More stories

  • in

    Biodiversity loss and climate extremes — study the feedbacks

    As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
    Nature-based solutions can help cool the planet — if we act now
    In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

    Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

    However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
    Biodiversity needs every tool in the box: use OECMs
    Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

  • in

    Eddy covariance-based differences in net ecosystem productivity values and spatial patterns between naturally regenerating forests and planted forests in China

    Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

  • in

    Field research stations are key to global conservation targets

    A theme is emerging in this year’s United Nations conferences on biodiversity (COP15), climate change (COP27) and the international wildlife trade (COP19): countries are struggling to meet key conservation targets. We argue that field research stations are an effective — but imperilled and overlooked — tool that can help policy frameworks to meet those targets. We write on behalf of 149 experts from 47 countries.
    Competing Interests
    The authors declare no competing interests. More

  • in

    Lack of host phylogenetic structure in the gut bacterial communities of New Zealand cicadas and their interspecific hybrids

    McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. 110, 3229–3236 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Archibald, J. M. Endosymbiosis and eukaryotic cell evolution. Curr. Biol. 25, R911–R921 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Moran, N. A. Symbiosis as an adaptive process and source of phenotypic complexity. Proc. Natl. Acad. Sci. U.S.A. 104(Suppl 1), 8627–8633 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hurst, G. D. D. Extended genomes: Symbiosis and evolution. Interface Focus. https://doi.org/10.1098/rsfs.2017.0001 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moran, N. A., McCutcheon, J. P. & Nakabachi, A. Genomics and evolution of heritable bacterial symbionts. Annu. Rev. Genet. 42, 165–190 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kikuchi, Y., Hosokawa, T. & Fukatsu, T. Insect-microbe mutualism without vertical transmission: A stinkbug acquires a beneficial gut symbiont from the environment every generation. Appl. Environ. Microbiol. 73, 4308–4316 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kikuchi, Y., Hosokawa, T. & Fukatsu, T. An ancient but promiscuous host-symbiont association between Burkholderia gut symbionts and their heteropteran hosts. ISME J. 5, 446–460 (2011).Article 
    PubMed 

    Google Scholar 
    Hu, Y. et al. Herbivorous turtle ants obtain essential nutrients from a conserved nitrogen-recycling gut microbiome. Nat. Commun. 9, 2440. https://doi.org/10.1038/s41467-018-03357-y (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salem, H. et al. Drastic genome reduction in an herbivore’s pectinolytic symbiont. Cell 171, 1520–1531 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bennett, G. M. & Moran, N. A. Heritable symbiosis: The advantages and perils of an evolutionary rabbit hole. Proc. Natl. Acad. Sci. 112, 10169–10176 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Campbell, M. A. et al. Changes in endosymbiont complexity drive host-level compensatory adaptations in cicadas. MBio 9, e02104-18 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Buchner, P. Symbiosis in animals which suck plant juices. In Endosymbiosis of Animals with Plant Microorganisms 210–432 (Interscience, 1965).
    Google Scholar 
    McCutcheon, J. P., McDonald, B. R. & Moran, N. A. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc. Natl. Acad. Sci. 106, 15394–15399 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Christensen, H. & Fogel, M. L. Feeding ecology and evidence for amino acid synthesis in the periodical cicada (Magicicada). J. Insect Physiol. 57, 211–219 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    McCutcheon, J. P., McDonald, B. R. & Moran, N. A. Origin of an alternative genetic code in the extremely small and GC-rich genome of a bacterial symbiont. PLoS Genet. 5, e1000565 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Campbell, M. A. et al. Genome expansion via lineage splitting and genome reduction in the cicada endosymbiont Hodgkinia. Proc. Natl. Acad. Sci. 112, 10192–10199 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Müller, H. J. Neuere vorstellungen über verbreitung und phylogenie der endosymbiosen der zikaden. Z. Morphol. Oekol. Tiere 61, 190–210 (1962).Article 

    Google Scholar 
    Müller, H. J. Zur systematik und phylogenie der zikaden-endosymbiosen. Biol. Zent. 68, 343–368 (1949).
    Google Scholar 
    Matsuura, Y. et al. Recurrent symbiont recruitment from fungal parasites in cicadas. Proc. Natl. Acad. Sci. 115, E5970–E5979 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhou, W. et al. Analysis of inter-individual bacterial variation in gut of cicada Meimuna mongolica (Hemiptera: Cicadidae). J. Insect Sci. 15, 1–6 (2015).Article 

    Google Scholar 
    Zheng, Z., Wang, D., He, H. & Wei, C. Bacterial diversity of bacteriomes and organs of reproductive, digestive and excretory systems in two cicada species (Hemiptera: Cicadidae). PLoS One 12, 1–21 (2017).
    Google Scholar 
    Wang, D., Huang, Z., He, H. & Wei, C. Comparative analysis of microbial communities associated with bacteriomes, reproductive organs and eggs of the cicada Subpsaltria yangi. Arch. Microbiol. 200, 227–235 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dillon, R. J. & Dillon, V. M. The gut bacteria of insects: Nonpathogenic interactions. Annu. Rev. Entomol. 49, 71–92 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ng, S. H., Stat, M., Bunce, M. & Simmons, L. W. The influence of diet and environment on the gut microbial community of field crickets. Ecol. Evol. 8, 4704–4720 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nishida, A. H. & Ochman, H. Rates of gut microbiome divergence in mammals. Mol. Ecol. 27, 1884–1897 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, A. E. & Werren, J. H. Holes in the hologenome: Why host–microbe symbioses are not holobionts. MBio 7, e02099 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grueneberg, J., Engelen, A. H., Costa, R. & Wichard, T. Macroalgal morphogenesis induced by waterborne compounds and bacteria in coastal seawater. PLoS One 11, e0146307 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lin, J. D., Lemay, M. A. & Parfrey, L. W. Diverse bacteria utilize alginate within the microbiome of the giant kelp Macrocystis pyrifera. Front. Microbiol. 9, 1914 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coon, K. L., Vogel, K. J., Brown, M. R. & Strand, M. R. Mosquitoes rely on their gut microbiota for development. Mol. Ecol. 23, 2727–2739 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coon, K. L., Brown, M. R. & Strand, M. R. Mosquitoes host communities of bacteria that are essential for development but vary greatly between local habitats. Mol. Ecol. 25, 5806–5826 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, 1–17 (2017).Article 

    Google Scholar 
    Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, 1–29 (2016).Article 

    Google Scholar 
    Kropáčková, L. et al. Codiversification of gastrointestinal microbiota and phylogeny in passerines is not explained by ecological divergence. Mol. Ecol. 26, 5292–5304 (2017).Article 
    PubMed 

    Google Scholar 
    Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403. https://doi.org/10.3389/fmicb.2015.01403 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hu, Y., Lukasik, P., Moreau, C. S. & Russell, J. A. Correlates of gut community composition across an ant species (Cephalotes varians) elucidate causes and consequences of symbiotic variability. Mol. Ecol. 23, 1284–1300 (2014).Article 
    PubMed 

    Google Scholar 
    Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnz117 (2019).Article 
    PubMed 

    Google Scholar 
    Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. 114, 9641–9646 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shapira, M. Gut microbiotas and host evolution: Scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).Article 
    PubMed 

    Google Scholar 
    Marshall, D. C. et al. Inflation of molecular clock rates and dates: Molecular phylogenetics, biogeography, and diversification of a global cicada radiation from Australasia (Hemiptera: Cicadidae: Cicadettini). Syst. Biol. 65, 16–34 (2016).Article 
    PubMed 

    Google Scholar 
    Lane, D. H. The recognition concept of speciation applied in an analysis of putative hybridization in New Zealand cicadas of the genus Kikihia (Insects: Hemiptera: Tibicinidae). Speciation and the Recognition Concept: Theory and Application (The Johns Hopkins Univ Press, 1995).
    Google Scholar 
    Cooley, J. R. & Marshall, D. C. Sexual signaling in periodical cicadas, Magicicada spp. (Hemiptera: Cicadidae). Behaviour 138, 827–855 (2001).Article 

    Google Scholar 
    Fleming, C. A. Adaptive Radiation in New Zealand Cicadas (American Philosophical Society, 1975).
    Google Scholar 
    Dugdale, J. S. & Fleming, C. A. New Zealand cicadas of the genus Maoricicada (Homoptera: Tibicinidae). N. Z. J. Zool. 5, 295–340 (1978).Article 

    Google Scholar 
    Marshall, D. C., Hill, K. B. R., Cooley, J. R. & Simon, C. Hybridization, mitochondrial DNA phylogeography, and prediction of the early stages of reproductive isolation: Lessons from New Zealand cicadas (genus Kikihia). Syst. Biol. 60, 482–502 (2011).Article 
    PubMed 

    Google Scholar 
    Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Report No.: e27295v2. PeerJ https://doi.org/10.7287/peerj.preprints.27295v2 (2018).Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, N. M., Proctor, D., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226. https://doi.org/10.1101/221499 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lemmon, A. R., Emme, S. A. & Lemmon, E. M. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst. Biol. 61, 727–744 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Simon, C. et al. Off-target capture data, endosymbiont genes and morphology reveal a relict lineage that is sister to all other singing cicadas. Biol. J. Linn. Soc. Lond. https://doi.org/10.1093/biolinnean/blz120 (2019).Article 

    Google Scholar 
    Owen, C. L. et al. Detecting and removing sample contamination in phylogenomic data: An example and its implications for Cicadidae phylogeny (Insecta: Hemiptera). Syst. Biol. 71, 1504–1523 (2022).Article 
    PubMed 

    Google Scholar 
    Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. Report No.: LBNL-7065E. https://www.osti.gov/biblio/1241166-bbmap-fast-accurate-splice-aware-aligner (Lawrence Berkeley National Lab. (LBNL), 2014).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nurk, S. et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In Research in Computational Molecular Biology 158–170 (Springer, 2013).Chapter 

    Google Scholar 
    Łukasik, P. et al. One hundred mitochondrial genomes of cicadas. J. Hered. 110, 247–256 (2019).Article 
    PubMed 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. https://doi.org/10.1093/bib/bbx108 (2017).Article 
    PubMed Central 

    Google Scholar 
    Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—A baiting and iterative mapping approach. Nucleic Acids Res. 41, e129 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Miller, M. A., Pfeiffer, W., Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop (GCE) 1–8 (2010).Buckley, T. R., Cordeiro, M., Marshall, D. C. & Simon, C. Differentiating between hypotheses of lineage sorting and introgression in New Zealand alpine cicadas (Maoricicada Dugdale). Syst. Biol. 55, 411–425 (2006).Article 
    PubMed 

    Google Scholar 
    Marshall, D. C., Slon, K., Cooley, J. R., Hill, K. B. R. & Simon, C. Steady Plio-Pleistocene diversification and a 2-million-year sympatry threshold in a New Zealand cicada radiation. Mol. Phylogenet. Evol. 48, 1054–1066 (2008).Article 
    PubMed 

    Google Scholar 
    Bator, J., Marshall, D. C., Leston, A., Cooley, J. & Simon, C. Phylogeography of the endemic red-tailed cicadas of New Zealand (Hemiptera: Cicadidae: Rhodopsalta): Molecular, morphological and bioacoustical confirmation of the existence of Hudson’s Rhodopsalta microdora. Zool. J. Linn. Soc. 195, 1219–1244 (2022).Article 

    Google Scholar 
    Brumfield, K. D. et al. Gut microbiome insights from 16S rRNA analysis of 17-year periodical cicadas (Hemiptera: Magicicada spp.) Broods II, VI, and X. Sci. Rep. 12, 16967. https://doi.org/10.1038/s41598-022-20527-7 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rakitov, R. A. Structure and function of the Malpighian tubules, and related behaviors in juvenile cicadas: Evidence of homology with spittlebugs (Hemiptera: Cicadoidea & Cercopoidea). Zool. Anz. 241, 117–130 (2002).Article 

    Google Scholar 
    Andersen, P. C., Brodbeck, B. V. & Mizell, R. F. Feeding by the leafhopper, Homalodisca coagulata, in relation to xylem fluid chemistry and tension. J. Insect Physiol. 38, 611–622 (1992).Article 
    CAS 

    Google Scholar 
    Cheung, W. W. K. & Marshall, A. T. Water and ion regulation in cicadas in relation to xylem feeding. J. Insect Physiol. 19, 1801–1816 (1973).Article 
    CAS 

    Google Scholar 
    Williams, K. S. & Simon, C. The ecology, behavior, and evolution of periodical cicadas. Annu. Rev. Entomol. 40, 269–295 (1995).Article 
    CAS 

    Google Scholar 
    Logan, D. P., Rowe, C. A. & Maher, B. J. Life history of chorus cicada, an endemic pest of kiwifruit (Cicadidae: Homoptera). N. Z. Entomol. 37, 96–106 (2014).Article 

    Google Scholar 
    Buckley, T. R. & Simon, C. Evolutionary radiation of the cicada genus Maoricicada Dugdale (Hemiptera: Cicadoidea) and the origins of the New Zealand alpine biota. Biol. J. Linn. Soc. Lond. 91, 419–435 (2007).Article 

    Google Scholar 
    Banker, S. E., Wade, E. J. & Simon, C. The confounding effects of hybridization on phylogenetic estimation in the New Zealand cicada genus Kikihia. Mol. Phylogenet. Evol. 116, 172–181 (2017).Article 
    PubMed 

    Google Scholar 
    Groussin, M. et al. Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat. Commun. 8, 14319 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sanders, J. G. et al. Stability and phylogenetic correlation in gut microbiota: Lessons from ants and apes. Mol. Ecol. 23, 1268–1283 (2014).Article 
    PubMed 

    Google Scholar 
    Wang, J. et al. Analysis of intestinal microbiota in hybrid house mice reveals evolutionary divergence in a vertebrate hologenome. Nat. Commun. 6, 6440 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Brucker, R. M. & Bordenstein, S. R. The hologenomic basis of speciation. Science 466, 667–669 (2013).Article 

    Google Scholar 
    Chandler, J. A. & Turelli, M. Comment on “The hologenomic basis of speciation: Gut bacteria cause hybrid lethality in the genus Nasonia”. Science 345, 1011 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, Z. et al. Changes in the rumen microbiome and metabolites reveal the effect of host genetics on hybrid crosses. Environ. Microbiol. Rep. 8, 1016–1023 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Weintraub, P. G. & Beanland, L. Insect vectors of phytoplasmas. Annu. Rev. Entomol. 51, 91–111 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hopkins, D. L. Xylella fastidiosa: Xylem-limited bacterial pathogen of plants. Annu. Rev. Phytopathol. 27, 271–290 (1989).Article 

    Google Scholar 
    Karban, R. Why cicadas (Hemiptera: Cicadidae) develop so slowly. Biol. J. Linn. Soc. Lond. 135, 291–298 (2021).Article 

    Google Scholar 
    Krell, R. K., Boyd, E. A., Nay, J. E., Park, Y.-L. & Perring, T. M. Mechanical and insect transmission of Xylella fastidiosa to Vitis vinifera. Am. J. Enol. Vitic. 58, 211–216 (2007).Article 
    CAS 

    Google Scholar 
    Paião, F., Meneguim, A. M., Casagrande, E. C., Lovato, L. & Leite, R. P. Levantamento de espécies de cigarras e transmissão de Xylella fastidiosa em cafeeiro. http://www.sbicafe.ufv.br/handle/123456789/1457 (2003).Elbeaino, T. et al. Identification of three potential insect vectors of Xylella fastidiosa in southern Italy. Phytopathol. Mediterr. 53, 328–332 (2014).
    Google Scholar  More

  • in

    Memory for own actions in parrots

    Zimmer, H. D. et al. Memory for Action: A Distinct Form of Episodic Memory? (Oxford University Press, 2001).
    Google Scholar 
    Goswami, U. The Wiley-Blackwell Handbook of Childhood Cognitive Development (Wiley, 2013).
    Google Scholar 
    Fujita, K., Morisaki, A., Takaoka, A., Maeda, T. & Hori, Y. Incidental memory in dogs (Canis familiaris): Adaptive behavioral solution at an unexpected memory test. Anim. Cogn. 15, 1055–1063 (2012).Article 
    PubMed 

    Google Scholar 
    Lind, J., Enquist, M. & Ghirlanda, S. Animal memory: A review of delayed matching-to-sample data. Behav. Processes 117, 52–58 (2015).Article 
    PubMed 

    Google Scholar 
    Kuczaj, S. A. II. & Eskelinen, H. C. (2014) The “creative dolphin” revisited: What do dolphins do when asked to vary their behavior. Anim. Behav. Cogn. 1, 66–77 (2014).Article 

    Google Scholar 
    Tulving, E. Episodic and semantic memory. Organ. Mem. 1, 381–403 (1972).
    Google Scholar 
    Tulving, E. How many memory systems are there?. Am. Psychol. 40, 385 (1985).Article 

    Google Scholar 
    Fugazza, C., Pongrácz, P., Pogány, Á., Lenkei, R. & Miklósi, Á. Mental representation and episodic-like memory of own actions in dogs. Sci. Rep. 10, 1–8 (2020).Article 

    Google Scholar 
    Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).Article 
    PubMed 

    Google Scholar 
    Conway, M. A. Memory and the self. J. Mem. Lang. 53, 594–628 (2005).Article 

    Google Scholar 
    Scagel, A. & Mercado, E. III. Do that again! Memory for self-performed actions in dogs (Canis familiaris). J. Comp. Psychol. 20, 25 (2022).
    Google Scholar 
    Mercado, E., Murray, S. O., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for recent actions in the bottlenosed dolphin (Tursiops truncatus): Repetition of arbitrary behaviors using an abstract rule. Learn. Behav. 26, 210–218 (1998).Article 

    Google Scholar 
    Paukner, A., Anderson, J. R., Donaldson, D. I. & Ferrari, P. F. Cued repetition of self-directed behaviors in macaques (Macaca nemestrina). J. Exp. Psychol. Anim. Behav. Process. 33, 139 (2007).Article 
    PubMed 

    Google Scholar 
    Smeele, S. Q. et al. Memory for own behaviour in pinnipeds. Anim. Cogn. 20, 1–12 (2019).
    Google Scholar 
    Clayton, N. S. Episodic-like memory and mental time travel in animals. (2017).Clayton, N. S., Griffiths, D. P. & Dickinson, A. Declarative and episodic-like memory in animals: Personal musings of a Scrub Jay (2000).Clayton, N. S. & Dickinson, A. Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274 (1998).Article 
    PubMed 

    Google Scholar 
    Tulving, E. Episodic memory and autonoesis: Uniquely human. Missing Link Cogn. Orig. Self-Reflect. Conscious 20, 3–56 (2005).
    Google Scholar 
    Suddendorf, T. & Corballis, M. C. Mental time travel and the evolution of the human mind. Genet. Soc. Gen. Psychol. Monogr. 123, 133–167 (1997).PubMed 

    Google Scholar 
    Suddendorf, T. & Corballis, M. C. The evolution of foresight: What is mental time travel, and is it unique to humans?. Behav. Brain Sci. 30, 299–313 (2007).Article 
    PubMed 

    Google Scholar 
    Crystal, J. D. Evaluating evidence from animal models of episodic memory. J. Exp. Psychol. Anim. Learn. Cogn. 47, 337 (2021).Article 
    PubMed 

    Google Scholar 
    Mercado, E. III., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for action events in the bottlenosed dolphin. Anim. Cogn. 2, 17–25 (1999).Article 

    Google Scholar 
    Zentall, T. R. Coding of stimuli by animals: Retrospection, prospection, episodic memory and future planning. Learn. Motiv. 41, 225–240 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).Article 
    PubMed 

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

    Google Scholar 
    Emery, N. J. Cognitive ornithology: The evolution of avian intelligence. Philos. Trans. R. Soc. B Biol. Sci. 361, 23–43 (2006).Article 

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

    Google Scholar 
    Emery, N. J. & Clayton, N. S. Evolution of the avian brain and intelligence. Curr. Biol. 15, R946–R950 (2005).Article 
    PubMed 

    Google Scholar 
    Bradbury, J. W. & Balsby, T. J. The functions of vocal learning in parrots. Behav. Ecol. Sociobiol. 70, 293–312 (2016).Article 

    Google Scholar 
    Baciadonna, L., Cornero, F. M., Emery, N. J. & Clayton, N. S. Convergent evolution of complex cognition: Insights from the field of avian cognition into the study of self-awareness. Learn. Behav. 49, 9–22 (2021).Article 
    PubMed 

    Google Scholar 
    Osvath, M., Kabadayi, C. & Jacobs, I. Independent evolution of similar complex cognitive skills (2014).Zentall, T. R., Clement, T. S., Bhatt, R. S. & Allen, J. Episodic-like memory in pigeons. Psychon. Bull. Rev. 8, 685–690 (2001).Article 
    PubMed 

    Google Scholar 
    Zentall, T. R., Singer, R. A. & Stagner, J. P. Episodic-like memory: Pigeons can report location pecked when unexpectedly asked. Behav. Processes 79, 93–98 (2008).Article 
    PubMed 

    Google Scholar 
    Healy, S. D. & Hurly, T. A. Spatial learning and memory in birds. Brain. Behav. Evol. 63, 211–220 (2004).Article 
    PubMed 

    Google Scholar 
    Taylor, A. H. Corvid cognition. Wiley Interdiscip. Rev. Cogn. Sci. 5, 361–372 (2014).Article 
    PubMed 

    Google Scholar 
    Boeckle, M. & Bugnyar, T. Long-term memory for affiliates in ravens. Curr. Biol. 22, 801–806 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marzluff, J. M., Walls, J., Cornell, H. N., Withey, J. C. & Craig, D. P. Lasting recognition of threatening people by wild American crows. Anim. Behav. 79, 699–707 (2010).Article 

    Google Scholar 
    Pepperberg, I. M. & Pepperberg, I. M. The Alex Studies: Cognitive and Communicative Abilities of Grey Parrots (Harvard University Press, 2009).Book 

    Google Scholar 
    Emery, N. J. & Clayton, N. S. Effects of experience and social context on prospective caching strategies by scrub jays. Nature 414, 443–446 (2001).Article 
    PubMed 

    Google Scholar 
    Herzog, S. K. et al. First systematic sampling approach to estimating the global population size of the Critically Endangered Blue-throated Macaw Ara glaucogularis. Bird Conserv. Int. 31, 293–311 (2021).Article 

    Google Scholar 
    Auersperg, A. M. & von Bayern, A. M. Who’sa clever bird—now? A brief history of parrot cognition. Behaviour 156, 391–407 (2019).Article 

    Google Scholar 
    Tassin de Montaigu, C., Durdevic, K., Brucks, D., Krasheninnikova, A. & von Bayern, A. Blue-throated macaws (Ara glaucogularis) succeed in a cooperative task without coordinating their actions. Ethology 126, 267–277 (2020).Article 

    Google Scholar 
    Auersperg, A. M. et al. Social transmission of tool use and tool manufacture in Goffin cockatoos (Cacatua goffini). Proc. R. Soc. B Biol. Sci. 281, 20140972 (2014).Article 

    Google Scholar 
    Brucks, D. & von Bayern, A. M. Parrots voluntarily help each other to obtain food rewards. Curr. Biol. 30, 292–297 (2020).Article 
    PubMed 

    Google Scholar 
    Krasheninnikova, A., Höner, F., O’Neill, L., Penna, E. & von Bayern, A. M. Economic decision-making in parrots. Sci. Rep. 8, 1–10 (2018).Article 

    Google Scholar 
    Jarvis, E. D. et al. Avian brains and a new understanding of vertebrate brain evolution. Nat. Rev. Neurosci. 6, 151–159 (2005).Article 
    PubMed 

    Google Scholar 
    Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic-midbrain-cerebellar circuit. Sci. Rep. 8, 1–11 (2018).Article 

    Google Scholar 
    Smeele, S. Q. et al. Coevolution of relative brain size and life expectancy in parrots. Proc. R. Soc. B 289, 20212397 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kirsch, J. A., Güntürkün, O. & Rose, J. Insight without cortex: Lessons from the avian brain. Conscious. Cogn. 17, 475–483 (2008).Article 
    PubMed 

    Google Scholar 
    Dunbar, R. I. & Shultz, S. Evolution in the social brain. Science 317, 1344–1347 (2007).Article 
    PubMed 

    Google Scholar 
    Wright, A. A. & Katz, J. S. Mechanisms of same/different concept learning in primates and avians. Behav. Processes 72, 234–254 (2006).Article 
    PubMed 

    Google Scholar 
    Smirnova, A. A., Obozova, T. A., Zorina, Z. A. & Wasserman, E. A. How do crows and parrots come to spontaneously perceive relations-between-relations?. Curr. Opin. Behav. Sci. 37, 109–117 (2021).Article 

    Google Scholar 
    Schusterman, R. J. & Kastak, D. A California sea lion (Zalophus californianus) is capable of forming equivalence relations. Psychol. Rec. 43, 823–839 (1993).Article 

    Google Scholar 
    Kastak, D. & Schusterman, R. J. Transfer of visual identity matching-to-sample in two California sea lions (Zalophus californianus). Anim. Learn. Behav. 22, 427–435 (1994).Article 

    Google Scholar 
    Zentall, T. R., Wasserman, E. A., Lazareva, O. F., Thompson, R. K. & Rattermann, M. J. Concept learning in animals. Comp. Cogn. Behav. Rev. 20, 25 (2008).
    Google Scholar 
    Marino, L. Convergence of complex cognitive abilities in cetaceans and primates. Brain. Behav. Evol. 59, 21–32 (2002).Article 
    PubMed 

    Google Scholar 
    Huber, L., Range, F. & Virányi, Z. Dog imitation and its possible origins. In Domestic dog Cognition and Behavior 79–100 (Springer, 2014).Chapter 

    Google Scholar 
    Schmidjell, T., Range, F., Huber, L. & Virányi, Z. Do owners have a Clever Hans effect on dogs? Results of a pointing study. Front. Psychol. 3, 558 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hare, B., Brown, M., Williamson, C. & Tomasello, M. The domestication of social cognition in dogs. Science 298, 1634–1636 (2002).Article 
    PubMed 

    Google Scholar 
    Lindqvist, C. & Jensen, P. Domestication and stress effects on contrafreeloading and spatial learning performance in red jungle fowl (Gallus gallus) and White Leghorn layers. Behav. Processes 81, 80–84 (2009).Article 
    PubMed 

    Google Scholar 
    Pack, A. A., Herman, L. M. & Roitblat, H. L. Generalization of visual matching and delayed matching by a California sea lion (Zalophus californianus). Anim. Learn. Behav. 19, 37–48 (1991).Article 

    Google Scholar 
    Bennett, M. S. Five breakthroughs: A first approximation of brain evolution from early bilaterians to humans. Front. Neuroanat. 15, 25 (2021).Article 

    Google Scholar 
    Cisek, P. Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81, 2265–2287 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Toft, C. A. & Wright, T. F. Parrots of the wild. Nat. Hist. World’s Most Captiv. Birds 20, 25 (2015).
    Google Scholar 
    Merkle, J. A., Sigaud, M. & Fortin, D. To follow or not? How animals in fusion–fission societies handle conflicting information during group decision-making. Ecol. Lett. 18, 799–806 (2015).Article 
    PubMed 

    Google Scholar 
    Stevens, J. R. & Gilby, I. C. A conceptual framework for nonkin food sharing: Timing and currency of benefits. Anim. Behav. 67, 603–614 (2004).Article 

    Google Scholar 
    Kamil, A. C. & Roitblat, H. L. The ecology of foraging behavior—Implications for animal learning and memory. Annu. Rev. Psychol. 36, 141–169 (1985).Article 
    PubMed 

    Google Scholar 
    Ortiz, S. T., Castro, A. C., Balsby, T. J. S. & Larsen, O. N. Problem-solving in a cooperative task in peach-fronted conures (Eupsittula aurea). Anim. Cogn. 23, 265–275 (2020).Article 

    Google Scholar 
    Krasheninnikova, A., Brucks, D., Blanc, S. & von Bayern, A. M. Assessing African grey parrots’ prosocial tendencies in a token choice paradigm. R. Soc. Open Sci. 6, 190696 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krasheninnikova, A. et al. Parrots do not show inequity aversion. Sci. Rep. 9, 1–12 (2019).Article 

    Google Scholar 
    Clayton, N. S., Griffiths, D. P., Emery, N. J. & Dickinson, A. Elements of episodic–like memory in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1483–1491 (2001).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall, 2020).Book 

    Google Scholar  More

  • in

    Long-term, basin-scale salinity impacts from desalination in the Arabian/Persian Gulf

    Al-Mutawa, A. M., Al Murbati, W. M., Al Ruwaili, N. A., Al Orafi, A. S., Al Orafi, A., Al Arafati, A., Nasrullah, A., Al Bahow, M. R., Al Anzi, S. M., Rashisi, M. & Al Moosa, S. Z. Desalination in the gcc. the history, the present & the future. Available from: https://www.gcc-sg.org/en-us/CognitiveSources/DigitalLibrary/Lists/DigitalLibrary/WaterandElectricity/1414489603.pdf Second edition, The Cooperation Council for the Arab States of the Gulf (GCC) General Secretariat (2014).Global Water Intelligence. DesalData. https://www.desaldata.com/. Accessed 2022-05-01 (2022).Sharifinia, M., Afshari Bahmanbeigloo, Z., Smith Jr, W. O., Yap, C. K. & Keshavarzifard, M. Prevention is better than cure: Persian gulf biodiversity vulnerability to the impacts of desalination plants. Glob. Change Biol. 25(12), 4022–4033 (2019).Article 

    Google Scholar 
    Connor, R. The United Nations World Water Development Report 2015: Water for a Sustainable World. Number 79. UNESCO, (2015).Al-Senafy, M., Al-Fahad, K. & Hadi, K. Water management strategies in the Arabian gulf countries. In Developments in Water Science, volume 50, pages 221–224. Elsevier, (2003).Ulrichsen, K.C.. Internal and external security in the arab gulf states. Middle East Policy16(2), 39 (2009).Verner, D. Adaptation to a changing climate in the Arab countries: a case for adaptation governance and leadership in building climate resilience. Number 79. World Bank Publications, (2012).Einav, R., Harussi, K. & Perry, D. The footprint of the desalination processes on the environment. Desalination 152(1–3), 141–154 (2003).Article 

    Google Scholar 
    Dawoud, M. A. Environmental impacts of seawater desalination: Arabian Gulf case study. Int. J. Environ. Sustain.1(3) (2012).Chow, A. C. et al. Numerical prediction of background buildup of salinity due to desalination brine discharges into the Northern Arabian Gulf. Water 11(11), 2284 (2019).Article 

    Google Scholar 
    Lee, K. & Jepson, W. Environmental impact of desalination: A systematic review of life cycle assessment. Desalination 509, 115066 (2021).Article 

    Google Scholar 
    Hosseini, H. et al. Marine health of the Arabian gulf: Drivers of pollution and assessment approaches focusing on desalination activities. Mar. Pollut. Bull. 164, 112085 (2021).Article 
    PubMed 

    Google Scholar 
    Le Quesne, W. J. F. et al. Is the development of desalination compatible with sustainable development of the Arabian Gulf?. Mar. Pollut. Bull. 173, 112940 (2021).Article 
    PubMed 

    Google Scholar 
    Kress, N., & Galil, B. Impact of seawater desalination by reverse osmosis on the marine environment. Efficient Desalination by Reverse Osmosis: A guide to RO practice. IWA, London, UK, pp. 177–202 (2015).Reynolds, R. M. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman: Results from the Mt Mitchell expedition. Mar. Pollut. Bull. 27, 35–59 (1993).Article 

    Google Scholar 
    Swift, S. A. & Bower, A. S. Formation and circulation of dense water in the Persian/Arabian Gulf. J. Geophys. Res. Oceans 108(C1), 1–4 (2003).Article 

    Google Scholar 
    Pous, S. P., Carton, X., & Lazure, P. Hydrology and circulation in the strait of hormuz and the Gulf of Oman: Results from the gogp99 experiment: 1. strait of hormuz. J. Geophys. Res. Oceans109(C12), (2004).Pous, S., Lazure, P. & Carton, X. A model of the general circulation in the persian gulf and in the strait of hormuz: Intraseasonal to interannual variability. Cont. Shelf Res. 94, 55–70 (2015).Article 

    Google Scholar 
    Johns, W. E., Yao, F., Olson, D. B., Josey, S. A., Grist, J. P. & Smeed, D. A. Observations of seasonal exchange through the Straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. J. Geophys. Res. Oceans108(C12) (2003).Hassanzadeh, S., Hosseinibalam, F. & Rezaei-Latifi, A. Numerical modelling of salinity variations due to wind and thermohaline forcing in the Persian gulf. Appl. Math. Model. 35(3), 1512–1537 (2011).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Price, A. R. G. Western Arabian gulf echinoderms in high salinity waters and the occurrence of dwarfism. J. Nat. Hist. 16(4), 519–527 (1982).Article 

    Google Scholar 
    Sheppard, C. R. C. Similar trends, different causes: Responses of corals to stressed environments in Arabian seas. In Proceedings of the 6th International Coral Reef Symposium Townsville, Australia, volume 3, pp. 297–302 (1988).Coles, S. L. & Jokiel, P. L. Effects of salinity on coral reefs. In Connell, D. W., & Hawker, D. W. editors, Pollution in tropical aquatic systems, pp. 147–166. CRC Press, Florida (1992).Coles, S. L. Coral species diversity and environmental factors in the Arabian gulf and the Gulf of Oman: A comparison to the Indo-Pacific region. Atoll Res. Bull. (2003).D’Agostino, D. et al. Growth impacts in a changing ocean: Insights from two coral reef fishes in an extreme environment. Coral Reefs 40(2), 433–446 (2021).Article 

    Google Scholar 
    Bœuf, G. & Payan, P. How should salinity influence fish growth?. Compar. Biochem. Physiol. Part C Toxicol. Pharmacol. 130(4), 411–423 (2001).Article 

    Google Scholar 
    Baudron, A. R., Needle, C. L., Rijnsdorp, A. D. & Marshall, C. T. Warming temperatures and smaller body sizes: Synchronous changes in growth of north sea fishes. Glob. Change Biol. 20(4), 1023–1031 (2014).Article 

    Google Scholar 
    Dore, M. H. I. Forecasting the economic costs of desalination technology. Desalination 172(3), 207–214 (2005).Article 

    Google Scholar 
    Karagiannis, I. C. & Soldatos, P. G. Water desalination cost literature: Review and assessment. Desalination 223(1–3), 448–456 (2008).Article 

    Google Scholar 
    Al Barwani, H. H. & Purnama, A. Evaluating the effect of producing desalinated seawater on hypersaline Arabian Gulf. Eur. J. Sci. Res. 22(2), 279–285 (2008).
    Google Scholar 
    Lee, W. & Kaihatu, J. M. Effects of desalination on hydrodynamic process in Persian Gulf. Coast. Eng. Proc. 36, 3–3 (2018).Article 

    Google Scholar 
    Ibrahim, H. D. & Eltahir, E. A. B. Impact of brine discharge from seawater desalination plants on Persian/Arabian gulf salinity. J. Environ. Eng. 145(12), 04019084 (2019).Article 

    Google Scholar 
    Campos, E. J. D. et al. Impacts of brine disposal from water desalination plants on the physical environment in the Persian/Arabian Gulf. Environ. Res. Commun. 2(12), 125003 (2020).Article 

    Google Scholar 
    Ibrahim, H. D., Xue, P. & Eltahir, E. A. B. Multiple salinity equilibria and resilience of Persian/Arabian Gulf basin salinity to brine discharge. Front. Mar. Sci. 7, 573 (2020).Article 

    Google Scholar 
    Ibrahim, H. D. Simulated effects of seawater desalination on Persian/Arabian Gulf exchange flow. J. Environ. Eng. 148(4), 04022012 (2022).Article 

    Google Scholar 
    Purnama, A. Assessing the environmental impacts of seawater desalination on the hypersalinity of arabian/persian gulf. In The Arabian Seas: Biodiversity, Environmental Challenges and Conservation Measures, pp. 1229–1245. Springer, (2021).GEBCO Compilation Group. The GEBCO_2021 grid: A continuous terrain model of the global oceans and land, (2021).Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13(2), 224–230 (1961).Article 

    Google Scholar 
    Nakamura, M., Stone, P. H. & Marotzke, J. Destabilization of the thermohaline circulation by atmospheric eddy transports. J. Clim. 7(12), 1870–1882 (1994).Article 

    Google Scholar 
    Pasquero, C. & Tziperman, E. Effects of a wind-driven gyre on thermohaline circulation variability. J. Phys. Oceanogr. 34(4), 805–816 (2004).Article 

    Google Scholar 
    Lucarini, V. & Stone, P. H. Thermohaline circulation stability: A box model study. part ii: coupled atmosphere-ocean model. J. Clim. 18(4), 514–529 (2005).Article 

    Google Scholar 
    Wunsch, C. Thermohaline loops, stommel box models, and the sandström theorem. Tellus A Dyn. Meteorol. Oceanogr. 57(1), 84–99 (2005).
    Google Scholar 
    Privett, D. W. Monthly charts of evaporation from the N. Indian Ocean (including the Red Sea and the Persian Gulf). Q. J. R. Meteorol. Soc. 85(366), 424–428 (1959).Article 

    Google Scholar 
    Chao, S.-Y., Kao, T. W. & Al-Hajri, K. R. A numerical investigation of circulation in the Arabian Gulf. J. Geophys. Res. Oceans 97(C7), 11219–11236 (1992).Article 

    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730), 1999–2049 (2020).Article 

    Google Scholar 
    Thoppil, P. G. & Hogan, P. J. Persian Gulf response to a wintertime shamal wind event. Deep Sea Res. Part I 57(8), 946–955 (2010).Article 

    Google Scholar 
    Paparella, F., Chenhao, X., Vaughan, G. O. & Burt, J. A. Coral bleaching in the Persian/Arabian Gulf is modulated by summer winds. Front. Mar. Sci. 6, 205 (2019).Article 

    Google Scholar 
    Gutiérrez, J.M., Jones, R. G., Narisma, G.T., Alves, L.M., Amjad, M., Gorodetskaya, I.V., Grose, M., Klutse, N.A.B., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L.O., Mernild, S.H., Ngo-Duc, T., van den Hurk, B. & Yoon, J.-H. Atlas. In V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, editors, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, (2021). Available from http://interactive-atlas.ipcc.ch/.Alosairi, Y., Imberger, J., & Falconer, R. A. Mixing and flushing in the Persian Gulf (Arabian Gulf). J. Geophys. Res. Oceans116(C3) (2011).Whitehead, J. A. Internal hydraulic control in rotating fluids – applications to oceans. Geophys. Astrophys. Fluid Dyn. 48(1–3), 169–192 (1989).Article 
    MATH 

    Google Scholar 
    Dougherty, W. W., Yates, D. N., Pereira, J. E., Monaghan, A., Steinhoff, D., Ferrero, B., Wainer, I., Flores-Lopez, F., Galaitsi, S., & Kucera, P., et al. The energy–water–health nexus under climate change in the united arab emirates: Impacts and implications. In Climate Change and Energy Dynamics in the Middle East, pp. 131–180. Springer, (2019).Al-Shehhi, M. R., Song, H., Scott, J. & Marshall, J. Water mass transformation and overturning circulation in the Arabian gulf. J. Phys. Oceanogr. 51(11), 3513–3527 (2021).
    Google Scholar 
    Hausfather, Z. & Peters, G. P. Emissions-the “business as usual’’ story is misleading. Nature 577, 618–620 (2020).Article 
    PubMed 

    Google Scholar 
    Al-Ghouti, M. A., Al-Kaabi, M. A., Ashfaq, M. Y. & Da’na, D. A. Produced water characteristics, treatment and reuse: A review. J. Water Process Eng. 28, 222–239 (2019).Article 

    Google Scholar 
    Riegl, B. M. & Purkis, S. J. Coral reefs of the gulf: adaptation to climatic extremes in the world’s hottest sea. In Coral reefs of the Gulf, pp. 1–4. Springer, (2012).Burt, J. A. et al. Insights from extreme coral reefs in a changing world. Coral Reefs 39(3), 495–507 (2020).Article 

    Google Scholar 
    D’Agostino, D. et al. The influence of thermal extremes on coral reef fish behaviour in the Arabian/Persian gulf. Coral Reefs 39(3), 733–744 (2020).Article 

    Google Scholar 
    Lachkar, Z., Mehari, M., Lévy, M., Paparella, F., & Burt, J.A. Recent expansion and intensification of hypoxia in the Arabian gulf and its drivers. Front. Mar. Sci. 1616 (2022).De Verneil, A., Burt, J. A., Mitchell, M., & Paparella, F. Summer oxygen dynamics on a southern Arabian Gulf coral reef. Front. Mar. Sci. 1676 (2021).Petersen, K. L. et al. Impact of brine and antiscalants on reef-building corals in the gulf of aqaba-potential effects from desalination plants. Water Res. 144, 183–191 (2018).Article 
    PubMed 

    Google Scholar 
    Sanchez-Lizaso, J. L. et al. Salinity tolerance of the mediterranean seagrass posidonia oceanica: recommendations to minimize the impact of brine discharges from desalination plants. Desalination 221(1–3), 602–607 (2008).Article 

    Google Scholar 
    Cambridge, M. L., Zavala-Perez, A., Cawthray, G. R., Mondon, J. & Kendrick, G. A. Effects of high salinity from desalination brine on growth, photosynthesis, water relations and osmolyte concentrations of seagrass posidonia australis. Mar. Pollut. Bull. 115(1–2), 252–260 (2017).Article 
    PubMed 

    Google Scholar 
    Cambridge, M. L. et al. Effects of desalination brine and seawater with the same elevated salinity on growth, physiology and seedling development of the seagrass posidonia australis. Mar. Pollut. Bull. 140, 462–471 (2019).Article 
    PubMed 

    Google Scholar 
    Kelaher, B. P., Clark, G. F., Johnston, E. L. & Coleman, M. A. Effect of desalination discharge on the abundance and diversity of reef fishes. Environ. Sci. Technol. 54(2), 735–744 (2019).Article 
    PubMed 

    Google Scholar 
    Gegner, H. M. et al. High salinity conveys thermotolerance in the coral model aiptasia. Biol. Open 6(12), 1943–1948 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Ochsenkühn, M. A., Röthig, T., D’Angelo, C., Wiedenmann, J. & Voolstra, C. R. The role of floridoside in osmoadaptation of coral-associated algal endosymbionts to high-salinity conditions. Sci. Adv. 3(8), e1602047 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gegner, H. M. et al. High levels of floridoside at high salinity link osmoadaptation with bleaching susceptibility in the cnidarian-algal endosymbiosis. Biol. Open 8(12), bio045591 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thoppil, P. G. & Hogan, P. J. A modeling study of circulation and eddies in the Persian Gulf. J. Phys. Oceanogr. 40(9), 2122–2134 (2010).Article 

    Google Scholar 
    Pous, S., Carton, X. & Lazure, P. A process study of the tidal circulation in the Persian gulf. Open J. Mar. Sci. 2(04), 131–140 (2012).Article 

    Google Scholar 
    Haney, R. L. Surface thermal boundary condition for ocean circulation models. J. Phys. Oceanogr. 1(4), 241–248 (1971).Article 

    Google Scholar  More

  • in

    Effect of a temperature gradient on the behaviour of an endangered Mexican topminnow and an invasive freshwater fish

    Time using the rock as refugeTemperature had an effect in the refuge usage of both species when analysed together (lme.zig: F3,192 = 7.97, p = 0.0001; Fig. 1A). However, species behaved differently (lme.zig: F1,192 = 14.79, p = 0.0004; Fig. 1A). As hypothesised, there was an interaction between temperature and species (lme.zig: F3,192 = 11.90, p  0.14, Fig. 1B).Size had an effect in the time exploring the rock (lme: F1,192 = 6.91, p = 0.012, Fig. 3) when species were analysed together, but there was no interaction with temperatures (lme: F3,192 = 0.42, p = 0.74, Fig. 3). We found that the interaction between species and size was close to be significant (lme: F1,192 = 3.62, p = 0.064, Fig. 3), implying that possibly smaller fish spent more time exploring the rock than bigger fish. However, when analysed separately, we did not find an effect of size in the exploring behaviour neither for twoline skiffias (lme: F1,96 = 2.99, p = 0.099, Fig. 3) nor for guppies (lme: F1,96 = 0.33, p = 0.569, Fig. 3).Figure 3Proportion of the total time observed (600 s) fish of different sizes spent exploring the rock. Lines represent the areas where the density of data is higher.Full size imageTime spent swimmingTemperature had an effect in the time spent swimming for both species when analysed together (lme: F3,192 = 23.48, p  More

  • in

    Spatial and temporal changes in moth assemblages along an altitudinal gradient, Jeju-do island

    Thornton, I. Island Colonization: The Origin and Development of Island Communities (Cambridge University Press, 2007).Book 

    Google Scholar 
    Weigelt, P., Jetz, W. & Kreft, H. Bioclimatic and physical characterization of the world’s islands. Proc. Natl Acad. Sci. 110, 15307–15312 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vitousek, P., Adsersen, H. & Loope, L. Introduction. In Islands: Biological Diversity and Ecosystem Function (eds Vitousek, P. et al.) 1–6 (Berlin, 1995).Chapter 

    Google Scholar 
    Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 2007).
    Google Scholar 
    Lomolino, M., Brown, J. & Sax, D. Island biogeography theory. In The Theory of Island Biogeography Revisited (eds Losos, J. & Ricklefs, R.) 13–51 (Princeton University Press, 2010).
    Google Scholar 
    Colom, P., Carreras, D. & Stefanescu, C. Long-term monitoring of Menorcan butterfly populations reveals widespread insular biogeographical patterns and negative trends. Biodivers. Conserv. 28, 1837–1851 (2019).Article 

    Google Scholar 
    Preston, F. W. The canonical distribution of commonness and rarity, part II. Ecology 43, 410–432 (1962).Article 

    Google Scholar 
    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, 1995).Book 

    Google Scholar 
    Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on species–area relationships. Ecol. Lett. 9(2), 215–227 (2006).Article 
    PubMed 

    Google Scholar 
    Field, R. et al. Spatial species-richness gradients across scales: A meta-analysis. J. Biogeogr. 36, 132–147 (2009).Article 

    Google Scholar 
    Brehm, G., Süssenbach, D. & Fiedler, K. Unique elevational diversity patterns of geometrid moths in an Andean montane rainforest. Ecography 26, 456–466 (2003).Article 

    Google Scholar 
    Brehm, G., Colwell, R. K. & Kluge, J. The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Glob. Ecol. Biogeogr. 16, 205–219 (2007).Article 

    Google Scholar 
    Beck, J. & Kitching, I. J. Drivers of moth species richness on tropical altitudinal gradients: A cross-regional comparison. Glob. Ecol. Biogeogr. 18, 361–371 (2009).Article 

    Google Scholar 
    Ashton, L. A. et al. Altitudinal patterns of moth diversity in tropical and subtropical A ustralian rainforests. Aust. Ecol. 41, 197–208 (2016).Article 

    Google Scholar 
    Maunsell, S. C. et al. Elevational turnover in the composition of leaf miners and their interactions with host plants in Australian subtropical rainforest. Aust. Ecol. 41, 238–247 (2016).Article 

    Google Scholar 
    McCain, C. M. Global analysis of reptile elevational diversity. Glob. Ecol. Biogeogr. 19, 541–553 (2010).
    Google Scholar 
    Yu, X. D., Lü, L., Luo, T. H. & Zhou, H. Z. Elevational gradient in species richness pattern of epigaeic beetles and underlying mechanisms at east slope of Balang Mountain in Southwestern China. PLoS ONE 8, e69177 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beck, J. et al. Elevational species richness gradients in a hyperdiverse insect taxon: A global meta-study on geometrid moths. Glob. Ecol. Biogeogr. 26, 412–424 (2017).Article 

    Google Scholar 
    Szewczyk, T. & McCain, C. M. A systematic review of global drivers of ant elevational diversity. PLoS ONE 11, e0155404 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rahbek, C. The elevational gradient of species richness: A uniform pattern?. Ecography 18, 200–205 (1995).Article 

    Google Scholar 
    Vitousek, P. M. Oceanic islands as model systems for ecological studies. J. Biogeogr. 29, 573–582 (2002).Article 

    Google Scholar 
    Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Global Ecol. Conserv. 19, e00670 (2019).Article 

    Google Scholar 
    Meyer, W. M. III. et al. Ground-dwelling arthropod communities of a sky island mountain range in Southeastern Arizona, USA: Obtaining a baseline for assessing the effects of climate change. PLoS ONE 10, e0135210 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kong, W. S. Biogeography of Korean plants 335 (Geobook, 2007) (in Korean).
    Google Scholar 
    Kitching, R. L. et al. Moth assemblages as indicators of environmental quality in remnants of upland Australian rain forest. J. Appl. Ecol. 37, 284–297 (2000).Article 

    Google Scholar 
    Froidevaux, J. S., Broyles, M. & Jones, G. Moth responses to sympathetic hedgerow management in temperate farmland. Agric. Ecosyst. Environ. 270, 55–64 (2019).Article 
    PubMed 

    Google Scholar 
    Fox, R. The decline of moths in Great Britain: A review of possible causes. Insect Conserv. Div. 6, 5–19 (2013).Article 

    Google Scholar 
    Keret, N. M., Mutanen, M. J., Orell, M. I., Itämies, J. H. & Välimäki, P. M. Climate change-driven elevational changes among boreal nocturnal moths. Oecologia 192, 1085–1098 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wenzel, M., Schmitt, T., Weitzel, M. & Seitz, A. The severe decline of butterflies on western German calcareous grasslands during the last 30 years: A conservation problem. Biol. Conserv. 128, 542–552 (2006).Article 

    Google Scholar 
    Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).Article 
    PubMed 

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

    Google Scholar 
    Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

    Google Scholar 
    Zenker, M. M. et al. Diversity and composition of Arctiinae moth assemblages along elevational and spatial dimensions in Brazilian Atlantic Forest. J. Insect Conserv. 19, 129–140 (2015).Article 

    Google Scholar 
    Brehm, G. & Fiedler, K. Faunal composition of geometrid moths changes with altitude in an Andean montane rain forest. J. Biogeogr. 30, 431–440 (2003).Article 

    Google Scholar 
    McCain, C. M. & Grytnes, J. A. Elevational gradients in species richness. In Encyclopedia of Life Sciences (Wiley, Chichester, 2010).
    Google Scholar 
    Heinrich, B. The Hot-Blooded Insects: Strategies and Mechanisms of Thermoregulation 601 (Harvard University Press, 1993).Book 

    Google Scholar 
    Heinrich, B. Thermoregulation in Endothermic Insects: Body temperature is closely attuned to activity and energy supplies. Science 185, 747–756 (1974).Article 
    PubMed 

    Google Scholar 
    May, M. L. Insect thermoregulation. Annu. Rev. Entomol. 24, 313–349 (1979).Article 

    Google Scholar 
    Heidrich, L. et al. Noctuid and geometrid moth assemblages show divergent elevational gradients in body size and color lightness. Ecography 44, 1169–1179 (2021).Article 
    MathSciNet 

    Google Scholar 
    Holloway, J. D. Macrolepidoptera diversity in the Indo-Australian tropics, geographic, biotopic and taxonomic variations. Biol. J. Linn. Soc. 30, 325–341 (1987).Article 

    Google Scholar 
    Axmacher, J. C. et al. Diversity of geometrid moths (Lepidoptera: Geometridae) along an Afrotropical elevational rainforest transect. Divers. Distrib. 10, 293–302 (2004).Article 

    Google Scholar 
    Heinrich, B. & Mommsen, T. P. Flight of winter moths near 0°C. Science 228, 177–179 (1985).Article 
    PubMed 

    Google Scholar 
    Rydell, J. & Lancaster, W. C. Flight and thermoregulation in moths were shaped by predation from bats. Oikos 88, 13–18 (2000).Article 

    Google Scholar 
    Skou, P. The geometroid moths of North Europe. Entomonograph, Vol. 6. Brill, Leiden. (1986).Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, noctuoidea). Syst. Entomol. 37, 102–124 (2012).Article 

    Google Scholar 
    Fiedler, K., Brehm, G., Hilt, N., Sussenbach, D. & Hauser, C. L. Variation of diversity patterns across moth families along a tropical altitudinal gradient. Ecol. Stud. 198, 167–179 (2008).Article 

    Google Scholar 
    Longino, J. T. & Colwell, R. K. Density compensation, species composition, and richness of ants on a neotropical elevational gradient. Ecosphere 2, 1–20 (2011).Article 

    Google Scholar 
    Beck, J. & Chey, V. K. Explaining the elevational diversity pattern of geometrid moths from Borneo: A test of five hypotheses. J. Biogeogr. 35, 1452–1464 (2008).Article 

    Google Scholar 
    Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).Article 
    PubMed 

    Google Scholar 
    Kwon, T. S. Ants foraging on grasses in South Korea: High diversity in Jeju Island and negative correlation with aphids. J. Asia-Pac. Biodivers. 10, 465–471 (2017).Article 

    Google Scholar 
    Han, E. K. et al. A disjunctive marginal edge of evergreen broad-leaved oak (Quercus gilva) in East Asia: The high genetic distinctiveness and unusual diversity of Jeju island populations and insight into a massive, independent postglacial colonization. Genes 11, 1114 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chi, Y., Shi, H., Wang, Y., Guo, Z. & Wang, E. Evaluation on island ecological vulnerability and its spatial heterogeneity. Mar. Pollut. Bull. 125, 216–241 (2017).Article 
    PubMed 

    Google Scholar 
    Vehviläinen, H., Koricheva, J. & Ruohomäki, K. Tree species diversity influences herbivore abundance and damage: Meta-analysis of long-term forest experiments. Oecologia 152, 287–298 (2007).Article 
    PubMed 

    Google Scholar 
    Root, R. B. Organization of plant–arthropod association in simple and diverse habitats: The fauna of collards (I. Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

    Google Scholar 
    Otway, S. J., Hector, A. & Lawton, J. H. Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. J. Anim. Ecol. 74, 234–240 (2005).Article 

    Google Scholar 
    Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).Article 

    Google Scholar 
    Qian, H. Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales. Ecol. Res. 25, 629–637 (2010).Article 

    Google Scholar 
    Major, J. A climatic index to vascular plant activity. Ecology 44, 485–498 (1963).Article 

    Google Scholar 
    Latham, R. E. & Ricklefs, R. E. Global patterns of tree species richness in moist forests: Energy-diversity theory does not account for variation in species richness. Oikos 67, 325–333 (1993).Article 

    Google Scholar 
    Francis, A. P. & Currie, D. J. A globally consistent richness-climate relationship for angiosperms. Am. Nat. 161, 523–536 (2003).Article 
    PubMed 

    Google Scholar 
    Storch, D. et al. Energy, range dynamics and global species richness patterns: Reconciling mid-domain effects and environmental determinants of avian diversity. Ecol. Lett. 9, 1308–1320 (2006).Article 
    PubMed 

    Google Scholar 
    Intachat, J., Holloway, J. D. & Staines, H. Effects of weather and phenology on the abundance and diversity of geometroid moths in a natural Malaysian tropical rain forest. J. Trop. Ecol. 17, 411–429 (2001).Article 

    Google Scholar 
    Choi, S. W. Effects of weather factors on the abundance and diversity of moths in a temperate deciduous mixed forest of Korea. Zool. Sci. 25, 53–58 (2008).Article 

    Google Scholar 
    Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lennon, J. J., Koleff, P., Greenwood, J. J. D. & Gaston, K. J. The geographical structure of British bird distributions: Diversity, spatial turnover and scale. J. Anim. Ecol. 70, 966–979 (2001).Article 

    Google Scholar 
    Choi, S. W. A high mountain moth assemblage quickly recovers after fire. Ann. Entomol. Soc. Am. 111, 304–311 (2018).
    Google Scholar 
    van Swaay, C., Warren, M. & Loïs, G. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209 (2006).Article 

    Google Scholar 
    De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. 110, 18561–18565 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).Article 
    PubMed 

    Google Scholar 
    Conrad, K. F., Warren, M. S., Fox, R., Parsons, M. S. & Woiwod, I. P. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132, 279–291 (2006).Article 

    Google Scholar 
    White, E. R. Minimum time required to detect population trends: The need for long-term monitoring programs. Bioscience 69, 40–46 (2019).Article 

    Google Scholar 
    Forister, M. L., Pelton, E. M. & Black, S. H. Declines in insect abundance and diversity: We know enough to act now. Conserv. Sci. Pract. 1, e80 (2019).
    Google Scholar 
    Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Div. 13, 103–114 (2020).Article 

    Google Scholar 
    Kim, J. W., Boo, K. O., Choi, J. T. & Byun, Y. H. Climate Change of 100 Years on the Korean Peninsula (National Institute of Meteorological Science, 2018).
    Google Scholar 
    Kim, S. S., Beljaev, E. A. & Oh, S. H. Illustrated Catalogue of Geometridae in Korea (Lepidoptera: Geometrinae, Ennominae) (Korea Research Institute of Bioscience and Biotechnology & Center for Insect Systematics, 2001).
    Google Scholar 
    Kononenko, V.S., Ahn, S.B. & Ronkay, L. Illustrated catalogue of Noctuidae in Korea (Lepidoptera). Insects of Korea 3. KRIBB & CIS, Junghaengsa (1998).Shin, Y.H. Coloured illustrations of the moths of Korea. Academybook (2001).Kim, S.S., Choi, S.W., Sohn, J.C., Kim, T. & Lee, B.W. The Geometrid moths of Korea (Lepidoptera: Geometridae). Junghaengsa (2016).Kim, C. G. & Kim, N. W. Altitudinal pattern of evapotranspiration and water need for upland crops in Jeju Island. J. Korea Water Resour. Assoc. 48, 915–923 (2015).Article 

    Google Scholar 
    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Magurran, A. E. Ecological Diversity and its Measurement (Princeton University Press, 1988).Book 

    Google Scholar 
    Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

    Google Scholar 
    R Development Core Team. R 4.0.3. R: A language and environment for statistical computing. R Foundation for statistical computing Vienna. Austria. URL http://www.R-project.org. (2020).Pohlert, T. Non-parametric trend tests and change-point detection. R-package version 0.0.1. (2020).Hipel, K. W. & McLeod, A. I. Time Series Modelling of Water Resources and Environmental Systems (Elsevier, 1994).
    Google Scholar 
    Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T. J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371 (2006).Article 
    MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    Colwell, R. K. Estiamtes, Version 91: Statistical Estimation of Species Richness and Shared Species from Samples (University of Connecticut, 2013).
    Google Scholar 
    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Global Ecol. Biogeogr. 19, 134–143 (2010).Article 

    Google Scholar 
    Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

    Google Scholar  More