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    Off the hook: electrical device keeps sharks away from fishing lines

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    More than 30% of shark and ray species are edging towards extinction, mainly because they are unintentionally caught by fishers targeting tuna and other commercially valuable species. A new device might help to keep some of these threatened species away from fishing hooks.

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    doi: https://doi.org/10.1038/d41586-022-03776-4

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    Parasitic infection increases risk-taking in a social, intermediate host carnivore

    Dubey, J. P. Toxoplasmosis of animals and humans. (CRC Press, 2010).Robert-Gangneux, F. & Dardé, M. L. Epidemiology of and diagnostic strategies for toxoplasmosis. Clin. Microbiol Rev. 25, 264–296 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wong, S. & Remington, J. S. Toxoplasmosis in Pregnancy. Clin. Infect. Dis. 18, 853–861 (1994).Article 
    CAS 
    PubMed 

    Google Scholar 
    Arantes, T. P. et al. Toxoplasma gondii: Evidence for the transmission by semen in dogs. Exp. Parasitol. 123, 190–194 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stibbs, H. H. Changes in brain concentrations of catecholamines and indoleamines in Toxoplasma gondii infected mice. Ann. Trop. Med Parasitol. 79, 153–157 (1985).Article 
    CAS 
    PubMed 

    Google Scholar 
    McConkey, G. A., Martin, H. L., Bristow, G. C. & Webster, J. P. Toxoplasma gondii infection and behaviour – Location, location, location? J. Exp. Biol. 216, 113–119 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lim, A., Kumar, V., Hari Dass, S. A. & Vyas, A. Toxoplasma gondii infection enhances testicular steroidogenesis in rats. Mol. Ecol. 22, 102–110 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zouei, N., Shojaee, S., Mohebali, M. & Keshavarz, H. The association of latent toxoplasmosis and level of serum testosterone in humans. BMC Res Notes 11, 365 (2018).Arnott, M. A., Cassella, J. P., Aitken, P. P. & Hay, J. Social interactions of mice congenital Toxoplasma infection. Ann. Trop. Med Parasitol. 84, 149–156 (1990).Article 
    CAS 
    PubMed 

    Google Scholar 
    Coccaro, E. F. et al. Toxoplasma gondii infection: Relationship with aggression in psychiatric subjects. J. Clin. Psychiatry 77, 334–341 (2016).Article 
    PubMed 

    Google Scholar 
    Webster, J. P., Brunton, C. F. A. & Macdonald, D. W. Effect of Toxoplasma Gondii Upon Neophobic Behaviour in Wild Brown Rats, Rattus Norvegicus. Parasitology 109, 37–43 (1994).Article 
    PubMed 

    Google Scholar 
    Berdoy, M., Webster, J. P. & Mcdonald, D. W. Fatal attraction in rats infected with Toxoplasma gondii. Proc. R. Soc. B: Biol. Sci. 267, 1591–1594 (2000).Article 
    CAS 

    Google Scholar 
    Poirotte, C. et al. Morbid attraction to leopard urine in toxoplasma-infected chimpanzees. Curr. Biol. 26, R98–R99, https://doi.org/10.1016/j.cub.2015.12.020 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gering, E. et al. Toxoplasma gondii infections are associated with costly boldness toward felids in a wild host. Nat. Commun. 12, 3842 (2021).Smith, D. W., Stahler, D. R. & MacNulty, D. R. Yellowstone Wolves: Science and Discovery in the World’s First National Park. (University of Chicago Press, 2020).Ruth, T. K., Buotte, P. C., Hornocker, M., Murphy, K. M. & Smith, D. W. Patterns of Resource Use Prior to and during Wolf Restoration. in Yellowstone Cougars: Ecology Before And During Wolf Restoration (eds. Ruth, T. K., Buotte, P. C. & Hornocker, M.) 151–175 (University Press of Colorado, 2019).Brandell, E. E. et al. Patterns and processes of pathogen exposure in gray wolves across North America. Sci. Rep. 11, 3722 (2021).Watts, D. E. & Benson, A. M. Prevalence of antibodies for selected canine pathogens among wolves (Canis lupus) from the Alaska Peninsula, USA. J. Wildl. Dis. 52, 506–515 (2016).Article 
    PubMed 

    Google Scholar 
    Galván-Ramírez, M. D. L. L., Gutíerrez-Maldonado, A. F., Verduzco-Grijalva, F. & Judith Marcela, D. J. The role of hormones on toxoplasma gondii infection: A systematic review. Front. Microbiol. 5, 503 (2014).Kreeger, T. J. The Internal Wolf: Physiology, Pathology, and Pharmacology. in Wolves: Behavior, Ecology, and Conservation (eds. Mech, L. D. & Boitani, L.) 192–217 (University of Chicago Press, 2003).Sands, J. & Creel, S. Social dominance, aggression and faecal glucocorticoid levels in a wild population of wolves, Canis lupus. Anim. Behav. 67, 387–396 (2004).Article 

    Google Scholar 
    Cassidy, K. A., Mech, L. D., MacNulty, D. R., Stahler, D. R. & Smith, D. W. Sexually dimorphic aggression indicates male gray wolves specialize in pack defense against conspecific groups. Behavioural Process. 136, 64–72 (2017).Article 

    Google Scholar 
    Ganz, T. Defensins: Antimicrobial peptides of innate immunity. Nat. Rev. Immunol. 3, 710–720 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Anderson, T. M. et al. Molecular and evolutionary history of melanism in North American gray wolves. Science (1979) 323, 1339–1343 (2009).CAS 

    Google Scholar 
    Smith, D. W. et al. Population Dynamics and Demography. in Yellowstone Wolves: Science and Discovery in the World’s First National Park (eds. Smith, D. W., Stahler, D. R. & MacNulty, D. R.) 77–92 (University of Chicago Press, 2020).Geremia, C. et al. Integrating population- and individual-level information in a movement model of Yellowstone bison. Ecol. Appl. 24, 346–362 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Houston, D. B. Elk as Winter-Spring Food for Carnivores in Northern Yellowstone National Park. J. Appl. Ecol. 15, 653–661 (1978).Article 

    Google Scholar 
    White, P. J. et al. Migration of northern yellowstone elk: Implications of spatial structuring. J. Mammal. 91, 827–837 (2010).Article 

    Google Scholar 
    Jimenez, M. D. et al. Wolf dispersal in the Rocky Mountains, Western United States: 1993–2008. J. Wildl. Manag. 81, 581–592 (2017).Article 

    Google Scholar 
    Fuller, T. K., Mech, L. D. & Cochrane, J. F. Wolf population dynamics. in Wolves: Behavior, Ecology, and Conservation2 (eds. Mech, L. D. & Boitani, L.) 161–191 (University of Chicago Press, 2003).Clutton-Brock, T. Mammal Societies. (John Wiley & Sons, 2016).Dass, S. A. H. et al. Protozoan parasite Toxoplasma gondii manipulates mate choice in rats by enhancing attractiveness of males. PLoS One 6, 1–6 (2011).Article 

    Google Scholar 
    Packard, J. M. Wolf Behavior: Reproductive, Social and Intelligent. in Wolves: Behavior, Ecology, and Conservation (eds. Mech, L. D. & Boitani, L.) (University of Chicago Press, 2003).Stahler, D. R. et al. Ecology of Family Dynamics in Yellowstone Wolf Packs. in Yellowstone Wolves: Science and Discovery in the World’s First National Park (eds. Smith, D. W., Stahler, D. R. & MacNulty, D. R.) 42–60 (University of Chicago Press, 2020).Sikes, R. S. 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mammal. 97, 663–688 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Murphy, K. M. et al. Distribution of Canada lynx in Yellowstone National Park. Northwest Sci. 80, 199–206 (2006).
    Google Scholar 
    Murphy, K. M. The ecology of the cougar (Puma concolor) in the northern Yellowstone ecosystem: Interactions with prey, bears, and humans. (University of Idaho, Moscow, USA, 1998).Ruth, T. K., Buotte, P. C. & Quigley, H. B. Comparing Ground Telemetry and Global Positioning System Methods to Determine Cougar Kill Rates. J. Wildl. Manag. 74, 1122–1133 (2010).Article 

    Google Scholar 
    Anton, C. B. The demography and comparative ethology of top predators in a multi-carnivore system. 211 (2020).Cassidy, K. A. et al. Yellowstone Wolf Project Annual Report. (2021).Ruth, T. K., Buotte, P. C. & Hornocker, M. Spatial Responses of Cougars to Wolf Presence. in Yellowstone Cougars: Ecology Before And During Wolf Restoration (eds. Ruth, T. K., Buotte, P. C. & Hornocker, M.) 129–150 (University Press of Colorado, 2019).Sawaya, M. A. et al. Evaluation of noninvasive genetic sampling methods for cougars in Yellowstone National Park. J. Wildl. Manag. 75, 612–622 (2011).Article 

    Google Scholar 
    Metz, M. C. et al. Accounting for imperfect detection in observational studies: modeling wolf sightability in Yellowstone National Park. Ecosphere 11, e03152 (2020).Rothman, R. J. & Mech, L. D. Scent-marking in lone wolves and newly formed pairs. Anim. Behav. 27, 750–760 (1979).Article 

    Google Scholar 
    Liesenfeld, O., Nguyen, T. A., Pharke, C. & Suzuki, Y. Importance of gender and sex hormones in regulation of susceptibility of the small intestine to peroral infection with Toxoplasma gondii tissue cysts. J. Parasitol. 87, 1491–1493 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Molnar, B. et al. Environmental and intrinsic correlates of stress in free-ranging wolves. PLoS One 10, 1–25 (2015).Article 

    Google Scholar 
    Anton, C. B. et al. Gray wolf habitat use in response to visitor activity along roadways in Yellowstone National Park. Ecosphere 11, e03164 (2020). More

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    Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

    Data acquisition and preparationStructured datasetsWe used structured North American Breeding Bird Survey (BBS) data, which is conducted annually over  > 2500 routes across the United States and Canada11,12 during the peak of the breeding season (May and June). BBS routes were approximately 40 km long with 50 stops spaced 0.8 km apart. At each stop a 3-min point count was conducted, where all species seen or heard were recorded12. We downloaded the entire dataset, 1966–2019, to identify each observer’s first year and account for differences in survey experience. We created a binary variable for the observers’ first year, with 1 indicating the first year they provided data, and 0 indicating all subsequent years. We then subset the data to years 2010–2019 to align with available community science data. We zero-filled BBS data by adding zeros for each species on routes in which birds were not detected in each year.Semi-structured datasetWe used the eBird Basic Dataset as a semi-structured dataset. We used checklists within the US and Canada during June and July from 2010 to 2019. Data were filtered to impose structure on the observation process and minimize effects of unequal spatial and temporal sampling using the auk package in program R24,25,56,59,60. Data were filtered to only include complete checklists where observers recorded counts of all species detected to reduce effects of preferential species reporting61. We also filtered data based on observer effort to only include checklists  More

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    Intracellular common gardens reveal niche differentiation in transposable element community during bacterial adaptive evolution

    Bacterial strains, primers, and growth conditionsBacterial strains, plasmids, and primers used in this study are shown in Supplementary Table S1. Escherichia coli strains carrying plasmids used in conjugation experiments were grown at 37 °C in LB medium. S. fredii CCBAU25509 (SF2) and its derivatives were grown at 28 °C in TY medium (5 g tryptone, 3 g yeast extract, 0.6 g CaCl2 per liter). To screen and purify conjugants or obtain pure cultures of bacteria, antibiotics were supplemented as required at the following concentrations (μg/mL): for E. coli, gentamicin (Gen), 30; and kanamycin (Km), 100; for Sinorhizobium strains, trimethoprim (Tmp), 10; nalidixic acid (Na), 30; and kanamycin (Km), 100. To screen sacB mutants from SF2 derivatives, firstly SF2 tolerance of 8%-30% sucrose in the TY medium was measured by the growth curve using Bioscreen C (Oy Growth Curves Ab Ltd, Raisio, Finland), and then the TY medium containing 10% sucrose was chosen as the selection medium.Construction of S. fredii derivatives harboring xenogeneic PsacB-sacB
    The multipartite genome of SF2 consists of a chromosome (Ch, GC% = 62.6%), a chromid (pB, GC% = 62%) [31], and a symbiosis plasmid (pA, GC% = 59%) [26]. Within each replicon, an insertion position, with GC% of its 10 kb flanking region being the same as the replicon average, was chosen for subsequent experiments (Fig. 1A). The suicide plasmid pJQ200SK carries the wild-type sacB gene (characterized by its low GC content of 38.8%; 1422 bp) and its promoter region PsacB (GC% = 36.1%, 446 bp) from Bacillus subtilis subsp. subtilis str. 168 [32]. A Km-resistant cassette from pBBR1MCS-2 [33] was amplified and assembled with a linearized pJQ200SK lacking the Gm-resistant cassette using a seamless cloning kit (Taihe Biotechnology, Beijing, China) as described previously [34]. This generated pJQ-L carrying the wild-type low GC% sacB (38.8%; 1422 bp; L-GC). The sacB gene with medium (54.6%; M-GC) or high GC (61.6%; H-GC) content in its synonymous codons was synthesized (Fig. S1), and used to replace the wild-type low GC% sacB gene of pJQ-L to generate pJQ-M and pJQ-H. This was also performed using the seamless cloning method as described above with the linearized pJQ-L lacking the wild-type sacB. Three genomic segments of SF2 (pA:330682-331687, pB:702541-703493, Ch:674057-675207) were individually cloned into each of pJQ-L, pJQ-M, and pJQ-H at the SmaI site using the seamless cloning method, which allowed subsequent integration of xenogeneic cassettes into three replicons. This generated nine plasmids (pJQ-L_pA, pJQ-L_pB, pJQ-L_Ch; pJQ-M_pA, pJQ-M_pB, pJQ-M_Ch; pJQ-H_pA, pJQ-H_pB, pJQ-H_Ch), which were transformed into E. coli DH5α and verified by Sanger sequencing before conjugation into rhizobia via triparental mating with helper plasmid pRK2013 [35]. This generated nine SF2 derivatives individually carrying a xenogeneic cassette in a replicon (Fig. 1A). The correct insertion of the xenogeneic cassette was checked by PCR.Fig. 1: Screening mutations in xenogeneic sacB of different GC content.A The xenogeneic cassettes harboring sacB of L-GC, M-GC, or H-GC were individually inserted into the symbiosis plasmid (pA; GC% = 59%), chromid (pB; GC% = 62%), or chromosome (Ch; GC% = 62.6%) of Sinorhizobium fredii CCBAU25509. Gene IDs surrounding each insertion position are shown. GC% of the three sacB versions were 38.8% (L-GC, the wild-type version from Bacillus subtilis subsp. subtilis str. 168), 54.6% (M-GC, synthesized), and 61.6% (H-GC, synthesized). The wild-type PsacB (GC% = 36.1%, 446 bp) of B. subtilis 168 was cloned together with each of the three versions of sacB. The number of A, T, C, or G in the 1422 bp sacB gene is indicated. B Growth curves in TY medium. C Levansucrase enzyme activity assay of crude proteins collected at OD600 = 1.2 in TY medium. Different letters indicate significant difference (Average ± SEM; ANOVA followed by Duncan’s test, alpha = 0.05). D Growth curves in TY medium supplemented with 10% sucrose. E Schematic view of culturing, mutant screening, and mutation identification in this work. sacB, levansucrase gene; km, kanamycin resistance gene.Full size imageThe xenogeneic silencer MucR prefers low GC% DNA targets [29, 30], and its potential role in niche differentiation for IS community members was tested. SF2 has two mucR copies, and the in-frame deletion mutant ΔmucR1R2 was constructed by using an allelic exchange strategy: upstream and downstream ~500 bp flanking regions of mucR1 or mucR2 were amplified and assembled with the linearized allelic exchange vector pJQ200SK. The pJQ200SK derivative used to delete mucR1 was linearized and then cloned seamlessly with the sequence coding MucR1 and C-terminal fused FLAG-tag. The resultant plasmid was conjugated into SF2 to generate SF2MucR1FLAG. The xenogeneic cassettes carrying plasmids (pJQ-L_pA, pJQ-M_pA, pJQ-H_pA) were then inserted into the same position of pA in ΔmucR1R2 and SF2MucR1FLAG, and verified by PCR.Mutant screening and calculation of mutation frequencyTo screen sacB mutants from SF2 derivatives, single colonies of S. fredii derivatives were inoculated and grown to an OD600 = 0.2, 0.6, 1.2, and 2.0, and dilutions were applied to plates with and without 10% sucrose respectively. The number of colonies on the 10% sucrose TY plates was recorded as “A” at the dilution of 10−a, and the number of colonies on the sucrose-free TY plates was recorded as “B” at the dilution of 10−b. The total mutation frequency was then calculated by (A·10-a)/(B·10-b). Independent colonies on the 10% sucrose TY plates were further purified on the same medium plates, and the full length of PsacB-sacB fragment was amplified by colony PCR. Gene loss, SNPs or short InDels, or large insertion mutations were identified by electrophoresis analysis of PCR products. Representative clones with large insertion mutations were selected for Sanger sequencing. Three independent experiments were performed for all test strains.Enzyme activity assay for levansucraseTo evaluate the function of xenogeneic sacB in SF2 derivatives, sucrose was dissolved in the buffer solution (0.1 M CH3COONa, pH 5.5), and the total protein extract of bacteria was added (calibrated to the same concentration) to make the final concentration of sucrose 1%, and the reaction system was incubated at 28°C for 12 h. After adding the color development solution (3,5-dinitrosalicylic acid 6.3 g, sodium hydroxide 21.0 g, potassium sodium tartrate 182.0 g, phenol 5.0 g, sodium metabisulfite 5.0 g in 1000 mL water; BOXBIO, Beijing, China), the enzyme was inactivated at 95 °C for 5 min, and the absorbance value at 540 nm was measured to calculate the glucose content. Determination of the release of glucose and fructose from sucrose allowed calculation of the total activity of the levansucrase. One unit (U) of enzyme is defined as the amount of enzyme required for producing 1 µmol glucose per min in reaction buffer. The specific activity of levansucrase hydrolysis activity is the activity units per mg of protein (U/mg).5′RACETo determine the transcription start site of the sacB gene, a 5′RACE experiment was performed with the 5′RACE kit (Sangon, Beijing, China) for Rapid Amplification of cDNA Ends using three gene-specific primers (Table S1) that anneal to the known region and an adapter primer that targets the 5′ end. Products generated by 5′RACE were subcloned into the TOPO-TA vector and individual colonies were sequenced.RNA extraction and RT-qPCRTo determine transcriptional levels of the major active ISs in SF2 and its ΔmucR1R2 mutant, strains were grown in 50 mL TY liquid medium to an OD600 of 1.2. A bacterial total RNA Kit (Zomanbio, Beijing, China) was used for total RNA extraction. cDNA was synthesized using FastKing-RT SuperMix (TIANGEN, Beijing, China). qPCR was performed by using QuantStudio 6 Flex and 2× RealStar Green Mixture (Genstar, Beijing, China). The primer pairs used are listed in Table S1. The 16S rRNA gene was used as an internal reference to normalize the expression level. Three independent biological replicates were performed.ChIP-qPCRTo test the potential recruitment of MucR in the xenogeneic PsacB-sacB region, three SF2 derivative strains harboring sacB of different GC% in the pA replicon and MucR1-FLAG (Table S1; MucR1-FLAG: L-GC, MucR1-FLAG: M-GC, MucR1-FLAG: H-GC) were cultured until the OD600 had reached 1.2. Formaldehyde was added into the TY medium to a final concentration of 1%, which was then incubated at 28 °C for 15 min. To stop crosslinking, glycine was added to a final concentration of 0.1 M. The cross-linked samples were harvested (5000 × g, 5 min, 4 °C) and washed twice with cold phosphate-buffered saline (PBS). After the pellets were ground into fine powder in liquid nitrogen, the samples were resuspended in buffer containing 1% SDS and 1 mM phenylmethanesulfonyl fluoride, and lysed by sonication using a sonicator (Q800R3, QSonica). Chromatin immunoprecipitation (ChIP) was performed using the ChIP assay kit (Beyotime, Shanghai, China) according to the manufacturer’s recommendations. The supernatant was collected and chromatin was immunoprecipitated with Anti-FLAG M2 antibody (Sigma). Input control and DNA obtained from the immunoprecipitation were amplified by PCR using primers listed in Table S1. The recruitment level of FLAG-tagged MucR1 in multiple regions within the PsacB-sacB fragment inserted by ISs at high frequency was detected by ChIP-qPCR.Crosslinking and western blotting assayTo test the ability of MucR1 to form homodimer in SF2 derivatives carrying sacB in pA, rhizobial cells (SF2MucR1FLAG, MucR1-FLAG: L-GC, MucR1-FLAG: M-GC, and MucR1-FLAG: H-GC) were cultured in 50 mL TY medium to an OD600 of 1.2. Formaldehyde was added at a final concentration of 1% in the culture which was then shaken at 28 °C, 100 rpm for 15 min to allow crosslinking. The crosslinking reaction was terminated by adding a final concentration of 100 mM glycine (28 °C, 100 rpm, 5 min). 1 mL of the above solution was centrifuged (5000 × g, 4 °C, 1 min), resuspended in 50 µL SDS loading buffer to a uniform cell density, and then boiled for 10 minutes for lysis. Next, lysates were separated on 12% SDS-PAGE and transferred to a nitrocellulose membrane. For immunodetection of individual proteins, the method described previously was used [30]. Briefly, mouse monoclonal Anti-FLAG M2 antibody (Sigma), HRP (horseradish peroxidase) conjugated goat Anti-mouse IgG (Abcam), and eECL Western blot kit (CWBIO, Beijing, China) were used, and chemiluminescence signals were visualized using Fusion FX6 (Vilber) and Evolution-Capt Edge software.Protein purificationTo purify MucR1 protein, E. coli BL21(DE3) carrying His6-SUMO-tagged MucR1 in the pET30a [29] was cultured in 500 mL LB medium until OD600 reached 0.8. The procedure described previously was used [30]. IPTG was then added to the culture to a final concentration of 0.6 mM and switched to 18 °C at 150 rpm for 12 h. Cells were harvested by centrifugation (5000 × g, 5 min, 4 °C) and resuspended in 30 mL of lysis buffer (25 mM Tris, pH 8.0, 250 mM NaCl, 10 mM imidazole) supplemented with 0.1 mg/mL DNase I, 0.4 mg/mL of lysozyme, and protease inhibitor mixture (Roche). After 30 min incubation and 120 sonication cycles (300 W, 10 s on, 10 s off), lysates were removed by centrifugation (18,000 × g, 4 °C, 30 min) and filtration through a 0.22 μm membrane. The supernatant was loaded onto Ni-Agarose Resin (CWBIO, Beijing, China) pre-washed using lysis buffer, washed 3 times with wash buffer (lysis buffer containing 20 mM imidazole), and then eluted by lysis buffer containing imidazole gradient (100, 200, 300 mM imidazole). The purified proteins were finally concentrated by ultrafiltration and redissolved in storage buffer (25 mM Tris, pH 8.0, 250 mM NaCl, 10% glycerol) prior to use or storage at −80 °C.DNA bridging assayTo determine if MucR1 can form DNA-MucR1-DNA complex with various regions of xenogeneic PsacB-sacB fragment, a DNA bridging assay described earlier [30, 36] was performed with modifications. DNA probes were prepared by annealing of synthesized complementary strands (PsacB −90~−24) or by PCR amplification (PsacB −90~+3, sacB +710~+802, sacB +908~+1007) using 5′-biotin-labeled or 5′-Cy5 primers (Table S1). In each bridging assay, 100 μL of hydrophilic streptavidin magnetic beads (NEB) were washed twice with 500 μL of PBS and then resuspended in 500 μL of coupling buffer (20 mM Tris-HCl, pH 7.4, 1 mM EDTA, 500 mM NaCl). Then, the suspension was supplied with 10 pmol of biotin-labeled DNA and incubated with the beads for 30 min at room temperature with gentle rotation. The resulting beads were washed twice with 500 μL of incubation buffer (20 mM Tris, pH 7.4, 150 mM NaCl, 1 mM dithiothreitol, 5% glycerol (vol/vol), 0.05% Tween 20) and resuspended after the addition of 10 pmol Cy5-labeled DNA and 10 μL HRV 3C protease to a final volume of 500 μL. The HRV 3C protease was used herein to remove SUMO. A twofold serial dilution of the protein sample was added to each 50 μL aliquot of bead suspension, and supplemented with incubation buffer to 60 μL final volume. After 30 minutes of incubation with gentle rotation at room temperature, the mixture was placed on a magnetic stand for 5 minutes. The supernatant was collected and labeled as Sample A. The beads were mixed with 60 μL of elution buffer (incubation buffer with 0.1% SDS and 20 μg/mL biotin) and incubated in a boiling water bath for 10 min. The eluted samples were labeled as Sample B. Cy5 fluorescence signals of Sample A and B were detected by a Microscale Thermophoresis Monolith NT.115 system (NanoTemper). The Cy5 fluorescence signal of the Sample A from the treatment without MucR1 was defined as 100% input signal.Statistical analysesAnalysis of variance (ANOVA) followed by Duncan’s test, Student’s t-test, and Fisher’s exact test were performed using GraphPad Prism 8. The closest homolog of individual active ISs and their family identification were determined using ISfinder [37]. Target sequence logos of ISs were generated by multiple sequence alignments of insertion sites within xenogeneic PsacB-sacB or genomic background using the program WebLogo [38].Although the fundamental niche, not constrained by biological interactions, cannot be determined by observation [15], the realized niche, representing a proportion of the fundamental niche where organisms actually live under abiotic and biotic interactions, can be estimated by correlative approaches [15, 39]. In order to address the influence of intracellular variables on biased IS insertions into nine common gardens, the within outlying mean index analysis developed for niche differentiation analysis was carried out using the R package “subniche” [40, 41]. The intracellular environmental gradients were determined by Principal Component Analysis (PCA) based on variables as follows: GC% of different sacB versions, replicon GC%, the number of each IS in the corresponding replicon where sacB is inserted, available insertion sites of ISs in different sacB versions, and levansucrase activity of strains carrying different sacB versions. Within this multidimensional Euclidean space (environmental space), mean positions in realized (sub)niches and parameters of each IS were obtained for the whole data set (realized niches in environmental space defined by nine common gardens) or various subsets (realized subniches in sub-environmental spaces identified by the hierarchical clustering analysis with the ward.D method based on the Euclidean distance matrix) [41]. Two and three subsets rather than four and more subsets were statistically analyzable. By comparing to the overall average habitat conditions (G) or the average subset habitat conditions (GK) of the spatial domain, ISs selecting for a less common habitat were indicated by their significantly higher niche marginality values compared to the simulated values, based on a Monte Carlo test with 1,000 permutations, under the hypothesis that each IS is indifferent to its intracellular environment [40]. More

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    Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

    Zhao, Q. G., Huang, G. Q. & Ma, Y. Q. The ecological environment conditions and construction of an ecological civilization in China. Acta Ecol. Sin. 36, 6328–6335 (2016).
    Google Scholar 
    Jiang, Y. China’s water scarcity. J. Environ. Manag. 90, 3185–3196 (2009).Article 

    Google Scholar 
    Jacob, D. J. & Winner, D. A. Effect of climate change on air quality. Atmos. Environ. 43, 51–63 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Shahmohamadi, P., Che-Ani, A. I., Ramly, A., Maulud, K. N. A. & Mohd-Nor, M. F. I. Reducing urban heat island effects: A systematic review to achieve energy consumption balance. Int. J. Phys. Sci. 5, 626–636 (2010).
    Google Scholar 
    Shan, W. et al. Ecological environment quality assessment based on remote sensing data for land consolidation. J. Clean. Prod. 239, 118126 (2019).Article 

    Google Scholar 
    Cheng, R. et al. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest. Biogeosciences 17, 4523–4544 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Ochoa-Gaona, S. et al. A multi-criterion index for the evaluation of local tropical forest conditions in Mexico. For. Ecol. Manag. 260, 618–627 (2010).Article 

    Google Scholar 
    Zuromski, L. M. et al. Solar-induced fluorescence detects interannual variation in gross primary production of coniferous forests in the western United States. Geophys. Res. Lett. 45, 7184–7193 (2018).Article 
    ADS 

    Google Scholar 
    Wingard, G. L. & Lorenz, J. J. Integrated conceptual ecological model and habitat indices for the southwest Florida coastal wetlands. Ecol. Ind. 44, 92–107 (2014).Article 

    Google Scholar 
    Zhou, X. H., Zhang, F., Zhang, H. W., Zhang, X. L. & Yuan, J. A study of soil salinity inversion based on multispectral remote sensing index in Ebinur lake wetland nature reserve. Spectrosc. Spectral Anal. 39, 1229–1235 (2019).CAS 

    Google Scholar 
    Jiang, M. Z., Chen, H. Y., Chen, Q. H., Wu, H. Y. & Chen, P. Wetland ecosystem integrity and its variation in an estuary using the EBLE index. Ecol. Ind. 48, 252–262 (2015).Article 

    Google Scholar 
    Lv, J. X. et al. Wetland loss identification and evaluation based on landscape and remote sensing indices in Xiong’an new area. Remote Sens. 11, 2834 (2019).Article 
    ADS 

    Google Scholar 
    Bi, X. et al. Assessment of spatio-temporal variation and driving mechanism of ecological environment quality in the arid regions of Central Asia, Xinjiang. Int. J. Environ. Res. Public Health 18, 7111 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leroux, L. et al. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. Eur. J. Agron. 108, 11–26 (2019).Article 

    Google Scholar 
    Liran, O., Shir, O. M., Levy, S., Grunfeld, A. & Shelly, Y. Novel remote sensing index of electron transport rate predicts primary production and crop health in L. sativa and Z. mays. Remote Sens. 12, 1718 (2020).Article 
    ADS 

    Google Scholar 
    Zang, Y. Z. et al. Remote sensing index for mapping canola flowers using MODIS data. Remote Sens. 12, 3912 (2020).Article 
    ADS 

    Google Scholar 
    Jia, T. X., Zhang, X. Q. & Dong, R. C. Long-term spatial and temporal monitoring of cyanobacteria blooms using MODIS on Google Earth Engine: A case study in Taihu lake. Remote Sens. 11, 2269 (2019).Article 
    ADS 

    Google Scholar 
    Bai, Y. Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data. Ecol. Ind. 129, 108029 (2021).Article 

    Google Scholar 
    Zhang, M., Lin, H., Long, X. R. & Cai, Y. T. Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000–2019 time-series Landsat data. Sci. Total Environ. 780, 146615 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Qu, C., Li, P. J. & Zhang, C. M. A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS J. Photogramm. Remote Sens. 175, 431–447 (2021).Article 
    ADS 

    Google Scholar 
    Fu, Y. C., Lu, X. Y., Zhao, Y. L., Zeng, X. T. & Xia, L. L. Assessment impacts of weather and land use/land cover (LULC) change on urban vegetation net primary productivity (NPP): A case study in Guangzhou, China. Remote Sens. 5, 4125–4144 (2013).Article 
    ADS 

    Google Scholar 
    Kulkarni, K. & Vijaya, P. NDBI based prediction of land use land cover change. J. Indian Soc. Remote Sens. 49, 2523–2537 (2021).Article 

    Google Scholar 
    Li, C. Y. & Zhang, N. Analysis of the daytime urban heat island mechanism in East China. J. Geophys. Res.-Atmos. 126, 2020 (2021).
    Google Scholar 
    Wang, Z. A. et al. Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecol. Indic. 128, 107845 (2021).Article 

    Google Scholar 
    Zhao, Y. J. et al. Impact of urban expansion on rain island effect in Jinan City, North China. Remote Sens. 13, 2989 (2021).Article 
    ADS 

    Google Scholar 
    Xu, H. Q. A remote sensing urban ecological index and its application. Acta Ecol. Sin. 33, 7853–7862 (2013).
    Google Scholar 
    Gou, R. K. & Zhao, J. Eco-environmental quality monitoring in Beijing, China, using an RSEI-based approach combined with random forest algorithms. IEEE Access 8, 196657–196666 (2020).Article 

    Google Scholar 
    Jing, Y. Q. et al. Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecol. Indic. 110, 107518 (2020).Article 

    Google Scholar 
    Airiken, M., Zhang, F., Chan, N. W. & Kung, H. T. Assessment of spatial and temporal ecological environment quality under land use change of urban agglomeration in the North Slope of Tianshan, China. Environ. Sci. Pollut. Res. 29, 12282–12299 (2022).Article 

    Google Scholar 
    Ji, J. W., Wang, S. X., Zhou, Y., Liu, W. L. & Wang, L. T. Studying the eco-environmental quality variations of Jing-Jin-Ji urban agglomeration and its driving factors in different ecosystem service regions from 2001 to 2015. IEEE Access 8, 154940–154952 (2020).Article 

    Google Scholar 
    Liu, Z. S., Wang, L. Y. & Li, B. Quality assessment of ecological environment based on Google Earth Engine: A case study of the Zhoushan Islands. Front. Ecol. Evol. 10, 918756 (2022).Article 

    Google Scholar 
    Xiong, Y. et al. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecol. Indic. 125, 107518 (2021).Article 

    Google Scholar 
    Zhang, Q. F. et al. Recent oasis dynamics and ecological security in the Tarim River Basin, Central Asia. Sustainability 14, 3372 (2022).Article 

    Google Scholar 
    Yuan, B. D. et al. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. J. Clean. Prod. 302, 126995 (2021).Article 

    Google Scholar 
    Gao, W. L., Zhang, S. W., Rao, X. Y., Lin, X. & Li, R. S. Landsat TM/OLI-based ecological and environmental quality survey of Yellow River Basin, Inner Mongolia section. Remote Sens. 13, 4477 (2021).Article 
    ADS 

    Google Scholar 
    Zhu, Q. et al. Relationship between ecological quality and ecosystem services in a red soil hilly watershed in southern China. Ecol. Ind. 121, 107119 (2021).Article 

    Google Scholar 
    Huang, H. P., Chen, W., Zhang, Y., Qiao, L. & Du, Y. Y. Analysis of ecological quality in Lhasa metropolitan area during 1990–2017 based on remote sensing and Google Earth Engine platform. J. Geogr. Sci. 31, 265–280 (2021).Article 

    Google Scholar 
    Fan, C., Gui, F., Wang, L. Z. & Zhao, S. Evaluation of environmental quality based on remote sensing data in the coastal lands of eastern China. J. Coastal Res. 36, 1229–1236 (2020).Article 

    Google Scholar 
    Phan, T. N., Kuch, V. & Lehnert, L. W. Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sens. 12, 2411 (2020).Article 
    ADS 

    Google Scholar 
    Binh, N. A. et al. Thirty-year dynamics of LULC at the Dong Thap Muoi area, southern Vietnam, using Google Earth Engine. ISPRS Int. J. Geo Inf. 10, 226 (2021).Article 

    Google Scholar 
    Yang, G. X. et al. AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 102, 102446 (2021).Inman, V. L. & Lyons, M. B. Automated inundation mapping over large areas using Landsat data and Google Earth Engine. Remote Sens. 12, 1348 (2020).Article 
    ADS 

    Google Scholar 
    Long, X. R., Li, X. Y., Lin, H. & Zhang, M. Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 102, 102453 (2021).
    Google Scholar 
    Hu, Y. F., Dong, Y. & Nacun, B. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS J. Photogram. Remote Sens. 146, 347–359 (2018).Article 
    ADS 

    Google Scholar 
    Mahdianpari, M. et al. A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: A case study in Newfoundland. Gisci. Remote Sens. 57, 1102–1124 (2020).Article 

    Google Scholar 
    Brovelli, M. A., Sun, Y. & Yordanov, V. Monitoring forest change in the Amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS Int. J. Geo Inf. 9, 580 (2020).Article 

    Google Scholar 
    Yin, H. R. et al. Analysis of spatial heterogeneity and influencing factors of ecological environment quality in China’s north-south transitional zone. Int. J. Environ. Res. Public Health 19, 2236 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xinran, N., Zhenqi, H., Mengying, R., Qi, Z. & Huang, S. Remote-sensing evaluation and temporal and spatial change detection of ecological environment quality in coal-mining areas. Remote Sens. 14, 345 (2022).Article 

    Google Scholar 
    Li, H. et al. Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR. Stoch. Environ. Res. Risk Assess. 35, 2173–2186 (2021).Article 

    Google Scholar 
    Wang, J. F. & Xu, C. D. Geodetector: Principle and prospective. Acta Geogr. Sin. 72, 116–134 (2017).
    Google Scholar 
    Peng, S., Ding, Y., Liu, W. & Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data. 11, 1931–1946 (2019).Article 
    ADS 

    Google Scholar 
    Hu, X. S. & Xu, H. Q. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecol. Indic. 89, 11–21 (2018).Article 

    Google Scholar 
    Yu, G. Q., Yang, H. B., Tian, Z. Z. & Zhang, B. S. Eco-environment quality assessment of Miyun county based on RS and GIS. Proc. Environ. Sci. 10, 2601–2607 (2011).Article 

    Google Scholar 
    Chen, S. L., Zhu, Z. H., Liu, X. T. & Yang, L. Variation in vegetation and its driving force in the Pearl river delta region of China. Int. J. Environ. Res. Public Health 19, 10343 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhu, D. Y., Chen, T., Zhen, N. & Niu, R. Q. Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environ. Sci. Pollut. Res. 27, 15716–15728 (2020).Article 

    Google Scholar 
    Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A. & Skakun, S. Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci. 5, 1–10 (2017).Article 

    Google Scholar 
    Kumar, L. & Mutanga, O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 10, 1509 (2018).Article 
    ADS 

    Google Scholar 
    Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
    ADS 

    Google Scholar 
    Parastatidis, D., Mitraka, Z., Chrysoulakis, N. & Abrams, M. Online global land surface temperature estimation from Landsat. Remote Sens. 9, 1208 (2017).Article 
    ADS 

    Google Scholar 
    Kennedy, R. E. et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens. 10, 691 (2018).Article 
    ADS 

    Google Scholar 
    Huang, H. B. et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176 (2017).Article 
    ADS 

    Google Scholar 
    Ying, L. et al. Estimation of remote sensing based ecological index along the Grand Canal based on PCA-AHP-TOPSIS methodology. Ecol. Ind. 122, 107214 (2021).Article 

    Google Scholar 
    He, X., Li, M., Guo, H. & Tian, Z. Evaluation of ecological environment of Songshan scenic area based on GF-1 data. in IOP Conference Series: Materials Science and Engineering. Vol. 392. 042029 (2018).Yi, Z., Jiyun, S., Xiangren, L. & Meng, Z. Spatio-temporal evolution and driving factors of eco-environmental quality based on RSEI in Chang-Zhu-Tan metropolitan circle, central China. Ecol. Ind. 144, 109436 (2022).Article 

    Google Scholar 
    Wan, H. L., Huo, F., Niu, Y. F., Zhang, W. & Zhang, Q. R. Dynamic monitoring and analysis of ecological environment change in Cangzhou city based on RSEI model considering PM2.5 concentration. Prog. Geophys. 36, 953–960 (2021).
    Google Scholar 
    Wang, J., Ma, J. L., Xie, F. F. & Xu, X. J. Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example. Chin. J. Appl. Ecol. 31, 3795–3804 (2020).
    Google Scholar  More

  • in

    Resource sharing is sufficient for the emergence of division of labour

    Ulrich, Y., Saragosti, J., Tokita, C. K., Tarnita, C. E. & Kronauer, D. J. C. Fitness benefits and emergent division of labour at the onset of group living. Nature 560, 635–638 (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Duarte, A., Weissing, F. J., Pen, I. & Keller, L. An evolutionary perspective on self-organized division of labor in social insects. Annu Rev. Ecol. Evol. Syst. 42, 91–110 (2011).Article 

    Google Scholar 
    West, S. A. & Cooper, G. A. Division of labour in microorganisms: an evolutionary perspective. Nat. Rev. Microbiol. 14, 716–723 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Oster, G. F. & Wilson, E. O. Caste and ecology in the social insects. (Princeton University Press, 1978).Arnold, K. E., Owens, I. P. F. & Goldizen, A. W. Division of labour within cooperatively breeding groups. Behav 142, 1577–1590 (2005).Article 

    Google Scholar 
    Bruintjes, R. & Taborsky, M. Size-dependent task specialization in a cooperative cichlid in response to experimental variation of demand. Anim. Behav. 81, 387–394 (2011).Article 

    Google Scholar 
    Bergmüller, R. & Taborsky, M. Adaptive behavioural syndromes due to strategic niche specialization. BMC Ecol. 7, 12 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schaller, G. B. The Serengeti lion: a study of predator-prey relations. (University of Chicago press, 2009).Bonabeau, E., Theraulaz, G. & Deneubourg, J.-L. Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies. Proc. Biol. Sci. 263, 1565–1569 (1996).Article 

    Google Scholar 
    Bonabeau, E. Fixed response thresholds and the regulation of division of labor in insect societies. Bull. Math. Biol. 60, 753–807 (1998).Article 
    MATH 

    Google Scholar 
    Graham, S., Myerscough, M. R., Jones, J. C. & Oldroyd, B. P. Modelling the role of intracolonial genetic diversity on regulation of brood temperature in honey bee (Apis mellifera L.) colonies. Insect Soc. 53, 226–232 (2006).Article 

    Google Scholar 
    Jeanson, R., Fewell, J. H., Gorelick, R. & Bertram, S. M. Emergence of increased division of labor as a function of group size. Behav. Ecol. Sociobiol. 62, 289–298 (2007).Article 

    Google Scholar 
    Gove, R., Hayworth, M., Chhetri, M. & Rueppell, O. Division of labour and social insect colony performance in relation to task and mating number under two alternative response threshold models. Insect. Soc. 56, 319–331 (2009).Article 

    Google Scholar 
    Ulrich, Y. et al. Response thresholds alone cannot explain empirical patterns of division of labor in social insects. PLoS Biol. 19, e3001269 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jeanson, R. & Weidenmüller, A. Interindividual variability in social insects – proximate causes and ultimate consequences. Biol. Rev. 89, 671–687 (2014).Article 
    PubMed 

    Google Scholar 
    Toth, A. L. & Robinson, G. E. Worker nutrition and division of labour in honeybees. Anim. Behav. 69, 427–435 (2005).Article 

    Google Scholar 
    Smith, C. R. et al. Nutritional asymmetries are related to division of labor in a queenless ant. PLoS ONE 6, e24011 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bernadou, A. et al. Stress and early experience underlie dominance status and division of labour in a clonal insect. Proc. R. Soc. B 285, 20181468 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bernadou, A., Hoffacker, E., Pable, J. & Heinze, J. Lipid content influences division of labour in a clonal ant. J. Exp. Biol. 223, jeb.219238 (2020).Article 

    Google Scholar 
    Dussutour, A., Poissonnier, L.-A., Buhl, J. & Simpson, S. J. Resistance to nutritional stress in ants: when being fat is advantageous. J. Exp. Biol. 219, 824–833 (2016).Article 
    PubMed 

    Google Scholar 
    Blanchard, G. B., Orledge, G. M., Reynolds, S. E. & Franks, N. R. Division of labour and seasonality in the ant Leptothorax albipennis: worker corpulence and its influence on behaviour. Anim. Behav. 59, 723–738 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Toth, A. L., Kantarovich, S., Meisel, A. F. & Robinson, G. E. Nutritional status influences socially regulated foraging ontogeny in honey bees. J. Exp. Biol. 208, 4641–4649 (2005).Article 
    PubMed 

    Google Scholar 
    Carter, G. G. & Wilkinson, G. S. Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment. Proc. R. Soc. B 280, 20122573 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Meurville, Marie-Pierre & LeBoeuf, AdriaC. Trophallaxis: the functions and evolution of social fluid exchange in ant colonies (Hymenoptera: Formicidae). Myrmecol N. 31, 1–30 (2021).
    Google Scholar 
    Duarte, A., Pen, I., Keller, L. & Weissing, F. J. Evolution of self-organized division of labor in a response threshold model. Behav. Ecol. Sociobiol. 66, 947–957 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moll, K., Federle, W. & Roces, F. The energetics of running stability: costs of transport in grass-cutting ants depend on fragment shape. J. Exp. Biol. 215, 161–168 (2012).Article 
    PubMed 

    Google Scholar 
    Ostwald, M. M., Fox, T. P., Harrison, J. F. & Fewell, J. H. Social consequences of energetically costly nest construction in a facultatively social bee. Proc. R. Soc. B 288, 20210033 (2021). rspb.2021.0033.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Molina, Y. & O’Donnell, S. A developmental test of the dominance-nutrition hypothesis: linking adult feeding, aggression, and reproductive potential in the paperwasp Mischocyttarus mastigophorus. Ethol. Ecol. Evol. 20, 125–139 (2008).Article 

    Google Scholar 
    Fiocca, K. et al. Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions. Behav. Ecol. Sociobiol. 74, 114 (2020).Article 
    MathSciNet 

    Google Scholar 
    Wcislo, W. T. & Gonzalez, V. H. Social and ecological contexts of trophallaxis in facultatively social sweat bees, Megalopta genalis and M. ecuadoria (Hymenoptera, Halictidae). Insect Soc. 53, 220–225 (2006).Article 

    Google Scholar 
    Gautrais, J., Theraulaz, G., Deneubourg, J.-L. & Anderson, C. Emergent polyethism as a consequence of increased colony size in insect societies. J. Theor. Biol. 215, 363–373 (2002).Article 
    ADS 
    PubMed 

    Google Scholar 
    Ferguson-Gow, H., Sumner, S., Bourke, A. F. G. & Jones, K. E. Colony size predicts division of labour in attine ants. Proc. R. Soc. B 281, 20141411 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dornhaus, A., Holley, J.-A. & Franks, N. R. Larger colonies do not have more specialized workers in the ant Temnothorax albipennis. Behav. Ecol. 20, 922–929 (2009).Article 

    Google Scholar 
    Ackermann, M. et al. Self-destructive cooperation mediated by phenotypic noise. Nature 454, 987–990 (2008).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Dubnau, D. & Losick, R. Bistability in bacteria. Mol. Microbiol 61, 564–572 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Honegger, K. & de Bivort, B. Stochasticity, individuality and behavior. Curr. Biol. 28, R8–R12 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schiessl, K. T. et al. Individual- versus group-optimality in the production of secreted bacterial compounds. Evolution 73, 675–688 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elsner, D., Hartfelder, K. & Korb, J. Molecular underpinnings of division of labour among workers in a socially complex termite. Sci. Rep. 11, 18269 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kohlmeier, P., Feldmeyer, B. & Foitzik, S. Vitellogenin-like A–associated shifts in social cue responsiveness regulate behavioral task specialization in an ant. PLoS Biol. 16, e2005747 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morandin, C., Hietala, A. & Helanterä, H. Vitellogenin and vitellogenin-like gene expression patterns in relation to caste and task in the ant Formica fusca. Insect Soc. 66, 519–531 (2019).Article 

    Google Scholar 
    Cooper, G. A. & West, S. A. Division of labour and the evolution of extreme specialization. Nat. Ecol. Evol. 2, 1161–1167 (2018).Article 
    PubMed 

    Google Scholar 
    Ferrante, E., Turgut, A. E., Duéñez-Guzmán, E., Dorigo, M. & Wenseleers, T. Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput. Biol. 11, e1004273 (2015).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    West-Eberhard, M.J. Wasp societies as microcosms for the study of development and evolution. in Natural history and evolution of paper-wasps (eds. Turillazzi, S. & West-Eberhard, M. J.) 290–317 (Oxford University Press, 1996).West-Eberhard, M. J. Flexible strategy and social evolution. in Animal societies: theories and facts (eds. Itō, Y., Brown, J. L. & Kikkawa, J.) 35–51 (Japan Scientific Societies Press, 1987).Amdam, G. V., Csondes, A., Fondrk, M. K. & Page, R. E. Complex social behaviour derived from maternal reproductive traits. Nature 439, 76–78 (2006).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krishnan, J. U., Brahma, A., Chavan, S. K. & Gadagkar, R. Nutrition induced direct fitness for workers in a primitively eusocial wasp. Insect Soc. 68, 319–325 (2021).Article 

    Google Scholar 
    O’Donnell, S. et al. Adult nutrition and reproductive physiology: a stable isotope analysis in a eusocial paper wasp (Mischocyttarus mastigophorus, Hymenoptera: Vespidae). Behav. Ecol. Sociobiol. 72, 86 (2018).Article 

    Google Scholar 
    Salomon, M., Mayntz, D. & Lubin, Y. Colony nutrition skews reproduction in a social spider. Behav. Ecol. 19, 605–611 (2008).Article 

    Google Scholar 
    Hunt, J. H. & Amdam, G. V. Bivoltinism as an antecedent to eusociality in the paper wasp genus Polistes. Science 308, 264–267 (2005).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hunt, J. H., Buck, N. A. & Wheeler, D. E. Storage proteins in vespid wasps: characterization, developmental pattern, and occurrence in adults. J. Insect Physiol. 49, 785–794 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hunt, J. H. et al. Differential gene expression and protein abundance evince ontogenetic bias toward castes in a primitively eusocial wasp. PLoS ONE 5, e10674 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smith, C. R., Toth, A. L., Suarez, A. V. & Robinson, G. E. Genetic and genomic analyses of the division of labour in insect societies. Nat. Rev. Genet. 9, 735–748 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sumner, S., Pereboom, J. J. M. & Jordan, W. C. Differential gene expression and phenotypic plasticity in behavioural castes of the primitively eusocial wasp, Polistes canadensis. Proc. R. Soc. B 273, 19–26 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gräff, J., Jemielity, S., Parker, J. D., Parker, K. M. & Keller, L. Differential gene expression between adult queens and workers in the ant Lasius niger. Mol. Ecol. 16, 675–683 (2007).Article 
    PubMed 

    Google Scholar 
    Nelson, C. M., Ihle, K. E., Fondrk, M. K., Page, R. E. & Amdam, G. V. The gene vitellogenin has multiple coordinating effects on social organization. PLoS Biol. 5, e62 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Corona, M. et al. Vitellogenin underwent subfunctionalization to acquire caste and behavioral specific expression in the harvester ant Pogonomyrmex barbatus. PLoS Genet 9, e1003730 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fewell, J. H. & Page, R. E. Jr The emergence of division of labour in forced associations of normally solitary ant queens. Evolut. Ecol. Res. 1, 537–548 (1999).
    Google Scholar 
    Kalina, J. Nest intruders, nest defence and foraging behaviour in the Black-and-white Casqued Hornbill Bycanistes subcylindricus. Ibis 131, 567–571 (1988).Article 

    Google Scholar 
    Heinsohn, R. & Legge, S. Breeding biology of the reverse-dichromatic, co-operative parrot Eclectus roratus. J. Zool. 259, 197–208 (2003).Article 

    Google Scholar 
    Zárybnická, M. & Vojar, J. Effect of male provisioning on the parental behavior of female Boreal Owls Aegolius funereus. Zool. Stud. 52, 36 (2013).Article 

    Google Scholar 
    Flores, E. & Herrero, A. Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nat. Rev. Microbiol. 8, 39–50 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Maynard Smith, J. & Szathmáry, E. The major transitions in evolution. (W.H. Freeman, 1995).West, S. A., Fisher, R. M., Gardner, A. & Kiers, E. T. Major evolutionary transitions in individuality. Proc. Natl Acad. Sci. 112, 10112–10119 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gorelick, R., Bertram, S. M., Killeen, P. R. & Fewell, J. H. Normalized mutual entropy in biology: quantifying division of labor. Am. Naturalist 164, 677–682 (2004).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).Wickham, H. ggplot2: elegant graphics for data analysis. (Springer-Verlag New York, 2016).Auguie, B. gridExtra: miscellaneous functions for ‘Grid’ graphics. (https://CRAN.R-project.org/package=gridExtra, 2017).Wilke, C. O. cowplot: streamlined plot theme and plot annotations for ‘ggplot2’. (https://CRAN.R-project.org/package=cowplot, 2019).Mills, B. R. MetBrewer: color palettes inspired by works at the Metropolitan Museum of Art. (https://CRAN.R-project.org/package=MetBrewer, 2021). More

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    Extinction magnitude of animals in the near future

    Selection of environmental-biotic events to be studiedIn global warming events associated with mass extinctions, the current environmental changes are similar to those recorded during the end-Ordovician, end-Guadalupian, and end-Permian mass extinctions. Therefore, I analyzed global surface temperature anomalies, mercury pollution concentrations, and deforestation percentages in these three mass extinctions and in the current crisis. The asteroid impact at the K–Pg boundary and nuclear war cause the formation of stratospheric soot aerosols distributed globally, thus inducing sunlight reductions and global cooling (impact winter and nuclear winter). I also analyzed stratospheric soot aerosols as a possible cause of future extinctions.Most likely case and worst caseThe most likely case corresponds to the reduction of CO2 emissions resulting from human conduct, the protection of forests, and the introduction of anti-pollution measures in the future under the Paris Agreement on Climate change and Sustainable Development Goals (SDGs). The worst case corresponds to the scenario in which humans fail to stop increasing global surface temperatures, pollution, and deforestation until 2100–2200 CE.I use the average of the RCP4.5 and RCP6.0 cases in the Intergovernmental Panel on Climate Change (IPCC)8 as the most likely case of GHG emissions, representing the middle of the four potential GHG emissions cases (RCP2.6, 4.5, 6.0, and 8.5) in Fifth Assessment Report of the IPCC8, approximately corresponding to the middle of SSP2-4.5 and SSP3-7.0 in Sixth Assessment Report of the IPCC9. The timing of decreased global GHG emissions is 2060–2080 CE. Therefore, I use the average GHG emissions and global surface temperature anomalies of the RCP4.5 and RCP6.0 cases as the most likely values and those of the RCP8.5 case as the worst-case scenario, marked by stopping GHG emissions from 2090 to 2100 CE8,9, as this case corresponds to the highest GHG emissions8,9.Surface temperature anomaly, environment, and extinction magnitude dataData on surface temperature anomalies and extinction percentages are from Kaiho4. Changes in industrial GHG emissions and global surface temperature anomalies are sourced from the Fifth and Sixth Assessment Report of the IPCC8,9.Pollution can be represented by mercury concentrations measured in sedimentary rocks recording mass extinctions8 and in recent sediments deposited in seas and lakes25,26 because mercury is toxic to plants and animals and because its sources include volcanic eruptions, meteorite impacts, and the combustion of fossil fuels10,33, which are common sources of pollutants, and because it can be commonly measured from sedimentary rocks recording mass extinctions33. The mercury concentration is related to the CO2 emission amount during global warming because of the common sources of mercury and CO2 (volcanism and fossil fuel combustion influencing global warming). Thus, the future mercury concentrations are estimated based on the CO2 emission amounts estimated by the IPCC8,9. Since mercury and the other pollutants mainly come from oil, coal, and vegetation33, the amount of mercury released should change in parallel with industrial CO2 emissions because there is a good correlation between mercury and CO2 emissions11.Deforestation occurs by the expansion of agricultural areas and urban areas, which are strongly related to human populations13,28. Thus, future deforestation percentages are estimated based on estimated future population data27 (Supplementary Table S2). The severity of deforestation in each event is expressed by the occupancy % of the deforested area in the pre-event forest area in (i) the Permian–Triassic transition marked by the largest mass extinction based on plant fossil records24 and (ii) 2005–2015 CE as a representative of the Anthropocene epoch12,13,28 based on the actual forest area relative to the pre-agriculture phase before 4000 BP. Deforestation is related to the human population because agriculture and urbanization have caused deforestation13,28. I estimate the past and future deforestation percentage using human population data in the past and future21 based on the parallel growth of the human population and deforestation13,28.Amount of stratospheric soot was calculated using a method of Kaiho and Oshima34 (Supplementary Table S1). I obtained global surface temperature anomaly caused by stratospheric soot using Fig. 5 of Kaiho and Oshima34.I then use those data to estimate the future extinction magnitude based on the assumption that the Earth and contemporary life at the time of each crisis are more or less mutually comparable throughout time and to the present day.I estimate the magnitude of the species animal extinction crisis between 2000 and 2500 CE using Figs. 1, 2 and Supplementary Tables S1 and S2 in each cause under the most likely case and worst case under three nuclear war scenarios (zero, minor, and major; Fig. 2d)15 in the PETM and mass extinction cases, respectively (Supplementary Tables S3, S4; Fig. 3). Finally, I estimate the magnitude of current animal extinction crisis by the four causes as an average of the species extinction magnitude by the four causes in Fig. 3. I use two different contribution rates of temperature anomalies, pollution, deforestation, and stratospheric soot by nuclear wars, 1:0.2:0.1:1 for marine animals and 1:0.5:1:1 for terrestrial tetrapods (different contribution case considering lower influence of pollution and deforestation to marine animals rather than terrestrial animals) and 1:1:1:1 for marine animals and 1:1:1:1 for terrestrial tetrapods (equal contribution case considering high influence of pollution and deforestation to marine animals via rain and soil erosion) (Supplementary Tables S5–S9). These contribution rates are estimated as end-members to show ranges of animal species extinction magnitude (%). More

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    Ecologists should create space for a wide range of expertise

    Madhusudan Katti says ecology would benefit from including perspectives from all of Earth’s inhabitants.Credit: Marc Hall

    Decolonizing science

    Science is steeped in injustice and exploitation. Scientific insights from marginalized people have been erased, natural history specimens have been taken without consent and genetics data have been manipulated to back eugenics movements. Without acknowledgement and redress of this legacy, many people from minority ethnic groups have little trust in science and certainly don’t feel welcome in academia — an ongoing barrier to the levels of diversity that many universities claim to pursue.
    In the next of a short series of articles about decolonizing the biosciences, Madhusudan Katti suggests five shifts that ecologists need to make to unravel the effects of colonization on their field. Katti, an evolutionary ecologist at North Carolina State University in Raleigh, would also like to see stronger inclusion of uncredentialed experts and Indigenous communities in research.

    Last year, my colleagues and I wrote a paper highlighting five shifts that would help to decolonize ecology (C. H. Trisos et al. Nature Ecol. Evol. 5, 1205–1212; 2021). Ecologists need to improve how they incorporate varied perspectives, approaches and interpretations from the diverse peoples inhabiting Earth’s natural environments. The five shifts are: the individual need to decolonize one’s mind; understand the history of colonization and how it shaped Western ecology; facilitate access to and dissemination of data; recognize diverse scientific expertise; and establish inclusive research groups. Although it can be difficult to make reforms given how resistant institutions are to change, we are optimistic because we have received invitations to speak on these issues. People are ready for these conversations.
    Decolonizing science toolkit
    My colleagues and I developed a workshop around the five shifts. We have conducted the workshop at my institution, and at the annual conference of the Society for Integrative and Comparative Biology. For each of the shifts, I have participants brainstorm and write down challenges and solutions that might lead to progress in these areas for their own research departments or institutions. We address them, shuffle groups and suggest policy changes and future action.Some organizations are already moving forward with some low-hanging fruit, such as making data and published results more accessible. However, open-access publishing models put an even greater burden of publication costs on authors and perpetuate inequalities, because early-career researchers and those in the global south often can’t afford them.The most contentious area tends to be the reluctance of academia to accept non-credentialed expertise such as traditional knowledge. Universities are in the business of giving out credentials in the form of degrees. If academia no longer requires a PhD, that can be a challenge to that model. There are also few, if any, incentives or rewards to spend time working towards decolonizing academia, even though it takes time and effort away from furthering individual careers.As an Indian American, I would like to see institutions expand antiracism conversations rather than introduce new checklists of things to do. For example, at annual meetings, it would be great to see scientific societies make more connections with the Indigenous communities where we work and invite them to share their perspectives.
    This interview has been edited for length and clarity. More