More stories

  • in

    Brazil: heed price of marine mining for an alternative fertilizer

    Brazil’s government risks fuelling the climate and biodiversity crisis by offsetting the fertilizer shortage resulting from Russia’s invasion of Ukraine this year (J. Liu et al. Nature 604, 425 (2022); S. Osendarp et al. Nature 604, 620–624; 2022). To produce an alternative fertilizer, it plans to mine up to 12 million tonnes annually of rhodoliths taken from an area in the South Atlantic that is roughly the size of the United Kingdom (see go.nature.com/3yhiyio).A full list of co-signatories to this letter appears in Supplementary Information.
    Competing Interests
    The author declares no competing interests. More

  • in

    A feeding frenzy of 150 whales marks a species’ comeback

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

  • in

    Prediction of the potential distribution of the predatory mite Neoseiulus californicus (McGregor) in China under current and future climate scenarios

    Moraes, G. J., Mcmurtry, J. A., Denmark, H. A. & Campos, C. B. A revised catalog of the mite family Phytoseiidae. Zootaxa 434, 1–494 (2004).Article 

    Google Scholar 
    Fraulo, A. B. & Liburd, O. E. Biological control of twospotted spider mite, Tetranychus urticae, with predatory mite, Neoseiulus californicus, in strawberries. Exp. Appl. Acarol. 43, 109–119 (2007).PubMed 
    Article 

    Google Scholar 
    Kuştutan, O. & Cakmak, I. Development, fecundity, and prey consumption of Neoseiulus californicus (McGregor) fed Tetranychus cinnabarinus Boisduval. Turk. J. Agric. For. 33, 19–28 (2009).
    Google Scholar 
    Kishimoto, H. et al. Occurrence of Neoseiulus californicus (Acari: Phytoseiidae) on citrus in Kyushu district, Japan. J. Acarol. Soc. Japan 16, 129–137 (2007).Article 

    Google Scholar 
    Albayrak, T., Yorulmaz, S., İnak, E., Toprak, U. & Van Leeuwen, T. Pirimicarb resistance and associated mechanisms in field-collected and selected populations of Neoseiulus californicus. Pestic. Biochem. Phys. 180, 104984 (2022).CAS 
    Article 

    Google Scholar 
    Abdellah, A., Abdelaziz, Z., Philipe, A., Serge, K. & Abdelhamid, E. M. Seasonal trend of Eutetranychus orientalis in Moroccan citrus orchards and its potential control by Neoseiulus californicus and Stethorus punctillum. Syst. Appl. Acarol. 26, 1458–1480 (2021).
    Google Scholar 
    Vidrih, M., Turnšek, A., Rak Cizej, M., Bohinc, T. & Trdan, S. Results of the single release efficacy of the predatory mite Neoseiulus californicus (McGregor) against the two-spotted spider mite (Tetranychus urticae Koch) on a hop plantation. Appl. Sci. 11, 118 (2021).CAS 
    Article 

    Google Scholar 
    Jiang, C. X., Chen, L., Huang, T. T., Mumtaz, M. & Li, Q. Neoseiulus californicus (Acari: Phytoseiidae) shows good predation potential when reared on an artificial diet supplemented with Tetranychus cinnabarinus. Syst. Appl. Acarol. 26, 2229–2246 (2021).
    Google Scholar 
    Katayama, H. et al. Density suppression of the citrus red mite Panonychus citri (Acari: Tetranychidae) due to the occurrence of Neoseiulus californicus (McGregor) (Acari: Phytoseiidae) on Satsuma mandarin. Appl. Entomol. Zool. 41, 679–684 (2006).Article 

    Google Scholar 
    Zhu, R., Guo, J. J., Yi, T. C., Xiao, R. & Jin, D. C. Preying potential of predatory mite Neoseiulus californicus to broad mite Polyphagotarsonemus latus. J. Plant Prot. 46, 465–471 (2019) ([In Chinese]).
    Google Scholar 
    Silva, D. E. et al. Impact of vineyard agrochemicals against Panonychus ulmi (Acari: Tetranychidae) and its natural enemy, Neoseiulus californicus (Acari: Phytoseiidae) in Brazil. Crop Prot. 123, 5–11 (2019).CAS 
    Article 

    Google Scholar 
    Sato, M. E., Da Silva, M. Z., De Souza Filho, M. F., Matioli, A. L. & Raga, A. Management of Tetranychus urticae (Acari: Tetranychidae) in strawberry fields with Neoseiulus californicus (Acari: Phytoseiidae) and acaricides. Exp. Appl. Acarol. 42, 107–120 (2007).PubMed 
    Article 

    Google Scholar 
    De Souza-Pimentel, G. C. et al. Biological control of Tetranychus urticae (Tetranychidae) on rosebushes using Neoseiulus californicus (Phytoseiidae) and agrochemical selectivity. Rev. Colombi. Entomol. 40, 80–84 (2014).
    Google Scholar 
    Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

    Google Scholar 
    Peterson, A. T. & Shaw, J. Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models, predicted geographic distribution, and climate change effects. Int. J. Parasitol. 33, 919–931 (2003).PubMed 
    Article 

    Google Scholar 
    Peterson, A. T. & Soberón, J. Species distribution modeling and ecological niche modeling: Getting the Concepts Right. Nat. Conserv. 10, 102–107 (2012).Article 

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

    Google Scholar 
    Stockwell, D. & Peters, D. P. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).Article 

    Google Scholar 
    Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186, 251–270 (2005).Article 

    Google Scholar 
    Arslan, E. S. & Örücü, Ö. K. MaxEnt modelling of the potential distribution areas of cultural ecosystem services using social media data and GIS. Environ. Dev. Sustain. 23, 2655–2667 (2021).Article 

    Google Scholar 
    Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

    Google Scholar 
    Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species distributions areas. Biodivers. Inf. 2, 1–10 (2005).
    Google Scholar 
    Ab Lah, N. Z., Yusop, Z., Hashim, M., Salim, J. M. & Numata, S. Predicting the habitat suitability of Melaleuca cajuputi based on the MaxEnt Species Distribution Model. Forests 12, 1449 (2021).Article 

    Google Scholar 
    Ali, H. et al. Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS ONE 16, e0260031 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boral, D. & Moktan, S. Predictive distribution modeling of Swertia bimaculata in Darjeeling-Sikkim Eastern Himalaya using MaxEnt: current and future scenarios. Ecol. Process. 10, 1–16 (2021).Article 

    Google Scholar 
    Kamyo, T. & Asanok, L. Modeling habitat suitability of Dipterocarpus alatus (Dipterocarpaceae) using MaxEnt along the Chao Phraya River in Central Thailand. Forest Sci. Technol. 16, 1–7 (2020).ADS 
    Article 

    Google Scholar 
    Barber, R. A., Ball, S. G., Morris, R. K. A. & Gilbert, F. Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers. Distrib. 28, 128–141 (2022).Article 

    Google Scholar 
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

    Google Scholar 
    Comino, E., Fiorucci, A., Rosso, M., Terenziani, A. & Treves, A. Vegetation and Glacier Trends in the area of the Maritime Alps Natural Park (Italy): MaxEnt application to predict habitat development. Clim. 9, 54 (2021).Article 

    Google Scholar 
    Wang, R. L. et al. Prediction of the potential distribution of the predatory mite Neoseiulus californicus McGregor in China using MaxEnt. Glob. Ecol. Conserv. 29, e01733 (2021).Article 

    Google Scholar 
    Bertolino, S. et al. Spatially explicit models as tools for implementing effective management strategies for invasive alien mammals. Mamm. Rev. 50, 187–199 (2020).Article 

    Google Scholar 
    Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive Pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 
    Article 

    Google Scholar 
    Tang, J. T., Li, J. H., Lu, H., Lu, F. P. & Lu, B. Q. Potential distribution of an invasive pest, Euplatypus parallelus, in China as predicted by Maxent. Pest Manag. Sci. 75, 1630–1637 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, Y. et al. Predicting dynamics of the potential breeding habitat of Larus saundersi by MaxEnt model under changing land-use conditions in wetland nature reserve of Liaohe Estuary, China. Remote Sens. 14, 552 (2022).ADS 
    Article 

    Google Scholar 
    Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: The impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).Article 

    Google Scholar 
    Smeraldo, S. et al. Generalists yet different: distributional responses to climate change may vary in opportunistic bat species sharing similar ecological traits. Mamm. Rev. 51, 571–584 (2021).Article 

    Google Scholar 
    Pörtner, H. O. et al. Climate Change 2022: The Physical Science Basis. Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 15. https://www.ipcc.ch/report/ar6/wg3/ (2022).Ahmed, S. E. et al. Scientists and software–surveying the species distribution modelling community. Divers. Distrib. 21, 258–267 (2015).Article 

    Google Scholar 
    Tognelli, M. F., Roig-Juñent, S. A., Marvaldi, A. E., Flores, G. E. & Lobo, J. M. An evaluation of methods for modelling distribution of Patagonian insects. Rev. Chil. Hist. Nat. 82, 347–360 (2009).Article 

    Google Scholar 
    Pangga, I., Salvacion, A., Hamor, N. & Yap, S. Maximum entropy (MaxEnt) modeling of the potential distribution of Aspidiotus rigidus Reyne (Hemiptera: Diaspididae) in the Philippines. Philipp. Agric. Sci. 104, 1–7 (2021).
    Google Scholar 
    Zhou, R. B. et al. Projecting the potential distribution of Glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biol. 10, 1150 (2021).Article 

    Google Scholar 
    Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’s distribtional areas. Biodivers. Inform. 2, 1–10 (2005).Article 

    Google Scholar 
    Soberon, J. M. Niche and area of distribution modeling: a population ecology perspective. Ecography 33, 159–167 (2010).Article 

    Google Scholar 
    Soberon, J. M. & Nakamura, M. Niches and distributional areas: concepts, methods and assumptions. P. Natl. Acad. Sci. USA 106, 19644–19650 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhang, Y. X., Ji, J., Chen, X., Lin, J. Z. & Chen, B. L. The effect of temperature on reproduction and development duration of Neoseiulus (Amblyseius) californicus (Mcgregor). Fujian J. Agric. Sci. 27, 157–161 (2012) ([In Chinese]).
    Google Scholar 
    Neto, M. P., Reis, P. R., Zacarias, M. S. & Silva, R. A. Influence of rainfall on mite distribution in organic and conventional coffee systems. Coffee Sci. 5, 67–74 (2010).
    Google Scholar 
    Hu, Z., Gui, L. Y., Hua, D. K. & Luo, J. Effect of simulated rainfall on laboratory population dynamics of Tetranychus cinnabarinus. J. Environ. Entomol. 38, 936–941 (2016) ([In Chinese]).
    Google Scholar 
    Lawler, J. J. Climate change adaptation strategies for resource management and conservation planning. Ann. N. Y. Acad. Sci. 1162, 79–98 (2009).ADS 
    PubMed 
    Article 

    Google Scholar 
    www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained.Gotoh, T., Yamaguchi, K. & Mori, K. Effect of temperature on life history of the predatory mite Amblyseius (Neoseiulus) californicus (Acari: Phytoseiidae). Exp. Appl. Acarol. 32, 15–30 (2004).PubMed 
    Article 

    Google Scholar 
    Yuan, X. P., Wang, X. D., Wang, J. W. & Zhao, Y. Y. Effects of brief exposure to high temperature on Neoseiulus californicus. Ying Yong Sheng Tai Xue Bao 26, 853–858 (2015) ([In Chinese]).PubMed 

    Google Scholar 
    Zhang, G. H. et al. Intraspecific variations on thermal susceptibility in the predatory mite Neoseiulus barkeri Hughes (Acari: Phytoseiidae): responding to long-term heat acclimations and frequent heat hardenings. Biol. Control 121, 208–215 (2018).Article 

    Google Scholar 
    Phillips, S. J., Dudík, M. & Schapire, R. E.[Internet] Maxent software for modeling species niches and distributions (Version 3.4.1). url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 17 March 2022.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. url: https://www.R-project.org/ (2021).Seyedizadeh, S., Ghane-Jahromi, M., Sedaratian-Jahromi, A. & Faraji, F. Discovery of the predatory mite Neoseiulus californicus (Acari: Phytoseiidae) in some rose greenhouses in Iran and describing variation in spermathecal calyx shape. Pers. J. Acarol. 6, 67–70 (2017).
    Google Scholar 
    Fang, X. D., Nguyen, V. L., Ouyang, G. C. & Wu, W. N. Survey of phytoseiid mites (Acari: Mesostigmata, Phytoseiidae) in citrus orchards and a key for Amblyseiinae in Vietnam. Acarologia 60, 254–267 (2020).Article 

    Google Scholar 
    Greco, N. M., Tetzlaff, G. T. & Liljesthröm, G. G. Presence–absence sampling for Tetranychus urticae and its predator Neoseiulus californicus (Acari: Tetranychidae; Phytoseiidae) on strawberries. Int. J. Pest Manag. 50, 23–27 (2004).Article 

    Google Scholar 
    Beaulieu, F. & Beard, J. J. Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types. Zootaxa 4500, 451–507 (2018).Kawashima, M. & Jung, C. Effects of sheltered ground habitats on the overwintering potential of the predacious mite Neoseiulus californicus (Acari: Phytoseiidae) in apple orchards on mainland Korea. Exp. Appl. Acarol. 55, 375–388 (2011).PubMed 
    Article 

    Google Scholar 
    Koller, M., Knapp, M. & Schausberger, P. Direct and indirect adverse effects of tomato on the predatory mite Neoseiulus californicus feeding on the spider mite Tetranychus evansi. Entomol. Exp. Appl. 125, 297–305 (2007).Article 

    Google Scholar 
    Ohno, S. et al. Geographic distribution of phytoseiid mite species (Acari: Phytoseiidae) on crops in Okinawa, a subtropical area of Japan. Entomol. Sci. 15, 115–120 (2012).Article 

    Google Scholar 
    Tixier, M. S., Otto, J., Kreiter, S., Dos Santos, V. & Beard, J. Is Neoseiulus wearnei the Neoseiulus californicus of Australia? Exp. Appl. Acarol. 62, 267–277 (2014).PubMed 
    Article 

    Google Scholar 
    Vacacela Ajila, H. E. et al. Supplementary food for Neoseiulus californicus boosts biological control of Tetranychus urticae on strawberry. Pest Manag. Sci. 75, 1986–1992 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xu, X. N., Wang, B. M., Wang, E. D. & Zhang, Z. Q. Comments on the identity of Neoseiulus californicus sensu lato (Acari: Phytoseiidae) with a redescription of this species from southern China. Syst. Appl. Acarol. 18, 329–344 (2013).
    Google Scholar 
    Pringle, K. L. & Heunis, J. M. Biological control of phytophagous mites in apple orchards in the Elgin area of South Africa using the predatory mite, Neoseiulus californicus (McGregor) (Mesostigmata: Phytoseiidae): a benefit-cost analysis. Afr. Entomol. 14, 113–121 (2006).
    Google Scholar 
    Tai, Y. W. et al. R package ‘corrplot’: Visualization of a Correlation Matrix. url: https://github.com/taiyun/corrplot (2021).Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).Article 

    Google Scholar 
    Araujo, M. B., Pearson, R. G., Tuiller, W. & Erhard, M. Validation of species–climate impact models under climate change. Glob. Change Biol. 11, 1504–1513 (2005).ADS 
    Article 

    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

    Google Scholar  More

  • in

    Neuron numbers link innovativeness with both absolute and relative brain size in birds

    Shultz, S. & Dunbar, R. Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proc. Natl Acad. Sci. USA 107, 21582–21586 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jerison, H. J. Animal intelligence as encephalization. Phil. Trans. R. Soc. Lond. B 308, 21–35 (1985).CAS 
    Article 

    Google Scholar 
    Roth, G. & Dicke, U. Evolution of the brain and intelligence. Trends Cogn. Sci. 9, 250–257 (2005).PubMed 
    Article 

    Google Scholar 
    Lefebvre, L., Whitle, P., Lascaris, E. & Finkelstein, A. Feeding innovations and forebrain size in birds. Anim. Behav. 53, 549–560 (1997).Article 

    Google Scholar 
    Overington, S. E., Morand-Ferron, J., Boogert, N. J. & Lefebvre, L. Technical innovations drive the relationship between innovativeness and residual brain size in birds. Anim. Behav. 78, 1001–1010 (2009).Article 

    Google Scholar 
    Reader, S. M., Hager, Y. & Laland, K. N. The evolution of primate general and cultural intelligence. Phil. Trans. R. Soc. B 366, 1017–1027 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. Proc Natl Acad. Sci. USA 113, 2532–2537 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reader, S. M. & Laland, K. N. Social intelligence, innovation, and enhanced brain size in primates. Proc. Natl Acad. Sci. USA 99, 4436–4441 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fristoe, T. S., Iwaniuk, A. N. & Botero, C. A. Big brains stabilize populations and facilitate colonization of variable habitats in birds. Nat. Ecol. Evol. 1, 1706–1715 (2017).PubMed 
    Article 

    Google Scholar 
    van Woerden, J. T., van Schaik, C. P. & Isler, K. Effects of seasonality on brain size evolution: evidence from Strepsirrhine primates. Am. Nat. 176, 758–767 (2010).PubMed 
    Article 

    Google Scholar 
    Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 
    Article 

    Google Scholar 
    Herculano-Houzel, S. Brains matter, bodies maybe not: the case for examining neuron numbers irrespective of body size. Ann. NY Acad. Sci. 1225, 191–199 (2011).PubMed 
    Article 

    Google Scholar 
    Logan, C. J. et al. Beyond brain size: uncovering the neural correlates of behavioral and cognitive specialization. Comp. Cogn. Behav. Rev. 13, 55–89 (2018).Article 

    Google Scholar 
    Jerison, H. Evolution of the Brain and Intelligence (Academic Press, 1973).Herculano-Houzel, S. Numbers of neurons as biological correlates of cognitive capability. Curr. Opin. Behav. Sci. 16, 1–7 (2017).Article 

    Google Scholar 
    Van Schaik, C. P., Triki, Z., Bshary, R. & Heldstab, S. A. A farewell to the encephalization quotient: a new brain size measure for comparative primate cognition. Brain Behav. Evol. 96, 1–12 (2021).PubMed 
    Article 

    Google Scholar 
    Striedter, G. F. Principles of Brain Evolution (Sinauer Associates, 2005).
    Google Scholar 
    MacLean, E. L. et al. The evolution of self-control. Proc. Natl Acad. Sci. USA 111, E2140–E2148 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Matějů, J. et al. Absolute, not relative brain size correlates with sociality in ground squirrels. Proc. R. Soc. B 283, 20152725 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Deaner, R. O., Isler, K., Burkart, J. & Van Schaik, C. Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain Behav. Evol. 70, 115–124 (2007).PubMed 
    Article 

    Google Scholar 
    Smaers, J. B., Dechmann, D. K. N., Goswami, A., Soligo, C. & Safi, K. Comparative analyses of evolutionary rates reveal different pathways to encephalization in bats, carnivorans, and primates. Proc. Natl Acad. Sci. USA 109, 18006–18011 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smaers, J. B. et al. The evolution of mammalian brain size. Sci. Adv. 7, eabe2101 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Němec, P. & Osten, P. The evolution of brain structure captured in stereotyped cell count and cell type distributions. Curr. Opin. Neurobiol. 60, 176–183 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl Acad. Sci. USA 113, 7255–7260 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kverková, K. et al. The evolution of brain neuron numbers in amniotes. Proc. Natl Acad. Sci. USA 119, e2121624119 (2022).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Iwaniuk, A. N. & Hurd, P. L. The evolution of cerebrotypes in birds. Brain Behav. Evol. 65, 215–230 (2005).PubMed 
    Article 

    Google Scholar 
    Timmermans, S., Lefebvre, L., Boire, D. & Basu, P. Relative size of the hyperstriatum ventrale is the best predictor of feeding innovation rate in birds. Brain Behav. Evol. 56, 196–203 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sayol, F., Lefebvre, L. & Sol, D. Relative brain size and its relation with the associative pallium in birds. Brain Behav. Evol. 87, 69–77 (2016).PubMed 
    Article 

    Google Scholar 
    Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).Deaner, R. O., Barton, R. A. & van Schaik, C. P. in Primate Life Histories and Socioecology (eds Kappeler, P. M. & Pereira, M. E.) 233–265 (Univ. of Chicago Press, 2003).Sol, D., Sayol, F., Ducatez, S. & Lefebvre, L. The life-history basis of behavioural innovations. Phil. Trans. R. Soc. B 371, 20150187 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dukas, R. Evolutionary biology of animal cognition. Ann. Rev. Ecol. Evol. Syst. 35, 347–374 (2004).Article 

    Google Scholar 
    Ricklefs, R. E. The cognitive face of life histories. Wilson Bull. 116, 119–133 (2004).Article 

    Google Scholar 
    Martin, T. E., Oteyza, J. C., Boyce, A. J., Lloyd, P. & Ton, R. Adult mortality probability and nest predation rates explain parental effort in warming eggs with consequences for embryonic development time. Am. Nat. 186, 223–236 (2015).PubMed 
    Article 

    Google Scholar 
    Unzeta, M., Martin, T. E. & Sol, D. Daily nest predation rates decrease with body size in passerine birds. Am. Nat. 196, 743–754 (2020).PubMed 
    Article 

    Google Scholar 
    Charvet, C. J. & Striedter, G. F. Developmental modes and developmental mechanisms can channel brain evolution. Front. Neuroanat. 5, 4 (2011).Finlay, B. L. & Darlington, R. B. Linked regularities in the development and evolution of mammalian brains. Science 268, 1578–1584 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Herculano-Houzel, S. Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Massen, J. J. M. et al. Brain size and neuron numbers drive differences in yawn duration across mammals and birds. Commun. Biol. 4, 1–10 (2021).Article 

    Google Scholar 
    Ramsey, G., Bastian, M. L. & Schaik, C. Van Animal innovation defined and operationalized. Behav. Brain Sci. 30, 393–437 (2007).PubMed 
    Article 

    Google Scholar 
    Lefebvre, L. A global database of feeding innovations in birds. Wilson J. Ornithol. 132, 803–809 (2021).Article 

    Google Scholar 
    Barton, R. A. Embodied cognitive evolution and the cerebellum. Phil. Trans. R. Soc. B 367, 2097–2107 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic–midbrain–cerebellar circuit. Sci. Rep. 8, 9960 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Brieuc, M. S. O. O., Waters, C. D., Drinan, D. P. & Naish, K. A. A practical introduction to random forest for genetic association studies in ecology and evolution. Mol. Ecol. Res. 18, 755–766 (2018).Article 

    Google Scholar 
    Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Güntürkün, O., Ströckens, F., Scarf, D. & Colombo, M. Apes, feathered apes, and pigeons: differences and similarities. Curr. Opin. Behav. Sci. 16, 35–40 (2017).Article 

    Google Scholar 
    Ströckens, F. et al. High associative neuron numbers could drive cognitive performance in corvid species. J. Comp. Neurol. 530, 1588–1605 (2022).PubMed 
    Article 

    Google Scholar 
    Shanahan, M., Bingman, V. P., Shimizu, T., Wild, M. & Güntürkün, O. Large-scale network organisation in the avian forebrain: a connectivity matrix and theoretical analysis. Front. Comput. Neurosci. 7, 89 (2013).Emery, N. J. Cognitive ornithology: the evolution of avian intelligence. Phil. Trans. R. Soc. B 361, 23–43 (2006).PubMed 
    Article 

    Google Scholar 
    Lambert, M. L., Jacobs, I., Osvath, M. & von Bayern, A. M. P. Birds of a feather? Parrot and corvid cognition compared. Behaviour 156, 505–594 (2019).Article 

    Google Scholar 
    Ksepka, D. T. et al. Tempo and pattern of avian brain size evolution. Curr. Biol. 30, 2026–2036 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Herculano-Houzel, S., Manger, P. R. & Kaas, J. H. Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size. Front. Neuroanat. 8, 77 (2014).Smaers, J. B., Mongle, C. S., Safi, K. & Dechmann, D. K. N. Allometry, evolution and development of neocortex size in mammals. Prog. Brain Res. 250, 83–107 (2019).PubMed 
    Article 

    Google Scholar 
    Cárdenas, A. & Borrell, V. Molecular and cellular evolution of corticogenesis in amniotes. Cell Mol. Life Sci. 77, 435–1460 (2020).Article 
    CAS 

    Google Scholar 
    García-Moreno, F. & Molnár, Z. Variations of telencephalic development that paved the way for neocortical evolution. Prog. Neurobiol. 194, 101865 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Charvet, C. J. & Striedter, G. F. Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis. Proc. R. Soc. B 276, 3421–3427 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Striedter, G. F. & Charvet, C. J. Developmental origins of species differences in telencephalon and tectum size: morphometric comparisons between a parakeet (Melopsittacus undulatus) and a quail (Colinus virgianus). J. Comp. Neurol. 507, 1663–1675 (2008).PubMed 
    Article 

    Google Scholar 
    Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl Acad. Sci. USA 104, 17707–17712 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Uomini, N., Fairlie, J., Gray, R. D. & Griesser, M. Extended parenting and the evolution of cognition. Phil. Trans. R. Soc. Lond. B 375, 20190495 (2020).Article 

    Google Scholar 
    Reiner, A. et al. Revised nomenclature for avian telencephalon and some related brainstem nuclei. J. Comp. Neurol. 473, 377–414 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mullen, R. J., Buck, C. R. & Smith, A. M. NeuN, a neuronal specific nuclear protein in vertebrates. Development 116, 201–211 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mezey, S. et al. Postnatal changes in the distribution and density of neuronal nuclei and doublecortin antigens in domestic chicks (Gallus domesticus). J. Comp. Neurol. 520, 100–116 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    Ducatez, S. & Lefebvre, L. Patterns of research effort in birds. PLoS ONE 9, e89955 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).Cooney, C. R. et al. Ecology and allometry predict the evolution of avian developmental durations. Nat. Commun. 11, 2383 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Botelho, J. F. & Faunes, M. The evolution of developmental modes in the new avian phylogenetic tree. Evol. Dev. 17, 221–223 (2015).PubMed 
    Article 

    Google Scholar 
    Bürkner, P.-C. Brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

    Google Scholar 
    Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
    Article 

    Google Scholar 
    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Berk, R. A. Statistical Learning from a Regression Perspective (Springer International, 2017).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
    Google Scholar 
    Lleonart, J., Salat, J. & Torres, G. J. Removing allometric effects of body size in morphological analysis. J. Theor. Biol. 205, 85–93 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sayol, F., Downing, P. A., Iwaniuk, A. N., Maspons, J. & Sol, D. Predictable evolution towards larger brains in birds colonizing oceanic islands. Nat. Commun. 9, 2820 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Torres, C. R., Norell, M. A. & Clarke, J. A. Bird neurocranial and body mass evolution across the end-Cretaceous mass extinction: the avian brain shape left other dinosaurs behind. Sci. Adv. 7, eabg7099 (2021). More

  • in

    First tagging data on large Atlantic bluefin tuna returning to Nordic waters suggest repeated behaviour and skipped spawning

    Satellite tracking has yielded key information about the movements and behaviour of marine vertebrates in ways that were previously logistically impossible34. In the current study, we tagged the first 18 angler-caught ABFT in Skagerrak, and tracked their movements for up to one year. Despite the majority of tags detaching prematurely, our data provides new insights regarding the migration behaviour and habitat use of this species, both locally within the Nordic region and more widely throughout the northeast Atlantic and western Mediterranean Sea. Most fish (N = 9) left Skagerrak via the Norwegian Trench, heading north before exiting into the Atlantic. In addition, the two tags which remained deployed for approximately one full year showed a return migration into the Skagerrak from the northern North Sea and southern Norwegian Sea regions, re-entering north of the British Isles and through the Norwegian Trench. No fish exited or re-entered through the English Channel or the southern North Sea. These observations of entry/exit from the Skagerrak are similar to migration behaviour inferred from historical commercial fishery data in the region during the 1950s–1960s16,19. These historical records also demonstrated that some individuals migrated from the southern Norwegian Sea into the Skagerrak, Kattegat and Øresund, before leaving the area several weeks later, potentially indicating exploratory feeding on herring and mackerel, abundant in the area during this time of year. Our new tagging results confirm this behaviour among at least some of the ABFT migrating to these areas.The migration patterns revealed by our tagging study exposes tuna entering and exiting the Skagerrak, Kattegat and Øresund to targeted exploitation by regional commercial fishing vessels. Presently, these vessels catch ABFT under a Norwegian quota (315 tonnes in 2021) but additional countries in the region may acquire a quota in the future. Moreover, the relatively narrow size distribution of tunas caught indicates that this migratory behaviour may only be performed by a limited number of year classes35, meaning that the continued long-term migration of ABFT to these waters is highly dependent on recruitment and survival of younger year classes. These younger year classes, perhaps once they reach a certain size, could then also undertake a migration to Skagerrak–Kattegat–Øresund. However, the combination of local exploitation pressures, and the presently limited number of year classes found in Skagerrak could result in ABFT migrating into Skagerrak–Kattegat–Øresund being a short-lived phenomenon if those year classes are subject to a large yearly fishing mortality (both regionally within the Nordic region, and more generally throughout the population range) and no younger year classes appear. Additionally, currently there is no scientifically-derived estimates of ABFT abundance for this region. We suggest to monitor the size distribution and abundance of ABFT in Scandinavian waters in the coming years to (1) confirm that visiting ABFT consist of only a few year classes, and clarify if younger year classes begin to appear, (2) evaluate how the numbers migrating to the region annually may change over time (e.g., under different levels of exploitation, or in relation to environmental factors).While most of our tagged ABFT went north after exiting the Skagerrak, one individual turned south into the south-central North Sea before eventually leaving through the northern part of the North Sea. The region to which it migrated in the North Sea is congruent with earlier commercial catches and sightings in this region, including the Dogger Bank vicinity15,16. Although the exact routes that tagged individuals followed were not identical, no individuals used the shortest route to reach the Atlantic: from the Skagerrak through the North Sea to the English Channel, and further south to the Bay of Biscay and other southern regions. Migration along a northerly route probably reflects a trade-off between the potential for higher energetic gain from more abundant food and higher energy resources, and the longer migration distance. This could suggest that ABFT either follow the food, or simply follow the same route by which they came through learned behaviour.Three tags remained attached long enough to explore long-term migration patterns and showed widely different behaviours. One fish crossed the Atlantic and utilized areas near the Grand Banks, crossing the ICCAT management boundary between the Western and Eastern stocks of ABFT (the 45° meridian), while the other two fish remained in the eastern Atlantic. The area west of Ireland, the Bay of Biscay and the area west of Portugal appear to be important feeding areas when the fish are not in Skagerrak or the Norwegian Sea. These results reflect interconnected seascapes for foraging through the NE Atlantic. Connecting foraging grounds off Ireland and the Bay of Biscay, which was previously suggested by Ref.24 is further corroborated by one of the fish tagged in this study, which passed over the Irish continental shelf when returning to Skagerrak in 2018.Depth and temperature useWithin ICES Area 3a, ABFT were predominantly roaming the upper water column, with most observations in the upper 100 m. However, some ABFT did dive to much deeper depths, with the maximum depth recorded being 520 m, showing that they can use the majority of the depth range available in the area (max. depth in the Norwegian Trench is app. 725 m, but represents a relatively small area). The behaviour likely reflects foraging, as ABFT were also observed by both the scientific tagging crews and the anglers to actively chase prey fish, like garfish and mackerel, at the surface during the tagging operations. The temperature ranges recorded varied between 7 and 17 °C. Both the depths and temperatures recorded are well within the thermal and depth limits reported in the literature for ABFT36.SpawningABFT have been shown to successfully spawn at temperatures above 20 °C at night30,31, and to display a distinct dive pattern thought to represent courtship and spawning behaviour29. When matching this described behaviour with the data from fish 34859 in the Mediterranean Sea, almost identical behavioural patterns were detected on specific days (Fig. 4). In total, seven days aligned with temperatures above 20 °C and oscillatory movement past the thermocline. All detected spawning events occurred west of Sardinia, where fishing for mature ABFT has been conducted for centuries37.In light of the recently proposed third spawning area in the Slope Sea of northeast United States38 and other proposed areas outside the Mediterranean19, it is relevant to look for similar temperature and behavioural patterns for fish 34840, which did not enter the Mediterranean Sea, and instead stayed in the eastern Atlantic. We found that this fish did not display a similar oscillatory behaviour, and the temperature experienced during the alleged spawning period (June–July) was above 20 °C only once (20.4 °C on 11 July). In this period, the fish was on the continental shelf west of Ireland, likely feeding and not spawning. Due to the size of the fish (247 cm CFL), reflecting a likely age of 14–16 years (matching the strong 2003 cohort), and the assumption that all eastern ABFT above five years and western ABFT above eight years are mature, we find it unlikely that this fish was immature. As such, these observations may suggest that this fish skipped spawning in 2018. Fish 34861 surfaced on 25 April and the tag was not recovered. The transmitted data does not allow for a detailed analysis of potential spawning behaviour for this fish. It did however, display 6 days where maximum temperatures from the transmitted dataset reached 20 °C (observations from 15. March to 20 April, with temperatures ranging from 20 to 20.6 °C). Given the lack of detailed behaviour and the fact that this time is well outside the normal spawning time for Mediterranean ABFT, we propose that this ABFT did not spawn in that period. However, the documentation of spawning depends on the general applicability of the temperature limits and nightly spawning behaviour30,31. More studies documenting spawning behaviour will be needed to corroborate if this pattern is consistent among locations and stocks. We also suggest more studies with longer lasting tags to elucidate if skipped spawning is a common behaviour and if fish skip one or more consecutive spawning seasons. Skipped spawning has been demonstrated in many fish species, including both freshwater and marine fish39, and likely reflects physiological condition40. If a considerable proportion of the adult population skips spawning every season, current population models, which assume annual spawning by all adult fish, should be modified to more accurately reflect population egg production and reproductive output. Current population modelling may be even further challenged if the proportion of adults that skip spawning varies over time, perhaps depending on environmental conditions. However, we acknowledge that only one of two fish followed through the spawning season appeared to skip spawning, and therefore caution against broad general interpretations. More studies are needed to verify that skipped spawning is a common behaviour, and if so, to estimate just how common that behaviour is.
    Return migrationIn exploited fish populations, large adults are hypothesized to be important components of the spawning population because they contribute more to recruitment than smaller individuals due to a variety of maternal effects including higher fecundity, better quality of eggs and differences in spawning behaviour (e.g. time, location)41. Although such effects remain to be documented for ABFT, it may be prudent to conserve these large individuals as a precautionary measure, to maximize their potential contributions to reproduction and recruitment.In order to protect these fish, new knowledge about their movements and distribution is required. Data from ABFT deployed with long-term electronic tags suggests that after spawning in the Gulf of Mexico, the fish return to the feeding grounds where they were initially tagged, indicating a return feeding migration7. The same has been observed more recently from ABFT tagged in Ireland24, and other large highly migratory fish species (e.g., swordfish, Xiphias gladius42). In the current study, both ABFT that retained the tag for one year also returned to the same area, suggesting a similar seasonal return feeding migration. We also note that ABFT appeared to perform recurrent visits to the Norwegian Sea, Ireland and the Bay of Biscay on their way from Nordic waters and upon their return to the latter. Hence, we hypothesize that large ABFT in Nordic waters generally return to the same feeding area the following year, given suitable habitat features (e.g., food and temperature conditions), and follow a similar migration route as they do so. More studies are nonetheless needed to confirm this hypothesis, given few long-term deployments in the current study. For a deeper understanding of behavioural repeatability, and if/when shifts in the behaviour occur, it will be necessary to follow the same fish over multiple years. Such studies would also act as a highly valuable indicator of survival, independent of stock assessment-derived mortality estimates, and could be used to estimate the local abundance of larger ABFT43. Thus, a promising avenue for future research would be to deploy long-lasting ( > 5 to 10 years) acoustic tags and use existing infrastructure from networks such as the European Tracking Network to track these large fish over the next decade44. Given that ABFT appear to return to the area annually, we suggest that Skagerrak is a promising area for the future deployment and retrieval of PSATs and other long-lasting tags, because of the relatively easy access to locate and recover detached floating tags, given that the area is reachable from land within a few hours by boat. Retrieving PSATs that have detached from animals enables scientists to access full datasets (in the present case with 5 s resolution, rather than the much coarser and variable resolution typically transmitted). This much higher resolution enables much more detailed analysis, as shown in our analysis of spawning behaviour. Additionally, floating Pop-off Data Storage Tags (PDST) tags may also be a prominent and less costly avenue forward as the geographical region is densely populated and contains many sandy beaches and highly visited coastal areas, giving ample opportunity for tag recovery. Previous studies with floating DSTs in this area have shown remarkably high return rates45.The evidence that ABFT have returned to Nordic waters following many years of rarity or absence, and our findings that at least some individuals return to the same site for feeding in consecutive years, raises new questions about the mechanisms that underlie habitat discovery—or the return to previously used habitats—by highly migratory fish species. How individuals or entire schools have discovered this region again as a suitable feeding area after an absence of more than 50 years is unclear. In light of the positive stock development in the last 1–2 decades22 and modelling studies showing suitable habitat in the area46, density-dependent foraging and exploratory behaviour for new feeding areas may be a prominent hypothesis for their return, potentially accompanied by complex social learning interactions among individuals within the population47,48. New tagging data which documents the use of new or formerly occupied habitats will be essential for understanding these processes and how they might be affected by human pressures (e.g., exploitation, climate change). Such data can help to parameterize and validate advanced conceptual models of group movement behaviour, collective memory and habitat use49,50,51, as well as to inform modern stock assessment models used for management.
    Tag deploymentFollowing recommendations from experienced taggers previously operating in the Mediterranean, most fish were tagged in the water alongside the boat. All these tags surfaced prematurely, while two (out of three) tags deployed on tunas brought on board the tagging boat surfaced after approximately one year. Depending on the conditions at sea, tagging along the side of the boat may not be as precise as on-board tagging, and the quality of the tag anchoring cannot be properly assessed. We therefore suggest that tagging on-board a boat is superior to tagging in the water alongside the boat for the deployment of long-lasting tags. This was also suggested in Ref.24. Furthermore, on-board tagging makes biological sampling fast and feasible, as opposed to tagging in the water alongside the boat. However, our advice is limited by a small sample size, making it difficult to draw formal conclusions; more studies are necessary to assess the best method to tag large ABFT. More

  • in

    Enhanced leaf turnover and nitrogen recycling sustain CO2 fertilization effect on tree-ring growth

    Cole, C. T., Anderson, J. E., Lindroth, R. L. & Waller, D. M. Rising concentrations of atmospheric CO2 have increased growth in natural stands of quaking aspen (Populus tremuloides). Glob. Change Biol. 16, 2186–2197 (2010).Article 

    Google Scholar 
    Urrutia-Jalabert, R. et al. Increased water use efficiency but contrasting tree growth patterns in Fitzroya cupressoides forests of southern Chile during recent decades. J. Geophys. Res. Biogeosci. 120, 2505–2524 (2015).Article 

    Google Scholar 
    Cienciala, E. et al. Increased spruce tree growth in Central Europe since 1960s. Sci. Total Environ. 619–620, 1637–1647 (2018).PubMed 
    Article 

    Google Scholar 
    Mathias, J. M. & Thomas, R. B. Disentangling the effects of acidic air pollution, atmospheric CO2, and climate change on recent growth of red spruce trees in the Central Appalachian Mountains. Glob. Change Biol. 24, 3938–3953 (2018).Article 

    Google Scholar 
    Körner, C. et al. Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2. Science 309, 1360–1362 (2005).PubMed 
    Article 

    Google Scholar 
    Klein, T. et al. Growth and carbon relations of mature Picea abies trees under 5 years of free-air CO2 enrichment. J. Ecol. 104, 1720–1733 (2016).CAS 
    Article 

    Google Scholar 
    Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).Article 

    Google Scholar 
    Peñuelas, J., Canadell, J. G. & Ogaya, R. Increased water-use efficiency during the 20th century did not translate into enhanced tree growth. Glob. Ecol. Biogeogr. 20, 597–608 (2011).Article 

    Google Scholar 
    IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Dong, N. et al. Rising CO2 and warming reduce global canopy demand for nitrogen. New Phytol. https://doi.org/10.1111/nph.18076 (2022).Finzi, A. C., Allen, A. S., DeLucia, E. H., Ellsworth, D. S. & Schlesinger, W. H. Forest litter production, chemistry, and decomposition following two years of free-air CO2 enrichment. Ecology 82, 470–484 (2001).
    Google Scholar 
    Liberloo, M. et al. Elevated CO2 concentration, fertilization and their interaction: growth stimulation in a short-rotation poplar coppice (EUROFACE). Tree Physiol. 25, 179–189 (2005).PubMed 
    Article 

    Google Scholar 
    Hungate, B. A. et al. Nitrogen cycling during seven years of atmospheric CO2 enrichment in a scrub oak woodland. Ecology 87, 26–40 (2006).PubMed 
    Article 

    Google Scholar 
    Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).PubMed 
    Article 

    Google Scholar 
    Liberloo, M. et al. Coppicing shifts CO2 stimulation of poplar productivity to above-ground pools: a synthesis of leaf to stand level results from the POP/EUROFACE experiment. New Phytol. 182, 331–346 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    McCarthy, H. R. et al. Re-assessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: interactions of atmospheric [CO2] with nitrogen and water availability over stand development. New Phytol. 185, 514–528 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dawes, M. A. et al. Species-specific tree growth responses to 9 years of CO2 enrichment at the alpine treeline. J. Ecol. 99, 383–394 (2011).
    Google Scholar 
    Luo, Y. Q. et al. Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide. Bioscience 54, 731–739 (2004).Article 

    Google Scholar 
    Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Luo, Y. Q., Hui, D. F. & Zhang, D. Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology 87, 53–63 (2006).PubMed 
    Article 

    Google Scholar 
    Kikuzawa, K. A cost-benefit analysis of leaf habit and leaf longevity of trees and their geographical pattern. Am. Nat. 138, 1250–1263 (1991).Article 

    Google Scholar 
    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Luo, T. X. et al. Summer solstice marks a seasonal shift in temperature sensitivity of stem growth and nitrogen-use efficiency in cold-limited forests. Agric. For. Meteorol. 248, 469–478 (2018).Article 

    Google Scholar 
    Rossi, S. et al. Conifers in cold environments synchronize maximum growth rate of tree-ring formation with day length. New Phytol. 170, 301–310 (2006).PubMed 
    Article 

    Google Scholar 
    Bauerle, W. L. et al. Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. Proc. Natl Acad. Sci. USA 109, 8612–8617 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jarvis, P. & Linder, S. Constraints to growth of boreal forests. Nature 405, 904–905 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sullivan, P. F., Ellison, S. B., McNown, R. W., Brownlee, A. H. & Sveinbjörnsson, B. Evidence of soil nutrient availability as the proximate constraint on growth of treeline trees in northwest Alaska. Ecology 96, 716–727 (2015).PubMed 
    Article 

    Google Scholar 
    Dodd, A. N. et al. Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science 309, 630–633 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jackson, S. D. Plant responses to photoperiod. New Phytol. 181, 517–531 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chapin III, F. S., Matson, P. A. & Mooney, H. A. Principles of Terrestrial Ecosystem Ecology (Springer-Verlag, 2002).Hikosaka, K. Leaf canopy as a dynamic system: ecophysiology and optimality in leaf turnover. Ann. Bot. 95, 521–533 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jiang, M. K. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mathias, J. M. & Thomas, R. B. Global tree intrinsic water use efficiency is enhanced by increased atmospheric CO2 and modulated by climate and plant functional types. Proc. Natl Acad. Sci. USA 118, e2014286118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Finzi, A. C. et al. Increases in nitrogen uptake rather than nitrogen-use efficiency support higher rates of temperate forest productivity under elevated CO2. Proc. Natl Acad. Sci. USA 104, 14014–14019 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Soulé, P. T. & Knapp, P. A. Radial growth rate increases in naturally occurring ponderosa pine trees: a late-20th century CO2 fertilization effect? New Phytol. 171, 379–390 (2006).PubMed 
    Article 

    Google Scholar 
    Linares, J. C. & Camarero, J. J. From pattern to process: linking intrinsic water-use efficiency to drought-induced forest decline. Glob. Change Biol. 18, 1000–1015 (2012).Article 

    Google Scholar 
    Kagawa, A., Sugimoto, A. & Maximov, T. C. 13CO2 pulse-labelling of photoassimilates reveals carbon allocation within and between tree rings. Plant Cell Environ. 29, 1571–1584 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Epron, D. et al. Pulse-labelling trees to study carbon allocation dynamics: a review of methods, current knowledge and future prospects. Tree Physiol. 32, 776–798 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wiley, E. & Helliker, B. A re-evaluation of carbon storage in trees lends greater support for carbon limitation to growth. New Phytol. 195, 285–289 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rocha, A. V., Goulden, M. L., Dunn, A. L. & Wofsy, S. C. On linking interannual tree ring variability with observations of whole-forest CO2 flux. Glob. Change Biol. 12, 1378–1389 (2006).Article 

    Google Scholar 
    Zweifel, R. et al. Link between continuous stem radius changes and net ecosystem productivity of a subalpine Norway spruce forest in the Swiss Alps. New Phytol. 187, 819–830 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kong, G. Q., Luo, T. X., Liu, X. S., Zhang, L. & Liang, E. Y. Annual ring widths are good predictors of changes in net primary productivity of alpine Rhododendron shrubs in the Sergyemla Mountains, southeast Tibet. Plant Ecol. 213, 1843–1855 (2012).Article 

    Google Scholar 
    Teets, A. et al. Linking annual tree growth with eddy-flux measures of net ecosystem productivity across twenty years of observation in a mixed conifer forest. Agric. For. Meteorol. 249, 479–487 (2018).Article 

    Google Scholar 
    Luo, T. X., Li, M. C. & Luo, J. Seasonal variations in leaf δ13C and nitrogen associated with foliage turnover and carbon gain for a wet subalpine fir forest in the Gongga Mountains, eastern Tibetan Plateau. Ecol. Res. 26, 253–263 (2011).CAS 
    Article 

    Google Scholar 
    Kobe, R. K., Lepczyk, C. A. & Iyer, M. Resorption efficiency decreases with increasing green leaf nutrients in a global data set. Ecology 86, 2780–2792 (2005).Article 

    Google Scholar 
    Vergutz, L., Manzoni, S., Porporato, A., Novais, R. F. & Jackson, R. B. Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecol. Monogr. 82, 205–220 (2012).Article 

    Google Scholar 
    Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
    Google Scholar 
    Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Kluwer Academic Publishers, 1990).Editorial Board of Vegetation Map of China, Chinese Academy of Sciences. Vegetation Atlas of China (Science Press, 2001). More

  • in

    Reference database of teeth images from the Family Bovidae

    Fossil remains from the Family Bovidae, such as antelopes and buffalo, are frequently used to reconstruct past environments1,2,3. Bovids reflect distinct ecological adaptations in terms of diet, habitat, water dependence, and seasonal migrations that vary according to their respective ecological niches. Widespread cooling in the late Miocene led to a major adaptive radiation of the bovids, and increasingly they began to exploit more open environments4,5,6. Thus, by approximately 4 Ma, bovids came to dominate the African fauna, replacing the previously abundant suids7,8,9. The current distribution of bovids extends across the African continent in myriad environments that differ significantly in proportions of wood and grass cover.The importance of bovid remains to paleoanthropological research was established initially by Broom10,11 and Wells and Cooke12. This dependence has been expanded and now ranges from paleodietary studies and evolutionary trends to hominin behavioral patterns13,14,15. In addition, several studies have demonstrated that changes in the relative abundance of bovid taxa reflected in fossil assemblages are indicative of fluctuations in environmental conditions, as bovids appear to be particularly responsive to environmental changes16,17,18.Bovid teeth, in particular isolated teeth, make up a majority of the southern African fossil record. Thus, bovid teeth, coupled with their ecological tendencies, are important sources of information for reconstructing the paleoenvironments associated with the fossil hominins. Taxonomic identification of fossil bovid teeth, however, is often problematic; biasing factors such as age and degree of wear complicate identifications and often result in considerable overlap in the shape and size of teeth. Traditionally, researchers rely upon modern and fossil comparative collections to identify isolated bovid teeth. However, researchers are somewhat limited by travel and the specific type and number of bovids housed at each institution. Here, we present B.O.V.I.D. (Bovidae Occlusal Visual IDentification) which is a repository of images of the occlusal surface of bovid teeth (~3900). The purpose of the database is to allow researchers to visualize a large sample of teeth from different tribes, genera, and species. The sample includes the three upper and three lower molars in multiple states of wear from the seven most common tribes in the southern African fossil record and the twenty most common species from those tribes. This design will help researchers see the natural variation that exists within a specific tooth type of a taxon and, with the current sample, help taxonomically identify extant and fossil teeth with modern counterparts. More

  • in

    Routes of soil microbiome dispersal

    Dispersal is assumed to contribute to microbiome composition and function; however, it is difficult to measure. Walters et al. now set out a 6-month experiment looking at different dispersal routes of environmental microorganisms to the surface soil layer. They set up different ‘traps’, either glass slides or freshly cut grass, to determine the number, identity and function of incoming microorganisms. The traps ‘recorded’ dispersal through air, from plants and their litter, or from below through the decomposing litter and bulk soil. This was achieved by placing the traps either on a pedestal, closing them off at the bottom or leaving them open, respectively. The authors found that the overall dispersal rate was low, with little influence of the route, with only 0.5% incoming bacterial cells per day compared with the number of resident cells. However, the dispersal routes did influence microbiome composition, at least if from above and close to the surface. Finally, without dispersal, the initial decomposition of the cut grass was slower.
    This is a preview of subscription content More