More stories

  • in

    Global impacts of future urban expansion on terrestrial vertebrate diversity

    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

  • in

    Antennae of psychodid and sphaerocerid flies respond to a high variety of floral scent compounds of deceptive Arum maculatum L.

    Raguso, R. A. Wake up and smell the roses: the ecology and evolution of floral scent. Annu. Rev. Ecol. Evol. Syst. 39, 549–569 (2008).
    Google Scholar 
    Knudsen, J. T., Eriksson, R., Gershenzon, J. & Ståhl, B. Diversity and distribution of floral scent. Bot. Rev. 72, 1–120 (2006).
    Google Scholar 
    Hadacek, F. & Weber, M. Club-shaped organs as additional osmophores within the Sauromatum inflorescence: odour analysis, ultrastructural changes and pollination aspects. Plant Biol. 4, 367–383 (2002).CAS 

    Google Scholar 
    Schlumpberger, B. O. & Raguso, R. A. Geographic variation in floral scent of Echinopsis ancistrophora (Cactaceae); evidence for constraints on hawkmoth attraction. Oikos 117, 801–814 (2008).
    Google Scholar 
    Gfrerer, E. et al. Floral scents of a deceptive plant are hyperdiverse and under population-specific phenotypic selection. Front. Plant Sci. 12, https://doi.org/10.3389/fpls.2021.719092 (2021).Primante, C. & Dötterl, S. A syrphid fly uses olfactory cues to find a non-yellow flower. J. Chem. Ecol. 36, 1207–1210 (2010).CAS 
    PubMed 

    Google Scholar 
    Knauer, A. C. & Schiestl, F. P. Bees use honest floral signals as indicators of reward when visiting flowers. Ecol. Lett. 18, 135–143 (2015).CAS 
    PubMed 

    Google Scholar 
    Theis, N. Fragrance of Canada thistle (Cirsium arvense) attracts both floral herbivores and pollinators. J. Chem. Ecol. 32, 917–927 (2006).CAS 
    PubMed 

    Google Scholar 
    Bouwmeester, H., Schuurink, R. C., Bleeker, P. M. & Schiestl, F. The role of volatiles in plant communication. Plant J. 100, 892–907 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schiestl, F. P. et al. Orchid pollination by sexual swindle. Nature 399, 421–422 (1999).CAS 

    Google Scholar 
    Schäffler, I. et al. Diacetin, a reliable cue and private communication channel in a specialized pollination system. Sci. Rep. 5, 1–11 (2015).
    Google Scholar 
    Castañeda-Zárate, M., Johnson, S. D. & van der Niet, T. Food reward chemistry explains a novel pollinator shift and vestigialization of long floral spurs in an orchid. Curr. Biol. 31, 238–246 (2021).PubMed 

    Google Scholar 
    Dötterl, S., David, A., Boland, W., Silberbauer-Gottsberger, I. & Gottsberger, G. Evidence for behavioral attractiveness of methoxylated aromatics in a dynastid scarab beetle-pollinated Araceae. J. Chem. Ecol. 38, 1539–1543 (2012).PubMed 

    Google Scholar 
    Maia, A. C. D. et al. The key role of 4-methyl-5-vinylthiazole in the attraction of scarab beetle pollinators: a unique olfactory floral signal shared by Annonaceae and Araceae. J. Chem. Ecol. 38, 1072–1080 (2012).CAS 
    PubMed 

    Google Scholar 
    Stamm, P., Etl, F., Maia, A. C. D., Dötterl, S. & Schulz, S. Synthesis, absolute configurations, and biological activities of floral scent compounds from night-blooming Araceae. J. Org. Chem. 86, 5245–5254 (2021).CAS 
    PubMed 

    Google Scholar 
    Jürgens, A., Wee, S. L., Shuttleworth, A. & Johnson, S. D. Chemical mimicry of insect oviposition sites: a global analysis of convergence in angiosperms. Ecol. Lett. 16, 1157–1167 (2013).PubMed 

    Google Scholar 
    Zito, P., Sajeva, M., Raspi, A. & Dötterl, S. Dimethyl disulfide and dimethyl trisulfide: so similar yet so different in evoking biological responses in saprophilous flies. Chemoecology 24, 261–267 (2014).CAS 

    Google Scholar 
    El-Sayed, A. M. The Pherobase: database of pheromones and semiochemicals. https://www.pherobase.com (2021).Kite, G. C. The floral odour of Arum maculatum. Biochem. Syst. Ecol. 23, 343–354 (1995).CAS 

    Google Scholar 
    Chartier, M., Pélozuelo, L. & Gibernau, M. Do floral odor profiles geographically vary with the degree of specificity for pollinators? Investigation in two sapromyophilous Arum species (Araceae). Ann. Soc. Entomol. Fr. 47, 71–77 (2011).
    Google Scholar 
    Chartier, M., Pélozuelo, L., Buatois, B., Bessière, J. M. & Gibernau, M. Geographical variations of odour and pollinators, and test for local adaptation by reciprocal transplant of two European Arum species. Funct. Ecol. 27, 1367–1381 (2013).
    Google Scholar 
    Marotz-Clausen, G. et al. Incomplete synchrony of inflorescence scent and temperature patterns in Arum maculatum L. (Araceae). Phytochemistry 154, 77–84 (2018).Szenteczki, M. A. et al. Spatial and temporal heterogeneity in pollinator communities maintains within-species floral odour variation. Oikos 130, 1487–1499 (2021).
    Google Scholar 
    Espíndola, A., Pellissier, L. & Alvarez, N. Variation in the proportion of flower visitors of Arum maculatum along its distributional range in relation with community-based climatic niche analyses. Oikos 120, 728–734 (2011).
    Google Scholar 
    Laina, D. et al. Local insect availability partly explains geographical differences in floral visitor assemblages of Arum maculatum L. (Araceae). Front. Plant Sci. 13, https://doi.org/10.3389/fpls.2022.838391 (2022).Tonnoir, A. L. A synopsis of the British Psychodidae (Dipt.) with descriptions of new species. Trans. Soc. Br. Entomol. 7, 21–64 (1940).Roháček, J., Beck-Haug, I. & Dobat, K. Sphaeroceridae associated with flowering Arum maculatum (Araceae) in the vicinity of Tübingen, SW-Germany (Insecta: Diptera). Senckenb. Biol. 71, 259–268 (1990).
    Google Scholar 
    Sayers, T. D. J., Steinbauer, M. J., Farnier, K. & Miller, R. E. Dung mimicry in Typhonium (Araceae): explaining floral trait and pollinator divergence in a widespread species complex and a rare sister species. Bot. J. Linn. Soc. 193, 375–401 (2020).
    Google Scholar 
    Gibernau, M., Macquart, D. & Przetak, G. Pollination in the genus Arum: a review. Aroideana 27, 148–166 (2004).
    Google Scholar 
    Kakishima, S. & Okuyama, Y. Pollinator assemblages of Arisaema heterocephalum subsp. majus (Araceae), a critically endangered species endemic to Tokunoshima Island, Central Ryukyus. Bull. Natl. Mus. Nat. Sci., Ser. B 44, 173–179 (2018).Urru, I. et al. Pollination strategies in Cretan Arum lilies. Biol. J. Linn. Soc. 101, 991–1001 (2010).
    Google Scholar 
    Diaz, A. & Kite, G. C. A comparison of the pollination ecology of Arum maculatum and Arum italicum in England. Watsonia 24, 171–181 (2002).
    Google Scholar 
    Lack, A. J. & Diaz, A. The pollination of Arum maculatum L.: a historical review and new observations. Watsonia 18, 333–342 (1991).Kite, G. C. et al. Inflorescence odours and pollinators of Arum and Amorphophallus (Araceae). in Reproductive Biology (eds. Owens, S. J. & Rudall, P. J.) 295–315 (Kew Royal Botanic Gardens, 1998).Laurence, B. R. The larval inhabitants of cow pats. J. Anim. Ecol. 23, 234–260 (1954).
    Google Scholar 
    Wagner, R. Zur Kenntnis der Psychodidenfauna des Allgäus. Nachrichtenblatt der Bayer. Entomol. 26, 23–28 (1977).
    Google Scholar 
    Satchell, G. H. The ecology of the British species of Psychoda (Diptera: Psychodidae). Ann. Appl. Biol. 34, 611–621 (1947).CAS 
    PubMed 

    Google Scholar 
    Withers, P. & O’Connor, J. P. A preliminary account of the Irish species of moth fly (Diptera: Psychodidae). Proc. R. Ir. Acad. B. 92, 61–77 (1992).
    Google Scholar 
    Dormont, L., Jay-Robert, P., Bessière, J. M., Rapior, S. & Lumaret, J. P. Innate olfactory preferences in dung beetles. J. Exp. Biol. 213, 3177–3186 (2010).CAS 
    PubMed 

    Google Scholar 
    Sládeček, F. X. J., Dötterl, S., Schäffler, I., Segar, S. T. & Konvicka, M. Succession of dung-inhabiting beetles and flies reflects the succession of dung-emitted volatile compounds. J. Chem. Ecol. 47, 433–443 (2021).PubMed 

    Google Scholar 
    Scheven, H. J. GC/MS Untersuchungen des Appendixduftes blühender Pflanzen von Arum maculatum L. und Arum italicum MILLER; Nachweis der attraktiven Wirkung der Duftbestandteile Indol, Humulen und p-Kresol auf Psychoda phalaenoides L. (Philipps-Universität Marburg, 1994).Schiestl, F. P. & Marion-Poll, F. Detection of physiologically active flower volatiles using gas chromatography coupled with electroantennography. in Analysis of Taste and Aroma (eds. Jackson, J. F. & Linskens, H. F.) 173–198 (Springer Berlin Heidelberg, 2002).Jhumur, U. S., Dötterl, S. & Jürgens, A. Electrophysiological and behavioural responses of mosquitoes to volatiles of Silene otites (Caryophyllaceae). Arthropod. Plant. Interact. 1, 245–254 (2007).
    Google Scholar 
    Heiduk, A. et al. Ceropegia sandersonii mimics attacked honeybees to attract kleptoparasitic flies for pollination. Curr. Biol. 26, 1–7 (2016).
    Google Scholar 
    Suinyuy, T. N., Donaldson, J. S. & Johnson, S. D. Geographical matching of volatile signals and pollinator olfactory responses in a cycad brood-site mutualism. Proc. R. Soc. B Biol. Sci. 282, (2015). http://doi.org/10.1098/rspb.2015.2053Dötterl, S. et al. Nursery pollination by a moth in Silene latifolia: The role of odours in eliciting antennal and behavioural responses. New Phytol. 169, 707–718 (2005).
    Google Scholar 
    Schiestl, F. P. et al. The chemistry of sexual deception in an orchid-wasp pollination system. Science 80(302), 437–438 (2003).
    Google Scholar 
    Stensmyr, M. C. et al. Rotting smell of dead-horse arum florets. Nature 420, 625–626 (2002).CAS 
    PubMed 

    Google Scholar 
    Lukas, K., Harig, T., Schulz, S., Hadersdorfer, J. & Dötterl, S. Flowers of European pear release common and uncommon volatiles that can be detected by honey bee pollinators. Chemoecology 29, 211–223 (2019).
    Google Scholar 
    Bermadinger-Stabentheiner, E. & Stabentheiner, A. Dynamics of thermogenesis and structure of epidermal tissues in inflorescences of Arum maculatum. New Phytol. 131, 41–50 (1995).PubMed 

    Google Scholar 
    Dötterl, S., Füssel, U., Jürgens, A. & Aas, G. 1,4-Dimethoxybenzene, a floral scent compound in willows that attracts an oligolectic bee. J. Chem. Ecol. 31, 2993–2998 (2005).PubMed 

    Google Scholar 
    Dötterl, S. et al. Linalool and lilac aldehyde/alcohol in flower scents. Electrophysiological detection of lilac aldehyde stereoisomers by a moth. J. Chromatogr. A 1113, 231–238 (2006).Brandt, K. et al. Subtle chemical variations with strong ecological significance: stereoselective responses of male orchid bees to stereoisomers of carvone epoxide. J. Chem. Ecol. 45, 464–473 (2019).CAS 
    PubMed 

    Google Scholar 
    Zito, P., Dötterl, S. & Sajeva, M. Floral volatiles in a sapromyiophilous plant and their importance in attracting house fly pollinators. J. Chem. Ecol. 41, 340–349 (2015).CAS 
    PubMed 

    Google Scholar 
    Kováts, E. & Weisz, P. Über den Retentionsindex und seine Verwendung zur Aufstellung einer Polaritätsskala für Lösungsmittel. Berichte der Bunsengesellschaft für Phys. Chem. 69, 812–820 (1965).
    Google Scholar 
    Dougherty, M. J., Guerin, P. M., Ward, R. D. & Hamilton, J. G. C. Behavioural and electrophysiological responses of the phlebotomine sandfly Lutzomyia longipalpis (Diptera: Psychodidae) when exposed to canid host odour kairomones. Physiol. Entomol. 24, 251–262 (1999).CAS 

    Google Scholar 
    Sant’Ana, A. L., Eiras, A. E. & Cavalcante, R. R. Electroantennographic responses of the Lutzomyia (Lutzomyia) longipalpis (Lutz and Neiva) (Diptera: Psychodidae) to 1-octen-3-ol. Neotrop. Entomol. 31, 13–17 (2002).Adams, R. P. Identification of essential oil components by gas chromatography/mass spectrometry. (Allured Publishing Corporation, 2007).Johnson, S. D. & Jürgens, A. Convergent evolution of carrion and faecal scent mimicry in fly-pollinated angiosperm flowers and a stinkhorn fungus. S. Afr. J. Bot. 76, 796–807 (2010).CAS 

    Google Scholar 
    Thakeow, P., Angeli, S., Weißbecker, B. & Schütz, S. Antennal and behavioral responses of Cis boleti to fungal odor of Trametes gibbosa. Chem. Senses 33, 379–387 (2008).CAS 
    PubMed 

    Google Scholar 
    Junker, R. R. & Blüthgen, N. Floral scents repel facultative flower visitors, but attract obligate ones. Ann. Bot. 105, 777–782 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Junker, R. R. & Tholl, D. Volatile organic compound mediated interactions at the plant-microbe interface. J. Chem. Ecol. 39, 810–825 (2013).CAS 
    PubMed 

    Google Scholar 
    Abraham, J. et al. Behavioral and antennal responses of Drosophila suzukii (Diptera: Drosophilidae) to volatiles from fruit extracts. Environ. Entomol. 44, 356–367 (2015).CAS 
    PubMed 

    Google Scholar 
    Stökl, J. et al. Scent variation and hybridization cause the displacement of a sexually deceptive orchid species. Am. J. Bot. 95, 472–481 (2008).PubMed 

    Google Scholar 
    Salamanca, J., Souza, B., Lundgren, J. G. & Rodriguez-Saona, C. From laboratory to field: electro-antennographic and behavioral responsiveness of two insect predators to methyl salicylate. Chemoecology 27, 51–63 (2017).CAS 

    Google Scholar 
    Revel, N., Alvarez, N., Gibernau, M. & Espíndola, A. Investigating the relationship between pollination strategies and the size-advantage model in zoophilous plants using the reproductive biology of Arum cylindraceum and other European Arum species as case studies. Arthropod. Plant. Interact. 6, 35–44 (2012).
    Google Scholar  More

  • in

    Silicon improves ion homeostasis and growth of liquorice under salt stress by reducing plant Na+ uptake

    Zhao, S. et al. Regulation of plant responses to salt stress. Int. J. Mol. Sci. 22, 4609 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Acosta-Motos, J. R. et al. Plant responses to salt stress: Adaptive mechanisms. Agronomy-Basel 7, 18 (2017).
    Google Scholar 
    Munns, R. & Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651–681 (2008).CAS 
    PubMed 

    Google Scholar 
    Wu, H. H. Plant salt tolerance and Na+ sensing and transport. Crop J. 6, 215–225 (2018).
    Google Scholar 
    Ali, M. et al. Silicon mediated improvement in the growth and ion homeostasis by decreasing Na+ uptake in maize (Zea mays L.) cultivars exposed to salinity stress. Plant Physiol. Biochem. 158, 208–218 (2021).CAS 
    PubMed 

    Google Scholar 
    Javaid, T., Farooq, M. A., Akhtar, J., Saqib, Z. A. & Anwar-ul-Haq, M. Silicon nutrition improves growth of salt-stressed wheat by modulating flows and partitioning of Na+, Cl- and mineral ions. Plant Physiol. Biochem. 141, 291–299 (2019).CAS 
    PubMed 

    Google Scholar 
    Zelm, E. V., Zhang, Y. X. & Testerink, C. Salt tolerance mechanisms of plants. Annu. Rev. Plant Biol. 71, 403–433 (2020).PubMed 

    Google Scholar 
    Kumar, P. et al. Potassium: A key modulator for cell homeostasis. J. Biotechnol. 324, 198–210 (2020).CAS 
    PubMed 

    Google Scholar 
    Ahmad, P., Ahanger, M. A., Alam, P., Alyemeni, M. N. & Ashraf, M. Silicon (Si) supplementation alleviates NaCl toxicity in mung bean [Vigna radiata (L.) Wilczek] through the modifications of physio-biochemical attributes and key antioxidant enzymes. J. Plant Growth Regul. 38, 1–13 (2018).
    Google Scholar 
    Chiappero, J. et al. Antioxidant status of medicinal and aromatic plants under the influence of growth-promoting rhizobacteria and osmotic stress. Ind. Crops Prod. 167, 113541 (2021).CAS 

    Google Scholar 
    Conceicao, S. S. et al. Silicon modulates the activity of antioxidant enzymes and nitrogen compounds in sunflower plants under salt stress. Arch. Agron. Soil Sci. 65, 1237–1247 (2019).CAS 

    Google Scholar 
    Etesami, H. & Jeong, B. R. Silicon (Si): Review and future prospects on the action mechanisms in alleviating biotic and abiotic stresses in plants. Ecotoxicol. Environ. Saf. 147, 881–896 (2018).CAS 
    PubMed 

    Google Scholar 
    Epstein, E. Silicon. Annu. Rev. Plant Physiol. Plant Mol. Biol. 50, 641–664 (1999).CAS 
    PubMed 

    Google Scholar 
    Epstein, E. The anomaly of silicon in plant biology. Proc. Natl. Acad. Sci. U S A 91, 11–17 (1994).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jadhao, K. R., Bansal, A. & Rout, G. R. Silicon amendment induces synergistic plant defense mechanism against pink stem borer (Sesamia inferens Walker.) in finger millet (Eleusine coracana Gaertn.). Sci. Rep. 10, 15 (2020).
    Google Scholar 
    Li, Z. C. et al. Silicon enhancement of estimated plant biomass carbon accumulation under abiotic and biotic stresses. A meta-analysis. Agron. Sustain. Dev. 38, 19 (2018).CAS 

    Google Scholar 
    Yan, G. C. et al. Silicon improves rice salinity resistance by alleviating ionic toxicity and osmotic constraint in an organ-specific pattern. Front. Plant Sci. 11, 12 (2020).
    Google Scholar 
    Farouk, S., Elhindi, K. M. & Alotaibi, M. A. Silicon supplementation mitigates salinity stress on Ocimum basilicum L. via improving water balance, ion homeostasis, and antioxidant defense system. Ecotoxicol. Environ. Saf. 206, 11 (2020).
    Google Scholar 
    Yin, J. L. et al. Silicon enhances the salt tolerance of cucumber through increasing polyamine accumulation and decreasing oxidative damage. Ecotoxicol. Environ. Saf. 169, 8–17 (2019).CAS 
    PubMed 

    Google Scholar 
    Hurtado, A. C. et al. Different methods of silicon application attenuate salt stress in sorghum and sunflower by modifying the antioxidative defense mechanism. Ecotoxicol. Environ. Saf. 203, 11 (2020).
    Google Scholar 
    Gaur, S. et al. Fascinating impact of silicon and silicon transporters in plants: A review. Ecotoxicol. Environ. Saf. 202, 12 (2020).
    Google Scholar 
    Vandegeer, R. K. et al. Silicon deposition on guard cells increases stomatal sensitivity as mediated by K(+)efflux and consequently reduces stomatal conductance. Physiol. Plant 171, 358–370 (2021).CAS 
    PubMed 

    Google Scholar 
    Lina, et al. Silicon-mediated changes in polyamines participate in silicon-induced salt tolerance in Sorghum bicolor L.. Plant Cell Environ. 39, 245–258 (2016).
    Google Scholar 
    Hassanvand, F., Nejad, A. R. & Fanourakis, D. Morphological and physiological components mediating the silicon-induced enhancement of geranium essential oil yield under saline conditions. Ind. Crops Prod. 134, 19–25 (2019).CAS 

    Google Scholar 
    Altuntas, O., Dasgan, H. Y. & Akhoundnejad, Y. Silicon-induced salinity tolerance improves photosynthesis, leaf water status, membrane stability, and growth in pepper (Capsicum annuum L.). HortScience 53, 1820–1826 (2018).CAS 

    Google Scholar 
    Coskun, D. et al. The controversies of silicon’s role in plant biology. New Phytol. 221, 67–85 (2019).PubMed 

    Google Scholar 
    Jiang, M. Y. et al. An “essential herbal medicine”-licorice: A review of phytochemicals and its effects in combination preparations. J. Ethnopharmacol. 249, 14 (2020).
    Google Scholar 
    Zhang, X. Y. et al. Inhibition effect of glycyrrhiza polysaccharide (GCP) on tumor growth through regulation of the gut microbiota composition. J. Pharmacol. Sci. 137, 324–332 (2018).CAS 
    PubMed 

    Google Scholar 
    Baltina, L. A. et al. Glycyrrhetinic acid derivatives as Zika virus inhibitors: Synthesis and antiviral activity in vitro. Bioorg. Med. Chem. 41, 116204 (2021).CAS 
    PubMed 

    Google Scholar 
    Zhao, Z. Y. et al. Glycyrrhizic ccid nanoparticles as antiviral and anti-inflammatory agents for COVID-19 treatment. ACS Appl. Mater. Interfaces 13, 20995–21006 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lu, J. H., Lv, X., Wu, L. & Li, X. Y. Germination responses of three medicinal licorices to saline environments and their suitable ecological regions. Acta Pratacul. Sin. 22, 198–205 (2013).
    Google Scholar 
    Geng, G. Q. & Xie, X. R. Effect of drought and salt stress on the physiological and biochemical characteristics of Glycyrrhiza uralensis. Pratacult. Sci. 35, 113–120 (2018).
    Google Scholar 
    Cui, J. J., Zhang, X. H., Li, Y. T., Zhou, D. & Zhang, E. H. Effect of silicon addition on seedling morphological and physiological indicators of Glycyrrhiza uralensis under salt stress. Acta Pratacul. Sin. 24, 214–220 (2015).
    Google Scholar 
    Zhang, W. J. et al. Silicon alleviates salt and drought stress of Glycyrrhiza uralensis plants by improving photosynthesis and water status. Biol. Plant. 64, 302–313 (2020).CAS 

    Google Scholar 
    Zhang, W. J. et al. Silicon promotes growth and root yield of Glycyrrhiza uralensis under salt and drought stresses through enhancing osmotic adjustment and regulating antioxidant metabolism. Crop Prot. 107, 1–11 (2018).
    Google Scholar 
    Chen, D. Q. et al. Silicon moderated the K deficiency by improving the plant-water status in sorghum. Sci. Rep. 6, 14 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Cui, J. J., Zhang, E. H., Zhang, X. H. & Wang, Q. Silicon alleviates salinity stress in licorice (Glycyrrhiza uralensis) by regulating carbon and nitrogen metabolism. Sci. Rep. 11, 12 (2021).
    Google Scholar 
    Lichtenthaler, H. K. & Wellburn, A. R. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Analysis 11, 591–592 (1983).CAS 

    Google Scholar 
    Yan, K., Wu, C. W., Zhang, L. H. & Chen, X. B. Contrasting photosynthesis and photoinhibition in tetraploid and its autodiploid honeysuckle (Lonicera japonica Thunb.) under salt stress. Front. Plant Sci. 6, 9 (2015).
    Google Scholar 
    Li, H. S. Principles and Techniques of Plant Physiological and Biochemical Experiments (Higher Education Press, 2000).
    Google Scholar 
    Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
    PubMed 

    Google Scholar 
    Lutts, S., Kinet, J. M. & Bouharmont, J. NaCl-induced senescence in leaves of rice (Oryza sativa L) cultivars differing in salinity resistance. Ann. Bot. 78, 389–398 (1996).CAS 

    Google Scholar 
    Havir, E. A. & Mchale, N. A. Biochemical and developmental characterization of multiple forms of catalase in tobacco leaves. Plant Physiol. 84, 450–455 (1987).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rizwan, M. et al. Mechanisms of silicon-mediated alleviation of drought and salt stress in plants: A review. Environ. Sci. Pollut. Res. 22, 15416–15431 (2015).CAS 

    Google Scholar 
    Al-Huqail, A. A., Alqarawi, A. A., Hashem, A., Malik, J. A. & Abd Allah, E. F. Silicon supplementation modulates antioxidant system and osmolyte accumulation to balance salt stress in Acacia gerrardii Benth. Saudi J. Biol. Sci. 26, 1856–1864 (2019).CAS 
    PubMed 

    Google Scholar 
    Hurtado, A. C. et al. Silicon application induces changes C:N: P stoichiometry and enhances stoichiometric homeostasis of sorghum and sunflower plants under salt stress. Saudi J. Biol. Sci. 27, 3711–3719 (2020).
    Google Scholar 
    Zhang, X. H., Zhang, W. J., Lang, D. Y., Cui, J. J. & Li, Y. T. Silicon improves salt tolerance of Glycyrrhiza uralensis Fisch by ameliorating osmotic and oxidative stresses and improving phytohormonal balance. Environ. Sci. Pollut. Res. 25, 25916–25932 (2018).CAS 

    Google Scholar 
    Liang, W. J., Ma, X. L., Wan, P. & Liu, L. Y. Plant salt-tolerance mechanism: A review. Biochem. Biophys. Res. Commun. 495, 286–291 (2018).CAS 
    PubMed 

    Google Scholar 
    Tester, M. & Davenport, R. Na+ tolerance and Na+ transport in higher plants. Ann. Bot. 91, 503–527 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khan, W. U. D. et al. Silicon nutrition mitigates salinity stress in maize by modulating ion accumulation, photosynthesis, and antioxidants. Photosynthetica 56, 1047–1057 (2018).CAS 

    Google Scholar 
    Zahoor, R. et al. Potassium fertilizer improves drought stress alleviation potential in cotton by enhancing photosynthesis and carbohydrate metabolism. Environ. Exp. Bot. 137, 73–83 (2017).CAS 

    Google Scholar 
    Hurtado, A. C. et al. Silicon alleviates sodium toxicity in sorghum and sunflower plants by enhancing ionic homeostasis in roots and shoots and increasing dry matter accumulation. SILICON 13, 475–486 (2021).CAS 

    Google Scholar 
    Yan, G. C. et al. Silicon alleviates salt stress-induced potassium deficiency by promoting potassium uptake and translocation in rice (Oryza sativa L.). J. Plant Physiol. 258, 7 (2021).
    Google Scholar 
    Dhiman, P. et al. Fascinating role of silicon to combat salinity stress in plants: An updated overview. Plant Physiol. Biochem. 162, 110–123 (2021).CAS 
    PubMed 

    Google Scholar 
    Bosnic, P., Bosnic, D., Jasnic, J. & Nikolic, M. Silicon mediates sodium transport and partitioning in maize under moderate salt stress. Environ. Exp. Bot. 155, 681–687 (2018).CAS 

    Google Scholar 
    Alamri, S. et al. Silicon-induced postponement of leaf senescence is accompanied by modulation of antioxidative defense and ion homeostasis in mustard (Brassica juncea) seedlings exposed to salinity and drought stress. Plant Physiol. Biochem. 157, 47–59 (2020).CAS 
    PubMed 

    Google Scholar 
    Ahmad, P. et al. Nitric oxide mitigates salt stress by regulating levels of osmolytes and antioxidant enzymes in chickpea. Front. Plant Sci. 7, 1–11 (2016).
    Google Scholar 
    Zhu, Y. X. et al. Silicon confers cucumber resistance to salinity stress through regulation of proline and cytokinins. Plant Physiol. Biochem. 156, 209–220 (2020).CAS 
    PubMed 

    Google Scholar  More

  • in

    First titanosaur dinosaur nesting site from the Late Cretaceous of Brazil

    Carballido, J. L. et al. A new giant titanosaur sheds light on body mass evolution among sauropod dinosaurs. Proc. R. Soc. B Biol. Sci. 284, 20171219 (2017).
    Google Scholar 
    Hechenleitner, E. M. et al. Two Late Cretaceous sauropods reveal titanosaurian dispersal across South America. Commun. Biol. 3, 622 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Otero, A., Carballido, J. L., Salgado, L., Canudo, I. & Garrido, A. C. Report of a giant titanosaur sauropod from the Upper Cretaceous of Neuquén Province, Argentina. Cretaceous Res. 122, 104754 (2021).
    Google Scholar 
    Sander, P. M. et al. Biology of the sauropod dinosaurs: The evolution of gigantism. Biol. Rev. 86, 117–155 (2011).PubMed 

    Google Scholar 
    Gorscak, E. & O’Connor, P. M. A new African titanosaurian sauropod dinosaur from the middle Cretaceous Galula Formation (Mtuka Member), Rukwa Rift Basin, southwestern Tanzania. PLoS One 14, e0211412 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Poropat, S. F. et al. Second specimen of the Late Cretaceous Australian sauropod dinosaur Diamantinasaurus matildae provides new anatomical information on the skull and neck of early titanosaurs. Zool. J. Linn. Soc. 192, 610–674 (2021).
    Google Scholar 
    González Riga, B. J. et al. An overview of the appendicular skeletal anatomy of South American titanosaurian sauropods, with definition of a newly recognized clade. An. Acad. Bras. Ciênc. 91, e20180374 (2019).
    Google Scholar 
    Gorscak, E. & O’connor, P. M. Time-calibrated models support congruency between Cretaceous continental rifting and titanosaurian evolutionary history. Biol. Lett. 12, 20151047 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pérez Moreno, A., Carballido, J. L., Otero, A., Salgado, L. & Calvo, J. O. The axial skeleton of Rinconsaurus caudamirus (Sauropoda: Titanosauria) from the Late Cretaceous of Patagonia, Argentina. Ameghiniana 59, 1–46 (2022).
    Google Scholar 
    Chiappe, L. M., Jackson, F., Coria, R. A. & Dingus, L. Nesting titanosaurs from Auca Mahuevo and adjacent sites. In The Sauropods: Evolution and Paleobiology (eds Curry Rogers, K. A. & Wilson, J. A.) 285–302 (University of California Press, 2005).
    Google Scholar 
    Hechenleitner, E. M., Grellet-Tinner, G. & Fiorelli, L. E. What do giant titanosaur dinosaurs and modern Australasian megapodes have in common?. PeerJ 3, e1341 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Cojan, I., Renard, M. & Emmanuel, L. Palaeoenvironmental reconstruction of dinosaur nesting sites based on a geochemical approach to eggshells and associated palaeosols (Maastrichtian, Provence Basin, France). Palaeogeogr. Palaeoclimatol. Palaeoecol. 191, 111–138 (2003).
    Google Scholar 
    Grellet-Tinner, G., Codrea, V., Folie, A., Higa, A. & Smith, T. First evidence of reproductive adaptation to “island effect” of a dwarf Cretaceous Romanian titanosaur, with embryonic integument in ovo. PLoS One 7(3), e32051 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khosla, A. & Lucas, S. Late Cretaceous dinosaur eggs and eggshells of peninsular India. Top. Geobiol. 51, 1–295 (2020).
    Google Scholar 
    Vila, B., Galobart, A., Oms, O., Poza, B. & Bravo, A. M. Assessing the nesting strategies of Late Cretaceous titanosaurs: 3-D clutch geometry from a new megaloolithid egg site. Lethaia 43, 197–208 (2009).
    Google Scholar 
    Chiappe, L. M. et al. Sauropod dinosaur embryos from the Late Cretaceous of Patagonia. Nature 396, 258–261 (1998).ADS 
    CAS 

    Google Scholar 
    Salgado, L. et al. Upper Cretaceous dinosaur nesting sites of Río Negro (Salitral Ojo de Agua and Salinas de Trapalco-Salitral de Santa Rosa), Northern Patagonia, Argentina. Cretaceous Res. 28, 392–404 (2007).
    Google Scholar 
    Salgado, L., Magalhães Ribeiro, C. M., García, R. A. & Fernández, M. A. Late Cretaceous megaloolithid eggs from Salitral de Santa Rosa (Río Negro, Patagonia, Argentina): Inferences on the titanosaurian reproductive biology. Ameghiniana 46, 605–620 (2009).
    Google Scholar 
    Grellet-Tinner, G. & Fiorelli, L. E. A new Argentinean nesting site showing neosauropod dinosaur reproduction in a Cretaceous hydrothermal environment. Nat. Commun. 1, 32 (2010).ADS 
    PubMed 

    Google Scholar 
    Hechenleitner, E. M. et al. A new Upper Cretaceous titanosaur nesting site from La Rioja (NW Argentina), with implications for titanosaur nesting strategies. Palaeontology 59, 433–446 (2016).
    Google Scholar 
    Kundrát, M. et al. Specialized craniofacial anatomy of a titanosaurian embryo from Argentina. Curr. Biol. 30, 4263–4269 (2020).PubMed 

    Google Scholar 
    Chiappe, L. M., Salgado, L. & Coria, R. A. Embryonic skulls of titanosaur sauropod dinosaurs. Science 293, 2444–2446 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Grellet-Tinner, G., Chiappe, L. M., Norell, M. & Bottjer, D. Dinosaur eggs and nesting behaviors: A paleobiological investigation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 232, 294–321 (2006).
    Google Scholar 
    Coria, R. A. & Chiappe, L. M. Embryonic skin from Late Cretaceous sauropods (Dinosauria) of Auca Mahuevo, Patagonia, Argentina. J. Paleontol. 81, 1528–1532 (2007).
    Google Scholar 
    Leuzinger, L. et al. Life and reproduction of titanosaurians: Isotopic hallmark of mid-palaeolatitude eggshells and its significance for body temperature, diet, and nesting. Chem. Geol. 583, 120452 (2021).ADS 
    CAS 

    Google Scholar 
    Faccio, G. Dinosaurian eggs from the upper Cretaceous of Uruguay. In Dinosaur Eggs and Babies (eds Carpenter, K. et al.) 47–53 (Cambridge University Press, 1994).
    Google Scholar 
    Vianey-Liaud, M., Hirsch, K., Sahni, A. & Sigé, B. Late Cretaceous Peruvian eggshells and their relationships with Laurasian and Eastern Gondwanian material. Geobios 30, 75–90 (1997).
    Google Scholar 
    Magalhães Ribeiro, C. M. Microstructural analysis of dinosaur eggshells from Bauru Basin (Late Cretaceous), Minas Gerais, Brasil. In First International Symposium on Dinosaur Eggs and Babies (eds Bravo, A. M. & Reyes, T.) 117–122 (Isona I Conca Dellà Catalonia, 2000).
    Google Scholar 
    Magalhães Ribeiro, C. M. Ovo e fragmentos de cascas de ovos de dinossauros, provenientes de região de Peirópolis, Uberaba, Minas Gerais. Arq. Mus. Nac. 60, 223–228 (2002).
    Google Scholar 
    Grellet-Tinner, G. & Zaher, H. Taxonomic identification of the Megaloolithidae egg and eggshells from Cretaceous Bauru Basin (Minas Gerais, Brazil): Comparison with the Auca Mahuevo (Argentina) titanosaurid eggs. Papéis Avulsos Zool. 47, 105–112 (2007).
    Google Scholar 
    Martinelli, A. G. & Teixeira, V. P. A. The Late Cretaceous vertebrate record from the Bauru Group in the Triângulo Mineiro, southeastern Brazil. Bol. Geol. Minero 126, 129–158 (2015).
    Google Scholar 
    Soares, M. V. T. et al. Sedimentology of a distributive fluvial system: The Serra da Galga Formation, a new lithostratigraphic unit (Upper Cretaceous, Bauru Basin, Brazil). Geol. J. 56, 951–975 (2021).
    Google Scholar 
    Soares, M. V. T. et al. Landscape and depositional controls on palaeosols of a distributive fluvial system (Upper Cretaceous, Brazil). Sedim. Geol. 409, 105774 (2020).
    Google Scholar 
    Martinelli, A. G. et al. Palaeoecological implications of an Upper Cretaceous tetrapod burrow (Bauru Basin; Peirópolis, Minas Gerais, Brazil). Palaeogeogr. Palaeoclimatol. Palaeoecol. 528, 147–159 (2019).
    Google Scholar 
    Grellet-Tinner, G., Chiappe, L. M. & Coria, R. A. Eggs of titanosaurid sauropods from the Upper Cretaceous of Auca Mahuevo (Argentina). Can. J. Earth Sci. 41, 949–960 (2004).ADS 

    Google Scholar 
    Wilson, J. A., Mohabey, D. M., Peters, S. E. & Head, J. J. Predation upon hatchling dinosaurs by a new snake from the Late Cretaceous of India. PLoS Biol. 8, e1000322 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Garcia, G. & Vianey-Liaud, M. Nouvelles données sur les coquilles d’œufs de dinosaures Megaloolithidae du Sud de la France: Systématique et variabilité intraspécifique. C. R. Acad. Sci. A. 332, 185–191 (2001).
    Google Scholar 
    Vianey-Liaud, M., Khosla, A. & Garcia, G. Relationships between European and Indian dinosaur eggs and eggshells of the oofamily Megaloolithidae. J. Vertebr. Paleontol. 23, 575–585 (2003).
    Google Scholar 
    Campos, D. A., Kellner, A. W. A., Bertini, R. J. & Santucci, R. M. On a titanosaurid (Dinosauria, Sauropoda) vertebral column from the Bauru Group, Late Cretaceous of Brazil. Arq. Mus. Nac. 63, 565–593 (2005).
    Google Scholar 
    Kellner, A. W. A., Campos, D. A. & Trotta, M. N. F. Description of a titanosaurid caudal series from the Bauru Group, Late Cretaceous of Brazil. Arq. Mus. Nac. 63, 529–564 (2005).
    Google Scholar 
    Machado, E. B., Avilla, L. S., Nava, W. R., Campos, D. A. & Kellner, A. W. A. A new titanosaur sauropod from the Late Cretaceous of Brazil. Zootaxa 3701, 301–321 (2013).PubMed 

    Google Scholar 
    Silva Junior, J. C. G. et al. Reassessment of Aeolosaurus maximus, a titanosaur dinosaur from the Late Cretaceous of Southeastern Brazil. Hist. Biol. 34, 402–411 (2022).
    Google Scholar 
    Bandeira, K. L. N. et al. A new giant Titanosauria (Dinosauria: Sauropoda) from the Late Cretaceous Bauru Group, Brazil. PLoS One 11, e0163373 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pearce, J. M. Philopatry: A return to origins. Auk 124, 1085–1087 (2007).
    Google Scholar 
    Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: Perspectives for a changing world. Science 313, 789–791 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Basilici, G., Hechenleitner, E. M., Fiorelli, L. E., Dal Bó, P. F. & Mountney, N. P. Preservation of titanosaur egg clutches in Upper Cretaceous cumulative palaeosols (Los Llanos Formation, La Rioja, Argentina). Palaeogeogr. Palaeoclimatol. Palaeoecol. 482, 83–102 (2017).
    Google Scholar 
    Mueller-Töwe, I. J., Sander, P. M., Schüller, H. & Thies, D. Hatching and infilling of dinosaur eggs as revealed by computed tomography. Palaeontogr. Abt. A 267, 119–168 (2002).
    Google Scholar 
    Paganelli, C. V. The physics of gas exchange across the avian eggshell. Am. Zool. 20, 329–338 (1980).
    Google Scholar 
    Grellet-Tinner, G., Fiorelli, L. E. & Salvador, R. B. Water vapor conductance of the Lower Cretaceous dinosaurian eggs from Sanagasta, La Rioja, Argentina: Paleobiological and paleoecological implications for South American faveoloolithid and megaloolithid eggs. Palaios 27, 35–47 (2012).ADS 

    Google Scholar 
    Grellet-Tinner, G. Phylogenetic interpretation of eggs and eggshells: Implications for phylogeny of Palaeognathae. Alcheringa 30, 141–182 (2006).
    Google Scholar 
    Mikhailov, K. E., Bray, E. S. & Hirsch, K. F. Parataxonomy of fossil egg remains (Veterovata): Principles and applications. J. Vertebr. Paleontol. 16, 763–769 (1996).
    Google Scholar 
    Nys, Y., Gautron, J., Garcia-Ruiz, J. M. & Hincke, M. T. Avian eggshell mineralization: Biochemical and functional characterization of matrix proteins. C. R. Palevol 3, 549–562 (2004).
    Google Scholar 
    Fernández, M. S., Passalacqua, K., Arias, J. I. & Arias, J. L. Partial biomimetic reconstitution of avian eggshell formation. J. Struct. Biol. 148, 1–10 (2004).PubMed 

    Google Scholar 
    Arias, J. L. & Fernández, M. S. Biomimetic processes through the study of mineralized shells. Mater. Charact. 50, 189–195 (2003).CAS 

    Google Scholar 
    Arias, J. L., Mann, K., Nys, Y., Garcia Ruiz, J. M. & Fernández, M. S. Eggshell growth and matrix macromolecules. In Handbook of Biomineralization (ed. Baeuerlein, E.) 309–328 (Wiley, 2007).
    Google Scholar 
    Chien, Y.-C., Hincke, M. T., Vali, H. & Mckee, M. D. Ultrastructural matrix–mineral relationships in avian eggshell, and effects of osteopontin on calcite growth in vitro. J. Struct. Biol. 163, 84–99 (2008).CAS 
    PubMed 

    Google Scholar 
    Hincke, M. T. et al. The eggshell: Structure, composition and mineralization. Front. Biosci. 17, 1266–1280 (2012).CAS 

    Google Scholar 
    Stapane, L. et al. Avian eggshell formation reveals a new paradigm for vertebrate mineralization via vesicular amorphous calcium carbonate. J. Biol. Chem. 295, 15853–15869 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Norell, M. A. et al. A theropod dinosaur embryo and the affinities of the Flaming Cliffs dinosaur eggs. Science 266, 779–782 (1994).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Araújo, R. et al. Filling the gaps of dinosaur eggshell phylogeny: Late Jurassic Theropod clutch with embryos from Portugal. Sci. Rep. 3, 1924 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xing, L. et al. An exquisitely preserved in-ovo theropod dinosaur embryo sheds light on avian-like prehatching postures. iScience 103516, 24 (2021).
    Google Scholar 
    Sato, T., Cheng, Y.-N., Wu, X.-C., Zelenitsky, D. K. & Hsiao, Y.-F. A pair of shelled eggs inside a female dinosaur. Science 308, 375 (2005).CAS 
    PubMed 

    Google Scholar 
    Norell, M. A., Clark, J. M. & Chiappe, L. M. A nesting dinosaur. Nature 378, 774–776 (1995).ADS 
    CAS 

    Google Scholar 
    Dong, Z. M. & Currie, P. J. On the discovery of an oviraptorid skeleton on a nest of eggs at Bayan Mandahu, Inner Mongolia, People’s Republic of China. Can. J. Earth Sci. 33, 631–636 (1996).ADS 

    Google Scholar  More

  • in

    Water ecological security assessment and spatial autocorrelation analysis of prefectural regions involved in the Yellow River Basin

    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

  • in

    Tracing the invasion of a leaf-mining moth in the Palearctic through DNA barcoding of historical herbaria

    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

  • in

    Effects of COVID-19 lockdowns on shorebird assemblages in an urban South African sandy beach ecosystem

    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

  • in

    Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation

    Corwin, D. L. & Lesch, S. M. Apparent soil electrical conductivity measurements in agriculture. Comput. Electron. Agric. 46, 11–43 (2005).
    Google Scholar 
    Akramkhanov, A., Lamers, J. P. A. & Martius, C. Conversion factors to estimate soil salinity based on electrical conductivity for soils in Khorezm region, Uzbekistan. In Sustainable Management of Saline Waters and Salt-Affected Soils for Agriculture (ed. Qadir, S. et al.) 19–25 (Syria, 2009).Boettinger, J. L., Doolittle, J. A., West, N. E., Bork, E. W. & Schupp, E. W. Nondestructive assessment of rangeland soil depth to petrocalcic horizon using electromagnetic induction. Arid. Land Res. Manag. 11, 372–390 (1997).
    Google Scholar 
    Herrero, J., Ba, A. A. & Aragues, R. Soil salinity and its distribution determined by soil sampling and electromagnetic techniques. Soil Use Manag. 19, 119–126 (2003).
    Google Scholar 
    Corwin, D. L. Past, present, and future trends in soil electrical conductivity measurements using geophysical methods. In Handbook of Agricultural Geophysics (eds. Allred, B. J., Daniels, J. J. & Ehsani, M. R.) 17–44 (Boca Raton, 2008).Triantafillou, J., Lesch, S. M., La Lau, K. & Buchanan, S. M. Field level digital mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model. Aust. J. Soil Res. 47, 651–663 (2009).
    Google Scholar 
    Lardo, E., Arous, A., Palese, A. M., Nuzzo, V. & Celano, G. Electromagnetic induction: A support tool for the evaluation of soil CO2 emissions and soil organic carbon content in olive orchards under semi-arid conditions. Geoderma 264, 188–194 (2016).ADS 
    CAS 

    Google Scholar 
    Yao, R. J. et al. Geostatistical monitoring of soil salinity for precision management using proximally sensed electromagnetic induction (EMI) method. Environ. Earth Sci. 75(20), 1362. https://doi.org/10.1007/s12665-016-6179-z (2016).CAS 

    Google Scholar 
    Corwin, D. L. & Lesch, S. M. Protocols and guidelines for field-scale measurement of soil salinity distribution with ECa-directed soil sampling. J. Environ. Eng. Geophys. 18(1), 1–25 (2013).
    Google Scholar 
    Heil, K. & Schmidhalter, U. The application of EM38: Determination of soil parameters, selection of soil sampling points and use in agriculture and archaeology. Sensors. 17, 2540 (2017).ADS 
    PubMed Central 

    Google Scholar 
    Rhoades, J. D., Corwin, D. L. & Lesch, S. M. Geospatial measurements of soil electrical conductivity to assess soil salinity and diffuse salt loading from irrigation. In Assessment of Non-point Source Pollution in the Vadose Zone (eds. Corwin, D. L., Loague, K. & Ellsworth, T. R.) 197–215 (Geophysical Monogram, 1999).Sadler, E. J., Camp, C. R. & Evans, R. G. New and future technology. In Irrigation of Agricultural Crops (eds. Steward, B. A. & Nelson, D. R.) 609–626 (Agronomy Monograph, 2007).Carrow, R. N., Krum, J. M., Flitcroft, I. & Cline, V. Precision turfgrass management: Challenges and field application for mapping turfgrass soil and stress. Precis. Agric. 11, 115–134 (2010).
    Google Scholar 
    Devitt, D. A., Lockett, M. & Bird, B. M. Spatial and temporal distribution of salts on fairways and greens irrigated with reuse water. Agronomy 99, 692–700 (2007).
    Google Scholar 
    Corwin D.L., Lesch S.M. & Lobell D.B. Laboratory and field measurements. In Agricultural Salinity Assessment and Management (eds. Wallender, W. W. & Tanji, K. K.) (2012).Lesch, S. M., Rhoades, J. D., Corwin, D. L., Robinson, D. A. & Suárez, D. L. ESAP-RSSD version 2.30R. User manual and tutorial guide. Res. Report 148 in USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2002).Lesch, S. M., Rhoades, J. D., Corwin, D. L., Robinson, D. A. & Suárez, D. L. ESAP-SaltMapper version 2.30R. User manual and tutorial guide. Res. Report 149 USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2002).Lesch, S. M., Rhoades, J. D. & Corwin, D. L. ESAP-95 Version 2.01R: User manual and tutorial guide. Res. Rep. 146. USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2000).Lesch, S. M., Strauss, D. J. & Rhoades, J. D. Spatial prediction of soil salinity using electromagnetic induction techniques: 1. Statistical prediction models: A comparison of multiple linear regression and cokriging. Water Resour. Res. 31, 373–386 (1995).ADS 

    Google Scholar 
    Amezketa, E. Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: A case study. Span. J. Agric. Res. 5(1), 91–101 (2007).
    Google Scholar 
    Grieve C. M., Grattan, S. R. & Mass, E. V. Plant salt tolerance. In Agricultural Salinity Assessment and Management (eds. Walender W. W. & Tanji K.K.) (ASCE, 2012).Shahba, M. Interaction effects of salinity and mowing on performance and physiology of bermudagrass cultivars. Crop Sci. 50, 2620–2631 (2010).
    Google Scholar 
    Marcum, K. B. & Pessarakli, M. Salinity tolerance and salt gland excretion efficiency of bermudagrass turf cultivars. Crop Sci. 46, 2571–2574 (2006).
    Google Scholar 
    Xiang, M., Moss, J. Q., Martin, D. L., Su, K. & Dunn, B. L. Evaluating the salinity tolerance of clonal-type bermudagrass cultivars and an experimental selection. Hortic. Sci. 51(1), 185–191 (2017).
    Google Scholar 
    Ganjegunte, G. K. et al. Soil salinity of an urban park after long term irrigation with saline ground water. Agronomy 109, 3011–3018 (2017).CAS 

    Google Scholar 
    Keren, R. & Miyamoto, S. Reclamation of saline, sodic and boron affected soils. In Agricultural Salinity Assessment and Management (eds. Walender W. W. & Tanji K. K.) (ASCE, 2012).Thomas, G. W. & Phillips, R. E. Consequences of water-movement in macropores. J. Environ. Qual. 8, 149–152 (1979).
    Google Scholar 
    White, R. E. The influence of macropores on the transport of dissolved and suspended matter through soil. In Advances in Soil Science (ed. Stewart, B. A.) 95–120 (Springer, 1985).Workman, S. & Skaggs, R. PREFLO: A water management model capable of simulating preferential flow. Trans. ASAE. 33, 1939–1948 (1990).
    Google Scholar 
    Huang, B., Duncan, R. R. & Carrow, R. N. Drought-resistance mechanisms of seven warm-season turfgrasses under surface soil drying: II. Root aspects. Crop Sci. 7(6), 863–1869 (1997).
    Google Scholar 
    Liu, X. H. & Huang, B. R. Cytokinin effects on creeping bentgrass response to heat stress: II. Leaf senescence and antioxidant metabolism. Crop Sci. 42, 466–472 (2002).CAS 

    Google Scholar  More