More stories

  • in

    What manta rays remember: the best spots to get spruced up

    A reef manta ray visits a cleaning station at Lady Elliot Island, Australia. Credit: A. O. Armstrong et al./Ecol. Evol. (CC BY 4.0)

    Ecology
    08 April 2021
    What manta rays remember: the best spots to get spruced up

    Giant fish preserve a mental map of where cleaning fish provide the highest-quality pest removal.

    Share on Twitter
    Share on Twitter

    Share on Facebook
    Share on Facebook

    Share via E-Mail
    Share via E-Mail

    Even sea creatures need pampering. Manta rays make regular visits to ‘cleaning stations’, where small fish rid the rays of skin parasites at the coral-reef equivalent of a day spa. Now it seems that rays can identify and remember spots where they have received quality cleaning.Cleaning stations are often centred on corals inhabited by cleaner shrimp or fish. To understand how these stations influence rays’ movements, Asia Armstrong at the University of Queensland in St Lucia, Australia, and her colleagues tracked 34 reef manta rays (Mobula alfredi) off the coast of eastern Australia for about 1.5 years.The highest density of rays was found at places where cleaning fish called blue-streak cleaner wrasses (Labroides dimidiatus) were most abundant. Rays typically visited cleaning stations during the day, when cleaner wrasses are most active, and favoured stations close to foraging regions.Rays are thought to prefer stations that provide superior cleaning — where the cleaners don’t bite them, for example. The rays’ behaviour suggests that they have a mental map of spots that offer both high-quality cleaning and proximity to foraging grounds.

    Ecol. Evol. (2021)

    Ecology More

  • in

    Assessment of water resource security in karst area of Guizhou Province, China

    Solving the problem of engineering water shortage is key to ensure water resource security in the karst area It can be seen from the subsystems of the indices sorted by the absolute MIV that the engineering water shortage subsystem had the greatest impact on water resource security in the karst area, which is the main reason to promote its transformation.The water shortage in karst areas is caused by poor natural conditions and inadequate engineering conditions, that is, “engineering water shortage”. It is a serious problem in the Guizhou karst area. The main reasons are as follows. First, the karst hydrogeological and geomorphic conditions, with high mountains and deep rivers, make Guizhou a water shortage area. Second, the karst area is rich in water resources, but it is difficult to develop and utilize these resources. Inter annual variations of rainfall are not significant, but there are large differences within the year, which can easily lead to seasonal drought. Third, the layout of water conservancy projects such as water retention, water storage, and water transfer is unreasonable or insufficient, resulting in conditions of shortage of irrigation and the inadequacy of drinking water for people and livestock. Therefore, the Guizhou karst area has become an area of water shortage, especially engineering water shortage. This is the main bottleneck restricting the coordinated development of the region’s social economy and ecology.Water conservancy projects can determine the diversion and allocation of water resources across time and district to achieve reasonable allocation, efficient utilization, and protection. This indicates the need for higher requirements for engineering water storage and improving water resource utilization efficiency. Therefore, the construction of water conservancy projects is key to ensure future water resource security.The modes of development and utilization of water resources are also significant in the karst area In the past 15 years, Guizhou Province has attached great importance to the development and utilization of water resources. The subsystems of water resource carrying capacity and vulnerability in the Guizhou karst area have risen steadily, which has improved water resource security. However, the development and utilization of water resources will cause changes in the quantity and structure of water usage. This has both optimization and constraints on regional development. Therefore, the geological, hydrological, and hydrogeological characteristics of the karst area must be investigated. The development and utilization of water resources in the karst area should involve appropriate technologies or methods in accordance with these different hydrogeological structures. Geology, geomorphology, rainwater, distributions of farmland and residences, and hydrogeological structures in the karst area are the major factors to consider for solving water shortages in this area35. Rain collection, underground reservoirs, a decentralized water supply and runoff gathering are significant modes of development in the karst area.The situation of water resource security in karst area of Guizhou is gradually getting better This is achieved through water conservation projects and technological measures for water resource exploitation, utilization, projection, and reasonable allocation and control. Meanwhile, Guizhou achieves the security of regional water resource utilization and development through adjusting the regional economic pattern, water resource utilization technology, and so forth.From 2001 to 2006, the status of water resource security was serious, and there was a moderate warning level. At that time, the industrialization of Guizhou province was developing rapidly, and the construction of water conservancy and other infrastructure was also advancing rapidly. Increased attention was given to soil erosion, desertification, water resource pollution, and other problems. Despite high water consumption, the water environment was gradually improving. However, rapid economic and social development has exceeded the carrying capacity of the water resources during this period. Some problems persist in the study area, such as inadequacy of urban sewage treatment facilities, outdated water conservancy facilities, and insufficient prevention of environmental pollution. Urban water pollution treatment facilities and garbage treatment facilities are seriously outdated and cannot meet the requirements of urban development and water environmental protection. These problems have led to a low starting point for water resource security utilization in Guizhou Province. Although the situation has been improved and alleviated year by year, it is still in a moderate warning level, and the water resource security situation is still severe.After reaching the critical safety level in 2007, the water resource security of Guizhou Province declined slightly in 2009 and 2013, although a critical safety level was maintained; the safety level further deteriorated to a moderate warning level in 2011. This deterioration occurred because Guizhou suffered its worst drought in a century from 2009 to 2011, and another drought in 2013. According to the information provided by single indices, the treatment rate of urban waste water, proportion of water supply for water lifting and diversion projects, qualifying rate of water environment function zones, qualifying rate of industrial waste water, degree of development and utilization of groundwater, and density of large and medium-sized reservoirs all showed increasing trends year by year or showed relatively high levels. In contrast, the indices of irrigation water consumption per unit area, above moderate rocky desertification area ratio, water consumption per ten thousand yuan GDP, and water consumption per ten thousand yuan industrial output decreased year by year. All of these indices played a driving role in water utilization and water resource security in the study area. Although the once-in-a-century drought reduced the amount of water, Guizhou Province improved the utilization rate of water resources in the dry years, which alleviated the impact of the reduction of water resources to a certain extent, and allowed the water resource security in the study area to barely maintain the critical safety level. This finding is consistent with previous research conclusions: the engineering water shortage subsystem had largest effect on water resource security in the karst area, whereas the water quantity subsystem had the least influence.It can be inferred that the requirements for ensuring water resource security in the karst area are a good economic development model, environmental protection, pollution control, and improvement of basic water conservancy facilities. These measures can be conducive to actively coping with the impact of abnormal climate changes on the utilization of water resources. More

  • in

    Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit

    1.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals: ecological implications. Trends Ecol. Evol. 7, 384–388 (1992).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Mcnab, B. K. Food habits, energetics, and the population biology of mammals. Am. Nat. 116, 106–124 (1980).Article 

    Google Scholar 
    3.Hovey, F. W. & Harestad, A. S. Estimating effects of snow on shrub availability for black-tailed deer in southwestern British Columbia. Wildl. Soc. Bull. 20, 308–313 (1992).
    Google Scholar 
    4.Post, E. & Stenseth, N. Climatic variability, plant phenology, and northern ungulates. Ecology 80, 1322–1339 (1999).Article 

    Google Scholar 
    5.Moen, A. N. Seasonal changes in heart rates, activity, metabolism, and forage intake of white-tailed deer. J. Wildl. Manag. 42, 715–738 (1978).Article 

    Google Scholar 
    6.Holand, Ø., Mysterud, A., Wannag, A. & Linnell, J. D. C. Roe deer in northern environments: physiology and behaviour. In The European Roe Deer: Biology of Success (eds Andersen, R. et al.) 117–137 (Scandinavian University Press, 1998).
    Google Scholar 
    7.Foromozov, A. N. Snow Cover as an Integral Factor of the Environment and Its Importance in the Ecology of Mammals and Birds (The University of Alberta, 1963).
    Google Scholar 
    8.Cagnacci, F. et al. Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120, 1790–1802 (2011).Article 

    Google Scholar 
    9.Dussault, C., Courtois, R., Ouellet, J.-P. & Girard, I. Space use of moose in relation to food availability. Can. J. Zool. 83, 1431–1437 (2005).Article 

    Google Scholar 
    10.Mysterud, A. & Sæther, B.-E. Climate change and implications for the future distribution and management of ungulates in Europe. In Ungulate Management in Europe: Problems and Practices (eds Putman, R. et al.) 349–375 (Cambridge University Press, 2011).
    Google Scholar 
    11.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

    Google Scholar 
    12.Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R. & Appenzeller, C. Snow variability in the Swiss Alps 1864–2009. Int. J. Climatol. 33, 3162–3173 (2013).Article 

    Google Scholar 
    13.Milner, J. M., van Beest, F. M., Schmidt, K. T., Brook, R. K. & Storaas, T. To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates. J. Wildl. Manag. 78, 1322–1334 (2014).Article 

    Google Scholar 
    14.Ossi, F. et al. Plastic response by a small cervid to supplemental feeding in winter across a wide environmental gradient. Ecosphere 8, e01629 (2017).Article 

    Google Scholar 
    15.Putman, R. & Staines, B. W. Supplementary winter feeding of wild red deer Cervus elaphus in Europe and North America: justifications, feeding practice and effectiveness. Mamm. Rev. 34, 285–306 (2004).Article 

    Google Scholar 
    16.Cagnacci, F., Boitani, L., Powell, R. A. & Boyce, M. S. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos. Trans. R. Soc. B Biol. Sci. 365, 2157–2162 (2010).Article 

    Google Scholar 
    17.Peters, W. et al. Migration in geographic and ecological space by a large herbivore. Ecol. Monogr. 87, 297–320 (2017).Article 

    Google Scholar 
    18.Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).Article 

    Google Scholar 
    20.Ossi, F., Gaillard, J. M., Hebblewhite, M. & Cagnacci, F. Snow sinking depth and forest canopy drive winter resource selection more than supplemental feeding in an alpine population of roe deer. Eur. J. Wildl. Res. 61, 111–124 (2015).Article 

    Google Scholar 
    21.Mysterud, A. & Østbye, E. Bed-site selection by European roe deer (Capreolus capreolus) in southern Norway during winter. Can. J. Zool. 73, 924–932 (1995).Article 

    Google Scholar 
    22.Ramanzin, M., Sturaro, E. & Zanon, D. Seasonal migration and home range of roe deer (Capreolus capreolus) in the Italian eastern Alps. Can. J. Zool. 85, 280–289 (2007).Article 

    Google Scholar 
    23.Endrizzi, S., Gruber, S., Dall’Amico, M. & Rigon, R. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geosci. Model. Dev. 7, 2831–2857 (2014).Article 
    ADS 

    Google Scholar 
    24.Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

    Google Scholar 
    25.Thomson, A. M. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    26.Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    27.Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).Article 

    Google Scholar 
    28.Penteriani, V. et al. Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes. Mamm. Rev. 48, 180–193 (2018).Article 

    Google Scholar 
    29.Sorensen, A., van Beest, F. M. & Brook, R. K. Impacts of wildlife baiting and supplemental feeding on infectious disease transmission risk: a synthesis of knowledge. Prev. Vet. Med. 113, 356–363 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Mysterud, A., Viljugrein, H., Solberg, E. J. & Rolandsen, C. M. Legal regulation of supplementary cervid feeding facing chronic wasting disease. J. Wildl. Manag. 83, 1667–1675 (2019).Article 

    Google Scholar 
    31.Ceacero, F. et al. Benefits for dominant red deer hinds under a competitive feeding system: food access behavior, diet and nutrient selection. PLoS ONE 7, e32780 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    32.Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).Article 

    Google Scholar 
    33.Loe, L. E. et al. Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere 7, e01374 (2016).Article 

    Google Scholar 
    34.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    36.Mysterud, A. Still walking on the wild side? Management actions as steps towards ‘semi-domestication’ of hunted ungulates. J. Appl. Ecol. 47, 920–925 (2010).Article 

    Google Scholar 
    37.Felton, A. M. et al. Interactions between ungulates, forests, and supplementary feeding: the role of nutritional balancing in determining outcomes. Mamm. Res. 62, 1–7 (2017).Article 

    Google Scholar 
    38.Ricci, S. et al. Impact of supplemental winter feeding on ruminal microbiota of roe deer Capreolus capreolus. Wildl. Biol. 2019, wlb.00572 (2019).Article 

    Google Scholar 
    39.Lone, K. et al. Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos 123, 641–651 (2014).Article 

    Google Scholar 
    40.Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science (80-) 346, 1517–1519 (2014).CAS 
    Article 
    ADS 

    Google Scholar 
    41.Milanesi, P., Breiner, F. T., Puopolo, F. & Holderegger, R. European human-dominated landscapes provide ample space for the recolonization of large carnivore populations under future land change scenarios. Ecography (Cop.) 40, 1359–1368 (2017).Article 

    Google Scholar 
    42.Pascual-Rico, R. et al. Is diversionary feeding a useful tool to avoid human-ungulate conflicts? A case study with the aoudad. Eur. J. Wildl. Res. 64, 1–7 (2018).Article 

    Google Scholar 
    43.van Beest, F. M., Loe, L. E., Mysterud, A. & Milner, J. M. Comparative space use and habitat selection of moose around feeding stations. J. Wildl. Manag. 74, 219–227 (2010).Article 

    Google Scholar 
    44.Jerina, K. Roads and supplemental feeding affect home-range size of Slovenian red deer more than natural factors. J. Mamm. 93, 1139–1148 (2012).Article 

    Google Scholar 
    45.Ranc, N. et al. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports 10, 11946 (2020). 46.Brown, R. D. & Robinson, D. A. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere 5, 219–229 (2011).Article 
    ADS 

    Google Scholar 
    47.Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U. S. A. 109, 8606–8611 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    48.Gurarie, E. et al. A framework for modelling range shifts and migrations: asking when, whither, whether and will it return. J. Anim. Ecol. 86, 943–959 (2017).PubMed 
    Article 

    Google Scholar 
    49.Rivrud, I. M. et al. Leave before it’s too late: anthropogenic and environmental triggers of autumn migration in a hunted ungulate population. Ecology 97, 1058–1065 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Courtois, R., Dussault, C., Potvin, F. & Daigle, G. Habitat selection by moose (Alces alces) in clear-cut landscapes. Alces 38, 177–192 (2002).
    Google Scholar 
    51.Gilbert, S. L., Hundertmark, K. J., Person, D. K., Lindberg, M. S. & Boyce, M. S. Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter. J. Mamm. 98, 246–259 (2017).Article 

    Google Scholar 
    52.Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    53.Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science (80-) 344, 1242552 (2014).CAS 
    Article 

    Google Scholar 
    54.Mason, T. H. E., Stephens, P. A., Apollonio, M. & Willis, S. G. Predicting potential responses to future climate in an alpine ungulate: Interspecific interactions exceed climate effects. Glob. Change Biol. 20, 3872–3882 (2014).Article 
    ADS 

    Google Scholar 
    55.Carnevali, L., Pedrotti, L., Riga, F. & Toso, S. Banca dati ungulati: Status, distribuzione, consistenza, gestione e prelievo venatorio delle popolazioni di ungulati in Italia. Rapporto 2001–2005 Vol. 117 (Biologia e Conservazione della Fauna, 2009).
    Google Scholar 
    56.Provincia Autonoma di Trento. Analisi delle consistenze e dei prelievi di ungulati, tetraonidi e coturnice. Stagione Venatoria 2018 (Provincia Autonoma di Trento, 2018).
    Google Scholar 
    57.Rockel, B., Will, A. & Hense, A. The regional climate model COSMO-CLM (CCLM). Meteorol. Z. 17, 347–348 (2008).Article 

    Google Scholar 
    58.Boyce, M. S. & McDonald, L. L. Relating populations to habitats using resource selection functions. Trends Ecol. Evol. 14, 268–272 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).Article 

    Google Scholar 
    60.Benoit, T. & Achraf, E. suncalc: compute sun position, sunlight phases, moon position and lunar phase. R package version 0.5.0. https://cran.r-project.org/package=suncalc (2019).61.DeCesare, N. J. et al. Transcending scale dependece in identifying habitat with resource selection functions. Ecol. Appl. 22, 1068–1083 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Kendall, M. A new measure of rank correlation. Biometrika 30, 81–89 (1938).MATH 
    Article 

    Google Scholar 
    63.Cohen, J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213–220 (1968).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Gamer, M., Lemon, J., Fellows, I. & Singh, P. irr: various coefficients of interrater reliability and agreement. R package version 0.84.1. https://cran.r-project.org/package=irr (2019).65.Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: resource selection (probability) functions for use-availability data. R package version 0.3-5. https://cran.r-project.org/package=ResourceSelection (2019).66.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://cran.r-project.org/package=rgdal (2019).67.McLeod, A. I. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. https://cran.r-project.org/package=Kendall (2011).68.Bright Ross, J. G., Peters, W., Ossi, F., Moorcroft P. R., Cordano, E., Eccel, E., Bianchini, F., Ramanzin, M., and Cagnacci, F. Datasets for “Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit.” https://doi.org/10.5281/zenodo.4637674 (2021). More

  • in

    Assessing the potential for deep learning and computer vision to identify bumble bee species from images

    1.Alexandra-Maria, K. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

    Google Scholar 
    2.Winfree, R., Williams, N. M., Gaines, H., Ascher, J. S. & Kremen, C. Wild bee pollinators provide the majority of crop visitation across land-use gradients in New Jersey and Pennsylvania, USA. J. Appl. Ecol. 45, 793–802 (2008).Article 

    Google Scholar 
    3.Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl. Acad. Sci. 110, 13044–13048 (2013).CAS 
    Article 
    ADS 

    Google Scholar 
    4.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

    Google Scholar 
    5.Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. 108, 662–667 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    6.Koh, I. et al. Modeling the status, trends, and impacts of wild bee abundance in the United States. Proc. Natl. Acad. Sci. 113, 140–145 (2016).CAS 
    Article 
    ADS 

    Google Scholar 
    7.Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).CAS 
    Article 

    Google Scholar 
    8.Murray, T. E., Kuhlmann, M. & Potts, S. G. Conservation ecology of bees: populations, species and communities. Apidologie 40, 211–236 (2009).Article 

    Google Scholar 
    9.Michener, C. D. The Bees of the World (Johns Hopkins University Press, Baltimore, 2007).
    Google Scholar 
    10.Milam, J. et al. Validating morphometrics with DNA barcoding to reliably separate three cryptic species of bombus cresson (Hymenoptera: Apidae). Insects 11, 669 (2020).Article 

    Google Scholar 
    11.Williams, P. H. et al. Widespread polytypic species or complexes of local species? Revising bumblebees of the subgenus Melanobombus world-wide (Hymenoptera, Apidae, Bombus). Eur. J. Taxon. 719, 1–120 (2020).
    Google Scholar 
    12.Drew, L. W. Are we losing the science of taxonomy? As need grows, numbers and training are failing to keep up. Bioscience 61, 942–946 (2011).Article 

    Google Scholar 
    13.Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).Article 

    Google Scholar 
    14.Valan, M., Makonyi, K., Maki, A., Vondráček, D. & Ronquist, F. Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks. Syst. Biol. 68, 876–895 (2019).Article 

    Google Scholar 
    15.Gratton, C. & Zuckerberg, B. Citizen science data for mapping bumble bee populations, in Novel Quantitative Methods in Pollinator Ecology and Management (2019).16.MacPhail, V. J., Gibson, S. D., Hatfield, R. & Colla, S. R. Using Bumble Bee Watch to investigate the accuracy and perception of bumble bee (Bombus spp.) identification by community scientists. PeerJ 8, e9412 (2020).Article 

    Google Scholar 
    17.Weeks, P. J. D., Gauld, I. D., Gaston, K. J. & O’Neill, M. A. Automating the identification of insects: a new solution to an old problem. Bull. Entomol. Res. 87, 203–211 (1997).Article 

    Google Scholar 
    18.Schröder, S. et al. The new key to bees: Automated identification by image analysis of wings. in The Conservation Link Between Agriculture and Nature (eds. Kevan, P. & Imperatriz-Fonseca, V.) 209–216 (Ministry of Environment, 2002).19.MacLeod, N., Benfield, M. & Culverhouse, P. Time to automate identification. Nature 467, 154–155 (2010).CAS 
    Article 
    ADS 

    Google Scholar 
    20.Fuentes, A., Yoon, S., Kim, S. C. & Park, D. S. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17, 2022 (2017).Article 

    Google Scholar 
    21.Motta, D. et al. Application of convolutional neural networks for classification of adult mosquitoes in the field. PLoS ONE 14, e0210829 (2019).CAS 
    Article 

    Google Scholar 
    22.Bojarski, M. et al. End to end learning for self-driving cars. arXxiv:1604.07316 (2016).23.Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. & Mougiakakou, S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35, 1207–1216 (2016).Article 

    Google Scholar 
    24.Liu, Z., Gao, J., Yang, G., Zhang, H. & He, Y. Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci. Rep. 6, 20410 (2016).CAS 
    Article 
    ADS 

    Google Scholar 
    25.Martineau, M., Raveaux, R., Chatelain, C., Conte, D. & Venturini, G. Effective training of convolutional neural networks for insect image recognition. In Advanced Concepts for Intelligent Vision Systems, pp 426–437 (eds Blanc-Talon, J. et al.) (Springer International Publishing, Cham, 2018).
    Google Scholar 
    26.Marques, A. C. R. et al. Ant genera identification using an ensemble of convolutional neural networks. PLoS ONE 13, e0192011 (2018).Article 

    Google Scholar 
    27.Williams, P. H., Thorp, R. W., Richardson, L. L. & Colla, S. R. Bumble Bees of North America: An Identification Guide (Princeton University Press, Princeton, 2014).
    Google Scholar 
    28.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. CVPR 770–778 (2015).29.Zagoruyko, S. & Komodakis, N. Wide residual networks. arXxiv:1605.07146 (2017).30.Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. arXxiv:1512.00567 (2015).31.Tan, M. et al. MnasNet: Platform-aware neural architecture search for mobile. arXxiv:1807.11626 (2019).32.Deng, J. et al. ImageNet: A large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (2009).33.Hernández-García, A. & König, P. Further advantages of data augmentation on convolutional neural networks. arXxiv:1906.11052 11139, 95–103 (2018).34.Fard, F. S., Hollensen, P., Mcilory, S. & Trappenberg, T. Impact of biased mislabeling on learning with deep networks. in 2017 International Joint Conference on Neural Networks (IJCNN) 2652–2657 (2017).35.Clare, J. D. J., Townsend, P. A. & Zuckerberg, B. Generalized model-based solutions to false positive error in species detection/non-detection data. Ecology 102, e03241 (2021).Article 

    Google Scholar 
    36.Clare, J. D. J. et al. Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?. Ecol. Appl. 29, e01849 (2019).Article 

    Google Scholar 
    37.Tian, Z. et al. Discriminative CNN via metric learning for hyperspectral classification. in IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium 580–583 (2019).38.Nazki, H., Yoon, S., Fuentes, A. & Park, D. S. Unsupervised image translation using adversarial networks for improved plant disease recognition. Comput. Electron. Agric. 168, 105117 (2020).Article 

    Google Scholar 
    39.Wäldchen, J. & Mäder, P. Machine learning for image based species identification. Methods Ecol. Evol. 9, 2216–2225 (2018).Article 

    Google Scholar 
    40.Woodard, S. H. et al. Towards a U.S. national program for monitoring native bees. Biol. Conserv. 252, 108821 (2020).Article 

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

    Google Scholar 
    42.Montgomery, G. A. et al. Is the insect apocalypse upon us? How to find out. Biol. Conserv. 241, 108327 (2020).Article 

    Google Scholar 
    43.Høye, T. T., Mann, H. M. R. & Bjerge, K. Camera-based monitoring of insects on green roofs. DCE – Natl. Cent. Environ. Energy 18 (2020).44.Ärje, J. et al. Automatic image-based identification and biomass estimation of invertebrates. Methods Ecol. Evol. 11, 922–931 (2020).Article 

    Google Scholar 
    45.Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
    Article 

    Google Scholar 
    46.Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).Article 

    Google Scholar 
    47.Ghisbain, G. et al. Substantial genetic divergence and lack of recent gene flow support cryptic speciation in a colour polymorphic bumble bee (Bombus bifarius) species complex. Syst. Ecol. 45, 635–652 (2020).
    Google Scholar  More

  • in

    Cyanobacterial eagle killer

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    Testing of how and why the Terpios hoshinota sponge kills stony corals

    Experiment 1: Sponge fragmentsEvidence of bleaching first occurred 3 days after the treatment and was only evident in the group with fragments of T. hoshinota. No bleaching was detected in the other 2 groups with the black cloth (to block light) and white cloth (control) (Table 1). Chi-square tests confirmed that the occurrence of bleaching depended on the treatments (p  More

  • in

    Phylogeography and morphological evolution of Pseudechiniscus (Heterotardigrada: Echiniscidae)

    1.Gross, V. et al. Miniaturization of tardigrades (water bears): Morphological and genomic perspectives. Arthr. Struct. Dev. 48, 12–19 (2019).Article 

    Google Scholar 
    2.Møbjerg, N. et al. Survival in extreme environments – on the current knowledge of adaptations in tardigrades. Acta Physiol. 202, 409–420 (2011).Article 
    CAS 

    Google Scholar 
    3.Giribet, G. & Edgecombe, G. D. Current understanding of Ecdysozoa and its internal phylogenetic relationships. Integr. Comp. Biol. 57, 455–466 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Campbell, L. I. et al. MicroRNAs and phylogenomics resolve the relationships of Tardigrada and suggest that velvet worms are the sister group of Arthropoda. Proc. Natl Acad. Sci. USA 108, 15920–15924 (2011).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    5.Jørgensen, A., Møbjerg, N. & Kristensen, R. M. Phylogeny and evolution of the Echiniscidae (Echiniscoidea, Tardigrada) – an investigation of the congruence between molecules and morphology. J. Zool. Syst. Evol. Res. 49(Suppl. 1), 6–16 (2011).Article 

    Google Scholar 
    6.Bertolani, R. et al. Phylogeny of Eutardigrada: new molecular data and their morphological support lead to the identification of new evolutionary lineages. Mol. Phyl. Evol. 76, 110–126 (2014).Article 

    Google Scholar 
    7.Fujimoto, S., Jørgensen, A. & Hansen, J. G. A. molecular approach to arthrotardigrade phylogeny (Heterotardigrada, Tardigrada). Zool. Scr. 46, 496–505 (2017).Article 

    Google Scholar 
    8.Gąsiorek, P., Stec, D., Morek, W. & Michalczyk, Ł. Deceptive conservatism of claws: distinct phyletic lineages concealed within Isohypsibioidea (Eutardigrada) revealed by molecular and morphological evidence. Contrib. Zool. 88, 78–132 (2019).Article 

    Google Scholar 
    9.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Ann. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    10.Bartels, P. J., Apodaca, J. J., Mora, C. & Nelson, D. R. A global biodiversity estimate of a poorly known taxon: phylum Tardigrada. Zool. J. Linn. Soc. 178, 730–736 (2016).Article 

    Google Scholar 
    11.McInnes, S. J. Zoogeographic distribution of terrestrial/freshwater tardigrades from current literature. J. Nat. Hist. 28, 257–352 (1994).Article 

    Google Scholar 
    12.Morek, W., Stec, D., Gąsiorek, P., Surmacz, B. & Michalczyk, Ł. Milnesium tardigradum Doyère, 1840: The first integrative study of interpopulation variability in a tardigrade species. J. Zool. Syst. Evol. Res. 57, 1–23 (2019).Article 

    Google Scholar 
    13.Gąsiorek, P., Blagden, B. & Michalczyk, Ł. Towards a better understanding of echiniscid intraspecific variability: A redescription of Nebularmis reticulatus (Murray, 1905) (Heterotardigrada: Echiniscoidea). Zool. Anz. 283, 242–255 (2019).Article 

    Google Scholar 
    14.Gąsiorek, P. et al. Echiniscus virginicus complex: the first case of pseudocryptic allopatry and pantropical distribution in tardigrades. Biol. J. Linn. Soc. 128, 789–805 (2019).
    Google Scholar 
    15.Cesari, M., McInnes, S. J., Bertolani, R., Rebecchi, L. & Guidetti, R. Genetic diversity and biogeography of the south polar water bear Acutuncus antarcticus (Eutardigrada : Hypsibiidae) – evidence that it is a truly pan-Antarctic species. Invertebr. Syst. 30, 635–649 (2016).Article 

    Google Scholar 
    16.Guidetti, R., McInnes, S. J., Cesari, M., Rebecchi, L. & Rota-Stabelli, O. Evolutionary scenarios for the origin of an Antarctic tardigrade species based on molecular clock analyses and biogeographic data. Contrib. Zool. 86, 97–110 (2017).Article 

    Google Scholar 
    17.Stec, D., Krzywański, Ł, Zawierucha, K. & Michalczyk, Ł. Untangling systematics of the Paramacrobiotus areolatus species complex by an integrative redescription of the nominal species for the group, with multilocus phylogeny and species delineation in the genus Paramacrobiotus. Zool. J. Linn. Soc. 188, 694–716 (2020).Article 

    Google Scholar 
    18.Thulin, G. Beiträge zur Kenntnis der Tardigradenfauna Schwedens. Ark. Zool. 7, 1–60 (1911).
    Google Scholar 
    19.Kristensen, R. M. Generic revision of the Echiniscidae (Heterotardigrada), with a discussion of the origin of the family. In Biology of Tardigrada (ed. Bertolani, R.) 261–335 (U.Z.I. Modena, 1987).
    Google Scholar 
    20.Vecchi, M. et al. Integrative systematic studies on tardigrades from Antarctica identify new genera and new species within Macrobiotoidea and Echiniscoidea. Invertebr. Syst. 30, 303–322 (2016).Article 

    Google Scholar 
    21.Cesari, M. et al. An integrated study of the biodiversity within the Pseudechiniscus suillus–facettalis group (Heterotardigrada: Echiniscidae). Zool. J. Linn. Soc. 188, 717–732 (2020).
    Google Scholar 
    22.Tumanov, D. V. Analysis of non-morphometric morphological characters used in the taxonomy of the genus Pseudechiniscus (Tardigrada: Echiniscidae). Zool. J. Linn. Soc. 188, 753–775 (2020).
    Google Scholar 
    23.Grobys, D. et al. High diversity in the Pseudechiniscus suillus–facettalis complex (Heterotardigrada: Echiniscidae) with remarks on the morphology of the genus Pseudechiniscus. Zool. J. Linn. Soc. 188, 733–752 (2020).Article 

    Google Scholar 
    24.Roszkowska, M. et al. Integrative description of five Pseudechiniscus species (Heterotardigrada: Echiniscidae: the suillus-facettalis complex). Zootaxa 4763, 451–484 (2020).Article 

    Google Scholar 
    25.Gąsiorek, P. et al. New Asian and Nearctic Hypechiniscus species (Heterotardigrada: Echiniscidae) signalise a pseudocryptic horn of plenty. Zool. J. Linn. Soc. (in press).26.Fontoura, P. & Morais, P. Assessment of traditional and geometric morphometrics for discriminating cryptic species of the Pseudechiniscus suillus complex (Tardigrada, Echiniscidae). J. Zool. Syst. Evol. Res. 49(Suppl. 1), 26–33 (2011).Article 

    Google Scholar 
    27.Yu, Y., Harris, A. J. & He, X. S-DIVA (Statistical Dispersal-Vicariance Analysis): a tool for inferring biogeographic histories. Mol. Phyl. Evol. 56, 848–850 (2010).Article 

    Google Scholar 
    28.Matzke, N. J. Probabilistic historical biogeography: new models for founder- event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 242–248 (2013).Article 

    Google Scholar 
    29.Pagel, M., Meade, A. & Barker, D. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53, 673–684 (2004).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    31.Rocha, A., Doma, I., Gonzalez-Reyes, A. & Lisi, O. Two new tardigrade species (Echiniscidae, Doryphoribiidae) from Salta province (Argentina). Zootaxa 4878, 267–286 (2020).Article 

    Google Scholar 
    32.Ehrenberg, C. G. Diagnoses novarum formarum. Verhandl. König. Preuss. Akad. Wiss Berlin 8, 526–533 (1853).
    Google Scholar 
    33.Mihelčič, F. Zwei neue Tardigradenarten aus Spanien. Zool. Anz. 155, 309–311 (1955).
    Google Scholar 
    34.Mihelčič, F. Beitrag zur Systematik de Tardigraden . Arch. Zool. Ital. 36, 57–103 (1951).
    Google Scholar 
    35.Iharos, A. Zwei neue Tardigraden-Arten. Zool. Anz. 115, 218–220 (1936).
    Google Scholar 
    36.Murray, J. Some South African Tardigrada. J. R. Microsc. Soc. 12, 515–524 (1907).Article 

    Google Scholar 
    37.Yang, T. Three new species and one new record of the Tardigrada from China. Acta Hydrobiol. Sin. 26, 504–507 (2002).
    Google Scholar 
    38.Mihelčič, F. Beiträge zur Kenntnis der Tardigrada Jugoslawiens. Zool. Anz. 121, 95–96 (1938).
    Google Scholar 
    39.Bartoš, E. Eine neue Tardigradenart aus der Tschechoslowakei. Zool. Anz. 106, 175–176 (1934).
    Google Scholar 
    40.Richters, F. Beitrag zur Kenntnis der Moosfauna Australiens und der Inseln des Pazifischen Ozeans. Zool. Jahrb. Abt. Syst. Ökol. Geogr. Tiere 26, 196–213 (1908).
    Google Scholar 
    41.Vončina, K., Kristensen, R. M. & Gąsiorek, P. Pseudechiniscus in Japan: re-description of Pseudechiniscus asper Abe et al., 1998 and description of Pseudechiniscus shintai sp. nov. Zoosyst. Evol. 96, 527–536 (2020).Article 

    Google Scholar 
    42.Wang, L. Tardigrades from the Yunnan-Guizhou Plateau (China) with description of two new species in the genera Mixibius (Eutardigrada: Hypsibiidae) and Pseudechiniscus (Heterotardigrada: Echiniscidae). J. Nat. Hist. 43, 2553–2570 (2009).Article 

    Google Scholar 
    43.Hulings, N. C. & Gray, J. S. A manual for the study of meiofauna. Smithson. Contrib. Zool. 78, 1–84 (1971).
    Google Scholar 
    44.Gąsiorek, P. & Michalczyk, Ł. Revised Cornechiniscus (Heterotardigrada) and new phylogenetic analyses negate echiniscid subfamilies and tribes. R. Soc. Open Sci. 7, 200581 (2020).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    45.Dastych, H. Notes on the revision of the genus Mopsechiniscus (Tardigrada). Zool. Anz. 240, 299–308 (2001).Article 

    Google Scholar 
    46.Rebecchi, L., Altiero, T., Eibye-Jacobsen, J., Bertolani, R. & Kristensen, R. M. A new discovery of Novechiniscus armadilloides (Schuster, 1975) (Tardigrada, Echiniscidae) from Utah, USA with considerations on non-marine Heterotardigrada phylogeny and biogeography. Org. Divers. Evol. 8, 58–65 (2008).Article 

    Google Scholar 
    47.Binda, M. G. & Kristensen, R. M. Notes on the genus Oreella (Oreellidae) and the systematic position of Carphania fluviatilis Binda, 1978 (Carphaniidae fam. nov., Heterotardigrada). Animalia 13, 9–20 (1986).
    Google Scholar 
    48.Binda, M. G. Risistemazione di alcuni Tardigradi con l’instituzione di un nuovo genere di Oreellidae e della nuova famiglia Archechiniscidae. Animalia 5, 307–314 (1978).
    Google Scholar 
    49.Kristensen, R. M. & Hallas, T. E. The tidal genus Echiniscoides and its variability, with erection of Echiniscoididae fam. n. (Tardigrada). Zool. Scr. 9, 113–127 (1980).Article 

    Google Scholar 
    50.Møbjerg, N., Kristensen, R. M. & Jørgensen, A. Data from new taxa infer Isoechiniscoides gen. nov. and increase the phylogenetic and evolutionary understanding of echiniscoidid tardigrades (Echiniscoidea: Tardigrada). Zool. J. Linn. Soc. 178, 804–818 (2016).Article 

    Google Scholar 
    51.Møbjerg, N., Jørgensen, A. & Kristensen, R. M. Ongoing revision of Echiniscoididae (Heterotardigrada: Echiniscoidea), with the description of a new interstitial species and genus with unique anal structures. Zool. J. Linn. Soc. 188, 663–680 (2020).Article 

    Google Scholar 
    52.Gąsiorek, P., Suzuki, A. C., Kristensen, R. M., Lachowska-Cierlik, D. & Michalczyk, Ł. Untangling the Echiniscus Gordian knot: Stellariscus gen. nov. (Heterotardigrada: Echiniscidae) from Far East Asia. Invertebr. Syst. 32, 1234–1247 (2018).Article 

    Google Scholar 
    53.Dastych, H. Echiniscus rackae sp. n., a new species of Tardigrada from the Himalayas. Entomol. Mitt. Zool. Mus. Hamburg 8, 246–250 (1986).
    Google Scholar 
    54.McInnes, S. J. Tardigrades from Signy Island, South Orkney Islands, with particular reference to freshwater species. J. Nat. Hist. 29, 1419–1445 (1995).Article 

    Google Scholar 
    55.Dastych, H. Two new species of Tardigrada from the Canadian Subarctic with some notes on sexual dimorphism in the family Echiniscidae. Entomol. Mitt. Zool. Mus. Hamburg 8, 319–334 (1987).
    Google Scholar 
    56.Pilato, G., Binda, M. G. & Lisi, O. Remarks on some Echiniscidae (Heterotardigrada) from New Zealand with the description of two new species. Zootaxa 1027, 27–45 (2005).Article 

    Google Scholar 
    57.Pilato, G., Binda, M. G., Napolitano, A. & Moncada, E. Notes on South American tardigrades with the description of two new species: Pseudechiniscus spinerectus and Macrobiotus danielae. Trop. Zool. 14, 223–231 (2001).Article 

    Google Scholar 
    58.Fontoura, P., Pilato, G. & Lisi, O. First record of Tardigrada from São Tomé (Gulf of Guinea, Western Equatorial Africa) and description of Pseudechiniscus santomensis sp. nov. (Heterotardigrada: Echiniscidae). Zootaxa 2564, 31–42 (2010).Article 

    Google Scholar 
    59.Bartoš, E. Die Tardigraden der Chinesischen und Javanischen Moosproben. Acta Soc. Zool. Bohem. 27, 108–114 (1963).
    Google Scholar 
    60.Beijerinck, M.W. De infusies en de ontdekking der backteriën. Jaarboek van de Koninklijke Akademie v. Wetenschappen. Amsterdam: Müller (1913).61.Baas-Becking, L. G. M. Geobiologie of inleiding tot de milieukunde (W.P. Van Stockum & Zoon, 1934).
    Google Scholar 
    62.Wallace, A. R. The geographical distribution of animals: with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth’s surface (Macmillan and Company, 1876).
    Google Scholar 
    63.Niedbała, W. The ptyctimous mites fauna of the Oriental and Australian regions and their centres of origin (Acari: Oribatida). Genus Suppl. 10, 1–493 (2000).
    Google Scholar 
    64.Niedbała, W. Ptyctimous mites (Acari: Oribatida) of South Africa. Ann. Zool. 56(Suppl. 1), 1–97 (2006).
    Google Scholar 
    65.Janion-Scheepers, C., Deharveng, L., Bedos, A. & Chown, S. Updated list of Collembola species currently recorded from South Africa. ZooKeys 503, 55–88 (2015).Article 

    Google Scholar 
    66.Kisielewski, J. Inland-water Gastrotricha from Brazil. Ann. Zool. 43(Suppl. 2), 1–168 (1991).
    Google Scholar 
    67.Tuxen, S. L. Ecology and zoogeography of the Brazilian Protura (Insecta). Stud. Neotrop. Fauna Environ. 12, 225–247 (1977).Article 

    Google Scholar 
    68.Greenslade, P. Why are there so many exotic springtails in Australia? A review. Soil Org. 90, 141–156 (2018).
    Google Scholar 
    69.Smit, H. Australian water mites of the subfamily Notoaturinae Besch (Acari: Hydrachnidia: Aturidae), with the description of 24 new species. Int. J. Acarol. 36, 101–146 (2010).Article 

    Google Scholar 
    70.Moir, M. L., Brennan, K. E. C. & Harvey, M. S. Diversity, endemism and species turnover of millipedes within the south-western Australian global biodiversity hotspot. J. Biogeogr. 36, 1958–1971 (2009).Article 

    Google Scholar 
    71.Harvey, M. S., Abrams, K. M., Beavis, A. S., Hillyer, M. J. & Huey, J. A. Pseudoscorpions of the family Feaellidae (Pseudoscorpiones: Feaelloidea) from the Pilbara region of Western Australia show extreme short-range endemism. Invertebr. Syst. 30, 491–508 (2016).Article 

    Google Scholar 
    72.Claxton, S.K. The taxonomy and distribution of Australian terrestrial tardigrades. PhD thesis, Macquarie University: Sydney (2004).73.Simpson, G. G. Tempo and mode in evolution (Columbia University Press, 1944).
    Google Scholar 
    74.Dastych, H. The Tardigrada of Poland. Monogr. Faun. Pol. 16, 1–255 (1988).
    Google Scholar 
    75.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    76.Petersen, B. The tardigrade fauna of Greenland. Medd. Grønl. 150, 1–94 (1951).
    Google Scholar 
    77.de Bruyn, M. et al. Borneo and Indochina are major evolutionary hotspots for Southeast Asian biodiversity. Syst. Biol. 63, 879–901 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Ugland, K. I., Gray, J. S. & Ellingsen, K. E. The species–accumulation curve and estimation of species richness. J. Anim. Ecol. 72, 888–897 (2003).Article 

    Google Scholar 
    79.Casquet, J. T., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12, 136–141 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Stec, D., Kristensen, R.M. & Michalczyk, Ł. An integrative description of Minibiotus ioculator sp. nov. from the Republic of South Africa with notes on Minibiotus pentannulatus Londoño et al., 2017 (Tardigrada: Macrobiotidae). Zool. Anz. 286, 117–134 (2020).81.Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

    Google Scholar 
    82.Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    84.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Meth. 14, 587–589 (2017).CAS 
    Article 

    Google Scholar 
    85.Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Katoh, K. & Toh, H. Recent developments in the MAFFT multiple sequence alignment program. Brief. Bioinform. 9, 286–298 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180 (2011).Article 

    Google Scholar 
    90.Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Suchard, M.A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 Virus Evol. 4, vey016 (2018).93.Rambaut, A., Suchard, M.A., Xie, D. & Drummond, A.J. Tracer v1.6 (2014). Available from http://beast.bio.ed.ac.uk/Tracer.94.Drummond, A. J. & Suchard, M. A. Bayesian random local clocks, or one rate to rule them all. BMC Biol. 8, 114 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    95.Ferreira, M. A. R. & Suchard, M. A. (2008) Bayesian analysis of elapsed times in continuous-time Markov chains. Can. J. Stat. 36, 355–368 (2008).MATH 
    Article 

    Google Scholar 
    96.Münkemüller, T. et al. How to measure and test phylogenetic signal. Meth. Ecol. Evol. 3, 743–756 (2012).Article 

    Google Scholar 
    97.Yu, Y., Harris, A. J., Blair, C. & He, X. J. RASP (Reconstruct Ancestral State in Phylogenies): a tool for historical biogeography. Mol. Phyl. Evol. 87, 46–49 (2015).Article 

    Google Scholar 
    98.Yu, Y., Blair, C. & He, X. J. RASP 4: ancestral state reconstruction tool for multiple genes and characters. Mol. Biol. Evol. 37, 604–606 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    99.Jombart, T., Balloux, F. & Dray, S. adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics 26, 1907–1909 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    100.Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30, 2216–2218 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades. Syst. Biol. 63, 951–970 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    102.Ree, R. H. & Sanmartín, I. Conceptual and statistical problems with the DEC+J model of founder-event speciation and its comparison with DEC via model selection. J. Biogeogr. 45, 741–749 (2018).Article 

    Google Scholar 
    103.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

    Google Scholar 
    104.Cobos, M. E., Peterson, A. T., Barve, N. & Osorio-Olvera, L. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ 7, e6281 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    105.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).106.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    107.Escobar, L. E., Lira-Noriega, A., Medina-Vogel, G. & Peterson, A. T. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: use of Maxent and NicheA to assure strict model transference. Geospat. Health 9, 221–229 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    108.Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Model. 213, 63–72 (2008).Article 

    Google Scholar 
    109.Anderson, R. P., Lew, D. & Peterson, A. T. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol. Model. 162, 211–232 (2003).Article 

    Google Scholar 
    110.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project (2020). More

  • in

    Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile

    1.Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).ADS 
    Article 

    Google Scholar 
    2.Yost, J. L. & Hartemink, A. E. How deep is the soil studied—an analysis of four soil science journals. Plant Soil 452, 5–18 (2020).CAS 
    Article 

    Google Scholar 
    3.Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).Article 

    Google Scholar 
    4.Dove, N. C. et al. Continental-scale patterns of extracellular enzyme activity in the subsoil: an overlooked reservoir of microbial activity. Environ. Res. Lett. 15, 1040a1 (2020).CAS 
    Article 

    Google Scholar 
    5.Hicks Pries, C. E., Castanha, C., Porras, R. C. & Torn, M. S. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Taş, N. et al. Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest. ISME J. 8, 1904–1919 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    7.Brewer, T. E. et al. Ecological and genomic attributes of novel bacterial taxa that thrive in subsurface soil horizons. mBio 10, e01318–e01319 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018).CAS 
    Article 

    Google Scholar 
    9.Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).CAS 
    Article 

    Google Scholar 
    10.Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Melillo, J. M. et al. Soil warming, carbon–nitrogen interactions, and forest carbon budgets. Proc. Natl Acad. Sci. USA 108, 9508–9512 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Feng, X., Simpson, A. J., Wilson, K. P., Dudley Williams, D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Dove, N. C., Stark, J. M., Newman, G. S. & Hart, S. C. Carbon control on terrestrial ecosystem function across contrasting site productivities: the carbon connection revisited. Ecology 100, e02695 (2019).PubMed 
    Article 

    Google Scholar 
    15.Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, nclimate1796 (2013).Article 
    CAS 

    Google Scholar 
    16.Olander, L. P. & Vitousek, P. M. Regulation of soil phosphatase and chitinase activity by N and P availability. Biogeochemistry 49, 175–190 (2000).CAS 
    Article 

    Google Scholar 
    17.DeAngelis, K. M. et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front. Microbiol. 6, 104–115 (2015).18.Cheng, L. et al. Warming enhances old organic carbon decomposition through altering functional microbial communities. ISME J. 11, 1825–1835 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Billings, S. A. & Ballantyne, F. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).ADS 
    Article 

    Google Scholar 
    20.Rasmussen, C., Torn, M. S. & Southard, R. J. Mineral assemblage and aggregates control carbon dynamics in a California conifer forest. Soil Sci. Soc. Am. J. 69, 1711–1721 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Jones, D. L. et al. Microbial competition for nitrogen and carbon is as intense in the subsoil as in the topsoil. Soil Biol. Biochem. 117, 72–82 (2018).CAS 
    Article 

    Google Scholar 
    22.Pold, G. et al. Carbon use efficiency and its temperature sensitivity covary in soil bacteria. mBio 11, e02293–19 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2018).24.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).PubMed 
    Article 

    Google Scholar 
    26.Sullivan, B. W. et al. Assessing nutrient limitation in complex forested ecosystems: alternatives to large‐scale fertilization experiments. Ecology 95, 668–681 (2014).PubMed 
    Article 

    Google Scholar 
    27.Hart, S. C., Firestone, M. K. & Paul, E. A. Decomposition and nutrient dynamics of ponderosa pine needles in a Mediterranean-type climate. Can. J. Forest Res. 22, 306–314 (1992).CAS 
    Article 

    Google Scholar 
    28.Dijkstra, P. et al. Effect of temperature on metabolic activity of intact microbial communities: evidence for altered metabolic pathway activity but not for increased maintenance respiration and reduced carbon use efficiency. Soil Biol. Biochem. 43, 2023–2031 (2011).CAS 
    Article 

    Google Scholar 
    29.Don, A., Rödenbeck, C. & Gleixner, G. Unexpected control of soil carbon turnover by soil carbon concentration. Environ. Chem. Lett. 11, 407–413 (2013).CAS 
    Article 

    Google Scholar 
    30.Rodriguez-R, L. M. & Konstantinidis, K. T. Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics 30, 629–635 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Rodriguez-R, L. M. & Konstantinidis, K. T. Estimating coverage in metagenomic data sets and why it matters. ISME J. 8, 2349–2351 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).CAS 
    Article 

    Google Scholar 
    33.Levasseur, A., Drula, E., Lombard, V., Coutinho, P. M. & Henrissat, B. Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes. Biotechnol. Biofuels 6, 41 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Benoit, I. et al. Degradation of different pectins by fungi: correlations and contrasts between the pectinolytic enzyme sets identified in genomes and the growth on pectins of different origin. BMC Genomics 13, 321 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Tveit, A. T., Urich, T. & Svenning, M. M. Metatranscriptomic analysis of arctic peat soil microbiota. Appl. Environ. Microbiol. 80, 5761–5772 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Huergo, L. F. & Dixon, R. The emergence of 2-Oxoglutarate as a master regulator metabolite. Microbiol. Mol. Biol. Rev. 79, 419–435 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Bai, X. et al. Expression of a β-mannosidase from Paenibacillus polymyxa A-8 in Escherichia coli and characterization of the recombinant enzyme. PLoS ONE 9, e111622 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    38.Sorensen, J. W., Dunivin, T. K., Tobin, T. C. & Shade, A. Ecological selection for small microbial genomes along a temperate-to-thermal soil gradient. Nat. Microbiol. 4, 55–61 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Pold, G., Grandy, A. S., Melillo, J. M. & DeAngelis, K. M. Changes in substrate availability drive carbon cycle response to chronic warming. Soil Biol. Biochem. 110, 68–78 (2017).CAS 
    Article 

    Google Scholar 
    40.Yue, H. et al. The microbe-mediated mechanisms affecting topsoil carbon stock in Tibetan grasslands. ISME J. 9, 2012–2020 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Xue, K. et al. Warming alters expressions of microbial functional genes important to ecosystem functioning. Front. Microbiol. 7, 668–681 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    42.Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Johnston, E. R. et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. Proc. Natl Acad. Sci. USA 116, 15096–15105 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Soong, J. L. et al. Microbial carbon limitation: the need for integrating microorganisms into our understanding of ecosystem carbon cycling. Glob. Change Biol. 26, 1953–1961 (2020).ADS 
    Article 

    Google Scholar 
    45.Chapin, F. S., Matson, P. A. & Vitousek, P. Principles of Terrestrial Ecosystem Ecology (Springer Science & Business Media, 2011).46.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    47.Oliverio, A. M., Bradford, M. A. & Fierer, N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob. Change Biol. 23, 2117–2129 (2017).ADS 
    Article 

    Google Scholar 
    48.Pold, G. et al. Long-term warming alters carbohydrate degradation potential in temperate forest soils. Appl. Environ. Microbiol. 82, 6518–6530 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Morrison, E. W. et al. Warming alters fungal communities and litter chemistry with implications for soil carbon stocks. Soil Biol. Biochem. 132, 120–130 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    50.Bai, W., Wang, G., Xi, J., Liu, Y. & Yin, P. Short-term responses of ecosystem respiration to warming and nitrogen addition in an alpine swamp meadow. Eur. J. Soil Biol. 92, 16–23 (2019).CAS 
    Article 

    Google Scholar 
    51.Tilman, D. Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc. Natl Acad. Sci. USA 101, 10854–10861 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. mBio 11, e00416–e00420 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Qi, J. et al. Drying-wetting cycles: effect on deep soil carbon. Soil Syst. 2, 3 (2018).CAS 
    Article 

    Google Scholar 
    54.Butcher, K. R., Nasto, M. K., Norton, J. M. & Stark, J. M. Physical mechanisms for soil moisture effects on microbial carbon-use efficiency in a sandy loam soil in the western United States. Soil Biol. Biochem. 150, 107969 (2020).CAS 
    Article 

    Google Scholar 
    55.Zhou, W. P., Shen, W. J., Li, Y. E. & Hui, D. F. Interactive effects of temperature and moisture on composition of the soil microbial community. Eur. J. Soil Biol. 68, 909–918 (2017).CAS 

    Google Scholar 
    56.Spohn, M., Klaus, K., Wanek, W. & Richter, A. Microbial carbon use efficiency and biomass turnover times depending on soil depth—Implications for carbon cycling. Soil Biol. Biochem. 96, 74–81 (2016).CAS 
    Article 

    Google Scholar 
    57.Saifuddin, M., Bhatnagar, J. M., Segrè, D. & Finzi, A. C. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat. Commun. 10, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    58.Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).CAS 
    Article 

    Google Scholar 
    59.Dove, N. C., Safford, H. D., Bohlman, G. N., Estes, B. L. & Hart, S. C. High-severity wildfire leads to multi-decadal impacts on soil biogeochemistry in mixed-conifer forests. Ecol. Appl. 30, e02072 (2020).PubMed 
    Article 

    Google Scholar 
    60.Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).PubMed 
    Article 

    Google Scholar 
    61.Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009–e00015 (2016).PubMed 
    Article 

    Google Scholar 
    62.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 
    63.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 
    64.Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Zhang, Z., Schwartz, S., Wagner, L. & Miller, W. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7, 203–214 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    66.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 
    67.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    70.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    71.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. No. LBNL-7065E (Ernest Orlando Lawrence Berkeley National Laboratory, 2014).74.Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Xue, Y., Jonassen, I., Øvreås, L. & Taş, N. Metagenome-assembled genome distribution and key functionality highlight importance of aerobic metabolism in Svalbard permafrost. FEMS Microbiol Ecol. 96, fiaa057 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    78.Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    81.Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
    Google Scholar 
    83.Aziz, R. K. et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics 9, 1–15 (2008).Article 
    CAS 

    Google Scholar 
    84.Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Zhou, Z., Tran, P., Liu, Y., Kieft, K. & Anantharaman, K. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. bioRxiv https://doi.org/10.1101/761643 (2019).86.Emiola, A. & Oh, J. High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage. Nat. Commun. 9, 1–8 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    87.Binkley, D. & Hart, S. C. in Advances in Soil Science (ed. Stewart, B. A.) 57–112 (Springer New York, 1989).88.Fox, J. & Weisberg, S. An R companion to applied regression Ch.4. (Sage, 2019).89.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    90.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 
    Article 

    Google Scholar 
    91.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    92.Oksanen, J. et al. vegan: Community Ecology Package. https://cran.r-project.org/web/packages/vegan/index.html (2013).93.Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    94.Bruns, T. D., White, T. J. & Taylor, J. W. Fungal molecular systematics. Annu. Rev. Ecol. Evol. Syst. 22, 525–564 (1991).Article 

    Google Scholar  More