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    Contrafreeloading in kea (Nestor notabilis) in comparison to Grey parrots (Psittacus erithacus)

    This study aimed to compare the extent of contrafreeloading in kea to that in Grey parrots, given that the two species exhibit very different levels of play: specifically, kea exhibit complex and frequent play29,30,35,36, whereas Greys exhibit considerably less play than several parrot species29. We found that, at the group level, although the overall amounts of kea classic contrafreeloading were nonsignificant, as a percentage of behaviour, kea generally contrafreeloaded more than Grey parrots in Experiment 1, whereas the opposite was true for Experiment 2. We compare the various behaviour patterns in detail, and propose explanations for our results below.The most interesting comparisons for Smith et al.’s hypothesis are the results from classic contrafreeloading. In Experiment 1, kea performed this behaviour at non-negligible levels, given the supposed rarity of the behaviour5 (two birds at 50%; the others varying between 39 and 47%). In contrast, although one Grey did classically contrafreeload at a statistically significant level, the other three were at ≤ 36%. These data suggest that the kea may have found the task more engaging than did the Greys. However, given that only two kea chose to pop the lid of an empty cup in control trials significantly above chance, whereas three of the four Greys did so significantly above chance and one at chance, we doubt that the kea found the task inherently rewarding. We note that this comparison between both species must be interpreted cautiously due to differences in methodology: For the Greys, the control trials were performed at the end of the study, by which point they may have learnt to associate lid-popping with reward. However, the data from experimental trials in Smith et al.13 are such that their birds would have been primed in the opposite direction: For example, three of those four birds rarely chose the empty lidded cup when free food was available, nor did they classically or super contrafreeload to any significant extent13; an association-driven explanation is therefore unlikely. In contrast, the kea experienced this control condition at the start of the experiment, allowing them 20 trials to become acquainted with the affordances of both options that would be available throughout the study (lid-popping versus not lid-popping). This opportunity was important for kea, as this species has been previously shown to learn about object properties through extensive object manipulation37. That kea popped lids at or above chance in these first 20 control trials suggested two possibilities: (1) After these 20 trials, the task may have been familiar enough to no longer be of much interest (i.e., no longer novel and worthy of consideration) by the time rewarded trials began (recall nonsignificant downward trends for Harley Quinn and Blofeld). (2) They acquired some interest in popping the lids. This latter case seems more likely, as the lid-popping task still likely provided some added value. Kea engaged in non-negligible levels of classic contrafreeloading, such that the chance to pop a lid and eat could be considered more interesting than simply eating an identical but freely available reward. Furthermore, three kea chose a lidded, empty cup over a free, least-preferred reward at least half the time, again suggesting that the activity held some appeal of its own.In Experiment 2 (which corresponds to classic contrafreeloading), all kea preferred freeloading for the walnut without a shell; two Greys, in contrast, nut contrafreeloaded at a statistically significant extent. This variability in behaviour at both the individual and species levels reveals the significance of a task’s proximate and potentially ultimate values in parrots’ choice to contrafreeload. Interestingly, although species like kea are hypothesized to prefer food items requiring high manipulation38,39, nut-cracking—chosen as an activity to provide direct comparison with the Greys13—is not prevalent in kea diet40, and that activity thus may not have been appropriate as an ethologically relevant one for kea. Greys, in contrast, are known to crack nuts in nature41. Future research could use a more ecologically relevant task for the kea, such as working to access food via digging or scraping32.As with Smith et al.’s Greys13, kea in Experiment 1 performed calculated contrafreeloading to a statistically significant extent. All kea did so on over 83% of trials; for the Greys, three birds were close to 90% but one was at only 67%. Kea consistently selected their preferred food out of the two options provided, suggesting that the lid-popping action did not deter kea from selecting their preferred reward. In related trials, where the lid-status of food paired with an empty cup varied, kea, like some Greys13, preferred lidded food over an empty lidless cup, again showing that lid-popping for food was an acceptable task.When examining situations in which food was discarded after contrafreeloading, we found that this choice in Experiment 1 was most common for Bruce. Notably, Bruce lacks a top mandible, making many of the manipulative behaviours more difficult to execute42. Bruce demonstrated consistent food preferences throughout the experiment, however, indicating that the reason some foods were discarded was, indeed, because they were too difficult for him to manipulate. In Experiment 2, Harley Quinn was the most likely to discard the nut, and did so exclusively in trials in which she chose the walnut without the shell (freeloaded). In these occasions, Harley Quinn was observed choosing the nut by tapping on it or the cup.Like the Greys, the kea failed to super contrafreeload to a statistically significant extent. Furthermore, contrafreeloading trials in which a lid was popped but the food underneath was not consumed occurred most often with the least-preferred food. Given kea’s performance on control trials, the super contrafreeloading results are not surprising. Interestingly, when lid-status of food paired with an empty cup varied, some Greys very rarely—and depending on food desirability—preferred to pop the empty cup’s lid rather than consume the free food; as noted earlier, three of eight kea did so on at least half the trials when the food in the lidless cup was their least preferred option (sultanas). Both kea and Greys thus likely placed the appeal of the task along some “value scale” along with that of the available food rewards, the combination influencing their behaviour when the two variables were presented in various permutations. Notably, even in control trials, where no food was involved, no bird of either species found the task aversive, engaging in the behaviour at least 50% of the time. Future research could investigate how a different, more rewarding task would influence this balance and thus contrafreeloading for both species.One possible alternative explanation for kea’s higher rates of contrafreeloading relative to those of Greys could be their natural tendency to probe and manipulate objects, thus causing them to pry off cup lids rather than manipulate lidless (open) cups. Were this action exploratory in nature, we would have observed significant decreases in behaviour as the experiment progressed, but note that we found no significant changes in any bird. Were they consistently drawn to lids and this behaviour were hard-wired, then we should have observed lid-popping appear significantly above chance across all three types of contrafreeloading. However, as discussed previously, kea did not significantly contrafreeload in the classic condition and actively freeloaded in super contrafreeloading conditions, suggesting that they were not simply interacting with lidded cups preferentially, but rather attending to the contents in the two cups and avoiding the additional manipulation of the lid when it led to a less (or, more often than not, equally) preferred food reward.Another potential explanation for the differences observed between kea and Greys might be found in the theoretical overlap between contrafreeloading and play, and how individuals might view the contrafreeloading action as a type of play. As a seemingly nonfunctional, intrinsically motivating behaviour occurring in low-stress environments, incurring a positive mood, varying between conspecifics, and often incomplete and/or repeated14,15, play shares many proximate-level attributes with contrafreeloading13. Our results demonstrate that kea subjects inhabiting a low-stress, captive environment repeatedly chose to engage in classic contrafreeloading to a non-negligible extent and calculated contrafreeloading to a significant extent, varied in their behaviour between individuals, and at times, left the task incomplete (e.g., left food uneaten). Furthermore, evidence for intrinsic motivation to perform a given task is suggested by the kea’s overall differential behaviour between the two experiments, as well as inter-individual differences.Importantly, this study serves only as a first step into determining whether play manifests as a form of contrafreeloading, but cannot ascertain that this is the only possible explanation for the presence or degree of contrafreeloading in the two species. Several alternative explanatory theories regarding the occurrence of contrafreeloading are enumerated in the discussion of Smith et al. (e.g., work ethic; information gathering; relief from boredom)13, and various other potential explanations (beyond playfulness) may reside at the species-level. Grey parrots (Psittacidae) and kea (Strigopidae) are separated by 50–80 million years of evolution43 and differ in their neurobiology (i.e., the size of the shell region related to vocal and possible cognitive abilities44). Differing ecological evolutionary pressures are also likely relevant: an island-based habitat39, a lack of natural predators30,45, and generalist diets40,46,47 are thought to have shaped the playfulness and cognitive abilities of kea30,40,46,47. Greys, in contrast, evolved predominantly on a continent (i.e., although they can be found on islands such as Principe, the Congo Grey is endemic to central Africa48,49), are subject to considerable predation48,50,51,52, and have a relatively less generalist diet (diverse but almost exclusively vegetarian and in which nuts play a significant role; see review in50). Such disparate evolutionary trajectories may offer other potential explanations for the differences in contrafreeloading observed between the two species, and future research could examine differences at genetic and/or neurological levels.The varying rates of contrafreeloading observed between the species could have also been influenced by other factors. For example, although both parrot groups studied here inhabit enriched environments, are habituated to participating in experimental trials, and have access to food ad libitum, their habitats are markedly different. Notably, the Grey subjects live in “man-made” settings (i.e., Griffin and Athena in a lab; Pepper, Franco, and Lucci in private homes), whereas the kea inhabit a naturalistic zoo enclosure. Physical enrichment, although somewhat different in kind, is unlikely to have differed in quantity, as all birds are provided routine naturalistic foraging, and Lucci lives in a free-flight aviary. More likely is the difference in sociality: Relatively more subjects reside together in the kea group (15) compared to the Greys (two groups of two Greys and one Grey living with two birds of differing species), and thus variables such as social stimulation and flock-based foraging techniques could have contributed to the expression of contrafreeloading (note that subadult male kea are known to obtain food through kleptoparasitism32). In order to elucidate the role of habitat on contrafreeloading, future studies could examine the behaviour of species residing in more comparable captive conditions.Future work should aim not only to apply these same methodologies to a broader range of parrot species, but also objectively quantify frequency and complexity of play across a wide range of parrots to allow a direct correlation between play and contrafreeloading over phylogeny in the parrot order. The apparent link between play behaviour and encephalisation in parrots53 offers another possible avenue for cross-species comparisons on contrafreeloading. Future research could also employ cognitive bias tests to quantify the mood of birds before and following contrafreeloading54, directly manipulate subjects’ participation in play behaviours or other control behaviours and observe whether engaging in play can increase contrafreeloading rates at the individual level, or perform behavioural coding of playfulness and/or arousal before and after contrafreeloading. Future research could incorporate more ecologically relevant contrafreeloading tasks to examine this behaviour at both the individual and species level, and approach the phenomenon by using both genetic and neuroscience techniques.In sum, contrafreeloading is, by its very nature, an enigma whose study presents many difficulties. It varies across the diverse contexts within which it is studied, and given that it is rarely exhibited to a statistically significant extent, analyses that require comparing nonsignificant behaviour patterns across individuals and/or species is a challenging undertaking. Many explanations have been proposed, but contrafreeloading is still poorly understood, and its correlation with play is likely only one of several logical rationales. Nevertheless, our findings suggest that interest in play should not be discounted as a contributing factor. More

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    Chemolithoautotroph distributions across the subsurface of a convergent margin

    Kelemen PB, Manning CE. Reevaluating carbon fluxes in subduction zones, what goes down, mostly comes up. Proc Natl Acad Sci USA. 2015;112:E3997–4006.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vitale Brovarone A, Sverjensky DA, Piccoli F, Ressico F, Giovannelli D, Daniel I. Subduction hides high-pressure sources of energy that may feed the deep subsurface biosphere. Nat Commun. 2020;11:1–1.Article 

    Google Scholar 
    Harris RN, Wang K. Thermal models of the middle America trench at the Nicoya Peninsula, Costa Rica. Geophys Res Lett. 2002;29:6–1.Article 

    Google Scholar 
    Plümper O, King HE, Geisler T, Liu Y, Pabst S, Savov IP, et al. Subduction zone forearc serpentinites as incubators for deep microbial life. Proc Natl Acad Sci USA. 2017;114:4324–9.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee H, Fischer TP, de Moor JM, Sharp ZD, Takahata N, Sano Y. Nitrogen recycling at the Costa Rican subduction zone: the role of incoming plate structure. Sci Rep. 2017;7:1–10.
    Google Scholar 
    Stern RJ. Subduction zones. Rev Geophys. 2002;40:3–38.Article 

    Google Scholar 
    Fullerton KM, Schrenk MO, Yücel M, Manini E, Basili M, Rogers TJ, et al. Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin. Nat Geosci. 2021;14:301–6.CAS 
    Article 

    Google Scholar 
    Barry PH, de Moor JM, Giovannelli D, Schrenk M, Hummer DR, Lopez T, et al. Forearc carbon sink reduces long-term volatile recycling into the mantle. Nature. 2019;568:487–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    Moore EK, Jelen BI, Giovannelli D, Raanan H, Falkowski PG. Metal availability and the expanding network of microbial metabolisms in the Archaean eon. Nat Geosci. 2017;10:629–36.CAS 
    Article 

    Google Scholar 
    Barnes JD, Cullen J, Barker S, Agostini S, Penniston-Dorland S, Lassiter JC, et al. The role of the upper plate in controlling fluid-mobile element (Cl, Li, B) cycling through subduction zones: Hikurangi forearc, New Zealand. Geosphere. 2019;15:642–58.Article 

    Google Scholar 
    Clift P, Vannucchi P. Controls on tectonic accretion versus erosion in subduction zones: Implications for the origin and recycling of the continental crust. Rev Geophys. 2004;42:1–31.Article 

    Google Scholar 
    Rüpke LH, Morgan JP, Hort M, Connolly JA. Serpentine and the subduction zone water cycle. Earth Planet Sci Lett. 2004;223:17–34.Article 

    Google Scholar 
    Carr MJ, Feigenson MD, Bennett EA. Incompatible element and isotopic evidence for tectonic control of source mixing and melt extraction along the Central American arc. Contrib Miner Pet. 1990;105:369–80.CAS 
    Article 

    Google Scholar 
    Gazel E, Carr MJ, Hoernle K, Feigenson MD, Szymanski D, Hauff F, et al. Galapagos‐OIB signature in southern Central America: mantle refertilization by arc–hot spot interaction. Geochem Geophys Geosyst. 2009;10:1–32.Article 

    Google Scholar 
    Trembath-Reichert E, Butterfield DA, Huber JA. Active subseafloor microbial communities from Mariana back-arc venting fluids share metabolic strategies across different thermal niches and taxa. ISME J. 2019;13:2264–79. https://doi.org/10.1038/s41396-019-0431-y.Power JF, Carere CR, Lee CK, Wakerley GL, Evans DW, Button M, et al. Microbial biogeography of 925 geothermal springs in New Zealand. Nat Commun. 2018;9:1–2.CAS 
    Article 

    Google Scholar 
    Acocella V, Spinks K, Cole J, Nicol A. Oblique back arc rifting of Taupo Volcanic zone. NZ Tecton. 2003;22:1–18.
    Google Scholar 
    Curtis AC, Wheat CG, Fryer P, Moyer CL. Mariana forearc serpentinite mud volcanoes harbor novel communities of extremophilic archaea. Geomicrobiol J. 2013;30:430–41.Article 

    Google Scholar 
    Inskeep WP, Jay ZJ, Herrgard MJ, Kozubal MA, Rusch DB, Tringe SG, et al. Phylogenetic and functional analysis of metagenome sequence from high-temperature archaeal habitats demonstrate linkages between metabolic potential and geochemistry. Front Microbiol. 2013;4:1–21.Article 

    Google Scholar 
    Colman DR, Lindsay MR, Amenabar MJ, Boyd ES. The intersection of geology, geochemistry, and microbiology in continental hydrothermal systems. Astrobiology. 2019;19:1505–22.CAS 
    PubMed 
    Article 

    Google Scholar 
    Inskeep WP, Jay ZJ, Tringe SG, Herrgård MJ, Rusch DB, YNP Metagenome Project Steering Committee and Working Group Members. The YNP metagenome project: environmental parameters responsible for microbial distribution in the Yellowstone geothermal ecosystem. Front Microbiol. 2013;4:1–15.Article 

    Google Scholar 
    Hou W, Wang S, Dong H, Jiang H, Briggs BR, Peacock JP, et al. A comprehensive census of microbial diversity in hot springs of Tengchong, Yunnan Province China using 16S rRNA gene pyrosequencing. PloS One. 2013;8:1–15.
    Google Scholar 
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TB, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Probst AJ, Castelle CJ, Singh A, Brown CT, Anantharaman K, Sharon I, et al. Genomic resolution of a cold subsurface aquifer community provides metabolic insights for novel microbes adapted to high CO2 concentrations. Environ Microbiol. 2017;19:459–74.CAS 
    PubMed 
    Article 

    Google Scholar 
    Probst AJ, Ladd B, Jarett JK, Geller-McGrath DE, Sieber CM, Emerson JB, et al. Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nat Microbiol. 2018;3:328–36.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    He C, Keren R, Whittaker M, Farag IF, Doudna J, Cate JH, et al. Genome-resoled metagenomics reveals site-specific diversity of episymbiotic CPR bacteria and DPANN archaea in groundwater ecosystems. Nat. Microbiol. 2021;6:354–65.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grettenberger CL, Hamilton TL. Metagenome-assembled genomes of novel taxa from an acid mine drainage environment. Appl Environ Microbiol. 2021;87:e0077221. https://doi.org/10.1101/2020.07.02.185728.Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP–a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:1–3.Article 

    Google Scholar 
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA, et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front Microbiol. 2020;11:37. https://doi.org/10.3389/fmicb.2020.00037.Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    Graham ED, Heidelberg JF, Tully BJ. Potential for primary productivity in a globally distributed bacterial phototroph. ISME J. 2018;12:1861–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–27.CAS 

    Google Scholar 
    Berg IA, Kockelkorn D, Ramos-Vera WH, Say RF, Zarzycki J, Hügler M, et al. Autotrophic carbon fixation in archaea. Nat Rev Microbiol. 2010;8:447–60.CAS 
    PubMed 
    Article 

    Google Scholar 
    Berg IA. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl Environ Microbiol. 2011;77:1925–36.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Youssef NH, Farag IF, Hahn CR, Jarett J, Becraft E, Eloe-Fadrosh E, et al. Genomic characterization of candidate division LCP-89 reveals an atypical cell wall structure, microcompartment production, and dual respiratory and fermentative capacities. Appl Environ Microbiol. 2019;85:1–19.Article 

    Google Scholar 
    Nigro LM, King GM. Disparate distributions of chemolithotrophs containing form IA or IC large subunit genes for ribulose-1, 5-bisphosphate carboxylase/oxygenase in intertidal marine and littoral lake sediments. FEMS Microbiol Ecol. 2007;60:113–25.CAS 
    PubMed 
    Article 

    Google Scholar 
    Aminuddin M, Nicholas DJ. Electron transfer during sulphide and sulphite oxidation in Thiobacillus denitrificans. Microbiology. 1974;82:115–23.
    Google Scholar 
    Giovannelli D, Sievert SM, Hügler M, Markert S, Becher D, Schweder T, et al. Insight into the evolution of microbial metabolism from the deep-branching bacterium, Thermovibrio ammonificans. eLife. 2017;6:1–31.Article 

    Google Scholar 
    Nakagawa S, Shataih Z, Banta A, Beveridge TJ, Sako Y, Reysenbach AL. Sulfurihydrogenibium yellowstonense sp. nov., an extremely thermophilic, facultatively heterotrophic, sulfur-oxidizing bacterium from Yellowstone National Park, and emended descriptions of the genus Sulfurihydrogenibium, Sulfurihydrogenibium subterraneum. Int J Syst Evol Microbiol. 2005;55:2263–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Leclerque A, Kleespies RGA. Rickettsiella bacterium from the hard tick, Ixodes woodi: molecular taxonomy combining multilocus sequence typing (MLST) with significance testing. PLoS One. 2012;7:e38062. https://doi.org/10.1371/journal.pone.0038062.Quatrini R, Johnson DB. Acidithiobacillus ferrooxidans. Trends Microbiol. 2019;27:282–3.CAS 
    PubMed 
    Article 

    Google Scholar 
    Spang A, Poehlein A, Offre P, Zumbrägel S, Haider S, Rychlik N, et al. The genome of the ammonia‐oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen CY, Chen PC, Weng FC, Shaw GT, Wang D. Habitat and indigenous gut microbes contribute to the plasticity of gut microbiome in oriental river prawn during rapid environmental change. PLoS One. 2017;12:e0181427. https://doi.org/10.1371/journal.pone.0181427.Garcia R, Müller R. The family Myxococcaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin: Springer; 2014. p. 191–212.Garcia R, Müller R. Simulacricoccus ruber gen. nov., sp. nov., a microaerotolerant, non-fruiting, myxospore-forming soil myxobacterium and emended description of the family Myxococcaceae. Int J Syst Evol Microbiol. 2018;68:3101–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Iino T. The family Ignavibacteriaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: other major lineages of bacteria and the archaea. New York, NY: Springer Science + Business Media; 2014. p. 701–3.Petrie L, North NN, Dollhopf SL, Balkwill DL, Kostka JE. Enumeration and characterization of iron (III)-reducing microbial communities from acidic subsurface sediments contaminated with uranium (VI). Appl Environ Microbiol. 2003;69:7467–79.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fincker M, Huber JA, Orphan VJ, Rappé MS, Teske A, Spormann AM. Metabolic strategies of marine subseafloor Chloroflexi inferred from genome reconstructions. Environ Microbiol. 2020;22:3188–204.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen YJ, Leung PM, Wood JL, Bay SK, Hugenholtz P, Kessler AJ, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15:2986–3004.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Flieder M, Buongiorno J, Herbold CW, Hausmann B, Rattei T, Lloyd KG, et al. Novel taxa of Acidobacteriota implicated in seafloor sulfur cycling. ISME J. 2021;15:3159–80.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kim M, Wilpiszeski RL, Wells M, Wymore AM, Gionfriddo CM, Brooks SC, et al. Metagenome-assembled genome sequences of novel prokaryotic species from the mercury-contaminated East Fork Poplar Creek, Oak Ridge, Tennessee, USA. Microbiol Resour Announc. 2021;10:e00153–21. https://doi.org/10.1128/MRA.00153-21.Santos‐Júnior CD, Logares R, Henrique‐Silva F. Microbial population genomes from the Amazon River reveal possible modulation of the organic matter degradation process in tropical freshwaters. Mol Ecol. 2022;31:206–19.PubMed 
    Article 

    Google Scholar 
    Yamada T, Sekiguchi Y. Cultivation of uncultured Chloroflexi subphyla: significance and ecophysiology of formerly uncultured Chloroflexi ‘subphylum I’ with natural and biotechnological relevance. Microbes Environ. 2009;24:205–16.PubMed 
    Article 

    Google Scholar 
    Sheik CS, Reese BK, Twing KI, Sylvan JB, Grim SL, Schrenk MO, et al. Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the census of deep life. Front Microbiol. 2018;9:840. https://doi.org/10.3389/fmicb.2018.00840.Doughari HJ, Ndakidemi PA, Human IS, Benade S. The ecology, biology and pathogenesis of Acinetobacter spp.: an overview. Microbes Environ. 2011;26:101–12.PubMed 
    Article 

    Google Scholar 
    Han XY, Han FS, Segal J. Chromobacterium haemolyticum sp. nov., a strongly haemolytic species. Int J Syst Evol Microbiol. 2008;58:1398–403.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lau MC, Kieft TL, Kuloyo O, Linage-Alvarez B, Van Heerden E, Lindsay MR, et al. An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers. Proc Natl Acad Sci USA. 2016;113:E7927–36.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Momper L, Jungbluth SP, Lee MD, Amend JP. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 2017;11:2319–33.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Worzewski T, Jegen M, Kopp H, Brasse H, Taylor Castillo W. Magnetotelluric image of the fluid cycle in the Costa Rican subduction zone. Nat Geosci. 2011;4:108–11.CAS 
    Article 

    Google Scholar 
    Hensen C, Wallmann K, Schmidt M, Ranero CR, Suess E. Fluid expulsion related to mud extrusion off Costa Rica—a window to the subducting slab. Geology. 2004;32:201–4.CAS 
    Article 

    Google Scholar 
    Simpson DR. Aluminum phosphate variants of feldspar. Am Miner. 1977;62:351–5.CAS 

    Google Scholar 
    London DA, Cerny P, Loomis J, Pan JJ. Phosphorus in alkali feldspars of rare-element granitic pegmatites. Can Miner. 1990;28:771–86.CAS 

    Google Scholar 
    Petrillo C, Castaldi S, Lanzilli M, Selci M, Cordone A, Giovannelli D, et al. Genomic and physiological characterization of Bacilli isolated from salt-pans with plant growth promoting features. Front Microbiol. 2021;12:715678. https://doi.org/10.3389/fmicb.2021.715678.Ghiorse WC, Wilson JT. Microbial ecology of the terrestrial subsurface. Adv Appl Microbiol. 1988;33:107–72.CAS 
    PubMed 
    Article 

    Google Scholar 
    Barker WW, Welch SA, Chu S, Banfield JF. Experimental observations of the effects of bacteria on aluminosilicate weathering. Am Miner. 1998;83:1551–63.CAS 
    Article 

    Google Scholar 
    Bennett PC, Rogers JR, Choi WJ, Hiebert FK. Silicates, silicate weathering, and microbial ecology. Geomicrobiol J. 2001;18:3–19.CAS 
    Article 

    Google Scholar 
    Hügler M, Sievert SM. Beyond the Calvin cycle: autotrophic carbon fixation in the ocean. Ann Rev Mar Sci. 2011;3:261–89.PubMed 
    Article 

    Google Scholar 
    Markert S, Arndt C, Felbeck H, Becher D, Sievert SM, Hügler M, et al. Physiological proteomics of the uncultured endosymbiont of Riftia pachyptila. Science. 2007;315:247–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bar-Even A, Noor E, Milo R. A survey of carbon fixation pathways through a quantitative lens. J Exp Bot. 2012;63:2325–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    Stevens TO, McKinley JP. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science. 1995;270:450–5.CAS 
    Article 

    Google Scholar 
    Barker WW, Welch SA, Banfield JF. Biogeochemical weathering of silicate minerals. Rev Miner Geochem. 1997;35:391–428.CAS 

    Google Scholar 
    Frank YA, Kadnikov VV, Lukina AP, Banks D, Beletsky AV, Mardanov AV, et al. Characterization and genome analysis of the first facultatively alkaliphilic Thermodesulfovibrio isolated from the deep terrestrial subsurface. Front Microbiol. 2016;7:2000. https://doi.org/10.3389/fmicb.2016.02000.Woycheese KM, Meyer-Dombard DA, Cardace D, Argayosa AM, Arcilla CA. Out of the dark: transitional subsurface-to-surface microbial diversity in a terrestrial serpentinizing seep (Manleluag, Pangasinan, the Philippines). Front Microbiol. 2015;6:1–12.Article 

    Google Scholar 
    Brazelton WJ, Morrill PL, Szponar N, Schrenk MO. Bacterial communities associated with subsurface geochemical processes in continental serpentinite springs. Appl Environ Microbiol. 2013;79:3906–16.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moser DP, Gihring TM, Brockman FJ, Fredrickson JK, Balkwill DL, Dollhopf ME, et al. Desulfotomaculum and Methanobacterium spp. dominate a 4-to 5-kilometer-deep fault. Appl Environ Microbiol. 2005;71:8773–83.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schwarzenbach EM, Gill BC, Gazel E, Madrigal P. Sulfur and carbon geochemistry of the Santa Elena peridotites: comparing oceanic and continental processes during peridotite alteration. Lithos. 2016;252:92–108.Article 

    Google Scholar 
    Sánchez‐Murillo R, Gazel E, Schwarzenbach EM, Crespo‐Medina M, Schrenk MO, Boll J, et al. Geochemical evidence for active tropical serpentinization in the Santa Elena Ophiolite, Costa Rica: an analog of a humid early Earth? Geochem Geophys Geosyst. 2014;15:1783–800.Article 

    Google Scholar 
    Crespo-Medina M, Twing KI, Sánchez-Murillo R, Brazelton WJ, McCollom TM, Schrenk MO. Methane dynamics in a tropical serpentinizing environment: the Santa Elena Ophiolite, Costa Rica. Front Microbiol. 2017;8:916. https://doi.org/10.3389/fmicb.2017.00916.DeShon HR, Schwartz SY. Evidence for serpentinization of the forearc mantle wedge along the Nicoya Peninsula, Costa Rica. Geophys Res Lett. 2004;31. https://doi.org/10.1029/2004GL021179.Delmelle P, Stix J. Volcanic gases. In: Sigurdsson H, Houghton B, McNutt S, Rymer H, Stix J, editors. Encyclopedia of volcanoes. New York, NY: Elsevier; 2000. p 803–15.Kharaka YK, Mariner RH. Geothermal systems. In: Sigurdsson H, Houghton B, McNutt S, Rymer H, Stix J, editors. Encyclopedia of volcanoes. New York, NY: Elsevier; 2000. p. 817–34.Badger MR, Bek EJ. Multiple Rubisco forms in proteobacteria: their functional significance in relation to CO2 acquisition by the CBB cycle. J Exp Bot. 2008;59:1525–41.CAS 
    PubMed 
    Article 

    Google Scholar 
    West-Roberts JA, Carnevali PB, Scholmerich MC, Al-Shayeb B, Thomas A, Sharrar AM, et al. The Chloroflexi supergroup is metabolically diverse and representatives have novel genes for non-photosynthesis based CO2 fixation. bioRxiv [Preprint]. 2021. Available from: https://doi.org/10.1101/2020.05.14.094862.Lloyd KG, Steen AD, Ladau J, Yin J, Crosby L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems. 2018;3:1–12.Article 

    Google Scholar 
    Colman DR, Lindsay MR, Boyd ES. Mixing of meteoric and geothermal fluids supports hyperdiverse chemosynthetic hydrothermal communities. Nat Commun. 2019;10:1–3.Article 

    Google Scholar  More

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    Invasive plant species carry legacy of colonialism

    Similar non-native and invasive flora, such as the fever tree (pictured) are found in regions previously occupied by the same European empire.Credit: Alamy

    In 1860, a British expedition raided the highland forests of South America, looking for a hot commodity: Cinchona seeds. The bark of these ‘fever’ trees produces the anti-malarial compound quinine, and the British Empire sought a stable source of the drug for its soldiers and civil service in India. After cultivation in the United Kingdom, young Cinchona trees were planted across southern India and what is now Sri Lanka.The British quinine scheme failed — instead, a species introduced to Java, now part of Indonesia, by the Dutch Empire later dominated the global market — but Cinchona trees are still common in parts of India.Such botanical legacies of imperial rule are common, finds a study published on 17 October in Nature Ecology & Evolution1. Regions that were once occupied by the same European colonial power — such as India and Sri Lanka — tend to have similar species of non-native and invasive plants. The longer the regions were occupied, the more their populations of invasive species resemble each other, the research found.Alien floraThe link between European colonialism and invasive species is intuitive, and has been noted by other researchers, says Bernd Lenzner, a macro-ecologist at the University of Vienna who led the study. To test the association, his team turned to the Global Naturalized Alien Flora database, which maps the distribution of nearly 14,000 invasive plant species.
    The imperial roots of climate science
    Across more than 1,100 regions, including 404 islands, the researchers found that regions once occupied by the British Empire had more similarities in their invasive flora than did ‘artificial’ empires that the team assembled from random regions. This was also the case for regions once part of the Dutch Empire (former Spanish and Portuguese colonies had alien-plant compositions similar to those of the artificial empires).Climate and geography play an important part in explaining the overlap in the diversity of invasive species, modelling by Lenzner’s team found, but so does the length of time regions were occupied by an imperial power. Regions that were central to trade, such as southern India for the British Empire and Indonesia for the Dutch Empire, formed clusters with considerable overlap in invasive-plant composition.The analysis did not look at when individual plant species were introduced or why. But anecdotally, many of the plants that were commonly taken to former empires were once of economic value and their populations were probably established on purpose, says Lenzner.Global trade impactsThe study’s conclusions might be “super obvious”, but they have important implications for conservation, says Nussaïbah Raja, a palaeontologist at Friedrich-Alexander University of Erlangen–Nürnberg in Erlangen, Germany. “We should be taking this history into consideration when we think about management of species.” Appreciating the history of introduced plants — as well as their place in today’s ecosystems — could help conservationists to handle future changes in biodiversity, such as those driven by climate change, Raja adds.Global trade is beginning to overwrite the colonial legacy of introduced plants. For example, the analysis showed similarities between invasive plant populations in Fujian, China, and some parts of Australia. Although both places were once connected by the British Empire, more recent global trade might also be partly responsible for the overlap.“We are still seeing these imprints of the colonial-empire legacies from centuries ago,” Lenzner says. “So what we’re doing and the species we’re redistributing today will be visible far into the future.” More

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    Benthic exometabolites and their ecological significance on threatened Caribbean coral reefs

    Benthic organism exudate collectionsExudate collections from benthic organisms were conducted on board the R/V Walton Smith in November 2018 in Lameshur Bay, St. John, U.S. Virgin Islands within the Virgin Islands National Park. In brief, we collected six species of benthic organisms (n = 6 specimens), incubated these organisms in separate containers for 8 h, and harvested the incubation water to characterize the composition of dissolved metabolites in their exudates. A description of the exudate collections is included below (additional details available in Supplementary Methods).Before each organism experiment, 58 l of surface (non-reef) seawater was collected ~1 mile offshore (18 17.127° N, 064 44.312° W, 31.6 m depth). Cells and particles were removed using peristaltic pressure through a 0.2 µm filter (47 mm, Omnipore, EMD Millipore Corporation, Billerica, MA, USA) using metabolomics-grade tubing and this filtrate (filtered seawater) was collected for the incubations. Additionally, two to three, 2 l filtrate subsets per experiment were acidified with concentrated hydrochloric acid (final concentration 1% volume/volume) and subjected to solid-phase-extraction (SPE) using a negative vacuum pressure of –3.7 to –5 100xkPA in Hg, to serve as controls. Before SPE, 6 ml, 1 gm Bond Elut PPL cartridges (Agilent, Santa Clara, CA, USA) were pre-conditioned with 6 ml of 100% HPLC-grade methanol.For the experiments, six species of benthic organisms were collected from reefs around Lameshur Bay by SCUBA divers. Experiments were completed on three stony corals (Porites astreoides, Siderastrea siderea, and Psuedodiploria strigosa), two octocorals (Plexaura homomalla and Gorgonia ventalina), and one encrusting alga (Ramicrusta textilis) (Table S1). P. astreoides, S. siderea, and R. textilis were held in a seawater table for 24 h (hrs) before the incubations and colonies from the other three species were held for 2-3 h due to timing constraints. Coral and algal fragments were generally small (2.5-5.0 cm in length).For each incubation, nine, acid-washed, 10 l polycarbonate bins (with lids) containing filtered seawater (4 l) were secured into an illuminated aquarium table (Prime HD, Aqua illumination, Bethlehem, PA, USA) (Photosynthetically Active Radiation = ~350–600 µmol quanta m−2 s−1). Air bubblers with sterilized Fluorinated Ethylene Propylene (FEP) tubing (890 Tubing, Nalgene, Thermo Scientific, Waltham, MA, USA) were used to inject air into each bin. Surface seawater was circulated through the aquarium table to maintain reef seawater temperature (29.5 °C). Six colonies/fragments of one species were randomly placed into 6 bins. The other 3 bins were reserved for control incubations containing filtered seawater only. A sensor (8 K HOBO/PAR loggers; Onset, Wareham, MA) monitored temperature and light conditions (data not shown). At the end of each 8 h experiment, colonies/fragments were wrapped in combusted aluminum foil and flash frozen in a charged dry shipper. The water in all incubations was re-filtered (as outlined above) and 2 l of each filtrate were acidified and subjected to SPE as described above. SPE cartridges were wrapped in combusted aluminum foil, placed in Whirl-Pak (Nasco, Madison, WI, USA) bags, and frozen at –20 °C.Metabolomics analyses and data processingAt the Woods Hole Oceanographic Institution (WHOI), metabolites were eluted from the thawed cartridges into combusted, borosilicate test tubes using 100% methanol (Optima grade) within 3 months of collection. The eluents were transferred into combusted amber 8 ml vials and nearly dried using a vacuum centrifuge. Samples were reconstituted in 200 µL of 95:5 (v/v) Milli-Q (MQ, Millipore Sigma, Burlington, MA, USA) water: acetonitrile with a deuterated standard mix added as an internal control (Table S2), vortexed, and prepared for targeted and untargeted metabolomics analyses in both positive and negative ion modes as described previously [16]. Samples prepared for untargeted analyses were further diluted (1:200) with the reconstitution solvent. A pooled sample (technical replicate) was made by combining aliquots from all samples and was injected repeatedly to assess instrument drift over the course of the run and for downstream sample processing. Samples prepared for targeted metabolomics were analyzed using an ultra-high performance liquid chromatography system (UHPLC; Accela Open Autosampler and Accela 1250 Pump, Thermo Scientific, Waltham, MA, USA) coupled to a heated electrospray ionization source (H-ESI) and a triple stage quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific), operated in selected reaction monitoring (SRM) mode. Samples prepared for untargeted metabolomics were analyzed with a UHPLC system (Vanquish UHPLC, Thermo Scientific) coupled to an ultra-high resolution mass spectrometer (Orbitrap Fusion Lumos, Thermo Scientific). MS/MS spectra were collected in a data-dependent manner using higher energy collisional dissociation (HCD) with a normalized collision energy of 35% (detailed methods provided in [16]). A Waters Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm) equipped with a Vanguard pre-column was used for chromatographic separation at 40 °C for targeted and untargeted analyses. Sample order was randomized and the pooled sample was analyzed after every six samples.For targeted metabolomics analysis, tandem MS/MS data files were converted into .mzML files using msconvert and processed with El-MAVEN [49]. Calibration curves for each compound (8 points each) were constructed based on the integrated peak areas using El-MAVEN. The concentrations of metabolites in the original samples were determined by dividing each concentration by the volume of the filtrate that passed through each PPL column. Finally, metabolite concentrations above the limits of detection and quantification were corrected for extraction efficiency using in-house values determined using standard protocols [50]. Statistical analyses of targeted metabolite concentrations were conducted using Welch’s independent t-tests and ANOVAs or Wilcoxon rank sum tests if data were not normally distributed (additional details in Supplementary Methods). We determined the mass of each colony and conducted Pearson correlations to investigate if colony size significantly correlated with concentrations of targeted metabolites, but no correlations were found.For the untargeted metabolomics analyses, raw files containing MS1 and MS/MS data were converted into .mzML files using msconvert and processed using XCMS [51]. Ion modes were analyzed separately. Before processing with XCMS, the R package AutoTuner [52] was used to find XCMS processing parameters appropriate for the data. In XCMS, the CentWave algorithm picked peaks using a gaussian fit. The specific parameters for peak picking for both ion modes were: noise = 10,000, peak-width = 3–15, ppm = 15, prefilter = c(2,168.600), integrate = 2, mzdiff = –0.005, snthresh = 10. Obiwarp was used to adjust retention times and this step was followed by correspondence analysis. For statistical analyses, including permutational PERMANOVA adonis tests and non-metric multidimensional scaling analysis (NMDS), MS1 features (defined as unique pairings of mass-to-charge (m/z) values with retention times) in both ion modes were culled following XCMS if they: (1) had >1 average fold change in the MQ blanks compared to the other samples, (2) occurred in less than 20% of samples (excluding pooled controls), and/or (3) were invariant (relative standard deviation of More

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    Colonialism shaped today’s biodiversity

    IPCC Climate Change 2022: Summary for Policymakers. (eds Pörtner, H. et al.) (Cambridge Univ. Press, 2022).Lewis, S. L. & Maslin, M. A. The human planet: How we created the Anthropocene. (Yale University Press, 2018).Lenzner, B. et al. Nat. Ecol. Evol. https://doi.org/s41559-022-01865-1 (2022).van Kleunen, M. et al. Nature 525, 100–103 (2015).Article 

    Google Scholar 
    Dawson, W. et al. Nat. Ecol. Evol. 1, 0186 (2017).Article 

    Google Scholar 
    Dyer, E. E. et al. PLoS Biol. 15, e2000942 (2017).Article 

    Google Scholar 
    Mohammed, R. S. et al. Am. Nat. 200, 140–155 (2022).Article 

    Google Scholar 
    Rodrigues, A. S. L. et al. Phil. Trans. R. Soc. Lond. B 374, 20190220 (2019).Article 

    Google Scholar 
    Reddin, C. J., Aberhan, M., Raja, N. B. & Kocsis, Á. T. Glob. Change Biol. 28, 5793–5807 (2022).CAS 
    Article 

    Google Scholar 
    Elton, C. S. The Ecology of Invasions by Animals and Plants. (University of Chicago Press, 1958).Goode, E. Invasive Species Aren’t Always Unwanted. The New York Times https://www.nytimes.com/2016/03/01/science/invasive-species.html (2016).Reo, N. J. & Ogden, L. A. Sustain. Sci. 13, 1443–1452 (2018).Article 

    Google Scholar 
    Simberloff, D. Nature 475, 36 (2011).CAS 
    Article 

    Google Scholar  More

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    Strength-mass scaling law governs mass distribution inside honey bee swarms

    Our experimental data reveals a scaling law between the mass of a layer along the vertical coordinate, M(z), and the weight that it supports, W(z), namely: (W(z) sim M(z)^a) with (a approx 1.5). To better understand the physical mechanism that yields this scaling law, we derive the force balance equation of a layer of the swarm and solve for W(z). We then equate the analytical expression for W(z) with the experimentally determined scaling law, (W(z) sim M(z)^a), to connect the swarm mass distribution to the exponent a and formulate the expressions for M(z) and W(z) in terms of a. We then consider a dimensional analysis of the strength of each layer of the swarm, S, or the maximum weight that it can support before the grip of the bees on one another breaks. As will be described in detail below, we find that (S sim M^{1.5}), which is close to the experimentally determined (a = 1.53). Deviation from this value increases the fraction of maximum strength exerted by different parts of the swarm.Force balance model of the weight distribution in the swarmWe assume that the swarm is at quasi-equilibrium (the shape does not change although individual bees may move), that all of the bees in each layer contribute equally to supporting the weight of the bees underneath that layer, that the layer thickness is very small, and that the swarm is radially symmetrical about the z-axis. We use a cylindrical coordinate system with a vertical coordinate z, as shown in Fig. 1e, and we consider layers of the swarm along the z-axis of thickness dz. Variables labeled with a tilde, as in (tilde{W}(z)), represent analytically derived expressions; variables without a tilde, as in W(z), represent values determined with power law fits to experimental data.We begin our analysis by applying the force balance principle to each layer of a swarm. As shown by the free body diagram in Fig. 1f, the force with which each layer of bees has to grasp the layer above it is equal to the weight of that layer and all of the layers underneath it: (tilde{F} = tilde{W}(z)). We express (tilde{W}(z)) using the force balance equation (a continuous version of the discrete definition in Eq. (5).):$$begin{aligned} tilde{W}(z) = g int _z^L tilde{M}(z) dz, end{aligned}$$
    (8)
    where the mass of bees per layer is (tilde{M}(z)), the swarm length is L, and g is the gravitational constant. Inspired by our experimental observation that the mass of the layers near the base is highest and the mass of the layers at the tip of the swarm is lowest in Fig. 3a, we model (tilde{M}(z)) as a monotonically decreasing function of z. To keep the units consistent, we normalize the z coordiante by the length of the swarm:$$begin{aligned} tilde{M}(z) = c left( 1-frac{z}{L}right) ^{tilde{b}}, end{aligned}$$
    (9)
    where the c factor in this expression ensures that the units of the mass per layer are mass/length, and (tilde{b}) is an unknown exponent. Choosing this function form allows us to easily integrate the expression for (tilde{W}(z)) when we substitute (tilde{M}(z)) into it, set this force balance derivation for (tilde{W}(z)) equal to the experimentally determined expression (W(z) = C M(z)^a), and compare the exponents a and (tilde{b}).To solve the expression for (tilde{W}(z)), we substitute the expression for (tilde{M}(z)), Eq. (9), into Eq. (8) and integrate. We then express (tilde{b}) in terms of the experimentally determined a by equating this expression for (tilde{W}(z)) to the scaling law we observe in our experiments, Eq. (7), (W(z) = C tilde{M}(z)^a). The exponent in the expression for (tilde{M(z)}), Eq. (9), is$$begin{aligned} tilde{b} = frac{1}{a-1}. end{aligned}$$
    (10)
    The weight supported by each layer is then:$$begin{aligned} tilde{W}(z) = cLg left( 1 – frac{1}{a}right) left( 1-frac{z}{L}right) ^{frac{a}{a-1}}. end{aligned}$$
    (11)
    Next, we test how well our force balance model predicts the data by comparing the predicted value of (tilde{b}) using the force balance to the value of b calculated using experimental fits. We first separate the expression for the layer mass, Eq. (9) into the product of the layer area, (tilde{A}(z)) and the layer density, (tilde{rho }(z)):$$begin{aligned} tilde{M}(z) sim tilde{A}(z) tilde{rho }(z). end{aligned}$$
    (12)
    To simplify our analysis, we model (tilde{A}(z)) and (tilde{rho }(z)) with a similar monotonically decreasing function to that in Eq. (9):$$begin{aligned} tilde{A}(z) = c_1 left( 1-frac{z}{L}right) ^{tilde{b}_1}, end{aligned}$$
    (13)
    and$$begin{aligned} tilde{rho }(z) =c_2 left( 1-frac{z}{L}right) ^{tilde{b}_2} end{aligned}$$
    (14)
    we can then separately measure the effect of the changes in area and density on the exponent in the mass per layer expression in Eq. (9), (tilde{b} = tilde{b}_1 + tilde{b}_2).We first calculate (tilde{b}) using the expression derived from the force balance, Eq. (10), and our experimental result for a, which yields (tilde{b} = 2 pm 0.47). Second, we calculate b by separately calculating power law fits to the data for A(z) in Fig. 2e according to Eq. (13) and (rho (z)) in Fig. 2d according to Eq. (14), which yields (b_1 = 1.38 pm 0.2) and (b_2 = 0.51 pm 0.09). Thus, (b = b_1 + b_2 = 1.89 pm 0.25). See Supplementary Fig. S5(a–c) for log-log plots of M(z), A(z) and (rho (z)), and Supplementary Fig. S5(d–f) for plots of the resulting b, (b_1), and (b_2).We calculate the deviation of (tilde{b}) from b, (frac{tilde{b} – b}{tilde{b}} = 0.03 pm 0.11), and plot the deviation of b from (tilde{b}) in Supplementary Fig. S5(g) as a comparison for the individual CT scans. The values of b and (tilde{b}) being on the same order of magnitude validates the model and allows us to compare (tilde{W}(z)) to a maximum strength of each layer, which we find with dimensional analysis in the following section.Strength of a swarm layer and individual beesThe strength of the layer, (tilde{S}(z)), or the maximum weight that it could support, can be greater than or equal to (tilde{W}(z)): (tilde{S}(z) ge tilde{W}(z)). If the weight of the bees underneath a layer were to exceed its strength (tilde{S}(z)), the layer would not be able to support the weight of those bees, and the swarm would break apart. We perform a dimensional analysis on the strength of each layer to find the relationship between the mass of a layer and its maximum strength, (tilde{S}(z) sim tilde{M}(z)^{alpha }). Force is proportional to mass, which is proprtional to volume, or a length cubed, so a layer’s strength scales with length cubed, (tilde{S}(z) propto L^3). The mass of each layer, with units of mass/length, is proportional to an area, or a length squared, so (tilde{M}(z)) scales with length squared, (tilde{M}(z) propto L^2). Thus, (alpha) must be 1.5 for (tilde{S}(z) sim tilde{M}(z)^{alpha }) to be dimensionally correct. This is similar to the relationship between weightifting capacity and body weight in Ref.16.Estimating (tilde{W}(z)/tilde{S}(z)) gives a measure of how much of its maximum strength each layer uses to hold up the rest of the swarm:$$begin{aligned} frac{tilde{W}(z)}{tilde{S}(z)} sim left( 1-frac{1}{a}right) left( 1-frac{z}{L}right) ^frac{2a-3}{2a-2} end{aligned}$$
    (15)
    The average number of bees that a bee in a swarm layer supports, (tilde{F}_{bee}(z)), is equal to the mass of bees supported by a layer divided by the sum of the mass of bees in a layer of bees that has the thickness of the length of a bee, (l approx 1.5), as a continuous version of the discrete equation in Eq. (6):$$begin{aligned} tilde{F}_{bee}(z) =frac{int _z^L tilde{M}(z) dz}{int _z^{z+l} tilde{M}(z) dz}. end{aligned}$$
    (16)
    After integrating, we get an expression for (tilde{F}_{bee} (z)):$$begin{aligned} tilde{F}_{bee}(z)= frac{left( 1-frac{z}{L}right) ^{frac{a}{a-1}}}{left( 1-frac{z}{L}right) ^{frac{a}{a-1}} – left( 1-frac{z + l}{L}right) ^{frac{a}{a-1}}}. end{aligned}$$
    (17)
    We use the expression for (frac{tilde{W}(z)}{tilde{S}(z)}), Eq. (15), and (tilde{F}_{bee}(z)), Eq. (17), in the next section to evaluate how the force distribution in the swarm would change for swarms with different values of a.Effect of a on the mass of each layer, the fraction of its maximum stregnth it uses, and the average force per beeWe now consider the effect of varying a on the mass and force distribution inside the swarm. To visualize the effect of a on the distribution of bees, we plot the mass per layer of a 1000-g, 12.5 cm long swarm, (tilde{M}(z)) vs. z/L, with (a = 1.5, 1.01, 1000), and (-0.2) in Fig. 3c and the corresponding average force per bee, (F_{bee}(z)) vs. z/L in Fig. 3d. These values of a are example values for the four possible cases of mass distribution in the swarm. We then evaluate how these values of a affect the fraction of maximum strength each layer uses to support the layers underneath it using Eq. (15).If (a approx alpha), as we found in our experiments, layers with higher mass near the attachment surface support the less massive layers under them, as in the solid black line in Fig. 3c. Correspondingly, Fig. 3d shows (tilde{F}_{bee}(z=0) approx 3) at the top of the swarm, and decreases towards the tip. The strength of each layer and the weight it supports are proportional to one another, (tilde{W}(z)/tilde{S}(z) sim 1/3), meaning that the fraction of maximum strength used by a layer is the same for all z. If (1< a < alpha), the swarm approaches one massive layer of bees, as in the dashed purple line in Fig. 3c. The dimensional analysis results in a very small fraction of the total strength used by this layer, (tilde{W}(z)/tilde{S}(z) rightarrow 0 (1-frac{z}{L})^{-infty }). The force supported by each bee in Fig. 3d shows (tilde{F}_{bee}(z) = 1) for the entire swarm, meaning that each bee only supports its own weight. This configuration would either require packing a large number of bees into one very dense or one very wide layer. A swarm with one very dense layer at the top would compress all of the bees; a swarm with one very wide layer would require a large surface area, which would put the swarm in danger from predators and changes in weather. Thus, despite a potentially lower fraction of strength used by the largest layer of bees, this configuration would put the swarm in danger by requiring a large surface area.For values of (a > alpha), as (a rightarrow infty), all the layers of the swarm have the same mass, as in the dash-dot red line in Fig. 3c. The force per bee in Fig. 3d shows (tilde{F}_{bee}(z=0) approx 8) at the top of the swarm, 2.5 times that of the (a = alpha) configuration. In this configuration, the top layers use a higher percentage of their available strength than the lower layers, (tilde{W}(z)/tilde{S}(z) rightarrow (1-frac{z}{L})). Thus, for large swarms, the bees that support the swarm would be under more strain, and the swarm would be more likely to break under external perturbation.Finally, (a < 0) ((0 le a le 1) results in negative values for (tilde{W}(z))) would suggest that the top layers of the swarm have a lower mass than the bottom layers, as in the dotted orange line in Fig. 3c. This is not a realistic range of values for a, but we include it here as a demonstration of a potential mass distribution with the largest layers being on the bottom of the swarm. This configuration would put even more strain on the layers of bees at the top of the swarm, as smaller layers near the attachment surface have a smaller maximum strength. As (a rightarrow 0) on the (a < 0) side, (tilde{W}(z)/tilde{S}(z) rightarrow infty (1-z/L)^{1.5}), and bees in the top layers use a much greater fraction of their strength than bees in the bottom layers. Accordingly, the mean force per bee in Fig. 3d exceeds the maximum bee grip strength of 35 bee weights, and the swarm could not support itself in this configuration.The swarm configuration with (a approx 1.5) uses the full strength of each layer and puts a lower strain on the bees than most other values of a, and avoids weight distributions that could expose a large number of bees to external danger. More

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    Effects of Rhizophagus intraradices on soybean yield and the composition of microbial communities in the rhizosphere soil of continuous cropping soybean

    Liu, X. Q. et al. Geographic differentiation and phylogeographic relationships among world soybean populations. Crop J. 8(2), 260–272 (2020).Article 

    Google Scholar 
    Coleman, K. et al. The potential for soybean to diversify the production of plant-based protein in the UK. Sci. Total Environ. 767(3), 144903 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, W. W., Feng, Z. Z., Wang, X. K., Liu, X. B. & Hu, E. Z. Quantification of ozone exposure- and stomatal uptake-yield response relationships for soybean in Northeast China. Sci. Total Environ. 599–600, 710–720 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Strom, N., Hu, W. M., Haarith, D. & Chen, S. Y. Interactions between soil properties, fungal communities, the soybean cyst nematode, and crop yield under continuous corn and soybean monoculture. Appl. Soil Ecol. 147, 103388 (2019).Article 

    Google Scholar 
    Fernandez-Gnecco, G. et al. Microbial community analysis of soils under different soybean cropping regimes in the Argentinean south-eastern Humid Pampas. Fems Microbiol. Ecol. 97(3), 007 (2021).Article 

    Google Scholar 
    Bai, L., Cui, J. Q., Jie, W. G. & Cai, B. Y. Analysis of the community compositions of rhizosphere fungi in soybeans continuous cropping fields. Microbiol. Res. 180, 49–56 (2015).PubMed 
    Article 

    Google Scholar 
    Liu, J. J., Yu, Z. H., Yao, Q. & Hu, X. J. Distinct soil bacterial communities in response to the cropping system in a Mollisol of northeast China. Appl. Soil Ecol. 119, 407–416 (2017).Article 

    Google Scholar 
    Zeng, H. L. et al. The influence of Bt maize cultivation on communities of arbuscular mycorrhizal fungi revealed by MiSeq sequencing. Front. Microbiol. 9, 3275 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Barbosa, M. V. et al. Aggregation of a ferruginous nodular gleysol in a pasture area in Cuba under the influence of Arbuscular mycorrhizal fungi associated with hybrid Urochloa. Soil Till. Res. 208(1), 104905 (2021).Article 

    Google Scholar 
    Zhang, F. G., Liu, M. H., Li, Y., Che, Y. & Xiao, Y. Effects of arbuscular mycorrhizal fungi, biochar and cadmium on the yield and element uptake of Medicago sativa. Sci. Total Environ. 655, 1150–1158 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Kokkoris, V. et al. Host identity influences nuclear dynamics in arbuscular mycorrhizal fungi. Curr. Biol. 31(7), 1531–1538 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Prates, J. P. et al. Agroecological coffee management increases arbuscular mycorrhizal fungi diversity. PLoS ONE 14(1), e0209093 (2019).Article 

    Google Scholar 
    Silvana, V. B., Longo, S., Marro, N. & Urcelay, C. The global invader Ligustrum lucidum accumulates beneficial arbuscular mycorrhizal fungi in a novel range. Plant Ecol. 222, 397–408 (2021).Article 

    Google Scholar 
    Chang, Q. et al. Effects of arbuscular mycorrhizal symbiosis on growth, nutrient and metal uptake by maize seedlings (Zea mays L.) grown in soils spiked with Lanthanum and Cadmium. Environ. Pollut. 2018(241), 607 (2018).Article 

    Google Scholar 
    Bi, Y. et al. Arbuscular mycorrhizal fungi alleviate root damage stress induced by simulated coal mining subsidence ground fissures. Sci. Total Environ. 652, 398–405 (2019).ADS 
    PubMed 
    Article 

    Google Scholar 
    Ma, X. N., Luo, W. Q., Li, J. & Wu, F. Arbuscular mycorrhizal fungi increase both concentrations and bioavilability of Zn in wheat (Triticum aestivum L.) grain on Zn-spiked soils. Appl. Soil Ecol. 135, 91–97 (2019).Article 

    Google Scholar 
    Srivastava, S., Johny, L. & Adholeya, A. Review of patents for agricultural use of arbuscular mycorrhizal fungi. Mycorrhiza 31(2), 127–136 (2021).PubMed 
    Article 

    Google Scholar 
    Kabdwal, B. C., Sharma, R. & Tewari, R. Field efficacy of different combinations of Trichoderma harzianum, Pseudomonas fluorescens, and arbuscular mycorrhiza fungus against the major diseases of tomato in Uttarakhand (India). Egypt. J. Biol. Pest Control 29, 1 (2019).Article 

    Google Scholar 
    Jie, W. G., Bai, L., Yu, W. J. & Cai, B. Y. Analysis of interspecific relationships between Funneliformis mosseae and Fusarium oxysporum in the continuous cropping of soybean rhizosphere soil during the branching period. Biocontrol Sci. Technol. 25(9), 1036–1051 (2015).Article 

    Google Scholar 
    Jie, W. G., Lin, J. X., Guo, N., Cai, B. Y. & Yan, X. F. Community composition of rhizosphere fungi as affected by Funneliformis mosseae in soybean continuous cropping soil during seedling period. Chil. J. Agric. Res. 79(3), 356–365 (2019).Article 

    Google Scholar 
    Jie, W. G., Lin, J. X., Guo, N., Cai, B. Y. & Yan, X. F. Effects of Funneliformis mosseae on mycorrhizal colonization, plant growth and the composition of bacterial community in the rhizosphere of continuous cropping soybean at seedling stage. Int. J. Agric. Biol. 22(5), 1173–1180 (2019).CAS 

    Google Scholar 
    Jie, W. G., Yao, Y. X., Guo, N., Zhang, Y. Z. & Qiao, W. Effects of Rhizophagus intraradices on plant growth and the composition of microbial communities in the roots of continuous cropping soybean at maturity. Sustainability 13, 6623 (2021).CAS 
    Article 

    Google Scholar 
    Yang, Y. R. et al. Interactive effects of exogenous melatonin and Rhizophagus intraradices on saline-alkaline stress tolerance in Leymus chinensis. Mycorrhiza 30(2), 357–371 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Phillips, J. M. & Hayman, D. S. Improved procedures for clearing roots and staining parasitic and vesicula-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans. Br. Mycol. Soc. 55(1), 158–161 (1970).Article 

    Google Scholar 
    Geng, Y. F., Qiu, Q., Mao, J. H. & Jing, Y. B. Effects of arbuscular mycorrhizal fungi inoculation and different inoculation amount on seedlings of Mesua ferrea. J. Fujian For. Sci. Technol. 43(03), 67–71 (2016).
    Google Scholar 
    Schütz, L., Saharan, K., Mäder, P., Boller, T. & Mathimaran, N. Rate of hyphal spread of arbuscular mycorrhizal fungi from pigeon pea to finger millet and their contribution to plant growth and nutrient uptake in experimental microcosms. Appl. Soil Ecol. 169(248), 104156 (2022).Article 

    Google Scholar 
    Fehr, W. R. & Caviness, C. E. Stages of Soybean Development. Special Report 80. Ames Cooperative Extension Service, Agriculture and Home Economic Experiment Station 1–11 (Iowa State University Press, 1977).
    Google Scholar 
    Zhou, N., Liu, P., Wang, Z. Y. & Xu, G. D. The effects of rapeseed root exudates on the forms of aluminum in aluminum stressed rhizosphere soil. Crop Prot. 30(6), 631–636 (2011).CAS 
    Article 

    Google Scholar 
    Dorn-In, S., Bassitta, R., Schwaiger, K., Bauer, J. & Holzel, C. S. Specific amplification of bacterial DNA by optimized so-called universal bacterial primers in samples rich of plant DNA. J. Microbiol. Methods 113, 50–56 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Smith, D. P. & Peay, K. G. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE 9(2), e90234 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7(5), 335–336 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Magoc, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21), 2957–2963 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C., Haas, B. J., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2200 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen, H. B. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 35 (2011).Article 

    Google Scholar 
    Spagnoletti, F. N., Balestrasse, K., Lavado, R. S. & Giacometti, R. Arbuscular mycorrhiza detoxifying response against arsenic and pathogenic fungus in soybean. Ecotoxicol. Environ. Safe 133(11), 47–56 (2016).CAS 
    Article 

    Google Scholar 
    Song, Y. Y., Chen, D. M., Lu, K., Sun, Z. X. & Zeng, R. S. Enhanced tomato disease resistance primed by arbuscular mycorrhizal fungus. Front. Plant Sci. 6, 786 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ramadan, A., Muroi, A. & Arimura, G. Herbivore-induced maize volatiles serve as priming cues for resistance against post-attack by the specialist armyworm Mythimna separata. J. Plant Interact. 6(2–3), 155–158 (2011).CAS 
    Article 

    Google Scholar 
    Spagnoletti, F. N., Leiva, M., Chiocchio, V. & Lavado, R. S. Phosphorus fertilization reduces the severity of charcoal rot (Macrophomina phaseolina) and the arbuscular mycorrhizal protection in soybean. J. Plant Nutr. Soil Sci. 181, 855–860 (2018).CAS 
    Article 

    Google Scholar 
    Wehner, J., Antunes, P. M., Powell, J. R., Mazukatow, J. & Rillig, M. C. Plant pathogen protection by arbuscular mycorrhizas: A role for fungal diversity? Pedobiologia 53(3), 197–201 (2010).Article 

    Google Scholar 
    Al-Askar, A. A. & Rashad, Y. M. Arbuscular mycorrhizal fungi: A biocontrol agent against common. Plant Pathol. 9, 31–38 (2010).Article 

    Google Scholar 
    Marschner, P. M., Crowley, D. E. & Lieberei, R. L. Arbuscular mycorrhizal infection changes the bacterial 16s rDNA community composition in the rhizosphere of maize. Mycorrhiza 11(6), 297–302 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turrini, A., Avio, L., Giovannetti, M. & Agnolucci, M. Functional complementarity of arbuscular mycorrhizal fungi and associated microbiota: The challenge of translational research. Front. Plant Sci. 9, 1407 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Giovannetti, M., Avio, L. & Sbrana, C. Fungal spore germination and pre-symbiotic mycelial growth-physiological and genetic aspects. In Arbuscular Mycorrhizas Physiology and Function (eds Koltai, H. & Kapulnik, Y.) 3–32 (Springer, 2010).Chapter 

    Google Scholar 
    Linderman, R. G. Mycorrhizal interactions with the rhizosphere microflora-the mycorrhizosphere effect. Phytopathology 78(3), 366–371 (1988).
    Google Scholar 
    Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 1, 541–556 (2009).Article 

    Google Scholar 
    Shoresh, M., Harman, G. E. & Mastouri, F. Induced systemic resistance and plant responses to fungal biocontrol agents. Annu. Rev. Phytopathol. 48(1), 21–43 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wang, E. et al. A common signaling process that promotes mycorrhizal and oomycete colonization of plants. Curr. Biol. 22(23), 2242–2246 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zamioudis, C. & Pieterse, C. M. J. Modulation of host immunity by beneficial microbes. Mol. Plant Microbe 25(2), 139–150 (2012).CAS 
    Article 

    Google Scholar 
    Haichar, F. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2(12), 1221–1230 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Linderman, R. G. Vesicular arbuscular mycorrhizae and soil microbial interactions, in Mycorrhizae in sustainable agriculture. ASA Spec. Publ. 54, 45–70 (1992).
    Google Scholar 
    Harrier Lucy, A. & Watson, C. A. The potential role of arbuscular mycorrhizal (AM) fungi in the bioprotection of plants against soil-borne pathogens in organic and/or other sustainable farming systems. Pest Manag. Sci. 60(2), 149–157 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Smith, G. S. The role of phosphorous nutrition in interactions of vesicular arbuscular mycorrhizal fungi with soilborne nematodes and fungi. Phytopathology 78(3), 371–374 (1988).CAS 

    Google Scholar 
    Schwob, I., Ducher, M. & Coudret, A. Effects of climatic factors on native arbuscular mycorrhizae and Meloidogyne exigua in a Brazilian rubber tree (Hevea brasilensis) plantation. Plant Pathol. 48(1), 19–25 (2010).Article 

    Google Scholar  More

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    Ecological sensitivity and vulnerability of fishing fleet landings to climate change across regions

    Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 1–8 (2020).Article 

    Google Scholar 
    Sumaila, U. R. et al. Benefits of the paris agreement to ocean life, economies, and people. Sci. Adv. 5, 1–10 (2019).Article 

    Google Scholar 
    Beaudreau, A. H. et al. Thirty years of change and the future of Alaskan fisheries: Shifts in fishing participation and diversification in response to environmental, regulatory and economic pressures. Fish Fish. 20, 601–619 (2019).
    Google Scholar 
    Finkbeiner, E. M. The role of diversification in dynamic small-scale fisheries: Lessons from Baja California Sur. Mexico. Glob. Environ. Chang. 32, 139–152 (2015).Article 

    Google Scholar 
    Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

    Google Scholar 
    IPCC. Climate Change 2007: Synthesis Report. Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. (2007).Johnson, J. E. & Welch, D. J. Climate change implications for Torres Strait fisheries: Assessing vulnerability to inform adaptation. Clim. Change 135, 611–624 (2016).ADS 
    Article 

    Google Scholar 
    IPCC. Annex I: Glossary. in IPCC special report on the ocean and cryosphere in a changing climate e [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)] 677–702 (Cambridge University Press, 2019). https://doi.org/10.1017/9781009157964.010Cheung, W. W. L., Watson, R., Morato, T., Pitcher, T. J. & Pauly, D. Intrinsic vulnerability in the global fish catch. Mar. Ecol. Prog. Ser. 333, 1–12 (2007).ADS 
    Article 

    Google Scholar 
    Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing down marine food webs. Science 80(279), 860 (1998).ADS 
    Article 

    Google Scholar 
    Lam, V. W. Y., Cheung, W. W. L., Reygondeau, G. & Rashid Sumaila, U. Projected change in global fisheries revenues under climate change. Sci. Rep. 6(6), 13 (2016).
    Google Scholar 
    Heck, N. et al. Fisheries at risk: Vulnerability of fisheries to climate change (Nat. Conserv. Tech. Rep, 2020).
    Google Scholar 
    Allison, E. H. et al. Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish. 10, 173–196 (2009).Article 

    Google Scholar 
    DuFour, M. R. et al. Portfolio theory as a management tool to guide conservation and restoration of multi-stock fish populations. Ecosphere 6(12), 1 (2015).Article 

    Google Scholar 
    Kasperski, S. & Holland, D. S. Income diversification and risk for fishermen. Proc. Natl. Acad. Sci. U. S. A. 110, 2076–2081 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bahri, T. et al. Adaptive management of fisheries in response to climate change. FAO Fisheries and Aquaculture Technical Paper 667, (FAO, 2021).Barker, M. J. & Schluessel, V. Managing global shark fisheries: Suggestions for prioritizing management strategies. Aquat. Conserv. Mar. Freshw. Ecosyst. 15, 325–347 (2005).Article 

    Google Scholar 
    Fletcher, W. J. F. & Fletcher, W. J. The application of qualitative risk assessment methodology to prioritize issues for fisheries management. ICES J. Mar. Sci. 62, 1576–1587 (2005).Article 

    Google Scholar 
    Cheung, W. W. L. The future of fishes and fisheries in the changing oceans. J. Fish Biol. 92, 790–803 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cinner, J. E. et al. Evaluating social and ecological vulnerability of coral reef fisheries to climate change. PLoS ONE 8(9), e74321 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Colburn, L. L. et al. Indicators of climate change and social vulnerability in fishing dependent communities along the Eastern and Gulf Coasts of the United States. Mar. Policy 74, 323–333 (2016).Article 

    Google Scholar 
    Pinnegar, J. K. et al. Assessing vulnerability and adaptive capacity of the fisheries sector in Dominica: Long-term climate change and catastrophic hurricanes. ICES J. Mar. Sci. 76, 1353–1367 (2019).
    Google Scholar 
    Aragão, G. M. et al. The importance of regional differences in vulnerability to climate change for demersal fisheries. ICES J. Mar. Sci. 1, 1–13 (2021).
    Google Scholar 
    Payne, M. R., Kudahl, M., Engelhard, G. H., Peck, M. A. & Pinnegar, J. K. Climate risk to European fisheries and coastal communities. Proc. Natl. Acad. Sci. U. S. A. 118, e2018086118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baptista, V., Silva, P. L., Relvas, P., Teodósio, M. A. & Leitão, F. Sea surface temperature variability along the Portuguese coast since 1950. Int. J. Climatol. 38, 1145–1160 (2018).Article 

    Google Scholar 
    Leitão, F. et al. (2019) A 60-year time series analyses of the upwelling along the Portuguese coast. Water 11(11), 1285 (2019).Article 

    Google Scholar 
    Leitão, F., Relvas, P., Cánovas, F., Baptista, V. & Teodósio, A. Northerly wind trends along the Portuguese marine coast since 1950. Theor. Appl. Climatol. 137(1), 19 (2018).
    Google Scholar 
    Bueno-Pardo, J. et al. Trends and drivers of marine fish landings in Portugal since its entrance in the European Union. ICES J. Mar. Sci. 77, 988–1001 (2020).Article 

    Google Scholar 
    Leitão, F., Maharaj, R. R., Vieira, V. M. N. C. S., Teodósio, A. & Cheung, W. W. L. The effect of regional sea surface temperature rise on fisheries along the Portuguese Iberian Atlantic coast. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1351–1359 (2018).Article 

    Google Scholar 
    Leitão, F., Alms, V. & Erzini, K. A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries. J. Mar. Syst. 139, 128–138 (2014).Article 

    Google Scholar 
    Ullah, H., Leitão, F., Baptista, V. & Chícharo, L. An analysis of the impacts of climatic variability and hydrology on the coastal fisheries, Engraulis encrasicolus and Sepia officinalis, of Portugal. Ecohydrol. Hydrobiol. 12, 337–352 (2012).Article 

    Google Scholar 
    EUMOFA. The EU Fish Market – Highlights the EU in the world market supply consumption import-export landings in the EU aquaculture (2021) https://doi.org/10.2771/563899DGPM. Relatório de Monitorização da Estratégia Nacional para o Mar 2013–2020, Documento de Suporte às Políticas do Mar. (2020).Almeida, C., Karadzic, V. & Vaz, S. The seafood market in Portugal: Driving forces and consequences. Mar. Policy 61, 87–94 (2015).Article 

    Google Scholar 
    Pita, C. & Gaspar, M. (2020) Small-Scale Fisheries in Portugal: Current Situation, Challenges and Opportunities for the Future. In Small-Scale Fisheries in Europe: Status, Resilience and Governance. Springer, Cham 283–305https://doi.org/10.1007/978-3-030-37371-9_14Baeta, F., José Costa, M. & Cabral, H. Changes in the trophic level of Portuguese landings and fish market price variation in the last decades. Fish. Res. 97, 216–222 (2009).Article 

    Google Scholar 
    Leitão, F. Landing profiles of Portuguese fisheries: Assessing the state of stocks. Fish. Manag. Ecol. 22, 152–163 (2015).Article 

    Google Scholar 
    Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

    Google Scholar 
    Bueno-Pardo, J. et al. Climate change vulnerability assessment of the main marine commercial fish and invertebrates of Portugal. Sci. Rep. 11, 2958 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Szynaka, M. J., Erzini, K., Gonçalves, J. M. S. & Campos, A. Identifying métiers using landings profiles: An octopus-driven multi-gear coastal fleet. J. Mar. Sci. Eng. 9, 1022 (2021).Article 

    Google Scholar 
    Gamito, R., Teixeira, C. M., Costa, M. J. & Cabral, H. N. Climate-induced changes in fish landings of different fleet components of Portuguese fisheries. Reg. Environ. Chang. 13, 413–421 (2013).Article 

    Google Scholar 
    Leitão, F., Baptista, V., Zeller, D. & Erzini, K. Reconstructed catches and trends for mainland Portugal fisheries between 1938 and 2009: Implications for sustainability, domestic fish supply and imports. Fish. Res. 155, 33–50 (2014).Article 

    Google Scholar 
    Teixeira, C. M. et al. Trends in landings of fish species potentially affected by climate change in Portuguese fisheries. Reg. Environ. Chang. 14, 657–669 (2014).Article 

    Google Scholar 
    Wickham, H. ggplot2: Elegant graphics for data analysis (Springer-Verlag, 2016).MATH 
    Book 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 3–900051–07–0 (2020).Zuur, A. F., Fryer, R. J., Jolliffe, I. T., Dekker, R. & Beukema, J. J. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14, 665–685 (2003).Article 

    Google Scholar 
    Zuur, A. F., Ieno, E. N. & Smith, G. M. (2007) Analysing Ecological Data. https://doi.org/10.1007/978-0-387-45972-1Anderson, M., Gorley, R. & Clarke, K. PERMANOVA for PRIMER: Guide to software and statistical methods. (PRIMER-E Ltd., 2008).Heppell, S. S., Heppell, S. a, Read, A. J. & Crowder, L. B. Effects of fishing on long-lived marine organisms. In Marine conservation biology: The science of maintaining the sea’s biodiversity (eds. Norse, E. & Crowder, L.) 211–231 (Island Press, 2005).Maynou, F. et al. Estimating trends of population decline in long-lived marine species in the Mediterranean sea based on fishers’ perceptions. PLoS ONE 6, e21818 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rolland, V., Barbraud, C. & Weimerskirch, H. Combined effects of fisheries and climate on a migratory long-lived marine predator. J. Appl. Ecol. 45, 4–13 (2008).Article 

    Google Scholar 
    Alves, L. M. F., Correia, J. P. S., Lemos, M. F. L., Novais, S. C. & Cabral, H. Assessment of trends in the Portuguese elasmobranch commercial landings over three decades (1986–2017). Fish. Res. 230, 105648 (2020).Article 

    Google Scholar 
    Correia, J. P., Morgado, F., Erzini, K. & Soares, A. M. V. M. Elasmobranch landings for the Portuguese commercial fishery from 1986 to 2009. Arquipel. Life Mar. Sci. 33, 81–109 (2016).
    Google Scholar 
    Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pinnegar, J. K. & Engelhard, G. H. The ‘shifting baseline’ phenomenon: A global perspective. Rev. Fish Biol. Fish. 18, 1–16 (2008).Article 

    Google Scholar 
    Moura, T. et al. Assessing spatio-temporal changes in marine communities along the Portuguese continental shelf and upper slope based on 25 years of bottom trawl surveys. Mar. Environ. Res. 160, 105044 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Martins, M. M., Skagen, D., Marques, V., Zwolinski, J. & Silva, A. Changes in the abundance and spatial distribution of the Atlantic chub mackerel (Scomber colias) in the pelagic ecosystem and fisheries off Portugal. Sci. Mar. 77, 551–563 (2013).Article 

    Google Scholar 
    Bordalo-Machado, P. & Figueiredo, I. The fishery for black scabbardfish (Aphanopus carbo Lowe, 1839) in the Portuguese continental slope. Rev. Fish Biol. Fish. 19, 49–67 (2009).Article 

    Google Scholar 
    Gordo, L. S. Black scabbardfish (Aphanopus carbo Lowe, 1839) in the southern Northeast Atlantic: Considerations on its fishery. Sci. Mar. 73, 11–16 (2009).Article 

    Google Scholar 
    Campos, A., Fonseca, P., Fonseca, T. & Parente, J. Definition of fleet components in the Portuguese bottom trawl fishery. Fish. Res. 83, 185–191 (2007).Article 

    Google Scholar 
    Bueno-Pardo, J. et al. Deep-sea crustacean trawling fisheries in Portugal: Quantification of effort and assessment of landings per unit effort using a Vessel Monitoring System (VMS). Sci. Rep. 7, 1–10 (2017).ADS 
    Article 

    Google Scholar 
    Gamito, R., Pita, C., Teixeira, C., Costa, M. J. & Cabral, H. N. Trends in landings and vulnerability to climate change in different fleet components in the Portuguese coast. Fish. Res. 181, 93–101 (2016).Article 

    Google Scholar 
    García-Seoane, E., Marques, V., Silva, A. & Angélico, M. M. Spatial and temporal variation in pelagic community of the western and southern Iberian Atlantic waters. Estuar. Coast. Shelf Sci. 221, 147–155 (2019).ADS 
    Article 

    Google Scholar 
    Vinagre, C., Duarte, F., Cabral, H. & Jose, M. Impact of climate warming upon the fish assemblages of the Portuguese coast under different scenarios. Reg. Environ. Change 11(4), 779. https://doi.org/10.1007/s10113-011-0215-z (2011).Article 

    Google Scholar 
    Goulart, P., Veiga, F. J. & Grilo, C. The evolution of fisheries in Portugal: A methodological reappraisal with insights from economics. Fish. Res. 199, 76–80 (2018).Article 

    Google Scholar 
    Pita, C., Pereira, J., Lourenço, S., Sonderblohm, C. & Pierce, G. J. (2015) The Traditional Small-Scale Octopus Fishery in Portugal: Framing Its Governability. 117–132. https://doi.org/10.1007/978-3-319-17034-3_7Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

    Google Scholar 
    Moreno, A. et al. Essential habitats for pre-recruit Octopus vulgaris along the Portuguese coast. Fish. Res. 152, 74–85 (2014).ADS 
    Article 

    Google Scholar 
    Sbrana, M. et al. Spatiotemporal abundance pattern of deep-water rose shrimp, parapenaeus longirostris, and Norway lobster, nephrops norvegicus, in european mediterranean waters. Sci. Mar. 83, 71–80 (2019).Article 

    Google Scholar 
    Quattrocchi, F., Fiorentino, F., Lauria, V. & Garofalo, G. The increasing temperature as driving force for spatial distribution patterns of Parapenaeus longirostris (Lucas 1846) in the Strait of Sicily (Central Mediterranean Sea). J. Sea Res. 158, 101871 (2020).Article 

    Google Scholar 
    Colloca, F., Mastrantonio, G., Lasinio, G. J., Ligas, A. & Sartor, P. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138, 29–39 (2014).Article 

    Google Scholar 
    Woods, P. J. et al. (2021) A review of adaptation options in fisheries management to support resilience and transition under socio-ecological change. ICES J. Mar. Sci. fsab146Gonzalez-Mon, B. et al. Spatial diversification as a mechanism to adapt to environmental changes in small-scale fisheries. Environ. Sci. Policy 116, 246–257 (2021).Article 

    Google Scholar 
    Garza-Gil, M. D., Torralba-Cano, J. & Varela-Lafuente, M. M. Evaluating the economic effects of climate change on the European sardine fishery. Reg. Environ. Chang. 11, 87–95 (2011).Article 

    Google Scholar 
    Borges, M. F., Santos, A. M. P., Crato, N., Mendes, H. & Mota, B. Sardine regime shifts off Portugal: A time series analysis of catches and wind conditions. Sci. Mar. 67, 235–244 (2003).Article 

    Google Scholar 
    Garrido, S. et al. Temperature and food-mediated variability of European Atlantic sardine recruitment. Prog. Oceanogr. 159, 267–275 (2017).ADS 
    Article 

    Google Scholar 
    ICES. Report of the working group on southern horse mackerel, anchovy and sardine (WGHANSA). (2018).Szalaj, D. et al. Food-web dynamics in the Portuguese continental shelf ecosystem between 1986 and 2017: Unravelling drivers of sardine decline. Estuar. Coast. Shelf Sci. 251, 107259 (2021).Article 

    Google Scholar 
    Feijó, D. et al. Catch and yield trends of the Portuguese purse seine fishery (2006–2018). Front. Mar. Sci. https://doi.org/10.3389/conf.fmars.2019.08.00013 (2019).Article 

    Google Scholar 
    Schickele, A., Francour, P. & Raybaud, V. European cephalopods distribution under climate-change scenarios. Sci. Rep. 11, 3930 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Purcell, S. W., Crona, B. I., Lalavanua, W. & Eriksson, H. Distribution of economic returns in small-scale fisheries for international markets: A value-chain analysis. Mar. Policy 86, 9–16 (2017).Article 

    Google Scholar 
    Thiao, D., Leport, J., Ndiaye, B. & Mbaye, A. Need for adaptive solutions to food vulnerability induced by fish scarcity and unaffordability in Senegal. Aquat. Living Resour. 31, 25 (2018).Article 

    Google Scholar 
    Education, A. & Variability, H. Cardoso, C., Lourenço, H., Costa, S., Gonçalves, S. & Leonor Nunes, M. Survey Into the Seafood Consumption Preferences and Patterns in the Portuguese Population. J. Food Prod. Mark. 22, 421–435 (2016).Article 

    Google Scholar 
    Holsten, A. & Kropp, J. P. An integrated and transferable climate change vulnerability assessment for regional application. Nat. Hazards 64, 1977–1999 (2012).Article 

    Google Scholar 
    Umweltbundesamt guidelines for climate impact and vulnerability assessments recommendations of the interministerial working group on adaptation to climate change of the German federal government for our environment. More