More stories

  • in

    Single-cell measurements and modelling reveal substantial organic carbon acquisition by Prochlorococcus

    Isotope labelling and phylogenetic analysis of a natural marine bacterioplankton population at seaMediterranean seawater was collected during August 2017 (station N1200, 32.45° N, 34.37 °E) from 11 depths by Niskin bottles and divided into triplicate 250 ml polycarbonate bottles. Two bottles from each depth were labelled with 1 mM sodium bicarbonate-13C and 1 mM ammonium-15N chloride (Sigma-Aldrich), and all three bottles (two labelled and one control) were incubated at the original depth and station at sea for 3.5 h around mid-day. The stable isotopes were chosen to enable direct comparison of C and N uptake in single cells, and the short incubation time was chosen to minimize isotope dilution and potential recycling and transfer of 13C and 15N between community members25. After incubation, bottles were brought back on board and the incubations were stopped by fixing with 2× electron-microscopy-grade glutaraldehyde (2.5% final concentration) and stored at 4 °C until sorting analysis. Cell sorting, NanoSIMS analyses and the calculation of uptake rates were performed as described in Roth-Rosenberg et al.26.DNA collection and extraction from seawaterSamples for DNA were collected on 0.22 µm Sterivex filters (Millipore). Excess water was removed using a syringe, 1 ml lysis buffer (40 mM EDTA, 50 mM Tris pH 8.3, and 0.75 M sucrose) was added and both ends of the filter were closed with parafilm. Samples were kept at −80 °C until extraction. DNA was extracted by using a semi-automated protocol including manual chemical cell lysis before automated steps using the QIAamp DNA Mini Protocol: DNA Purification from Blood or Body Fluids (Spin Protocol, starting from step 6, at the BioRap unit, Faculty of Medicine, Technion). The manual protocol began with thawing the samples, then the storage buffer was removed using a syringe and 170 µl lysis buffer added to the filters. Thirty microlitres of Lysozyme (20 mg ml−1) were added to the filters and incubated at 37 °C for 30 min. After incubation, 20 µl proteinase K and 200 µl buffer AL (from the Qiagen kit) were added to the tube for 1 h at 56 °C (with agitation). The supernatant was transferred to a new tube, and DNA was extracted using the QIAcube automated system. All DNA samples were eluted in 100 μl DNA-free distilled water.ITS PCR amplificationPCR amplification of the ITS was carried out with specific primers for Prochlorococcus CS1_16S_1247F (5′-ACACTGACGACATGGTTCTACACGTACTACAATGCTACGG) and Cs2_ITS_Ar (5′-TACGGTAGCAGAGACTTGGTCTGGACCTCACCCTTATCAGGG)21,22. The first PCR was performed in triplicate in a total volume of 25 μl containing 0.5 ng of template, 12.5 μl of MyTaq Red Mix (Bioline) and 0.5 μl of 10 μM of each primer. The amplification conditions comprised steps at 95 °C for 5 min, 28/25 (16 S/ITS) cycles at 95 °C for 30 s, 50 °C for 30 s and 72 °C for 1 min followed by one step of 5 min at 72 °C. All PCR products were validated on a 1% agarose gel, and triplicates were pooled. Subsequently, a second PCR amplification was performed to prepare libraries. These were pooled and after a quality control sequenced (2 × 250 paired-end reads) using an Illumina MiSeq sequencer. Library preparation and pooling were performed at the DNA Services facility, Research Resources Center, University of Illinois at Chicago. MiSeq sequencing was performed at the W.M. Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign.ITS sequence processingPaired-end reads were analysed using the Dada2 pipeline46. The quality of the sequences per sample was examined using the Dada2 ‘plotQualityProfile’ command. Quality filtering was performed using the Dada2 ‘filterAndTrim’ command with parameters for quality filtering truncLen=c(290,260), maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE, trimLeft=c(20,20). Following error estimation and dereplication, the Dada2 algorithm was used to correct sequences. Merging of the forward and reverse reads was done with minimum overlap of 4 bp. Detection and removal of suspected chimaeras was done with command ‘removeBimeraDenovo’. In total, 388,417 sequences in 484 amplicon sequence variants were counted. The amplicon sequence variants were aligned in MEGA6 (ref. 47), and the first ~295 nucleotides, corresponding to the 16S gene, were trimmed. The ITS sequences were then classified using BLASTn against a custom database of ITS sequences from cultured Prochlorococcus and Synechococcus strains as well as from uncultured HL and LL clades.Individual-based modelPlanktonIndividuals.jl (v0.1.9) was used to run the individual-based simulations48. Briefly, the cells fix inorganic carbon through photosynthesis and nitrogen, phosphorus and DOC from the water column into intracellular quotas and grow until division or grazing. Cell division is modelled as a probabilistic function of cell size. Grazing is represented by a quadratic probabilistic function of cell population. Cells consume nutrient resources, which are represented as Eulerian, density-based tracers. A full documentation of state variables and model equations are available online at https://juliaocean.github.io/PlanktonIndividuals.jl/dev/. Equations related to mixotrophy are shown below as an addition to the online documentation.$$V_{{mathrm{DOC}}} = V_{{mathrm{DOC}}}^{{mathrm{max}}} cdot {{mathrm{max}}}left( {0.0,{{mathrm{min}}}left( {1.0,,frac{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}}}{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}^{{mathrm{min}}}}}} right)} right) cdot frac{{{mathrm{DOC}}}}{{{mathrm{DOC}} + K_{{mathrm{DOC}}}^{{mathrm{sat}}}}}$$
    (1)
    $$f_{{mathrm{PS}}} = frac{{P_{mathrm{S}}}}{{P_{mathrm{S}} + V_{{mathrm{DOC}}}}}$$
    (2)
    $$V_{{mathrm{DOC}}} = 0,,{mathrm{if}},f_{{mathrm{PS}}} < f_{{mathrm{PS}}}^{{mathrm{min}}}$$ (3) where VDOC is the cell-specific DOC uptake rate (mol C cell−1 s−1), (V_{{mathrm{DOC}}}^{{mathrm{max}}}) is the maximum cell-specific DOC uptake rate (mol C cell−1 s−1), (q_{mathrm{C}}^{{mathrm{max}}}) is the maximum cell carbon quota (mol C cell−1), (q_{mathrm{C}}^{{mathrm{min}}}) is the minimum cell carbon quota (mol C cell−1). The maximum and minimum functions here is used to keep qC between (q_{mathrm{C}}^{{mathrm{min}}}) and (q_{mathrm{C}}^{{mathrm{max}}}). (K_{{mathrm{DOC}}}^{{mathrm{sat}}}) is the half-saturation constant for DOC uptake (mol C m−3). fPS is the fraction of fixed C originating from photosynthesis (PS, mol C cell−1 s−1). DOC uptake stops when fPS is smaller than (f_{{mathrm{PS}}}^{{mathrm{min}}})(minimum fraction of fixed C originating form photosynthesis, 1% by default) according to laboratory studies of Prochlorococcus that showed that they cannot survive long exposure to darkness (beyond several days) even when supplied with organic carbon sources13. (1 − fPS) is also shown in Fig. 3 as the contribution of DOC uptake.We set up two separate simulations; each of them has a population of either an obligate photo-autotroph or a mixotroph that also consumes DOC. The initial conditions and parameters (Supplementary Table 3) are the same for the two simulations except the ability of mixotrophy. The simulations were run with a timestep of 1 min for 360 simulated days to achieve a steady state. We run the two simulations for multiple times in order to get the range of the stochastic processes.Evaluation of autotrophic growth ratesWe evaluated the carbon-specific, daily-averaged carbon fixation rate, ℙ as a function of light intensity (I, µE), following Platt et al.33:$${Bbb P} = frac{1}{{Delta t}}{int}_0^{Delta t} {frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}} P_{mathrm{S}}^{{mathrm{Chl}}}left( {1 - e^{ - alpha _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}} right)e^{ - beta _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}Delta t$$ (4) Here, (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl are empirically determined coefficients representing the chlorophyll-a-specific carbon fixation rate (mol C (mol Chl)−1 s−1), the initial slope of the photosynthesis–light relationship and photo-inhibition effects at high photon fluxes, respectively. We impose empirically determined values for (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl from the published study of Moore and Chisholm24. The natural Prochlorococcus community comprises HL and LL ecotypes, which have different values of (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl, and the community growth rate is expected to be between that of HL extremes and LL extremes. Therefore, we use photo-physiological parameters for an HL-adapted ecotype (MIT9215), acclimated at 70 µmol photons m−2 s−1 and an LL-adapted ecotype (MIT9211), acclimated 9 µmol photons m−2 s−1. The models with these values are shown as the different lines in Fig. 2b,d. I is the hourly PAR, estimated by scaling the observed noon value at each depth with a diurnal variation evaluated from astronomical formulae based on geographic location and time of year37,38.(frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is the molar chlorophyll-a to carbon ratio, which is modelled as a function of growth rate and light intensity using the Inomura34 model (equation 17 therein) where parameters were calibrated with laboratory data from Healey49. In addition, the maximum growth rate ((mu _{{mathrm{max}}}^I)) based on macromolecular allocation is also estimated using the Inomura model (equation 30 therein). An initial guess of the growth rate and the empirically informed light intensity are used to estimate (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}), which is then used to evaluate the light-limited, photoautotrophic growth rate$${Bbb V}_{mathrm{C}}^{{mathrm{auto}}} = min left( {{Bbb P} - K_{mathrm{R}},mu _{{mathrm{max}}}^I} right)$$ (5) from which the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is again updated. The light-limited growth rate is used to re-evaluate the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}). Repeating this sequence until the values converge, ({Bbb V}_{mathrm{C}}^{{mathrm{auto}}}) and (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) are solved iteratively.The nitrogen-specific uptake rate of fixed nitrogen (day−1) is modelled as$${Bbb V}_{{{mathrm{N}}}} = {Bbb V}_{mathrm{N}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{N}}}}frac{N}{{N + K_{{{mathrm{N}}}}}}$$ (6) where values of the maximum uptake rate, ({Bbb V}_{mathrm{N}}^{{mathrm{max}}}), and half-saturation, KN, are determined from empirical allometric scalings35, along with a nitrogen cell quota QN from Bertilsson et al.39.The P-limited growth rate, or the phosphorus-specific uptake rate of phosphate (day−1), is modelled as$${Bbb V}_{mathrm{P}} = {Bbb V}_{mathrm{P}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{P}}}}frac{{{mathrm{PO}_{4}}^{3 - }}}{{{mathrm{PO}_{4}}^{3 - } + K_{mathrm{P}}}}$$ (7) where values of the maximum uptake rate, ({Bbb V}_{mathrm{P}}^{{mathrm{max}}}). and half-saturation, KP, are determined from empirical allometric scalings35, along with a nitrogen cell quota QP from Bertilsson et al.39.Iron uptake is modelled as a linear function of cell surface area (SA), with rate constant ((k_{{mathrm{Fe}}}^{{mathrm{SA}}})) following Lis et al.36.$${Bbb V}_{{mathrm{Fe}}} = k_{{mathrm{Fe}}}^{{mathrm{SA}}} cdot {mathrm{SA}}frac{1}{{Q_{{mathrm{Fe}}}}}{mathrm{Fe}}$$ (8) The potential light-, nitrogen-, phosphorus- and iron-limited growth rates (({Bbb V}_{mathrm{C}},{Bbb V}_{mathrm{N}},{Bbb V}_{mathrm{P}},{Bbb V}_{{mathrm{Fe}}})) were evaluated at each depth in the water column and the minimum is the local modelled photo-autotrophic growth rate estimate, assuming no mixotrophy (Fig. 2b,d, blue lines). Parameters used in this evaluation are listed in Supplementary Table 2.An important premise of this study is that heterotrophy is providing for the shortfall in carbon under very low light conditions, but not nitrogen. It is known that Prochlorococcus can assimilate amino acids9 and therefore the stoichiometry of the heterotrophic contribution might alter the interpretations. However, it is also known that Prochlorococcus can exude amino acids40, which might cancel out the effects on the stoichiometry of Prochlorococcus.For the estimates of phototrophic growth rate from local environmental conditions (Fig. 2) we employed photo-physiological parameters from laboratory cultures of Prochlorococcus24. For the purposes of this study, we have assumed that the photosynthetic rates predicted are net primary production, which means that autotrophic respiration has been accounted for in the measurement. However, the incubations in that study were of relatively short timescale (45 min), which might suggest they are perhaps more representative of gross primary production. If this is the case, our estimates of photo-autotrophic would be even lower after accounting for autotrophic respiration, and thus would demand a higher contribution from heterotrophic carbon uptake. In this regard, our estimates might be considered a lower bound for organic carbon assimilation.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    Mountain- and brown hare genetic polymorphisms to survey local adaptations and conservation status of the heath hare (Lepus timidus sylvaticus, Nilsson 1831)

    Angerbjörn, A. & Flux, J. E. C. Lepus timidus. Mamm. Species 1–11, https://doi.org/10.2307/3504302 (1995).Bergengren, A. On genetics, evolution and history of distribution of the heath-hare, a distinct population of the Arctic hare, Lepus timidus Lin. Swed. Wildl. (Viltrevy) 6, 381–460 (1969).
    Google Scholar 
    Thulin, C.-G. The distribution of mountain hares Lepus timidus in Europe: a challenge from brown hares L. europaeus? Mamm. Rev. 33, 29–42 (2003).Article 

    Google Scholar 
    Mills, L. S. et al. Camouflage mismatch in seasonal coat color due to decreased snow duration. Proc. Nat.Acad. Sci. 110, 7360–7365 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zimova, M. et al. Lack of phenological shift leads to increased camouflage mismatch in mountain hares. Proc.Royal Soc. B: Biol. Sci. 287, 20201786 (2020).Article 

    Google Scholar 
    Levänen, R., Kunnasranta, M. & Pohjoismäki, J. Mitochondrial DNA introgression at the northern edge of the brown hare (Lepus europaeus) range. Ann Zool Fennici 55, 15–24 (2018).Article 

    Google Scholar 
    Thulin, C.-G., Winiger, A., Tallian, A. G. & Kindberg, J. Hunting harvest data in Sweden indicate precipitous decline in the native mountain hare subspecies Lepus timidus sylvaticus (heath hare). J. Nat. Conserv. 64, 126069 (2021).Article 

    Google Scholar 
    Thulin, C.-G., Jaarola, M. & Tegelström, H. The occurrence of mountain hare mitochondrial DNA in wild brown hares. Mol. Ecol. 6, 463–467 (1997).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pohjoismäki, J. L. O., Michell, C., Levänen, R. & Smith, S. Hybridization with mountain hares increases the functional allelic repertoire in brown hares. Sci. Rep. 11, 15771 (2021).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoekstra, H. E. Genetics, development and evolution of adaptive pigmentation in vertebrates. Heredity (Edinb) 97, 222–234 (2006).Article 
    CAS 

    Google Scholar 
    Hamill, R. M., Doyle, D. & Duke, E. J. Spatial patterns of genetic diversity across European subspecies of the mountain hare, Lepus timidus L. Heredity (Edinb) 97, 355–365 (2006).Article 
    CAS 

    Google Scholar 
    Leach, K., Montgomery, W. I. & Reid, N. Biogeography, macroecology and species’ traits mediate competitive interactions in the order Lagomorpha. Mamm. Rev. 45, 88–102 (2015).Article 

    Google Scholar 
    Marques, J. P. et al. Data Descriptor: Mountain hare transcriptome and diagnostic markers as resources to monitor hybridization with European hares. Sci. Data 4, 1–11 (2017).Article 

    Google Scholar 
    NCBI Sequence Read Archive https://identifiers.org/insdc.sra:SRP358660 (2022).Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics. Preprint at http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).Article 

    Google Scholar 
    Marques, J. P. et al. An annotated draft genome of the mountain hare (Lepus timidus). Genome Biol. Evol. 12, 3656–3662 (2020).Article 
    CAS 
    PubMed 

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

    Google Scholar 
    Broad Institute. Picard toolkit. Broad Institute, GitHub repository. Preprint at https://broadinstitute.github.io/picard/ (2019).Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv 1207.3907 (2012).Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Michell, C. T., Pohjoismäki, J. L. O., Spong, G. & Thulin, C.-G. Mountain- and brown hare genetic polymorphisms to survey local adaptations and conservation status of the heath hare (Lepus timidus sylvaticus, Nilsson 1831), Dryad, https://doi.org/10.5061/dryad.3bk3j9kmp (2022).Khan, A. & Mathelier, A. Intervene: a tool for intersection and visualization of multiple gene or genomic region sets. BMC Bioinformatics 18, 287 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dierckxsens, N., Mardulyn, P. & Smits, G. NOVOPlasty: De novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 45 (2017).Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30 (2013).Trifinopoulos, J., Nguyen, L. T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44 (2016).Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Levänen, R., Thulin, C.-G., Spong, G. & Pohjoismäki, J. L. O. Widespread introgression of mountain hare genes into Fennoscandian brown hare populations. PloS One 13, e0191790 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Giska, I. et al. The evolutionary pathways for local adaptation in mountain hares. Mol. Ecol. 31, 1487–1503 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thulin, C.-G., Isaksson, M. & Tegelström, H. The origin of Scandinavian mountain hares (Lepus timidus). Gibier Faune Savage/Game and Wildlife 14, 463–475 (1997).
    Google Scholar 
    Ferreira, M. S. et al. The legacy of recurrent introgression during the radiation of hares. Syst. Biol. 70, 593–607 (2021).Article 
    PubMed 

    Google Scholar  More

  • in

    Fragmentation by major dams and implications for the future viability of platypus populations

    Zhou, Y. et al. Platypus and echidna genomes reveal mammalian biology and evolution. Nature 592, 756–762 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bino, G. et al. The platypus: evolutionary history, biology, and an uncertain future. J. Mammal. 100, 308–327 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Veyrunes, F. et al. Bird-like sex chromosomes of platypus imply recent origin of mammal sex chromosomes. Genome Res. 18, 965–973 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anich, P. S. et al. Biofluorescence in the platypus (Ornithorhynchus anatinus). Mammalia 85, 179–181 (2021).Article 

    Google Scholar 
    Pavoine, S., Ollier, S. & Dufour, A. B. Is the originality of a species measurable? Ecol. Lett. 8, 579–586 (2005).Article 

    Google Scholar 
    Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Woinarski, J. & Burbidge, A. In The IUCN Red List of Threatened Species 2016: e. T40488A21964009 (IUCN, 2016).Victoria Government Gazette. Authority of Victorian Government Printer (2021).Hawke, T., Bino, G. & Kingsford, R. T. A silent demise: Historical insights into population changes of the iconic platypus (Ornithorhynchus anatinus). Glob. Ecol. Conserv. 20, e00720 (2019).Article 

    Google Scholar 
    Grant, T. R. & Fanning, D. Platypus (CSIRO PUBLISHING, 2007).Bino, G., Kingsford, R. T. & Wintle, B. A. A stitch in time–Synergistic impacts to platypus metapopulation extinction risk. Biol. Conserv. 242, 108399 (2020).Article 

    Google Scholar 
    Hawke, T., Bino, G. & Kingsford, R. A National Assessment of the Conservation Status of the Platypus (University of New South Wales, 2021).Bino, G., Hawke, T. & Kingsford, R. T. Synergistic effects of a severe drought and fire on platypuses. Sci. Total Environ. 777, 146137 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Klamt, M., Thompson, R. & Davis, J. Early response of the platypus to climate warming. Glob. Change Biol. 17, 3011–3018 (2011).Article 

    Google Scholar 
    Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 
    PubMed 

    Google Scholar 
    Grill, G. et al. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales. Environ. Res. Lett. 10, 015001 (2015).Article 

    Google Scholar 
    Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dugan, P. J. et al. Fish migration, dams, and loss of ecosystem services in the Mekong basin. Ambio 39, 344–348 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Sci. Adv. 3, e1700611 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grant, T. R. & Temple-Smith, P. D. Conservation of the platypus, Ornithorhynchus anatinus: threats and challenges. Aquat. Ecosyst. Health Manag. 6, 5–18 (2003).Article 

    Google Scholar 
    Hawke, T., Bino, G. & Kingsford, R. T. Damming insights: variable impacts and implications of river regulation on platypus populations. Aquat. Conserv.: Mar. Freshw. Ecosyst. 31, 504–519 (2021).Article 

    Google Scholar 
    Bethge, P., Munks, S., Otley, H. & Nicol, S. Diving behaviour, dive cycles and aerobic dive limit in the platypus Ornithorhynchus anatinus. Comp. Biochem. Physiol. Part A: Mol. Integr. Physiol. 136, 799–809 (2003).Article 

    Google Scholar 
    Grant, T. & Llewellyn, L. C. The Biology and Management of the Platypus (Ornithorhynchus anatinus) in NSW (NSW National Parks and Wildlife Service, 1991).Grant, T. R. Captures, Capture Mortality, Age and Sex Ratios of Platypuses, Ornithorhynchus Anatinus, during Studies over 30 Years in the Upper Shoalhaven River in New South Wales (Linnean Society of New South Wales, 2004).Marchant, R. & Grant, T. The productivity of the macroinvertebrate prey of the platypus in the upper Shoalhaven River, New South Wales. Mar. Freshw. Res. 66, 1128–1137 (2015).Article 

    Google Scholar 
    Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M. & Turlure, C. Individual dispersal, landscape connectivity and ecological networks. Biol. Rev. 88, 310–326 (2013).Article 
    PubMed 

    Google Scholar 
    Frankham, R. et al. Genetic Management of Fragmented Animal and Plant Populations (Oxford University Press, 2017).Frankham, R. Genetic rescue of small inbred populations: meta‐analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).Article 
    PubMed 

    Google Scholar 
    Garant, D., Forde, S. E. & Hendry, A. P. The multifarious effects of dispersal and gene flow on contemporary adaptation. Funct. Ecol. 21, 434–443 (2007).Article 

    Google Scholar 
    Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).Article 
    PubMed 

    Google Scholar 
    Kolomyjec, S. H. The History and Relationships of Northern Platypus (Ornithorhynchus Anatinus) Populations: A Molecular Approach (James Cook University, 2010).Furlan, E. M. et al. Dispersal patterns and population structuring among platypuses, Ornithorhynchus anatinus, throughout south-eastern Australia. Conserv. Genet. 14, 837–853 (2013).Article 
    CAS 

    Google Scholar 
    Balkenhol, N., Cushman, S., Storfer, A. & Waits, L. Landscape Genetics: Concepts, Methods, Applications (John Wiley & Sons, 2015).Ramachandran, S. et al. Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc. Natl Acad. Sci. USA 102, 15942–15947 (2005).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hoffman, J. R., Willoughby, J. R., Swanson, B. J., Pangle, K. L. & Zanatta, D. T. Detection of barriers to dispersal is masked by long lifespans and large population sizes. Ecol. Evolution 7, 9613–9623 (2017).Article 

    Google Scholar 
    Meirmans, P. G. & Hedrick, P. W. Assessing population structure: F-ST and related measures. Mol. Ecol. Resour. 11, 5–18 (2011).Article 
    PubMed 

    Google Scholar 
    Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).Article 

    Google Scholar 
    Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—implications for brown trout conservation. Ecol. Evolution 9, 2106–2120 (2019).Article 

    Google Scholar 
    Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sherwin, W. B., Chao, A., Jost, L. & Smouse, P. E. Information theory broadens the spectrum of molecular ecology and evolution. Trends Ecol. Evol. 32, 948–963 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Serena, M. & Williams, G. Movements and cumulative range size of the platypus (Ornithorhynchus anatinus) inferred from mark–recapture studies. Aust. J. Zool. 60, 352–359 (2013).Article 

    Google Scholar 
    Hawke, T. et al. Fine‐scale movements and interactions of platypuses, and the impact of an environmental flushing flow. Freshw. Biol. 66, 177–188 (2021).Article 

    Google Scholar 
    Hawke, T. et al. Long-term movements and activity patterns of platypus on regulated rivers. Sci. Rep. 11, 1–11 (2021).Article 

    Google Scholar 
    Nislow, K. H., Hudy, M., Letcher, B. H. & Smith, E. P. Variation in local abundance and species richness of stream fishes in relation to dispersal barriers: implications for management and conservation. Freshw. Biol. 56, 2135–2144 (2011).Article 

    Google Scholar 
    Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Hoffmann, A. A., Miller, A. D. & Weeks, A. R. Genetic mixing for population management: From genetic rescue to provenancing. Evol. Appl. 14, 634–652 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mills, L. S. & Allendorf, F. W. The one-migrant-per-generation rule in conservation and management. Conserv. Biol. 10, 1509–1518 (1996).Article 

    Google Scholar 
    Brown, J. J. et al. Fish and hydropower on the US Atlantic coast: failed fisheries policies from half‐way technologies. Conserv. Lett. 6, 280–286 (2013).Article 

    Google Scholar 
    Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish. Fish. 19, 340–362 (2018).Article 

    Google Scholar 
    Broadhurst, B., Ebner, B., Lintermans, M., Thiem, J. & Clear, R. Jailbreak: a fishway releases the endangered Macquarie perch from confinement below an anthropogenic barrier. Mar. Freshw. Res. 64, 900–908 (2013).Article 

    Google Scholar 
    Sainsbury, A. W. & Vaughan‐Higgins, R. J. Analyzing disease risks associated with translocations. Conserv. Biol. 26, 442–452 (2012).Article 
    PubMed 

    Google Scholar 
    Kolomyjec, S. H., Grant, T. R., Johnson, C. N. & Blair, D. Regional population structuring and conservation units in the platypus (Ornithorhynchus anatinus). Aust. J. Zool. 61, 378–385 (2014).Article 

    Google Scholar 
    Drechsler, M. & Burgman, M. A. Combining population viability analysis with decision analysis. Biodivers. Conserv. 13, 115–139 (2004).Article 

    Google Scholar 
    Kolomyjec, S. H. et al. Population genetics of the platypus (Ornithorhynchus anatinus): a fine-scale look at adjacent river systems. Aust. J. Zool. 57, 225–234 (2009).Article 

    Google Scholar 
    Kolomyjec, S. H., Grant, T. R. & Blair, D. Ten polymorphic microsatellite DNA markers for the platypus, Ornithorhynchus anatinus. Mol. Ecol. Resour. 8, 1133–1135 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Martin, H. C. et al. Insights into platypus population structure and history from whole-genome sequencing. Mol. Biol. Evol. 35, 1238–1252 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bino, G., Kingsford, R. T., Grant, T., Taylor, M. D. & Vogelnest, L. Use of implanted acoustic tags to assess platypus movement behaviour across spatial and temporal scales. Sci. Rep. 8, 1–12 (2018).Article 
    CAS 

    Google Scholar 
    Kilian, A. et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89 (2012).Article 
    PubMed 

    Google Scholar 
    Georges, A. et al. Genomewide SNP markers breathe new life into phylogeography and species delimitation for the problematic short‐necked turtles (Chelidae: Emydura) of eastern Australia. Mol. Ecol. 27, 5195–5213 (2018).Article 
    PubMed 

    Google Scholar 
    Steane, D. A. et al. Population genetic analysis and phylogeny reconstruction in Eucalyptus (Myrtaceae) using high-throughput, genome-wide genotyping. Mol. Phylogenet. Evol. 59, 206–224 (2011).Article 
    PubMed 

    Google Scholar 
    Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schmidt, T. L., Jasper, M. E., Weeks, A. R. & Hoffmann, A. A. Unbiased population heterozygosity estimates from genome‐wide sequence data. Methods Ecol. Evolution 12, 1888–1898 (2021).Article 

    Google Scholar 
    Pew, J., Muir, P. H., Wang, J. & Frasier, T. R. related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol. Ecol. Resour. 15, 557–561 (2015).Article 
    PubMed 

    Google Scholar 
    Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).Article 
    PubMed 

    Google Scholar 
    Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89 (2013).Article 

    Google Scholar 
    Mijangos, J. L., Gruber, B., Berry, O., Pacioni, C. & Georges, A. dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture. Methods Ecol. Evol. 13, 2150–2158 (2022).McVean, G. A genealogical interpretation of principal components analysis. PLoS Genet. 5, e1000686 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Mijangos, J. et al. Datasets and R scripts for Fragmentation by major dams and implications for the future viability of platypus populations (2022).IUCN (International Union for Conservation of Nature) 2008. Ornithorhynchus anatinus. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org (2022).Crossman, S. & Li, O. Surface Hydrology Lines (National) (2015).Crossman, S. & Li, O. Surface Hydrology Polygons (National) (2015).Australian Bureau of Statistics (2021). States and Territories – 2021 – Shapefile [https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files] [Shapefile], Digital boundary files (2022).Australian National Committee on Large Dams Incorporated (ANCOLD). Register of Large Dams Australia (2022). More

  • in

    Mixotrophy in depth

    Rippka, R. et al. J. Gen. Microbiol. https://doi.org/10.1099/00221287-111-1-1 (1979).Article 

    Google Scholar 
    Muñoz-Marín, M. C. et al. ISME J. 14, 1065–1073 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yelton, A. P. et al. ISME J. 10, 2946–2957 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ward, B. A. & Follows, M. J. Proc. Natl Acad. Sci. USA 113, 2958–2963 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, Z. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01250-5 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Flombaum, P. et al. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zubkov, M. et al. Appl. Environ. Microbiol. 69, 1299–1304 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vila-Costa, M. et al. Science 314, 652–654 (2006).Article 
    PubMed 

    Google Scholar 
    Muñoz-Marín, M. C. et al. Proc. Natl Acad. Sci. USA 110, 8597–8602 (2013).Article 
    PubMed Central 

    Google Scholar 
    Gómez-Baena, G. et al. PLoS ONE 3, e3416 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coe, A. et al. Limnol. Oceanogr. 71, 1375–1388 (2016).Article 

    Google Scholar 
    Muñoz-Marín, M. C. et al. Microbiol. Spectr. https://doi.org/10.1101/2021.10.04.462702 (2022).Article 
    PubMed 

    Google Scholar  More

  • in

    Effects of different pioneer and exotic species on the changes of degraded soils

    Sacristán, D., Peñarroya, B., Recatalá, L. Increasing the Knowledge on the Management of Cu-Contaminated Agricultural Soils by Cropping Tomato (Solanum Lycopersicum L.). Land Degrad. Dev. 26, 587–595 (2015).FAO. Land Degradation Assessment in Drylands. Manual for Local Level Assessment of Land Degradation and Sustainable Land Management. Part 1: Planning and Methodological Approach, Analysis and Reporting. https://www.fao.org/3/i6362e/i6362e.pdf (Food and Agriculture Organization of the United Nations, 2011).Vlachodimos, K., Papatheodorou, E. M., Diamantopoulos, J. & Monokrousos, N. Assessment of Robinia pseudoacacia cultivations as a restoration strategy for reclaimed mine spoil heaps. Environ Monit. Assess. 185, 6921–6932 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Misano, G. & Di Pietro, R. Habitat 9250 “Quercus trojana woods” in Italy. Fitosociologia 44, 235–238 (2007).
    Google Scholar 
    Biondi, E. et al. A contribution towards the knowledge of semideciduous and evergreen woods of Apulia (south-eastern Italy). Fitosociologia 41(1), 3–28 (2004).MathSciNet 

    Google Scholar 
    Brunetti, G. et al. Remediation of a heavy metals contaminated soil using mycorrhized and non-mycorrhized Helichrysum italicum (Roth) Don. Land Degrad. Dev. 29, 91–104 (2017).Article 

    Google Scholar 
    Poblador, S. et al. The influence of the invasive alien nitrogen-fixing Robinia pseudoacacia L. on soil nitrogen availability in a mixed Mediterranean riparian forest. Eur. J. For. Res. 138, 1083–1093 (2019).Article 
    CAS 

    Google Scholar 
    Vítková, M., Müllerová, J., Sádlo, J., Pergl, J. & Pyšek, P. Black locust (Robinia pseudoacacia) beloved and despised: A story of an invasive tree in Central Europe. For. Ecol. Manag. 384, 287–302 (2017).Article 

    Google Scholar 
    Doran, J.W., Parkin, T.B. Quantitative indicators of soil quality: a minimum data set. in Methods for Assessing Soil Quality (eds. Doran, J.W., Jones, A.J.). 25–37 (Soil Science Society of America, 1996).Gil-Sotres, F., Trasar-Cepeda, C., Leirós, M. C. & Seoane, S. Different approaches to evaluating soil quality using biochemical properties. Soil Biol. Biochem. 37, 877–887 (2005).Article 
    CAS 

    Google Scholar 
    Andriani, G. F. & Walsh, N. An example of the effects of anthropogenic changes on natural environment in the Apulian karst (southern Italy). Environ. Geol. 58, 313–325 (2009).Article 
    ADS 

    Google Scholar 
    Bisantino, T., Pizzo, V., Polemio, M. & Gentile, F. Analysis of the flooding event of October 22–23, 2005 in a small basin in the province of Bari (Southern Italy). J. Agric. Eng. 531, 197–204 (2016).Article 

    Google Scholar 
    Soil Survey Staff. Keys to Soil Taxonomy 12th edn. (USDA-Natural Resources Conservation Service, 2014).
    Google Scholar 
    Tartarino, P. Inventario dei Boschi Spontanei e dei Rimboschimenti delle Provincie BAT e Bari e Stima del Loro Volume Legnoso e della sua Frazione Prelevabile nel Prossimo Ventennio. (Rapporto Tecnico Scientifico, 2011).Ismail, A. et al. Chemical composition and biological activities of Tunisian Cupressus arizonica Greene essential oils. Chem. Biodivers. 11, 150–160 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Navarro, A. et al. Feasibility of SRC Species for growing in Mediterranean conditions. Bioenerg. Res. 9, 208–223 (2015).Article 

    Google Scholar 
    Perrino, E. V., Brunetti, G. & Farrag, K. Plant communities in multi-metal contaminated soils: A case study in the National Park of Alta Murgia (Apulia Region-Southern Italy). Int. J. Phytoremediat. 16, 871–888 (2014).Article 
    CAS 

    Google Scholar 
    VV AA Perizia Studi per il Riequilibrio Socio-Economico dell’area Interessata dall’invaso sul Torrente Locone. Consorzio Di Bonifica Apulo Lucano (1986).Lavarra, P. et al. Il Sistema Carta della Natura della Regione Puglia. (ISPRA, Serie Rapporti 204, 2014).Sparks, D. L. et al. Method of Soil Analysis: Part 3 (American Society of Agronomy Inc, 1996).Book 

    Google Scholar 
    Brink, R. H. Jr., Dubach, P. & Lynch, D. L. Measurement of carbohydrates in soil hydrolyzates with anthrone. Soil Sci. 89, 157–166 (1960).Article 
    ADS 
    CAS 

    Google Scholar 
    Lowry, O. H., Rosebrough, N. J., Farr, A. L. & Randall, R. J. Protein measurement with the folin phenol reagent. J. Biol. Chem. 193, 265–275 (1951).Article 
    CAS 
    PubMed 

    Google Scholar 
    García, C., Hernandez, T. & Costa, F. Potential use of dehydrogenase activity as an index of microbial activity in degraded soils. Commun. Soil Sci. Plant Anal. 28, 123–134 (1997).Article 

    Google Scholar 
    Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).Article 
    CAS 

    Google Scholar 
    Gregorich, E. G., Wen, G., Voroney, R. P. & Kachanoski, R. G. Calibration of a rapid direct chloroform extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 22, 1009–1011 (1990).Article 
    CAS 

    Google Scholar 
    Nannipieri, P., Ceccanti, B., Cervelli, S. & Matarese, E. Extraction of phosphatase, urease, protease, organic carbon and nitrogen from soil. Soil Sci. Soc. Am. J. 44, 1011–1016 (1980).Article 
    ADS 
    CAS 

    Google Scholar 
    Tabatabai, M.A. (1994) Soil enzymes. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (eds. Weaver, R.W. et al.). 775–833 (Soil Science Society of America, Inc., 1996)Traversa, A., Said-Pullicino, D., D’Orazio, V., Gigliotti, G., & Senesi, N. Properties of humic acids in Mediterranean forest soils (Southern Italy): Influence of different plant covering. Eur. J. For. Res. 130, 1045–1054 (2011)De Marco, A. et al. Decomposition of black locust and black pine leaf litter in two coeval forest stands on Mount Vesuvius and dynamics of organic components assessed through proximate analysis and NMR spectroscopy. Soil Biol. Biochem. 51, 1–15 (2012).Article 
    CAS 

    Google Scholar 
    Wei, G. et al. Invasive Robinia pseudoacacia in China is nodulated by Mesorhizobium and Sinorhizobium species that share similar nodulation genes with native American symbionts. FEMS Microbiol. Ecol. 68, 320–328 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schulze, E. D., Gebauer, G., Ziegler, H. & Lange, O. L. Estimates of nitrogen fixation by trees on an aridity gradient in Namibia. Oecologia 88, 451–455 (1991).Article 
    ADS 
    PubMed 

    Google Scholar 
    Zahran, H. H. Rhizobium-legume symbiosis and nitrogen fixation under severe conditions and in an arid climate. Microbiol. Mol. Biol. Rev. 63, 968–989 (1999).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Veste, M. & Kriebitzsch, W. U. Influence of drought stress on photosynthesis, transpiration, and growth of juvenile black locust (Robinia pseudoacacia L.). Forstarchiv 84, 35–42 (2013).
    Google Scholar 
    Nicolescu, V. N. et al. Ecology, growth and management of black locust (Robinia pseudoacacia L.), a non-native species integrated into European forests. J. For. Res. 31, 1081–1101 (2020).Article 
    CAS 

    Google Scholar 
    Sposito, G. The Chemistry of Soil (Oxford University Press, 2008).
    Google Scholar 
    Margalef, O. et al. Global patterns of phosphatase activity in natural soils. Sci. Rep. 7, 1337. https://doi.org/10.1038/s41598-017-01418-8 (2017).Prescott, C. E. & Grayston, S. J. Tree species influence on microbial communities in litter and soil: Current knowledge and research needs. For. Ecol. Manag. 309, 19–27 (2013).Article 

    Google Scholar 
    Frankenberger, W. T. & Dick, W. A. Relationships between enzyme, activities and microbial growth and activity indices in soil. Soil Sci. Soc. Am. J. 47, 945–951 (1983).Article 
    ADS 
    CAS 

    Google Scholar 
    Frankenberger, W.T., Tabatabai, M.A. Amidase activity in soils III. Stability and distribution. Soil Sci. Soc. Am. J. 45, 333–338 (1981).Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: A brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fertil. Soils 54, 11–19 (2018).Article 
    CAS 

    Google Scholar 
    Pascual, J. A., Garcia, C., Hernandez, T., Moreno, J. L. & Ros, M. Soil microbial activity as a biomarker of degradation and remediation processes. Soil Biol. Biochem. 32, 1877–1883 (2000).Article 
    CAS 

    Google Scholar 
    García-Gil, J. C., Plaza, C., Solker-Rovira, P. & Polo, A. Long-term effects of municipal solid waste compost application on soil enzyme activities and microbial biomass. Soil Biol. Biochem. 32, 1907–1913 (2000).Article 

    Google Scholar 
    Insam, H. & Domsch, K. H. Relationship between soil organic carbon and microbial biomass on chronosequences of reclamation sites. Microb. Ecol. 15, 177–188 (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    Acosta-Martinez, V. & Tabatabai, M. Enzyme activities in a limed agricultural soil. Biol. Fertil. Soils 31, 85–91 (2000).Article 
    CAS 

    Google Scholar 
    Uselman, S. M., Qualls, R. G. & Thomas, R. B. A test of a potential short cut in the nitrogen cycle: the role of exudation of symbiotically fixed nitrogen from the roots of a N-fixing tree and the effects of increased atmospheric CO2 and temperature. Plant Soil 210, 21–32 (1999).Article 
    CAS 

    Google Scholar 
    De Marco, A., Esposito, F., Berg, B., Zarrelli, A. & Virzo De Santo, A. Litter inhibitory effects on soil microbial biomass activity, and catabolic diversity in two paired stands of Robinia pseudoacacia L. and Pinus nigra Arn. Forest 9, 766. https://doi.org/10.3390/f9120766 (2018).Article 

    Google Scholar 
    Haghverdi, K. & Kooch, Y. Effects of diversity of tree species on nutrient cycling and soil-related processes. CATENA 178, 335–344 (2019).Article 
    CAS 

    Google Scholar 
    Anderson, H. T. Microbial eco-physiological indicators to assess soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).Article 

    Google Scholar 
    Jenkinson, D.S., Ladd, J.N. Microbial biomass in soil: Measurement and turnover. in Soil Biochemistry (eds. Paul, E.A., Ladd, J.N.). 415–471 (Marcel Dekker Inc., 1981) More

  • in

    The red harvester ant

    Gordon, D. M. Anim. Behav. 49, 649–659 (1995).Article 

    Google Scholar 
    Gordon, D. M. The Ecology of Collective Behavior (Princeton Univ. Press, in the press).Gordon, D. M. Anim. Behav. 38, 194–204 (1989).Article 

    Google Scholar 
    Greene, M. J. & Gordon, D. M. Nature 423, 32 (2003).Article 
    CAS 

    Google Scholar 
    Pinter-Wollman, N. et al. Anim. Behav. 86, 197–207 (2013).Article 

    Google Scholar 
    Gordon, D. M., Guetz, A., Greene, M. J. & Holmes, S. Behav. Ecol. 22, 429–435 (2011).Article 

    Google Scholar 
    Prabhakar, B., Dektar, K. N. & Gordon, D. M. PLOS Comput. Biol. 8, e1002670 (2012).Article 
    CAS 

    Google Scholar 
    Davidson, J. D., Arauco-Aliaga, R. P., Crow, S., Gordon, D. M. & Goldman, M. S. Front. Ecol. Evol. 4, 115 (2016).Article 

    Google Scholar 
    Pagliara, R., Gordon, D. M. & Leonard, N. E. PLOS Comput. Biol. 14, e1006200 (2018).Article 

    Google Scholar 
    Friedman, D. A. et al. iScience 8, 283–294 (2018).Article 
    CAS 

    Google Scholar 
    Gordon, D. M. Ant Encounters: Interaction Networks and Colony Behavior (Princeton Univ. Press, 2010).Sundaram, M., Steiner, E. & Gordon, D. M. Ecol. Monogr. 92, e1503 (2022).Article 

    Google Scholar 
    Ingram, K. K., Pilko, A., Heer, J. & Gordon, D. M. J. Anim. Ecol. 82, 540–550 (2013).Article 

    Google Scholar 
    Gordon, D. M. Nature 498, 91–93 (2013).Article 
    CAS 

    Google Scholar  More

  • in

    Zebras of all stripes repel biting flies at close range

    The evolutionary origins of zebra stripes have been investigated—and debated—for centuries. The trait is rare, conspicuous, and intensely expressed, and thus appears to beg an adaptationist explanation. However, the utility of a complete coat of densely packed, starkly contrasting black-and-white stripes is not immediately apparent. Unlike many conspicuous visual traits, striped pelage is expressed with comparable intensity in both sexes and is thus unlikely to have arisen through sexual selection alone (although in plains zebras, Equus quagga, males have stripes closer to true black than females). Stripes are clearly not aposematic warning signals, nor do they provide camouflage in either the woodland or savannah habitats common across zebra ranges1,2. So, striping presents an ideal evolutionary puzzle: a trait so refined it seems it must be “for” something, but one that confers no clear advantage upon its bearers and imposes apparent costs (conspicuousness) that cannot be explained in Zahavian terms.Scientists have proposed and investigated several possible explanations for the evolution of zebra stripes (reviewed in3). The hypotheses suggest various ways in which stripes may provide a social function (species or individual recognition or social cohesion1,4), a temperature-regulation benefit5,6, an anti-predator effect7,8, or an anti-parasite effect9,10. There is continued debate over both the merits of individual hypotheses and the likelihood of stripes having arisen via a single driver vs. a confluence or alternation of multiple selective pressures6,11.The present study addresses the hypothesis that has thus far received the most empirical support: the anti-parasite hypothesis (also known as the ectoparasite hypothesis12). Zebras, like most ungulates, are harassed by tabanid, glossinid and Stomoxys species of biting flies, which can inflict significant blood loss, transmit disease, and weaken hosts when fly-avoidance behaviors reduce the host’s feeding rate9,13,14. Yet zebras are attacked far less than sympatric ungulates across their African range15,16, and also less than other equids9,17. Zebras also produce odors that may augment their anti-fly defenses18, but so do other sympatric ungulate species18,19, and a host of observations and experiments have demonstrated that black-and-white stripes alone are unattractive, or actively repellent to tabanid, glossinid, and Stomoxys flies17,20,21,22,23.Though the effect of stripes on flies is well-established, the source of the effect remains unexplained. Since Waage’s foundational studies in the 1970s and 1980s9,24 most hypotheses have suggested ways that stripes might interfere with the visual and navigational systems of flies, making it harder for them to locate, identify, or successfully land on striped targets. These hypothetical mechanisms can be roughly grouped by the distance (and the attendant phase of a fly’s orientation and landing behavior) at which they would likely operate:

    From afar: stripes might make it harder for flies to locate and distinguish zebras from background vegetation, perhaps by breaking up their outline9 or varying the way they polarize or reflect light17,31 especially from distances at which composite eyes support only low-resolution vision and cannot resolve zebra stripes as clear bands of alternating color on a single host (estimated at  > 2.0 m22,  > 4.4 m24, and even  > 20 m25).

    At close range (estimates range from 0.5 to 4.0 m26): stripes might interfere with orientation or landing behavior via any of several disruptive or ‘dazzle’-related visual effects27. For example, stripes might affect ‘optic flow’, or the fly’s perceived relative motion to its target as it approaches, by creating an illusion of false direction or speed of motion (e.g., via variants of the ‘barber pole’ or ‘wagon wheel’ effects28). Alternatively, relative motion to a striped pattern within the visual field may create the perception of self-rotation, inducing the fly’s involuntary ‘optomotor response’ and resulting in an avoidance turn in an effort to stay on a straight course29.

    Finally, stripes might cause confusion in the transition between long- and short-distance orientation. If zebras appear as blurred gray from a distance and then, at closer range, suddenly resolve into a sequence of floating black and white bars, this abrupt ‘visual transformation’26 might disrupt the behavioral sequence that facilitates landing.

    Within these categories, hypotheses have proliferated faster than experimental tests of many of the proposed mechanisms. The very active literature on this question has grown in somewhat haphazard fashion, as curious researchers test new possibilities without eliminating old ones6. Importantly, few experiments have controlled the distance from which flies are first able to view potential landing sites (but see23). While growing evidence supports a mechanism operating at close range22,26, failing to restrict the starting distance of the fly means that the full set of possible mechanisms outlined above all remain plausible contributors to most previous results.Additionally, while many studies have, appropriately, used artificial stimuli to isolate basic effects of color, pattern, brightness, and light polarization of (usually flat) test surfaces, possible contributions of several aspects of natural zebra pelage remain untested. Controlled experiments have used various landing substrates, including striped and solid oil tray traps, sticky plastic, smooth plastic17, cloth (Experiment 2 in22), horse blankets or sheets26, and paint on live animals30. These have all clearly demonstrated a broadly replicable visual effect: stripes, and some other juxtapositions of black and white (e.g., checkerboard patterns26), repel flies. However, insofar as specific features of zebra pelage factor into proposed mechanisms of fly repellence—the reflective properties of “smooth, shiny” coats31; the orientation of the stripes17,32; the light-polarizing effects of black and white hair vs. background vegetation25; and the complex structure of hair25—there is a need for more experiments that present natural targets to wild flies (but see22,33). Similarly, most experiments have compared landing preferences between black-and-white striped, solid black, solid white, and occasionally solid grey substrates, which have served as important controls for determining that light polarization, rather than a combination of polarization and brightness, is sufficient to induce the effect of stripe avoidance17. However, it is now time to refocus on the original question by presenting flies with more realistic choices. Since biting flies seeking a bloodmeal on the African savannah seldom encounter solid black hosts, and even more rarely solid white hosts, landing choices should be compared between zebra stripes and common coat colors of sympatric mammals, namely various shades of brown. Further, tabanid, glossinid, and Stomoxys flies all avoid landing on stripes that are the same width or narrower than the widest zebra stripes 17,23, and there is some evidence that narrower stripes are even more repellent to tabanids17. This pattern is potentially significant in the application of the anti-parasite hypothesis to an adaptive explanation for the striking variation in stripe width across zebra species and between the different areas of the body on individual zebras22, but must first be confirmed with experiments using real zebra pelage.Here, we present a simple experiment designed to address each of these gaps in the literature on the anti-fly benefits of zebra stripes. In this field experiment, the landing choices of flies were tested entirely within the range at which all estimates agree flies should be able to perceive the presented stripes ( More

  • in

    Sampling from four geographically divergent young female populations demonstrates forensic geolocation potential in microbiomes

    Cohort demographicsA total of 206 female participants were enrolled in the study and passed our quality control standards. All participants were required to be between the ages of 18–26 years old (22.5 ± 2.1) and to be born and at the time living in one of four geographically distinct regions of the world: Barbados; Santiago, Chile; Pretoria, S. Africa; and Bangkok, Thailand. The regions do, however, differ by an order of magnitude in their geographic spread as the intra-distance separating the residence neighborhood of participants ranged from 34 (Barbados) to 681 km (Pretoria, S. Africa) (Fig. S2). The Chilean and the South African datasets are further divided into two contiguous sub-regions, or neighborhoods, to allow for a micro-geographic analysis. The study population is largely dominated by individuals with self-identified Thai heritage (33%), followed by Black African (16%), Afro-Caribbean (14%) and white (14%) descent, although 19% of the Chilean population did not report ethnicity.Study participants, despite the divergent geographies, mostly have similar dietary and lifestyle habits (Table S1). Over half the study population (62%) have a normal BMI, with the mean BMI in this range (22.6 ± 5.5). The diets of the different cohorts are also similar as of the total cohort, 78% consume a starch heavy diet (≥ 4 days a week) of rice, bread and pasta, followed by 66% who frequently consume (≥ 4 days a week) vegetables and fruit and 49% who frequently consume dairy products. The study population is split by level of tobacco exposure, with 51% of the population having never smoked, and 43% being exposed to second-hand smoke through living with a smoker. Over half (56%) of the study population own one or more pets.Stool microbiomeThe OTUs identified using the UPARSE pipeline17 were used to compute the alpha diversity of the microbial communities using the Chao1 (species richness) and Shannon (species evenness) indices. The mean Shannon indices reveal that the microbiota diversity is only significant between Thailand-Chile with FDR  More