More stories

  • in

    Global decline of pelagic fauna in a warmer ocean

    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 

    Google Scholar 
    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

    Google Scholar 
    Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P. & Polovina, J. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9–25 (2016).
    Google Scholar 
    Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).CAS 

    Google Scholar 
    Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).CAS 

    Google Scholar 
    Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).CAS 

    Google Scholar 
    Angel, M. V. & de C. Baker, A. Vertical distribution of the standing crop of plankton and micronekton at three stations in the northeast Atlantic. Biol. Oceanogr. 2, 1–30 (1982).
    Google Scholar 
    Cook, A. B., Sutton, T. T., Galbraith, J. K. & Vecchione, M. Deep-pelagic (0–3000 m) fish assemblage structure over the Mid-Atlantic Ridge in the area of the Charlie-Gibbs Fracture Zone. Deep Sea Res. 2 98, 279–291 (2013).
    Google Scholar 
    Hidaka, K., Kawaguchi, K., Murakami, M. & Takahashi, M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep Sea Res. 1 48, 1923–1939 (2001).Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342 (2015).
    Google Scholar 
    Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1639–1664 (2021).CAS 

    Google Scholar 
    Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
    Google Scholar 
    Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 

    Google Scholar 
    Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).
    Google Scholar 
    Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).
    Google Scholar 
    Kwiatkowski, L., Aumont, O. & Bopp, L. Consistent trophic amplification of marine biomass declines under climate change. Glob. Change Biol. 25, 218–229 (2019).
    Google Scholar 
    Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).CAS 

    Google Scholar 
    Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
    Google Scholar 
    Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).
    Google Scholar 
    Reid, S. B., Hirota, J., Young, R. E. & Hallacher, L. E. Mesopelagic-boundary community in Hawaii: micronekton at the interface between neritic and oceanic ecosystems. Mar. Biol. 109, 427–440 (1991).
    Google Scholar 
    Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).
    Google Scholar 
    Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. PICES Scientific Report 38 (North Pacific Marine Science Organization, 2010).Kaartvedt, S., Staby, A. & Aksnes, D. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6 (2012).
    Google Scholar 
    Gjøsaeter, J. & Kawaguchi, K. A Review of the World Resources of Mesopelagic Fish Fisheries Technical Paper 193 (FAO, 1980).Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).
    Google Scholar 
    Benoit-Bird, K. J. & Lawson, G. L. Ecological insights from pelagic habitats acquired using active acoustic techniques. Annu. Rev. Mar. Sci. 8, 463–490 (2016).
    Google Scholar 
    Annasawmy, P. et al. Micronekton diel migration, community composition and trophic position within two biogeochemical provinces of the south west Indian Ocean: insight from acoustics and stable isotopes. Deep Sea Res. 1 138, 85–97 (2018).CAS 

    Google Scholar 
    Haris, K. et al. Sounding out life in the deep using acoustic data from ships of opportunity. Sci. Data 8, 23 (2021).CAS 

    Google Scholar 
    Irigoien, X. et al. The Simrad EK60 echosounder dataset from the Malaspina circumnavigation. Sci. Data 8, 259 (2021).
    Google Scholar 
    Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
    Google Scholar 
    Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).CAS 

    Google Scholar 
    Proud, R., Cox, M., Le Guen, C. & Brierley, A. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Mar. Ecol. Prog. Ser. 598, 35–48 (2018).
    Google Scholar 
    Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119 (2017).CAS 

    Google Scholar 
    Ramsay, J. O. & Silverman, B. W. Functional Data Analysis (Springer, 2005).Moriarty, R. & O’Brien, T. D. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).
    Google Scholar 
    Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, e1602468 (2017).
    Google Scholar 
    Bertrand, A., Ballón, M. & Chaigneau, A. Acoustic observation of living organisms reveals the upper limit of the oxygen minimum zone. PLoS ONE 5, e10330 (2010).
    Google Scholar 
    Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).CAS 

    Google Scholar 
    Godø, O. R., Patel, R. & Pedersen, G. Diel migration and swimbladder resonance of small fish: some implications for analyses of multifrequency echo data. ICES J. Mar. Sci. 66, 1143–1148 (2009).
    Google Scholar 
    Agersted, M. D. et al. Mass estimates of individual gas-bearing mesopelagic fish from in situ wideband acoustic measurements ground-truthed by biological net sampling. ICES J. Mar. Sci. 78, 3658–3673 (2021).
    Google Scholar 
    Backus, R. & Craddock, J. in Oceanic Sound Scattering Prediction (eds Anderson, N. R. & Zahuranec, B. J.) 529–547 (Springer, 1977).Longhurst, A. Ecological Geography of the Sea (Elsevier, 2010).Spalding, M. D., Agostini, V. N., Rice, J. & Grant, S. M. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean Coast. Manage. 60, 19–30 (2012).
    Google Scholar 
    Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).
    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge Univ. Press, 2010).Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 

    Google Scholar 
    Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).
    Google Scholar 
    Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J. & Brierley, A. S. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES J. Mar. Sci. 76, 718–733 (2019).
    Google Scholar 
    Chapman, R. P., Bluy, O. Z., Adlington, R. H. & Robison, A. E. Deep scattering layer spectra in the Atlantic and Pacific oceans and adjacent seas. J. Acoust. Soc. Am. 56, 1722–1734 (1974).
    Google Scholar 
    Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proc. R. Soc. B 286, 20190353 (2019).
    Google Scholar 
    Escobar-Flores, P. C., O’Driscoll, R. L., Montgomery, J. C., Ladroit, Y. & Jendersie, S. Estimates of density of mesopelagic fish in the Southern Ocean derived from bulk acoustic data collected by ships of opportunity. Polar Biol. 43, 43–61 (2020).
    Google Scholar 
    Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. Proc. R. Soc. B 289, 20211781 (2022).
    Google Scholar 
    Reygondeau, G. et al. Climate change-induced emergence of novel biogeochemical provinces. Front. Mar. Sci. 7, 657 (2020).
    Google Scholar 
    Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).
    Google Scholar 
    Bianchi, D., Carozza, D. A., Galbraith, E. D., Guiet, J. & DeVries, T. Estimating global biomass and biogeochemical cycling of marine fish with and without fishing. Sci. Adv. 7, eabd7554 (2021).
    Google Scholar 
    Grimaldo, E. et al. Investigating the potential for a commercial fishery in the northeast Atlantic utilizing mesopelagic species. ICES J. Mar. Sci. 77, 2541–2556 (2020).
    Google Scholar 
    Olsen, R. E. et al. Can mesopelagic mixed layers be used as feed sources for salmon aquaculture? Deep Sea Res. 2 180, 104722 (2020).CAS 

    Google Scholar 
    De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291 (2007).
    Google Scholar 
    Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493 (2015).
    Google Scholar 
    Perrot, Y. et al. Matecho: an open-source tool for processing fisheries acoustics data. Acoust. Aust. 46, 241–248 (2018).
    Google Scholar 
    Stanton, T. Review and recommendations for the modelling of acoustic scattering by fluid-like elongated zooplankton: euphausiids and copepods. ICES J. Mar. Sci. 57, 793–807 (2000).
    Google Scholar 
    GEBCO: A Continuous Terrain Model of the Global Oceans and Land (British Oceanographic Data Centre, 2019).EchoPY v.1.1: Fisheries Acoustic Data Processing in Python (Python, 2020); https://pypi.org/project/echopyde Boor, C. A Practical Guide to Splines (Springer, 1978).Clustering (SciKit Learn, 2021); https://scikit-learn.org/stable/modules/clusteringEyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
    Google Scholar 
    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
    Google Scholar 
    Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, eaay4740 (2020).
    Google Scholar 
    Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. J. Adv. Model. Earth Syst. 13, e2021MS002496 (2021).
    Google Scholar 
    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
    Google Scholar 
    Locarnini, R. et al. World Ocean Atlas 2018, Volume 1: Temperature NOAA Atlas NESDIS 81 (NOAA, 2018).García, H. et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation NOAA Atlas NESDIS 83 (NOAA, 2018).Sathyendranath, S. et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 19 May 2021; http://www.esa-oceancolour-cci.org More

  • in

    Diving in

    Nearly two years into the United Nations Decade of Ocean Science, research, including some featured in this month’s issue, shows that there is still a wealth of scientific secrets to uncover in the ocean depths.
    In many ways, considering the ocean as a single unit is overly broad. The global ocean covers 71% of the planet’s surface, reaches down to depths of over 10 kilometres, includes about 1.35 billion cubic kilometres of water and houses an approximated 2.2 million eukaryotic species. There are distinct regions, with distinct physical properties, and, in turn, there are distinct species. Yet, the world’s oceans do have a level of physical and thematic connectivity.
    Credit: Daria Zaseda / DigitalVision Vectors / GettyPhysically, a large part of the connection is related to the presence of large rotating ocean currents that transfer heat across latitudes and contribute to ocean mixing (thermohaline circulation). Some of these currents are warming at alarming rates — up to three times faster than the rest of the ocean, leading to questions about the underlying mechanisms of the warming and expectations for change.Focusing on western boundary currents (WBCs) in the Southern Hemisphere, in an Article in this issue of Nature Climate Change, Li and colleagues answer a long-debated question on the mechanisms of change, showing that temperature-gradient-related instabilities, rather than flow-speed-related instabilities are behind the shifts. In another Article, focusing on the global future changes of eddies (including eddy-rich WBCs), Beech and colleagues report the development of a flexible method that maximizes local model resolution while minimizing computational costs, to reveal the long-term geographical specificities and nonlinear temperature increases expected to 2100 (see also the News and Views article by Yang on these papers).A recent paper1 has demonstrated the important role of large ocean currents in defining plankton biogeography and dynamics, and WBC warming has previously been linked to impacts such as fishery collapses. The tight link between physical processes and biological responses is an underscoring theme of climate change ecology, but is perhaps more apparent in the open ocean, where physical processes can be easily (if imperfectly) linked to primary productivity using remotely sensed phytoplankton pigment absorption, and where life is generally less impacted by geographical, political or disturbance-based boundaries compared with land and freshwater systems. These aspects may facilitate modelling of current and future communities, while also allowing broader assumptions to be made about biological movement and connectivity.Despite these benefits, understanding ocean change comes with its own difficulties. Biological sampling, while easy enough in the surface waters, becomes increasingly difficult at depth. Although future habitats for various organisms have been projected on the basis of their thermal limits in the ocean, these predictions often still rely on temperatures at the surface of the sea. Addressing this, Santana-Falcón and colleagues report in an Article the global mapping of ocean temperature changes to depths of 1,000 metres, and reveal the complex depth-dependent changes in thermal upper and lower bounds that marine organisms will soon be subjected to. In another Article, Ariza and colleagues neatly address the issue of directly monitoring deep-ocean change by compiling a large database of sound-based observations, and subsequently classifying the ocean’s ‘echobiomes’, defined as sound-scattering communities with comparable structural and functional properties (see also the accompanying News and Views article by Hazen). Sound-based methods are also increasingly being used on land2, and represent an exciting tool for monitoring change, particularly in hard-to-reach places such as deep forests, high mountaintops or underground. While the sound reflection method used in the study by Ariza and colleagues has limits in its ability to identify organisms at the individual or species levels, it does provide a community-level focus on change, which remains much needed in the field of global change ecology.At the other end of the spatial spectrum, research by Lee and colleagues reported in an Article also in this issue dives deep into the DNA of a keystone ocean organism (a copepod), to understand the mechanisms that may allow longer-term adaptation to warming and pH stress. The work reveals remarkable adaptation over just a few short generations, which is linked to epigenetic changes. As climate change impacts continue to escalate, the ability of organisms to invoke both shorter- and longer-term adaptations has become an increasingly relevant area of research. Epigenetics has previously been reported as a quick-response method to cope with environmental stress, and may be particularly relevant in defining the adaptation of short-lived animals such as insects and the resilience of the communities they uphold.The five research pieces linked to the oceans in this issue reveal just some of the diversity of topics, methods and scales relevant to understanding global change. Also increasingly relevant are works on ocean conservation3 and on the social and economic impacts of ocean change4,5. Like climate change science, the topic of ocean change is less of a field, and more of a cross-disciplinary theme. More

  • in

    Assessing a megadiverse but poorly known community of fishes in a tropical mangrove estuary through environmental DNA (eDNA) metabarcoding

    Levin, L. A. et al. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4, 430–451 (2001).CAS 

    Google Scholar 
    Wagner, G. M. & Sallema-Mtui, R. in Estuaries: A Lifeline of Ecosystem Services in the Western Indian Ocean Estuaries of the World (eds S. Diop, P. Scheren, & J. Machiwa) 183–207 (2016).Brown, C. J. et al. The assessment of fishery status depends on fish habitats. Fish Fish. 20, 1–14 (2019).CAS 

    Google Scholar 
    De La Morinière, E. C., Pollux, B., Nagelkerken, I. & Van der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).ADS 

    Google Scholar 
    Branton, M. & Richardson, J. S. Assessing the value of the umbrella-species concept for conservation planning with meta-analysis. Conserv. Biol. 25, 9–20 (2011).PubMed 

    Google Scholar 
    Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).PubMed 

    Google Scholar 
    Zainal Abidin, D. H. et al. DNA-based taxonomy of a mangrove-associated community of fishes in Southeast Asia. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-021-97324-1 (2021).CAS 
    Article 

    Google Scholar 
    Gauthier, G. et al. Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra. Philos. Trans. Roy. Soc. B Biol. Sci. 368, 20120482 (2013).
    Google Scholar 
    Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
    PubMed 

    Google Scholar 
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chong, V. C., Lee, P. K. & Lau, C. M. Diversity, extinction risk and conservation of Malaysian fishes. J. Fish Biol. 76, 2009–2066. https://doi.org/10.1111/j.1095-8649.2010.02685.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zainal Abidin, D. H. et al. Ichthyofauna of Sungai Merbok Mangrove Forest Reserve, northwest Peninsular Malaysia, and its adjacent marine waters. Check List 17, 601–631. https://doi.org/10.15560/17.2.601 (2021).Article 

    Google Scholar 
    Ong, J. et al. in Hutan paya laut Merbok, Kedah: Pengurusan hutan, persekitaran fizikal dan kepelbagaian flora. Vol. 23 Siri kepelbagaian biologi hutan (ed Ku Aman KA Abd Rahim AR, Abu Hassan MN, Abdullah M, Nor Hazliza MB, Latiff A) 21–33 (Jabatan Perhutanan Semenanjung Malaysia, 2015).Hookham, B., Shau-Hwai, A. T., Dayrat, B. & Hintz, W. A baseline measure of tree and gastropod biodiversity in replanted and natural mangrove stands in Malaysia: Langkawi Island and Sungai Merbok. Trop. Life Sci. Res. 25, 1 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Jamaluddin, J. A. F. et al. DNA barcoding of shrimps from a mangrove biodiversity hotspot. Mitochondrial DNA Part A 30, 618–625. https://doi.org/10.1080/24701394.2019.1597073 (2019).CAS 
    Article 

    Google Scholar 
    Mansor, M., Mohammad-Zafrizal, M., Nur-Fadhilah, M., Khairun, Y. & Wan-Maznah, W. Temporal and spatial variations in fish assemblage structures in relation to the physicochemical parameters of the Merbok estuary, Kedah. J. Nat. Sci. Res. 2, 110–127 (2012).
    Google Scholar 
    Alshari, N. F. M. A. H. et al. Metabarcoding of Fish Larvae in the Merbok River reveals species diversity and distribution along its mangrove environment. Zool. Stud. 60, 60–76. https://doi.org/10.6620/ZS.2021 (2021).Article 

    Google Scholar 
    Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 1–9 (2016).
    Google Scholar 
    Hupało, K. et al. An urban Blitz with a twist: Rapid biodiversity assessment using aquatic environmental DNA. Environ. DNA 3, 200–213 (2020).
    Google Scholar 
    Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

    Google Scholar 
    Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
    PubMed 

    Google Scholar 
    Ahn, H. et al. Evaluation of fish biodiversity in estuaries using environmental DNA metabarcoding. PLoS ONE 15, e0231127 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Polanco, F. A. et al. Detecting aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53, 1606–1619 (2021).
    Google Scholar 
    Zhang, H., Yoshizawa, S., Iwasaki, W. & Xian, W. Seasonal fish assemblage structure using environmental DNA in the Yangtze Estuary and its adjacent waters. Front. Mar. Sci. 6, 515. https://doi.org/10.3389/fmars.2019.00515 (2019).Article 

    Google Scholar 
    Stat, M. et al. Ecosystem biomonitoring with eDNA: Metabarcoding across the tree of life in a tropical marine environment. Sci. Rep. 7, 1–11 (2017).ADS 
    CAS 

    Google Scholar 
    West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27, 1942–1957 (2021).
    Google Scholar 
    Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).
    Google Scholar 
    Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 1–12 (2021).
    Google Scholar 
    Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2021).PubMed 

    Google Scholar 
    Fujii, K. et al. Environmental DNA metabarcoding for fish community analysis in backwater lakes: A comparison of capture methods. PLoS ONE 14, e0210357 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshwat. Ecosyst. 29, 1785–1800 (2019).
    Google Scholar 
    Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Klymus, K. E., Marshall, N. T. & Stepien, C. A. Environmental DNA (eDNA) metabarcoding assays to detect invasive invertebrate species in the Great Lakes. PLoS ONE 12, 24. https://doi.org/10.1371/journal.pone.0177643 (2017).CAS 
    Article 

    Google Scholar 
    Wilson, C. et al. Tracking ghosts: Combined electrofishing and environmental DNA surveillance efforts for Asian carps in Ontario waters of Lake Erie. Manag. Biol. Invasion 5, 225–231. https://doi.org/10.3391/mbi.2014.5.3.05 (2014).Article 

    Google Scholar 
    Alexander, J. B. et al. Development of a multi-assay approach for monitoring coral diversity using eDNA metabarcoding. Coral Reefs 39, 159–171. https://doi.org/10.1007/s00338-019-01875-9 (2020).Article 

    Google Scholar 
    Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25, 527–541. https://doi.org/10.1111/mec.13481 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Fritts, A. K. et al. Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps. Biol. Invasions 21, 1143–1153 (2019).
    Google Scholar 
    Maruyama, A., Nakamura, K., Yamanaka, H., Kondoh, M. & Minamoto, T. The release rate of environmental DNA from juvenile and adult fish. PLoS ONE 9, e114639 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Amberg, J. J., Merkes, C. M., Stott, W., Rees, C. B. & Erickson, R. A. Environmental DNA as a tool to help inform zebra mussel, Dreissena polymorpha, management in inland lakes. Manag. Biol. Invasion 10, 96 (2019).
    Google Scholar 
    Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812. https://doi.org/10.1093/bioinformatics/btu393 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zainal Abidin, D. H. & Noor Adelyna, M. A. Environmental DNA (eDNA) Metabarcoding as a Sustainable Tool of Coastal Biodiversity Assessment in Universities as Living Labs for Sustainable Development 211–225 (Springer, 2020).Sard, N. M. et al. Comparison of fish detections, community diversity, and relative abundance using environmental DNA metabarcoding and traditional gears. Environ. DNA 1, 368–384 (2019).
    Google Scholar 
    Hoffman, J. C., Kelly, J. R., Trebitz, A. S., Peterson, G. S. & West, C. W. Effort and potential efficiencies for aquatic non-native species early detection. Can. J. Fish. Aquat. Sci. 68, 2064–2079 (2011).
    Google Scholar 
    Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 1–12 (2017).
    Google Scholar 
    Whitfield, A. K. Fish species in estuaries—From partial association to complete dependency. J. Fish Biol. 97, 1262–1264 (2020).PubMed 

    Google Scholar 
    Carpenter, K. & Niem, V. The living marine resources of the Western Central Pacific. Volume 5. Bony Fishes Part 3 (Menidae to Pomacentridae). Vol. 5, 2791–3380 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. FAO species identification guide for fishery purposes. The Living Marine Resources of the Western Central Pacific. Volume 6. Bony Fishes Part 4 (Labridae to Latimeriidae), Estuarine Crocodiles, Sea Turtles, Sea Snakes and Marine Mammals. Vol. 6, 3381–4218 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific: Batoid fishes, chimaera and bony fishes part 1 (Elopidae to Linophrynidae). Vol. 3, 1397–2068 (Food and Agriculture Organization of the United Nations, 1999).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific. Volume 4. Bony Fishes Part 2 (Mugilidae to Carangidae). Vol. 4, 2069–2790 (Food and Agriculture Organization of the United Nations, 1999).Benson, D. A. et al. GenBank. Nucleic Acids Res. 46, D41–D47 (2018).CAS 
    PubMed 

    Google Scholar 
    Pentinsaari, M., Ratnasingham, S., Miller, S. E. & Hebert, P. D. N. BOLD and GenBank revisited—Do identification errors arise in the lab or in the sequence libraries?. PLoS ONE 15, e0231814–e0231814. https://doi.org/10.1371/journal.pone.0231814 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ardura, A., Planes, S. & Garcia-Vazquez, E. Applications of DNA barcoding to fish landings: Authentication and diversity assessment. Zookeys 365, 49–65. https://doi.org/10.3897/zookeys.365.6409 (2013).Article 

    Google Scholar 
    ZainalAbidin, D. H. et al. Population genetics of the black scar oyster, Crassostrea iredalei: Repercussion of anthropogenic interference. Mitochondrial DNA Part A 27, 647–658 (2016).CAS 

    Google Scholar 
    Kelly, R. P. et al. Genetic and manual survey methods yield different and complementary views of an ecosystem. Front. Mar. Sci. 3, 283 (2017).
    Google Scholar 
    Ratnasingham, S. & Hebert, P. D. BOLD: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17. https://doi.org/10.1007/s10592-015-0775-4 (2016).CAS 
    Article 

    Google Scholar 
    Vasconcelos, R. P. et al. Global patterns and predictors of fish species richness in estuaries. J. Anim. Ecol. 84, 1331–1341 (2015).PubMed 

    Google Scholar 
    Shah, A. S. R. M., Hashim, Z. H. & Sah, S. A. M. Freshwater fishes of Gunung Jerai, Kedah Darul Aman: A preliminary study. Trop. Life Sci. Res. 20, 59 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Md. Zain, K. et al. Fish diversity along streams in Ulu Muda Forest Reserve, Kedah, Peninsular Malaysia. Malayan Nat. J. 73, 349–361 (2021).
    Google Scholar 
    Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
    PubMed 

    Google Scholar 
    Wang, S. et al. Methodology of fish eDNA and its applications in ecology and environment. Sci. Total Environ. 755, 142622. https://doi.org/10.1016/j.scitotenv.2020.142622 (2021).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

    Google Scholar 
    Southeast Asian Fisheries Development Centre (SEAFDEC). Status and trends of sharks fisheries in South East Asia in Malaysia Shark Fisheries (Fisheries and Resources Monitoring System (FIRMS), Rome, 2004).Zhang, S., Zhao, J. & Yao, M. A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol. Evol. 11, 1609–1625 (2020).ADS 

    Google Scholar 
    Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).CAS 

    Google Scholar 
    Hayami, K. et al. Effects of sampling seasons and locations on fish environmental DNA metabarcoding in dam reservoirs. Ecol. Evol. 10, 5354–5367 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morey, K. C., Bartley, T. J. & Hanner, R. H. Validating environmental DNA metabarcoding for marine fishes in diverse ecosystems using a public aquarium. Environ. DNA 2, 330–342 (2020).
    Google Scholar 
    Shaw, J. L. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).
    Google Scholar 
    Siegenthaler, A. et al. Metabarcoding of shrimp stomach content: Harnessing a natural sampler for fish biodiversity monitoring. Mol. Ecol. Resour. 19, 206–220. https://doi.org/10.1111/1755-0998.12956 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Stoeckle, M. Y., Das Mishu, M. & Charlop-Powers, Z. Improved environmental DNA reference library detects overlooked marine fishes in New Jersey, United States. Front. Mar. Sci. 7, 226 (2020).
    Google Scholar 
    Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).
    Google Scholar 
    Hebert, P. D., Ratnasingham, S. & De Waard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 270, S96–S99 (2003).CAS 

    Google Scholar 
    Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. Roy. Soc. Open Sci. 2, 150088 (2015).ADS 
    CAS 

    Google Scholar 
    Mariani, S., Baillie, C., Colosimo, G. & Riesgo, A. Sponges as natural environmental DNA samplers. Curr. Biol. 29, R401–R402 (2019).CAS 
    PubMed 

    Google Scholar 
    Bylemans, J., Gleeson, D. M., Duncan, R. P., Hardy, C. M. & Furlan, E. M. A performance evaluation of targeted eDNA and eDNA metabarcoding analyses for freshwater fishes. Environ. DNA 1, 402–414 (2019).
    Google Scholar 
    Chin, A. T. et al. Beta diversity changes in estuarine fish communities due to environmental change. Mar. Ecol. Prog. Ser. 603, 161–173 (2018).ADS 

    Google Scholar 
    Sloterdijk, H. et al. Composition and structure of the larval fish community related to environmental parameters in a tropical estuary impacted by climate change. Estuar. Coast. Shelf Sci. 197, 10–26 (2017).ADS 

    Google Scholar 
    Malaysian Meteorological Department. Tinjauan Cuaca bagi Tempoh November 2017 hingga April 2018. National Climate Centre: Ministry of Science, Technology and Innovation. Retrieved on February 1st, 2018, from https://www.met.gov.my/iklim/ramalanbermusim/ (2017).Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
    PubMed 

    Google Scholar 
    Illumina. 16S Metagenomic Sequencing Library Preparation. https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf 1–28 (2013).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. (Babraham Bioinformatics (Babraham Institute, 2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
    PubMed 

    Google Scholar 
    Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS ONE 12, e0176343 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
    Google Scholar 
    Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of Fishes: Genera, species, references. http://www.calacademy.org/scientists/catalog-of-fishes-family-group-names/ (2021).Ebert, D. A. & Fowler, S. Sharks of the World (Princeton University Press, 2013).
    Google Scholar 
    R Core Team. RStudio: integrated development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com42, 14 (2015).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oksanen, J. et al. Package ‘vegan’. Commun. Ecol. Pack. 2, 1–295 (2013).
    Google Scholar 
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

    Google Scholar  More

  • in

    Effects of different water management and fertilizer methods on soil temperature, radiation and rice growth

    General description of the experimental areaThe experiment was performed for two years at the National Key Irrigation Experimental Station located on the Songnen Plain in Heping town, Qing’an County, Suihua, Heilongjiang, China, with a geographical location of 45° 63′ N and 125° 44′ E at an elevation of 450 m above sea level (Fig. 1). This region consists of plain topography and has a semiarid cold temperate continental monsoon climate, i.e., a typical cold region with a black soil distribution area. The average annual temperature is 2.5 °C, the average annual precipitation is 550 mm, the precipitation is concentrated from June to September of each year, and the average annual surface evaporation is 750 mm. The growth period of crops is 156–171 days, and there is a frost-free period of approximately 128 days year−122. The soil at the study site is albic paddy soil with a mean bulk density of 1.01 g/cm3 and a porosity of 61.8% prevails. The basic physicochemical properties of the soil were as follows: the mass ratio of organic matter was 41.8 g/kg, pH value was 6.45, total nitrogen mass ratio was 15.06 g/kg, total phosphorus mass ratio was 15.23 g/kg, total potassium mass ratio was 20.11 g/kg, mass ratio of alkaline hydrolysis nitrogen was 198.29 mg/kg, available phosphorus mass ratio was 36.22 mg/kg and available potassium mass ratio was 112.06 mg/kg.Figure 1Location of the study area. The map and inset map in this image were drawn by the authors using ArcGIS software. The software version used was ArcGIS software v.10.2, and its URL is http://www.esri.com/.Full size imageHumic acid fertilizerHumic acid fertilizer was produced by Yunnan Kunming Grey Environmental Protection Engineering Co., Ltd., China (Fig. 2). The organic matter was ≥ 61.4%, and the total nutrients (nitrogen, phosphorus and potassium) were ≥ 18.23%, of which N ≥ 3.63%, P2O5 ≥ 2.03%, and K2O ≥ 12.57%. The moisture content was ≤ 2.51%, the pH value was 5.7, the worm egg mortality rate was ≥ 95%, and the amount of faecal colibacillosis was ≤ 3%. The fertilizer contained numerous elements necessary for plants. The contents of harmful elements, including arsenic, mercury, lead, cadmium and chromium, were ≤ 2.8%, 0.01%, 7.6%, 0.1% and 4.7%, respectively; these were lower than the test standard.Figure 2Humic acid fertilizer in powder form.Full size imageExperimental design and observation methodsIrrigationIn this experiment, three irrigation practices, namely, control irrigation (C), wet irrigation (W) and flood irrigation (F), were designed (Table 1).Table 1 Different irrigation methods.Full size tableControl irrigation (C) of rice had no water layer in the rest of the growing stages, except for the shallow water layer at the regreen stage of rice, which was maintained at 0–30 mm, and the natural dryness in the yellow stage. The irrigation time and irrigation quota were determined by the root soil moisture content as the control index. The upper limit of irrigation was the saturated moisture content of the soil, the lower limit of soil moisture at each growth stage was the percentage of saturated moisture content, and the TPIME-PICO64/32 soil moisture analyser was used to determine the soil moisture content at 7:00 a.m. and 18:00 p.m., respectively. When the soil moisture content was close to or lower than the lower limit of irrigation, artificial irrigation occurred until the upper irrigation limit was reached. The soil moisture content was maintained between the upper irrigation limit and the lower irrigation limit of the corresponding fertility stage. Under the wet irrigation (W) and flood irrigation (F) conditions, it was necessary to read the depth of the water layer through bricks and a vertical ruler embedded in the field before and after 8:00 am every day to determine if irrigation was needed. If irrigation was needed, then the water metre was recorded before and after each irrigation. The difference between before and after was the amount of irrigation23.FertilizationIn our research, five fertilization methods were applied, as shown in Table 2. In this experiment, the rice cultivar “Suijing No. 18” was selected. Urea and humic acid fertilizer were applied according to the proportion of base fertilizer:tillering fertilizer:heading fertilizer (5:3:2). The amounts of phosphorus and potassium fertilizers were the same for all treatments, and P2O5 (45 kg ha−1) and K2O (80 kg ha−1) were used. Phosphorus was applied once as a basal application. Potassium fertilizer was applied twice: once as a basal fertilizer and at 8.5 leaf age (panicle primordium differentiation stage) at a 1:1 ratio22.Table 2 The fertilizer methods.Full size tableThis study was performed with a randomized complete block design with three replications. Three irrigation practices and five fertilizer methods were applied, for a total of 15 treatments as follows: CT1, CT2, CT3, CT4, CT5; WT1, WT2, WT3, WT4, WT5; FT1, FT2, FT3, FT4, and FT5 (C, W, and F represent control irrigation, wet irrigation, and flood irrigation; T represents fertilizer treatment).Measurements of the samplesA soil temperature sensor (HZTJ1-1) was buried in each experimental plot to monitor the temperature of each soil layer (5 cm, 10 cm, 15 cm, 20 cm and 25 cm depth). The transmission of photosynthetically active radiation was measured from 11:00 to 13:00 by using a SunScan Canopy Analysis System (Delta T Devices, Ltd., Cambridge, UK), and data during the crop-growing season were recorded every day24.Plant measurements were taken during the periods of tillering to ripening on days with no wind and good light. The fluorescence parameters were measured by a portable fluorescence measurement system (Li-6400XT, America). The detection light intensity was 1500 μmol m−2 s−1, and the saturated pulsed light intensity was 7200 μmolm−2 s−1. The functional leaves were dark adapted for 30 min, and then the maximum photosynthetic efficiency of PSII (Fv/Fm) was measured. Photochemical quenching (QP) and nonphotochemical quenching (NPQ) were measured with natural light. Simultaneously, the leaf chlorophyll relative content (SPAD) was monitored using SPAD 502 (Konica Minolta, Inc., Tokyo, Japan). For plant agronomic characteristics, the distance from the stem base to the stem tip was measured with a straight ruler to quantify plant height24.Statistical analysisExperimental data obtained for different parameters were analysed statistically using the analysis of variance technique as applicable to randomized complete block design. Duncan’s multiple range test was employed to assess differences between the treatment means at a 5% probability level. All statistical analyses were performed using SPSS 22.0 for Windows24.
    Ethics approvalExperimental research and field studies on plants, including the collection of plant material, comply with relevant institutional, national, and international guidelines and legislation. We had appropriate permissions/licences to perform the experiment in the study area. More

  • in

    Decomposition stages as a clue for estimating the post-mortem interval in carcasses and providing accurate bird collision rates

    Barrientos, R. et al. A review of searcher efficiency and carcass persistence in infrastructure-driven mortality assessment studies. Biol. Conserv. 222, 146–153 (2018).
    Google Scholar 
    Stevens, B. S., Reese, K. P. & Connelly, J. W. Survival and detectability bias of avian fence collision surveys in sagebrush steppe. J. Wildl. Manag. 75, 437–449 (2011).
    Google Scholar 
    Hunting, K. A Roadmap for PIER Research on Avian Collisions with Power Lines in California. (2002).Barrientos, R. et al. Wire marking results in a small but significant reduction in avian mortality at power lines: A baci designed study. PLoS ONE 7, e32569 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Costantini, D., Gustin, M., Ferrarini, A. & Dell’Omo, G. Estimates of avian collision with power lines and carcass disappearance across differing environments. Anim. Conserv. 20, 173–181 (2017).
    Google Scholar 
    Jenkins, A. R. et al. Estimating the impacts of power line collisions on Ludwig’s Bustards Neotis ludwigii. Bird Conserv. Int. 21, 303–310 (2011).
    Google Scholar 
    Shaw, J. M., Reid, T. A., Schutgens, M., Jenkins, A. R. & Ryan, P. G. High power line collision mortality of threatened bustards at a regional scale in the Karoo, South Africa. Ibis (Lond. 1859) 1859(160), 431–446 (2018).
    Google Scholar 
    Gómez-Catasús, J. et al. Factors affecting differential underestimates of bird collision fatalities at electric lines: a case study in the Canary Islands. Ardeola 68, 71–94 (2021).
    Google Scholar 
    Ponce, C., Alonso, J. C., Argandoña, G., García Fernández, A. & Carrasco, M. Carcass removal by scavengers and search accuracy affect bird mortality estimates at power lines. Anim. Conserv. 13, 603–612 (2010).
    Google Scholar 
    Bernardino, J. et al. Bird collisions with power lines: State of the art and priority areas for research. Biol. Conserv. 222, 1–13 (2018).
    Google Scholar 
    Brooks, J. W. & Sutton, L. in Veterinary Forensic Pathology (ed. Brooks, J. W.) 43–63 (2018). https://doi.org/10.1007/978-3-319-67172-7_4Brooks, J. W. Postmortem changes in animal carcasses and estimation of the postmortem interval. Vet. Pathol. 53, 929–940 (2016).CAS 
    PubMed 

    Google Scholar 
    Ascensão, F. et al. Beware that the lack of wildlife mortality records can mask a serious impact of linear infrastructures. Glob. Ecol. Conserv. 19, e00661 (2019).
    Google Scholar 
    Hau, T. C., Hamzah, N. H., Lian, H. H. & Amir Hamzah, S. P. A. Decomposition process and post mortem changes: Review. Sains Malaysiana 43, 1873–1882 (2014).
    Google Scholar 
    Cooper, J. E. in Wildlife Forensic Investigation: Principles and Practice (eds. Cooper, J. & Cooper, M.) 237–324 (CRC Press, 2013). https://doi.org/10.1201/b14553Sutherland, A., Myburgh, J., Steyn, M. & Becker, P. J. The effect of body size on the rate of decomposition in a temperate region of South Africa. Forensic Sci. Int. 231, 257–262 (2013).CAS 
    PubMed 

    Google Scholar 
    Valverde, I., Espín, S., María-Mojica, P. & García-Fernández, A. J. Protocol to classify the stages of carcass decomposition and estimate the time of death in small-size raptors. Eur. J. Wildl. Res. 66, 1–13 (2020).
    Google Scholar 
    Goff, M. L. in Current Concepts in Forensic Entomology (eds. Amendt, J., Goff, M., Campobasso, C. & Grassberger, M.) 1–24 (Springer, 2010). https://doi.org/10.1007/978-1-4020-9684-6_1Pittner, S. et al. A field study to evaluate PMI estimation methods for advanced decomposition stages. Int. J. Legal Med. 134, 1361–1373 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Probst, C. et al. Estimating the postmortem interval of wild boar carcasses. Vet. Sci. 7, 6 (2020).PubMed Central 

    Google Scholar 
    Cambra-Moo, Ó., Delgado-Buscalioni, Á. & Delgado-Buscalioni, R. An approach to the study of variations in early stages of Gallus gallus decomposition. J. Taphon. 6, 21–40 (2008).
    Google Scholar 
    Oates, D., Coggin, J., Hartman, F. & Hoilien, G. Guide to Time of Death in Selected Wildlife Species. (Nebraska Technical Series No. 14. Lincoln, N.E., Nebraska Game and Parks Commission, 1984).Hewadikaram, K. A. & Goff, M. L. Effect of carcass size on rate of decomposition and arthropod succession patterns. Am. J. Forensic Med. Pathol. 12, 240–265 (1991).
    Google Scholar 
    Zhou, C. & Byard, R. W. Factors and processes causing accelerated decomposition in human cadavers—An overview. J. Forensic Leg. Med. 18, 6–9 (2011).PubMed 

    Google Scholar 
    Cockle, D. L. & Bell, L. S. Human decomposition and the reliability of a ‘Universal’ model for post mortem interval estimations. Forensic Sci. Int. 253(136), e1-136.e9 (2015).
    Google Scholar 
    Azevedo, R. R. & Krüger, R. F. The influence of temperature and humidity on abundance and richness of Calliphoridae (Diptera). Iheringia. Série Zool. 103, 145–152 (2013).
    Google Scholar 
    Barnes, K. M. in Wildlife Forensic Investigation: Principles and Practice (eds. Cooper, J. & Cooper, M.) 149–160 (CRC Press, 2013).Mann, R. W., Bass, W. M. & Meadows, L. Time since death and decomposition of the human body: Variables and observations in case and experimental field studies. J. Forensic Sci. 35, 103–111 (1990).CAS 
    PubMed 

    Google Scholar 
    Gliksman, D. et al. Biotic degradation at night, abiotic degradation at day: Positive feedbacks on litter decomposition in drylands. Glob. Change Biol. 23, 1564–1574 (2017).ADS 

    Google Scholar 
    Araujo, P. I., Grasso, A. A., González-Arzac, A., Méndez, M. S. & Austin, A. T. Sunlight and soil biota accelerate decomposition of crop residues in the Argentine Pampas. Agric. Ecosyst. Environ. 330, 107908 (2022).
    Google Scholar 
    Fernández-Palacios, J. M. & Martín-Esquivel, J. L. Naturaleza de las Islas Canarias: Ecología y Conservación. (Turquesa, 2001).Kenward, M. G. & Roger, J. H. An improved approximation to the precision of fixed effects from restricted maximum likelihood. Comput. Stat. Data Anal. 53, 2583–2595 (2009).MathSciNet 
    MATH 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org (2020).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
    Google Scholar 
    Halekoh, U. & Højsgaard, S. A Kenward–Roger approximation and parametric bootstrap methods for tests in linear mixed models-the R package pbkrtest. J. Stat. Softw. 59, 1–30 (2014).
    Google Scholar 
    Fox, J. & Weisberg, S. An {R} Companion to Applied Regression, Second Edition. (Sage, 2011).Bartoń, K. MuMIn: Multi-Model Inference. (R Package Version 1.43.6, 2019).De Rosario-Martinez, H., Fox, J. & R Core Team. Package ‘phia’ Title Post-Hoc Interaction Analysis. (R Package Version 0.2–1, 2015).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
    Google Scholar 
    Vass, A. Beyond the grave—Understanding human decomposition. Microbiol. Today 28, 190–192 (2001).
    Google Scholar 
    Gill-King, H. in Forensic Taphonomy: The Postmortem Fate of Human Remains (eds. Haglund, W. D. & Sorg, M. H.) 93–104 (CRC Press, 1996). https://doi.org/10.1201/9781439821923.sec2Campobasso, C. P., Di Vella, G. & Introna, F. Factors affecting decomposition and Diptera colonization. Forensic Sci. Int. 12, 18–27 (2001).
    Google Scholar 
    Austin, A. T., Araujo, P. I. & Leva, P. E. Interaction of position, litter type, and water pulses on decomposition of grasses from the semiarid Patagonian steppe. Ecology 90, 2642–2647 (2009).PubMed 

    Google Scholar 
    Brandt, L. A., Bonnet, C. & King, J. Y. Photochemically induced carbon dioxide production as a mechanism for carbon loss from plant litter in arid ecosystems. J. Geophys. Res. Biogeosci. 114, G02004 (2009).ADS 

    Google Scholar 
    Lee, H., Rahn, T. & Throop, H. An accounting of C-based trace gas release during abiotic plant litter degradation. Glob. Chang. Biol. 18, 1185–1195 (2012).ADS 

    Google Scholar 
    Zepp, R. G., Erickson, D. J., Paul, N. D. & Sulzberger, B. Interactive effects of solar UV radiation and climate change on biogeochemical cycling. Photochem. Photobiol. Sci. 6, 286–300 (2007).CAS 
    PubMed 

    Google Scholar 
    Archer, M. S. Rainfall and temperature effects on the decomposition rate of exposed neonatal remains. Sci. Justice J. Forensic Sci. Soc. 44, 35–41 (2004).Simmons, T., Adlam, R. E. & Moffatt, C. Debugging decomposition data—Comparative taphonomic studies and the influence of insects and carcass size on decomposition rate. J. Forensic Sci. 55, 8–13 (2010).PubMed 

    Google Scholar 
    Spicka, A., Johnson, R., Bushing, J., Higley, L. G. & Carter, D. O. Carcass mass can influence rate of decomposition and release of ninhydrin-reactive nitrogen into gravesoil. Forensic Sci. Int. 209, 80–85 (2011).CAS 
    PubMed 

    Google Scholar 
    Tracqui. in Encyclopaedia of Forensic Sciences (eds. Siegel, J. A., Saukko, P. J. & Max, M. H.) 1357–1363 (Academic Press, 2000).Riding, C. S. & Loss, S. R. Factors influencing experimental estimation of scavenger removal and observer detection in bird–window collision surveys. Ecol. Appl. 28, 2119–2129 (2018).PubMed 

    Google Scholar  More

  • in

    Identifying driving factors of urban land expansion using Google Earth Engine and machine-learning approaches in Mentougou District, China

    Orr, D. W. Land use and climate change. Conserv. Biol. 22(6), 1372–1374 (2010).
    Google Scholar 
    Zhang, X. D. et al. Tropospheric ozone perturbations induced by urban land expansion in China from 1980 to 2017. Environ. Sci. Technol. https://doi.org/10.1021/ACS.EST.1C06664 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Noojipady, P. et al. Forest carbon emissions from cropland expansion in the Brazilian cerrado biome. Environ. Res. Lett. 12(2), 025004. https://doi.org/10.1088/1748-9326/aa5986 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhu, B., Xun, Z., Ran, Z. & Zhao, X. Study of multiple land use planning based on the coordinated development of wetland farmland: A case study of Fuyuan City, China. Sustainability 11(1), 271. https://doi.org/10.3390/su11010271 (2019).Article 

    Google Scholar 
    Tong, D., Chu, J., Han, Q. & Liu, X. How land finance drives urban expansion under fiscal pressure: Evidence from Chinese cities. Land. 11(2), 253. https://doi.org/10.3390/land11020253 (2022).Article 

    Google Scholar 
    Chen, J., Chang, K. T., Karacsonyi, D. & Zhang, X. Comparing urban land expansion and its driving factors in Shenzhen and Dongguan, China. Habitat. Int. 43, 61–71. https://doi.org/10.1016/j.habitatint.2014.01.004 (2014).CAS 
    Article 

    Google Scholar 
    Shu, B. R., Zhang, H. H., Li, Y. L., Qu, Y. & Chen, L. Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China. Habitat. Int. 43, 181–190. https://doi.org/10.1016/j.habitatint.2014.02.004 (2014).Article 

    Google Scholar 
    Wang, R. Y., He, W. S., Wu, D., Zhang, L. & Li, Y. J. Urban Land expansion simulation considering the diffusional and aggregated growth simultaneously: A case study of Luoyang City. Sustainability. 13(17), 9781–9781. https://doi.org/10.3390/su13179781 (2021).Article 

    Google Scholar 
    Wei, Y. D. & Ye, X. Determinants of urban land expansion and environmental change in China. Stoch. Env. Res. Risk. A. 28(4), 757–765. https://doi.org/10.1007/s00477-013-0840-9 (2014).Article 

    Google Scholar 
    Yang, Q. K., Duan, X. J., Yang, L. & Wang, L. Spatial-Temporal patterns and driving factors of rapid urban land development in provincial China: A case study of Jiangsu. Sustainability. 9(12), 2371. https://doi.org/10.3390/su9122371 (2017).Article 

    Google Scholar 
    Zhong, Y., Lin, A. & Zhou, Z. Evolution of the pattern of spatial expansion of urban land use in the Poyang Lake ecological economic zone. Int. J. Environ. Res. Public. Health. 16(1), 117. https://doi.org/10.3390/ijerph16010117 (2019).Article 
    PubMed Central 

    Google Scholar 
    Wu, C., Huang, X. & Chen, B. Telecoupling mechanism of urban land expansion based on transportation accessibility: A case study of transitional Yangtze River economic Belt, China. Land Use Policy 96, 104687. https://doi.org/10.1016/j.landusepol.2020.104687 (2020).Article 

    Google Scholar 
    Zhao, P. Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat. Int. 34(2), 236–243. https://doi.org/10.1016/j.habitatint.2009.09.008 (2010).Article 

    Google Scholar 
    Cai, W. J. & Tu, F. Y. Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China. PLoS ONE 15(1), 0227299. https://doi.org/10.1371/journal.pone.0227299 (2020).CAS 
    Article 

    Google Scholar 
    Salvati, L., Carlucci, M., Grigoriadis, E. & Chelli, F. M. Uneven dispersion or adaptive polycentrism? Urban expansion, population dynamics and employment growth in an “ordinary” city. Rev. Region. Res. 38(1), 1–25. https://doi.org/10.1007/s10037-017-0115-x (2017).Article 

    Google Scholar 
    Cao, Y., Ba, I. Z., Zhou, W. & Zhang, X. Analyses of traits and driving forces on urban land expansion in a typical coal-resource-based city in a loess area. Environ. Earth. Sci. 75(16), 1191.1-11911.3. https://doi.org/10.1007/s12665-016-5926-5 (2016).Article 

    Google Scholar 
    Davies, R. G., Barbosa, O. D. & Fuller, R. A. City-wide relationships between green spaces, urban land use and topography. Urban Ecosyst. 11(3), 269. https://doi.org/10.1007/s11252-008-0062-y (2008).Article 

    Google Scholar 
    Cheng, L. L., Liu, M. & Zhan, J. Q. Land use scenario simulation of mountainous districts based on Dinamica EGO model. J. Mt. Sci. 17(2), 289–303. https://doi.org/10.1007/s11629-019-5491-y (2020).Article 

    Google Scholar 
    Liu, J. Y., Zhan, J. Y. & Deng, X. Z. Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. Ambio 34, 450–455. https://doi.org/10.1579/0044-7447-34.6.450 (2005).Article 
    PubMed 

    Google Scholar 
    Li, X. M., Zhou, W. & Quyang, Z. J. Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?. Appl. Geogr. 38, 1–10. https://doi.org/10.1016/j.apgeog.2012.11.004 (2013).Article 

    Google Scholar 
    Wang, Z. W. & Lu, C. H. Urban land expansion and its driving factors of mountain cities in China during 1990–2015. J. Geogr. Sci. 28(8), 1152–1166. https://doi.org/10.1007/s11442-018-1547-0 (2018).MathSciNet 
    Article 

    Google Scholar 
    Zhang, Y. W. & Xie, H. L. Interactive relationship among urban expansion, economic development, and population growth since the reform and opening up in China: An analysis based on a vector error correction model. Land 8(10), 153–153. https://doi.org/10.3390/land8100153 (2019).CAS 
    Article 

    Google Scholar 
    Deng, X., Huang, J., Rozelle, S. & Uchid, E. Growth, population and industrialization, and urban land expansion of China. J. Urban. Econ. 63(1), 96–115. https://doi.org/10.1016/j.jue.2006.12.006 (2006).Article 

    Google Scholar 
    Luo, J., Zhang, X. & Wu, Y. Urban land expansion and the floating population in China: For production or for living?. Cities 74(4), 219–228. https://doi.org/10.1016/j.cities.2017.12.007 (2018).Article 

    Google Scholar 
    Salem, M., Tsurusaki, N. & Divigalpitiya, P. Analyzing the driving factors causing urban expansion in the peri-urban areas using logistic regression: A case study of the greater Cairo region. Infrastructures 4(1), 4. https://doi.org/10.3390/infrastructures4010004 (2019).Article 

    Google Scholar 
    Salem, M., Bose, A. & Chowdhury, I. R. Urban expansion simulation based on various driving factors using a logistic regression model: Delhi as a case study. Sustainability 13(19), 1–17. https://doi.org/10.3390/su131910805 (2021).Article 

    Google Scholar 
    Su, Z. W. et al. Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China. Geomat. Nat. Hazards. Risk. 9(1), 1207–1229. https://doi.org/10.1080/19475705.2018.1505667 (2018).Article 

    Google Scholar 
    Hu, Y. & Hu, Y. Land cover changes and their driving mechanisms in central Asia from 2001 to 2017 supported by google earth engine. Remote. Sens-Basel. 11(5), 554. https://doi.org/10.3390/rs11050554 (2019).ADS 
    Article 

    Google Scholar 
    Liu, Y., Song, W. & Deng, X. Understanding the spatiotemporal variation of urban land expansion in oasis cities by integrating remote sensing and multi-dimensional dpsir-based indicators. Ecol. Indic. 2(96), 23–37. https://doi.org/10.1016/j.ecolind.2018.01.029 (2019).CAS 
    Article 

    Google Scholar 
    Tian, C., Cheng, L. L., Wang, Y. F., Sun, H. Y. & Yin, T. T. Comprehensive effectiveness evaluation and obstacle diagnosis of mining villages in the transition period. Trans. CSAE. 38(5), 241–249. https://doi.org/10.11975/j.issn.1002-6819.2022.05.029 (2022).Article 

    Google Scholar 
    Cheng, L. L., Sun, H. Y., Zhang, Y. & Zhen, S. Spatial structure optimization of mountainous abandoned mine land reuse based on system dynamics model and CLUE-S model. Int. J. Coal. Sci. Techn. 6, 113–126. https://doi.org/10.1007/s40789-019-0241-x (2019).CAS 
    Article 

    Google Scholar 
    Tian, C., Cheng, L. L. & Yin, T. T. Impacts of anthropogenic and biophysical factors on ecological land using logistic regression and random forest: A case study in Mentougou District, Beijing, China. J. Mt. Sci. 19, 433–445. https://doi.org/10.1007/s11629-021-7022-x (2022).Article 

    Google Scholar 
    Gorelick, N., Hanchr, M., Dixon, M., Ilyushchenko, S. & Moore, R. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote. Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 (2017).ADS 
    Article 

    Google Scholar 
    Feng, R. D., Wang, F. Y. & Wang, K. Y. Quantifying influences of anthropogenic-natural factors on ecological land evolution in mega-urban agglomeration: A case study of Guangdong-Hong Kong-Macao Greater Bay area. J. Clean. Prod. 283(9), 125304. https://doi.org/10.1016/j.jclepro.2020.125304 (2021).Article 

    Google Scholar 
    Sun, X., Lu, Z., Li, F. & Crittenden, J. C. Analyzing spatio-temporal changes and tradeoffs to support the supply of multiple ecosystem services in Beijing, China. Ecol. Indicat. 94, 117–129. https://doi.org/10.1016/j.ecolind.2018.06.049 (2018).Article 

    Google Scholar 
    Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A. & Pereira, J. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest. Ecol. Manag. 275, 117–129. https://doi.org/10.1016/j.foreco.2012.03.003 (2012).Article 

    Google Scholar 
    Ugur, A. Dynamic land cover mapping of urbanized cities with Landsat 8 multi-temporal images: Comparative evaluation of classification algorithms and dimension reduction methods. Isprs Int. J. Geo-Inf. 8(3), 139. https://doi.org/10.3390/ijgi8030139 (2019).Article 

    Google Scholar 
    Chapelle, O. Training a support vector machine in the primal. Neural. Comput. 19(5), 1155. https://doi.org/10.1162/neco.2007.19.5.1155 (2007).MathSciNet 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    Lin, Q. Y., Guo, J. Y., Yan, J. F. & Wang, H. Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images. Eur. J. Remote. Sens. 51(1), 1036–1048. https://doi.org/10.1080/22797254.2018.1534532 (2018).Article 

    Google Scholar 
    Devos, O., Ruckebusch, C., Duponchel, L. & Huvenne, J. P. Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation. Chemometr. Intell. Lab. 96(1), 27–33. https://doi.org/10.1016/j.chemolab.2008.11.005 (2009).CAS 
    Article 

    Google Scholar 
    Heumann, B. W. An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote. Sens-Basel. 3(11), 2440–2460. https://doi.org/10.3390/rs3112440 (2011).ADS 
    Article 

    Google Scholar 
    Hsu, C., Chang, C. C. & Lin, C. J. A practical guide to support vector classification, 15. Department of Computer Science, National Taiwan University. https://doi.org/10.1111/j.1365-3016.1995.tb00168.x (2009).Aspinall, R. Modelling land use change with generalized linear models-a multi-model analysis of change between 1860 and 2000 in Gallatin valley, Montana. J. Environ. Manage. 72(1–2), 91–103. https://doi.org/10.1016/j.jenvman.2004.02.009 (2004).Article 
    PubMed 

    Google Scholar 
    Wu, W. & Zhang, J. Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in north-eastern Puerto Rico. Appl. Geogr. 37, 52–62. https://doi.org/10.1016/j.apgeog.2012.10.012 (2013).Article 

    Google Scholar 
    Thomas, D. R., Zhu, P. C. & Decady, Y. J. Point estimates and confidence intervals for variable importance in multiple linear regression. J. Educ. Behav. Stat. 32(1), 61–91. https://doi.org/10.1002/bimj.201100134 (2007).Article 

    Google Scholar 
    Huang, B. & Boutros, P. C. The parameter sensitivity of random forests. BMC Bioinform. 17, 331. https://doi.org/10.1186/s12859-016-1228-x (2016).Article 

    Google Scholar 
    Pang, J., Chen, Y., He, S., Qiu, H. & Mao, L. Classification of friction and wear state of wind turbine gearboxes using decision tree and random forest algorithms. J. Tribol-T. Asme. 143(9), 1–28. https://doi.org/10.1115/1.4049257 (2020).CAS 
    Article 

    Google Scholar 
    Liu, M., Hu, S., Ge, Y., Heuvelink, G. & Huang, X. Using multiple linear regression and random forests to identify spatial poverty determinants in rural China. Spat. Stat.-Neth. 42, 100461. https://doi.org/10.1016/j.spasta.2020.100461 (2020).MathSciNet 
    Article 

    Google Scholar 
    Jutidamrongphan, W. Determine the land-use land-cover changes, urban expansion and their driving factors for sustainable development in Gazipur Bangladesh. Atmosphere 12(10), 1353. https://doi.org/10.3390/atmos12101353 (2021).ADS 
    Article 

    Google Scholar 
    Liu, M. & Tian, H. China’s land cover and land use change from 1700 to 2005: estimations from high-resolution satellite data and historical archives. Glob. Biogeochem. Cycles https://doi.org/10.1029/2009GB003687 (2010).Article 

    Google Scholar 
    Tong, Z., Yao, S., Hu, W. & Cui, F. Simulation of urban expansion in Guangzhou Foshan metropolitan area under the influence of accessibility. Scientia. Geographica. Sinica. 38(5), 737–746 (2018).
    Google Scholar 
    Poelmans, L. & Rompaey, A. V. Complexity and performance of urban expansion models. Comput. Environ. Urban Syst. 34(1), 17–27. https://doi.org/10.1016/j.compenvurbsys.2009.06.001 (2010).Article 

    Google Scholar 
    Galinato, S. P. & Gregma, I. The effects of government spending on deforestation due to agricultural land expansion and CO2 related emissions. Ecol. Econ. 122, 43–53. https://doi.org/10.1016/j.ecolecon.2015.10.025 (2016).Article 

    Google Scholar 
    Xie, X. F., Wu, T., Zhu, M., Jiang, G. J. & Xw, E. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol. Indic. 120, 106925. https://doi.org/10.1016/j.ecolind.2020.106925 (2021).CAS 
    Article 

    Google Scholar 
    Miller, M. D. The mpacts of Atlanta’s urban sprawl on forest cover and fragmentation. Appl. Geogr. 34, 171–179. https://doi.org/10.1016/j.apgeog.2011.11.010 (2012).ADS 
    Article 

    Google Scholar 
    Güneralp, B. & Seto, K. C. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/8/1/014025 (2013).Article 

    Google Scholar 
    Qiao, W. et al. Multi-dimensional expansion of urban space through the lens of land use: The case study of Nanjing city, China. J. Geogr. Sci. 29(5), 749–761. https://doi.org/10.1007/s11442-019-1625-y (2019).Article 

    Google Scholar 
    Yza, B., Lt, A. & Hw, A. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. J. Clean. Prod. 329, 129488. https://doi.org/10.1016/j.jclepro.2021.129488 (2021).Article 

    Google Scholar  More

  • in

    Honey bees save energy in honey processing by dehydrating nectar before returning to the nest

    Berenbaum, M. R. & Calla, B. Honey as a functional food for Apis mellifera. Annu. Rev. Entomol. 66, 185–208. https://doi.org/10.1146/annurev-ento-040320-074933 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Crane, E. Honey: A Comprehensive Survey (Heinemann, 1975).
    Google Scholar 
    Park, O. W. The storing and ripening of honey by honeybees. J. Econ. Entomol. 18, 405–410 (1925).Article 

    Google Scholar 
    Reinhardt, J. F. Ventilating the bee colony to facilitate the honey ripening process. J. Econ. Entomol. 32, 654–660. https://doi.org/10.1093/jee/32.5.654 (1939).Article 

    Google Scholar 
    Eyer, M., Neumann, P. & Dietemann, V. A look into the cell: Honey storage in honey bees, Apis mellifera. PLoS ONE 11(8), e0161059 (2016).Article 

    Google Scholar 
    Oertel, E., Fieger, E. A., Williams, V. R. & Andrews, E. A. Inversion of cane sugar in the honey stomach of the bee. J. Econ. Entomol. 44, 487–492 (1951).CAS 
    Article 

    Google Scholar 
    Park, O. W. Studies on the changes in nectar concentration produced by the honeybee, Apis mellifera. Part I. Changes which occur between the flower and the hive. Res. Bull. Iowa Agric. Exp. Station 151, 211–243 (1932).
    Google Scholar 
    Nicolson, S. W. & Human, H. Bees get a head start on honey production. Biol. Let. 4, 299–301. https://doi.org/10.1098/rsbl.2008.0034 (2008).Article 

    Google Scholar 
    Nicolson, S. W. & Louw, G. N. Simultaneous measurement of evaporative water loss, oxygen consumption, and thoracic temperature during flight in a carpenter bee. J. Exp. Zool. 222, 287–296 (1982).Article 

    Google Scholar 
    Schmid-Hempel, P., Kacelnik, A. & Houston, A. I. Honeybees maximize efficiency by not filling their crop. Behav. Ecol. Sociobiol. 17, 61–66 (1985).Article 

    Google Scholar 
    Kacelnik, A., Houston, A. I. & Schmid-Hempel, P. Central-place foraging in honey bees: The effect of travel time and nectar flow on crop filling. Behav. Ecol. Sociobiol. 19, 19–24. https://doi.org/10.1007/BF00303838 (1986).Article 

    Google Scholar 
    Wolf, T. J., Schmid-Hempel, P., Ellington, C. P. & Stevenson, R. D. Physiological correlates of foraging efforts in honey-bees: Oxygen consumption and nectar load. Funct. Ecol. 3, 417–424 (1989).Article 

    Google Scholar 
    Mitchell, D. Thermal efficiency extends distance and variety for honeybee foragers: Analysis of the energetics of nectar collection and desiccation by Apis mellifera. J. R. Soc. Interface 16, 20180879. https://doi.org/10.1098/rsif.2018.0879 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Corbet, S. A. et al. Native or exotic? Double or single? Evaluating plants for pollinator-friendly gardens. Ann. Bot. 87, 219–232 (2001).Article 

    Google Scholar 
    Harano, K. & Nakamura, J. Nectar loads as fuel for collecting nectar and pollen in honeybees: Adjustment by sugar concentration. J. Comp. Physiol. A. https://doi.org/10.1007/s00359-016-1088-x (2016).Article 

    Google Scholar 
    Nicolson, S. W. & van Wyk, B.-E. Nectar sugars in Proteaceae: Patterns and processes. Aust. J. Bot. 46, 489–504 (1998).Article 

    Google Scholar 
    Corbet, S. A. Nectar sugar content: Estimating standing crop and secretion rate in the field. Apidologie 34, 1–10. https://doi.org/10.1051/apido:2002049 (2003).CAS 
    Article 

    Google Scholar 
    Southwick, E. E. & Pimentel, D. Energy efficiency of honey production by bees. Bioscience 31, 730–732. https://doi.org/10.2307/1308779 (1981).Article 

    Google Scholar 
    Mitchell, D. Nectar, humidity, honey bees (Apis mellifera) and varroa in summer: A theoretical thermofluid analysis of the fate of water vapour from honey ripening and its implications on the control of Varroa destructor. J. R. Soc. Interface 16, 20190048. https://doi.org/10.1098/rsif.2019.0048 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Human, H., Nicolson, S. W. & Dietemann, V. Do honeybees, Apis mellifera scutellata, regulate humidity in their nest?. Naturwissenschaften 93, 397–401 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Ellis, M. B. Homeostasis: Humidity and water relations in honeybee colonies, MSc thesis, University of Pretoria (2008).Ellis, M., Nicolson, S., Crewe, R. & Dietemann, V. Hygropreference and brood care in the honeybee (Apis mellifera). J. Insect Physiol. 54, 1516–1521. https://doi.org/10.1016/j.jinsphys.2008.08.011 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Portman, Z. M., Ascher, J. S. & Cariveau, D. P. Nectar concentrating behavior by bees (Hymenoptera: Anthophila). Apidologie 52, 1169–1194. https://doi.org/10.1007/s13592-021-00895-1 (2021).Article 

    Google Scholar 
    Nicolson, S. W. Water homeostasis in bees, with the emphasis on sociality. J. Exp. Biol. 212, 429–434. https://doi.org/10.1242/jeb.022343 (2009).Article 
    PubMed 

    Google Scholar 
    Pokorny, T., Lunau, K. & Eltz, T. Raising the sugar content – orchid bees overcome the constraints of suction feeding through manipulation of nectar and pollen provisions. PLoS ONE 9(11), e113823. https://doi.org/10.1371/journal.pone.0113823 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lindauer, M. The water economy and temperature regulation of the honeybee colony. Bee World 36, 81–92 (1955).Article 

    Google Scholar 
    Heinrich, B. Mechanisms of body-temperature regulation in honeybees, Apis mellifera. I. Regulation of head temperature. J. Exp. Biol. 85, 61–72 (1980).Article 

    Google Scholar 
    Cooper, P. D., Schaffer, W. M. & Buchmann, S. L. Temperature regulation of honeybees (Apis mellifera) foraging in the Sonoran desert. J. Exp. Biol. 114, 1–15 (1985).Article 

    Google Scholar 
    Louw, G. N. & Hadley, N. F. Water economy of the honeybee: A stoichiometric accounting. J. Exp. Zool. 235, 147–150 (1985).Article 

    Google Scholar 
    Rodney, S. & Purdy, J. Dietary requirements of individual nectar foragers, and colony-level pollen and nectar consumption: A review to support pesticide exposure assessment for honey bees. Apidologie 51, 163–179. https://doi.org/10.1007/s13592-019-00694-9 (2020).Article 

    Google Scholar 
    Drezner-Levy, T., Smith, B. & Shafir, S. The effect of foraging specialization on various learning tasks in the honey bee (Apis mellifera). Behav. Ecol. Sociobiol. 64, 135–148. https://doi.org/10.1007/s00265-009-0829-z (2009).Article 

    Google Scholar 
    Afik, O. & Shafir, S. Effect of ambient temperature on crop loading in the honey bee, Apis mellifera (Hymenoptera: Apidae). Entomologia Generalis 29, 135–148 (2007).Article 

    Google Scholar 
    Seeley, T. D. Honey bee foragers as sensory units of their colonies. Behav. Ecol. Sociobiol. 34, 51–62 (1994).Article 

    Google Scholar 
    Waller, G. D. Evaluating responses of honeybees to sugar solutions using an artificial-flower feeder. Ann. Entomol. Soc. Am. 65, 857–862 (1972).CAS 
    Article 

    Google Scholar 
    Nicolson, S. W., de Veer, L., Köhler, A. & Pirk, C. W. W. Honeybees prefer warmer nectar and less viscous nectar, regardless of sugar concentration. Proc. R. Soc. B: Biol. Sci. 280, 20131597. https://doi.org/10.1098/rspb.2013.1597 (2013).Article 

    Google Scholar 
    Neff, J. L. & Simpson, B. B. The roles of phenology and reward structure in the pollination biology of wild sunflower (Helianthus annuus L., Asteraceae). Israel J. Bot. 39, 197–216 (1990).
    Google Scholar 
    Waller, G. D., Carpenter, E. W. & Ziehl, O. A. Potassium in onion nectar and its probable effect on attractiveness of onion flowers to honey bees. J. Am. Soc. Hortic. Sci. 97, 535–539 (1972).CAS 
    Article 

    Google Scholar 
    Roubik, D. W., Yanega, D., Aluja, M., Buchmann, S. L. & Inouye, D. W. On optimal nectar foraging by some tropical bees (Hymenoptera: Apidae). Apidologie 26, 197–211 (1995).Article 

    Google Scholar 
    Power, E. F., Stabler, D., Borland, A. M., Barnes, J. & Wright, G. A. Analysis of nectar from low-volume flowers: A comparison of collection methods for free amino acids. Methods Ecol. Evol. 9, 734–743. https://doi.org/10.1111/2041-210X.12928 (2018).Article 
    PubMed 

    Google Scholar 
    Pattrick, J. G., Symington, H. A., Federle, W. & Glover, B. J. The mechanics of nectar offloading in the bumblebee Bombus terrestris and implications for optimal concentrations during nectar foraging. J. R. Soc. Interface 17, 20190632. https://doi.org/10.1098/rsif.2019.0632 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strauss, U., Dietemann, V., Human, H., Crewe, R. M. & Pirk, C. W. W. Resistance rather than tolerance explains survival of savannah honeybees (Apis mellifera scutellata) to infestation by the parasitic mite Varroa destructor. Parasitology 143, 374–387. https://doi.org/10.1017/s0031182015001754 (2016).Article 
    PubMed 

    Google Scholar 
    Dyer, F. C. & Seeley, T. D. Interspecific comparisons of endothermy in honey-bees (Apis): Deviations from the expected size-related patterns. J. Exp. Biol. 127, 1–26. https://doi.org/10.1242/jeb.127.1.1 (1987).Article 

    Google Scholar  More

  • in

    How a COVID lockdown changed bird behaviour

    Sightings of some common bird species increased during the UK’s 2020 lockdown.Credit: Tolga Akmen/AFP via Getty

    People weren’t the only ones who changed their ways during the COVID-19 pandemic — birds did, too. Four out of five of the most commonly observed birds in the United Kingdom altered their behaviour during the nation’s first lockdown of 2020, although they did so in different ways depending on the species, according to an analysis.The study, published in Proceedings of the Royal Society B on 21 September1, is one of several that used the disruptions brought about by the pandemic — from a reduction in the number of cars on the roads to the closure of some national parks — to quantify the impact that humanity has on the natural world. Although some research has found that lockdowns had a largely positive effect on wildlife2, the latest data from the United Kingdom provide a much more nuanced picture (see Bird Behaviour).

    Credit: Warrington et al/Proceedings of the Royal Society B

    “People didn’t disappear during the lockdown,” says co-author Miyako Warrington, a behavioural ecologist at the University of Manitoba in Winnipeg, Canada. “We changed our behaviour, and wildlife responded.”Rare experimentIn the early months of the pandemic, social media was abuzz with reports of wild animals being seen in unusual places. These claims were partially validated when Warrington and her colleagues reported that, in 2020, many bird species in the United States and Canada were spotted moving into spaces usually occupied by people2.To see how a COVID-19 lockdown affected birds in the United Kingdom, Warrington and her colleagues tallied sightings of the 25 most common birds between March and July 2020 — during the country’s first lockdown — and compared their data set with data from previous years. In total, the study included around 870,000 observations.The team then compared this information to data showing how people split their time between home, essential shops and parks: three places people in the United Kingdom were allowed to be during the lockdown.Because people spent more time at home and in parks than before March 2020, the analysis found that 20 of the 25 bird species examined behaved differently during lockdown. Parks — which were flooded with visitors — saw an an uptick in the numbers of corvids and gulls, whereas smaller birds, such as Eurasian blue tits (Cyanistes caeruleus) and house sparrows (Passer domesticus), were spotted less frequently than in previous years. And because people spent more time at home, the number of avian species that visited domestic gardens also dropped, by around one-quarter, compared with previous years.Other species, including rock pigeons (Columba livia), didn’t react to the lockdown at all. Warrington found this surprising, because pigeons are city dwellers, so she thought they would be affected by the changes in people’s behaviour. “But they don’t give a crap about what we do,” she says.Adapting to changeThe birds that altered their habits during the lockdown were probably responding to changes in human behaviour, says Warrington. Tits and other birds whose numbers dipped might have fled when people and their pets started spending more time in parks and gardens. The reverse could be true for scavengers, such as gulls and corvids, which might have benefited from park visitors leaving behind rubbish for them to feed on.When combined with the results of other studies, the behaviour of British birds reveals the complex ways in which wildlife was affected by lockdowns and underlines the importance of reducing the disturbance of animals by people, says Raoul Manenti, a conservation zoologist at the University of Milan in Italy.For Warrington, that means acknowledging that lockdowns were not universally good for wildlife. “Our relationship with nature is complicated,” she says. By developing a better understanding of this relationship, “we know we can affect positive change as long as we do it in a thoughtful manner”. More