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    Author Correction: Areas of global importance for conserving terrestrial biodiversity, carbon and water

    Biodiversity and Natural Resources Program (BNR), International Institute for Applied Systems Analysis (IIASA), Laxenburg, AustriaMartin Jung, Matthew Lewis, Dmitry Schepaschenko, Myroslava Lesiv, Steffen Fritz, Michael Obersteiner & Piero ViscontiUN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UKAndy Arnell, Shaenandhoa García-Rangel, Jennifer Mark, Lera Miles, Corinna Ravilious, Oliver Tallowin, Arnout van Soesbergen, Valerie Kapos & Neil BurgessFood and Agriculture Organization of the United Nations (FAO), Rome, ItalyXavier de LamoDepartment of Zoology, University of Cambridge, Cambridge, UKMatthew LewisDepartment of Ecology and Evolutionary Biology, University of Connecticut, Stamford, CT, USACory MerowRoyal Botanic Gardens, Kew, Richmond, UKIan Ondo, Samuel Pironon & Rafaël GovaertsBotanic Gardens Conservation International, Richmondy, UKMalin RiversSiberian Federal University, Krasnoyarsk, RussiaDmitry SchepaschenkoDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABradley L. Boyle, Brian J. Enquist, Brian Maitner & Erica A. NewmanDepartment of Geography, Florida State University, Tallahassee, FL, USAXiao FengDepartment of Biological Sciences, Macquarie University, North Ryde, New South Wales, AustraliaRachael GallagherSchool of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelShai Meiri & Gali OferDepartment of Geography, King’s College London, London, UKMark MulliganMitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelUri RollCIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, Vairão, PortugalJeffrey O. HansonDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USAWalter Jetz & D. Scott RinnanCenter for Biodiversity and Global Change, Yale University, New Haven, CT, USAWalter Jetz & D. Scott RinnanDepartment of Biology and Biotechnologies, Sapienza University of Rome, Rome, ItalyMoreno Di MarcoThe Nature Conservancy, Arlington, VA, USAJennifer McGowanColumbia University, New York, NY, USAJeffrey D. SachsSchool of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Tasmania, AustraliaVanessa M. AdamsCSIRO Land and Water, Canberra, Australian Capital Territory, AustraliaSamuel C. AndrewDepartment of Biology, University of Kentucky, Lexington, KY, USAJoseph R. BurgerBetty and Gordon Moore Center for Science, Conservation International, Arlington, VA, USALee Hannah & Patrick R. RoehrdanzDepartamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, ChilePablo A. MarquetInstituto de Ecología y Biodiversidad (IEB), Santiago, ChilePablo A. MarquetCentro de Cambio Global UC, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, ChilePablo A. MarquetThe Santa Fe Institute, Santa Fe, NM, USAPablo A. MarquetInstituto de Sistemas Complejos de Valparaíso (ISCV), Valparaíso, ChilePablo A. MarquetManaaki Whenua—Landcare Research, Lincoln, New ZealandJames K. McCarthyCenter for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, DenmarkNaia Morueta-HolmeDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USADaniel S. ParkCenter for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus, DenmarkJens-Christian SvenningSection for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, DenmarkJens-Christian SvenningCEFE, Univ. Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, Montpellier, FranceCyrille ViolleNaturalis Biodiversity Center, Leiden, The NetherlandsJan J. WieringaWorld Resources Institute, London, UKGraham WynneRio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifical Catholic University, Rio de Janeiro, BrazilBernardo B. N. StrassburgInternational Institute for Sustainability, Rio de Janeiro, BrazilBernardo B. N. StrassburgPrograma de Pós Graduacão em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgBotanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgEnvironmental Change Institute, Centre for the Environment, Oxford University, Oxford, UKMichael ObersteinerUN Sustainable Development Solutions Network, Paris, FranceGuido Schmidt-TraubCorrespondence to
    Martin Jung or Piero Visconti. More

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    Effects of fertilizer under different dripline spacings on summer maize in northern China

    1.China. China statistical yearbook. (China Statistics Press, 2020).2.Shiferaw, B., Prasanna, B. M., Hellin, J. & Bänziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3, 307–327 (2011).Article 

    Google Scholar 
    3.Chen, M. P., Sun, F. & Shindo, J. China’s agricultural nitrogen flows in 2011: Environmental assessment and management scenarios. Resour. Conserv. Recycl. 111, 10–27 (2016).Article 

    Google Scholar 
    4.He, Y. X. et al. Tracking ammonia morning peak, sources and transport with 1 Hz measurements at a rural site in North China Plain. Atmos. Environ. 235, 117630 (2020).CAS 
    Article 

    Google Scholar 
    5.Zhang, Y. et al. Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 158, 490–501 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Ayars, J. E., Fulton, A. & Taylor, B. Subsurface drip irrigation in California—Here to stay?. Agric. Water Manag. 157, 39–47 (2015).Article 

    Google Scholar 
    7.Chauhdary, J. N., Bakhsh, A., Engel, B. A. & Ragab, R. Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach. Agric. Water Manag. 221, 449–461 (2019).Article 

    Google Scholar 
    8.Mali, S. S., Naik, S. K., Jha, B. K., Singh, A. K. & Bhatt, B. P. Planting geometry and growth stage linked fertigation patterns: Impact on yield, nutrient uptake and water productivity of Chilli pepper in hot and sub-humid climate. Sci. Hortic. (Amsterdam) 249, 289–298 (2019).Article 

    Google Scholar 
    9.Silber, A. et al. High fertigation frequency: the effects on uptake of nutrients, water and plant growth. Plant Soil 253, 467–477 (2003).CAS 
    Article 

    Google Scholar 
    10.Wu, D. L. et al. Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China. Agric. Water Manag. 213, 200–211 (2019).Article 

    Google Scholar 
    11.Ning, D. et al. Deficit irrigation combined with reduced N-fertilizer rate can mitigate the high nitrous oxide emissions from Chinese drip-fertigated maize field. Glob. Ecol. Conserv. 20, e00803 (2019).Article 

    Google Scholar 
    12.Sandhu, O. S. et al. Drip irrigation and nitrogen management for improving crop yields, nitrogen use efficiency and water productivity of maize-wheat system on permanent beds in north-west India. Agric. Water Manag. 219, 19–26 (2019).Article 

    Google Scholar 
    13.Li, H. et al. Effects of different nitrogen fertilizers on the yield, water- and nitrogen-use efficiencies of drip-fertigated wheat and maize in the North China Plain. Agric. Water Manag. 243, 106474 (2021).Article 

    Google Scholar 
    14.Lamm, F. R., Stone, L. R., Manges, H. L. & O’Brien, D. M. Optimum lateral spacing for subsurface drip-irrigated corn. Trans. ASAE 40, 1021–1027 (1997).Article 

    Google Scholar 
    15.Bozkurt, Y., Yazar, A., Gençel, B. & Sezen, M. S. Optimum lateral spacing for drip-irrigated corn in the Mediterranean Region of Turkey. Agric. Water Manag. 85, 113–120 (2006).Article 

    Google Scholar 
    16.Chen, R. et al. Lateral spacing in drip-irrigated wheat: The effects on soil moisture, yield, and water use efficiency. Field Crop. Res. 179, 52–62 (2015).Article 

    Google Scholar 
    17.Zhou, L. et al. Drip irrigation lateral spacing and mulching affects the wetting pattern, shoot-root regulation, and yield of maize in a sand-layered soil. Agric. Water Manag. 184, 114–123 (2017).Article 

    Google Scholar 
    18.Eissa, M. A. Efficiency of P fertigation for drip-irrigated potato grown on calcareous sandy soils. Potato Res. 62, 97–108 (2019).CAS 
    Article 

    Google Scholar 
    19.Irmak, S., Djaman, K. & Rudnick, D. R. Effect of full and limited irrigation amount and frequency on subsurface drip-irrigated maize evapotranspiration, yield, water use efficiency and yield response factors. Irrig. Sci. 34, 271–286 (2016).Article 

    Google Scholar 
    20.Yao, Y. L. et al. Urea deep placement for minimizing NH3 loss in an intensive rice cropping system. Field Crop. Res. 218, 254–266 (2018).Article 

    Google Scholar 
    21.Ziadi, N., Cambouris, A. N., Nyiraneza, J. & Nolin, M. C. Across a landscape, soil texture controls the optimum rate of N fertilizer for maize production. Field Crop. Res. 148, 78–85 (2013).Article 

    Google Scholar 
    22.Fang, H. et al. An optimized model for simulating grain-filling of maize and regulating nitrogen application rates under different film mulching and nitrogen fertilizer regimes on the Loess Plateau. China. Soil Tillage Res. 199, 104546 (2020).Article 

    Google Scholar 
    23.Zheng, J. et al. Interactive effects of mulching practice and nitrogen rate on grain yield, water productivity, fertilizer use efficiency and greenhouse gas emissions of rainfed summer maize in northwest China. Agric. Water Manag. 248, 106778 (2021).Article 

    Google Scholar 
    24.Qi, X. L. et al. Grain yield and apparent N recovery efficiency of dry direct-seeded rice under different N treatments aimed to reduce soil ammonia volatilization. Field Crop. Res. 134, 138–143 (2012).Article 

    Google Scholar 
    25.Han, K., Zhou, C. J. & Wang, L. Q. Reducing ammonia volatilization from maize fields with separation of nitrogen fertilizer and water in an alternating furrow irrigation system. J. Integr. Agric. 13, 1099–1112 (2014).CAS 
    Article 

    Google Scholar 
    26.Amin, A.E.-E.A.Z. Carbon sequestration, kinetics of ammonia volatilization and nutrient availability in alkaline sandy soil as a function on applying calotropis biochar produced at different pyrolysis temperatures. Sci. Total Environ. 726, 138489 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Li, H. T. et al. Film mulching, residue retention and N fertilization affect ammonia volatilization through soil labile N and C pools. Agric. Ecosyst. Environ. 308, 107272 (2021).CAS 
    Article 

    Google Scholar 
    28.Sun, B. et al. Bacillus subtilis biofertilizer mitigating agricultural ammonia emission and shifting soil nitrogen cycling microbiomes. Environ. Int. 144, 105989 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Tabli, N. et al. Plant growth promoting and inducible antifungal activities of irrigation well water-bacteria. Biol. Control 117, 78–86 (2018).Article 

    Google Scholar 
    30.Zhong, X. M. et al. Reducing ammonia volatilization and increasing nitrogen use efficiency in machine-transplanted rice with side-deep fertilization in a double-cropping rice system in Southern China. Agric. Ecosyst. Environ. 306, 107183 (2021).CAS 
    Article 

    Google Scholar 
    31.Li, C., Sun, M. X., Xu, X. B. & Zhang, L. X. Characteristics and influencing factors of mulch film use for pollution control in China: Microcosmic evidence from smallholder farmers. Resour. Conserv. Recycl. 164, 105222 (2021).Article 

    Google Scholar 
    32.Li, M. N., Wang, Y. L., Adeli, A. & Yan, H. J. Effects of application methods and urea rates on ammonia volatilization, yields and fine root biomass of alfalfa. Field Crop. Res. 218, 115–125 (2018).Article 

    Google Scholar 
    33.Pinheiro, P. L. et al. Straw removal reduces the mulch physical barrier and ammonia volatilization after urea application in sugarcane. Atmos. Environ. 194, 179–187 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Zhu, H. et al. Interactive effects of soil amendments (biochar and gypsum) and salinity on ammonia volatilization in coastal saline soil. CATENA 190, 104527 (2020).CAS 
    Article 

    Google Scholar 
    35.Oppong Danso, E. et al. Effect of different fertilization and irrigation methods on nitrogen uptake, intercepted radiation and yield of okra (Abelmoschus esculentum L.) grown in the Keta Sand Spit of Southeast Ghana. Agric. Water Manag. 147, 34–42 (2015).Article 

    Google Scholar 
    36.Liu, R. H. et al. Chemical fertilizer pollution control using drip fertigation for conservation of water quality in Danjiangkou Reservoir. Nutr. Cycl. Agroecosystems 98, 295–307 (2014).CAS 
    Article 

    Google Scholar 
    37.Sanz-Cobena, A. et al. Strategies for greenhouse gas emissions mitigation in mediterranean agriculture: A review. Agric. Ecosyst. Environ. 238, 5–24 (2017).CAS 
    Article 

    Google Scholar 
    38.Zhou, J. B., Xi, J. G., Chen, Z. J. & Li, S. X. Leaching and transformation of nitrogen fertilizers in soil after application of n with irrigation: A soil column method. Pedosphere 16, 245–252 (2006).CAS 
    Article 

    Google Scholar 
    39.Rosemary, F., Vitharana, U. W. A., Indraratne, S. P., Weerasooriya, R. & Mishra, U. Exploring the spatial variability of soil properties in an Alfisol soil catena. CATENA 150, 53–61 (2017).CAS 
    Article 

    Google Scholar 
    40.Liu, Y., Lv, J. S., Zhang, B. & Bi, J. Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, Eastern China. Sci. Total Environ. 450–451, 108–119 (2013).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    41.Vasu, D. et al. Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management. Soil Tillage Res. 169, 25–34 (2017).Article 

    Google Scholar 
    42.Jin, J. Y., Bai, Y. L. & Yang, L. P. High Efficiency Soil Nutrient Testing Technology and Equipment (China Agriculture Press, 2006) (in Chinese).
    Google Scholar 
    43.Tan, Y. et al. Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas. Sci. Rep. 11, 13821 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Ren, Y. et al. Effect of sowing proportion on above- and below-ground competition in maize–soybean intercrops. Sci. Rep. 11, 15760 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Wang, Z. H., Liu, X. J., Ju, X. T., Zhang, F. S. & Malhi, S. S. Ammonia volatilization loss from surface-broadcast urea: comparison of vented- and closed-chamber methods and loss in winter wheat–summer maize rotation in North China plain. Commun. Soil Sci. Plant Anal. 35, 2917–2939 (2004).CAS 
    Article 

    Google Scholar 
    46.Zhou, L. P. et al. Comparison of several slow-released nitrogen fertilizers in ammonia volatilization and nitrogen utilization in summer maize field. J. Plant Nutr. Fertil. 22, 1449–1457 (2016) (in Chinese).
    Google Scholar 
    47.Huang, T. M. et al. Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China. Soil Tillage Res. 191, 57–65 (2019).Article 

    Google Scholar 
    48.Wang, Z., Li, J. & Li, Y. Effects of drip system uniformity and nitrogen application rate on yield and nitrogen balance of spring maize in the North China Plain. Field. Crop. Res. 159, 10–20 (2014).Article 

    Google Scholar 
    49.Brar, H. S., Vashist, K. K. & Bedi, S. Phenology and yield of spring maize (Zea mays L.) under different drip irrigation regimes and planting methods. J. Agric. Sci. Technol. 18, 831–843 (2016).
    Google Scholar 
    50.Poch-Massegú, R., Jiménez-Martínez, J., Wallis, K. J., Ramírez de Cartagena, F. & Candela, L. Irrigation return flow and nitrate leaching under different crops and irrigation methods in Western Mediterranean weather conditions. Agric. Water Manag. 134, 1–13 (2014).Article 

    Google Scholar 
    51.Yuan, Z. Q. et al. Film mulch with irrigation and rainfed cultivations improves maize production and water use efficiency in Ethiopia. Ann. Appl. Biol. 175, 215–227 (2019).Article 

    Google Scholar 
    52.Wang, J. L. Research on the use of water and fertilizer for drip irrigation multiple cropping silage maize (Shihezi University, 2016) (in Chinese).
    Google Scholar 
    53.Lamm, F. R. & Trooien, T. P. Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrig. Sci. 22, 195–200 (2003).Article 

    Google Scholar 
    54.Yan, X. L., Jia, L. M. & Dai, T. F. Effects of water and nitrogen coupling under drip irrigation on tree growth and soil nitrogen content of Populus × euramericana cv. ‘Guariento’. Chin. J. Appl. Ecol. 29, 2195 (2018) (in Chinese).
    Google Scholar 
    55.Sun, W. T., Sun, Z. X., Wang, C. X., Gong, L. & Zhang, Y. L. Coupling effect of water and fertilizer on corn yield under drip fertigation. Sci. Agric. Sin. 39, 563–568 (2006) (in Chinese).
    Google Scholar 
    56.Banerjee, B., Pathak, H. & Aggarwal, P. Effects of dicyandiamide, farmyard manure and irrigation on crop yields and ammonia volatilization from an alluvial soil under a rice (Oryza sativa L.)-wheat (Triticum aestivum L.) cropping system. Biol. Fertil. Soils 36, 207–214 (2002).CAS 
    Article 

    Google Scholar 
    57.Yang, Q. L., Liu, P., Dong, S. T., Zhang, J. W. & Zhao, B. Effects of fertilizer type and rate on summer maize grain yield and ammonia volatilization loss in northern China. J. Soils Sediments 19, 2200–2211 (2019).CAS 
    Article 

    Google Scholar 
    58.Zhou, G. W. et al. Effects of saline water irrigation and N application rate on NH3 volatilization and N use efficiency in a drip-irrigated cotton field. Water Air Soil Pollut. 227, 103 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    59.Zheng, J., Kilasara, M. M., Mmari, W. N. & Funakawa, S. Ammonia volatilization following urea application at maize fields in the East African highlands with different soil properties. Biol. Fertil. Soils 54, 411–422 (2018).CAS 
    Article 

    Google Scholar 
    60.Li, Z. et al. Nitrogen use efficiency and ammonia oxidation of corn field with drip irrigation in Hetao irrigation district. J. Irrig. Drain. 37, 37–42,49 (2018) (in Chinese).61.Zheng, L. et al. Impact of fertilization on ammonia volatilization and N2O emissions in an open vegetable field. Chin. J. Appl. Ecol. 29, 4063–4070 (2018) (in Chinese).
    Google Scholar 
    62.Li, Y. Q., Liu, G., Hong, M., Wu, Y. & Chang, F. Effect of optimized nitrogen application on nitrous oxide emission and ammonia volatilization in Hetao irrigation area. Acta Sci. Circumst. 39, 578–584 (2019) (in Chinese).CAS 

    Google Scholar 
    63.Das, P. et al. Emissions of ammonia and nitric oxide from an agricultural site following application of different synthetic fertilizers and manures. Geosci. J. 12, 177–190 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    64.Cai, G. X. et al. Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain. Nutr. Cycl. Agroecosyst. 63, 187–195 (2002).CAS 
    Article 

    Google Scholar 
    65.Wang, X. L. et al. Corn compensatory growth upon post-drought rewatering based on the effects of rhizosphere soil nitrification on cytokinin. Agric. Water Manag. 241, 106436 (2020).Article 

    Google Scholar 
    66.Li, G. et al. Effect of drip fertigation on summer maize in north China. Sci. Agric. Sin. 52, 1930–1941 (2019) (in Chinese).
    Google Scholar  More

  • in

    Specialization directs habitat selection responses to a top predator in semiaquatic but not aquatic taxa

    1.Binckley, C. A. & Resetarits, W. J. Habitat selection determines abundance, richness and species composition of beetles in aquatic communities. Biol. Lett. 1, 370–374 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Foltz, S. J. & Dodson, S. I. Aquatic Hemiptera community structure in stormwater retention ponds: A watershed land cover approach. Hydrobiologia 621, 49–62 (2009).Article 

    Google Scholar 
    3.Goldberg, F. J., Quinzio, S. & Vaira, M. Oviposition-site selection by the toad Melanophryniscus rubriventris in an unpredictable environment in Argentina. Can. J. Zool. 84, 699–705 (2006).Article 

    Google Scholar 
    4.Blaustein, L. Oviposition site selection in response to risk of predation: Evidence from aquatic habitats and consequences for population dynamics and community. In Evolutionary Theory and Processes: Modern Perspectives (ed. Wasser, S. P.) 441–456 (Kluwer, 1999).5.Resetarits, W. J. & Binckley, C. A. Spatial contagion of predation risk affects colonization dynamics in experimental aquatic landscapes. Ecology 90, 869–876 (2009).PubMed 
    Article 

    Google Scholar 
    6.Kraus, J. M. & Vonesh, J. R. Feedbacks between community assembly and habitat selection shape variation in local colonization. J. Anim. Ecol. 79, 795–802 (2010).PubMed 

    Google Scholar 
    7.Resetarits, W. J. Oviposition site choice and life history evolution. Am. Zool. 36, 205–215 (1996).Article 

    Google Scholar 
    8.Morris, D. W. Toward an ecological synthesis: A case for habitat selection. Oecologia 136, 1–13 (2003).ADS 
    PubMed 
    Article 

    Google Scholar 
    9.Resetarits, W. J. & Wilbur, H. M. Choice of oviposition site by Hyla chrysoscelis: Role of predators and competitors. Ecology 70, 220–228 (1989).Article 

    Google Scholar 
    10.Resetarits, W. J., Binckley, C. A. & Chalcraft, D. R. Habitat selection, species interactions, and processes of community assembly in complex landscapes: A metacommunity perspective. In Metacommunities: Spatial Dynamics and Ecological Communities (eds. Holyoak, M., Leybold, A. & Holt, R. D.) 374–398 (University of Chicago Press, Chicago, 2005).11.Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    12.Langellotto, G. A. & Denno, R. F. Responses of invertebrate natural enemies to complex-structured habitats: A meta-analytical synthesis. Oecologia 139, 1–10 (2004).ADS 
    PubMed 
    Article 

    Google Scholar 
    13.Åbjörnsson, K., Brönmark, C. & Hansson, L.-A. The relative importance of lethal and non-lethal effects of fish on insect colonisation of ponds: Influence of fish on insect colonisation. Freshw. Biol. 47, 1489–1495 (2002).Article 

    Google Scholar 
    14.Pintar, M. R. & Resetarits, W. J. Jr. Out with the old, in with the new: Oviposition preference matches larval success in cope’s gray treefrog, Hyla chrysoscelis. J. Herpetol. 51, 186–189 (2017).Article 

    Google Scholar 
    15.Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Evol. Syst. 27, 337–363 (1996).Article 

    Google Scholar 
    16.Caudill, C. C. & Peckarsky, B. L. Lack of appropriate behavioral or developmental responses by mayfly larvae to trout predators. Ecology 84, 2133–2144 (2003).Article 

    Google Scholar 
    17.Binckley, C. A. & Resetarits, W. J. Functional equivalence of non-lethal effects: Generalized fish avoidance determines distribution of gray treefrog, Hyla chrysoscelis, larvae. Oikos 102, 623–629 (2003).Article 

    Google Scholar 
    18.Pollard, C. J. et al. Removal of an exotic fish influences amphibian breeding site selection: Exotic fish removal. J. Wildl. Manag. 81, 720–727 (2017).Article 

    Google Scholar 
    19.Petranka, J. W. & Fakhoury, K. Evidence of a chemically-mediated avoidance response of ovipositing insects to bluegills and green frog tadpoles. Copeia 1991, 234–239 (1991).Article 

    Google Scholar 
    20.McPeek, M. A. Differential dispersal tendencies among Enallagma damselflies (Odonata) inhabiting different habitats. Oikos 56, 187–195 (1989).Article 

    Google Scholar 
    21.Šigutová, H., Šigut, M. & Dolný, A. Intensive fish ponds as ecological traps for dragonflies: An imminent threat to the endangered species Sympetrum depressiusculum (Odonata: Libellulidae). J. Insect Conserv. 19, 961–974 (2015).Article 

    Google Scholar 
    22.Potts, K. M. Survival and development of larval odonates (Anisoptera) and female oviposition site choice in response to predatory fish. https://egrove.olemiss.edu/etd/1854 (2020).23.Blaustein, L., Kiflawi, M., Eitam, A., Mangel, M. & Cohen, J. E. Oviposition habitat selection in response to risk of predation in temporary pools: Mode of detection and consistency across experimental venue. Oecologia 138, 300–305 (2004).ADS 
    PubMed 
    Article 

    Google Scholar 
    24.Wildermuth, H. Habitat selection and oviposition site recognition by the dragonfly Aeshna juncea (L.): An experimental approach in natural habitats (Anisoptera: Aeshnidae). Odonatologica 22, 27–44 (1993).25.Wildermuth, H. Habitatselektion bei Libellen. Adv. Odonatol. 6, 223–257 (1994).
    Google Scholar 
    26.Laurila, A. Breeding habitat selection and larval performance of two anurans in freshwater rock-pools. Ecography 21, 484–494 (1998).Article 

    Google Scholar 
    27.Schwind, R. Spectral regions in which aquatic insects see reflected polarized light. J. Comp. Physiol. A 177, 439–448 (1995).Article 

    Google Scholar 
    28.Horváth, G. & Kriska, G. Polarization vision in aquatic insects and ecological traps for polarotactic insects in Aquatic Insects: Challenges to Populations (eds. Lancaster, J. & Briers, R. A.) 204–229 (CAB International Publishing, 2008).29.Schulte, L. M. et al. The smell of success: Choice of larval rearing sites by means of chemical cues in a Peruvian poison frog. Anim. Behav. 81, 1147–1154 (2011).Article 

    Google Scholar 
    30.Corbet, P. S. Dragonflies: Behavior and ecology of Odonata. (Harley Books, 1999).31.Nicolet, P. et al. The wetland plant and macroinvertebrate assemblages of temporary ponds in England and Wales. Biol. Conserv. 120, 261–278 (2004).Article 

    Google Scholar 
    32.Henrikson, B.-I. Sphagnum mosses as a microhabitat for invertebrates in acidified lakes and the colour adaptation and substrate preference in Leucorrhinia dubia (Odonata, Anisoptera). Ecography 16, 143–153 (1993).Article 

    Google Scholar 
    33.Kokko, H. & Sutherland, W. J. Ecological traps in changing environments: Ecological and evolutionary consequences of a behaviourally mediated Allee effect. Evol. Ecol. Res. 3, 537–551 (2001).
    Google Scholar 
    34.Gilroy, J. J. & Sutherland, W. J. Beyond ecological traps: Perceptual errors and undervalued resources. Trends Ecol. Evol. 22, 351–356 (2007).PubMed 
    Article 

    Google Scholar 
    35.Abrams, P. A., Cressman, R. & Křivan, V. The role of behavioral dynamics in determining the patch distributions of interacting species. Am. Nat. 169, 505–518 (2007).PubMed 
    Article 

    Google Scholar 
    36.Denton, J. & Beebee, T. J. C. Palatability of anuran eggs and embryos. Amphib. Reptil. 12, 111–112 (1991).Article 

    Google Scholar 
    37.Larson, D. J. The predaceous water beetles (Coleoptera: Dytiscidae) of Alberta: Systematics, natural history and distribution. Quaest. Entomol. 11, 245–498 (1985).
    Google Scholar 
    38.Mikolajewski, D. J. & Rolff, J. Benefits of morphological defence demonstrated by direct manipulation in larval dragonflies. Evol. Ecol. Res. 6, 619–626 (2004).
    Google Scholar 
    39.Relyea, R. A. Morphological and behavioral plasticity of larval anurans in response to different predators. Ecology 82, 523–540 (2001).Article 

    Google Scholar 
    40.Benard, M. F. Predator-induced phenotypic plasticity in organisms with complex life histories. Annu. Rev. Ecol. Evol. Syst. 35, 651–673 (2004).Article 

    Google Scholar 
    41.McCauley, S. J., Davis, C. J. & Werner, E. E. Predator induction of spine length in larval Leucorrhinia intacta (Odonata). Evol. Ecol. Res. 10, 435–447 (2008).
    Google Scholar 
    42.Nöllert, A. & Nöllert, C. Die Amphibien Europas. (Franckh-Kosmos Verlags-GmbH and Company, 1992).43.Maštera, J., Zavadil, V. & Dvořák, J. Vajíčka a larvy obojživelníků České republiky. (Academia, 2015).44.Speybroeck, J., Beukema, W., Bok, B. & Van der Voort, J. Field Guide to the Amphibians and Reptiles of Britain and Europe. (Bloomsbury Natural History, 2016).45.Sternberg, K. & Buchwald, R. Die Libellen Baden-Württembergs. Band 2: Großlibellen (Anisoptera). (Verlag Eugen Ulmer Gmbh & Co., 2000).46.Mikolajewski, D. J. & Johansson, F. Morphological and behavioral defenses in dragonfly larvae: Trait compensation and cospecialization. Behav. Ecol. 15, 614–620 (2004).Article 

    Google Scholar 
    47.Kjærstad, G., Dolmen, D., Olsvik, H. A. & Tilseth, E. The backswimmer Notonecta glauca L. (Hemiptera, Notonectidae) in Central Norway. Nor. J. Entomol. 56, 44–49 (2009).
    Google Scholar 
    48.Svensson, B. G., Tallmark, B. & Petersson, E. Habitat heterogeneity, coexistence and habitat utilization in five backswimmer species (Notonecta spp.; Hemiptera, Notonectidae). Aquat. Insects 22, 81–98 (2000).Article 

    Google Scholar 
    49.Macan, T. T. A twenty-one-year study of the water-bugs in a Moorland Fishpond. J. Anim. Ecol. 45, 913–922 (1976).Article 

    Google Scholar 
    50.Lock, K., Adriaens, T., Meutter, F. V. D. & Goethals, P. Effect of water quality on waterbugs (Hemiptera: Gerromorpha & Nepomorpha) in Flanders (Belgium): Results from a large-scale field survey. Ann. Limnol. Int. J. Limnol. 49, 121–128 (2013).Article 

    Google Scholar 
    51.Cook, W. L. & Streams, F. A. Fish predation on Notonecta (Hemiptera): Relationship between prey risk and habitat utilization. Oecologia 64, 177–183 (1984).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Swevers, L., Lambert, J. G. D. & De Loof, A. Synthesis and metabolism of vertebrate-type steroids by tissues of insects: A critical evaluation. Experientia 47, 687–698 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Bergsten, J. & Miller, K. B. Taxonomic revision of the Holarctic diving beetle genus Acilius Leach (Coleoptera: Dytiscidae): Acilius taxonomic revision. Syst. Entomol. 31, 145–197 (2005).Article 

    Google Scholar 
    54.Åbjörnsson, K., Wagner, B. M. A., Axelsson, A., Bjerselius, R. & Olsén, K. H. Responses of Acilius sulcatus (Coleoptera: Dytiscidae) to chemical cues from perch (Perca fluviatilis). Oecologia 111, 166–171 (1997).ADS 
    PubMed 
    Article 

    Google Scholar 
    55.Boukal, D. S. et al. Catalogue of water beetles of the Czech Republic. Klapalekiana 43(Suppl.), 1–289 (2007).
    Google Scholar 
    56.Gioria, M., Schaffers, A., Bacaro, G. & Feehan, J. The conservation value of farmland ponds: Predicting water beetle assemblages using vascular plants as a surrogate group. Biol. Conserv. 143, 1125–1133 (2010).Article 

    Google Scholar 
    57.Everard, M. Britain’s Freshwater Fishes. (Princeton University Press, 2013).58.Briers, R. A. & Warren, P. H. Competition between the nymphs of two regionally co-occurring species of Notonecta (Hemiptera: Notonectidae). Freshw. Biol. 42, 11–20 (1999).Article 

    Google Scholar 
    59.Wiggins, G. B., Mackay, R. J. & Smith, I. M. Evolutionary and ecological strategies of animals on annual temporary pools. Arch. Für Hydrobiol. Suppl. 58, 197–206 (1980).
    Google Scholar 
    60.Culler, L. E., Ohba, S. & Crumrine, P. Predator-Prey Interactions of Dytiscids. In Ecology, Systematics, and the Natural History of Predaceous Diving Beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 363–379 (Springer, 2014).61.Schuh, R. T. & Slater, J. A. True Bugs of the World (Hemiptera:Heteroptera): Classification and Natural History (Cornell University Press, Cornell, 1995).
    Google Scholar 
    62.Streams, F. A. Intrageneric predation by Notonecta (Hemiptera: Notonectidae) in the laboratory and in nature. Ann. Entomol. Soc. Am. 85, 265–273 (1992).Article 

    Google Scholar 
    63.Giacoma, C., Zugolaro, C. & Beani, L. The advertisement calls of the green toad (Bufo viridis): Variability and role in mate choice. Herpetologica 53, 454–464 (1997).
    Google Scholar 
    64.Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124, 86–93 (2018).Article 

    Google Scholar 
    65.Halekoh, U., Højsgaard, S. & Yan, J. The R Package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).Article 

    Google Scholar 
    66.R Core Team. R: A Language and Environment for Statistical Computing (The R Foundation for Statistical Computing, Vienna, Austria). https://www.r-project.org/ (2020).67.Wells, K. D. The Ecology and Behavior of Amphibians. (University of Chicago Press, 2007).68.Purrenhage, J. L. & Boone, M. D. Amphibian community response to variation in habitat structure and competitor density. Herpetologica 65, 14–30 (2009).Article 

    Google Scholar 
    69.Formanowicz, D. R. & Bobka, M. S. Predation risk and microhabitat preference: An experimental study of the behavioral responses of prey and predator. Am. Midl. Nat. 121, 379–386 (1989).Article 

    Google Scholar 
    70.Egan, R. S. & Paton, P. W. C. Within-pond parameters affecting oviposition by wood frogs and spotted salamanders. Wetlands 24, 1–13 (2004).Article 

    Google Scholar 
    71.Ward, S. A. Optimal habitat selection in time-limited dispersers. Am. Nat. 129, 568–579 (1987).Article 

    Google Scholar 
    72.Fretwell, S. D. & Lucas, H. L. On territorial behavior and other factors influencing habitat distribution in birds. I. Theoretical development. Biotheoretica 19, 16–36 (1970).Article 

    Google Scholar 
    73.Austad, S. N. A classification of alternative reproductive behaviors and methods for field-testing ESS models. Am. Zool. 24, 309–319 (1984).Article 

    Google Scholar 
    74.Crespo, J. G. A review of chemosensation and related behavior in aquatic insects. J. Insect Sci. 11, 1–39 (2011).Article 

    Google Scholar 
    75.Wildermuth, H. Dragonflies recognize the water of rendezvous and oviposition sites by horizontally polarized light: A behavioural field test. Naturwissenschaften 85, 297–302 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    76.Chislock, M. F., Doster, E., Zitomer, R. A. & Wilson, A. E. Eutrophication: Causes, consequences, and controls in aquatic ecosystems. Nat. Educ. Knowl. 4, 10 (2013).
    Google Scholar 
    77.Dolný, A., Mižičová, H. & Harabiš, F. Natal philopatry in four European species of dragonflies (Odonata: Sympetrinae) and possible implications for conservation management. J. Insect Conserv. 17, 821–829 (2013).Article 

    Google Scholar 
    78.Refsnider, J. M. & Janzen, F. J. Putting eggs in one basket: Ecological and evolutionary hypotheses for variation in oviposition-site choice. Annu. Rev. Ecol. Evol. Syst. 41, 39–57 (2010).Article 

    Google Scholar 
    79.Brodin, T., Mikolajewski, D. J. & Johansson, F. Behavioural and life history effects of predator diet cues during ontogeny in damselfly larvae. Oecologia 148, 162–169 (2006).ADS 
    PubMed 
    Article 

    Google Scholar 
    80.Kershenbaum, A., Spencer, M., Blaustein, L. & Cohen, J. E. Modelling evolutionarily stable strategies in oviposition site selection, with varying risks of predation and intraspecific competition. Evol. Ecol. 26, 955–974 (2012).Article 

    Google Scholar 
    81.Hopper, K. R. Risk-spreading and bet-hedging in insect population biology. Annu. Rev. Entomol. 44, 535–560 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Gioria, M. Habitats. In Ecology, Systematics, and the Natural History of predaceous diving beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 307–362 (Springer, Netherlands, 2014).
    Google Scholar 
    83.Diehl, S. Fish predation and benthic community structure: The role of omnivory and habitat complexity. Ecology 73, 1646–1661 (1992).Article 

    Google Scholar 
    84.Giller, P. S. & McNeill, S. Predation strategies, resource partitioning and habitat selection in Notonecta (Hemiptera/Heteroptera). J. Anim. Ecol. 50, 789–808 (1981).Article 

    Google Scholar 
    85.Ribera, I. & Nilsson, A. N. Morphometric patterns among diving beetles (Coleoptera: Noteridae, Hygrobiidae, and Dytiscidae). Can. J. Zool. 73, 2343–2360 (2011).Article 

    Google Scholar 
    86.Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).Article 

    Google Scholar 
    87.Schoeppner, N. M. & Relyea, R. A. Damage, digestion, and defence: The roles of alarm cues and kairomones for inducing prey defences. Ecol. Lett. 8, 505–512 (2005).PubMed 
    Article 

    Google Scholar 
    88.Schoeppner, N. M. & Relyea, R. A. Interpreting the smells of predation: How alarm cues and kairomones induce different prey defences. Funct. Ecol. 23, 1114–1121 (2009).Article 

    Google Scholar 
    89.McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Persistence and accumulation of environmental DNA from an endangered dragonfly

    We developed environmental DNA (eDNA) detection protocols to assist in habitat identification for conservation for the US federally endangered Hine’s emerald dragonfly (Somatochlora hineana). Larval S. hineana have been observed in groundwater-fed calcareous fen habitats in Illinois, Wisconsin, Michigan, and Missouri in the USA, and Ontario, Canada. Habitat destruction and fragmentation have been the primary cause of S. hineana population decline1. Therefore, a key part of conservation efforts to benefit S. hineana is the identification and protection of any remaining habitat areas. Conventional sampling for the presence of S. hineana often includes both adult and larval sampling.Larval S. hineana surveys include benthic-sampling and the pumping of crayfish burrows. Larval S. hineana are most often found in the burrows of Cambarus (= Lacunicambarus) diogenes throughout the year and are almost exclusively found in C. diogenes burrows during their overwintering period2. Comprehensive larval surveys can take months to complete, require intensive training of field personnel, are reliant on favorable weather conditions, and are only effective if late instar larvae can be collected for identification. Adult S. hineana surveys are difficult due to short flight season, habitat segregation by sex, large potential flight range (adults can range for many kilometers from larval habitat), risk of harm when netting adult dragonflies, and difficulty observing genitalia characteristics necessary for accurate species identification when in flight1.Given the restrictions of conventional sampling techniques, there has been a great need to develop a method to expedite field site identification. Environmental DNA can be used to guide and prioritize locations for conventional surveying methods, increasing the speed at which habitats can be identified for protection and restoration.Environmental DNA (eDNA) is a relatively new surveillance method used to detect the presence of a species within a habitat by collecting environmental samples (e.g., soil and water) that contain cell fragments and exogenous DNA3. Mitochondrial genes, which are more plentiful and have a higher resistance to degradation than nuclear genes, are targeted and amplified to determine species presence or absence4,5,6,7.Currently, there is a taxonomic skew toward fish, amphibian, and mollusk eDNA studies7,8 suggesting the need to determine if eDNA methods can be useful for detecting aquatic insects. Environmental DNA analysis from 27 taxa of freshwater arthropods had been published as of 2019; some of these taxa include Procambarus clarkii, Pacifastacus leniusculus, and Gammarus pulex8. Additionally, the critically endangered plecopteran Isogenus nubecula was detected using eDNA methods9.The potential advantages of using eDNA rather than traditional surveying methods include the reduction of field labor hours10, reduced impact to sensitive habitats7, and a lower threshold of detection11,12. Additionally, eDNA has proven to be an effective tool when traditional methods require timely/costly surveying efforts6 and for detecting cryptic invasive species10.Although there is always some risk of damaging the habitat when studying a system, environmental DNA sampling (i.e., water, soil, ice) is much less invasive and has far less potential for harming native and endangered species than many traditional surveying methods7. For example, electrofishing can cause damage in the form of removing/killing fish from the sample site13. Traditional sampling methods for larval populations of S. hineana include benthic sampling (monitoring populations in stream beds) and burrow-pumping (a novel technique used to locate larvae within crayfish burrows)2. These techniques can disrupt flow patterns within shallow streams, collapse burrows, and harm/kill sampled individuals.While there has been some speculation that eDNA sampling may have high false-positive rates due to ancient DNA contamination from extirpated populations, studies show that eDNA typically becomes undetectable in water within 1–44 days after source removal10,14,15,16,17,18,19,20,21 and approximately 144 days in soil22. This suggests that eDNA surveys are contemporaneous and can be used to inform conservation efforts.Environmental DNA degradation is likely more complex in a field setting, and the persistence (defined here as the length of time eDNA remains detectable within a habitat or mesocosm) and net-accumulation (defined here as the difference between the amount of eDNA produced and the amount of eDNA degraded over time) are likely to vary depending on numerous factors that alter source/sink dynamics3. Spatiotemporal dynamics are especially important in affecting the persistence and accumulation of eDNA in the field and need to be accounted for when developing eDNA methodologies23. Concentrations of eDNA may fluctuate spatially and/or temporally as a result of fluctuations in biomass18,24,25, transport through a flowing system17,26,27,28, age structuring of target populations7,16, feeding activity29, life-history events5, seasonal habitat preference13,30, water temperature24,31,32,33, hydrology13,27, inhibition13,27, and microbial activity34. Some studies show that water pH affects eDNA degradation rates19, while others do not35. Similarly, some studies show that UV light exposure affects eDNA degradation rates17, while others show no such effect36.In this study, we focused on the effects that seasonal shifts in temperature have on the persistence and net-accumulation of larval S. hineana eDNA. Since temperature drives the production of eDNA through metabolic processes31 and directly alters the rate of microbial degradation of eDNA32, it may be the most important variable driving seasonal shifts in eDNA detection.Somatochlora hineana larval molting activity varies with seasonal changes, the net-accumulation of S. hineana eDNA within a habitat. Adult S. hineana females lay eggs within streams and streamlets during their flight period (July–early August). Eggs typically mature over winter. In the following year, hatching of pro-larva from eggs occurs between April and June. All S. hineana larvae go through approximately 12 larval instars (F-11 to F-0). The first 6 larval instars (F-11 through F-6) occur rapidly within the first year, and the final 6 (F-5 through F-0) occur more slowly over a period of 2–4 years1. Since S. hineana larvae take several years to fully mature, they survive overwintering in shallow, partially frozen streams within Cambarus (= Lacunicambarus) diogenes crayfish burrows. While S. hineana larvae overwinter within burrows, they rarely consume food or molt, thus reducing the amount of eDNA shed2.The net-accumulation of larval S. hineana eDNA was likely to increase with increasing temperatures2,31,37, while the persistence of larval S. hineana eDNA was likely to decrease with increasing temperatures32. Therefore, we assessed the seasonal shift in persistence and net-accumulation of larval S. hineana eDNA in temperature-controlled mesocosms that reflect the larval overwintering period (5.0 °C) and the larval active period (16.0 °C). This study provided preliminary information regarding the seasonal shift in eDNA production for larval S. hineana. Understanding the seasonal dynamics of larval S. hineana eDNA is vital for efficient detection of this rare aquatic species using eDNA protocols. Our mesocosm results have informed subsequent field sampling of S. hineana eDNA. More

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    First microsatellite markers for the European Robin (Erithacus rubecula) and their application in analysis of parentage and genetic diversity

    1.Cramp, S. & Perrins, C. M. in The Birds of the Western Palearctic, Vol. 7 (eds. Cramp, S. & Perrins, C. M.) (Oxford University Press, 1993).2.Lack, D. Clutch and brood size in the Robin. Br. Birds 39(98–109), 130–135 (1946).
    Google Scholar 
    3.Lack, D. Further notes on clutch and brood size in the Robin. Br. Birds 41(98–104), 130–137 (1948).
    Google Scholar 
    4.Lack, D. The Life of Robin (Witherby, 1965).
    Google Scholar 
    5.Harper, D. G. C. Pairing strategies and mate choice in female Robins (Erithacus rubecula). Anim. Behav. 33, 862–875 (1985).Article 

    Google Scholar 
    6.Lebedeva, N. V. & Lomadze, N. H. in The Robin Erithacus Rubecula in the North-Western Caucasus (eds. Matishov, G. G. & Lebedeva, N. V.) 252–277 (SSC RAS Publishing, 2007).7.Knysh, N. P. Materials on the biology of Robin in forest-steppe deciduous forests of Sumy region. Berkut 17, 41–60 (2008).
    Google Scholar 
    8.Zimin V. B. in The Robin in the North of the Area, Vol. 1. Distribution. Number. Reproduction (ed. Zimin, V. B.) 401–422 (Karel’skiy nauchnyy centr RAN, 2009).9.Baranovskiy, A. V. & Ivanov, E. S. Features of reproductive biology of robins (Erithacus rubecula) in anthropogenic habitats (for example, the city of Ryazan). Principy èkologii 6, 17–25 (2017).
    Google Scholar 
    10.Wesołowski, T. Primeval conditions—What can we learn from them?. Ibis 149, 64–77 (2007).Article 

    Google Scholar 
    11.Tobias, J. & Seddon, N. Territoriality as a paternity guard in the European robin Erithacus rubecula. Anim. Behav. 60, 165–173 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Tobias, J. & Seddon, N. Female begging in European robins: Do neighbors eavesdrop for extrapair copulations?. Behav. Ecol. 13, 637–642 (2002).Article 

    Google Scholar 
    13.Lubjuhn, T., Strohbach, S., Brün, J., Gerken, T. & Epplen, J. T. Extra-pair paternity in great tits (Parus major)—A long term study. Behaviour 136, 1157–1172 (1999).Article 

    Google Scholar 
    14.Griffith, S. C., Owens, I. P. F. & Thuman, K. A. Extra pair paternity in birds: A review of interspecific variation and adaptive function. Mol. Ecol. 11, 2195–2212 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Cockburn, A. Prevalence of different modes of parental care in birds. Proc. Biol. Sci. 273, 1375–1383 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    16.Zagalska-Neubauer, M. & Dubiec, A. Techniki i markery molekularne w badaniach zmienności genetycznej ptaków. Not. Ornit. 48, 193–206 (2007).
    Google Scholar 
    17.Brouwer, L. & Griffith, S. C. Extra-pair paternity in birds. Mol. Ecol. 28, 4864–4882 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Petter, S. C., Miles, D. B. & White, M. M. Genetic evidence of mixed reproductive strategy in a monogamous bird. Condor 92, 702–708 (1990).Article 

    Google Scholar 
    19.Jennions, M. D. & Petrie, M. Why do females mate multiply? A review of the genetic benefits. Biol. Rev. 75, 21–64 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Akçay, E. & Roughgarden, J. Extra-pair paternity in birds: Review of the genetic benefits. Evol. Ecol. Res. 9, 855–868 (2007).
    Google Scholar 
    21.Dietzen, C., Witt, H.-H. & Wink, M. The phylogeographic differentiation of the European robin Erithacus rubecula on the Canary Islands revealed by mitochondrial DNA sequence data and morphometrics: Evidence for a new robin on Gran Canaria?. Avian Sci. 3, 115–131 (2003).
    Google Scholar 
    22.Rodrigues, P. et al. Phylogeography and genetic diversity of the Robin (Erithacus rubecula) in the Azores Islands: Evidence of a recent colonisation. J. Ornithol. 154, 889–900 (2013).Article 

    Google Scholar 
    23.Fulgione, D., Rippa, D., Manganiello, E., Caliendo, M. F. & Rastogi, R. K. Seasonal genetic structure analysis of a resident population of European Robin. Open Zool. J. 1, 11–17 (2008).CAS 
    Article 

    Google Scholar 
    24.Morin, P. A., Messier, J. & Woodruff, D. S. DNA extraction, amplification, and direct sequencing from hornbill feathers. J. Sci. Soc. Thail. 20, 31–41 (1994).CAS 
    Article 

    Google Scholar 
    25.Wright, T. F. et al. Microsatellite variation among divergent populations of stalk-eyed flies, genus Cyrtodiopsis. Genet. Res. 84, 27–40 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Yue, G.-H., Kovacs, B. & Orban, L. A new problem with cross-species amplification of microsatellites: Generation of non-homologous products. Dongwuxue Yanjiu 2, 131–140 (2010).
    Google Scholar 
    27.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Dąbrowski, M. J., Bornelöv, S., Kruczyk, M., Baltzer, N. & Komorowski, J. ‘True’ null allele detection in microsatellite loci: A comparison of methods, assessment of difficulties and survey of possible improvements. Mol. Ecol. Resour. 15, 477–488 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Primmer, C. R., Møller, A. P. & Ellegren, H. A wide-range survey of cross-species microsatellite amplification in birds. Mol. Ecol. 5, 365–378 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Jaroszewicz, B. et al. Białowieża forest—A relic of the high naturalness of European forests. Forests 10, 849 (2019).Article 

    Google Scholar 
    31.Campos, A. R. et al. How do Robins Erithacus rubecula resident in Iberia respond to seasonal flooding by conspecific migrants?. Bird Study 58, 435–442 (2011).Article 

    Google Scholar 
    32.Owen, J. C. Collecting, processing, and storing avian blood: A review. J. Field Ornithol. 82, 339–354 (2011).Article 

    Google Scholar 
    33.Horváth, M. B., Martínez-Cruz, B., Negro, J. J., Kalmár, L. & Godoy, J. A. An overlooked DNA source for non-invasive genetic analysis in birds. J. Avian Biol. 36, 84–88 (2005).Article 

    Google Scholar 
    34.Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Austin, J. D. et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2011–31 March 2011. Mol. Ecol. Resour. 11, 757–758 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Raymond, M. & Rousset, F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. Heredity 86, 248–249 (1995).Article 

    Google Scholar 
    39.Rousset, F. GENEPOP’007: A complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
    Article 

    Google Scholar 
    40.Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www.unil.ch/izea/softwares/fstat.htlm (2001).41.Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Grohme, M. A., Soler, R. F., Wink, M. & Frohme, M. Microsatellite marker discovery using single molecule real-time circular consensus sequencing on the Pacific Biosciences RS. Biotechniques 55, 253–256 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Liljegren, M. M., de Muinck, E. J. & Trosvik, P. Microsatellite length scoring by single molecule real time sequencing-effects of sequence structure and PCR regime. PLoS ONE 11, e0159232 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Dutta, N. et al. Microsatellite marker set for genetic diversity assessment of primitive Chitala chitala (Hamilton, 1822) derived through SMRT sequencing technology. Mol. Biol. Rep. 46, 41–49 (2018).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    45.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Corner, S., Yuzbasiyan-Gurkan, V., Agnew, D. & Venta, P. J. Development of a 12-plex of new microsatellite markers using a novel universal primer method to evaluate the genetic diversity of jaguars (Panthera onca) from North American zoological institutions. Conserv. Genet. Resour. 11, 487–497 (2019).Article 

    Google Scholar 
    47.Graham, B. A., Carpenter, A. M., Friesen, V. L. & Burg, T. M. A comparison of neutral genetic differentiation and genetic diversity among migratory and resident populations of Golden-crowned-Kinglets (Regulus satrapa). J. Ornithol. 161, 509–519 (2020).Article 

    Google Scholar 
    48.Bensch, S., Grahn, M., Müller, N., Gay, L. & Akesson, S. A. Genetic, morphological, and feather isotope variation of migratory willow warblers show gradual divergence in a ring. Mol. Ecol. 18, 3087–3096 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Kralj, J., Procházka, P., Fainová, D., Patzenhauerová, H. & Tutiš, V. Intraspecific variation in the wing shape and genetic differentiation of reed warblers Acrocephalus scirpaceus in Croatia. Acta Ornithol. 45, 51–58 (2010).Article 

    Google Scholar 
    50.Mettler, R. et al. Contrasting patterns of genetic differentiation among blackcaps (Sylvia atricapilla) with divergent migratory orientations in Europe. PLoS ONE 8, e81365 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Gyllensten, U., Jakonsson, S. & Temrin, H. No evidence for illegitimate young in monogamous and polygynous warblers. Nature 343, 168–170 (1990).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Gil, D., Slater, P. J. B. & Graves, J. A. Extrapair paternity and song characteristics in the willow warbler Phylloscopus trochilus. J. Avian Biol. 38, 291–297 (2007).Article 

    Google Scholar 
    53.Moskalenko, V. N., Belokon, M. M., Belokon, Y. S. & Goretskaia, M. I. Extrapair young in nests of the Wood Warbler (Phylloscopus sibilatrix) in the Middle Russia (poster). In 26th International Ornithological Congress (2014).54.Grendelmeier, A., Arlettaz, R., Olano-Marin, J. & Pasinelli, G. Experimentally provided conspecific cues boost bird territory density but not breeding performance. Behav. Ecol. 28, 174–185 (2017).Article 

    Google Scholar 
    55.Petrie, M. & Kempenaers, B. Extrapair paternity in birds: Explaining variation between species and populations. Trends Ecol. Evol. 13, 52–58 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Wagner, R. H. Hidden leks: sexual selection and the clustering of avian territories. Ornithol. Monogr. 49, 123–145 (1998).Article 

    Google Scholar 
    57.Fletcher, R. J. & Miller, C. W. On the evolution of hidden leks and the implications for reproductive and habitat selection behaviours. Anim. Behav. 71, 1247–1251 (2006).Article 

    Google Scholar 
    58.Broughton, R. K., Bubnicki, J. W. & Maziarz, M. Multi-scale settlement patterns of a migratory songbird in a European primeval forest. Behav. Ecol. Sociobiol. 74, 1–12 (2020).Article 

    Google Scholar  More

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    Evaluation of fish feeder manufactured from local raw materials

    Automatic feeder productivityTable 1 and Figs. 4, 5 and 6 show the automatic feeder productivity as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the automatic feeder productivity increases with increasing feed pellets size, air flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the automatic feeder productivity significantly increased from 11.16 to 13.87 (by 19.54%) kg min−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the automatic feeder productivity significantly increased from 11.02 to 14.03 (by 21.45%) kg min−1, while the automatic feeder productivity significantly increased from 3.33 to 21.46 (by 84.48%) kg min−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 1 Automatic feeder productivity at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 4Automatic feeder productivity at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 5Automatic feeder productivity at different feed pellet sizes and air flow rates.Full size imageFigure 6Automatic feeder productivity at different rotational speeds of screw and flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to increase the automatic feeder productivity from 3.04 to 3.79, 6.23 to 8.92, 11.86 to 14.10, 15.27 to 18.94 and 19.42 to 23.62 kg min−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the automatic feeder productivity increased from 3.04 to 19.42, 3.16 to 21.36 and 3.79 to 23.62 kg min−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 4.From statistical analysis, there were no significant different between feed pellets sizes 1 and 2 on the automatic feeder productivity, meanwhile, there were significant differences between feed pellets size 3 and sizes 1 and 2 on the productivity. Regarding the effect of air flow rate, there were significant differences between air flow rates on the automatic feeder productivity, the same trend was happened with the effect of rotational speed of screw on productivity. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 1.Regarding the effect of feed pellet size and air flow rate on the automatic feeder productivity, the results indicate that the automatic feeder productivity increases with increasing the feed pellets size and flow rate. It increased from 9.53 to 12.37, 11.23 to 13.82 and 12.73 to 15.43 kg min−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the automatic feeder productivity increased from 9.53 to 12.73, 11.16 to 13.92 and 12.37 to 15.43 kg min−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 5.The results also indicate that the automatic feeder productivity increased from 2.26 to 4.54, 6.39 to 8.90, 11.76 to 14.56, 15.25 to 18.68 and 19.44 to 23.45 kg min−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the automatic feeder productivity increased from 2.26 to 19.44, 3.19 to 21.50 and 4.54 to 23.45 kg min−1 at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 6.Multiple regression analysis was carried out to obtain a relationship between the automatic feeder productivity as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ Pr_{actual} = – 8.457 + 1.354PS + 0.301FR + 0.025RS{text{ R}}^{{2}} = 0.98{ ,} $$
    (13)
    where PS is the feed pellets size, mm; FR is the air flow rate, m3 min−1; RS is the rotational speed of screw, rpm.This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Automatic feeder efficiencyTable 2, Figs. 7, 8 and 9 show the automatic feeder efficiency as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that, when the feed pellets size increased from 1 to 3 mm, the automatic feeder efficiency significantly increased from 65.30 to 82.14 (by 20.50%) %. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the automatic feeder efficiency significantly increased from 62.58 to 85.07 (by 26.44%) %, while the automatic feeder efficiency significantly increased from 61.58 to 78.69 (by 21.74%) % when the rotational speed of screw increased from 180 to 900 rpm.Table 2 Automatic feeder efficiency at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 7Automatic feeder efficiency at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 8Automatic feeder efficiency at different feed pellet sizes and air flow rates.Full size imageFigure 9Automatic feeder efficiency at different rotational speeds of screw and air flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to increase the automatic feeder efficiency from 55.79 to 69.41, 57.10 to 81.78, 72.48 to 86.13, 69.96 to 86.81 and 71.19 to 86.58% at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the automatic feeder efficiency increased from 55.79 to 71.19, 57.98 to 78.29 and 69.41 to 86.58% at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 7.The statistical analysis showed that the differences between the obtained data of automatic feeder efficiency due to the effect of feed pellets size (A) and air flow rate (B) were significant. Regarding the effect of rotational speed of screw, there were significant differences between rotational speeds of screw 1, 2 and 3, meanwhile, there were no significant differences between rotational speeds of screw 3, 4 and 5. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 2.Regarding the effect of feed pellet size and air flow rate on the automatic feeder productivity, the results indicate that the automatic feeder efficiency increases with increasing the feed pellets size and flow rate. It increased from 53.91 to 70.69, 65.23 to 81.19 and 76.78 to 94.54% for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the automatic feeder efficiency increased from 53.91 to 76.78, 63.14 to 83.89 and 70.69 to 94.54% at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 8.The results also indicate that the automatic feeder efficiency increased from 41.37 to 83.28, 58.53 to 81.54, 71.85 to 84.96, 69.88 to 85.59 and 71.27 to 85.98% at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the automatic feeder efficiency increased from 41.37 to 71.27, 58.53 to 80.82 and 83.28 to 85.98% at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 9.Increasing the parameters seams to increase the productivity but regarding the efficiency, results show that the efficiency increases with increasing this parameter at (540 rpm) started to be constant and 720–900 rpm decreased in all treatments under study (Figs. 7, 9). It is concluded that efficiency with the parameters increased, became constant and decreased.Multiple regression analysis was carried out to obtain a relationship between the automatic feeder efficiency as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ eta = 9.566 + 8.417PS + 2.249FR + 0.025RS{text{ R}}^{{2}} = 0.89{ ,} $$
    (14)
    where this equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Specific energy consumptionTable 3, Figs. 10, 11 and 12 show the specific energy consumption of automatic feeder as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the specific energy consumption of automatic feeder decreases with increasing feed pellets size, air flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the specific energy consumption of automatic feeder significantly decreased from 8.93 to 6.74 (by 24.52%) W h kg−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the specific energy consumption of automatic feeder significantly decreased from 10.83 to 5.42 (by 49.95%) W h kg−1, while the specific energy consumption significantly decreased from 9.08 to 6.55 (by 27.86%) W h kg−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 3 Specific energy consumption at different feed pellets sizes, air flow rates and rotational speeds of screw.Full size tableFigure 10Specific energy consumption at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 11Specific energy consumption at different feed pellet sizes and air flow rates.Full size imageFigure 12Specific energy consumption at different rotational speeds of screw and air flow rates.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to decrease the specific energy consumption from 9.87 to 7.94, 9.18 to 7.63, 9.14 to 7.30, 8.65 to 6.63 and 7.79 to 4.20 W h kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the specific energy consumption decreased from 9.87 to 7.79, 9.42 to 7.65 and 7.94 to 4.20 W h kg−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 10.From statistical analysis, there were no significant differences between feed pellets sizes 1 and 2 on the specific energy consumption, meanwhile, there were significant differences between feed pellets size 3 and 1 and 2 on the specific energy consumption. Regarding the effect of air flow rate, there were significant differences between air flow rates and specific energy consumption. Regarding the effect of rotational speed of screw, there were significant differences between rotational speeds of screw 1, 2, 4 and 5 on the specific energy consumption, meanwhile, there were no significant differences between rotational speeds of screw 2 and 3 on the specific energy consumption. The analysis showed also that the interaction between both ABC was non-significant. On the other hand, the interaction between the effect of both AB, AC and BC on the data was significant as shown in Table 3.Regarding the effect of feed pellet size and air flow rate on the specific energy consumption, the results indicate that the specific energy consumption decreases with increasing the feed pellets size and flow rate. It decreased from 12.05 to 9.07, 8.81 to 6.56 and 5.92 to 4.59 W h kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the specific energy consumption decreased 12.05 to 5.92, 11.37 to 5.75 and 9.07 to 4.59 W h kg−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 11.The results also indicate that the specific energy consumption decreased from 12.31 to 6.18, 11.43 to 5.63, 11.21 to 5.63, 10.38 to 5.21 and 8.81 to 4.46 W h kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the specific energy consumption decreased from 12.31 to 8.81, 8.75 to 6.37 and 6.18 to 4.46 W h kg−1 at 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown Fig. 12.
    Multiple regression analysis was carried out to obtain a relationship between the specific energy consumption of automatic feeder as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:-$$ SEC = 20.045 – 1.095PS – 0.541FR – 0.003RS{text{ R}}^{{2}} = 0.92 , {.} $$
    (15)
    This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw.Total costs of automatic feederTable 4, Figs. 13, 14 and 15 show the total cost of automatic feeder as affected by the different feed pellets sizes (1, 2 and 3 mm), air flow rates (10, 15 and 20 m3 min−1) and rotational speeds of screw (180, 360, 540, 720 and 900 rpm). The results indicate that the total cost of automatic feeder decreases with increasing feed pellets size, flow rate and rotational speed of screw. It indicates that when the feed pellets size increased from 1 to 3 mm, the total cost of automatic feeder significantly decreased from 0.15 to 0.11 (by 26.27%) EGP kg−1. It also indicates that when the air flow rate increased from 10 to 20 m3 min−1, the total cost of automatic feeder significantly decreased from 0.16 to 0.09 (by 43.75%) EGP kg−1, while the total cost of automatic feeder significantly decreased from 0.16 to 0.10 (by 37.50%) EGP kg−1 when the rotational speed of screw increased from 180 to 900 rpm.Table 4 Total cost of automatic feeder at different feed pellets sizes, air flow rate and rotational speeds of screw.Full size tableFigure 13Total cost of automatic feeder at different feed pellet sizes and rotational speeds of screw.Full size imageFigure 14Total cost of automatic feeder at different feed pellet sizes and air flow rates.Full size imageFigure 15Total cost of automatic feeder at different rotational speeds of screw and air flow rate.Full size imageIt could be noticed that increasing the feed pellets size from 1 to 3 mm, tends to decrease the total cost of automatic feeder from 0.18 to 0.14, 0.16 to 0.12, 0.15 to 0.11, 0.13 to 0.09 and 0.12 to 0.08 EGP kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively. The results also indicate that the total cost of automatic feeder decreased from 0.18 to 0.12, 0.17 to 0.10 and 0.14 to 0.08 EGP kg−1 at 1, 2 and 3 mm feed pellets sizes, respectively when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 13.From statistical analysis, there were no significant differences between feed pellets sizes 1 and 2 on the total cost of automatic feeder, meanwhile, there were significant differences between feed pellets size 3 and 1 and 2 on the total cost of automatic feeder. Regarding the effect of air flow rate, there were significant differences between air flow rates and specific energy consumption. Regarding the effect of rotational speed of screw, there were no significant differences between rotational speeds of screw 1 and 2, also 3 and 4 on the total cost of automatic feeder, meanwhile, there were significant differences between rotational speeds of screw 2 and 3 on the total cost of automatic feeder.Regarding the effect of feed pellet size and flow rate on the total cost of automatic feeder, the results indicate that the total cost of automatic feeder decreases with increasing the feed pellets size and air flow rate. It decreased from 0.18 to 0.13, 0.16 to 0.11 and 0.10 to 0.08 EGP kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the feed pellets size increased from 1 to 3 mm. The results also indicate that the total cost of automatic feeder decreased from 0.18 to 0.10, 0.16 to 0.10 and 0.13 to 0.08 EGP kg−1 at 1, 2 and 3 mm feed pellets size, respectively, when the air flow rate increased from 10 to 20 m3 min−1 as shown in Fig. 14.The results also indicate that the total cost of automatic feeder decreased from 0.22 to 0.11, 0.18 to 0.10, 0.16 to 0.10, 0.13 to 0.09 and 0.12 to 0.07 EGP kg−1 at 180, 360, 540, 720 and 900 rpm rotational speed of screw, respectively, when the air flow rate increased from 10 to 20 m3 min−1. The results also indicate that the total cost of automatic feeder decreased from 0.22 to 0.12, 0.16 to 0.11 and 0.11 to 0.07 EGP kg−1 for 10, 15 and 20 m3 min−1 air flow rate, respectively, when the rotational speed of screw increased from 180 to 900 rpm as shown in Fig. 15.
    Multiple regression analysis was carried out to obtain a relationship between the total costs of automatic feeder as dependent variable and different of feed pellets size, air flow rate and rotational speed of screw as independent variables. The best fit for this relationship is presented in the following equation:$$ TC = 0.315 – 0.020PS – 0.006FR – 8.8 times 10^{ – 5} RS{text{ R}}^{{2}} = 0.87{,} $$
    (16)
    where: TC is the total cost of automatic feeder, EGP kg−1.This equation could be applied in the range of 1 to 3 mm feed pellets size, 10 to 20 m3 min−1 air flow rate and from 180 to 900 rpm of rotational speed of screw. More

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    The three major axes of terrestrial ecosystem function

    FLUXNET dataThe data used in this study belong to the FLUXNET LaThuile9 and FLUXNET2015 Tier 1 and Tier 2 datasets10, which make up the global network of CO2, water vapour and energy flux measurements. We merged the two FLUXNET releases and retained the FLUXNET2015 (the most recent and with a robust quality check) version of the data when the site was present in both datasets. Croplands were removed to avoid the inclusion of sites that are heavily managed in the analysis (for example, fertilization and irrigation).The sites used cover a wide variety of climate zones (from tropical to Mediterranean to Arctic) and vegetation types (wetlands, shrublands, grasslands, savanna, evergreen and deciduous forests). It should be noted though that tropical forests are underrepresented in the FLUXNET database (Extended Data Figs. 1, 3).Sites were excluded in cases in which: (i) data on precipitation or radiation were not available or completely gap-filled; (ii) the calculation of functional properties failed because of low availability of measured data (see ‘Calculation of ecosystem functions from FLUXNET’); and (iii) fluxes showed clear discontinuities in time series indicating a change of instrumentation set-up (for example, changes in the height of the ultrasonic anemometer or gas analyser).The final number of sites selected was 203 (1,484 site years). The geographical distribution is shown in Extended Data Fig. 1, the distribution in the climate space is shown in Extended Data Fig. 2 and the fraction of sites for each climate classes is reported in Extended Data Fig. 3.For each site, we downloaded the following variables at half-hourly temporal resolution: (i) gross primary productivity (GPP, μmol CO2 m–2 s–1) derived from the night-time flux partitioning26 (GPP_NT_VUT_50 in FLUXNET 2015 and GPP_f in LaThuile), (ii) net ecosystem exchange (NEE, μmol CO2 m–2 s–1) measurements filtered using annual friction velocity (u*, m s−1) threshold (NEE_VUT_50 in FLUXNET 2015; NEE in LaThuile); (iii) latent heat (LE, W m−2) fluxes, which were converted to evapotranspiration (ET, mm); (iv) sensible heat (H, W m−2) fluxes; (v) air temperature (Tair, °C); (vi) vapour pressure deficit (VPD, hPa); (vii) global shortwave incoming radiation (SWin, W m−2); viii) net radiation (Rn, W m−2); (ix) ground heat flux (G, W m−2); (x) friction velocity u* (m s−1); and (xi) wind speed (u, m s−1). For the energy fluxes (H, LE) we selected the fluxes not corrected for the energy balance closure to guarantee consistency between the two FLUXNET datasets (in the LaThuile dataset energy fluxes were not corrected).The cumulative soil water index (CSWI, mm) was computed as a measure of water availability according to a previous report27. Half-hourly values of transpiration estimates (T, mm) were calculated with the transpiration estimation algorithm (TEA)28. The TEA has been shown to perform well against both model simulations and independent sap flow data28.For 101 sites, ecosystem scale foliar N content (N%, gN 100 g−1) was computed as the community weighted average of foliar N% of the major species at the site sampled at the peak of the growing season or gathered from the literature29,30,31,32. Foliar N% for additional sites was derived from the FLUXNET Biological Ancillary Data Management (BADM) product and/or provided by site principal investigators (Supplementary Table 1, Extended Data Fig. 1). It should be noted that this compilation of N% data might suffer from uncertainties resulting from the scaling from leaves to the eddy covariance footprint, the sampling strategy (including the position along the vertical canopy profile), the species selection and the timing of sampling. About 30% of the data comes from a coordinated effort that minimized these uncertainties29,30, and for the others we collected N% data that were representative for the eddy covariance footprint31,32.Maximum leaf area index (LAImax, m2 m−2) and maximum canopy height (Hc, m) were also collected for 153 and 199 sites, respectively, from the literature32,33, the BADM product, and/or site principal investigators.Earth observation retrievals of above-ground biomass (AGB, tons of dry matter per hectare (t DM ha−1)) were extracted from the GlobBiomass dataset34 at its original resolution (grid cell 100 × 100 m) for each site location. All the grid cells in a 300 × 300 m and 500 × 500 m window around each location were selected to estimate the median and 95th percentiles of AGB for each site. The median of AGB was selected to avoid the contribution of potential outliers to the expected value of AGB. The analysis further explored the contribution of higher percentiles in the local variation of AGB as previous studies have highlighted the contribution of older and larger trees in uneven stand age plots to ecosystem functioning35. According to the evaluation against AGB measured at 71 FLUXNET sites (Extended Data Fig. 10), we decided to use the product with median AGB values extracted from the 500 × 500 m window.A total of 94 sites have all the data on vegetation structure (N%, LAImax, Hc, and AGB).The list of sites is reported in Supplementary Table 1 along with the plant functional type (PFT), Köppen-Geiger classification, coordinates, and when available N%, LAImax, Hc and AGB.In this study we did not make use of satellite information, with the exception of the AGB data product. Future studies will benefit from new missions such as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), the fluorescence explorer (FLEX), hyperspectral, and radar and laser detection and ranging (LiDAR) missions (for example, Global Ecosystem Dynamics Investigation (GEDI)), to characterize a multivariate space of structural and functional properties.Calculation of ecosystem functions from FLUXNETStarting from half-hourly data, we calculated at each site a single value for each of the ecosystem functions listed below. For the calculations of functional properties we used, unless otherwise indicated, good-quality data: quality flag 0 (measured data) and 1 (good-quality gap-filled data) in the FLUXNET dataset.Gross primary productivity at light saturation (GPPsat)GPP at light saturation using photosynthetically active radiation as driving radiation and 2,000 μmol m−2 s−1 as saturating light. GPPsat represents the ecosystem-scale maximum photosynthetic CO2 uptake15,30,36. The GPPsat was estimated from half-hourly data by fitting the hyperbolic light response curves with a moving window of 5 days and assigned at the centre of the moving window30,37. For each site the 90th percentile from the GPPsat estimates was then extracted.Maximum net ecosystem productivity (NEPmax)This was computed as the 90th percentile of the half-hourly net ecosystem production (NEP = −NEE) in the growing season (that is, when daily GPP is higher than 30% of the GPP amplitude). This metric represents the maximum net CO2 uptake of the ecosystem.Basal ecosystem respiration (Rb and Rbmax)Basal ecosystem respiration at reference temperature of 15 °C was derived from night-time NEE measurements26. Daily basal ecosystem respiration (Rbd) was derived by fitting an Arrhenius type equation over a five-day moving window and by keeping the sensitivity to temperature parameter (E0) fixed as in the night-time partitioning algorithms26,38. Rbd varies across seasons because it is affected by short-term variations in productivity33,39, phenology40 and water stress41. For each site, the mean of the Rbd (Rb) and the 95th percentile (Rbmax) were computed. The calculations were conducted with the REddyProc R package v.1.2.2 (ref. 38).Apparent carbon-use efficiency (aCUE)The aCUE as defined in this study is the efficiency of an ecosystem to sequester the carbon assimilated with photosynthesis39. aCUE is an indication of the proportion of respired carbon with respect to assimilated carbon within one season. A previous report6 showed that little of the variability in aCUE can be explained by climate or conventional site characteristics, and suggested an underlying control by plant, faunal and microbial traits, in addition to site disturbance history. Daily aCUE (aCUEd) is defined as aCUEd = 1 − (Rbd/GPPd), where GPPd is daily mean GPP and Rbd is derived as described above. For each site, aCUE was computed as the median of aCUEd.Metrics of water-use efficiency (WUE)Various metrics of WUE are described below: stomatal slope or slope coefficient (G1), underlying water-use efficiency (uWUE), and water-use efficiency based on transpiration (WUEt). The three metrics were used because they are complementary, as shown in previous studies11,42.Stomatal slope or slope coefficient (G1)This is the marginal carbon cost of water to the plant carbon uptake. G1 is the key parameter of the optimal stomatal model derived previously43. G1 is inversely related to leaf-level WUE. At leaf level, G1 is calculated using nonlinear regression and can be interpreted as the slope between stomatal conductance and net CO2 assimilation, normalized for VPD and CO2 concentration43. A previous report42 showed the potential of the use of G1 at ecosystem scale, where stomatal conductance is replaced by surface conductance (Gs), and net assimilation by GPP. The methodology is implemented in the bigleaf R package44. The metric was computed in the following situations: (i) incoming shortwave radiation (SWin) greater than 200 W m−2; (ii) no precipitation event for the last 24 h45, when precipitation data are available; and (iii) during the growing season: daily GPP > 30% of its seasonal amplitude44.Underlying water-use efficiency (uWUE)The underlying WUE was computed following a previous method46. uWUE is a metric of water-use efficiency that is negatively correlated to G1 at canopy scale44:$${rm{uWUE}}=frac{{rm{GPP}}sqrt{{rm{VPD}}}}{{rm{ET}}}.$$uWUE was calculated using the same filtering that was applied for the calculation of G1. The median of the half-hourly retained uWUE values was computed for each site and used as a functional property.Water-use efficiency based on transpiration (WUEt)The WUE based on transpiration (T) was computed to reduce the confounding effect resulting from soil evaporation11,28:$${{rm{WUE}}}_{{rm{t}}}=frac{{rm{GPP}}}{T},$$where T is the mean annual transpiration calculated with the transpiration estimation algorithm (TEA) developed by in a previous study28 and GPP is the mean annual GPP.Maximum surface conductance (G
    smax)Surface conductance (Gs) was computed by inverting the Penman–Monteith equation after calculating the aerodynamic conductance (Ga).Among the different formulations of Ga (m s–1) in the literature, we chose to use here the calculation of the canopy (quasi-laminar) boundary layer conductance to heat transfer, which ranges from empirical to physically based (for example, ref. 47). Other studies48,49 suggested an empirical relationship between Ga, the horizontal wind speed (u) and the friction velocity, u*:$${G}_{{rm{a}}}=frac{1}{(frac{u}{{u}^{* 2}}+6.2u{* }^{-0.67})}$$Gs (m s−1) is computed by inverting the Penman–Monteith equation:$${G}_{{rm{s}}}=frac{{{rm{LEG}}}_{{rm{a}}}gamma }{Delta ({R}_{{rm{n}}}-G-S)+rho {C}_{{rm{p}}}{G}_{{rm{a}}}{rm{VPD}}-{rm{LE}}(Delta +gamma )}$$where Δ is the slope of the saturation vapour pressure curve (kPa K−1), ρ is the air density (kg m−3), Cp is the specific heat of the air (J K−1 kg−1), γ is the psychrometric constant (kPa K−1), VPD (kPa), Rn (W m−2), G (W m−2) and S is the sum of all energy storage fluxes (W m−2) and set to 0 as not available in the dataset. When not available, G also was set to 0.Gs represents the combined conductance of the vegetation and the soil to water vapour transfer. To retain the values with a clear physiological interpretation, we filtered the data as we did for the calculation of G1.For each site, the 90th percentile of the half-hourly Gs was calculated and retained as the maximum surface conductance of each site (Gsmax). Gs was computed using the bigleaf R package44.Maximum evapotranspiration in the growing season (ETmax)This metric represents the maximum evapotranspiration computed as the 95th percentile of ET in the growing season and using the data retained after the same filtering applied for the G1 calculation.Evaporative fraction (EF)EF is the ratio between LE and the available energy, here calculated as the sum of H + LE (ref. 50). For the calculation of EF, we used the same filtering strategy as for G1. We first calculated mean daytime EF. We then computed  the EF per site as the growing season average of daytime EF. We also computed the amplitude of the EF in the growing season by calculating the interquartile distance of the distribution of mean daytime EF (EFampl).Principal component analysisA PCA was conducted on the multivariate space of the ecosystem functions. Each variable (ecosystem functional property, EFP) was standardized using z-transformation (that is, by subtracting its mean value and then dividing by its standard deviation). From the PCA results we extracted the explained variance of each component and the loadings of the EFPs, indicating the contribution of each variable to the component. We performed the PCA using the function PCA() implemented in the R package FactoMineR51.We justify using PCA over nonlinear methods because it is an exploratory technique that is highly suited to the analysis of the data volume used in this study, whereas other nonlinear methods applied to such data would be over-parameterized. For the same reason, PCA was used in previous work concerning the global spectrum of leaf and plant traits, and fluxes1,3,52.To test the significance of dimensionality of the PCA, we used a previously described methodology53. We used the R package ade4 (ref. 54) and evaluated the number of significant components of the PCA to be retained to minimize both redundancy and loss of information (Supplementary Information 2). We tested the significance of the PCA loadings using a combination of the bootstrapped eigenvector method55 and a threshold selected using the number of dimensions56 (Supplementary Information 2).Predictive variable importanceA random forests (RF) analysis was used to identify the vegetation structure and climate variables that contribute the most to the variability of the significant principal components, which were identified with the PCA analysis (see ‘Principal component analysis’). In the main text we refer to the results of this analysis as ‘predictive variable importance’ to distinguish this to the ‘causal variable importance’ described below.The analysis was conducted using the following predictor variables: as structural variables, N% (gN 100 g−1), LAImax (m2 m−2), AGB (t DM ha−1) and Hc (m); as climatic variables, mean annual precipitation (P, mm), mean VPD during the growing season (VPD, hPa), mean shortwave radiation (SWin, W m−2), mean air temperature (Tair, °C); and the cumulative soil water index (CSWI, −), as indicator of site water availability.We used partial dependencies of variables to assess the relationship between individual predictors and the response variable (that is, PC1, PC2 and PC3).The results from the partial dependency analysis can be used to determine the effects of individual variables on the response, without the influence of the other variables. The partial dependence function was calculated using the pdp R package57.The partial dependencies were calculated restricted to the values that lie within the convex hull of their training values to reduce the risk of interpreting the partial dependence plot outside the range of the data (extrapolation).Invariant causal regression models and causal variable importanceWe have quantified the dependence of the principal components on the different structural and climatic variables using nonlinear regression. Such dependencies can only be interpreted causally if the regression models are in fact causal regression models (see Supplementary Information 3 for a formal definition), which may not be the case if there are hidden confounders. To see whether the regression models allow for a causal interpretation, we use invariant causal prediction58. This method investigates whether the regression models are stable with respect to different patterns of heterogeneity in the data, encoded by different environments (that is, subsets of the original dataset). The rationale is that a causal model, describing the full causal mechanism for the response variable, should be invariant with respect to changes in the environment if the latter does not directly influence the response variable13,59. Other non-causal models may be invariant, too, but a non-invariant model cannot be considered causal.How to choose the environments is a modelling choice that must satisfy the following criteria. First, it should be possible to assign each data point to exactly one environment. Second, the environments should induce heterogeneity in the data, so that, for example, the predictor variables have different distributions across environments. Third, the environments must not directly affect the response variable, only via predictors, although the distribution of the response may still change between environments. The third criterion can be verified by expert knowledge and is assumed to hold for our analysis. In addition, if it is violated, then, usually, no set is invariant58, which can be detected from data.In our analysis, we assigned each data point (that is, each site) to one of two environments (two subsets of the original dataset): the first includes forest sites in North America, Europe or Asia; and the second includes non-forest and forest ecosystems from South America, Africa or Oceania, and non-forest ecosystems from North America, Europe or Asia (see Supplementary Information 3.1.3.1 for details). Our choice satisfies the method’s assumption that the distribution of the predictors is different between the two environments (that is, they induce heterogeneity in the data; see Supplementary Fig. 3.1). Environments that are too small or too homogeneous do not provide any evidence against the full set of covariates being a candidate for the set of causal predictors. Other choices of environments than the one presented here yield consistent results (Supplementary Information 3.2.1, Supplementary Fig. 3.4).For each subset of predictors, we test whether the corresponding regression model is invariant (yielding the same model fit in each environment). Although many models were rejected and considered non-invariant, the full model (with all the nine predictors and used in the predictive variable importance analysis) was accepted as invariant, establishing the full set of covariates as a reasonable candidate for the set of direct causal predictors. We used both RF (randomForest package in R60) and generalized additive models, GAM61 (mgcv package62 in R) to fit the models. Both methods lead to comparable results but with a better average performance of the RF: GAM led to slightly better results than RF for PC1, whereas for PC2 and PC3 RF showed a much better model performance (Supplementary Table 3.1, Supplementary Information 3.2.2). Therefore, in the main text we showed only the results from the RF (except for PC1).If, indeed, the considered regression models are causal, this allows us to make several statements. First, we can test for the existence of causal effects by testing for statistical significance of the respective predictors in the fitted models. Second, we can use the response curves of the fitted model to define a variable importance measure with a causal interpretation. In the main text we refer to this variable importance as ‘causal variable importance’. For details, see Supplementary Information 3.1.2. More formally, we considered the expected value of the predicted variables (the principal components) under joint interventions on all covariates (AGB, Hc, LAImax, N%, Tair, VPD, SWin, CSWI and P) at once, and then, to define the importance, we quantified how this expected value depends on the different covariates. We applied the same analysis to groups of vegetation structural and climate covariates (see ‘Groupwise variable importance’ in Supplementary Information 3.1.2.3, 3.2.3).The details of the methodology and the results are described in Supplementary Information 3, in which we also provide further details on the choice of environment variable and on the statistical tests that we use to test for invariance. An overview of the invariance-based methodology is shown in Supplementary Fig. 3.1.Land surface model runsWe run two widely used land surface models: Orchidee-CN (OCN) and Jena Scheme for Biosphere Atmosphere Coupling in Hamburg (JSBACH):OCNThe dynamic global vegetation model OCN is a model of the coupled terrestrial carbon and nitrogen cycles63,64, derived from the ORCHIDEE land surface model. It operates at a half-hourly timescale and simulates diurnal net carbon, heat and water exchanges, as well as nitrogen trace gas emissions, which jointly affect the daily changes in leaf area index, foliar nitrogen, and vegetation structure and growth. The main purpose of the model is to analyse the longer-term (interannual to decadal) implication of nutrient cycling for the modelling of land–climate interactions64,65. The model can run offline, driven by observed meteorological parameters, or coupled to the global circulation model.JSBACHJSBACH v.3 is the land surface model of the MPI Earth System Model66,67. The model operates at a half-hourly time step and simulates the diurnal net exchange of momentum, heat, water and carbon with the atmosphere. Daily changes in leaf area index and leaf photosynthetic capacity are derived from a prognostic scheme assuming a PFT-specific set maximum leaf area index and a set of climate responses modulating the seasonal course of leaf area index. Carbon pools are prognostic allowing for simulating the seasonal course of net land–atmosphere carbon exchanges.We selected OCN and JSBACH because they are widely used land surface models with different structures. JSBACH is a parsimonious representation of the terrestrial energy, water and carbon exchanges used to study the coupling of land and atmosphere processes in an Earth system model67. OCN has also been derived from the land surface model ORCHIDEE68, but it includes a more comprehensive representation of plant physiology, including a detailed representation of the tight coupling of the C and N cycling63. Both models contribute to the annual global carbon budget of the Global Carbon Project69 and have shown good performance compared to a number of global benchmarks. OCN was further used in several model syntheses focused on the interaction between changing N deposition and CO2 fertilization70,71,72. Both OCN and JSBACH can operate at a half-hourly timescale and simulate net and gross carbon exchanges, water and energy fluxes, and therefore are ideal for the extraction of ecosystem functional properties, as done with the eddy covariance data.The models were driven by half-hourly meteorological variables (shortwave and longwave downward flux, air temperature and humidity, precipitation, wind speed and atmospheric CO2 concentrations) observed at the eddy covariance sites. OCN was furthermore driven by N deposition fields73. Vegetation type, soil texture and plant available water were prescribed on the basis of site observations, but no additional site-specific parameterization was used. Both models were brought into equilibrium with respect to their ecosystem water storage and biogeochemical pools by repeatedly looping over the available site years. We added random noise (mean equal to 0 and standard deviation of 5% of the flux value) to the fluxes simulated by the models to mimic the random noise of the eddy covariance flux observations. An additional test conducted without noise addition showed only a marginal effect on the calculations of the functional properties and the ecosystem functional space.We used runs of the JSBACH and OCN model for 48 FLUXNET sites (Supplementary Table 1). The simulated fluxes were evaluated against the observation to assess the performance of the models at the selected sites. From the model outputs and from each site we derived the ecosystem functions using the same methodology described above. Then the PCA analysis was performed on the three datasets (FLUXNET, OCN and JSBACH) and restricted to the 48 sites used to run the models. We ran the models only on the subset of sites for which the information for the parameterization and high-quality forcing was available. However, the different ecosystem functions emerge from the model structure and climatological conditions. Therefore, even with a smaller set of site we can evaluate whether models reproduce the key dimensions of terrestrial ecosystem function by comparing the PCA results from FLUXNET and the model runs.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

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    Fecal filtrate transplantation protects against necrotizing enterocolitis

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