Ecology

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

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•    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

Water ecological security evaluation results of Yellow River BasinIndex weight analysisThis study selects the index weights in 2009, 2014, and 2019 for comparative analysis. As shown in Table 3, in terms of space, in the pressure layer, indicator A6 (Water area) has the most prominent weight, and indicator A3 (Natural population growth rate) has the most negligible weight; in the state layer, indicator B6 (Proportion of wetland area to total area) has the most prominent weight, and B1 (COD emissions per 10,000 yuan GDP) has the most negligible weight; in the response layer, indicator C3 (Green area rate of built-up area) has the most prominent weight, and indicator C2 (Centralized treatment rate of urban domestic sewage) has the most negligible weight. In summary, water area, wetland area, and built-up green space are the key indicators affecting the water ecology of the Yellow River Basin, including natural factors and economic and social factors.Table 3 Water ecological security index weight.Full size tableIn terms of time, indicators A6 and B6 have equal weights in three years and have always been in an important position. The weight of indicator C1 (the rate of stable compliance of wastewater discharge by industrial enterprises) has fallen for three consecutive years, from 0.38 to 0.09. It shows that after years of environmental management in various cities, the rate of compliance with wastewater discharge standards of industrial enterprises has been continuously increasing. It plays a positive role in the construction of water ecological security. The weight of indicator C3 has increased significantly in three years, from 0.31 in 2009 to 0.90 in 2019, indicating that with the continuous development of urbanization, the built-up area has become larger and larger, which has a massive impact on water ecological security. Therefore, the green area in the built-up area is vital, which is the key to ensuring the urban ecological environment. It is also a critical factor in maintaining the water ecological security.Trend analysis of water ecological securityThis study is based on Eq. (4) to calculate the WESI of the nine provinces in the past ten years, as shown in Fig. 3. From the perspective of the changes in WESI from 2009 to 2019, the overall trend is slowly increasing. Compared with 2009, WESI increased by 5.96% in 2019, but the average annual growth rate was only 0.59%. The sharp rise stage was in 2009–2012, with an average annual growth rate of 1.84%. Since 2009, there has been no inferior V water in the main stream of the Yellow River, and the water quality has been improving year by year. During this period, the nine provinces implemented the Yellow River Basin Flood Control Plan under the guidance of The State Council. The plan calls for strengthening infrastructure construction in the Yellow River Basin and conducting work such as river improvement and soil and water conservation. Therefore, we will promote the restoration of water ecology in the river basin and improve the safety of water ecology. From 2012 to 2019, WESI showed a trend of ups and downs. This is because the provinces have gradually shifted their development focus to the economy after achieving significant results in restoring water ecology in the river basin. The rapid economic development has brought more significant pressure to environmental governance and hindered water ecological safety improvement.Figure 3Trend map of water ecological security index (WESI) of nine provinces.Full size imageCriterion layer quantitative resultsTo further study and appraise the water ecological security of the study area, this paper quantifies the criteria layers (i.e., pressure, state, response) on account of the SMI-P method. It selects 2009, 2014, and 2019 for comparative analysis. As shown in Fig. 4, the criterion layer has undergone specific changes over time. First of all, the distribution of pressure in 62 cities has not changed much in three years. The areas with more tremendous pressure on water ecological security are mainly concentrated in eastern cities, including Shuozhou, Taiyuan, Jinzhou, Luliang, Linfen, Jincheng, and Changzhi, Anyang, Hebi, Jiaozuo, Puyang, Liaocheng, and other cities. Areas with less pressure are mainly concentrated in western and eastern cities, including Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, Haibei Tibetan Autonomous Prefecture, Ordos, Bayannaoer, Yulin, and other cities. In 2009, the precipitation in spring and winter in Lanzhou is less, the degree of drought is serious, and the flood disaster is more severe in flood season, which brings tremendous pressure to the water ecological security. After 2015, Lanzhou continued to implement the Action Plan for Prevention and Control of Water Pollution and then the river chief system was implemented. In 2019, The Work Plan of Lanzhou Municipal Water Pollution Prevention and Control Action in 2019 was issued and implemented. All these measures and actions have laid a foundation for water ecological security. On the contrary, with the rapid development of urbanization and economy and society, the pressure of water ecological security in Jinan has increased.Figure 4Quantitative spatial distribution map of the 62 cities in the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageThe larger the value of the status layer, the better the aquatic ecological status. On the contrary, the worse the aquatic ecological security. The overall spatial distribution of the status layer has not changed significantly in the past three years, and the changes are mainly concentrated in some cities. For example, the water ecological security status of Wuhan and Ulan Chab has gradually deteriorated in three years. The reason is that the urban population is becoming denser and sewage discharge is increasing, but related management and measures have not been fully implemented. In Dongying, the water ecological security status improved in 2014 and 2019. According to the Environmental Status Bulletin, in 2014, Dongying deepened its drainage basin pollution control system, continuously strengthened the restraint mechanism to improve river water quality, and carried out a pilot wetland ecological restoration.In the three years of 2009, 2014, and 2019, the response layer has changed more significantly than the pressure and status layers. It can be seen that the degree of response scarcity has gradually shifted from western cities to eastern cities. The reason can be understood as that due to their superior natural conditions, western cities have relatively weak awareness of water ecological protection and governance, and their ability to respond to emergencies is insufficient. However, with the increasingly prominent ecological and environmental problems, the awareness of maintaining water ecological safety is increasing, and the protection and governance measures are constantly improving. For example, Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, and Haibei Tibetan Autonomous Prefecture. Eastern cities are densely populated, urbanization development is faster than western cities, and environmental problems occur more frequently. Therefore, the awareness of ecological and environmental protection is more substantial, the governance system is relatively complete, and responsiveness is relatively good. However, as time progresses, some cities have somewhat slackened their ecological environment governance, and therefore their responsiveness has also weakened. For example, Shuozhou, Jinzhou, Lvliang, Linfen, and other places.Final quantitative resultsIn order to show the water ecological security status of 62 cities more intuitively, this paper shows the water ecological status level in Table 2 through the GIS spatial distribution map (Fig. 5).Figure 5Distribution map of water ecological security status in 62 cities of the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageLooking at the overall situation in the past three years, the water ecological security status is relatively stable, with little overall change. The reasons mainly include natural geographical location and economic and social development. In terms of physical geography, the safer areas are concentrated in the upper reaches of the Yellow River Basin, all of which have the characteristics of large land and sparsely populated areas and relatively superior natural conditions. They provide good conditions and foundations for the construction of water ecological security. The moderate warning cities are primarily located in the Loess Plateau and the North China Plain, where water resources are scarce, and the dense population, posing a threat to water ecological security. In terms of economic and social development, relatively safe areas are located in remote areas with inconvenient transportation. The region is dominated by agriculture and animal husbandry, with relatively backward economic development and a low level of urbanization. In addition, the threat to water ecological security is relatively tiny. Residents in the moderate warning area have a significant living demand, and the over-exploitation and utilization of natural resources have led to the destruction of the ecological environment. Therefore, it poses a more significant threat to water ecological security.Combining Fig. 5 and Table A.2 of appendix, it can be seen that in 2009, there were 8 safer cities, 22 with early warning level, and 32 with moderate warning. Relatively safe cities are concentrated in the southwest and north of the Yellow River Basin; cities with moderate warning level are distributed in the central and eastern areas. In 2014, the number of safer cities increased to 10, and the number of cities with moderate warning level decreased to 30. The means that water ecological security has received more and more attention, and cities have consciously strengthened the protection and governance of water ecology to maintain water ecological security. In 2019, there are 11 relatively safe cities, 21 cities with warning level, and 30 cities with moderate warning level. The overall situation has not changed much, and some cities have changed significantly. For example, Erdos had increased from an early warning status in 2009 to a safer status in 2014, and its safety index has risen from 0.57 to 0.65. Wuzhong has been upgraded from the warning level in 2009 (0.39) to the relatively safe in 2014 (0.44), and the safety index (0.47) in 2019 has also increased. Binzhou had improved from its early warning status (0.60) in 2009 to a relatively safe level (0.64) in 2014, and its safety index (0.66) has also increased in 2019, but the increase is not significant. On the contrary, Jinan has deteriorated from the early warning level in 2009 and 2014 to the moderate warning level in 2019, indicating that the water ecological security of Jinan has been seriously threatened in the process of rapid development.Spatial autocorrelation analysis of 62 cities in the Yellow River BasinGlobal spatial autocorrelation analysisThis paper selects 2009, 2014 and 2019, and analyzes the global spatial autocorrelation based on GeoDa. Combining Table 4 and Fig. 6, the Moran index for these three years was 0.298, 0.359, and 0.334 respectively, which were all in the [0,1] interval, indicating the water ecological security of 62 cities in the past three years showed significant spatial autocorrelation. Moreover, there is a positive spatial correlation, and the spatial autocorrelation is strong. The four quadrants of the scatter chart are high-high (i.e., first quadrant) aggregation area, low–high (i.e., second quadrant) aggregation area, low-low (i.e., third quadrant) aggregation area, and high-low (i.e., fourth quadrant) aggregation area. After testing, z-value  > 1.96, p-value  More

Detection of archival Phyllonorycter mines in historical herbariaOnly 1.5% (225 out of 15,009) of herbarium specimens of Tilia spp. examined from the Palearctic contained Ph. issikii leaf mines. These 225 herbarium specimens occurred in 185 geographical locations across the Palearctic, with the westernmost point in Germany (Hessen; the herbarium specimen dated by 2004) to the most eastern locations in Japan (on the island of Hokkaido; 1885–1974) (Fig. 1).Figure 1The localities where herbarium specimens of Tilia spp. carrying Phyllonorycter mines were collected in the Palearctic in the last 253 years. The dotted line divides Ph. issikii range to native (below the line) and invaded (above the line). The map was generated using ArcGIS 9.3 (Release 9.3. New York St., Redlands, CA. Environmental Systems Research Institute, http://www.esri.com/software/arcgis/eval-help/arcgis-93).Full size imageMost specimens with leaf mines (90%; 203/225) originated from Eastern Palearctic, in particular from the Russian Far East (RFE) (67.5%, 137/203) (Fig. 2a). In some cases, leaves were severely attacked, carrying up to 12 mines per leaf (as documented in the Russian Far East in 1930s–1960s). On the other hand, we found only 22 herbarium specimens with mines (10%; 22/225) from the putative invaded region in Western Palearctic, with the majority of herbarium specimens with mines (7% 15/225) from European Russia (Fig. 2b).Figure 2The presence of Phyllonorycter issikii mines in the herbarium specimens collected in the putative native (a) and invaded (b) ranges over the past 253 years (1764–2016). The number of herbarium specimens with and without mines and the percentage of the specimens with mines in each region or country from all herbarium specimens examined in a region or country (in brackets) are given next to each graph. The total number of herbarium specimens, including those with and without mines, is given for Eastern (a) and Western Palearctic (b) separately and altogether (a + b).Full size imageThe average number of leaf mines per herbarium specimen found in native (5.68 ± 0.77) and invaded regions (6.09 ± 1.70) was not significantly different (Mann–Whitney U-test: U = 20,145; Z = 0.43; p = 0.43). However, the infestation rate by Ph. issikii, i.e. percentage of leaves with mines per herbarium specimen was statistically higher in the West than in the East: 35% ± 8.19 versus 23% ± 1.94 (Mann–Whitney U-test: U = 1339; Z = 2.30; p = 0.02).Leaf mines from the East were significantly older than those from the West (Mann–Whitney U-test: U = 81; Z =  − 4.4; p  400 bp) were obtained for 71 archival specimens that were between 7 and 162 years old (Fig. 4, the points in dashed frame) (Table S4). Nine of these 71 specimens were over one century old (106–162-year-old): eight originated from the Palearctic and one from the Nearctic (Fig. 4, the points in gray cloud).In the Palearctic, the oldest successfully DNA barcoded Ph. issikii specimen (obtained sequence length 408 bp) was a 162-year-old larva dissected from the leaf mine on Tilia amurensis from the RFE (village Busse, Amur Oblast, the year 1859), sequence ID LMINH119-19 (Fig. 5, Table S5). In the Nearctic, the oldest sequenced specimen (obtained sequence length 658 bp) was 127-year-old larva of Ph. tiliacella on T. americana from USA, Pennsylvania (Fig. 5, Table S5).Figure 5A maximum likelihood tree of 81 COI sequences of Phyllonorycter spp. Overall, 71 archival sequenced specimens were dissected from herbaria collected in the Palearctic and the Nearctic in 1859–2014 and ten specimens (highlighted in blue) originated from the modern range20. The tree was generated with the K2P nucleotide substitution model and bootstrap method (2500 iterations), p  More

Graded lockdowns imposed by the South African government to manage the COVID-19 pandemic27,28,29 has afforded us a unique opportunity to quantify shorebird responses to increasing human density in Muizenberg Beach over 8 months in 2020, including a 2-month period of virtual human exclusion. In spite of our study being limited to one beach over 2 years, we were able to take advantage of data collected prior to- (2019) and during the 2020 COVID lockdowns, to better understand a pervasive feature of sandy beach ecosystems (human recreation) that is predicted to intensify in future10.Findings for the 2019–2020 component of our study generally conformed to hypotheses posed. Firstly, shorebird abundance was inversely associated with human abundance and was positively related to lockdown level in 2020. Secondly, shorebird abundance was generally greatest during lockdown levels 5 and 4, when humans were effectively absent from the beach. To contextualise, shorebird abundance was roughly six times greater at the start of lockdown level 5 (2020) than the equivalent period in 2019. Thirdly, lowest shorebird abundance occurred during lockdown level 1 when human abundance was greatest in 2020. Collectively, these findings indicate a strong inverse association between shorebird- and human abundance on Muizenberg Beach and align with results of other studies36,37,38,39. Cumulatively, our findings, allied with prior research highlight the potential for human recreational activity, particularly at high intensities, to impact shorebird utilisation of sandy beach ecosystems, which may in turn affect ecological functions they provide that contribute to ecosystem multifunctionality.The inverse relationship that we recorded between human- and shorebird abundance likely manifests through the diverse ways in which recreational activity impacts fundamental processes and ecosystem components, which in turn link ecologically to shorebirds10,36,37,38,39,40. Muizenberg Beach is popular for surfing, bait-harvesting and general recreational activities, and it is these activities that likely drive the human-shorebird relationship that we report, particularly in 2020. When carried out under high human densities, such activities can lead to a reduction in space available, rendering the ecosystem less suitable as a substrate for birds36. Noise pollution and the presence of dogs may further depress habitat suitability41. Repeated trampling of sediment can negatively impact macrofaunal populations, which together with altered sedimentary biogeochemistry (e.g. increased anoxia), can reduce trophic resource availability to shorebirds, with benthic bait-collecting compounding these effects42,43. At the start of our data collection in 2020, we were unable to identify shorebird species due to lockdown levels 5 and 4 prohibiting human presence on the beach27,28,29. It is probable though that shorebird assemblages during lockdown levels 5 and 4 were not the same as those we identified between lockdown level 3 to 1 (mainly gulls; Table 3). This is based on research showing that increasing environmental disturbances can induce switches in biotic assemblages to those that can tolerate human activities44. Thus, the shorebird assemblages we identified during lockdown levels 3 to 1 is potentially the end-result of the mechanisms highlighted above (space reduction, noise, reduced resource availability) acting on shorebird assemblages in the absence of humans (lockdown levels 5 and 4) following humans being permitted onto the beach.At an inter-annual level, our data revealed idiosyncratic patterns that raise interesting questions about human-shorebird relationships. In 2019, in the absence of any lockdowns, shorebird abundance rose over the winter period (May–August). Winter peaks in abundance have previously been recorded in the literature45,46,47, including for kelp gulls (Larus dominicanus), which were the dominant shorebird in Muizenberg Beach. Specifically, winter abundance peaks for this species have been recorded in sandy beaches in the Eastern Cape, the Swartkops Estuary and Algoa Bay in South Africa (southeast coast)45,46,47. However, the absence of a winter abundance peak in 2020 raises the possibility that the 2019 winter-peak was not seasonal but an opportunistic response to decreased human abundance (see Fig. 4A). In South Africa, coastal ecosystems generally experience greatest human numbers in summer, due to warmer conditions and long end-of-year-vacation periods, based on our observations and experiences.The second inter-annual trend worth noting in our findings is that shorebird abundance was greater in 2019 than 2020, despite lockdowns being implemented in 2020. This counterintuitive finding is likely due to lockdowns that excluded people from the beach in 2020 (levels 5 to 3) being too short in duration to facilitate increases in bird numbers in 2020 beyond the 2019 level. This is supported by our data showing that humans were excluded from the beach for a total of 2 months (April and May 2020; levels 5-4) out of the 8-month period during which photographs were analysed. It would have been expected at the onset of the study that humans would be excluded from the beach during lockdown level 329, which would have resulted in an additional two and a half months of human exclusion and potentially a higher mean shorebird abundance for 2020. However, it is clear from our data that humans were present on the beach during level 3. On closer inspection, it is evident that human numbers increased even prior to the end of lockdown level 4. In fact, human abundance was greater under lockdown level 3 in 2020 than in the same period in 2019. Such high numbers of humans on the beach despite prohibitions are likely due to a lack of compliance, confusion around regulations and/or ‘covid fatigue’, which describes the propensity of humans to grow tired of COVID-19 regulations48. An additional consideration is that human numbers on the beach increased dramatically during lockdown levels 2 and 1, being almost twice the level recorded in 2019 in the same period. The lower 2020 bird count that we recorded is thus likely a product of the short duration of human exclusions in 2020 (lockdown levels 4 and 5) and the magnitude and rate of increase in human numbers thereafter (levels 3-1). Separately, our findings additionally suggest that surrogates (lockdown levels in our case) are unreliable estimators of human presence or abundance and align with findings elsewhere24.The last noteworthy inter-annual trend in our data was the difference in strength of human-shorebird relationships. While the inverse relationship between human and shorebird numbers was evident in both years, it was only during 2020, when humans were excluded from Muizenberg Beach, that the extent of this relationship was revealed. Specifically, in 2020, human exclusion at the start of lockdown level 5 was accompanied by a six-fold increase in shorebird abundance relative to 2019 at the same period. Additional support for the difference in strength of the human-shorebird relationship is the (1) significant interaction recorded between human numbers and year in explaining shorebird abundance and (2) the almost twofold stronger negative relationship (based on regression slopes) between shorebird and human abundance in 2020 vs 2019. These findings suggest that were it not for the COVID lockdowns in 2020, the extent of increasing human numbers on shorebirds may have been masked. However, it must be borne in mind that inter-annual variation may have played some role in the difference in trends recorded for 2019 versus 2020, though we cannot quantify this, given that we only have data for 2 years. Nevertheless, we suggest that when making conservation/management recommendations, decision-makers need to be cognisant of the potential for human effects on sandy beach ecosystems to be underestimated in studies based on variation in human density, in which human exclusion at appropriate spatial and temporal scales is absent24. Concerns have been expressed in the past about the failure of studies to consistently detect large-scale changes in sandy beach ecosystems, including those induced by recreational activities19. We suggest that such deficiencies may relate in part to the scarcity of true human exclusions in disturbance studies at meaningful scales in space and time.Findings from the in situ component of our study suggested that shorebird assemblages were negligibly affected by the transition from lockdown level 3 to 1, but that spatial differences among zones were more prominent. The lack of cases in which lockdown levels interacted statistically with zones (Tables 2, 4) further reinforces our conclusion regarding lockdown effects. Shorebird assemblage structure did vary between lockdown levels 3 and 2, due mainly to increasing contributions of Chroicocephalus hartlaubii (Hartlaub’s Gull) from level 3 to 2 and the opposite for L. dominicanus. Contrary to our hypothesis, differences in assemblage (Shannon–Wiener diversity was the exception) and species metrics were not detected among lockdown levels. This was likely due to the gradient in human abundance being weak among lockdown levels 3 to 1, relative to levels 5 and 4, with there being no virtual exclusion of humans under level 3 lockdown, as would have been expected given government regulations29. It is also possible that under lockdown levels 3, 2 and 1, the shorebird assemblage was simplified and comprised species tolerant of human activities44. The increase in Shannon–Wiener diversity value from lockdown level 3 to 2 was counter expectation, but likely reflects increased evenness during lockdown level 2, brought on by the declining dominance of L. dominicanus and a greater contribution of C. hartlaubii.Taken in its entirety, our findings provide valuable perspectives on human-shorebird interactions in sandy beaches. Based on our 2020 data spanning lockdowns of decreasing severity, our findings suggest that shorebirds are likely to benefit from human-free periods. This benefit is in reality likely to extend across multiple-trophic levels and is unlikely to be shorebird-specific, based on prior research reporting positive organism metrics at lower trophic levels in low human and/or human-free conditions in beach ecosystems20. Broadly, our findings attest to the value of using current and future lockdowns associated with managing the global COVID-19 pandemic to provide data on responses of birds and other organism groups to human-free spaces and times25,26,49. These human-free conditions can additionally provide invaluable data on sensitivities of ecosystem components and processes to increasing human density25,26,49. Data collected during lockdowns can provide better approximations of baseline conditions in sandy beach ecosystems, thereby providing a more meaningful basis for (1) evaluating future ecosystem change in response to human and global change stressors and (2) developing ecosystem restoration programs. This would be central to preventing long-term ecosystem degradation through the shifting base-line syndrome, where successive generations of decision makers/scientists judge the magnitude of change experienced by ecosystem components against increasingly deteriorating conditions over generational time-scales50. We also advocate for data emanating from COVID lockdown studies to be used in public education initiatives, so that beach users are made aware of the ways in which recreational activities can influence beach ecosystems. Such initiatives can improve involvement of public stakeholders in management of sandy beach ecosystems, which has been shown to provide cost-effective and sound decision-making, while increasing support for conservation initiatives51,52,53.Lastly, our findings have shed light on the sensitivity of shorebirds to increasing human numbers, mainly for recreational purposes. By moving beyond binary contrasts of human presence/absence, our work has also shown the magnitude of increasing human numbers on shorebirds, by virtue of the 34.18% increase in human abundance in our study corresponding with a 79.63% decline in bird numbers during the transition from lockdown level 4 to 3 in 2020. This finding is highly relevant considering that our work was based on an urban ecosystem—such systems are thought to have avian communities that are more disturbance tolerant relative to rural or suburban ecosystems54. Broadly, our work emphasises the need for environmental managers and city planners to be cognisant of the sensitivity of shorebirds to human recreational activities, even in urban settings, and to develop appropriate management plans in conjunction with scientists and stakeholders51,52,53. It should be noted that bird responses that we recorded in 2020 are unlikely to be driven solely by changing human numbers in Muizenberg Beach. Processes influencing bird assemblages in beaches surrounding our focal study area, including changes in human numbers and behaviour, may also have been influential determinants of trends recorded. We lack the data to comment meaningfully on this, but is an area worth exploring in future studies.Concluding perspectivesThe global COVID-19 anthropause has been described as the greatest large-scale experiment in modern history. This period has afforded scientists a unique opportunity to refine understanding of the consequences of human activities on Earth’s natural environments25,26,49. This is particularly relevant for human-dominated ecosystems such as sandy beaches, which are arguably the most utilised of Earth’s ecosystems for recreational purposes. In the absence of the COVID-19 anthropause, it is doubtful whether human exclusions could be carried out at scales that would allow meaningful detection of responses to human recreational disturbance. Our findings broadly attest to the points raised thus far, illustrating not only the potential for conventional approaches to underestimate human effects in sandy beaches, but also the sensitivity of shorebirds to human recreation and the magnitude of human influence. We hope that our findings stimulate further research on human recreational effects on sandy beach ecosystems, particularly with a view towards quantifying disturbance sensitivities and response thresholds of fundamental processes that drive multifunctionality in these heavily utilised, yet highly significant coastal ecosystems. We suggest that this is an imperative, given the exponential human population growth expected in the future, particularly along the coast, and the increasing demand predicted on sandy beach ecosystems from recreation, tourism and commercial sectors10,18. At its broadest level, our work dovetails with prior calls for scientists to capitalise on current and future COVID lockdowns to refine our understanding of human-nature interactions25, so that ecosystems and socio-ecological services provided can be sustainably utilised in the future. More