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    The future of Viscum album L. in Europe will be shaped by temperature and host availability

    Walas, Ł, Ganatsas, P., Iszkuło, G., Thomas, P. A. & Dering, M. Spatial genetic structure and diversity of natural populations of Aesculus hippocastanum L. in Greece. PLoS ONE 14, e0226225 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Song, Y. G. et al. Past, present and future suitable areas for the relict tree Pterocarya fraxinifolia (Juglandaceae): Integrating fossil records, niche modeling, and phylogeography for conservation. Eur. J. For. Res. 140, 1323–1339 (2021).Article 

    Google Scholar 
    Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 
    Article 

    Google Scholar 
    Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change. Ann. For. Sci. 78, 1–18 (2021).Article 

    Google Scholar 
    Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576 (2009).Article 

    Google Scholar 
    Watling, J. I. et al. Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecol. Modell. 309, 48–59 (2015).ADS 
    Article 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    Phillips, S. J., Dudík, M. & Schapire, R. E. [Internet] Maxent software for modeling species niches and distributions. url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 13 July 2022.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

    Google Scholar 
    Marcer, A., Sáez, L., Molowny-Horas, R., Pons, X. & Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Conserv. 166, 221–230 (2013).Article 

    Google Scholar 
    Rigling, A., Eilmann, B., Koechli, R. & Dobbertin, M. Mistletoe-induced crown degradation in Scots pine in a xeric environment. Tree Physiol. 30, 845–852 (2010).PubMed 
    Article 

    Google Scholar 
    Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Mistletoe effects on Scots pine decline following drought events: Insights from within-tree spatial patterns, growth and carbohydrates. Tree Physiol. 32, 585–598 (2012).PubMed 
    Article 

    Google Scholar 
    Kollas, C., Gutsch, M., Hommel, R., Lasch-Born, P. & Suckow, F. Mistletoe-induced growth reductions at the forest stand scale. Tree Physiol. 38, 735–744 (2018).PubMed 
    Article 

    Google Scholar 
    Schulze, E. D. & Ehleringer, J. R. The effect of nitrogen supply on growth and water-use efficiency of xylem-tapping mistletoes. Planta 162, 268–275 (1984).PubMed 
    Article 

    Google Scholar 
    Escher, P. et al. Transpiration, CO2 assimilation, WUE, and stomatal aperture in leaves of Viscum album L: Effect of abscisic acid (ABA) in the xylem sap of its host (Populus x euamericana). Plant Physiol. Biochem. 46, 64–70 (2008).PubMed 
    Article 

    Google Scholar 
    Zweifel, R., Bangerter, S., Rigling, A. & Sterck, F. J. Pine and mistletoes: How to live with a leak in the water flow and storage system?. J. Exp. Bot. 63, 2565–2578 (2012).PubMed 
    Article 

    Google Scholar 
    Mutlu, S., Osma, E., Ilhan, V., Turkoglu, H. I. & Atici, O. Mistletoe (Viscum album) reduces the growth of the Scots pine by accumulating essential nutrient elements in its structure as a trap. Trees 30, 815–824 (2016).Article 

    Google Scholar 
    Tsopelas, P., Angelopoulos, A., Economou, A. & Soulioti, N. Mistletoe (Viscum album) in the fir forest of Mount Parnis Greece. For. Ecol. Manag. 202, 59–65 (2004).Article 

    Google Scholar 
    Dobbertin, M. & Rigling, A. Pine mistletoe (Viscum album ssp. austriacum) contributes to Scots pine (Pinus sylvestris) mortality in the Rhone valley of Switzerland. For. Pathol. 36, 309–322 (2006).Article 

    Google Scholar 
    Lech, P., Żółciak, A. & Hildebrand, R. Occurrence of European mistletoe (Viscum album L.) on forest trees in Poland and its dynamics of spread in the period 2008–2018. Forests 11, 83 (2020).Article 

    Google Scholar 
    Iszkuło, G. et al. Jemioła jako zagrożenie dla zdrowotności drzewostanów iglastych. Sylwan 164, 226–236 (2020) ([In Polish]).
    Google Scholar 
    Mellado, A., Morillas, L., Gallardo, A. & Zamora, R. Temporal dynamic of parasite-mediated linkages between the forest canopy and soil processes and the microbial community. New Phytol. 211, 1382–1392 (2016).PubMed 
    Article 

    Google Scholar 
    Mellado, A. & Zamora, R. Generalist birds govern the seed dispersal of a parasitic plant with strong recruitment constraints. Oecologia 176, 139–147 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    Hódar, J. A., Lázaro-González, A. & Zamora, R. Beneath the mistletoe: parasitized trees host a more diverse herbaceous vegetation and are more visited by rabbits. Ann. For. Sci. 75, 1–8 (2018).Article 

    Google Scholar 
    Zuber, D. Biological flora of Central Europe: Viscum album L. Flora Morphol. Distrib Funct. Ecol. Plants 199, 181–203 (2004).Article 

    Google Scholar 
    Urech, K. & Baumgartner, S. Chemical constituents of Viscum album L.: Implications for the pharmaceutical preparation of mistletoe. In: Mistletoe: From mythology to evidence-based medicine. (eds. Zänker, K.S. & Kaveri, S. V.), 11–23. (S. Karger AG, Basel, Switzerland, 2015).Singh, B. N. et al. European Viscum album: a potent phytotherapeutic agent with multifarious phytochemicals, pharmacological properties and clinical evidence. RSC Adv. 6, 23837–23857 (2016).ADS 
    Article 

    Google Scholar 
    Jeffree, C. E. & Jeffree, E. P. Redistribution of the potential geographical ranges of mistletoe and colorado beetle in Europe in response to the temperature component of climate change. Funct. Ecol. 10, 562–577 (1996).Article 

    Google Scholar 
    Troels-Smith, J. Ivy, mistletoe and elm climate indicators-fodder plants. A contribution to the interpretation of the pollen zone border VII-VIII. Dan. Geol. Undersøg. IV Række 4, 1–32 (1960).
    Google Scholar 
    Dobbertin, M. et al. The upward shift in altitude of pine mistletoe (Viscum album ssp. austriacum) in Switzerland—the result of climate warming?. Int. J. Biometeorol. 50, 40–47 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    Zamora, R. & Mellado, A. Identifying the abiotic and biotic drivers behind the elevational distribution shift of a parasitic plant. Plant Biol. 21, 307–317 (2019).PubMed 
    Article 

    Google Scholar 
    Barney, C. W., Hawksworth, F. G. & Geils, B. W. Hosts of Viscum album. Eur. J. Plant Pathol. 28, 187–208 (1998).
    Google Scholar 
    Böhling, N. et al. Notes on the Cretan mistletoe, Viscum album subsp. creticum subsp. nova (Loranthaceae/Viscaceae). Isr. J. Plant Sci. 50, 77–84 (2002).
    Google Scholar 
    Plants of the World Online [Internet] url: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:921668-1. Accessed 13 July 2022.Zuber, D. & Widmer, A. Phylogeography and host race differentiation in the European mistletoe (Viscum album L.). Mol. Ecol. 18, 1946–1962 (2009).PubMed 
    Article 

    Google Scholar 
    Schaller, G., Urech, K., Grazi, G. & Giannattasio, M. Viscotoxin composition of the three European subspecies of Viscum album. Planta Med 64, 677–678 (1998).PubMed 
    Article 

    Google Scholar 
    Kahle-Zuber, D. Biology and evolution of the European mistletoe (Viscum album). Doctoral Thesis. ETH Zurich. (2008).Zuber, D. & Widmer, A. Genetic evidence for host specificity in the hemi-parasitic Viscum album L. (Viscaceae). Mol. Ecol. 9, 1069–1073 (2000).PubMed 
    Article 

    Google Scholar 
    Mejnartowicz, L. Relationship and genetic diversity of mistletoe [Viscum album L.] subspecies. Acta Soc. Bot. Pol. Pol. 75, 39–49 (2006).Article 

    Google Scholar 
    Xie, W., Adolf, J. & Melzig, M. F. Identification of Viscum album L. miRNAs and prediction of their medicinal values. PLoS ONE 12, e0187776 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Valle, A. C. V., de Carvalho, A. C. & Andrade, R. V. Viscum album-literature review. Int. J. Sci. Res 10, 63–71 (2021).
    Google Scholar 
    Schröder, L. et al. The gene space of European mistletoe (Viscum album). Plant J. 109, 278–294 (2022).PubMed 
    Article 

    Google Scholar 
    Sangüesa-Barreda, G. et al. Delineating limits: Confronting predicted climatic suitability to field performance in mistletoe populations. J. Ecol. 106, 2218–2229 (2018).Article 

    Google Scholar 
    GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.zw6f5q. Accessed 27 July 2021.GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.6wmc9d. Accessed 6 August 2021.FloraWeb [Internet] url: https://www.floraweb.de. Accessed 10 December 2021.Pladias – Database of the Czech Flora and Vegetation. [Internet] url: www.pladias.cz. Accessed 14 July 2022.Zając, A., Zając, M., Tertil, R. & Harman, I. Atlas rozmieszczenia roślin naczyniowych w Polsce. 593 (Instytut Botaniki Uniwersytetu Jagiellońskiego, Kraków, 2001) [In Polish].Idžojtić, M., Kogelnik, M., Franjić, J. & Škvorc, Ž. Hosts and distribution of Viscum album L. ssp. album in Croatia and Slovenia. Plant Biosyst. 140, 50–55 (2006).Article 

    Google Scholar 
    Varga, I. et al. Changes in the Distribution of European Mistletoe (Viscum album) in Hungary During the Last Hundred Years. Folia Geobot 49, 559–577 (2014).Article 

    Google Scholar 
    Wild, J. et al. Plant distribution data for the Czech Republic integrated in the Pladias database. Preslia 91, 1–24 (2019).Article 

    Google Scholar 
    Krasylenko, Y. et al. The European mistletoe (Viscum album L.): Distribution, host range, biotic interactions, and management worldwide with special emphasis on Ukraine. Botany 98, 499–516 (2020).Article 

    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Karger D. N., et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository (2018).Gutjahr, O. et al. Max planck institute earth system model (MPI-ESM1. 2) for the high-resolution model intercomparison project (HighResMIP). Geosci. Model Dev. 12, 3241–3281 (2019).ADS 
    Article 

    Google Scholar 
    Hijmans, R. J., & van Etten, J. raster: Geographic analysis and modeling with raster data. R package version 2.0-12. (2012).R Core Team. The Comprehensive R Archive Network. [Internet] url: https://cran.r-project.org/ Accessed 14 July 2022.Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with European tree species distribution models under climate change (Version v1). Zenodo https://doi.org/10.5281/zenodo.3686918 (2020).Wang, Z., Chang, Y. I., Ying, Z., Zhu, L. & Yang, Y. A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve. Bioinformatics 23, 2788–2794 (2007).PubMed 
    Article 

    Google Scholar 
    Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

    Google Scholar 
    QGIS Development Team. QGIS Geographic Information Sys-tem. Open Source Geospatial Foundation Project. [Internet]. url: https://www.qgis.org/en/site/. Accessed 14 July 2022.Fischer, J. T. Water relations of mistletoes and their hosts. In: The biology of mistletoes. (eds. Calder, M., & Bernhard, T.), 163–184 (Academic Press, Sydney, 1983).Skre, O. The regional distribution of vascular plants in Scandinavia with requirements for high summer temperatures. Norweg. J. Bot. 26, 295–318 (1979).
    Google Scholar 
    Wangerin, B. Loranthaceae. In: Lebensgeschichte der Blütenpflanzen Mitteleuropas (eds. Kirchner, O. V., Loew, E., & Schroeter, C.) 2, 953–1146 (E. Ulmer, Stuttgart, 1937).Rybalka, I. A. Relationship between density of the white mistletoe (Viscum album L.) and some landscape and environmental characteristics of urban areas in the case of Kharkiv. Ekologicheskiy Vestnik 1, 87–97 (2017).
    Google Scholar 
    Patykowski, J. & Kołodziejek, J. Comparative analysis of antioxidant activity in leaves of different hosts infected by mistletoe (Viscum album L. subsp. album). Arch. Biol. Sci. 65, 851–861 (2013).Article 

    Google Scholar 
    Skrypnik, L., Maslennikov, P., Feduraev, P., Pungin, A. & Belov, N. Ecological and landscape factors affecting the spread of European mistletoe (Viscum album L.) in urban areas (A Case Study of the Kaliningrad City, Russia). Plants 9, 394 (2020).PubMed Central 
    Article 

    Google Scholar 
    Kunick, W. Veränderungen von Flora und Vegetation einer Grosstadt dargestellt am Beispiel von Berlin (West). PhD Thesis, Technische Universität (1974). [In German].Kołodziejek, J., Patykowski, J. & Kołodziejek, R. Distribution, frequency and host patterns of European mistletoe (Viscum album subsp. album) in the major city of Lodz Poland. Biol. 68, 55–64 (2013).
    Google Scholar 
    Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Brief 12, 662–666 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691, 4–9 (2012).
    Google Scholar 
    Luther, P., Becker, H. & Leroi, R. Die Mistel: Botanik, Lektine, medizinische Anwendung. Springer (1987).Gazol, A. et al. Distinct effects of climate warming on populations of silver fir (Abies alba) across Europe. J. Biogeogr. 42, 1150–1162 (2015).Article 

    Google Scholar 
    Tikkanen, O. P. et al. Freezing tolerance of seeds can explain differences in the distribution of two widespread mistletoe subspecies in Europe. For. Ecol. Manag. 482, 118806 (2021).Article 

    Google Scholar 
    Pilichowski, S. et al. Wpływ Viscum album ssp. austriacum (Wiesb.) Vollm. na przyrost radialny Pinus sylvestris L. Sylwan 162, 452–459 (2018) ([In Polish]).
    Google Scholar 
    Szmidla, H., Tkaczyk, M., Plewa, R., Tarwacki, G. & Sierota, Z. Impact of common mistletoe (Viscum album L.) on scots pine forests—A call for action. Forests 10, 847 (2019).Article 

    Google Scholar 
    Wójcik, R. & Kędziora, W. Abundance of Viscum in central Poland: Results from a large-scale mistletoe inventory. Environ. Sci. Proc. 3, 98 (2020).
    Google Scholar 
    Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Drought and mistletoe reduce growth and water-use efficiency of Scots pine. For. Ecol. Manag. 296, 64–73 (2013).Article 

    Google Scholar 
    Mathiasen, R. L., Nickrent, D. L., Shaw, D. C. & Watson, D. M. Mistletoes: Pathology, systematics, ecology, and management. Plant Dis. 92, 988–1006 (2008).PubMed 
    Article 

    Google Scholar 
    Catal, Y. & Carus, S. Effect of pine mistletoe on radial growth of crimean pine (Pinus nigra) in Turkey. J. Environ. Biol. 32, 263 (2011).PubMed 

    Google Scholar 
    Skre, O. High temperature demands for growth and development in Norway Spruce [Picea abies (L.) Karst.] in Scandinavia. Meld Nor Landbrukshøgsk 51, 1–29 (1971).
    Google Scholar 
    Utaaker, K. A temperature-growth index—the respiration equivalent—used in climatic studies on the meso-scale in Norway. Agric. Meteorol. 5, 351–359 (1968).Article 

    Google Scholar 
    Iversen, J. Viscum, Hedera and Ilex as climate indicators: A contribution to the study of the post-glacial temperature climate. Geol. fören. Stockh. förh. 66, 463–483 (1944).Article 

    Google Scholar 
    Briggs, J. Mistletoe, Viscum album (Santalaceae), in Britain and Ireland; a discussion and review of current status and trends. Brit. Ir. Bot. 3, 419–454 (2021).
    Google Scholar  More

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    Marine subsidies produce cactus forests on desert islands

    Bartz, K. K. & Naiman, R. J. Effects of Salmon-Borne nutrients on riparian soils and vegetation in Southwest Alaska. Ecosystems 8, 529–545 (2005).Article 

    Google Scholar 
    Erskine, P. D. et al. Subantarctic Macquarie Island—a model ecosystem for studying animal-derived nitrogen sources using 15N natural abundance. Oecologia 117, 187–193 (1998).ADS 
    PubMed 
    Article 

    Google Scholar 
    Hocking, M. D. & Reimchen, T. E. Salmon species, density and watershed size predict magnitude of marine enrichment in riparian food webs. Oikos 118(9), 1307–1318 (2009).Article 

    Google Scholar 
    Hocking, M. D. & Reynolds, J. D. Impacts of salmon on riparian plant diversity. Science 331, 1609–1612 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Hocking, M. D., & Reimchen, T. E. Salmon-derived nitrogen in terrestrial invertebrates from coniferous forests of the Pacific Northwest. BMC Ecol. 2, 4. https://doi.org/10.1186/1472-6785-2-4 (2002).Bilby, R. E., Fransen, B. R. & Bisson, P. A. Incorporation of nitrogen and carbon from spawning coho salmon into the trophic system of small streams: Evidence from stable isotopes. Can. J. Fish Aquat. Sci. 53, 164–173 (1996).Article 

    Google Scholar 
    Talley, D. M. et al. Research challenges at the land–sea interface. Estuar. Coast. Shelf Sci. 58, 699–702 (2003).ADS 
    Article 

    Google Scholar 
    Mizutani, H. & Wada, E. Nitrogen and carbon isotope ratios in seabird rookeries and their ecological implications. Ecology 69(2), 340–349 (1988).Article 

    Google Scholar 
    Rowe, J. A., Litton, C. M., Lepczyk, C. A. & Popp, B. N. Impacts of endangered seabirds on nutrient cycling in montane forest ecosystems of Hawai’i. Pac. Sci. 71(4), 495–509 (2017).Article 

    Google Scholar 
    Sanchez-Pinero, F. & Polis, G. A. Bottom-up dynamics of allochthonous input: Direct and indirect effects of seabirds on islands. Ecology 81(11), 3117–3132 (2000).Article 

    Google Scholar 
    Wait, D. A., Aubrey, D. P. & Anderson, W. B. Seabird guano influences on desert islands: Soil chemistry and herbaceous species richness and productivity. J. Arid Environ. 60, 681–695 (2005).ADS 
    Article 

    Google Scholar 
    Stapp, P., Polis, G. A. & Pinero, F. S. Stable isotopes reveal strong marine and El Nino effects on island food webs. Nature 401, 467–469 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    Anderson, W. B., Wait, D. A. & Stapp, P. Resources from another place and time: Responses to pulses in a spatially subsidized system. Ecology 89(3), 660–670 (2008).PubMed 
    Article 

    Google Scholar 
    Ellis, J. C. Marine birds on land: A review of plant biomass, species richness, and community composition in seabird colonies. Plant Ecol. 181(2), 227–241 (2005).Article 

    Google Scholar 
    Fukami, T. et al. Above- and below-ground impacts of introduced predators in seabird-dominated island ecosystems. Ecol. Lett. 9, 1299–1307 (2006).PubMed 
    Article 

    Google Scholar 
    Wootton, J. T. Direct and indirect effects of nutrients on intertidal community structure: Variable consequences of seabird guano. J. Exp. Mar. Biol. Ecol. 151, 139–153 (1991).Article 

    Google Scholar 
    McCauley, D. J., et al., From wing to wing: the persistence of long ecological interaction chains in less-disturbed ecosystems. Sci. Rep. 2, 409. https://doi.org/10.1038/srep00409 (2012).Young, H. S., McCauley, D. J., Dunbar, R. B. & Dirzo, R. Plants cause ecosystem nutrient depletion via the interruption of bird-derived spatial subsidies. Proc. Natl. Acad. Sci. U.S.A. 107(5), 2072–2077 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lindeboom, H. J. The nitrogen pathway in a Penguin rookery. Ecology 65(1), 269–277 (1984).CAS 
    Article 

    Google Scholar 
    Mizutani, H., Kabaya, Y. & Wada, E. Ammonia volatilization and high 15N/14N ratio in a penguin rookery in Antarctica. Geochem. J. 19(6), 323–327 (1985).ADS 
    CAS 
    Article 

    Google Scholar 
    Anderson, W. B. & Polis, G. A. Nutrient fluxes from water to land: seabirds affect plant nutrient status on Gulf of California islands. Oecologia 118, 324–332 (1999).ADS 
    PubMed 
    Article 

    Google Scholar 
    Polis, G. A. & Hurd, S. D. Linking marine and terrestrial food webs: Allochthonous input from the ocean supports high secondary productivity on small islands and coastal land communities. Am. Nat. 147, 396–423 (1996).Article 

    Google Scholar 
    Goss, N. S. New and rare birds found breeding on the San Pedro Martir Isle. University of California Press 5, 240–244 (1888).
    Google Scholar 
    Velarde, E., et al., Nesting seabirds of the Gulf of California’s Offshore islands: Diversity, ecology and conservation. in Biodiversity, Ecosystems, and Conservation in Northern Mexico, Carton, J.-L. E., Ceballos, G., Felger, R. S. Eds. (Oxford University Press, 2005) pp. 452–470.Wilder, B. T., Felger, R. S. & Ezcurra, E. Controls of plant diversity and composition on a desert archipelago. PeerJ 7, e7286. https://doi.org/10.7717/peerj.7286 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ellis, J., Fariña, J. & Witman, J. Nutrient transfer from sea to land: the case of gulls and cormorants in the Gulf of Maine. J. Anim. Ecol. 75, 565–574 (2006).PubMed 
    Article 

    Google Scholar 
    Wilder, B. T., Felger, R. S. & Morales, H. R. Succulent plant diversity of the Sonoran Islands, Gulf of California Mexico. Haseltonia 2008(14), 127–160 (2008).Article 

    Google Scholar 
    Lucassen, F. et al. The stable isotope composition of nitrogen and carbon and elemental contents in modern and fossil seabird guano from Northern Chile—Marine sources and diagenetic effects. PLoS ONE 12(6), e0179440. https://doi.org/10.1371/journal.pone.0179440 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16(3), 153–162 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Szpak, P., Longstaffe, F. J., Millaire, J.-F. & White, C. D. Stable isotope biogeochemistry of seabird guano fertilization: Results from growth chamber studies with maize (Zea mays). PLoS ONE 7(3), e33741. https://doi.org/10.1371/journal.pone.0033741 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ezcurra, E., et al. Natural History and Evolution of the World’s Deserts. Global Deserts Outlook. United Nations Environment Programme (UNEP), 1–26 (2006).Yetman, D. The Great Cacti: Ethnobotany and biogeography (University of Arizona Press, 2007).
    Google Scholar 
    Álvarez-Borrego, S. Physical oceanography. in A New Island Biogeography of the Sea of Cortés, Case, T. J., Cody, M. L., Ezcurra, E. Eds. (Oxford University Press, 2002), pp. 41–59.Douglas, R., Gonzalez-Yajimovich, O., Ledesma-Vazquez, J. & Staines-Urias, F. Climate forcing, primary production and the distribution of Holocene biogenic sediments in the Gulf of California. Quatern. Sci. Rev. 26, 115–129 (2007).ADS 
    Article 

    Google Scholar 
    Urbán, J. Marine mammals of the Gulf of California: An overview of diversity and conservation status. in The Gulf of California: Biodiversity and conservation, R. C. Brusca, Ed. (The University of Arizona Press and the Arizona-Sonora Desert Museum, 2010), pp. 188–209.Hastings, P. A., Findley, L. T., & Van der Heiden, A. M. Fishes of the Gulf of California. in: Brusca, R. C., (eds) The Gulf of California: Biodiversity and conservation 96–118, The University of Arizona Press and the Arizona-Sonora Desert Museum (2010).
    Google Scholar 
    Polis, G. A., Hurd, S. D., Jackson, C. T. & Sanchez Piñero, F. El Niño effects on the dynamics and control of an Island ecosystem in the Gulf of California. Ecology 78, 1884–1897 (1997).
    Google Scholar 
    Wilder, B. T. & Felger, R. S. Dwarf giants, guano, and isolation: The flora and vegetation of San Pedro Mártir Island, Gulf of California, Mexico. Proc. San Diego Soc. Nat. Hist. 42, 1–24 (2010).
    Google Scholar 
    Medel-Narvaez, A., Leon Luz, J. L., Freaner-Martinez, F. & Molina-Freaner, F. Patterns of abundance and population structure of Pachycereus pringlei (Cactaceae), a columnar cactus of the Sonoran Desert. Plant Ecol. 187, 1–14 (2006).Article 

    Google Scholar 
    Felger, R.S., Wilder, B.T. in collaboration with Romero-Morales, H. Plant Life of a Desert Archipelago: Flora of the Sonoran Islands in the Gulf of California. Tucson, University of Arizona Press (2012).Wilkinson, C. E., Hocking, M. D. & Reimchen, T. E. Uptake of salmon-derived nitrogen by mosses and liverworts in Coastal British Columbia. Oikos 108, 85–98 (2005).CAS 
    Article 

    Google Scholar 
    Barrett, K., Wait, D. A. & Anderson, W. B. Small island biogeography in the Gulf of California: Lizards, the subsidized island biogeography hypothesis, and the small island effect. J. Biogeogr. 30, 1575–1581 (2003).Article 

    Google Scholar 
    Young, H. S., McCauley, D. J. & Dirzo, R. Differential responses to guano fertilization among tropical tree species with varying functional traits. Am. J. Bot. 98, 207–214 (2011).PubMed 
    Article 

    Google Scholar 
    Nobel, P. S. Environmental Biology of Agaves and Cacti. Cambridge University Press (2003).Ramirez, K. S., Craine, J. M. & Fierer, N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob. Change Biol. 18(6), 1918–1927 (2012).ADS 
    Article 

    Google Scholar 
    Craine, J. M. et al. Ecological interpretations of nitrogen isotope ratios of terrestrial plants and soils. Plant Soil 396, 1–26 (2015).CAS 
    Article 

    Google Scholar 
    Schoeninger, M. J. & DeNiro, M. J. Nitrogen and carbon isotope composition of bone collagen from marine and terrestrial animals. Geochim. Cosmochim. Acta 48(4), 625–639 (1984).ADS 
    CAS 
    Article 

    Google Scholar 
    Amundson, R. et al. Global patterns of the isotopic composition of soil and plant nitrogen. Global Biogeochem. Cycles 17(1), 1031. https://doi.org/10.1029/2002GB001903 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    Kahmen, A., Wanek, W. & Buchmann, N. Foliar δ15N values characterize soil N cycling and reflect nitrate or ammonium preference of plants along a temperate grassland gradient. Oecologia 156, 861–870 (2008).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bowen, T. Unknown Island: Seri Indians, Europeans, and San Esteban Island in the Gulf of California (University of New Mexico Press, 2000).
    Google Scholar 
    Evans, R. D. Physiological mechanisms influencing plant nitrogen isotope composition. Trends Plant Sci. 6(3), 121–126 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dolby, G., Bennett, S. E. K., Lira-Noriega, A., Wilder, B. T. & Munguia-Vega, A. Assessing the geological and climatic forcing of biodiversity and evolution surrounding the Gulf of California. J. Southw. 57, 391–455 (2015).Article 

    Google Scholar 
    Case, T. J., Cody, M. L., & Ezcurra, E. A New Island Biogeography of the Sea of Cortés (Oxford University Press, 2002).Book 

    Google Scholar 
    Tershy, B. R. & Breese, D. The birds of San Pedro Mártir Island, Gulf of California Mexico. West. Birds 28, 96–107 (1997).
    Google Scholar 
    Tershy, B. R., Breese, D. & Croll, D. A. Human perturbations and conservation strategies for San Pedro Mártir Island, Islas de Golfo de California Reserve México. Environ. Conserv. 24, 261–270 (1997).Article 

    Google Scholar 
    Wilder, B. T. Historical biogeography of the Midriff Islands in the Gulf of California, Mexico. Dissertation. Riverside: UC, Riverside (2014).Post, D. M. et al. Getting to the fat of the matter: Models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152, 179–189 (2007).ADS 
    PubMed 
    Article 

    Google Scholar 
    Kiljunen, M. et al. A revised model for lipid-normalizing δ13C values from aquatic organisms, with implications for isotope mixing models. J. Appl. Ecol. 43, 1213–1222 (2006).CAS 
    Article 

    Google Scholar 
    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

    Google Scholar 
    R Core Team, R: A language and environment for statistical computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2022). More

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    Early-season plant-to-plant spatial uniformity can affect soybean yields

    Sites description and field operationsA total of six field studies were conducted in two different regions over two seasons. Four studies (two dryland and two irrigated) were in Kansas, United States (dryland: 39°4′30″ N, − 96°44′43″ W, irrigated: 39°4′25″N, − 96°43′12″ W) during the 2019 and 2020 growing seasons (hereafter referred to as USDry19, USIrr19, USDry20, and USIrr20 studies). The remaining two studies (dryland) were in Entre Rios, Argentina (31°50′49″ S; 60°32′16″ W) during the 2018/2019 and 2019/2020 growing seasons (hereafter referred to as Arg19 and Arg20 studies). The soils were Fluventic Hapludolls [silt loam, 40% sand, 13% clay, 47% silt, organic matter (OM) 1.7%, 7.7 pH, 31.1 ppm P (Bray−1)] at the US dryland studies, and Pachic Argiudolls [silty clay loam, 10.1% sand, 30.6% clay and 59.3% silt, OM 3.2%, 6.8 pH, 34.7 ppm P (Bray−1)] at the US irrigated studies. At the Argentinian studies soil was a Vertic Argiudoll in 2019 [silty clay loam to clay loam, 3.9% sand, 27.6% clay, 67.9% silt, OM 2.65%, 7.2 pH, 12.5 ppm P (Bray−1)] and an Acuic Argiudoll in 2020 [silt loam to silty-clay-loam, 5.6% sand, 28.6% clay, 65.8% silt, OM 3.33%].The US dryland and irrigated studies were sown on June 4, 2019, and May 20, 2020. In 2019, the dryland study was replanted on June 29 due to poor emergence after the first sowing. The studies in Argentina were sown on December 5 in 2018 and November 20 in 2019. At all six studies, plots were kept free of weeds, pests, and diseases through recommended chemical control.The genotypes used in the US were P40A47X (MG 4.0) and P39A58X (MG 3.9) (Corteva Agriscience, Johnston, IA, USA) in 2019 and 2020, respectively. Both varieties are tolerant to glyphosate and dicamba herbicides (RR2X) and have low lodging probability. For the northeast region of Kansas, recommended sowing dates range from May 15 to June 15 along with MG 421. In addition, recommended seeding rates are between 270 and 355 thousand seeds ha−1 for low-yielding environments and 190 to 285 thousand seeds ha−1 for medium- and high-yielding environments13. In Argentina, the genotype AW5815IPRO (MG 5.8, Bayer, Leverkusen, Germany) was used both in 2020 and 2021, it is tolerant to glyphosate and sulfonylureas, and has low lodging probability. Recommended sowing dates for Entre Rios considering soybeans as a single crop range from October 20 to December 10, and MG usually range from 4 to 6; lastly, seeding rate recommendations are between 200 and 250 thousand seeds ha−1 in the region22.Study designThe studies carried out in the US were arranged as a split plot design with three replicates in both 2019 and 2020. In 2019, the main plot treatment factor was planter type with two levels [John Deere (Moline, Illinois, US) Max Emerge planter (ME, 12 rows), and John Deere Exact Emerge Planter (EE, 16 rows)], and the split-plot treatment factor was seeding rate with two levels (160 and 321 thousand seeds ha−1). In 2020 the main plot treatment factor was also planter type with two levels (ME and EE), and the split-plot treatment factor was seeding rate with four levels (160, 215, 270 and 321 thousand seeds ha−1). Planting speed was 7 km h−1 in both studies and years, plots were 24 and 32 rows wide when planted with ME and EE, respectively, with 0.76 m row spacing. Plot length was 80 m in the dryland studies and 160 m in the irrigated studies. The studies in Argentina were arranged as a single replicate of each seeding rate (100, 230, 360 and 550 thousand seeds ha−1) in both years. Planting speed was 5.5 km h−1 in both years, and plots were 10 rows wide with 0.52 m row spacing and 350 m in length.All treatment factors in US studies were evaluated with the overall goal of producing substantial variation in the variable of interest, plant-to-plant spatial uniformity, rather than to make an inference of their effect on yield. The Argentinian studies were only used for selection of stand uniformity variables due to the single replicate. Plant spatial uniformity variables were first fitted using the data from US studies (details below), and then the best explanatory metrics were selected to re-fit the relationships combining both data sets from US and Argentina. Finally, sowing dates, maturity groups, and seeding rates evaluated in this study at both locations (Arg and US) were aligned with those recommended for each region.Data collection and spacing uniformity variablesTwo segments of 2 m in length were established early in the season inside each plot. At the V5 (US studies) and R1 (Arg studies) soybean development stage23, the cumulative distance of the plants within each segment was measured and then used to calculate multiple derived variables. Plant spacing (cm) was calculated as the average distance between neighboring plants. In addition, the distance from a plant to each neighboring plant was classified as shorter or longer than the plant spacing (named nearest and farthest neighbor distance, respectively). Achieved versus Target Evenness Index (ATEI, dimensionless) was calculated as the ratio between the observed plant spacing and the theoretical plant spacing (TPS, cm), where TPS is the expected plant spacing derived from a specific seeding rate and row width (Eq. 1).$$ATEI = frac{Spacing;(cm) }{{TPS;(cm)}}$$
    (1)
    The ATEI index was designed to account for the proximity of the observed plant spacing to the TPS. Values closer to 1 indicate that the plant spacing is close to the TPS and values that are below or above 1 indicate that the plant spacing is lower or higher than the TPS, respectively; thereby departing from an ideal plant spacing. Hence, ATEI values greater than 1 depict both (i) non-uniform plant-to-plant spacing distribution and (ii) plant densities below the target (seeding rate). To further understand the meaning of ATEI, the relative density (rd) was calculated as the ratio between plant density (based on the number of plants in the 2 m segment) and seeding rate.To account for the unevenness of distance from a plant to both neighboring plants within the row, we used the Evenness Index (EI, dimensionless), calculated as the ratio between the distance to the nearest neighbor (cm) and the plant spacing (cm) of a given plant (Eq. 2). The Evenness Index values range from 0 to 1, a value closer to 1 indicates that a plant is equidistantly spaced to both of its neighboring plants within the row, if zero then those plants are occupying the same position (as doubles). It is important to note that EI does not provide information on the spacing (in distance, cm) or how close the spacing is compared to the TPS, but only describes the unevenness distance of a plant to its neighboring plants within a row.$$Evenness ;Index; (EI) = frac{nearest; neighbor ;(cm)}{{Spacing; (cm)}}$$
    (2)
    In addition, the distance from a plant to its preceding neighboring plant, and the TPS were used to classify the position of each plant into one of eight classes (Fig. 1). Plants were classified in classes ranging from “double” (preceding plant distance  Double-skip) as a function of seeding rate, planter type and their interaction (fixed effects), and block nested in site-year (random effect) (Tables 1 and 2). Independent models for each of the 4 US studies were built assessing the effects of planter type, seeding rate, and their interaction (fixed effects), and seeding rate nested in planter type, and in block (random effects) on the same variables previously mentioned (Supplementary Table 1). The models were run using the lmer function from lme4 package in R (R Core Team, 2021). In addition, the US and Arg studies were combined to evaluate the effect of site-year on yield, plant density, and all stand uniformity variables (Supplementary Fig. 1) using the lm function from package stats. Means separation were performed using Fisher’s LSD (Least Significance Difference) test (alpha = 0.05) with emmeans function from package emmeans.Table 1 Effect of planter type, seeding rate, and their interaction on variables from plant position classification for all US studies. References: percentage of perfectly spaced plants (Perfect), percentage of plants misplaced by 66% (Mis 66), percentage of plants misplaced by 33% (Mis 33), percentage of double plants (Double), percentage of short skips plants (Short-skip), percentage of long skip plants (Long-skip), percentage of double skips plants (Double-skip), and percentage of greater than double skip plants ( > Double-skip).Full size tableTable 2 Effect of planter type, seeding rate, and their interaction on yield and stand uniformity variables for all US studies. References: Spacing between plants standard deviation (Spacing sd), achieved versus targeted evenness index mean and standard deviation (ATEI and ATEI sd, respectively), and evenness index mean and standard deviation (EI and EI sd, respectively).Full size tableCommunity-scale data from the four US studies were combined and fitted to bivariate linear regression models with yield as the response variable and each of the stand spatial uniformity variables as the explanatory variable. Significant models (alpha = 0.05) were further evaluated by calculating the coefficient of determination (R2) and root mean squared error (RMSE) (Fig. 2). Models with the lower RMSE and higher R2 were selected as those that best captured the effect of non-uniform stands on soybean yield. After variables were selected, both US and Arg data sets were combined and the linear regressions between the selected variables and yield were re-fitted to assess the consistency of the relationships when an independent data set was included. Community-scale yield from US and Arg studies was modelled as a function of the selected stand uniformity variable, country (US and Arg), and their interaction (fixed effects) (Fig. 3). The spatial uniformity metric showing the most consistent relationship for both US and Arg studies (i.e., non-significant interaction between stand uniformity metric and country), was selected to continue the analysis. The bivariate linear regression models were run with function lm.Figure 2Relationship between stand uniformity variables and soybean yield for US studies. ATEI mean and sd achieved versus targeted evenness index mean and standard deviation, EI mean and sd evenness index mean and standard deviation, Perfect percentage of perfectly spaced plants, R2 coefficient of determination, RMSE root mean square error. All stand uniformity variables presented a significant slope at alpha = 0.05.Full size imageFigure 3Relationship of spacing standard deviation (Spacing sd, cm) and achieved versus targeted evenness index standard deviation (ATEI sd) to soybean yield. Different colors and line types denote different countries (Argentina, Arg—full line, red points; United States, US—dashed line, blue points). R2 coefficient of determination, RMSE root mean square error.Full size imageDifferent environmental conditions and seeding rate levels may modify the effect of plant spatial uniformity on yield. To explore this, each of the studies from Arg and US were separated into low- (USDry19 and ArgDry20, mean of 2.7 Mg ha−1), medium- (USIrr19, USDry20 and ArgDry19, mean of 3.0 Mg ha−1), and high- (USIrr20, mean of 4.3 Mg ha−1) yield environments based on the effect of site-year on yield (Supplementary Fig. 1). Additionally, the tested seeding rates were separated in low ( 300 thousand seeds ha−1) levels based on the current optimal seeding rate for medium yielding environments (235 thousand seeds ha−1, 4 Mg ha−1)13 and the extreme values proposed by Suhre et al.11 (148 and 445 thousand seeds ha−1). This classification was used to model yield as a function of (i) the selected stand uniformity metric, yield environment, and their interaction, and (ii) the selected stand uniformity metric, seeding rate levels, and their interaction. These models were tested to obtain a robust conclusion on the overall effect of yield environment and seeding rate levels, and their interactions (all treated as fixed effects) with plant-to-plant spatial uniformity relative to the response variable, soybean yield. The Akaike information criteria (AIC) was used to compare the full (with interactions) relative to the reduced models (single effects).Ethics declarationsExperimental research and field studies on plants including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation. More

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    Protecting boreal caribou habitat can help conserve biodiversity and safeguard large quantities of soil carbon in Canada

    Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57. https://doi.org/10.1038/nature09678 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Ceballos, G. et al. Accelerated human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, 5. https://doi.org/10.1126/sciadv.1400253 (2015).Article 

    Google Scholar 
    Purvis, A. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Nature https://doi.org/10.5281/zenodo.5517457.svg (2019).Balvernara, P. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Drivers. Change https://doi.org/10.5281/zenodo.5517423 (2019).Carrol, C. & Noss, R. F. Rewilding in the face of climate change. Conserv. Biol. 35, 155–167. https://doi.org/10.1111/cobi.13531 (2020).Article 

    Google Scholar 
    Barr, S. L., Larson, B. M. H., Beechey, T. J. & Scott, D. J. Assessing climate change adaptation progress in Canada’s protected areas. Can. Geog. 65, 152–165. https://doi.org/10.1111/cag.12635 (2020).Article 

    Google Scholar 
    Convention on Biological Diversity. Aichi Target 11, Convention on Biological Diversity. https://www.cbd.int/aichi-targets/target/11. Accessed 14 May 2021.United Nations. Climate Change Pathways. https://unfccc.int/climate-action/marrakech-partnership/reporting-and-tracking/climate_action_pathways. Accessed 12 Sept 2022.Government of Canada. Canada’s nature legacy: Protecting our nature conservation/nature-legacy.html (2021).Coristine, L. E. et al. Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond. Facets 3, 531–562. https://doi.org/10.1139/facets-2017-0102 (2017).Article 

    Google Scholar 
    De Barros, A. E. et al. Identification of areas in Brazil that optimize areas that optimize conservation of forest carbon, Jaguars and Biodiversity. Conserv. Biol. 28, 580–593. https://doi.org/10.1111/cobi.12202 (2013).Article 
    PubMed 

    Google Scholar 
    Jantz, P., Scott, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Change 4, 138–142. https://doi.org/10.1038/nclimate2105 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Beaudrot, L. et al. Limited carbon and biodiversity co-benefits for tropical mammals and birds. Ecol. Appl. 26, 10998–11111. https://doi.org/10.1890/15-0935 (2016).Article 

    Google Scholar 
    Morelli, T. L. et al. Climate-change refugia: Biodiversity in a slow lane. Front. Ecol. Environ. 18, 228–234. https://doi.org/10.1002/fee.2189 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stralberg, et al. Macrorefugia for North American trees ad songbirds: Climatic limiting factors and multi-scale topographic influences. Glob. Ecol. Biogeogr. 27, 690–703. https://doi.org/10.1111/geb.12731 (2018).Article 

    Google Scholar 
    Caroll, C. & Ray, J. C. Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change. Glob. Chang Biol. 27, 3395–3414. https://doi.org/10.1111/gcb.15645 (2020).Article 

    Google Scholar 
    Bradshaw, C. J., Warkentin, I. G. & Sodhi, N. S. Urgent preservation of boreal carbon stocks and biodiversity. Trends Ecol. Evol. 24, 541–548. https://doi.org/10.1016/j.tree.2009.03.019 (2009).Article 
    PubMed 

    Google Scholar 
    Harris, L. I. et al. The essential carbon service provided by northern peatlands. Front. Ecol. Environ. 20, 222–230 (2022).Article 

    Google Scholar 
    Environment and Climate Change Canada. Canadian Environmental Sustainability Indicators: Canada’s conserved areas. environmental-indicators/conserved-areas.html (2020).Office of the Auditor General of Canada. Lessen learnt from 30 years of climate change challenges and opportunities. https://www.oag-bvg.gc.ca/internet/English/att__e_43948.html#hd3l (2020).Shea, T. et al. Canada’s Conservation Vision: A report of the National Advisory Panel. Government of Canada, 43 pp (2018).Environment and Climate Change Canada. Pan-Canadian Approach to transforming species at risk conservation in Canada. species-at-risk-conservation.html (2018).Bergerund, A. T. Caribou, wolves and man. Trends Ecol. Evol. 3, 68–72. https://doi.org/10.1016/0169-5347(88)90019-5 (1988).Article 

    Google Scholar 
    Vernier, L. A. et al. Effects of natural resource development on the terrestrial biodiversity of Canadian boreal forests. Environ. Rev. 22, 457–490. https://doi.org/10.1139/er-2013-0075 (2014).Article 

    Google Scholar 
    Wells, J. V., Dawson, N., Culver, N., Reid, F. A. & Slegers, S. M. The state of conservation in North America’s Borel Forest: Issues and opportunities. Front. For. Glob. Change 3, 90. https://doi.org/10.3389/ffgc.2020.00090/full (2020).Article 

    Google Scholar 
    COSEWIC. COSEWIC assessment and update status report on the woodland caribou Rangifer tarandus caribou in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xi + 98 pp. (2002).COSEWIC. COSEWIC assessment and status report on the caribou Rangifer tarandus, Newfoundland population, Atlantic-Gaspésie population and Boreal population, in Canada. Committee on the Status of Endangered Wildlifein Canada. Ottawa. xxiii + 128 pp. (2014).Environment and Climate Change Canada. Amended Recovery Strategy for the Woodland Caribou (Rangifer tarandus caribou), Boreal Population, in Canada. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. xiii + 143pp. (2020).Environment and Climate Change Canada. Report on the Progress of Recovery Strategy Implementation for the Woodland Caribou (Rangifer tarandus caribou), Boreal population in Canada for the Period 2012–2017. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. ix + 94 (2017).Hebblewhite, M. Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biol. Conserv. 206, 102–111. https://doi.org/10.1016/j.biocon.2016 (2017).Article 

    Google Scholar 
    Fortin, D., McLoughlin, P. D. & Hebblewhite, M. When the protection of a threatened species depends on the economy of a foreign nation. PLoS ONE 15, e0229555. https://doi.org/10.1371/journal.pone.0229555 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Drever, R. C. et al. Conservation through co-occurrence: Woodland caribou as a focal species for boreal biodiversity. Biol. Conserv. 232, 238–252. https://doi.org/10.1016/j.biocon.2019.01.026 (2019).Article 

    Google Scholar 
    Government of Canada. Pan-Canadian Framework on clean growth and climate change climatechange/pan-canadian-framework.html.Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet Chang 128, 24–30. https://doi.org/10.1016/j.gloplacha.2015.02.004 (2015).ADS 
    Article 

    Google Scholar 
    Jennings, M. D. Gap analysis: Concept, methods, recent results. Land Ecol. 5, 15–20 (2010).
    Google Scholar 
    Environment and Climate Change Canada. Canadian Protected and Conserved Areas database. national-wildlife-areas/protected-conserved-areas-database (2019).DeLuca, T. H. & Boisvenue, C. Boreal forest soil carbon: Distribution function and modelling. Forestry 85, 161–184. https://doi.org/10.1093/forestry/cps003 (2012).Article 

    Google Scholar 
    Price, et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365. https://doi.org/10.1139/er-2013-0042 (2013).Article 

    Google Scholar 
    Southee, F. M., Edwards, B. A., Chetkiewicz, C. B. & O’Connor, C. M. Freshwater conservation planning in the far north of Ontario, Canada: Identifying priority watersheds for conservation of fish biodiversity in an intact boreal landscape. Facets 6, 90–117. https://doi.org/10.1139/facets-2020-0015 (2021).Article 

    Google Scholar 
    Mitchell, M. G. E. et al. Identifying key ecosystem service providing areas to inform national-scale conservation planning. Environ. Res. Lett. 16, 014038. https://doi.org/10.1088/1748-9326/abc121 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Labadie, G. P. D., McLoughlin, M. H. & Fortin, D. Insect-mediated apparent competition between mammals in a boreal food web. Proc. Natl. Acad. Sci. U S A. 118, e2022892118. https://doi.org/10.1073/pnas.2022892118 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cameron, V. & Hargreaves, A. L. Spatial distribution and conservation hotspots of mammals in Canada. Facets 5, 692–703. https://doi.org/10.1139/facets-2020-0018 (2020).Article 

    Google Scholar 
    Ceballos, G. & Ehrlich, P. R. Global mammal distributions, biodiversity hotspots, and conservation. PNAS 103, 19374–19379. https://doi.org/10.1073/pnas.0609334103 (2016).ADS 
    Article 

    Google Scholar 
    Anielski, M. & Wilson, S. Counting Canada’s natural capital: Assessing the real value of Canada’s boreal ecosystems. Ottawa, On: Canadian Boreal Initiative and Pembina Institute counting-canadas-natural-capital (2009).Kumaraswamy, S. & Udyakumar, M. Biodiversity banking: A strategic conservation mechanism. Biodiver. Conserv. 20, 1155–1165. https://doi.org/10.1007/s10531-011-0020-5 (2011).Article 

    Google Scholar 
    Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374. https://doi.org/10.1038/s41893-018-0100-6 (2018).Article 

    Google Scholar 
    Godden, L. & Cowell, S. Conservation planning and Indigenous governance in Australia’s Indigenous Protected Areas. Restor. Ecol. 24, 692–697. https://doi.org/10.1111/rec.12394 (2016).Article 

    Google Scholar 
    Greg Brown, B. & Fagerholm, N. Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Ecol. Ser. 13, 119–133. https://doi.org/10.1016/j.ecoser.2014.10.007 (2021).Article 

    Google Scholar 
    Martin, A. E., Neave, E., Kirby, P., Drever, C. R. & Johnson, C. A. Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap. Sci. Rep. 12, 11895. https://doi.org/10.1038/s41598-022-15274-8 (2022).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    COSEWIC. Canadian Wildlife Species at Risk. Committee on the Status of Endangered Wildlife in Canada (2018).Alberta Environment and Parks and Alberta Conservation Association. Status of the Arctic Grayling (Thymallus arcticus) in Alberta: Update 2015. Alberta Environment and Parks. Alberta Wildlife Status Report No. 57 (Update 2015). Edmonton, AB. 96 pp. (2015).Environment and Climate Change Canada (ECCC). 2016. Range map extents, species at risk, Canada. Government of Canada. Open Government Dataset. https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121.Magurran, A. E. Measuring Biological Diversity 256 (Blackwell Publishing, 2004).
    Google Scholar 
    Caissy, P., Klemet-N’Guessan, S., Jackiw, R., Eckert, C. G. & Hargreaves, A. L. High conservation priority of range-edge plant populations not matched by habitat protection or research effort. Biol. Conserv. 249, 108732 (2020).Article 

    Google Scholar 
    Gaston, K. J. Rarity 201 (Chapman & Hall, 1994).Book 

    Google Scholar 
    Stralberg, D. Velocity-based macrorefugia for North American ecoregions. Zenodo. https://doi.org/10.5281/zenodo.2579337 (2019).Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853. https://doi.org/10.1038/nclimate2392 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, I., Hill, J. K., Ohlemüller, R. D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026. https://doi.org/10.1126/science.1206432 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Woodall, C. W. et al. An indicator of tree migration in forests of the eastern United States. For. Ecol. Manag. 257, 1434–1444 (2009).Article 

    Google Scholar 
    Iverson, L. R., Schwartz, M. W. & Prasad, A. M. How fast and far might tree species migrate in the eastern United States due to climate change? Glob. Ecol. Biogeogr. 13, 209–219 (2004).Article 

    Google Scholar 
    McLachlan, J. S., Hellmann, J. J. & Schwartz, M. W. A framework for debate of assisted migration in an era of climate change. Conserv. Biol. 21, 297–302 (2007).Article 

    Google Scholar 
    Sittaro, F., Paquette, A., Messier, C. & Nock, C. A. Tree range expansion in eastern North America fails to keep pace with climate warming at northern range limits. Glob. Change Biol. 23, 3292–3301. https://doi.org/10.1111/gcb.13622 (2017).ADS 
    Article 

    Google Scholar 
    Ping, C. L. et al. Carbon stores and biogeochemical properties of soils under black spruce forest, Alaska. Soil Sci. Soc. Am. J. 74, 969–978. https://doi.org/10.2136/sssaj2009.0152 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    Hengl, T. et al. SoilGrids250m: Global soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

    Google Scholar 
    Chung, N. C., Miasojedow, B., Startek, M. & Gambin, A. Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinform. 29, 644. https://doi.org/10.1186/s12859-019-3118-5 (2019).Article 

    Google Scholar 
    Chung, N. C., Miasojedow, B., Startek, M. & Gambin A. Jaccard: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients. R package version 0.1.0. https://CRAN.R-project.org/package=jaccard (2018). More

  • in

    Global hotspots for soil nature conservation

    Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wall, D. H. et al. (eds) Soil Ecology and Ecosystem Services (Oxford University Press, 2012).Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl Acad. Sci. USA 110, 14296–14301 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adhikari, K. & Hartemink, A. E. Linking soils to ecosystem services—a global review. Geoderma 262, 101–111 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Pereira, P., Bogunovic, I., Muñoz-Rojas, M. & Brevik, E. C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 5, 7–13 (2018).Article 

    Google Scholar 
    Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Chang. 10, 550–554 (2020).ADS 
    Article 

    Google Scholar 
    Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Global vulnerability of soil ecosystems to erosion. Landsc. Ecol. 35, 823–842 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the Anthropocene. Curr. Biol. 29, R1036–R1044 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5, 1499–1509 (2021).PubMed 
    Article 

    Google Scholar 
    Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat. Ecol. Evol. 5, 411–418 (2021).PubMed 
    Article 

    Google Scholar 
    Díaz, S. et al. (eds). Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); https://zenodo.org/record/3553579#.YyhIsXbMK70Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 480–485 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).ADS 
    PubMed 
    Article 

    Google Scholar 
    Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).ADS 
    Article 

    Google Scholar 
    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
    Article 

    Google Scholar 
    Xu, X., Thornton, P. E. & Post, W. M. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems: global soil microbial biomass C, N and P. Glob. Ecol. Biogeogr. 22, 737–749 (2013).Article 

    Google Scholar 
    Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).Guerra, C. A. et al. Global projections of the soil microbiome in the Anthropocene. Glob. Ecol. Biogeogr. 30, 987–999 (2021).PubMed 
    Article 

    Google Scholar 
    Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
    Article 

    Google Scholar 
    El Moujahid, L. et al. Effect of plant diversity on the diversity of soil organic compounds. PLoS One 12, e0170494 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11, 3870 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in Northern Europe. Front. Microbiol. 11, 1953 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).Article 

    Google Scholar 
    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Egoh, B., Reyers, B., Rouget, M., Bode, M. & Richardson, D. M. Spatial congruence between biodiversity and ecosystem services in South Africa. Biol. Conserv. 142, 553–562 (2009).Article 

    Google Scholar 
    Jürgens, N. et al. The BIOTA Biodiversity Observatories in Africa—a standardized framework for large-scale environmental monitoring. Environ. Monit. Assess. 184, 655–678 (2012).PubMed 
    Article 

    Google Scholar 
    Wyborn, C. & Evans, M. C. Conservation needs to break free from global priority mapping. Nat. Ecol. Evol. 5, 1322–1324 (2021).PubMed 
    Article 

    Google Scholar 
    Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 
    Article 

    Google Scholar 
    Zhou, Z., Wang, C. & Luo, Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat. Commun. 11, 3072 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eisenhauer, N., Schulz, W., Scheu, S. & Jousset, A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct. Ecol. 27, 282–288 (2013).Article 

    Google Scholar 
    Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haines-Young, R. H. & Potschin, M. B. in Ecosystems Ecology: A New Synthesis (eds Raffaelli, D. G. & Frid, C. L. J.) Ch. 6 (2012).Smith, L. C. et al. Large‐scale drivers of relationships between soil microbial properties and organic carbon across Europe. Glob. Ecol. Biogeogr. 30, 2070–2083 (2021).Article 

    Google Scholar 
    Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tanneberger, F. et al. The power of nature‐based solutions: how peatlands can help us to achieve key EU sustainability objectives. Adv. Sustain. Syst. 5, 2000146 (2021).CAS 
    Article 

    Google Scholar 
    Johnston, A. et al. Observed and predicted effects of climate change on species abundance in protected areas. Nat. Clim. Chang. 3, 1055–1061 (2013).ADS 
    Article 

    Google Scholar 
    Hannah, L. et al. Protected area needs in a changing climate. Front. Ecol. Environ. 5, 131–138 (2007).Article 

    Google Scholar 
    Gallardo, B. et al. Protected areas offer refuge from invasive species spreading under climate change. Glob. Chang. Biol. 23, 5331–5343 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).Article 

    Google Scholar 
    Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Glob. Environ. Change 71, 102368 (2021).Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Science 376, 1094–1101 (2022).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Changes in belowground biodiversity during ecosystem development. Proc. Natl Acad. Sci. USA. 116, 6891–6896 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS One 4, e6372 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. Biol. Sci. 281, 20141988 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv https://doi.org/10.1101/081257 (2016).Tedersoo, L. et al. Towards understanding diversity, endemicity and global change vulnerability of soil fungi. Preprint at bioRxiv https://doi.org/10.1101/2022.03.17.484796 (2022).Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Phillips, H. R. P., Heintz-Buschart, A. & Eisenhauer, N. Putting soil invertebrate diversity on the map. Mol. Ecol. 29, 655–657 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xiong, W. et al. A global overview of the trophic structure within microbiomes across ecosystems. Environ. Int. 151, 106438 (2021).PubMed 
    Article 

    Google Scholar 
    Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).CAS 
    Article 

    Google Scholar 
    Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).CAS 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
    Article 

    Google Scholar 
    Carter, M. R. & Gregorich, E. G. (eds) Soil Sampling and Methods of Analysis (CRC Press, 2007).Sparks, D. L. et al. (eds) Methods of Soil Analysis, Part 3: Chemical Methods (Wiley, 2020).Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

    Google Scholar 
    Bell, C. W. et al. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J. Vis. Exp. 81, e50961 (2013).Wang, L. et al. Diversifying livestock promotes multidiversity and multifunctionality in managed grasslands. Proc. Natl Acad. Sci. USA. 116, 6187–6192 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Durán, J., Delgado-Baquerizo, M., Rodríguez, A., Covelo, F. & Gallardo, A. Ionic exchange membranes (IEMs): a good indicator of soil inorganic N production. Soil Biol. Biochem. 57, 964–968 (2013).Article 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Sharma, N. XGBoost. The Extreme Gradient Boosting for Mining Applications (GRIN Verlag, 2018).Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).Wilson. ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters. R version 1 https://CRAN.R-project.org/package=ParBayesianOptimization (2021).Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001).Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).Article 

    Google Scholar 
    Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).MATH 
    Article 

    Google Scholar 
    Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (Routledge, 1984).Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (2010).Article 

    Google Scholar 
    Getis, A. & Ord, J. K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 24, 189–206 (2010).Article 

    Google Scholar 
    Prasannakumar, V., Vijith, H., Charutha, R. & Geetha, N. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc. Behav. Sci. 21, 317–325 (2011).Article 

    Google Scholar 
    Lin, G. Comparing spatial clustering tests based on rare to common spatial events. Comput. Environ. Urban Syst. 28, 691–699 (2004).Article 

    Google Scholar 
    Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).Article 

    Google Scholar 
    Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013).ADS 
    Article 

    Google Scholar 
    Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).ADS 
    Article 

    Google Scholar 
    Kim, H. et al. A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios. Geosci. Model Dev. 11, 4537–4562 (2018).Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117 (2011).ADS 
    Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

    Google Scholar 
    O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).ADS 
    Article 

    Google Scholar 
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).ADS 
    Article 

    Google Scholar  More

  • in

    Independent origin of large labyrinth size in turtles

    Steinhausen, W. Über die Beobachtungen der Cupula in den Bogengangsampullen des Labyrinthes des Lebendes Hechts. Pflug. Arch. 232, 500–512 (1933).Article 

    Google Scholar 
    Wever, E. G. The reptile ear. (Princeton University Press, 1978).Wilson, V. J. & Melvill Jones, G. Mammalian vestibular physiology. (Plenum Press, 1979).Spoor, F. & Zonneveld, F. Comparative review of the human bony labyrinth. Yearb. Phys. Anthropol. 41, 211–251 (1998).Article 

    Google Scholar 
    Rabbitt, R. D., Damiano, E. R. & Grant, J. W. Biomechanics of the semicircular canals and otolith organs. In: Highstein, F. M., Ray, R. R., Popper, A. N. (eds) Springer Handbook Of Auditory Research, vol. 19, The Vestibular System, pp. 153–201 (Springer, New York, 2004).Georgi, J. A. & Sipla, J. S. Comparative and functional anatomy of balance in aquatic reptiles and birds. In: Thewissen, J. G. M., Nummela, S. (eds) Sensory Evolution On The Threshold, Adaptations In Secondarily Aquatic Vertebrates.pp. 233–256 (University of California Press, 2008).David, R. et al. Motion from the past. A new method to infer vestibular capacities of extinct species. C. R. Palevol. 9, 397–410 (2010).Article 

    Google Scholar 
    Oman, C. M., Marcus, E. N. & Curthoys, I. S. The influence of the semicircular canal morphology on endolymph flow dynamics. Acta Otolaryngol. 103, 1–13 (1987).CAS 
    PubMed 
    Article 

    Google Scholar 
    Georgi, J. A., Sipla, L. S. & Forster, C. A. Turning semicircular canal function on its head: dinosaurs and a novel vestibular analysis. PLoS One 8, e58517 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Spoor, F., Bajpai, S., Hussain, S. T., Kumar, K. & Thewissen, J. G. M. Vestibular evidence for the evolution of aquatic behaviour in early cetaceans. Nature 417, 163–166 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Spoor, F. et al. The primate semicircular canal system and locomotion. Proc. Nat. Acad. Sci. USA 104, 10808–10812 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cox, P. G. & Jeffery, N. Geometry of the semicircular canals and extraocular muscles in rodents, lagomorphs, felids and modern humans. J. Anat. 213, 83–596 (2008).
    Google Scholar 
    Cox, P. G. & Jeffery, N. Semicircular canals and agility: the influence of size and shape measures. J. Anat. 216, 37–47 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Silcox, M. T. et al. Semicircular canal system in early primates. J. Hum. Evol. 56, 315–327 (2009).PubMed 
    Article 

    Google Scholar 
    Lebrun, R. et al. Deep evolutionary roots of strepsirrhine primate labyrinthine morphology. J. Anat. 216, 368–380 (2010).PubMed 
    Article 

    Google Scholar 
    Billet, G. et al. High morphological variation of vestibular system accompanies slow and infrequent locomotion in three-toed sloths. Proc. R. Soc. Lond. B. 279, 3932–3939 (2012).
    Google Scholar 
    Gunz, P., Ramsier, M., Kuhrig, M., Hublin, J.-J. & Spoor, F. The mammalian bony labyrinth reconsidered, introducing a comprehensive geometric morphometric approach. J. Anat. 220, 529–543 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Malinzak, M. D., Kaya, R. F. & Hullar, T. E. Locomotor head movements and semicircular canal morphology in primates. Proc. Natl Acad. Sci. USA 109, 914–919 (2012).Article 

    Google Scholar 
    Alloing-Séguier, L. et al. The bony labyrinth in diprotodontian marsupial mammals: diversity in extant and extinct forms and relationships with size and phylogeny. J. Mamm. Evol. 20, 191–198 (2013).Article 

    Google Scholar 
    Berlin, J. C., Kirk, E. C. & Rowe, T. B. Functional implications of ubiquitous semicircular canal non-orthogonality in mammals. PLoS One 8, e79585 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Davies, K. T. J., Bates, P. J. J., Maryanto, I., Cotton, J. A. & Rossiter, S. J. The evolution of bat vestibular systems in the face of potential antagonistic selection pressures for flight and echolocation. PLoS One 8, e61998 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grohé, C. et al. Bony labyrinth shape variation in extant Carnivora: a case study of Musteloidea. J. Anat. 228, 366–383 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pfaff, C., Martin, T. & Ruf, I. Bony labyrinth morphometry indicates locomotor adaptations in the squirrel-related clade (Rodentia, Mammalia). Proc. R. Soc. B 282, 20150744 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Melville Jones, G. & Spells, K. E. A theoretical and comparative study of the functional dependence of the semicircular canal upon its physical dimensions. Proc. R. Soc. Lond. B Biol. Sci. 157, 403–419 (1963).ADS 
    Article 

    Google Scholar 
    Kemp, A. D. & Kirk, E. C. Eye size and visual acuity influence vestibular anatomy in mammals. Anat. Rec. 297, 781–790 (2014).Article 

    Google Scholar 
    Ekdale, E. G. Form and function of the mammalian ear. J. Anat. 228, 324–337 (2016).PubMed 
    Article 

    Google Scholar 
    Goyens, J. High ellipticity reduces semicircular canal sensitivity in squamates compared to mammals. Sci. Rep. 9, 16428 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Witmer, L. M., Chatterjee, S., Franzosa, J. & Rowe, T. Neuroanatomy of flying reptiles and implications for flight, posture and behaviour. Nature 425, 950–953 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lautenschlager, S., Rayfield, E. J., Altangerel, P., Zanno, L. E. & Witmer, L. M. The endocranial anatomy of Therizinosauria and its implications for sensory and cognitive function. PLoS ONE 7, e52289 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cuthbertson, R. S., Maddin, H. C., Holmes, R. B. & Anderson, J. S. The braincase and endosseous labyrinth of Plioplatecarpus peckensis (Mosasauridae, Plioplatecarpinae), with functional implications for locomotor behavior. Anat. Rec. 298, 1597–1611 (2015).Article 

    Google Scholar 
    Schade, M., Rauhut, O. W. M. & Evers, S. W. Neuroanatomy of the spinosaurid Irritator challengeri (Dinosauria: Theropoda) indicates potential adaptations for piscivory. Sci. Rep. 10, 9259 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Benson, R. B. J., Starmer-Jones, E., Close, R. A. & Walsh, S. A. Comparative analysis of vestibular ecomorphology in birds. J. Anat. 231, 990–1018 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dudgeon, T. W., Maddin, H. C., Evans, D. C. & Mallon, J. C. The internal cranial anatomy of Champsosaurus (Choristodera: Champsosauridae): implications for neurosensory function. Sci. Rep. 10, 7122 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bronzati, M. et al. Deep evolutionary diversification of semicircular canals in archosaurs. Curr. Biol. 31, 2520–2529 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hansen, M., Hoffman, E. A., Norell, M. A. & Bhullar, B.-A. S. The early origin of a birdlike inner ear and the evolution of dinosaurian movement and vocalization. Science 372, 601–609 (2021).ADS 
    Article 

    Google Scholar 
    Ernst, C. H. & Barbour, R. W. Turtles Of The World. (Smithsonian Institution Press, Washington, D.C., 1989).Evers, S. W. & Benson, R. B. J. A new phylogenetic hypothesis of turtles with implications for the timing and number of evolutionary transitions to marine lifestyles in the group. Palaeontology 62, 93–134 (2019).Article 

    Google Scholar 
    Joyce, W. G. A review of the fossil record of basal Mesozoic turtles. Bull. Peabody Mus. Nat. Hist. 58, 65–113 (2017).Article 

    Google Scholar 
    Lautenschlager, S., Ferreira, G. S. & Werneburg, I. Sensory evolution and ecology of early turtles revealed by digital endocranial reconstructions. Front. Ecol. Evol. 6, 1–7 (2018).Article 

    Google Scholar 
    Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 123, 1–15 (1985).Article 

    Google Scholar 
    Sugiura, N. Further analysis of the data by Akaike’s information criterion and the finite corrections. Commun. Stat. Theory Methods 7, 13–26 (1978).MATH 
    Article 

    Google Scholar 
    Foth, C. et al. Comparative analysis of the shape and size of the middle ear cavity of turtles reveals no correlation with habitat ecology. J. Anat. 235, 1078–1097 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Neenan, J. M. et al. Evolution of the sauropterygian labyrinth with increasingly pelagic lifestyles. Curr. Biol. 27, 3852–3858 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Loza, C. M., Latimer, A. E., Sánchez-Villagra, M. R. & Carlini, A. A. Sensory anatomy of the most aquatic of carnivorans: the Antarctic Ross seal, and convergences with other mammals. Biol. Lett. 13, 20170489 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Werneburg, I. & Maier, W. Diverging development of akinetic skulls in cryptodire and pleurodire turtles: an ontogenetic and phylogenetic study. Vertebr. Zool. 69, 113–143 (2019).
    Google Scholar 
    Ferreira, G. S. & Werneburg, I. Evolution, diversity, and development of the craniocervical system in turtles with special reference to jaw musculature. In: Ziermann, J., Diaz, R. R. Jr, Diogo, R. (eds) Heads, Jaws and Muscles: Evolution, Development, Anatomical Diversity And Function (Springer, Cham, 2019).David, R. J. A. et al. Comment on “The early origin of a birdlike inner ear and the evolution of dinosaurian movement and vocalization”, Science (in press).Schwab, J. A. et al. Inner ear sensory system changes as extinct crocodylomorphs transitioned from land to water. Proc. Nat. Acad. Sci. USA 117, 10422–10428 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yang, L. M. & Ornitz, D. M. Sculpturing the skull through neurosensory epithelial-mesenchymal signaling. Dev. Dyn. 248, 88–97 (2019).PubMed 
    Article 

    Google Scholar 
    Kandel, B. M. & Hullar, T. E. The relationship of head movements to semicircular canal size in cetaceans. J. Exp. Biol. 213, 1175–1181 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moll, D. Food and feeding behavior of the turtle, Dermatemys mawei, in Belize. J. Herpetol. 23, 445–447 (1989).Article 

    Google Scholar 
    Evers, S. W. et al. Neurovascular anatomy of the protostegid turtle Rhinochelys pulchriceps and comparisons of membranous and endosseous labyrinth shape in an extant turtle. Zool. J. Linn. Soci. 187, 800–828 (2019).
    Google Scholar 
    Ekdale, E. G. Comparative anatomy of the bony labyrinth (inner ear) of placental mammals. PLoS One 8, e66624 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Joyce, W. G. Phylogenetic relationships of Mesozoic turtles. Bull. Peabody Mus. Nat. Hist. 48, 3–102 (2007).Article 

    Google Scholar 
    Sterli, J. & De La Fuente, M. S. Anatomy of Condorchelys antiqua Sterli, 2008, and the origin of the modern jaw closure mechanism in turtles. J. Vertebr. Paleontol. 30, 351–366 (2010).Article 

    Google Scholar 
    Ferreira, G. S. et al. Feeding biomechanics suggests progressive correlation of skull architecture and neck evolution in turtles. Sci. Rep. 10, 5505 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Aerts, P., Van Damme, J. & Herrel, A. Intrinsic mechanics and control of fast cranio-cervical movements in aquatic feeding turtles. Am. Zool. 41, 1299–1310 (2001).
    Google Scholar 
    Herrel, A., Van Damme, J. & Aerts, P. Cervical anatomy and function in turtles. In Biology Of Turtles. In: Wyneken, J., Godfrey, M. H., Bels, V. (eds) pp. 163–185 (CRC Press, Boca Raton, 2008).Narazaki, T., Sato, K., Abernathy, K. J., Marshall, G. J. & Miyazaki, N. Loggerhead turtles (Caretta caretta) use vision to forage on gelatinous prey in mid-water. PLoS One 8, e66043 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guthrie, D. M. “Role of vision in fish behaviour”. In: T. J. Pitcher (eds) The Behaviour Of Teleost Fishes. pp. 75–113 (Springer, Boston, 1986).Sterli, J. & Joyce, W. G. The cranial anatomy of the Early Jurassic turtle Kayentachelys aprix. Acta Paleontol. Pol. 52, 675–694 (2007).
    Google Scholar 
    Werneburg, I. The tendinous framework in the temporal skull region of turtles and considerations about its morphological implications in amniotes: a review. Zool. Sci. 30, 141–153 (2013).Article 

    Google Scholar 
    Werneburg, I. Neck motion in turtles and its relation to the shape of the temporal skull region. C. R. Palevol. 14, 527–548 (2015).Article 

    Google Scholar 
    TTWG, Turtle Taxonomy Working Group, Rhodin, A. G. J. et al. Turtles of the world, 8th edition: annotated checklist of taxonomy, synonymy, distribution with maps, and conservation status. Chelonian Res. Monogr. 7, 1–292 (2017).
    Google Scholar 
    Gower, J. C. Generalized Procrustes analysis. Psychometrika 40, 33–50 (1975).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Adams, D. C., Collyer, M. L., Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. https://cran.r-project.org/package=geomorph (2019).R Core Team, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/ (2019).Rholf, E. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).Article 

    Google Scholar 
    Adams, D. C. & Felice, R. N. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS One 9, e94335 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kendall, D. G. The diffusion of shape. Adv. Appl. Probab. 9, 428–430 (1977).Article 

    Google Scholar 
    Bookstein, F. L. Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med. Image Anal. 1, 97–118 (1997).Article 

    Google Scholar 
    Gunz, P., Mitteroecker, P. & Bookstein, F. L. “Semilandmarks in three dimensions. In: Slice, D. E. (ed) Modern Morphometrics in Physical Anthropology, pp. 73–98 (Kluwer Academic, 2005).Webster, M. & Sheets, H. A practical introduction to land- mark-based geometric morphometrics. In: Alroy, J., Hunt, G. (eds) Quantitative Methods in Paleobiology. Paleontological Society Papers 16, pp. 163–188 (Paleontological Society, 2010).Gunz, P. & Mitteroecker, P. Semilandmarks: a method for quantifying curves and surfaces. Hystrix 24, 103–109 (2013).
    Google Scholar 
    Bookstein, F. L. Size and shape spaces for landmark data in two dimensions. Stat. Sci. 1, 181–242 (1986).MATH 

    Google Scholar 
    Pereira, A. G., Sterli, J., Moreira, F. R. R. & Schrago, C. G. Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Mol. Phylogenet. Evol. 113, 59–66 (2017).PubMed 
    Article 

    Google Scholar 
    Bapst, D. W. paleotree: an R package for paleontological and phylogenetic analyses of evolution. Methods Ecol. Evol. 3, 803–807 (2012).Article 

    Google Scholar 
    Lloyd, G. T. Estimating morphological diversity and tempo with discrete character-taxon matrices: implementation, challenges, progress, and future directions. Biol. J. Linn. Soc. 118, 131–151 (2016).Article 

    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ferreira, G. S., Bronzati, M., Langer, M. C. & Sterli, J. Phylogeny, biogeography, and diversification patterns of side-necked turtles (Testudines: Pleurodira). R. Soc. Open Sci. 5, 171773 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bapst, D. W. A stochastic rate-calibrated method for time-scaling phylogenies of fossil taxa. Methods Ecol. Evol. 4, 724–733 (2013).Article 

    Google Scholar 
    Laurin, M. The evolution of body size, Cope’s Rule and the origin of amniotes. Syst. Biol. 53, 594–622 (2004).PubMed 
    Article 

    Google Scholar 
    Pace, C. M., Blob, R. W. & Westneat, M. W. Comparative kinematics of the forelimb during swimming in red-eared slider (Trachemys scripta) and spiny softshell (Apalone spinifera) turtles. J. Exp. Biol. 204, 3261–3271 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Claude, J., Paradis, E., Tong, H. & Auffray, J.-C. A geometric morphometric assessment of the effects of environment and cladogenesis on the evolution of the turtle shell. Biol. J. Linn. Soc. 79, 485–501 (2003).Article 

    Google Scholar 
    Angielczyk, K. D., Feldman, C. R. & Miller, G. R. Adaptive evolution of plastron shape in emydine turtles. Evolution 65, 377–394 (2011).PubMed 
    Article 

    Google Scholar 
    Angielczyk, K. D., Burroughs, R. W. & Feldman, C. R. Do turtles follow the rules? Latitudinal gradients in species richness, body size, and geographic range area of the World’s turtles. J. Exp. Zool. Mol. Dev. Evol. 324, 270–294 (2015).Article 

    Google Scholar 
    Pritchard, P. C. H. Oiscivory in turtles, and evolution of the long-necked Chelidae. Symp. Zool. Soc. Lond. 52, 87–110 (1984).
    Google Scholar 
    Joyce, W. G. et al. A new pelomedusoid turtle, Sahonachelys mailakavava, from the Late Cretaceous of Madagascar provides evidence for convergent evolution of specialized suction feeding among pleurodires. R. Soc. Open Sci. 8, 210098 (2021).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adams, D. C. A method for assessing phylogenetic least squares models for shape and other high‐dimensional multivariate data. Evolution 68, 2675–2688 (2014).PubMed 
    Article 

    Google Scholar 
    Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Multivariate phylogenetic comparative methods: evaluations, comparisons, and recommendations. Syst. Biol. 67, 14–31 (2018).PubMed 
    Article 

    Google Scholar 
    Collyer, M. L., Sekora, D. J. & Adams, D. C. A method for analysis of phenotypic change for phenotypes described by high-dimensional data. Heredity 115, 357–365 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lowi-Merri, T. M., Benson, R. B. J., Claramunt, S. & Evans, D. C. The relationship between sternum variation and mode of locomotion in birds. BMC Biol. 19, 1–23 (2021).Article 

    Google Scholar 
    Adams, D. C. & Collyer, M. L. Phylogenetic ANOVA: group-clade aggregation, biological challanges, and a refined permutation procedure. Evolution 72, 1204–1215 (2018).PubMed 
    Article 

    Google Scholar 
    Friedman, S. T., Martinez, C. M., Price, S. A. & Wainwright, P. C. The influence of size on body shape diversification across Indo-Pacific shore fishes. Evolution 73, 1873–1884 (2019).PubMed 
    Article 

    Google Scholar 
    Foth, C., Rabi, M. & Joyce, W. G. Skull variation in extant and extinct Testudinata and its relation to habitat and feeding ecology. Acta Zool. 98, 310–325 (2017).Article 

    Google Scholar 
    Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 326, 119–157 (1989).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Ritz, C. & Spiess, A.-N. qpcR: an R package for sigmoidal model selection in quantitative real-rime polymerase chain reaction analysis. Bioinformatics 24, 1549–1551 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Akaike, H. Information Theory As An extension Of The Maximum Likelihood Principle. In: Petrov, B. N., Csaki, F. (eds) Second International Symposium on Information Theory, pp. 267–281 (Akademiai Kiado, New York, 1973).Burnham, K. P., Anderson, D. Model selection and multi-model inference: a practical information-theoretic approach. (Springer, New York, 2002).Nagelkerke, N. J. D. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D., R. Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–141, URL: https://CRAN.R-project.org/package=nlme. (2019).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Racicot, R. A. & Colbert, M. W. Morphology and variation in porpoise (Cetacea: Phocoenidae) cranial endocasts. Anat. Rec. 296, 979–992 (2013).Article 

    Google Scholar 
    Evers, S. W. Code and Data to “Independent origin of large labyrinth size in turtles”. Zenodo https://doi.org/10.5281/zenodo.7024572 (2022).Article 

    Google Scholar  More

  • in

    Biological invasions as a selective filter driving behavioral divergence

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, (2017).IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES secretariat, 2019). https://doi.org/10.5281/zenodo.3831673.Elton, C. S. The Ecology of Invasions by Animals and Plants. (University of Chicago Press, 1958).Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion Ecology. (Wiley-Blackwell, 2013).O’Dowd, D. J., Green, P. T. & Lake, P. S. Invasional “meltdown” on an oceanic island. Ecol. Lett. 6, 812–817 (2003).
    Google Scholar 
    Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl Acad. Sci. 113, 11261–11265 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spatz, D. R. et al. Globally threatened vertebrates on islands with invasive species. Sci. Adv. 3, (2017).Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agriculture, Ecosyst. Environ. 84, 1–20 (2001).
    Google Scholar 
    Hoffmann, B. D. & Broadhurst, L. M. The economic cost of managing invasive species in Australia. NeoBiota 31, 1–18 (2016).
    Google Scholar 
    Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evolution 16, 199–204 (2001).
    Google Scholar 
    Jeschke, J. M. & Strayer, D. L. Invasion success of vertebrates in Europe and North America. Proc. Natl Acad. Sci. 102, 7198–7202 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lovell, R. S. L., Blackburn, T. M., Dyer, E. E. & Pigot, A. L. Environmental resistance predicts the spread of alien species. Nat. Ecol. Evolution 5, 322–329 (2021).
    Google Scholar 
    Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evolution 26, 333–339 (2011).
    Google Scholar 
    Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions? Trends Ecol. Evolution 27, 57–64 (2012).
    Google Scholar 
    Chapple, D. G. & Wong, B. B. M. The role of behavioural variation across different stages of the introduction process. in Biological Invasions and Animal Behaviour (eds. Weis, Judith, S. & Sol, Daniel.) 7–25 (Cambridge University Press, 2016).Holway, D. & Suarez, A. Animal behavior: an essential component of invasion biology. Trends Ecol. Evolution 14, 328–330 (1999).CAS 

    Google Scholar 
    Felden, A. et al. Behavioural variation and plasticity along an invasive ant introduction pathway. J. Anim. Ecol. 87, 1653–1666 (2018).PubMed 

    Google Scholar 
    D’Amore, D. M., Popescu, V. D. & Morris, M. R. The influence of the invasive process on behaviours in an intentionally introduced hybrid, Xiphophorus helleri-maculatus. Anim. Behav. 156, 79–85 (2019).
    Google Scholar 
    Perkins, T. A., Boettiger, C. & Phillips, B. L. After the games are over: life‐history trade‐offs drive dispersal attenuation following range expansion. Ecol. Evolution 6, 6425–6434 (2016).
    Google Scholar 
    Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s Paradox revisited: the evolution of dispersal kernels during range expansion. Am. Naturalist 172, S34–S48 (2008).
    Google Scholar 
    Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than through time. Proc. Natl Acad. Sci. 108, 5708–5711 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl Acad. Sci. 110, 13452–13456 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Heger, T. & Jeschke, J. M. The enemy release hypothesis as a hierarchy of hypotheses. Oikos 123, 741–750 (2014).
    Google Scholar 
    Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).
    Google Scholar 
    Wilson, J. R. U., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: dispersal pathways affect invasion success. Trends Ecol. Evolution 24, 136–144 (2009).
    Google Scholar 
    Wilson, S. & Swan, G. A complete guide to reptiles of Australia. (New Holland Publishers, 2021).Chapple, D. G., Miller, K. A., Kraus, F. & Thompson, M. B. Divergent introduction histories among invasive populations of the delicate skink (Lampropholis delicata): has the importance of genetic admixture in the success of biological invasions been overemphasized? Diversity Distrib. 19, 134–146 (2013).
    Google Scholar 
    Chapple, D., Knegtmans, J., Kikillus, H. & van Winkel, D. Biosecurity of exotic reptiles and amphibians in New Zealand: building upon Tony Whitaker’s legacy. J. R. Soc. N.Z. 46, 66–84 (2016).
    Google Scholar 
    Chapple, D. G., Whitaker, A. H., Chapple, S. N. J., Miller, K. A. & Thompson, M. B. Biosecurity interceptions of an invasive lizard: Origin of stowaways and human-assisted spread within New Zealand. Evolut. Appl. 6, 324–339 (2013).
    Google Scholar 
    Tingley, R., Thompson, M. B., Hartley, S. & Chapple, D. G. Patterns of niche filling and expansion across the invaded ranges of an Australian lizard. Ecography 39, 270–280 (2016).
    Google Scholar 
    Chapple, D. G. et al. Biology of the invasive delicate skink (Lampropholis delicata) on Lord Howe Island. Aust. J. Zool. 62, 498–506 (2014).
    Google Scholar 
    Moule, H. et al. A matter of time: temporal variation in the introduction history and population genetic structuring of an invasive lizard. Curr. Zool. 61, 456–464 (2015).CAS 

    Google Scholar 
    Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Know when to run, know when to hide: can behavioral differences explain the divergent invasion success of two sympatric lizards? Ecol. Evolution 1, 278–289 (2011).
    Google Scholar 
    Cromie, G. L. & Chapple, D. G. Impact of tail loss on the behaviour and locomotor performance of two sympatric Lampropholis skink species. PLoS ONE 7, e34732 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brand, J. A. et al. Rapid shifts in behavioural traits during a recent fish invasion. Behav. Ecol. Sociobiol. 75, 134 (2021).
    Google Scholar 
    Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).
    Google Scholar 
    Pintor, L. M., Sih, A. & Bauer, M. L. Differences in aggression, activity and boldness between native and introduced populations of an invasive crayfish. Oikos 117, 1629–1636 (2008).
    Google Scholar 
    Mueller, J. C. et al. Selection on a behaviour-related gene during the first stages of the biological invasion pathway. Mol. Ecol. 26, 6110–6121 (2017).MathSciNet 
    CAS 
    PubMed 

    Google Scholar 
    Snell-Rood, E. C. An overview of the evolutionary causes and consequences of behavioural plasticity. Anim. Behav. 85, 1004–1011 (2013).
    Google Scholar 
    Niemelä, P. T., Niehoff, P. P., Gasparini, C., Dingemanse, N. J. & Tuni, C. Crickets become behaviourally more stable when raised under higher temperatures. Behav. Ecol. Sociobiol. 73, 81 (2019).
    Google Scholar 
    Polverino, G. et al. Psychoactive pollution suppresses individual differences in fish behaviour. Proc. R. Soc. B: Biol. Sci. 288, 20202294 (2021).
    Google Scholar 
    Royauté, R., Garrison, C., Dalos, J., Berdal, M. A. & Dochtermann, N. A. Current energy state interacts with the developmental environment to influence behavioural plasticity. Anim. Behav. 148, 39–51 (2019).
    Google Scholar 
    Michelangeli, M., Chapple, D. G., Goulet, C. T., Bertram, M. G. & Wong, B. B. M. Behavioral syndromes vary among geographically distinct populations in a reptile. Behav. Ecol. 30, 393–401 (2019).
    Google Scholar 
    Nicolaus, M., Tinbergen, J. M., Ubels, R., Both, C. & Dingemanse, N. J. Density fluctuations represent a key process maintaining personality variation in a wild passerine bird. Ecol. Lett. 19, 478–486 (2016).PubMed 

    Google Scholar 
    Lapiedra, O., Schoener, T. W., Leal, M., Losos, J. B. & Kolbe, J. J. Predator-driven natural selection on risk-taking behavior in anole lizards. Science 360, 1017–1020 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gruber, J., Brown, G., Whiting, M. J. & Shine, R. Geographic divergence in dispersal-related behaviour in cane toads from range-front versus range-core populations in Australia. Behav. Ecol. Sociobiol. 71, 38 (2017).
    Google Scholar 
    Gruber, J., Brown, G., Whiting, M. J. & Shine, R. Is the behavioural divergence between range-core and range-edge populations of cane toads (Rhinella marina) due to evolutionary change or developmental plasticity? R. Soc. Open Sci. 4, 170789 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morgan, D., Waas, J. R. & Innes, J. Do territorial and non-breeding Australian Magpies Gymnorhina tibicen influence the local movements of rural birds in New Zealand? Ibis 148, 330–342 (2006).
    Google Scholar 
    O’leary, R. A. & Jones, D. N. Foraging by suburban Australian magpies during dry conditions. Corella 26, 53–54 (2002).
    Google Scholar 
    Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. Behavioral flexibility and species invasions: the adaptive flexibility hypothesis. Ethol. Ecol. Evolution 22, 393–404 (2010).
    Google Scholar 
    Dingemanse, N. J. & Wolf, M. Between-individual differences in behavioural plasticity within populations: causes and consequences. Anim. Behav. 85, 1031–1039 (2013).
    Google Scholar 
    Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evolution 4, 788–793 (2020).
    Google Scholar 
    Cole, E. F. & Quinn, J. L. Personality and problem-solving performance explain competitive ability in the wild. Proc. R. Soc. B: Biol. Sci. 279, 1168–1175 (2012).
    Google Scholar 
    Webster, M. M., Ward, A. J. W. & Hart, P. J. B. Individual boldness affects interspecific interactions in sticklebacks. Behav. Ecol. Sociobiol. 63, 511–520 (2009).
    Google Scholar 
    McGhee, K. E., Pintor, L. M. & Bell, A. M. Reciprocal behavioral plasticity and behavioral types during predator-prey interactions. Am. Naturalist 182, 704–717 (2013).
    Google Scholar 
    Ioannou, C. C., Payne, M. & Krause, J. Ecological consequences of the bold–shy continuum: the effect of predator boldness on prey risk. Oecologia 157, 177–182 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Moran, N. P., Wong, B. B. M. & Thompson, R. M. Weaving animal temperament into food webs: implications for biodiversity. Oikos 126, 917–930 (2017).
    Google Scholar 
    Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12, 20150623 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Moule, H., Michelangeli, M., Thompson, M. B. & Chapple, D. G. The influence of urbanization on the behaviour of an Australian lizard and the presence of an activity–exploratory behavioural syndrome. J. Zool. 298, 103–111 (2016).
    Google Scholar 
    Michelangeli, M., Wong, B. B. M. & Chapple, D. G. It’s a trap: sampling bias due to animal personality is not always inevitable. Behav. Ecol. 27, 62–67 (2016).
    Google Scholar 
    Michelangeli, M., Melki-Wegner, B., Laskowski, K., Wong, B. B. M. & Chapple, D. G. Impacts of caudal autotomy on personality. Anim. Behav. 162, 67–78 (2020).
    Google Scholar 
    Shine, R. Locomotor speeds of gravid lizards: Placing “costs of reproduction” within an ecological context. Funct. Ecol. 17, 526–533 (2003).
    Google Scholar 
    Naimo, A. C., Jones, C., Chapple, D. G. & Wong, B. B. M. Has an invasive lizard lost its antipredator behaviours following 40 generations of isolation from snake predators? Behav. Ecol. Sociobiol. 75, 131 (2021).
    Google Scholar 
    Brand, J. A. et al. Population differences in the effect of context on personality in an invasive lizard. Behav. Ecol. 32, 1363–1371 (2021).
    Google Scholar 
    Goulet, C. T., Thompson, M. B., Michelangeli, M., Wong, B. B. M. & Chapple, D. G. Thermal physiology: a new dimension of the pace‐of‐life syndrome. J. Anim. Ecol. 86, 1269–1280 (2017).PubMed 

    Google Scholar 
    Michelangeli, M., Goulet, C. T., Kang, H. S., Wong, B. B. M. & Chapple, D. G. Integrating thermal physiology within a syndrome: locomotion, personality and habitat selection in an ectotherm. Funct. Ecol. 32, 970–981 (2018).
    Google Scholar 
    Bell, A. M. Randomized or fixed order for studies of behavioral syndromes? Behav. Ecol. 24, 16–20 (2013).PubMed 

    Google Scholar 
    Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evolution 7, 1325–1330 (2016).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. (2019).Bürkner, P. C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    Munson, A. A., Michelangeli, M. & Sih, A. Stable social groups foster conformity and among-group differences. Anim. Behav. 174, 197–206 (2021).
    Google Scholar 
    Royauté, R. & Dochtermann, N. A. Comparing ecological and evolutionary variability within datasets. Behav. Ecol. Sociobiol. 75, 127 (2021).
    Google Scholar 
    Dalos, J., Royauté, R., Hedrick, A. V. & Dochtermann, N. A. Phylogenetic conservation of behavioural variation and behavioural syndromes. J. Evolut. Biol. 35, 311–321 (2022).
    Google Scholar 
    Miller, K. A., Duran, A., Melville, J., Thompson, M. B. & Chapple, D. G. Sex-specific shifts in morphology and colour pattern polymorphism during range expansion of an invasive lizard. J. Biogeogr. 44, 2778–2788 (2017).
    Google Scholar 
    Michelangeli, M., Chapple, D. G. & Wong, B. B. M. Are behavioural syndromes sex specific? Personality in a widespread lizard species. Behav. Ecol. Sociobiol. 70, 1911–1919 (2016).
    Google Scholar 
    Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
    MATH 

    Google Scholar 
    Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. 85, 935–956 (2010).PubMed 

    Google Scholar 
    Chapple, D. G. et al. Data from Chapple et al. “Biological invasions as a selective filter driving behavioral divergence”. Monash University. Dataset. https://doi.org/10.26180/18851036.v2 (2022). More

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    Pollinator biological traits and ecological interactions mediate the impacts of mosquito-targeting malathion application

    Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14(10), 1062–1072 (2011).PubMed 
    Article 

    Google Scholar 
    Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: A conceptual framework for the effects of land-use change. Ecol. Lett. 10(4), 299–314 (2007).PubMed 
    Article 

    Google Scholar 
    Kluser, S. & Peduzzi, P. Global pollinator decline: A literature review. Preprint at http://archive-ouverte.unige.ch/unige 32258 (2007).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25(6), 345–353 (2010).PubMed 
    Article 

    Google Scholar 
    Rhodes, C. J. Pollinator decline—an ecological calamity in the making?. Sci. Prog. 101(2), 121–160 (2018).PubMed 
    Article 

    Google Scholar 
    Huang, H. & D’Odorico, P. Critical transitions in plant-pollinator systems induced by positive inbreeding-reward-pollinator feedbacks. Iscience 23(2), 100819 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krishnan, N. et al. Assessing field-scale risks of foliar insecticide applications to monarch butterfly (Danaus plexippus) larvae. Environ. Toxicol. Chem. 39(4), 923–941 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bargar, T. A., Hladik, M. L. & Daniels, J. C. Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption. PeerJ 8, e8669 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Emmel, T. C. & Tucker, J. C. In Mosquito Control Pesticides: Ecological Impacts and Management Alternatives (eds Emmel, T. C. & Tucker, J. C.) 105 (Scientific Publishers, 1991).Johnson, R. M., Ellis, M. D., Mullin, C. A. & Frazier, M. Pesticides and honey bee toxicity–USA. Apidologie 41(3), 312–331 (2010).CAS 
    Article 

    Google Scholar 
    Olaya-Arenas, P., Scharf, M. E. & Kaplan, I. Do pollinators prefer pesticide-free plants? An experimental test with monarchs and milkweeds. J. Appl. Ecol. 57(10), 2019–2030 (2020).CAS 
    Article 

    Google Scholar 
    Berryman, A. A. What causes population cycles of forest Lepidoptera?. Trends Ecol. Evol. 11(1), 28–32 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    Elkinton, J. & Boettner, G. Benefits and harm caused by the introduced generalist tachinid, Compsilura concinnata North America. Biol. Control 57(2), 277–288 (2012).
    Google Scholar 
    Beschta, R. L. & Ripple, W. J. Riparian vegetation recovery in Yellowstone: The first two decades after wolf reintroduction. Biol. Conserv. 198, 93–103 (2016).Article 

    Google Scholar 
    Oberhauser, K. et al. Lacewings wasps and fliesoh my insect enemies take a bite out of monarchs. In Monarchs in a Changing World: Biology and Conservation of an iconic insect (eds Oberhauser, K. S. et al.) 71–82 (Cornell University Press, 2015).Chapter 

    Google Scholar 
    Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval Lepidoptera. Annu. Rev. Entomol. 47(1), 361–393 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hermann, S. L., Blackledge, C., Haan, N. L., Myers, A. T. & Landis, D. A. Predators of monarch butterfly eggs and neonate larvae are more diverse than previously recognised. Sci. Rep. 9(1), 1–9 (2019).CAS 
    Article 

    Google Scholar 
    McCoshum, S. M., Andreoli, S. L., Stenoien, C. M., Oberhauser, K. S. & Baum, K. A. Species distribution models for natural enemies of monarch butterfly (Danaus plexippus) larvae and pupae: Distribution patterns and implications for conservation. J. Insect Conserv. 20(2), 223–237 (2016).Article 

    Google Scholar 
    Geest, E. A., Wolfenbarger, L. L. & McCarty, J. P. Recruitment, survival and parasitism of monarch butterflies (Danaus plexippus) in milkweed gardens and conservation areas. J. Insect Conserv. 23(2), 211–224 (2019).Article 

    Google Scholar 
    Stenoien, C. et al. Monarchs in decline: A collateral landscape-level effect of modern agriculture. Insect Sci. 25(4), 528–541 (2018).PubMed 
    Article 

    Google Scholar 
    Crone, E. E., Pelton, E. M., Brown, L. M., Thomas, C. C. & Schultz, C. B. Why are monarch butterflies declining in the west? Understanding the importance of multiple correlated drivers. Ecol. Appl. 29(7), e01975 (2019).PubMed 
    Article 

    Google Scholar 
    Brower, L. P. et al. Effect of the 2010–2011 drought on the lipid content of monarchs migrating through Texas to overwintering sites in Mexico. In The Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds Oberhauser, K. S. et al.) 117–129 (Cornell University Press, 2015).
    Google Scholar 
    Thogmartin, W. E. et al. Monarch butterfly population decline in North America: Identifying the threatening processes. R. Soc. Open Sci. 4(9), 170760 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Olaya-Arenas, P. & Kaplan, I. Quantifying pesticide exposure risk for monarch caterpillars on milkweeds bordering agricultural land. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00223 (2019).
    Article 

    Google Scholar 
    Olaya-Arenas, P., Hauri, K., Scharf, M. E. & Kaplan, I. Larval pesticide exposure impacts monarch butterfly performance. Sci. Rep. 10(1), 1–12 (2020).Article 

    Google Scholar 
    Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. PNAS 108(2), 662–667 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Epstein, L. Fifty years since silent spring. Annu. Rev. Phytopathol. 52, 377–402 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rayor, L. S. Effects of monarch larval host plant chemistry and body size on Polistes wasp predation. In The Monarch Butterfly Biology and Conservation (eds Oberhauser, K. S. & Solensky, M. J.) 39–46 (Cornell University Press, 2004).
    Google Scholar 
    Baker, A. M. & Potter, D. A. Invasive paper wasp turns urban pollinator gardens into ecological traps for monarch butterfly larvae. Sci. Rep. 10(1), 1–7 (2020).Article 

    Google Scholar 
    Castellanos, I. & Barbosa, P. Dropping from host plants in response to predators by a polyphagous caterpillar. J. Lepid. Soc. 65(4), 270–272 (2011).
    Google Scholar 
    Kessler, S. C. et al. Bees prefer foods containing neonicotinoid pesticides. Nature 521(7550), 74–76 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liao, L.-H., Wu, W.-Y. & Berenbaum, M. R. Behavioral responses of honey bees (Apis mellifera) to natural and synthetic xenobiotics in food. Sci. Rep. 7(1), 1–8 (2017).Article 

    Google Scholar 
    Musser, R. O. et al. Caterpillar saliva beats plant defences. Nature 416(6881), 599–600 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schmidt, J. & Smith, J. Host examination walk and oviposition site selection of Trichogramma minutum: Studies on spherical hosts. J. Insect Behav. 2(2), 143–171 (1989).Article 

    Google Scholar 
    Ramos, R. S. et al. Investigation of the lethal and behavioral effects of commercial insecticides on the parasitoid wasp Copidosoma truncatellum. Chemosphere 191, 770–778 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Chareonviriyaphap, T. et al. Pesticide avoidance behavior in Anopheles albimanus, a malaria vector in the Americas. J. Am. Mosq. Control Assoc. 13(2), 171–183 (1997).CAS 
    PubMed 

    Google Scholar 
    Nansen, C., Baissac, O., Nansen, M., Powis, K. & Baker, G. Behavioral avoidance-will physiological insecticide resistance level of insect strains affect their oviposition and movement responses?. PLoS ONE 11(3), e0149994 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martini, X., Kincy, N. & Nansen, C. Quantitative impact assessment of spray coverage and pest behavior on contact pesticide performance. Pest Manag. Sci. 68(11), 1471–1477 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bull, D. & Coleman, R. Effects of pesticides on Trichogramma spp. Southwest. Entomol. Suppl. 8, 156–168 (1985).CAS 

    Google Scholar 
    Thubru, D., Firake, D. & Behere, G. Assessing risks of pesticides targeting lepidopteran pests in cruciferous ecosystems to eggs parasitoid, Trichogramma brassicae (Bezdenko). Saudi J. Biol. Sci. 25(4), 680–688 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Selwood, K. & Zimmer, H. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

    Google Scholar 
    Chmiel, J. A., Daisley, B. A., Pitek, A. P., Thompson, G. J. & Reid, G. Understanding the effects of sublethal pesticide exposure on honey bees: A role for probiotics as mediators of environmental stress. Front. Ecol. Evol. 8, 22 (2020).Article 

    Google Scholar 
    Chittka, L., Williams, N., Rasmussen, H. & Thomson, J. Navigation without vision: Bumblebee orientation in complete darkness. Proc. R. Soc. B 266(1414), 45–50 (1999).PubMed Central 
    Article 

    Google Scholar 
    Young, M. W. & Kay, S. A. Time zones: A comparative genetics of circadian clocks. Nat. Rev. Genet. 2(9), 702–715 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mallet, J. Gregarious roosting and home range in Heliconius butterflies. Natl. Geogr. Res. 2(2), 198–215 (1986).
    Google Scholar 
    Chang, Y.-M. et al. Roosting site usage, gregarious roosting and behavioral interactions during roost-assembly of two Lycaenidae butterflies. Zool. Stud. 59, e10 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Vulinec, K. Collective security aggregation by insects as a defence. In Insect Defences. Adaptive Mechanisms of Prey and Predators (eds Evans, D. L. & Schmidt, J. O.) 251–288 (State University of New York, 1990).
    Google Scholar 
    Salcedo, C. Environmental elements involved in communal roosting in Heliconius butterflies (Lepidoptera: Nymphalidae). Environ. Entomol. 39(3), 907–911 (2010).PubMed 
    Article 

    Google Scholar 
    Giordano, B. V., McGregor, B. L., Runkel, A. E. IV. & Burkett-Cadena, N. D. Distance diminishes the effect of deltamethrin exposure on the monarch butterfly, Danaus plexippus. J. Am. Mosq. Control Assoc. 36(3), 181–188 (2020).PubMed 
    Article 

    Google Scholar 
    Matzrafi, M. Climate change exacerbates pest damage through reduced pesticide efficacy. Pest Manag. Sci. 75(1), 9–13 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hewitt, A. Spray drift: Impact of requirements to protect the environment. Crop Prot. 19(8–10), 623–627 (2000).Article 

    Google Scholar 
    Nail, K. R., Stenoien, C. & Oberhauser, K. S. Immature monarch survival: Effects of site characteristics, density and time. Ann. Entomol. Soc. 108(5), 680–690 (2015).Article 

    Google Scholar 
    Payne, C. C. & Mertens, P. P. Cytoplasmic polyhedrosis viruses. In The Reoviridae (ed. Joklik, K.) 425–504 (Springer, 1983).Chapter 

    Google Scholar 
    Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26(5), 547–555 (2001).Article 

    Google Scholar 
    Salvato, M. Influence of mosquito control chemicals on butterflies (Nymphalidae, Lycaenidae, Hesperiidae) of the lower Florida keys. J. Lepid. Soc. 55(1), 8–14 (2001).
    Google Scholar 
    Frey, D. F. & Leong, K. L. Can microhabitat selection or differences in ‘catchability’ explain male-biased sex ratios in overwintering populations of monarch butterflies?. Anim. Behav. 45(5), 1025 (1993).Article 

    Google Scholar 
    Macgregor, C. J. & Scott-Brown, A. S. Nocturnal pollination: An overlooked ecosystem service vulnerable to environmental change. Emerg. Top. Life Sci. 4(1), 19–32 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More