faunengebietes. Vol. 1. Th. Famil. Papilionidae-Hepialidae (R. Friedländer & Sohn, 1901).Filipiev, I. Lepidoptera [In Russian]. Russkoe Entomologicheskoe Obozrenie 16, 376–378 (1916).
Google Scholar 
Fabritius, G. R. Anmärkningsvärda fynd av fjärilar, bland dessa den för Europa nya Callimorpha menetriesii Ev. [In Finnish]. Meddeland. Soc. Fauna Fl. Fenn. 40, 47–49 (1914).
Google Scholar 
Carpelan, J. Callimorpha menetriesii Ev. återfunnen [In Finnish]. Meddeland. Soc. Fauna Fl. Fenn. 48, 108–109 (1921).
Google Scholar 
Kurentzov, A. I. Zoogeography of the Amur Region [In Russian] (Nauka Publisher, 1965).Dubatolov, V. V. Tiger moths (Lepidoptera, Arctiidae: Arctiinae) of South Siberian mountains (report 2) [In Russian] in Arthropods and Helminths, Fauna of Siberia Series (ed. Zolotarenko, G. S.) 139–169 (Nauka Publisher, 1990).Klitin, A. K. New record of the tiger moth Borearctia menetriesii on Sakhalin Island [In Russian]. Bulletin of Sakhalin Museum 16, 269–271 (2009).
Google Scholar 
Nupponen, K. & Fibiger, M. Additions to the checklist of Bombycoidea and Noctuoidea of the Volgo-Ural region. Part II. (Lepidoptera: Lasiocampidae, Erebidae, Nolidae, Noctuidae). Nota Lepidopterol. 35, 33–50 (2012).
Google Scholar 
Koshkin, E. S. Preliminary results of the examination of the fauna of Higher Moths (Macroheterocera, excluding Geometridae and Noctuidae) of the upper Bureya River basin (Khabarovsk Region) [In Russian]. Proceedings of Grodekovsky Museum (Nature of the Far East) 24, 65–75 (2010).
Google Scholar 
Marttila, O., Saarinen, K., Haahtela, T. & Pajari, M. Idänsiilikäs Borearctia menetriesi (Eversmann, 1846) [In Finnish] in Suomen kiitäjät ja kehrääjät [Macrolepidoptera of Finland] 265–266 (Kirjayhtymä Oy, 1996).Lappi, E., Mikkola, K. & Ryynänen, J. Idänsiilikäs Borearctia menetriesii, tervetuloa takaisin! [Welcome back Borearctia menetriesii] [In Finnish]. Baptria 29, 28–29 (2004).
Google Scholar 
Silvonen, K. Borearctia Dubatolov, 1985 [online]. Kimmo’s Lepidoptera Site, Finland. http://www.kolumbus.fi/~kr5298/lnel/a/bormenet.htm (2010).Bolotov, I. N. et al. Menetries’ Tiger Moth Range and Ecology Database (1840s-2020). figshare https://doi.org/10.6084/m9.figshare.15000399 (2022).Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016).
Google Scholar 
Conrad, K. F., Warren, M. S., Fox, R., Parsons, M. S. & Woiwod, I. P. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132, 279–291 (2006).
Google Scholar 
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Google Scholar 
Simmons, B. I. et al. Worldwide insect declines: An important message, but interpret with caution. Ecol. Evol. 9, 3678–3680 (2019).PubMed 
PubMed Central 

Google Scholar 
Didham, R. K. et al. Interpreting insect declines: seven challenges and a way forward. Insect Conserv. Diver. 13, 103–114 (2020).
Google Scholar 
Boyes, D. H., Evans, D. M., Fox, R., Parsons, M. S. & Pocock, M. J. Is light pollution driving moth population declines? A review of causal mechanisms across the life cycle. Insect Conserv. Diver. 14, 167–187 (2021).
Google Scholar 
Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. USA 118, e2002548117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: Death by a thousand cuts. Proc. Natl. Acad. Sci. USA 118, e2023989118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schowalter, T. D., Pandey, M., Presley, S. J., Willig, M. R. & Zimmerman, J. K. Arthropods are not declining but are responsive to disturbance in the Luquillo Experimental Forest, Puerto Rico. Proc. Natl. Acad. Sci. USA 118, e2002556117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Berry, P. A. M., Smith, R. G. & Benveniste, J. ACE2: the new global digital elevation model in Gravity, Geoid and Earth Observation (ed. Mertikas, S. P.) 231–237 (Springer, 2010).Kurentzov, A. I. My travels [In Russian] (Far Eastern Publishing House, 1973).Dubatolov, V. V. A catalogue of type specimens of Palaearctic tiger moths (Lepidoptera, Arctiidae, Arctiinae) preserved in the collection of the Zoological Institute of Russian Academy of Sciences (St. Petersburg) [In Russian]. Entomol. Rev. 75, 338–356 (1996).
Google Scholar 
Bailey, R. G. Explanatory Supplement to Ecoregions Map of the Continents. Environ. Conserv. 16, 307–309 (1989).
Google Scholar 
Olson, D. M. & Dinerstein, E. The Global 200: Priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 89, 199–224 (2002).
Google Scholar 
Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 
Beaumont, L. J. et al. Impacts of climate change on the world’s most exceptional ecoregions. Proc. Natl. Acad. Sci. USA 108, 2306–2311 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).PubMed 

Google Scholar  More

Direct habitat lossAccording to the global projections of urban expansion under five SSPs17 (Supplementary Note 3 and Supplementary Fig. 1), 36–74 million hectares (Mha) of land areas will be urbanized by 2100, representing a 54–111% increase compared with the baseline year of 2015. Among these, 11–33 Mha natural habitats (Supplementary Table 1) will become urban areas by 2100. Across SSP scenarios, the patterns of change in losses of total habitat, forest, shrubland, and grassland are consistent with the global projections of urban expansion (Fig. 1). In terms of urban encroachment on wetlands, wetland will undergo the largest loss under scenario SSP4 than under other scenarios. However, if the sustainable pathway of scenario SSP1 is properly implemented, this will enable us to conserve the global wetland. The greatest loss of other habitat will occur under scenario SSP3, but the minimal loss of other habitat will occur under scenario SSP1. Under the five different SSP scenarios, the United States, Nigeria, Australia, Germany, and the UK are consistently predicted to have greater habitat loss due to urban expansion (Supplementary Table 2).Fig. 1: Future direct habitat loss due to urban expansion under SSP scenarios.a The habitat loss by 2100 for each habitat type. Bars indicate the mean habitat loss area (five scenarios) for each habitat type. Error bars represent mean values ± 1 SEM for the loss of each habitat type under five scenarios, n = 5 scenarios. Points represent data in five scenarios. b The losses in total area, forest, shrubland, grassland, wetland, and other land.Full size imageThere are obvious disparities in the hot spots and cold spots of habitat loss under the five SSP scenarios (Fig. 2 and Supplementary Figs. 2–6). Potential hot spots of habitat loss are concentrated in regions such as the northeastern, southern, and western coasts of the United States, the Gulf of Guinea coastal areas, Sub-Saharan Africa, and the Persian Gulf coastal areas. Under scenario SSP5, parts of central and western Europe will also become hot spots. However, under other scenarios, the cold spots will be particularly concentrated in eastern and southern Europe. East Asia and South Asia, which are represented by China, India, and Japan, are dominated by cold spots (Supplementary Figs. 2–6), because these regions may experience a decline in urban land demand from 2050 to 2100 (for examples in China, see Supplementary Figs. 7–11), although they are currently the most populous regions in the world.Fig. 2: Future hot spots and cold spots of habitat loss due to urban expansion under SSP scenarios by 2100.Figures for the United States (a), Europe (b), Africa (c), and China (d) are presented separately. The Gi_Bin identifies statistically significant hot spots and cold spots. Statistical significance was based on the p-value and z-score (two-sided), and no adjustments were made for multiple comparisons.Full size imageOur scenario projections show that the largest natural habitat loss is expected to occur in the temperate broadleaf and mixed forests biome (except for scenario SSP3). In addition, many biomes will experience proportionate loss of natural habitat. These biomes include the tropical and subtropical coniferous forests biome, the temperate coniferous forests biome, the flooded grasslands and savannas biome, the Mediterranean forests, woodlands, and scrub biome, and the mangroves biome (Supplementary Table 3). Although the rate of future habitat loss is small at the global scale, it can be large in some areas. For example, the habitat in the temperate broadleaf and mixed forests may decrease by 1.4% under scenario SSP5. At the ecoregion scale, about 9% of 867 terrestrial ecoregions will lose more than 1% of habitat due to urban expansion (Supplementary Fig. 12). In the future, four ecoregions—the Atlantic coastal pine barrens, the coastal forests of the northeastern United States, and the Puerto Rican moist and dry forests—will experience more than 20% of habitat loss.Urban expansion threatens biodiversity prioritization schemesTo reflect the potential impact of urban expansion on protected areas (Supplementary Note 4), the analyses presented here were based on the assumption that urban expansion within protected areas is not strictly restricted and can even occur in the currently gazetted protected areas (Supplementary Note 5, Supplementary Figs. 13 and 14). In 2015, urban areas with a total area of 30,594 km2 were distributed in 28,152 protected areas, accounting for 12.6% of global protected areas (Supplementary Figs. 15 and 16). Moreover, 38% of the urban land-use changes within protected areas were due to the conversion of natural habitats into urban land between 1992 and 2015. If urban expansion continues without strict restrictions, 13.2–19.8% of the protected areas will be affected by urban land by 2100, and urban land will occur in 29,563–44,400 protected areas with a total urban land area of up to 46,705–89,901 km2 across the five SSP scenarios (the lowest and highest proportions of urban land in each protected area by 2100 under SSP3 and SSP5 scenarios are presented in Supplementary Figs. 17 and 18).We also found that 0.90% of all terrestrial biodiversity hotspots (Supplementary Note 6), which are the world’s most biologically rich yet threatened terrestrial regions24, were urbanized in 2015. And this proportion (0.90%) is higher than that located in the rest of the Earth’s surface (0.51%) in 2015. By 2100, the new urban expansion will additionally occupy 1.5–1.8% of hotspot areas under the five SSP scenarios (Supplementary Table 4). Five biodiversity hotspots are projected to suffer the largest proportion of urban land conversion: the California Floristic Province (6–11%), Japan (6–8%), the North American Coastal Plain (4–8%), the Guinean Forests of West Africa (4–8%), and the Forests of East Australia (2–6%). In contrast, the East Melanesian Islands and the New Caledonia are almost unaffected by urban expansion. Biodiversity hotspots (e.g., the Guinean Forests of West Africa, the Coastal Forests of Eastern Africa, Eastern Afromontane, and the Polynesia-Micronesia) with few human disturbances in 2015 are projected to experience the highest percentage of future urban growth. Compared with the urban areas in 2015, by 2100, the urban areas in these four biodiversity hotspots will experience a disproportionate increase of 281–708, 294–535, 169–305, and 33–337%, respectively.The World Wildlife Fund (WWF) selected the ecoregions that are most crucial to the conservation of global biodiversity as Global 20025 (Supplementary Note 7). However, about 93% of the Global 200 ecoregions will be affected by future urban expansion. Although the proportion of urban land in each ecoregion will be less than 1% in 2100, the urban area located in these ecoregions will experience an increase of 74–160% from 2015 to 2100 across the five SSP scenarios (Supplementary Table 4). Four ecologically vulnerable ecoregions that have the highest urban growth rates are the Sudd-Sahelian Flooded Grasslands and Savannas, the East African Acacia Savannas, the Hawaii Moist Forest, and the Congolian Coastal Forests. By 2100, the urban areas in these four ecoregions will increase by 877–9955, 527–646, 18–902, and 500–1037%, respectively.The five SSP scenarios showed that the urban area is expected to increase by only 73–213 km2 in the Last of the Wild areas26 (see Supplementary Note 8 for descriptions about the Last of the Wild areas) by 2100 (Supplementary Table 4).Impacts of urban expansion on habitat fragmentationThe increasing exposures of natural habitat to urbanized land use may cause long-term changes in the function and structure of the natural habitat that is adjacent to urban areas13. To examine this proximity effect, we investigated the impact of future urban expansion on the nearest distance between urban areas and natural habitat (i.e., the distance from patch edges of urban areas to patch edges of the nearest natural habitats) under different SSP scenarios. Although the global urban area is expected to increase by 36–74 Mha by 2100, the impacts of future urban expansion on adjacent natural habitat are disproportionately large. Future urban expansion will make urban areas much closer to patch edges of 34–40 Mha natural habitat, which will inevitably threaten the natural habitat and increase the risk of biodiversity decline. The effects of urban expansion on adjacent patch edges of natural habitats are remarkably different across different scenarios. Specifically, the area of affected adjacent natural habitat is expected to be 38.45, 34.24, 40.31, 37.84, and 39.42 Mha under SSP1 to SSP5 scenarios by 2100, with the smallest effect under scenario SSP2, and the largest effect under scenario SSP3. Moreover, the scale of urban expansion does not correspond directly with the size of the impact. Several countries, including Mauritania, Algeria, Saudi Arabia, Western Sahara, and the United States, will have a large change in the distance from future urban areas to natural habitats due to urban expansion (Supplementary Table 5). Such effects also varied across different natural habitat types. The distance from the patch edges of urban areas to patch edges of (a) wetland, other land, and forest, (b) grassland, and (c) shrubland will generally be shortened by ~2000, ~1500 and ~900 m, respectively.In addition to the effect on the distance to the habitat edge, urban-caused habitat fragmentation is also reflected in reducing mean patch size (MPS)13, increasing mean edge index (edge density (ED), i.e., edge length on a per-unit area)27, and enlarging isolation (mean Euclidean nearest neighbor distance, ENN_MN)28 (Fig. 3). Taking the global ecoregions as the analysis unit, we found that within a 5 km buffer of urban areas, the median of MPS of natural habitats tends to show an overall decline trend, and the segmentation and subdivision of habitats become more obvious as future urban land expands. The median of MPS is the largest under scenario SSP1, followed by SSP4, SPP2, and SSP3 with some fluctuations in between, and the smallest MPS is found with the most fragmented landscape under scenario SSP5. A smaller patch size indicates that the inner parts of the habitat are subject to higher risk of being influenced by external disturbance. Future urban expansion also tends to cause an increase in the ED of natural habitat, which is often linked with smaller patches or more irregular shapes, and therefore poses a threat to biodiversity that influences many ecological processes (e.g., the spread of dispersal and predation)13,27,28. Scenario SSP1 shows the best performance in maintaining a low habitat ED and a high level of biodiversity conservation. However, under scenario SSP5, ED will experience a rapid increase in the second half of the 21st century. Meanwhile, the ENN_MN will increase substantially in the future, suggesting that areas with the same habitat type will become increasingly isolated, irregular, dispersed, or unevenly distributed due to the barrier of urban land. This will affect the speed of dispersal and patch recolonization. Scenario SSP1 is also most conducive to maintaining the proximity of natural habitats with the same habitat type. Other scenarios show relatively similar performance.Fig. 3: Future urban expansion effects on habitat fragmentation under SSP scenarios.a Mean patch size (MPS), b edge density (ED), c mean Euclidean nearest-neighbor distance (ENN_MN).Full size imageImpacts of urban expansion on terrestrial biodiversityWe focus on biodiversity in three common vertebrate taxa (i.e., amphibians, mammals, and birds) in our analyses. Future land system conversion to urban land will cause an average of 34% loss in the overall relative species richness. Land conversion from dense forest, mosaic grassland and open forest, mosaic grassland, and bare and natural grassland to urban land will cause the highest overall relative biodiversity loss (48%, 95% confidence interval (CI): 34–59% on a 1 km grid). These land systems with a high risk of biodiversity loss are concentrated in the United States, Europe, and Sub-Saharan Africa (Supplementary Fig. 19). Overall, the negative effect of future urban expansion on the total abundance of species will be more pronounced than that on species richness. Urban land changes will result in an average of 52% overall loss in relative total abundance of species. In particular, the losses of dense forest, natural grassland, and mosaic grassland, due to conversion to urban land, will lead to a high risk of species loss (62%, 95% CI: 38–76%).In terms of the number of species (i.e., all amphibians, mammals, and birds), future urban expansion will cause an average loss of 7–9 species and a loss of up to ~197 species per 10 km grid cell by 2100 across the five SSP scenarios (Fig. 4 and Supplementary Fig. 20). Species loss is most likely to be concentrated in Sub-Saharan Africa (particularly the Gulf of Guinea coast), the United States, and Europe. In addition, southeastern Brazil, India, and the eastern coast of Australia are also relatively high-risk areas. However, the specific effects of urban expansion vary substantially across different SSP scenarios. For instance, under scenario SSP5, urban expansion will pose a fatal threat to the global species richness in areas with urban development potential (species richness loss will occur in ~740 Mha land areas), whereas under the divided pathway (SSP4) and regional rivalry pathway (SSP3) scenarios, urban expansion will threaten the richest biodiversity hotspots, such as Sub-Saharan Africa and Latin America (Supplementary Fig. 20).Fig. 4: Potential biodiversity loss due to future urban expansion under SSP scenarios.The biodiversity loss in terms of the number of terrestrial vertebrate species (amphibians, mammals, and birds) lost per 10 km grid cell in the North America (a), Europe (b), the Gulf of Guinea coast (c), and East Asia (d).Full size imageWe also found a loss of up to 12 species of threatened amphibians, mammals, and birds (including vulnerable, endangered, or critically endangered categories defined in the IUCN Red List), and a loss of up to 40 species of small-ranged amphibians, mammals, and birds (small-ranged species are species with a geographic range size smaller than the median range size for that taxon)29 due to future urban expansion by 2100. There are a few scattered areas that will be hotspots for the loss of threatened species, such as West Africa, East Africa, northern India, and the eastern coast of Australia (Supplementary Fig. 21). The loss of small-ranged species will concentrate in fewer areas (Supplementary Fig. 22). We have identified 30 conservation priority ecoregions with high risks of habitat loss and small-ranged species loss due to future urban expansion (Supplementary Table 6). These conservation priority ecoregions are all found in Latin America and Sub-Saharan Africa (Supplementary Fig. 23). However, some hotspots outside of these conservation priority regions, such as tropical Southeast Asia, the west coast of the United States, and northern New Zealand, will also be affected (Supplementary Fig. 23).The top 5% 10 km grid cells with the highest loss in species richness (28–38 species potentially being lost) scatter across adjacent urban areas. However, only 6.4–8.6% of these regions are covered by the current global network of protected areas. These areas are often overlooked, and thus receive relatively low conservation spending. Ecoregions in Sub-Saharan African, Central and South America, Southeast Asia, and Australia will be responsible for the top 43% of average species loss across the SSP scenarios (Fig. 5). Kenya, Swaziland, Brunei, Zambia, Republic of Congo, and Zimbabwe will face the largest potential species richness loss (approximately > 29 species lost per 10 km grid cell) under all five SSP scenarios (Supplementary Fig. 24 and Supplementary Table 7).Fig. 5: Average potential biodiversity loss per 10 km grid cell in ecoregions due to future urban expansion under SSP scenarios.The mean potential biodiversity loss represents the average number of terrestrial vertebrate species (amphibians, mammals, and birds) lost per 10 km grid cell.Full size image More

Ceballos, G., García, A. & Ehrlich, P. R. The sixth extinction crisis: Loss of animal populations and species. J. Cosmol. 8, 31 (2010).
Google Scholar 
Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).CAS 
PubMed 

Google Scholar 
Scott, J. M., Goble, D. D., Haines, A. M., Wiens, J. A. & Neel, M. C. Conservation-reliant species and the future of conservation. Conserv. Lett. 3, 91–97 (2010).
Google Scholar 
Johnson, M. A., Kirby, R., Wang, S. & Losos, J. What drives variation in habitat use by Anolis lizards: Habitat availability or selectivity?. Can. J. Zool. 84, 877–886 (2006).
Google Scholar 
Gaston, K. J., Blackburn, T. M. & Lawton, J. H. Interspecific abundance-range size relationships: an appraisal of mechanisms. J. Anim. Ecol. 66, 579–601 (1997).
Google Scholar 
Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).
Google Scholar 
Razgour, O., Hanmer, J. & Jones, G. Using multi-scale modelling to predict habitat suitability for species of conservation concern: The grey long-eared bat as a case study. Biol. Cons. 144, 2922–2930 (2011).
Google Scholar 
Jetz, W., Sekercioglu, C. H. & Watson, J. E. Ecological correlates and conservation implications of overestimating species geographic ranges. Conserv. Biol. 22, 110–119 (2008).PubMed 

Google Scholar 
Seddon, P. J. From reintroduction to assisted colonization: Moving along the conservation translocation spectrum. Restor. Ecol. 18, 796–802 (2010).
Google Scholar 
Tomlinson, S., Lewandrowski, W., Elliott, C. P., Miller, B. P. & Turner, S. R. High-resolution distribution modeling of a threatened short-range endemic plant informed by edaphic factors. Ecol. Evol. 10, 763–773 (2019).PubMed 
PubMed Central 

Google Scholar 
Tomlinson, S., Webber, B. L., Bradshaw, S. D., Dixon, K. W. & Renton, M. Incorporating biophysical ecology into high-resolution restoration targets: insect pollinator habitat suitability models. Restor. Ecol. 26, 338–347 (2018).
Google Scholar 
Glen, A. S., Sutherland, D. R. & Cruz, J. An improved method of microhabitat assessment relevant to predation risk. Ecol. Res. 25, 311–314 (2010).
Google Scholar 
Limberger, D., Trillmich, F., Biebach, H. & Stevenson, R. D. Temperature regulation and microhabitat choice by free-ranging Galapagos fur seal pups (Arctocephalus galapagoensis). Oecologia 69, 53–59 (1986).PubMed 

Google Scholar 
Parmenter, R. R., Parmenter, C. A. & Cheney, C. D. Factors influencing microhabitat partitioning in arid-land darkling beetles (Tenebrionidae): temperature and water conservation. J. Arid Environ. 17, 57–67 (1989).
Google Scholar 
Kleckova, I., Konvicka, M. & Klecka, J. Thermoregulation and microhabitat use in mountain butterflies of the genus Erebia: importance of fine-scale habitat heterogeneity. J. Therm. Biol 41, 50–58 (2014).PubMed 

Google Scholar 
Napierała, A. & Błoszyk, J. Unstable microhabitats (merocenoses) as specific habitats of Uropodina mites (Acari: Mesostigmata). Exp. Appl. Acarol. 60, 163–180 (2013).PubMed 
PubMed Central 

Google Scholar 
Marshall, K. L., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 1–10 (2016).
Google Scholar 
Lovell, P. G., Ruxton, G. D., Langridge, K. V. & Spencer, K. A. Egg-laying substrate selection for optimal camouflage by quail. Curr. Biol. 23, 260–264 (2013).CAS 
PubMed 

Google Scholar 
Wrege, P. H., Rowland, E. D., Keen, S. & Shiu, Y. Acoustic monitoring for conservation in tropical forests: Examples from forest elephants. Methods Ecol. Evol. 8, 1292–1301 (2017).
Google Scholar 
Measey, G. J., Stevenson, B. C., Scott, T., Altwegg, R. & Borchers, D. L. Counting chirps: Acoustic monitoring of cryptic frogs. J. Appl. Ecol. 54, 894–902 (2017).
Google Scholar 
Lambert, K. T. & McDonald, P. G. A low-cost, yet simple and highly repeatable system for acoustically surveying cryptic species. Austral Ecol. 39, 779–785 (2014).
Google Scholar 
Picciulin, M., Kéver, L., Parmentier, E. & Bolgan, M. Listening to the unseen: Passive Acoustic Monitoring reveals the presence of a cryptic fish species. Aquat. Conserv. Mar. Freshwat. Ecosyst. 29, 202–210 (2019).
Google Scholar 
Linkie, M. et al. Cryptic mammals caught on camera: assessing the utility of range wide camera trap data for conserving the endangered Asian tapir. Biol. Cons. 162, 107–115 (2013).
Google Scholar 
Balme, G. A., Hunter, L. T. & Slotow, R. Evaluating methods for counting cryptic carnivores. J. Wildl. Manag. 73, 433–441 (2009).
Google Scholar 
Carbone, C. et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals in Animal Conservation forum. 75–79 (2001) (Cambridge University Press).Russell, J. C., Hasler, N., Klette, R. & Rosenhahn, B. Automatic track recognition of footprints for identifying cryptic species. Ecology 90, 2007–2013 (2009).PubMed 

Google Scholar 
Jarvie, S. & Monks, J. Step on it: can footprints from tracking tunnels be used to identify lizard species?. N. Z. J. Zool. 41, 210–217 (2014).
Google Scholar 
Watts, C., Thornburrow, D., Rohan, M. & Stringer, I. Effective monitoring of arboreal giant weta (Deinacrida heteracantha and D. mahoenui; Orthoptera: Anostostomatidae) using footprint tracking tunnels. J. Orthop. Res. 22, 93–100 (2013).
Google Scholar 
Williams, E. M. Developing monitoring methods for cryptic species: a case study of the Australasian bittern, Botaurus poiciloptilus: a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Manawatū, New Zealand, Massey University (2016).Hacking, J., Abom, R. & Schwarzkopf, L. Why do lizards avoid weeds?. Biol. Invasions 16, 935–947 (2014).
Google Scholar 
Valentine, L. E. Habitat avoidance of an introduced weed by native lizards. Austral. Ecol. 31, 732–735 (2006).
Google Scholar 
Hawkins, J. P., Roberts, C. M. & Clark, V. The threatened status of restricted-range coral reef fish species in Animal Conservation forum. 81–88 (2000) (Cambridge University Press).Mason, L. D., Bateman, P. W. & Wardell-Johnson, G. W. The pitfalls of short-range endemism: High vulnerability to ecological and landscape traps. PeerJ 6, e4715 (2018).PubMed 
PubMed Central 

Google Scholar 
Dassot, M., Constant, T. & Fournier, M. The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges. Ann. For. Sci. 68, 959–974 (2011).
Google Scholar 
Weber, H. LiDAR Sensor Functionality and Variants (2018).Michel, P., Jenkins, J., Mason, N., Dickinson, K. & Jamieson, I. Assessing the ecological application of lasergrammetric techniques to measure fine-scale vegetation structure. Eco. Inform. 3, 309–320 (2008).
Google Scholar 
Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. LiDAR remote sensing of forest structure. Prog. Phys. Geogr. 27, 88–106 (2003).
Google Scholar 
Anderson, L. & Burgin, S. Patterns of bird predation on reptiles in small woodland remnant edges in peri-urban north-western Sydney, Australia. Landsc. Ecol. 23, 1039–1047 (2008).
Google Scholar 
Hannam, M. & Moskal, L. M. Terrestrial laser scanning reveals seagrass microhabitat structure on a tideflat. Remote Sensing 7, 3037–3055 (2015).
Google Scholar 
Zavalas, R., Ierodiaconou, D., Ryan, D., Rattray, A. & Monk, J. Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR. Remote Sens. 6, 2154–2175 (2014).
Google Scholar 
Mandlburger, G., Hauer, C., Wieser, M. & Pfeifer, N. Topo-bathymetric LiDAR for monitoring river morphodynamics and instream habitats—A case study at the Pielach River. Remote Sens. 7, 6160–6195 (2015).
Google Scholar 
Laize, C. et al. Use of LIDAR to characterise river morphology (2014).Cooper, C. & Withers, P. Physiological significance of the microclimate in night refuges of the numbat Myrmecobius fasciatus. Austral. Mammal. 27, 169–174 (2005).
Google Scholar 
Orell, P. & Morris, K. Chuditch recovery plan. Western Austral. Wildl. Manag. Program 13, 1 (1994).
Google Scholar 
Pearson, D. Western Spiny-Tailed Skink (Egernia stokesii) Recovery Plan (Department of Environment and Conservation, 2012).
Google Scholar 
McPeek, M. A., Cook, B. & McComb, W. Habitat selection by small mammals. Trans. Kentucky Acad. Sci. 44, 68–73 (1983).
Google Scholar 
Armstrong, K. The distribution and roost habitat of the orange leaf-nosed bat, Rhinonicteris aurantius, in the Pilbara region of Western Australia. Wildl. Res. 28, 95–104 (2001).
Google Scholar 
Mancina, C. et al. Endemics under threat: an assessment of the conservation status of Cuban bats. Hystrix Ital. J. Mammal. 18, 3–15 (2007).
Google Scholar 
Webb, M. H., Holdsworth, M. C. & Webb, J. Nesting requirements of the endangered Swift Parrot (Lathamus discolor). Emu-Austral. Ornithol. 112, 181–188 (2012).
Google Scholar 
Watson, S. J., Watson, D. M., Luck, G. W. & Spooner, P. G. Effects of landscape composition and connectivity on the distribution of an endangered parrot in agricultural landscapes. Landsc. Ecol. 29, 1249–1259 (2014).
Google Scholar 
Duffield, G. & Bull, M. Stable social aggregations in an Australian lizard, Egernia stokesii. Naturwissenschaften 89, 424–427 (2002).CAS 
PubMed 

Google Scholar 
Duffield, G. A. & Bull, M. Characteristics of the litter of the gidgee skink, Egernia stokesii. Wildl. Res. 23, 337–341 (1996).
Google Scholar 
Ecoscape. Blue Hills – Mungada East Terrestrial Fauna Assessment. (Sinosteel Midwest Corporation, 2016).Silver Lake Resources. Department of Water and Environmental Regulation Prescribe Premise Licence Application. (Egan Street Resources Limited, 2021).Maptek. I-Site 8800 Scanning System Solutions for Mining (2010).SoilWater Group. 3D LiDAR Scanning (2018).United States Department of Transportation. Ground-Based LiDAR Rock Slope Mapping and Assessment (2008).R Core Team. R: a language and environment for statistical computing, https://www.R-project.org/ (2017).Bartoń, K. Package ‘MuMIn’, https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf (2020).Converse, S. J., White, G. C. & Block, W. M. Small mammal responses to thinning and wildfire in ponderosa pine-dominated forests of the southwestern United States. J. Wildl. Manag. 70, 1711–1722 (2006).
Google Scholar 
Vieira, I. C. G. et al. Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazonia. Remote Sens. Environ. 87, 470–481 (2003).
Google Scholar 
Whitford, K. & Williams, M. Hollows in jarrah (Eucalyptus marginata) and marri (Corymbia calophylla) trees: II. Selecting trees to retain for hollow dependent fauna. For. Ecol. Manag. 160, 215–232 (2002).
Google Scholar 
Salmona, J., Dixon, K. M. & Banks, S. C. The effects of fire history on hollow-bearing tree abundance in montane and subalpine eucalypt forests in southeastern Australia. For. Ecol. Manag. 428, 93–103 (2018).
Google Scholar 
Lindenmayer, D., Cunningham, R., Donnelly, C., Tanton, M. & Nix, H. The abundance and development of cavities in Eucalyptus trees: a case study in the montane forests of Victoria, southeastern Australia. For. Ecol. Manage. 60, 77–104 (1993).
Google Scholar 
Craig, M. D. et al. How many mature microhabitats does a slow-recolonising reptile require? Implications for restoration of bauxite minesites in south-western Australia. Aust. J. Zool. 59, 9–17 (2011).
Google Scholar 
Schwarzkopf, L., Barnes, M. & Goodman, B. Belly up: Reduced crevice accessibility as a cost of reproduction caused by increased girth in a rock-using lizard. Austral Ecol. 35, 82–86 (2010).
Google Scholar 
Cooper, W. E. Jr. & Whiting, M. J. Islands in a sea of sand: Use of Acacia trees by tree skinks in the Kalahari Desert. J. Arid Environ. 44, 373–381 (2000).
Google Scholar 
Webb, J. K. & Shine, R. Out on a limb: conservation implications of tree-hollow use by a threatened snake species (Hoplocephalus bungaroides: Serpentes, Elapidae). Biol. Cons. 81, 21–33 (1997).
Google Scholar 
Fitzgerald, M., Shine, R. & Lemckert, F. Radiotelemetric study of habitat use by the arboreal snake Hoplocephalus stephensii (Elapidae) in eastern Australia. Copeia 2002, 321–332 (2002).
Google Scholar 
Grimm-Seyfarth, A., Mihoub, J. B. & Henle, K. Too hot to die? The effects of vegetation shading on past, present, and future activity budgets of two diurnal skinks from arid Australia. Ecol. Evol. 7, 6803–6813 (2017).PubMed 
PubMed Central 

Google Scholar 
Attum, O., Eason, P., Cobbs, G. & El Din, S. M. B. Response of a desert lizard community to habitat degradation: Do ideas about habitat specialists/generalists hold?. Biol. Cons. 133, 52–62 (2006).
Google Scholar 
Melville, J. & Schulte Ii, J. A. Correlates of active body temperatures and microhabitat occupation in nine species of central Australian agamid lizards. Austral. Ecol. 26, 660–669. https://doi.org/10.1046/j.1442-9993.2001.01152.x (2001).Article 

Google Scholar 
Munguia-Vega, A., Rodriguez-Estrella, R., Shaw, W. W. & Culver, M. Localized extinction of an arboreal desert lizard caused by habitat fragmentation. Biol. Cons. 157, 11–20 (2013).
Google Scholar 
Pietrek, A., Walker, R. & Novaro, A. Susceptibility of lizards to predation under two levels of vegetative cover. J. Arid Environ. 73, 574–577 (2009).
Google Scholar 
Moreno, S., Delibes, M. & Villafuerte, R. Cover is safe during the day but dangerous at night: The use of vegetation by European wild rabbits. Can. J. Zool. 74, 1656–1660 (1996).
Google Scholar 
Tchabovsky, A. V., Krasnov, B., Khokhlova, I. S. & Shenbrot, G. I. The effect of vegetation cover on vigilance and foraging tactics in the fat sand rat Psammomys obesus. J. Ethol. 19, 105–113 (2001).
Google Scholar 
Pizzuto, T. A., Finlayson, G. R., Crowther, M. S. & Dickman, C. R. Microhabitat use by the brush-tailed bettong (Bettongia penicillata) and burrowing bettong (B. lesueur) in semiarid New South Wales: Implications for reintroduction programs. Wildl. Res. 34, 271–279 (2007).
Google Scholar 
Hawlena, D., Saltz, D., Abramsky, Z. & Bouskila, A. Ecological trap for desert lizards caused by anthropogenic changes in habitat structure that favor predator activity. Conserv. Biol. 24, 803–809 (2010).PubMed 

Google Scholar 
Oversby, W., Ferguson, S., Davis, R. A. & Bateman, P. Bad news for bobtails: Understanding predatory behaviour of a resource-subsidised corvid towards an island endemic reptile. Wildl. Res. 45, 595–601 (2018).
Google Scholar 
Pianka, E. R. Rarity in A ustralian desert lizards. Austral Ecol. 39, 214–224 (2014).
Google Scholar 
Germano, J. M. & Bishop, P. J. Suitability of amphibians and reptiles for translocation. Conserv. Biol. 23, 7–15 (2009).PubMed 

Google Scholar 
Tsiouvaras, C., Havlik, N. & Bartolome, J. Effects of goats on understory vegetation and fire hazard reduction in a coastal forest in California. For. Sci. 35, 1125–1131 (1989).
Google Scholar 
Tasker, E. M. & Bradstock, R. A. Influence of cattle grazing practices on forest understorey structure in north-eastern New South Wales. Austral. Ecol. 31, 490–502 (2006).
Google Scholar 
Payne, A., Van Vreeswyk, A., Leighton, K., Pringle, H. & Hennig, P. An inventory and condition survey of the Sandstone-Yalgoo-Paynes Find area, Western Australia (1998).Shoo, L. P., Freebody, K., Kanowski, J. & Catterall, C. P. Slow recovery of tropical old-field rainforest regrowth and the value and limitations of active restoration. Conserv. Biol. 30, 121–132 (2016).PubMed 

Google Scholar 
Lamb, D. in Regreening the Bare Hills 325–358 (Springer, 2011).Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).PubMed 

Google Scholar 
Stow, A. J., Sunnucks, P., Briscoe, D. & Gardner, M. The impact of habitat fragmentation on dispersal of Cunningham’s skink (Egernia cunninghami): Evidence from allelic and genotypic analyses of microsatellites. Mol. Ecol. 10, 867–878 (2001).CAS 
PubMed 

Google Scholar 
Stow, A. & Sunnucks, P. High mate and site fidelity in Cunningham’s skinks (Egernia cunninghami) in natural and fragmented habitat. Mol. Ecol. 13, 419–430 (2004).CAS 
PubMed 

Google Scholar  More

If you are a scientist hoping to photograph and share your own research:
•    Don’t underestimate the power of modern media and social-media platforms. Content is changing the world and people’s lives, and it can easily change your life. Stay at the forefront of media technology, or at least be aware of developments. It’s a never-ending race, but it’s easy to get into.
•    If you plan to share your work with others, imagine what will be of interest to them. If you can excitingly describe your work to a 5-year-old, you won’t have any trouble getting anyone interested. Beautiful pictures help, but the story always comes first.

•    You will stand out much more if you have a niche and unique story. It could be your rare field of science or a special angle that you use to tell the story of your work. Being different is awesome.
•    Set the bar very high. You can find dozens of examples of truly high-quality content on the Internet. And you can almost always find resources that can help you to learn how to create work of the same calibre. With practice, your skills will inevitably rise — but at any given time, it’s important to know the level you should aim for.
•    Find people who are cooler than you. Don’t hesitate to ask them for advice or to shadow them. Have them share their experiences, stand behind them and observe their work if they’ll let you. Few things are more useful than real work experience, both your own and that of others.
•    Take on a project. This could be a an illustrated workbook for colleagues or students, a guide book, a lecture for schoolchildren with compelling visuals, a course for students or a documentary on your topic.
•    If you work in a team, you can raise the bar even higher. Use each other’s strengths, share experiences, make plans, apply for grants and take on challenging science-communication projects together. This multiplies the fun and the results. More

Tsering, D. Transport development in Tibet since the democratic reform in 1959. J. Tibetan Stud. 76–85 (2019).Yan, X. et al. Relationships between heavy metal concentrations in roadside topsoil and distance to road edge based on field observations in the Qinghai-Tibet Plateau, China. Int. J. Environ. Res. Public Health. 10(3), 762–765 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Berling-Wolff, S. & Jianguo, W. U. Modeling urban landscape dynamics: A case study in Phoenix, USA. J. Urban Ecosyst. 7(3), 215–240 (2004).
Google Scholar 
Jianzhou, G., Yansui, L. & Beicheng, X. Spatial heterogeneity of urban land-cover landscape in Guangzhou from 1990 to 2005. J. Geogr. Sci. 19(2), 213–224 (2009).
Google Scholar 
Pan, L., Zhang, H. & Liu, A. Analysis of threshold of road networks effecting landscape fragmentation in Chongqing. J. Ecol. Sci. 34(5), 45–51 (2015).CAS 

Google Scholar 
Paukert, C. P., Pitts, K. L., Whittier, J. B. & Oldenc, J. D. Development and assessment of a landscape-scale ecological threat index for the Lower Colorado River Basin. J. Ecol. Indic. 11(2), 304–310 (2011).
Google Scholar 
Li, H., Yu, Q., Li, N., Wang, J. & Yang, Y. Study on landscape dynamics and driving mechanisms of the Shudu Lake catchment wetlands in Northwest Yunnan. J. West China Forest. Sci. 42(3), 34–39 (2013).Yunqing, H., Jinxi, W. & Hong, J. The dynamics of land cover change pattern and landscape fragmentation in Jiuzhaigou Nature Reserve, China. J. Proc. SPIE Int. Soc. Opt. Eng. 7498, 74983P (2009).Hu, L. et al. Landscape pattern in Nanwenghe nature reserve and its driving forces. J. Protect. Forest Sci. Technol. 0(7), 18–21 (2015).Li, X. et al. Land use/cover and landscape pattern changes in Manas River Basin based on remote sensing. J. Int. J. Agric. Biol. Eng. 13(5), 141–152 (2020).Andrejs, R. & Merkurjevs, J. Software tool implementing the fuzzy AHP method in ecological risk assessment. J. Inform. Technol. Manag. Sci. 20(1), 34–39 (2017).Peng, J., Dang, W., Liu, Y., Zong, M. & Hu, X. Research progress and prospect of landscape ecological risk assessment. J. Acta Geogr. Sin. 70(04), 664–677 (2015).
Google Scholar 
Xu, Y., Fu, B. & Lü, H. Research on landscape pattern and ecological processes based on landscape models. J. Acta Ecol. Sin. 30(1), 212–220 (2010).
Google Scholar 
Forman, R. Road ecology: A solution for the giant embracing us. J. Landsc. Ecol. 13(4), 3–5 (1998).
Google Scholar 
Minxi, W., Shiliang, L., Baoshan, C. & Min, Y. Impacts of hydroelectric project construction on nature reserve and assessment. J. Acta Ecol. Sin. 28(4), 1663–1671 (2008).
Google Scholar 
Yang, K., Deng, X., Xue-Ling, L. I. & Wen, P. Impacts of hydroelectric cascade exploitation on river ecosystem and landscape: A review. J. Acta Ecol. Sin. 22(5), 1359–1367 (2011).
Google Scholar 
Chen, L. D., Wang, J. P., Jiang, C. L. & Zhang, H. P. Quantitative study on effect of linear project construction on landscape pattern along pipeline. J. Sci. Geogr. Sin. 30(2), 161–167 (2010).
Google Scholar 
Qianqian, H., Luomeng, C. & Shanlin, W. Impact of expressway on land use and landscape pattern: A case study of Putan Guai to Chenghao section of Inner Mongolia Provincial Highway 103. J. Environ. Protect. Sci. 35(05), 58–61 (2009).
Google Scholar 
Huang, Y., Li, Y. & Ying, H. Responses of Chongqing-Yi Expressway to land use change and landscape pattern. J. Nat. Resourc. 30(09), 1449–1460 (2015).
Google Scholar 
Keken, Z., Sebkova, M. & Skalos, J. Analyzing land cover change—The impact of the motorway construction and their operation on landscape structure. J. Geogr. Inform. Syst. 6(5), 559–571 (2014).Mengna, H. & Ting, M. Assessing the impacts of China’s road network on landscape fragmentation and protected areas. J. Geo-inform. Sci. 21(8), 1183–1195 (2019).
Google Scholar 
Mothorpe, C., Hanson, A. & Schnier, K. The impact of interstate highways on land use conversion. J. Ann. Reg. Sci. 51(3), 833–870 (2013).
Google Scholar 
Wu, C.-F., Lin, Y.-P., Chiang, L.-C. & Huang, T. Assessing highway’s impacts on landscape patterns and ecosystem services: a case study in Puli Township, Taiwan. J. Landsc. Urban Plan. 128, 60–71 (2014).
Google Scholar 
Jia, L., Lei, T. & Yan, S. H. Environmental impact analysis and control measures in tunnel construction. J. Appl. Mech. Mater. 90–93, 3250–3253 (2011).ADS 

Google Scholar 
Wang, M. Analysis of high-speed railway construction on ecological environment impact and environmental protection contribution. J. Railway Constr. Technol. https://doi.org/10.3969/j.issn.1009-4539.2015.04.019 (2015).Article 

Google Scholar 
He, Y. & Xiong, C. Environmental impact of waste slurry in pile foundation construction of high-speed railway bridges and its countermeasures. J. Adv. Mater. Res. 383–390, 3690–3694 (2011).
Google Scholar 
Jing, C. et al. Influence of cross-sea bridge project on water quality and ecological environment of nearby sea and its tracking, monitoring and verification. J. Ocean Dev. Manag. 37(10), 96–100 (2020).
Google Scholar 
Jianhua, X., Mingquan, W., Shijian, Z. & Zheng, N. Remote sensing monitoring of ecological and economic impacts of major Railway construction along the Belt and Road. J. Sci. Technol. Eng. 20(11), 9 (2020).
Google Scholar 
Fang, L. On ecological environment impact assessment of metal mine construction project. J. Nonferrous Metals (Min. Sect.). 64(03), 58–60 (2012).
Google Scholar 
Bian, B., Lin, C. & Wu, H. S. Contamination and risk assessment of metals in road-deposited sediments in a medium-sized city of China. J. Ecotoxicol. Environ. Saf. 112, 87–95 (2015).CAS 

Google Scholar 
Limin, Y., Yanhai, Z., Rongzu, Q. & Xisheng, H. The influence of regional road construction on landscape ecology on both sides: A case study of Jiangle County, Fujian Province. J. Sichuan Agric. Univ. 33(2), 159–165 (2015).
Google Scholar 
Liang, Z. & Nianlai, C. Analysis on the impact of Jinwu Expressway on ecological environment based on comprehensive index evaluation method. J. Environ. Sustain. Dev. 44(3), 137–139 (2019).
Google Scholar 
Ting, W. & Zongmin, W. Study on eco-environmental impact assessment system of highway construction. J. Resourc. Econom. Environ. Protect. 3, 129–132. (2015).
Google Scholar 
Igondova, E., Pavlickova, K. & Majzlan, O. The ecological impact assessment of a proposed road development (the Slovak approach). J. Environ. Impact Assessm. Rev. 59, 43–54 (2016).
Google Scholar 
Chen, L., Fu, B. & Zhao, W. Source-sink landscape theory and its ecological significance. J. Front. Biol. China 3(2), 131–136 (2008).
Google Scholar 
Wu, J. et al. Spatial differentiation of landscape ecological risk in opencast mining area. J. Acta Ecol. Sin. 33(12), 3816–3824 (2013).
Google Scholar 
Wang, J., Cui, B., Liu, J., Yao, H. & Juan, H. The effect of land use and its change on ecological risk in the Lancang River watershed of Yunnan Province at the landscape scale. J. Acta Sci. Circumstan. 2, 269–277 (2008).CAS 
Article 

Google Scholar 
Xie, H. Regional eco-risk analysis based on landscape structure and spatial statistics. J. Acta Ecol. Sin. 28(10), 5020–5026 (2008).
Google Scholar 
Jinggang, L. I., Chunyang, H. E. & Xiaobing, L. I. Landscape ecological risk assessment of natural/semi-natural landscapes in fast urbanization regions——A case study in Beijing, China. J. Nat. Resourc. 23(1), 33–47 (2008).
Google Scholar 
Jie, W., Wanqi, B. & Guoxing, T. Temporal and spatial characteristics of landscape ecological risk in Qinghai-Tibet Plateau. J. Resour. Sci. 42(9), 1739–1749 (2020).
Google Scholar 
Xuegong, X., Huiping, L., Zaiyi, F. & Rencang, B. Ecological risk assessment of wetland area in Yellow River Delta. J. Acta Sci. Nat. Univ. Pekinensis. 01, 111–120 (2001).
Google Scholar 
Malekmohammadi, B. & Blouchi, L. Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. J. Ecol. Indic. 41, 133–144 (2014).
Google Scholar 
Campos, P., Paz, T., Lenz, L., Qiu, Y. & Paz, I. Multi-criteria decision method for sustainable watercourse management in urban areas. J. Sustain. 12(16), 6493–6514 (2020).
Google Scholar 
Peng, L. et al. Research on ecological risk assessment in land use model of Shengjin Lake in Anhui province, China. J. Environ. Geochem. Health. 41(6), 2665–2679 (2019).CAS 

Google Scholar 
Zhang, D., Yang, S., Wang, Z., Yang, C. & Chen, Y. Assessment of ecological environment impact in highway construction activities with improved group AHP-FCE approach in China. J. Environ. Monit. Assess. 192(7), 451–469 (2020).
Google Scholar 
Luan, B. et al. Evaluating green stormwater infrastructure strategies efficiencies in a rapidly urbanizing catchment using SWMM-based topsis. J. Clean. Prod. 223, 680–691 (2019).
Google Scholar 
Ramya, S. & Devadas, V. Integration of GIS, AHP and TOPSIS in evaluating suitable locations for industrial development: A case of Tehri Garhwal district, Uttarakhand, India. J. Clean. Prod. 238, 117872 (2019).Koc, K., Ekmekciolu, M. & Zger, M. An integrated framework for the comprehensive evaluation of low impact development strategies. J. Environ. Manag. 294, 113023 (2021).Xiumei, T., Yu, L., Yanmin, R., Yuchun, P. & Xingyao, H. Study on change of land use and ecosystem service value along expressway. J. China Agric. Univ. 21(2), 132–139 (2016).
Google Scholar 
Fei, Z., Shanjiang, Y. & Dongfang, W. Ecological risk assessment due to land use/cover changes (LUCC) in Jinghe County, Xinjiang, China from 1990 to 2014 based on landscape patterns and spatial statistics. J. Environ. Earth. Sci. 77(13), 491 (2018).
Google Scholar 
Rangel-Buitrago, N., Neal, W. J. & de Jonge, V. N. Risk assessment as tool for coastal erosion management. J. Ocean Coast. Manag. 186, 105099 (2020).
Google Scholar 
Mo, W., Wang, Y., Zhang, Y. & Zhuang, D. Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing. J. Sci. Total Environ. 574, 1000–1011 (2017).ADS 
CAS 

Google Scholar 
Getis, A. & Ord, J. K. The analysis of spatial association by use of distance statistics. J. Springer Berlin Heidelberg. 24(3), 189–206 (2010).
Google Scholar 
Yingxue, Z., Wenbo, M., Yong, W. & Dafang, Z. Impact of land use change on landscape pattern around expressways in Beijing. J. Geo-Inform. Sci. 19(001), 28–38 (2017).
Google Scholar 
Gang, Z., HuiJun, G. & Guang, Z. Changes of wetland landscape pattern in arid inland area of Northwest China: A case study of inner flow area in Junggar, Xinjiang. J. Arid Land Resourc. Environ. 28(8), 77–82 (2014).
Google Scholar 
Haihang, W., Qianhui, Z., Jiayao, Z. & Chunguo, Z. Analysis on dynamic change of landscape pattern of land use in Zhushan County. J. Forest Resourc. Manag. 6, 76–83 (2018).
Google Scholar 
Shiliang, L., Zhifeng, Y., Baoshan, C. & Shu, G. Impact of road on landscape and ecological risk assessment: A case study of Lancang River Basin. J. Chin. J. Ecol. 8, 897–901 (2005).
Google Scholar 
Yuan, Y. et al. Flood-landscape ecological risk assessment under the background of urbanization. J. Water. 11(7), 1418 (2019).Xie, H., Wang, P. & Huang, H. Ecological risk assessment of land use change in the Poyang lake Eco-economic zone, China. J. Int. J. Environ. Res. Public Health. 10(1), 328–346 (2013).
Google Scholar 
Fengjiao, X. & Xiao, L. Ecological risk pattern in coastal areas of Jiangsu Province based on land use change. J. Acta Ecol. Sin. 38(20), 7312–7325 (2018).
Google Scholar 
Mann, D., Anees, M. M., Rankavat, S. & Joshi, P. K. Spatio-temporal variations in landscape ecological risk related to road network in the Central Himalaya. J. Hum. Ecol. Risk Assess. https://doi.org/10.1080/10807039.2019.1710693 (2020).Article 

Google Scholar 
Oliveira, B. R. D., Costa, E. L. D., Carvalho-Ribeiro, S. M. & Maia-Barbosa, P. M. Land use dynamics and future scenarios of the Rio Doce State Park buffer zone, Minas Gerais, Brazil. J. Environ. Monit. Assessm. 192(1), 39.1–39.12 (2020).
Google Scholar 
Li, Y., Sun, Y. & Li, J. Heterogeneous effects of climate change and human activities on annual landscape change in coastal cities of mainland China. J. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2021.107561 (2021).Dadashpoor, H., Azizi, P. & Moghadasi, M. Land use change, urbanization, and change in landscape pattern in a metropolitan area. J. Sci. Total Environ. 655(10), 707–709 (2019).ADS 
CAS 

Google Scholar  More

ModelsConsider an IPD game with misperception such as implementation errors and observation errors22,23,31. Due to the misperception, the parameter in the real game changes from (omega _1=[T_1,R_1,P_1,S_1]) to (omega _2=[T_2,R_2,P_2,S_2]), and only player X notices the change. Thus, player Y’s cognition of the parameter is (omega _1), while player X’s cognition of the parameter is (omega _2). In each round, player X chooses a strategy from its strategy set (Omega _X={{mathbf {p}}=[p_{cc},p_{cd},p_{dc},p_{dd}]^T|p_{xy} in [0,1],xyin {cc,cd,dc,dd}}), e.g., (p_{xy}) is player X’s probability for cooperating with given previous outcome (xyin {cc,cd,dc,dd}). Similar to (Omega _X), player Y’s strategy set is (Omega _Y={{mathbf {q}}=[q_{cc},q_{dc},q_{cd},q_{dd}]^T|q_{xy} in [0,1],xyin {cc,dc,cd,dd}}). According to Press and Dyson7, this game can be characterized by a Markov chain with a state transition matrix (M=[M_{jk}]_{4times 4}) (see “Notations” for details). Denote ({mathbf {v}}=[v_{cc},v_{cd},v_{dc},v_{dd}]^T) as a probability vector such that ({mathbf {v}}^T M={mathbf {v}}^T) and (v_{cc}+v_{cd}+v_{dc}+v_{dd}=1). Let ({mathbf {S}}^{omega _i}_{X}=[R_i,S_i,T_i,P_i]^T), and ({mathbf {S}}^{omega _i}_{Y}=[R_i,T_i,S_i,P_i]^T,) (iin {1,2}). The expected utility functions of players are as follows:$$begin{aligned} begin{aligned} u_X^{omega _i}({mathbf {p}},{mathbf {q}})={mathbf {v}} cdot {mathbf {S}}^{omega _i}_{X}, u_Y^{omega _i}({mathbf {p}},{mathbf {q}})={mathbf {v}} cdot {mathbf {S}}^{omega _i}_{Y},iin {1,2}. end{aligned} end{aligned}$$Denote (G_1 = {{mathbf {P}}, {varvec{Omega }}, {mathbf {u}}, omega _1}), and (G_2={{mathbf {P}},{varvec{Omega }},{mathbf {u}},omega _2}), where ({mathbf {P}}={X,Y}), ({varvec{Omega }}=Omega _Xtimes Omega _Y), and ({mathbf {u}}={u_X^{omega _i},u_Y^{omega _i}}, iin {1,2}). Thus, the actual utilities of players are obtained through (G_2), and in the view of player Y, they are playing game (G_1). In the view of player X, they are playing game (G_2) but player X knows that player Y’s cognition is (G_1). (G_1) and (G_2) are shown in Table 2.Table 2 Utility matrices in IPD games with misperception.Full size tableLet ({mathbf {p}}_0=[1,1,0,0]^T). For (iin {1,2}), ({mathbf {p}}=alpha {mathbf {S}}^{omega _i}_{X} +beta {mathbf {S}}^{omega _i}_Y +gamma {mathbf {1}}+{mathbf {p}}_0), where (alpha ,beta ,gamma in {mathbb {R}}), is called a ZD strategy7 of player X in (G_i) since the strategy makes the two players’ expected utilities subjected to a linear relation:$$begin{aligned} alpha u_X^{omega _i}({mathbf {p}},{mathbf {q}})+beta u_Y^{omega _i}({mathbf {p}},{mathbf {q}})+gamma =0, end{aligned}$$for any player Y’s strategy ({mathbf {q}}). All available ZD strategies for player X in G can be expressed as (Xi (omega _i)={{mathbf {p}}in Omega _X|{mathbf {p}}=alpha {mathbf {S}}^{omega _i}_{X} +beta {mathbf {S}}^{omega _i}_Y +gamma {mathbf {1}}+{mathbf {p}}_0,alpha ,beta ,gamma in {mathbb {R}} }.) Also, the three special ZD strategies are denoted as:

(1)

equalizer strategy7,12: ({mathbf {p}}=beta {mathbf {S}}^{omega _i}_{Y}+gamma {mathbf {1}}+{mathbf {p}}_0);

(2)

extortion strategy7,13: ({mathbf {p}}=phi [({mathbf {S}}^{omega _i}_X-P_i{mathbf {1}})-chi ({mathbf {S}}^{omega _i}_Y-P_i{mathbf {1}})]+{mathbf {p}}_0,chi geqslant 1);

(3)

generous strategy14,15: ({mathbf {p}}=phi [({mathbf {S}}^{omega _i}_X-R_i{mathbf {1}})-chi ({mathbf {S}}^{omega _i}_Y-R_i{mathbf {1}})] +{mathbf {p}}_0,chi geqslant 1).

Based on the past experience, player Y knows that player X prefers ZD strategies, which has been widely considered in many IPD games7,9. To avoid that player Y notices the change, which may result in potential decrease of player X’s utility21 or collapse of the model28, player X keeps choosing ZD strategies according to (G_1), such that the strategy sequence matches player Y’s anticipation. To sum up, in our formulation,

the real game is (G_2);

player Y thinks that they are playing game (G_1), and player X thinks that they are playing game (G_2);

player X knows that player Y’s cognition is (G_1);

player Y believes that player X chooses ZD strategies;

player X tends to choose a ZD strategy according to (G_1) to avoid player Y’s suspicion of misperception.

In fact, player X can benefit from the misperception through the ZD strategy. For example, player X can adopt a generous strategy in (G_1) to not only promote player Y’s cooperation behavior, but also make player X’s utility higher than that of player Y, if the generous strategy is an extortion strategy in (G_2). A beneficial strategy for player X is able to maintain a linear relationship between players’ utilities or improve the supremum or the infimum of its utility in its own cognition. In the following, we aim to analyze player X’s implementation of a ZD strategy in IPD with misperception, and proofs are given in the Supplementary Information.Invariance of ZD strategyPlayer X’s ZD strategies may be kept in IPD games with misperception from implementation errors or observation errors. In particular, player X keeps choosing a ZD strategy ({mathbf {p}}) in (G_1) to avoid player Y’s suspicion about possible misperception. In the view of player X, it can also enforce players’ expected utilities subjected to a linear relationship if ({mathbf {p}}) is also a ZD strategy in (G_2). The following theorem provides a necessary and sufficient condition for the invariance of the linear relationship between players’ utilities.Theorem 1
Any ZD strategy ({mathbf {p}}) of player X in (G_1) is also a ZD strategy in (G_2) if and only if$$begin{aligned} frac{R_1-P_1}{2R_1-S_1-T_1}=frac{R_2-P_2}{2R_2-S_2-T_2}. end{aligned}$$
(1)

If (1) holds, player X can ignore the misperception and choose an arbitrary ZD strategy based on its opponent’s anticipation since it also leads to a linear relationship between players’ utilities, as shown in Fig. 1; otherwise, player X can not unscrupulously choose ZD strategies based on player Y’s cognition. There is a player X’s ZD strategy in player Y’s cognition which is not the ZD strategy in player X’s cognition. Further, because of the symmetry of (omega _1) and (omega _2), player X’s any available ZD strategy ({mathbf {p}}) in (G_2) is also a ZD strategy in (G_1) if and only if (1) holds. It indicates that (Xi (omega _1)=Xi (omega _2)) and player X can choose any ZD strategy based on its own cognition, which does not cause suspicion of the opponent since it is also consistent with player Y’s anticipation. Additionally, the slopes of linear relations between players’ utilities may be different, as also shown in Fig. 1, and player X can benefit from the misperception by choosing a ZD strategy to improve the corresponding slope.In fact, (1) covers the following two cases:

(1)

(2P_i=T_i+S_i), (iin {1,2}), is a sufficient condition of (1). Thus, when (2P_i=T_i+S_i), (iin {1,2}), player X’s any ZD strategy ({mathbf {p}}) in (G_1) is also a ZD strategy in (G_2). Actually, (2P_i=T_i+S_i), (iin {1,2}), means that the sum of players’ utilities when players mutual defect is equal to that when only one player chooses defective strategies.

(2)

(R_i+P_i=T_i+S_i), (iin {1,2}), is another sufficient condition of (1). Thus, when (R_i+P_i=T_i+S_i), (iin {1,2}), player X’s any ZD strategy ({mathbf {p}}) in (G_1) is also a ZD strategy in (G_2). Actually, (R_i+P_i=T_i+S_i), (iin {1,2}), means that the game has a balanced structure in utilities32. At this point, the relationship between cooperation rate and efficiency is monotonous, i.e., the higher the cooperation rate of both sides, the greater the efficiency (the sum of players’ utilities).

Furthermore, for the three special ZD strategies, player X can also maintain a linear relationship between players’ utilities in the IPD game with misperception.Figure 1Player X can also enforce a linear relationship between players’ utilities in its own cognition. Let (omega _1=[T,R_1,P_1,S]=[5,3,1,0]) and (omega _2=[T,R_2,P_2,S]=[5,frac{23}{7},frac{1}{7},0]), which satisfy (1). Consider that player X chooses two different ZD strategies in (a) and (b), respectively, and the red lines describe the relationships between players’ utilities in (G_1). We randomly generate 100 player Y’s strategies, and blue circles are ((u^{omega _2}_X,u^{omega _2}_Y)), correspondingly. Notice that blue circles are indeed on a cyan line in both (a) and (b).Full size imageEqualizer strategyBy choosing equalizer strategies according to player Y’s cognition, player X can unilaterally set player Y’s utilities, as shown in the following corollary.
Corollary 1
Player X’s any equalizer strategy ({mathbf {p}}) in (G_1) is also an equalizer strategy in (G_2) if and only if$$begin{aligned} frac{R_1-P_1}{R_2-P_2}=frac{R_1-T_1}{R_2-T_2}=frac{R_1-S_1}{R_2-S_2}. end{aligned}$$
(2)

(2) is also a sufficient condition of (1). If (2) holds, player X can unilaterally set player Y’s utility by choosing any equalizer strategy in (G_1) even though they have different cognitions; otherwise, player X can not unscrupulously choose an equalizer strategy based on player Y’s cognition since it may not be an equalizer strategy in player X’s cognition.Extortion strategyBy choosing extortion strategies according to player Y’s cognition, player X can get an extortionate share, as shown in the following corollary.
Corollary 2
For player X’s extortion strategy ({mathbf {p}}) with extortion factor (chi >1) in (G_1), ({mathbf {p}}) is also an extortion strategy in (G_2) if (1) and the following inequality hold:$$begin{aligned} begin{aligned} (S_1-P_1)(R_2-P_2)-(R_1-P_1)(T_2-P_2)-chi ((T_1-P_1)(R_2-P_2)-(R_1-P_1)(T_2-P_2))1) in (G_1), ({mathbf {p}}) is also a generous strategy in (G_2) if (1) and the following inequality hold:$$begin{aligned} begin{aligned}(S_1-R_1)(R_2-P_2)-(R_1-P_1)(T_2-R_2)-chi ((T_1-R_1)(R_2-P_2)-(R_1-P_1)(T_2-R_2))b^1_i, iin {1,2}, end{aligned} end{aligned}$$
(5)
where (a^1_i) and (b^1_i,iin {1,2}) are parameters shown in “Notations”.
Actually, when player Y chooses the always cooperate (ALLC) strategy35, i.e., ({mathbf {q}}=[1,1,1,1]^T), player X gets the supremum of the expected utility in (G_1) and player X’s utility is improved in the IPD game with misperception.Figure 4Player X can use either equalizer strategies and extortion strategies to raise the supremum of its expected utility or generous strategies to raise the infimum of its expected utility. (a) and (b) consider that (omega _1=[T,R_1,P,S]) and (omega _2=[T,R_2,P,S]), where (R_1ne R_2); (c) considers that (omega _1=[T,R,P_1,S]) and (omega _2=[T,R,P_2,S]), where (P_1ne P_2). The red lines in (a), (b), and (c) describe utilities’ relationships when player X chooses an equalizer strategy, an extortion strategy, and a generous strategy in (G_1), respectively; The yellow area contains all possible relationships between players’ utilities in (G_2) if player X does not change its strategy. In (a) and (b), r is the supremum of player X’s utility in (G_1), and (r’) is lower than the supremum of player X’s utility in (G_2); In (c), l is the infimum of player X’s utility in (G_1), and (l’) is lower than the infimum of player X’s utility in (G_2).Full size imageExtortion strategyBy choosing extortion strategies according to player Y’s cognition, player X can also improve the supremum of its expected utility.
Corollary 5
For player X’s extortion strategy ({mathbf {p}}) with extortion factor (chi >1) in (G_1), the supremum of player X’s expected utility in (G_2) is larger than that in (G_1) if$$begin{aligned} begin{aligned}a^2_ichi ^2+b^2_ichi +c^2_i1), the infimum of player X’s expected utility in (G_2) is larger than that in (G_1) if$$begin{aligned} begin{aligned}a^3_ichi ^2+b^3_ichi +c^3_i More

Comparison of T
ag calculated from publicly available data and actual measurement dataThe calculated Tag (m2/kg-FM) in each year is summarized for each species in Supplemental Table 1:

The geometric means (GMs) of Tag values calculated using the collected samples ranged from 8.1 × 10−6 to 2.5 × 10−2 m2/kg-FM; the minimum was for western bracken fern in 2019 and the maximum was for koshiabura in 2018 at Kawamata, Fukushima.

The GMs of Tag values calculated using the publicly available data ranged from 1.6 × 10−5 to 1.2 × 10−2 m2/kg-FM and thus were similar to the actual measurement data. The minimum GM was for udo in 2019 and the maximum was for koshiabura in 2019. The geometric standard deviation (GSD) range was 1.5–4.5.

Annual GMs of Tag values calculated from publicly available data and actual measurement data are compared in Fig. 1. The values for individual years are represented by different points. The Tag values were distributed close to the 1:1 line, which suggested that Tag values calculated from the publicly available data generally agreed with those calculated from actual measurements. Hence, an obvious overestimation of Tag from the publicly available data described above was not observed in the present data. We confirmed that Tag calculated from the publicly available food monitoring data and the total deposition data from the airborne survey are reliable surrogates for actual measurement samples. We discuss Tag calculated from the publicly available data hereafter.Figure 1Comparison of annual geometric means of the aggregated transfer factor (Tag) calculated from publicly available data and actual measurement data. Circles, diamonds, and triangles indicate deciduous perennial spermatophytes, deciduous tree spermatophytes, and deciduous perennial pteridophytes, respectively. Values for individual years are represented by different points. Error bars indicate the geometric standard deviation in cases where more than three samples were available.Full size imageRelationship between soil deposition and radioactivity in edible wild plants from publicly available dataWe confirmed the relationship between deposition and concentration of 137Cs for the publicly available data for butterbur scape, fatsia sprout, and western bracken fern in a year (Fig. 2), as a representative deciduous perennial and tree spermatophyte, and deciduous perennial pteridophyte, respectively, in the year of the maximum number of detections. Butterbur scape, fatsia sprout, and western bracken fern showed positive significant, nonsignificant, and weak negative significant correlations, respectively (Spearman’s rank correlation, butterbur scape, p = 0.001, rs = 0.45; fatsia sprout, p = 0.85, rs = − 0.03; western bracken fern, p = 0.03, rs = − 0.21). Among 29 subdata with more than 20 detections for each species in a year, in addition to the data shown in Fig. 2, 13 showed statistically significant positive correlations (Butterbur scape in 2014 and 2016; bamboo shoot in 2012, and 2014 − 2019; fatsia sprout in 2013 and 2016; koshiabura in 2013; and ostrich fern in 2012), and western bracken fern in 2017 showed a significant negative correlation. These weak correlations may be affected by uncertainty in the deposition data. We used a representative deposition value for each municipality and the original deposition data grid was of low resolution (see the “Methods” section Radiocesium deposition data from airborne survey). Especially for the cases lacking a clear positive correlation, the degree of radiocesium absorption by edible wild plants was largely different even in the same deposition. Radiocesium uptake by plants in an environment is also affected by other factors (e.g., soil characteristics25,26). The edible wild plants targeted in the present study were not cultivated but were collected in a variety of environments, such as forests with high organic matter content in the soil and paddy field margins with poorly drained soil high in clay content, although we cannot precisely confirm the growth environment of each species included in the present study.Figure 2Correlation between deposition and concentration of 137Cs in three edible wild plants. Circles, diamonds, and triangles indicate butterbur scape, fatsia sprout, and western bracken fern, respectively. The three species are representative deciduous perennial and tree spermatophyte, and deciduous perennial pteridophyte, respectively, in the year of the maximum number of detections.Full size imageTemporal change in T
ag
The time-dependence of Tag for each species in the period 2012–2019 is shown in Fig. 3. The Tag values of deciduous perennial spermatophytes and pteridophytes showed a decreasing trend with time. Given that the bioavailability of 137Cs in the soil in the plant root zone decreased with time, as observed in previous studies27,28, we also observed a decrease in Tag. The Tag of deciduous trees did not show a decreasing trend with time.Figure 3Temporal change in the aggregated transfer factor (Tag) in the period 2012–2019. Circles, diamonds, and triangles indicate deciduous perennial spermatophytes, deciduous tree spermatophytes (including bamboo shoot), and deciduous perennial pteridophytes, respectively. Single exponential fitted lines are shown. Solid lines indicate statistically significant parameters (see Table 2).Full size imageAfter the Chernobyl nuclear accident, radiocesium concentrations in deciduous tree leaves decreased with time owing to the effect of direct deposition at an early stage and the following root uptake effect29, and the Tag of tree leaves decreased accordingly. In previous studies conducted in orchards after the Chernobyl and Fukushima accidents, radiocesium concentrations in deciduous tree leaves showed a decreasing trend30,31. The lack of a declining trend for woody edible wild plants Tag in the present study may be due to a smaller effect of direct deposition at the early stage resulting from interception by tall tree canopies in the vicinity. The height of trees with edible wild plants is usually at eye level. The samples collected soon after the accident were possibly affected by direct deposition, whereas in the latter study period, many of the data were from trees grown after the accident. If the effect of direct deposition was large, a declining trend in Tag might have been observed as observed in orchards. Thus, the absence of a declining trend in Tag indicates that the effect of direct deposition was relatively small.As an additional possibility for the absence of a declining trend in tree Tag, the continuous supply of bioavailable radiocesium from the organic layer on the forest floor may affect the temporal change in Tag. Compared with the managed conditions in orchards of previous studies30,31, an organic layer develops on the soil surface in a forest and, therefore, reabsorption of radiocesium from the organic layer via the roots may be more active. Imamura et al.17 also observed a similar trend to that in the present study, namely that radiocesium concentrations in leaves of the canopies of the deciduous tree konara oak (Quercus serrata) did not show a temporal change from 2011 to 2015 in two Fukushima forests. These authors’ results included the effect of direct deposition on the tree bodies at an early stage of the accident, although the emergence of leaves was after the deposition. Nevertheless, a clear decreasing trend in the radiocesium concentration was not observed, which implies that a deciduous tree actively absorbs radiocesium via the roots in Fukushima forests, and a sufficient amount of radiocesium is absorbed to conceal a decline at an early stage owing to the effect of direct deposition.Single exponential fitted lines for each species are shown in Fig. 3. The estimated parameters and the Teff (year) calculated with Eq. (2) in “Methods” section are presented in Table 2. The Teff for Tag values that showed a decreasing trend was approximately 2 years, except for bamboo shoot. Tagami and Uchida10 reported that the Teff of the slow loss component for three edible wild plants of deciduous perennial spermatophytes was 970–3830 days. The 137Cs decline in pteridophytes, and deciduous shrub and herbaceous species on the floor of European forests was reported to be 1.2–8 years for Teff excluding the rapid loss component after the Chernobyl nuclear accident32. The present results are thus within the range of previous studies.Table 2 Estimated parameters and standard errors for correlations of Tag (m2/kg-FM) in the period 2012–2019 with time (day) calculated using Eq. (3) and effective half-lives [Teff, (year)] calculated using Eq. (2) for 11 parts of 10 edible wild plant species. A0 is estimated initial Tag, and λ (/day) is the 137Cs loss rate in edible parts of the plants.Full size tableFor bamboo shoot, applying a single exponential function, a relatively long Teff of 8.3 years was estimated. The Tag decreased between 2012 and 2014, and thereafter no notable change was observed. This observation may reflect the effect of rapid and a slow loss components. Indeed, we applied a two-component exponential function for bamboo shoot, and observed Teff of 0.7 years and − 7.8 years for the rapid and slow loss components, respectively. For edible wild tree species, statistically significant single exponential fitted lines were not observed, which reflected the absence of change in Tag with time, as discussed above in this section.The Tag varied for all species, varying by 1–3 orders of magnitude within a year that included more than two detections (Fig. 3, Supplemental Table 1). As demonstrated in previous studies5, the present study also showed substantial variation in Tag values, which may be for several reasons. Recently, Tagami et al.12 calculated Tag using the radiocesium concentration in edible wild plants measured by local municipalities from higher-resolution publicly available data (accurate to district level) for giant butterbur, bamboo shoot, fatsia sprout, and koshiabura. The municipalities in these authors’ study are located within the present study area. These authors’ results differed in being one or two orders of magnitude smaller than the present results. The lower resolution of the present deposition data may be one of the causes of the greater Tag variation. The other source of variation is the site dependency of radiocesium absorption by edible wild plants from the soil as described above. Clarification of factors that contribute to the variation in Tag other than 137Cs deposition, and its trends consistent with species, is necessary, which will decrease uncertainty and lead to more accurate estimation of Tag of 137Cs with wild plants.Summary of T
ag for estimation of long-term ingestion dose to the publicTo estimate long-term potential ingestion dose to the public, Tag with small temporal variability excluding high values at the early stage after the accident is required. However, for the edible wild plant species in the present study, no Tag information in an equilibrium condition from before the Fukushima accident is available. Therefore, average values of Tag for the period after the decrease in Tag has weakened and a certain number of samples is available would be appropriate. The Teff for Tag showing a decreasing trend was approximately 2 years except for bamboo shoot, which has not shown any temporal variation since 2014. The Tag for the other species, udo, uwabamisou, momijigasa, fatsia sprout, koshiabura and Japanese royal fern, has not shown temporal variation throughout 2012–2019 (see the “Results and discussion” section Temporal change in Tag). Therefore, Tag values since 2014 are applicable for estimation of long-term potential ingestion dose to the public. The GMs and GSDs of the Tag values for 2014–2019 for each species are shown in Table 3 listed in order of decreasing GM.Table 3 Aggregated transfer factor (m2/kg-FM) calculated from publicly available data for 2014–2019 for 11 parts of 10 edible wild plant species.Full size tableSignificant differences in Tag were observed among the species (one-way ANOVA with Tukey’s post hoc test, p  More

Erebia pronoe exhibits highly structured and strongly differentiated mitochondrial lineages, which are consistent with the distribution of previously described morphotaxa and analyses of Dincă et al.10 These genetic lineages are also reflected to varying degrees in the nuclear markers. The observed mito-nuclear discordances can be explained by different evolutionary rates of genetic markers, the effects of Wolbachia infections, and introgression. These aspects are discussed in more detail in the following sections on the phylogeographic history of this species complex.Mito-nuclear discordance and the systematic status of Erebia melas
Based on genital morphology and nuclear markers, E. melas represents a distinct group to E. pronoe. The common area of origin of both species was probably located in the eastern Alps, which is supported by a RASP analysis based on the nuclear markers. However, E. melas acts as an ingroup of E. pronoe based on the mitochondrial markers, and a RASP analysis indicates a common origin for both taxa in the Carpathian region. Since most Erebia species in Europe have at least parts of their distribution in the Alps21 and are adapted to Alpine environments and habitats22,23, we consider an eastern Alpine origin of the ancestor of E. pronoe and E. melas more likely. This hypothesis subsumes the assumption that the genetic proximity on the mitochondrial level was probably caused by hybridisation and introgression events, which could have occurred as a result of several eastward advances of E. pronoe to the Balkan Peninsula (see below). This seems plausible, because the ability and tendency of E. pronoe to hybridise with other Erebia species have been demonstrated repeatedly12,24,25.The existence of Wolbachia strain 2 in both species, and its distribution from the Pyrenees (in E. pronoe) to the Balkan Peninsula (in E. melas) also speaks for a common origin of both species. Thus, Wolbachia strain 2 might represent the ancient strain present in the common ancestor of this species group, surviving today at the geographic margins (i.e. Pyrenees, western Alps, Balkan Peninsula), but which at some time was replaced in the centre of the butterfly’s range (i.e. the eastern and central Alps) by strain 1. The link between co-occurrence in a common area and prevalence of one Wolbachia strain was also recently demonstrated in other Erebia species26 and might facilitate mitochondrial introgression27.Intraspecific differentiation and glacial refugia of Erebia pronoe
The Pyrenean region is inhabited by one of the oldest and most differentiated intraspecific lineages of E. pronoe. The high genetic diversity in the Pyrenees speaks for large effective population sizes throughout time, enabled by mostly altitudinal shifts in response to climatic cycles, and a lack of major genetic bottlenecks. Compared to the Pyrenean group, the genetic diversity of the western Alpine populations, also well differentiated from all other groups, is lower. This lower diversity was probably the result of repeated cold stage retreat to a geographically more restricted refugium at the foot of the south-western Alps, a well-known refugial area for numerous species28.We cannot say conclusively whether the populations in the Pyrenean region or in the western Alps differentiated first, due to the contradictory genetical markers. The higher evolutionary rate of the mitochondrial markers, the allopatric distribution, and the hybridisation with diverse Erebia species may have led to a greater differentiation of the Pyrenees and/or a loss of the genetic link between the western Alps and the Pyrenees. Since a link between the western Alps and the Pyrenees is still well reflected in the nuclear data set and by the shared Wolbachia strain 2, we consider the most likely scenario to be an early Pleistocene or even Pliocene expansion from the western Alps to the Pyrenees, with subsequent isolation and differentiation. Thus, the Pyrenees-western Alps populations might first have separated as one group from an eastern Alps group s.l., as suggested by nuclear information, and not in two independent events, as suggested by mitochondrial genes.Simultaneously to the split between western Alps and Pyrenees, a separation of the eastern Alpine group s.l. into a southern Alpine subgroup and an eastern Alpine subgroup should have occurred. The southern Alpine subgroup displays a high genetic diversity in their nuclear markers, but a significantly lower diversity in the mtDNA. This might be explained by the existence of a cold-stage refugial area in the southern Alps or their margin, supporting the constant survival of large populations, but also a reshaping of the mtDNA patterns through introgression from the eastern Alpine subgroup during secondary contact when both subgroups expanded into formerly glaciated east-central Alpine areas. The isolated occurrence of Wolbachia strain 1 and mitochondrial haplotypes H29 and H30 (shared with the eastern Alps subgroup) in the southern Alps further support the hypothesis of gene flow from the eastern Alpine region into the southern Alpine populations and vice versa.The eastern Alpine subgroup probably survived glacial periods in a large, cohesive refugium at the eastern edge of the Alps, as has been demonstrated for numerous other species28. This area is also seen as a potential centre of origin of the entire taxon. From there, a recent (most likely postglacial) dispersal must have taken place, which should be responsible at least partly for the star-like pattern of this group in both mitochondrial and nuclear haplotype networks. However, further dispersal events out of the eastern Alps during previous interglacials and maybe even going back to the Pliocene have to be postulated to explain the entire range dynamics in E. pronoe.Apparently, multiple advances out of the eastern Alps into the Balkan mountain systems have taken place from several independent glacial refugia in the region, as indicated by the different mtDNA lineages in Slovenia, western Balkan mountains, and eastern Balkan mountains. A separation between the eastern and western Balkans, and hence also separate glacial refugia in both areas, was frequently observed for mountain taxa28,31. This pattern may have resulted from a succession of independent dispersal events from the eastern Alps throughout the younger Pleistocene, with subsequent regional extinction events and/or independent dispersal events across the Carpathians, as has been demonstrated for numerous other species29.A similar pattern of two independent colonisation events also applies to the Carpathians. Thus, the highly isolated populations in the south-eastern Carpathians must go back to an older expansion out of the eastern Alps. This probably took place during one of the last interglacial phases. The route most likely followed the Carpathian arc, but only a few populations survived at their south-eastern edge. This underlines the phylogeographic independence of this part of the Romanian Carpathians, which is also supported by studies on numerous other mountain species30,31,32. On the other hand, the Tatra mountains, as the northernmost part of the Carpathians, were colonised very recently, most likely postglacially, out of the eastern Alpine area. The strong and rather recent link between these two areas is also supported by phylogeographic studies on many taxa30,33,34.Because of the slower evolutionary rate of nuclear DNA and the resulting incomplete lineage sorting, nuclear markers can contribute little to the reconstruction of these presumably recent events. In line with that, the Valais lineage also has little nuclear differentiation but is clearly distinguished from the western and eastern Alpine lineages by the exclusive mtDNA haplotype H17 and Wolbachia strain 3. The presence of a single, highly differentiated mtDNA haplotype and an exclusive Wolbachia strain indicates a selective sweep. This lineage most likely represents a chronological relict of an interglacial expansion of the eastern Alpine subgroup to the western-central Alps surviving since then in this area, finding glacial refugia in nearby unglaciated areas and becoming infested by a Wolbachia strain not present in any other E. pronoe lineage, hence accelerating its differentiation.Another selective sweep was probably the cause of the mito-nuclear unconformity in the southern Alps lineage. The occurrence of the mtDNA haplotypes H29 and H30 and the Wolbachia strain 1 indicate mitochondrial hybridisation between the eastern and southern Alpine lineages during an expansive interglacial phase. As a result, Wolbachia infection probably occurred, which might have impoverished the mitochondrial diversity of the southern Alps lineage.Consequences for subspecific differentiation in Erebia pronoe
In general, the support given by our data for the so-far described subspecies decreases from west to east. Erebia pronoe glottis Fruhstorfer, 1920, distributed in the Pyrenees, represents the best-supported subspecies. Fixed mitochondrial amino acid changes emphasize the distinctness of this taxon, which might be well advanced in the process of speciation; we cannot even exclude the possibility that it has already reached full species rank. The genetic separation of the western Alps from the Valais, geographically separated along the main Alpine ridge, justifies the recognition of the taxa E. pronoe vergy (Ochsenheimer, 1807) and E. pronoe psathura Fruhstorfer, 1920, respectively, and is supported by both marker sets as well as by the existence of two different Wolbachia strains. The eastern Alpine subgroup resembles the nominotypical E. pronoe pronoe. The existence of at least one lineage in the southern Alpine area is supported by both marker sets. A finer separation based on the mitochondrial markers is not possible, because of recent introgression events affecting east Alpine haplotypes, as also indicated by the existence of Wolbachia strain 1. This population group could be assigned to the taxon E. pronoe gardeina Schawerda, 1924, or to E. pronoe tarcenta Fruhstorfer, 1920, considering their ranges. Nevertheless, a final decision requires further regional studies. Erebia pronoe fruhstorferi Warren, 1933 was accepted to be widely distributed in the Balkan mountain systems. However, our data suggest independent lineages in the western and eastern Balkan mountain systems of which only the eastern populations can be assigned to this taxon. The lineage of the Slovenian Alps is primarily based on mitochondrial markers and morphological characteristics7. The existence of an independent lineage for the highly isolated populations in the southern Carpathians, justifies the subspecies status of E. pronoe regalis Hormuzachi, 1937. Both marker sets display a differentiation, which was more pronounced in the nuclear than in the mitochondrial DNA. More