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    Rapid evolution of bacterial mutualism in the plant rhizosphere

    Bacterial strain and growth conditionsWe used Pseudomonas protegens (formerly Pseudomonas fluorescens)55 CHA0 as a model strain, which was initially isolated from tobacco roots56. The strain was chromosomally tagged with GFP and a kanamycin resistance cassette to enable specific tracking of the strain and detection of contaminations19. This bacterium has the genetic potential to produce various bioactive metabolites, including the plant hormone indole-3-acetic acid (IAA), antimicrobial compounds and lytic enzymes25. Prior to the experiment, bacteria were grown for 48 h on a King′s medium B57 (KB) agar plate supplemented with 50 µg ml−1 kanamycin, a single colony was randomly picked and grown for 12 h in KB at 28 °C with agitation. The cell culture was then washed for three times in 10 mM MgSO4 and adjusted to 107 cells ml−1 and used as inoculant for all plants. This inoculant was also stored at −80 °C as frozen ancestral stock, from which ʻAncestorʼ isolates were picked in later experiments.Host plant and growth conditionsWe used Arabidopsis thaliana ecotype Col-0 as a model host plant. Surface-sterilized seeds were first sown in Petri dishes with agar-solidified (1.5% agar (w/v)) modified Hoagland′s medium: (KNO3 (3 mM), MgSO4 (0.5 mM), CaCl2 (1.5 mM), K2SO4 (1.5 mM), NaH2PO4 (1.5 mM), H3BO3 (25 μM), MnSO4 (1 μM), ZnSO4 (0.5 μM), (NH4)6Mo7O24 (0.05 μM), CuSO4 (0.3 μM), MES (2.5 mM) and 50 μM Fe(III)EDTA, pH = 5.8) and stratified for 2 days at 4 °C after Petri dishes were positioned vertically and transferred to a growth chamber (20 °C, 10 h light/14 h dark, light intensity 100 μmol m−2 s−1). After 2 weeks of incubation, two seedlings were transferred to closed and sterile ECO2 boxes (http://www.eco2box.com/ov80xxl_nl.htm) for selection experiment. The ECO2 boxes were filled with 260 g of dry, carbon-free silver sand that was previously washed with MilliQ water to remove dissolvable chemical elements and heated to 550 °C for 24 h to remove remaining organic material. Prior to transplantation the sand was amended with 13 ml of modified Hoagland medium.Design of the selection experimentThe selection experiment was conducted in a gnotobiotic system to remove confounding effects that may emerge as a result of competitive interactions with other microorganisms, and to place the focus on plant-mediated selective pressures. Moreover, we allowed only the bacteria to evolve during the experiment and used new clonal plants at every bacterial transfer. We set up five independent plant–bacterium replicate lines, which were grown for six independent plant growth cycles (see Fig. S1 for an overview of the experimental design). The experiment was started by inoculating 106 cells of the stock P. protegens CHA0 culture (from here on abbreviated as ʻancestorʼ) into the rhizosphere of 2-week-old A. thaliana seedlings growing in sterile silver sand within ECO2 boxes (two plants per replicate selection line). Inoculated plants were then grown for 4 weeks (20 °C, 10 h light/14 h dark, light intensity 100 μmol m−2 s−1) after which the plant growth cycle was terminated and root-associated bacteria were harvested by placing the roots of both plants into a 1.5 ml Eppendorf tubes filled with 1 ml 10 mM MgSO4 and two glass beads. Rhizosphere bacteria were suspended into the liquid using a TissueLyser II at a frequency of 20 s−1 for 1 min after which bacterial cell densities were determined using flow cytometry (BD Accuri™ C6 Plus, thresholds for FSC: 2000, SSC: 8000). After this, 106 cells were inoculated to the rhizosphere of new A. thaliana plants to initiate the next plant growth cycle. Possible contaminations were checked by plating the suspension on 3 g l−1 tryptic soy agar (TSA) plates and it was verified that all colonies carried the GFP marker gene, as observed under UV light.Bacterial life-history traits measurementsIndividual bacterial colonies were isolated from all replicate plant selection lines for life-history measurements at the end of the second, fourth and sixth plant growth cycle by dilution plating the rhizosphere suspension on 3 g l−1 TSA plates. After incubation at 28 °C for 24 h, 16 colonies were randomly picked from each replicate selection lines resulting in a total of 240 evolved and 16 ancestral colonies. All these colonies were characterized for a set of key bacterial life-history traits representative of growth, stress resistance and traits linked with plant–microbe interactions.

    a.

    Bacterial growth yield in KB medium
    All the bacterial isolates were grown in 96-well plates with 160 µl 1/3 strength liquid KB, at 20 °C without shaking. Bacterial yield was determined as the maximum optical density at 600 nm after 3 days of growth using a spectrophotometer (SPECTROstar Nano).

    b.

    Bacterial stress resistance
    We measured bacterial resistance to a range of different stresses using various 96-well microplate assays. Abiotic stress resistance was determined by growing bacteria in 160 µl of 1 g l−1 TSB containing 0.0025% H2O2 (oxidative stress), 15% polyethylene glycol (PEG)−6000 (water potential stress) or 2% NaCl (salt stress). We used resistance to antibiotics commonly produced by rhizosphere microorganisms as indicator of biotic stress resistance. Antibiotic resistance was tested in 160 µl of 1 g l−1 TSB supplemented with 1 µg ml−1 streptomycin, 1 µg ml−1 tetracycline, or 5 µg ml−1 penicillin, respectively. Bacterial growth were determined after 3 days of growth at 20 °C without shaking as optical density at 600 nm.

    c.

    Traits linked with plant–microbe interactions
    P. protegens CHA0 harbours several traits that are linked to plant growth including production of antibiotics and plant hormones. To assess these traits, we grew each bacterial colony in 96-well plates containing 160 µl of 1/3 strength liquid KB per well at 20 °C without shaking for 72 h. Cell-free supernatants were obtained by filter sterilization (0.22 µm) using Multiscreen HTS 96-well filtration plates (1000 × g, 30 min), which were used to measure the production of the plant hormone auxin (Indole-3-acetic acid (IAA)), iron-chelating siderophores and proteolytic activity. Furthermore, we also measured antifungal and antibacterial activity of all colonies.

    IAA detectionThe production of the plant hormone auxin was determined with a colorimetric test58. Briefly, 30 µl P. protegens CHA0 cell-free filtrate was incubated with 30 µl R1 reagent (12 g l−1 FeCl3, 7.9 M H2SO4) for 12 h in the dark and optical density read at 530 nm of the colorimetric complex was used as a measurement of IAA concentration.Siderophore activityIron-chelating ability was measured as a proxy for siderophore production59. To this end, 100 µl of P. protegens CHA0 cell-free filtrate was mixed with 100 µl of modified CAS solution (with 0.15 mM FeCl3) and optical density read at 630 nm after 3 h of incubation was used as a proxy of siderophore production. The iron-chelating ability was calculated based on the standard curve based on modified CAS assay solution with a range of iron concentration (0, 0.0015, 0.003, 0.006, 0.009, 0.012, 0.015 mM FeCl3).Proteolytic activityThe proteolytic activity assay we used was adapted from Smeltzer et al.60. Briefly, 15 µl of P. protegens CHA0 cell-free filtrate was incubated with 25 µl of azocasein (2% w/v in 50 mM Tris-HCl pH 8.0) at 40 °C for 24 h. One hundred and twenty-five of 10% w/v cold trichloroacetic acid (TCA) was added to precipitate superfluous azocasein, and then 100 µl supernatant was neutralized with 100 µl of 1 M NaOH after centrifugation at 5000g for 30 min. Optical density read at 440 nm was used as a proxy of exoprotease activity.Tryptophan side chain oxidase (TSO) activityTSO activity, an indicator of quorum sensing activity in P. protegens CHA0, was measured based on an modified established colorimetric assay61: Three-day-old bacterial cultures grown in 1/3 strength liquid KB were mixed at a 1:1 ratio with a reagent solution (5 g l−1 SDS, 37.6 g l−1 glycine 2.04 l−1 g tryptophan, pH 3.0) and TSO activity was measured as optical density at 600 nm after overnight incubation.Biofilm formationWe quantified bacterial biofilm formation using a standard protocol62. Briefly, bacteria were grown at 20 °C for 72 h in 160 µl 1 g l−1 TSB in a 96-well microtiter plate with TSP lid (TSP, NUNC, Roskilde, Denmark). Planktonic cells were removed by immersing the lid with pegs three times in phosphate-buffered saline solution (PBS). Subsequently, the biofilm on the pegs was stained for 20 min in 160 µl 1% crystal violet solution. Pegs were washed five times in PBS after which the crystal violet was extracted for 20 min from the biofilm in a new 96-well microtiter plate containing 200 µl 96% ethanol per well. Biofilm formation was defined as the optical density at 590 nm of the ethanol extracted crystal violet63.Inhibition of other microorganismsAntimicrobial activity was defined as the relative growth of the target organism in P. protegens supernatant compared to the control treatment. Antifungal activity of the cell-free supernatant was assessed against the ascomycete Verticillium dahliae. The fungus was grown on potato dextrose agar at 28 °C for 4 days, after which plugs of fungal mycelium were incubated in potato dextrose broth medium at 28 °C and gentle shaking for 5 days. Fungal spores were collected by filtering out the mycelium from this culture over glass wool. Subsequently, spores were washed and resuspended in water and the OD595 of the suspension was adjusted to 1. Five microlitres of this spore suspension was then inoculated with 15 µl P. protegens CHA0 cell-free filtrate and incubated in 160 µl of 1 g l−1 PDB medium for 2 days at 20 °C in 96-well plates. Fungal growth was measured as optical density at 595 nm after 2 days of growth and contrasted with the growth in the control treatment (PDB medium without P. protegens supernatant). Antibacterial activity was determined using the plant pathogen Ralstonia solanacearum as a target organism. R. solanacearum was grown in 160 µl of 1 g l−1 TSB medium supplemented with 15 μl of P. protegens CHA0 cell-free filtrate or 15 µl of 1/3 strength liquid KB as a control for 2 days at 20 °C. R. solanacearum growth was measured as optical density at 600 nm.Determining changes in P. protegens CHA0 interactions with A. thaliana after the selection experimentBased on the life-history trait measurements, five distinct bacterial phenotypes were identified using K-means clustering analysis (Fig. S2). In order to assess whether phenotypic changes reflected shifts in the strength and type of plant–bacterium interaction, we chose five isolates from each bacterial phenotype group representing each replicate selection line and five ancestral isolates for further measurements (a total of 30 isolates, Table S2).Effects of ancestor and evolved bacteria on plant performanceFor each isolate we measured root colonizing ability and impact on plant performance. All 30 bacterial isolates were incubated overnight in 1/3 KB strength liquid at 20 °C. The culture was centrifuged twice for 5 min at 5000 × g and the pellet was washed and finally resuspended in 10 mM MgSO4. The resulting suspension was adjusted to an OD600 of 0.01 for each strain64. Ten microlitres of the bacterial suspension (or 10 mM MgSO4 as a control) was applied to the roots of three 10-day old sterile Arabidopsis thaliana Col-0 seedlings (excluding 2 days of stratification at 4 °C) grown on vertically positioned Petri dishes with agar-solidified (1.5% agar (w/v)) modified Hoagland′s medium (n = 3 biological plant replicates, each containing 3 seedlings). Plants were grown for 14 days before harvesting. Plants were photographed before and 14 days after bacterial inoculation.Bacterial effects on plant health were quantified as leaf ʻgreennessʼ as the presence of ancestral strain was observed to lead to bleaching and loss of chlorophyll in A. thaliana leaves. The ʻgreennessʼ was quantified from photographs by measuring the number of green pixels. To this end, photographs were first transformed in batch using Adobe Photoshop 2021 by sequentially selecting only green areas followed by thresholding balancing green tissue over background noise (Level 80). This resulted in black-and-white images for further analysis, and the mean number of white pixels per fixed-sized region-of-interest of the aboveground tissue was subsequently determined as ʻgreennessʼ using ImageJ (version 1.50i). The numbers of lateral roots and the primary root length were also measured using ImageJ. The root morphology data measured at the end of the experiment was normalized with the data collected at the time of inoculation for each individual seedling.To determine shoot biomass, the rosette of each plant was separated from the root system with a razor blade and weighted. The roots were placed into a pre-weighted 1.5 ml Eppendorf tubes to quantify the root biomass. To determine the bacterial abundance per plant, these tubes were subsequently filled with 1 ml 10 mM MgSO4 buffer solution and two glass beads. The rhizosphere bacteria were suspended into the buffer solution using a TissueLyser II at a frequency of 20 s−1 for 1 min after which bacterial densities were determined using flow cytometry (BD Accuri™ C6 Plus, thresholds for FSC: 2000, SSC: 8000). Shoot biomass, root biomass, root length and number of lateral roots were used in a principal component analysis (PCA) to calculate an overall impact of the bacteria on plant performance (Fig. 2e). The first principal component (PC1) explained 79.9% of the variation and was normalized against the control treatment to be used as a proxy of ʻPlant performanceʼ in which positive values reflect plant growth promotion and negative values plant growth inhibition.Root derived carbon source utilizationTo measure changes in bacterial growth on potential root derived carbon sources, we measured the growth of all 256 isolates using modified Ornston and Stanier (OS) minimal medium65 supplemented with single carbon sources at a final concentration of 0.5 g l−1 in 96-well plates containing 160 µl carbon supplemented OS medium per well. The following carbon sources were selected based on their relatively high abundance in Arabidopsis root exudates21: alanine, arabinose, butyrolactam, fructose, galactose, glucose, glycerol, glycine, lactic acid, putrescine, serine, succinic acid, threonine and valine. Bacterial growth was determined by measuring optical density at 600 nm after 3 days incubation at 20 °C.GUS histochemical staining assay and bacterial growth under scopoletin stressTo investigate effects of the ancestor and evolved strains of P. protegens CHA0 on expression of MYB72, we applied a GUS histochemical staining assay to the 30 selected isolates (Table S2). MYB72 is a transcription factor involved in production of the coumarin scopoletin in Arabidopsis roots and specific rhizobacteria can upregulate expression of MYB72 in the roots66. Scopoletin is an iron-mobilizing phenolic compound with selective antimicrobial activity22. Seedlings of the A. thaliana MYB72pro:GFP-GUS24 reporter line were prepared as described above. Seven-day-old seedlings were inoculated directly below the hypocotyls with 10 μl of a bacterial suspension (OD660 = 0.1)24. At 2 days after inoculation, the roots were separated from the shoots and washed in MilliQ water (Milliport Corp., Bedford, MA) to remove all the adhered bacteria. GUS staining of the roots was performed in 12-well microtiter plates where each well contained roots of 5–6 seedlings and 1 ml of freshly prepared GUS substrate solution (50 mM sodium phosphate with a pH at 7, 10 mM EDTA, 0.5 mM K4[Fe(CN)6], 0.5 mM K3[Fe(CN)6], 0.5 mM X-Gluc, and 0.01% Silwet L-77)67. Plates were incubated in the dark at room temperature for 16 h. The roots were fixed overnight in 1 ml ethanol:acetic acid (3:1 v/v) solution at 4 °C and transferred to 75% ethanol. Then the pictures of each microtiter plates were taken, and GUS activity was quantified by counting the number of blue pixels in each well of the microtiter plates using image analysis in ImageJ (version 1.52t). To assess the effects of scopoletin on ancestral and evolved P. protegens CHA0 isolates, we applied a sensitivity assay to the 30 selected isolates (Table S2). In brief, growth of bacterial isolates was measured in 1 g l−1 TSB medium (160 µl) supplemented with scopoletin at final concentrations of 0 µM (control), 500 µM, 1000 µM, and 2 mM using optical density at 600 nm after 72 h incubation at 20 °C without shaking in 96-well microtiter plates. Maximal effect (Emax) of scopoletin was calculated via R package ʻGRmetricsʼ68 as an indication of scopoletin tolerance.Whole-genome sequencingAll 30 isolated phenotypes were whole genome sequenced to identify possible mutations and affected genes. To this end, isolates were cultured overnight at 28 °C in 1/3 strength liquid KB. Chromosomal DNA was isolated from each culture using the GenElute™ Bacterial Genomic DNA Kit Protocol (NA2100). DNA samples were sheared on a Covaris E-220 Focused-ultrasonicator and sheared DNA was then used to prepare Illumina sequencing libraries with the NEBNext® Ultra™ DNA Library Prep Kit (New England Biolabs. France) and the NEBNext® Multiplex Oligos for Illumina® (96 Index Primers). The final libraries were sequenced in multiplex on the NextSeq 500 platform (2 × 75 bp paired-end) by the Utrecht Sequencing Facility (http://www.useq.nl) yielding between 1.0 and 6.4 million reads per sample equivalent to ~10–70-fold coverage (based on comparison with the original 6.8 Mbp reference genome NCBI GenBank: CP003190.1).Variant calling analysisWe first constructed an updated reference genome of P. protegens CHA0, carrying the GFP marker gene on its chromosome, from the ancestral strain using the A5 pipeline with default parameters69. The input dataset for this sample consisted of 3,1M reads and totals an approximate 34-fold coverage. The size of the updated reference genome is 6.8 Mbp, with a G + C content of 63.4%, and it comprises 80 scaffolds, with a N50 value of 343 kbp. We subsequently used PROKKA70 (version 1.12; https://github.com/tseemann/prokka) for full annotation of the updated reference genome, and this resulted in the identification of 6147 genes. The updated genome is deposited in NCBI GenBank with following reference: RCSR00000000.1.Having established the ancestral genome sequence, we subsequently used Snippy (version 3.2-dev; https://github.com/tseemann/snippy) to identify and functionally annotate single-nucleotide polymorphisms and small insertions and deletions (indels) for each individual strain. In addition, we investigated the breadth of coverage for each gene per sample with BedTools71 to identify genes with large insertions or deletions. An overview of the polymorphisms is shown in Supplementary Table S3. Raw sequencing data for this study are deposited at the NCBI database under BioProject PRJNA473919.Relative competitive fitness of gac mutants measured in vivo and in vitroThe relative competitive fitness of selected gac mutants was measured in direct competition with their direct ancestors both in vivo in the rhizosphere of A. thaliana and in vitro in different standard culture media. Relative fitness was measured as deviation from initial 1:1 ratio of bacterial clone pairs based on PCR-based high-resolution melting profile (RQ-HRM) analysis. Three pairs of isolates were selected: (A) evolved gacA ID 242 (genotype oafAY335X ∙ RS17350A77A.fsX14 ∙ gacAD49Y) and its direct ancestral genotype 133 (genotype oafAY335X ∙ RS17350A77A.fsX14) from evolutionary line 1; (B) evolved gacA ID 220 (genotype galEV32M ∙ accCE413K ∙ gacAD54Y) and its direct ancestral genotype 28 (genotype galEV32M ∙ accCE413K) from line 2; (c) evolved gacS ID 222 (genotype oafAK338S.fsX18 ∙ gacSG27D) and its direct ancestral genotype 66 (genotype oafAK338S.fsX18) from line 3. Bacterial isolates were first grown overnight in KB medium at 28 °C, centrifuged at 5000g for 10 min and the pellet resuspended in 10 mM MgSO4. This washing procedure was repeated twice. The resulting bacterial suspensions were diluted to OD600 = 0.05. The initial inoculum for the competition assays was then generated by mixing equal volumes of evolved and ancestral competitors in a ratio of 1:1.Measuring competitive fitness in A. thaliana rhizosphereThis assay was performed on the roots of 10-day old A. thaliana seedlings grown on full strength Hoagland agar plates, which were prepared as described earlier. Twenty microlitres of the initial inoculum, containing a total of 106 bacterial cells, was inoculated on to the root–shoot junction of each seedling. After 14 days of growth, bacterial populations were isolated from the roots and stored at −80 °C in 42.5% glycerol for relative abundance measurements.Measuring competitive fitness in culture mediaCompetition assays were also performed in three commonly used nutrient-rich growth media: KB, LB and TSB. KB contained 20 g proteose peptone, 1.5 g MgSO4.7H2O, 1.2 g KH2PO4 and 10 g glycerol per litre and the pH was adjusted to 7.3 ± 0.2. TSB contained 30 g tryptic soy broth per litre and pH was adjusted to 7.3 ± 0.2. LB contained 10 g peptone, 5 g yeast extract and 5 g NaCl per litre. Twenty microlitres inoculum of competing strains, containing about 106 bacterial cells, were added into wells containing 140 μl fresh medium in a 96-well plate. The microplates were incubated at 28 °C without shaking for 48 after 80 μl sample was harvested and stored at −80 °C in 42.5% glycerol from each well for relative abundance measurements.RQ-HRM assay for quantifying changes in genotype frequencies after competitionWe used a high-resolution melting (HRM) curve profile assay with integrated LunaProbes to quantify the ratio of mutant to wild-type genotypes72,73,74. The probes and primers used in this study are listed in Table S4. Primers were designed using Primer3. Probes were designed with the single-nucleotide polymorphism (SNP) located in the middle of the sequence, and the 3′ end was blocked by carbon spacer C3. The primer asymmetry was set to 2:1 (excess primer: limiting primer) in all cases. Pre-PCR was performed in a 10-μl reaction system, with 0.25 μM excess primer, 0.125 μM limiting primer, 0.25 μM probe, 0.5 μl bacterial sample culture (100-fold diluted saved sample, OD600 is about 0.01), 1× LightScanner Master Mix (BioFire Defense). DMSO with the final concentration 5% was supplemented in all reactions to ensure the targeted melting domains are within the detection limit of the LightScanner (Idaho Technology Inc.). Finally, MQ water was used to supplement up to 10 μl. A 96-well black microtiter plate with white wells was used to minimize background fluorescence. Before amplification, 25 μl mineral oil was loaded in each well to prevent evaporation, and the plate was covered with a foil seal to prevent the degradation of fluorescent molecules. Amplification was initiated by a holding at 95 °C for 3 min, followed by 55 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s and extension at 72 °C for 30 s and then kept at 72 °C for 10 min. After amplification, samples were heated in a ThermalCycler (Bio-Rad) shortly to 95 °C for 30 s to denature all double-stranded structures followed by a rapid cooling to 25 °C for 30 s to facilitate successful hybridization between probes and the target strands. The plate was then transferred to a LightScanner (Idaho Technology Inc.). Melting profiles of each well were collected by monitoring the continuous loss of fluorescence with a steady increase of the temperature from 35 to 97 °C with a ramp rate of 0.1 °C/s. The relative quantification was based on the negative first derivative plots using software MATLAB. The areas of probe-target duplexes melting peaks were auto-calculated by ʻAutoFit Peaks I Residualsʼ function in software PeakFit (SeaSolve Software Inc.). The mutant frequency X was calculated using the formula shown below:$$X=frac{{rm{Area}}_{{mathrm{mutant}}}}{{{{mathrm{{Area}}}}}_{{mathrm{{mutant}}}}+{{rm{Area}}}_{{mathrm{{WT}}}}}$$
    (1)
    To validate the RQ-HRM method, standard curves were generated by measuring mixed samples with known proportions of mutant templates: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%. Measurements for each sample were done in triplicate. Linear regression formula of each mutant between actual frequencies and measured frequencies are shown in Fig. S7. The high R2 values, and nearly equal to 1 slope values of these equations, confirmed that the RQ-HRM method can accurately detect mutantsʼ frequency in a mixed population.The relative fitness of the evolved strains was calculated according to previous studies using the following equation75,76:$${mathrm{{relative}}; {mathrm{{fitness}}}}(r)=frac{{{{X}}}_{1}(1-{{{X}}}_{0})}{{{{X}}}_{0}(1-{{{X}}}_{1})}$$
    (2)
    where X0 is the initial mutant frequency; (1−X0) the initial ancestor frequency; X1 the final mutant frequency; and (1−X1) is the final ancestor frequency.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Effects of thinning and understory removal on the soil water-holding capacity in Pinus massoniana plantations

    1.Wen, X. F. et al. Soil moisture effect on the temperature dependence of ecosystem respiration in a subtropical Pinus plantation of southeastern China. Agric. For. Meteorol. 137, 166–175 (2006).ADS 
    Article 

    Google Scholar 
    2.Meißner, M., Köhler, M., Schwendenmann, L., Hölscher, D. & Dyckmans, J. Soil water uptake by trees using water stable isotopes (δ2H and δ18O)a method test regarding soil moisture, texture and carbonate. Plant Soil 376, 327–335 (2014).Article 
    CAS 

    Google Scholar 
    3.Sprenger, M. et al. Storage, mixing, and fluxes of water in the critical zone across northern environments inferred by stable isotopes of soil water. Hydrol. Process. 32, 1720–1737 (2018).ADS 
    Article 

    Google Scholar 
    4.Zhang, B. B. et al. Higher soil capacity of intercepting heavy rainfall in mixed stands than in pure stands in riparian forests. Sci. Total Environ. 658, 1514–1522 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Lee, K. S., Kim, J. M., Lee, D. R., Kim, Y. & Lee, D. Analysis of water movement through an unsaturated soil zone in Jeju Island, Korea using stable oxygen and hydrogen isotopes. J. Hydrol. 345, 199–211 (2007).ADS 
    Article 

    Google Scholar 
    6.Lozano-Parra, J., Schnabel, S. & Ceballos-Barbancho, A. The role of vegetation covers on soil wetting processes at rainfall event scale in scattered tree woodland of Mediterranean climate. J. Hydrol. 529, 951–961 (2015).ADS 
    Article 

    Google Scholar 
    7.Wan, H. & Liu, W. G. An isotope study (δ18O and δD) of water movements on the Loess Plateau of China in arid and semiarid climates. Ecol. Eng. 93, 226–233 (2016).Article 

    Google Scholar 
    8.Liu, Z. Q., Yu, X. X. & Jia, G. D. Water uptake by coniferous and broad-leaved forest in a rocky mountainous area of northern China. Agric. For. Meteorol. 265, 381–389 (2019).ADS 
    Article 

    Google Scholar 
    9.Easterling, D. R. et al. Climate extremes: Observations, modeling, and impacts. Science 289, 2068–2074 (2000).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Dai, E. F., Wang, X. L., Zhu, J. J. & Xi, W. M. Quantifying ecosystem service trade-offs for plantation forest management to benefit provisioning and regulating services. Ecol. Evol. 7, 7807–7821 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Ontl, T. A. et al. Adaptation pathways: Ecoregion and land ownership influences on climate adaptation decision-making in forest management. Clim. Chang. 146, 75–88 (2017).ADS 
    Article 

    Google Scholar 
    12.Di Prima, S. et al. Impacts of thinning of a Mediterranean oak forest on soil properties influencing water infiltration. J. Hydrol. Hydromech. 65, 276–286 (2017).Article 
    CAS 

    Google Scholar 
    13.Wang, Z., He, Q. H., Hu, B., Pang, X. Y. & Bao, W. K. Gap thinning improves soil water content, changes the vertical water distribution, and decreases the fluctuation. Can. J. For. Res. 48, 1042–1048 (2018).CAS 
    Article 

    Google Scholar 
    14.Del Campo, A. D. et al. Effectiveness of water-oriented thinning in two semiarid forests: The redistribution of increased net rainfall into soil water, drainage and runoff. For. Ecol. Manag. 438, 163–175 (2019).Article 

    Google Scholar 
    15.He, Z. B. et al. Responses of soil organic carbon, soil respiration, and associated soil properties to long-term thinning in a semiarid spruce plantation in northwestern China. Land Degrad. Dev. 29, 4387–4396 (2018).Article 

    Google Scholar 
    16.Giuggiola, A., Zweifel, R., Feichtinger, L., M., Vollenweider, P. & Bugmann, H. Competition for water in a xeric forest ecosystem—Effects of understory removal on soil micro-climate, growth and physiology of dominant Scots pine trees. For. Ecol. Manag. 409, 241–249 (2018).17.Prévosto, B., Helluy, M., Gavinet, J., Fernandez, C. & Balandier, P. Microclimate in Mediterranean pine forests: What is the influence of the shrub layer? Agric. For. Meteorol. 282–283, 107856 (2020).18.Sohn, J. A., Saha, S. & Bauhus, J. Potential of forest thinning to mitigate drought stress: A meta-analysis. For. Ecol. Manag. 380, 261–273 (2016).Article 

    Google Scholar 
    19.Vilà-Cabrera, A., Coll, L., Martínez-Vilalta, J. & Retana, J. Forest management for adaptation to climate change in the Mediterranean basin: A synthesis of evidence. For. Ecol. Manag. 407, 16–22 (2018).Article 

    Google Scholar 
    20.Bréda, N., Granier, A. & Aussenac, G. Effects of thinning on soil and tree water relations, transpiration and growth in an oak forest (Quercus petraea (Matt.) Liebl.). Tree Physiol. 15, 295–306 (1995).21.Martínez, G. G., Pachepsky, Y. A. & Vereecken, H. Effect of soil hydraulic properties on the relationship between the spatial mean and variability of soil moisture. J. Hydrol. 516, 154–160 (2014).ADS 
    Article 

    Google Scholar 
    22.Buchanan, B. P. et al. Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrol. Earth Syst. Sci. 18, 3279–3299 (2014).ADS 
    Article 

    Google Scholar 
    23.Gwak, Y. & Kim, S. Factors affecting soil moisture spatial variability for a humid forest hillslope. Hydrol. Process. 31, 431–445 (2016).ADS 
    Article 

    Google Scholar 
    24.Knighton, J. et al. Seasonal and topographic variations in ecohydrological separation within a small, temperate, snow-influenced catchment. Water Resour. Res. 55, 6417–6435 (2019).ADS 
    Article 

    Google Scholar 
    25.Metzger, J. C. et al. Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties. Hydrol. Process. 31, 3783–3795 (2017).ADS 
    Article 

    Google Scholar 
    26.Hasselquist, N. J., Benegas, L., Roupsard, O., Malmer, A. & Ilstedt, U. Canopy cover effects on local soil water dynamics in a tropical agroforestry system: Evaporation drives soil water isotopic enrichment. Hydrol. Process. 32, 994–1004 (2018).ADS 
    Article 

    Google Scholar 
    27.Heiskanen, J. & Mäkitalo, K. Soil water-retention characteristics of Scots pine and Norway spruce forest sites in Finnish Lapland. For. Ecol. Manag. 162, 137–152 (2002).Article 

    Google Scholar 
    28.Geris, J., Tetzlaff, D., Mcdonnell, J. & Soulsby, C. The relative role of soil type and tree cover on water storage and transmission in northern headwater catchments. Hydrol. Process. 29, 1844–1860 (2015).ADS 
    Article 

    Google Scholar 
    29.Sun, L. et al. Tracing the soil water response to autumn rainfall in different land uses at multi-day timescale in a subtropical zone. CATENA 180, 355–364 (2019).CAS 
    Article 

    Google Scholar 
    30.Del Campo, A. D., González-Sanchis, M., Lidón, A., Ceacero, C. J. & García-Prats, A. Rainfall partitioning after thinning in two low-biomass semiarid forests: Impact of meteorological variables and forest structure on the effectiveness of water-oriented treatments. J. Hydrol. 565, 74–86 (2018).Article 

    Google Scholar 
    31.Cabon, A. et al. Thinning increases tree growth by delaying drought-induced growth cessation in a Mediterranean evergreen oak coppice. For. Ecol. Manag. 409, 333–342 (2018).Article 

    Google Scholar 
    32.Xiong, Y. M., Xia, H. P., Li, Z. A., Cai, X. A. & Fu, S. L. Impacts of litter and understory removal on soil properties in a subtropical Acacia mangium plantation in China. Plant Soil 304, 179–188 (2008).CAS 
    Article 

    Google Scholar 
    33.Su, W. H., Zhu, X. W., Fan, S. H., Zeng, X. L. & Liu, G. L. Review of effects of harvesting on forest ecosystem. For. Resour. Manag. 3, 35–40 (2017).
    Google Scholar 
    34.Nijzink, R. et al. The evolution of root-zone moisture capacities after deforestation: A step towards hydrological predictions under change?. Hydrol. Earth Syst. Sci. 20, 4775–4799 (2016).ADS 
    Article 

    Google Scholar 
    35.Xiao, W. F. et al. Rates of litter decomposition and soil respiration in relation to soil temperature and water in different-aged Pinus massoniana forests in the Three Gorges Reservoir Area, China. PLoS One 9, e101890 (2014).36.Shen, Y. F. et al. Labile organic carbon pools and enzyme activities of Pinus massoniana plantation soil as affected by understory vegetation removal and thinning. Sci. Rep. 8, 573 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Lei, L. et al. Thinning but not understory removal increased heterotrophic respiration and total soil respiration in Pinus massoniana stands. Sci. Total Environ. 621, 1360–1369 (2017).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    38.Wang, X. R. et al. Short-terms effects of tending thinnng on soil labile organic carbon in Pinus massoniana stands. Chin. J. Ecol. 40, 1049–1061 (2021).
    Google Scholar 
    39.Zhao, P., Tang, X. Y., Zhao, P., Zhang, W. & Tang, J. L. Mixing of event and pre-event water in a shallow Entisol in sloping farmland based on isotopic and hydrometric measurements, SW China. Hydrol. Process. 30, 3478–3493 (2016).ADS 
    Article 

    Google Scholar 
    40.Lin, G. H., Phillips, S. L. & Ehleringer, J. R. Monsoonal precipitation responses of shrubs in a cold desert community on Colorado Plateau. Oecologia 106, 8–17 (1996).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.West, A. G., Patrickson, S. J. & Ehleringer, J. R. Water extraction times for plant and soil materials used in stable isotope analysis. Rapid Commun. Mass Sp. 20, 1317–1321 (2006).CAS 
    Article 

    Google Scholar 
    42.Tetzlaff, D., Birkel, C., Dick, J., Geris, J. & Soulsby, C. Storage dynamics in hydropedological units control hillslope connectivity, runoff generation, and the evolution of catchment transit time distributions. Water Resour. Res. 50, 969–985 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Liu, Y. et al. Variations of soil water isotopes and effective contribution times of precipitation and throughfall to alpine soil water, in Wolong Nature Reserve, China. CATENA 126, 201–208 (2015).Article 

    Google Scholar 
    44.Zhang, Z., Jin, G. Q., Zhou, Z. C. & Sun, L. S. Biomass allocation differences with Pinus massoniana in Guangdong and Hubei provenances. J. Zhejiang A&F Univ. 36, 271–278 (2019).
    Google Scholar 
    45.Özcan, M., GÖkbulak, F. & Hizal, A. Exclosure effects on recovery of selected soil properties in a mixed broadleaf forest recreation site. Land Degrad. Dev. 24, 266–276 (2013).46.Fan, Y. et al. Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecol. Process. 5, 19–31 (2016).ADS 
    Article 

    Google Scholar 
    47.Xu, Q., Liu, S. R., Wan, X. C., Jiang, C. Q. & Wang, J. X. Effects of rainfall on soil moisture and water movement in a subalpine dark coniferous forest in southwestern China. Hydrol. Process. 26, 3800–3809 (2012).ADS 
    Article 

    Google Scholar 
    48.Sprenger, M., Leistert, H., Gimbel, K. & Weiler, M. Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes. Rev. Geophys. 54, 674–704 (2016).ADS 
    Article 

    Google Scholar 
    49.Zheng, W. B., Wang, S. Q., Sprenger, M., Liu, B. X. & Cao, J. S. Response of soil water movement and groundwater recharge to extreme precipitation in a headwater catchment in the North China Plain. J. Hydrol. 576, 466–477 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    50.Hsueh, Y. H., Allen, S. T. & Keim, R. F. Fine-scale spatial variability of throughfall amount and isotopic composition under a hardwood forest canopy. Hydrol. Process. 30, 1796–1803 (2016).ADS 
    Article 

    Google Scholar 
    51.Allen, S. T., Keim, R. F., Barnard, H. R., Mcdonnell, J. J. & Renée Brooks, J. The role of stable isotopes in understanding rainfall interception processes: A review. Wires. Water 4, e1187 (2017).52.Shaw, S. B., McHardy, T. M. & Riha, S. J. Evaluating the influence of watershed moisture storage on variations in base flow recession rates during prolonged rain-free periods in medium-sized catchments in New York and Illinois, USA. Water Resour. Res. 49, 6022–6028 (2013).ADS 
    Article 

    Google Scholar 
    53.Zhao, J., Xu, Z. & Singh, V. P. Estimation of root zone storage capacity at the catchment scale using improved mass curve technique. J. Hydrol. 540, 959–972 (2016).ADS 
    Article 

    Google Scholar 
    54.Zhang, Y. K. & Schilling, K. E. Effects of land cover on water table, soil moisture, evapotranspiration, and groundwater recharge: A field observation and analysis. J. Hydrol. 319, 328–338 (2006).ADS 
    Article 

    Google Scholar 
    55.Deng, L., Yan, W. M., Zhang, Y. W. & Shangguan, Z. P. Severe depletion of soil moisture following land-use changes for ecological restoration: Evidence from northern China. For. Ecol. Manag. 366, 1–10 (2016).Article 

    Google Scholar 
    56.Imaizumi, F., Sidle, R. C. & Kamei, R. Effects of forest harvesting on the occurrence of landslides and debris flows in steep terrain of central Japan. Earth Surf. Proc. Land 33, 827–840 (2010).ADS 
    Article 

    Google Scholar 
    57.Nyssen, J. et al. Impact of soil and water conservation measures on catchment hydrological response-a case in north Ethiopia. Hydrol. Process. 24, 1880–1895 (2010).ADS 
    Article 

    Google Scholar 
    58.Zheng, H., Gao, J., Teng, Y., Feng, C. & Tian, M. Temporal variations in soil moisture for three typical vegetation types in Inner Mongolia, Northern China. Plos One 10, e0118964 (2015).59.Oswald, C. J., Richardson, M. C. & Branfireun, B. A. Water storage dynamics and runoff response of a boreal Shield headwater catchment. Hydrol. Process. 25, 3042–3060 (2011).
    Google Scholar 
    60.De Boer-Euser, T., McMillan, H. K., Hrachowitz, M., Winsemius, H. C. & Savenije, H. H. Influence of soil and climate on root zone storage capacity. Water Resour. Res. 52, 2009–2024 (2016).ADS 
    Article 

    Google Scholar 
    61.Zhou, X. N. & Lin, H. M. Effect on soil physical and chemical properties by different harvesting methods. Sci. Silva. Sin. 34, 18–25 (1998).
    Google Scholar 
    62.Meier, I. C., Knutzen, F., Eder, L. M., Müller-Haubold, H. & Leuschner, C. The deep root system of Fagus sylvatica on sandy soil: Structure and variation across a precipitation gradient. Ecosystems 21, 280–296 (2017).Article 
    CAS 

    Google Scholar 
    63.Liu, Y., Cui, Z., Huang, Z., López-Vicente, M. & Wu, G. Influence of soil moisture and plant roots on the soil infiltration capacity at different stages in arid grasslands of China. Catena 182, 104147 (2019).64.Beven, K. & Germann, P. Macropores and water flow in soils revisited. Water Resour. Res. 49, 3071–3092 (2013).ADS 
    Article 

    Google Scholar 
    65.Bronick, C. J. & Lal, R. Soil structure and management: A review. Geoderma 124, 3–22 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Périé, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315–325 (2008).Article 

    Google Scholar 
    67.Kooch, Y., Samadzadeh, B. & Hosseini, S. M. The effects of broad-leaved tree species on litter quality and soil properties in a plain forest stand. CATENA 150, 223–229 (2017).CAS 
    Article 

    Google Scholar 
    68.Mishra, S. et al. Understanding the relationship between soil properties and litter chemistry in three forest communities in tropical forest ecosystem. Environ. Monit. Assess. 191, 797 (2019).CAS 
    Article 

    Google Scholar 
    69.Yang, B., Wen, X. F. & Sun, X. M. Seasonal variations in depth of water uptake for a subtropical coniferous plantation subjected to drought in an East Asian monsoon region. Agric. For. Meteorol. 201, 218–228 (2015).ADS 
    Article 

    Google Scholar 
    70.Ungar, E. D. et al. Transpiration and annual water balance of Aleppo pine in a semiarid region: Implications for forest management. For. Ecol. Manag. 298, 39–51 (2013).Article 

    Google Scholar 
    71.Oerter, E. J. & Bowen, G. J. Spatio-temporal heterogeneity in soil water stable isotopic composition and its ecohydrologic implications in semiarid ecosystems. Hydrol. Process. 33, 1724–1738 (2019).ADS 
    Article 

    Google Scholar  More

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    Ancient oaks of Europe are archives — protect them

    CORRESPONDENCE
    22 June 2021

    Ancient oaks of Europe are archives — protect them

    Christian Sonne

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    Changlei Xia

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    Su Shiung Lam

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    Christian Sonne

    Aarhus University, Roskilde, Denmark.

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    Changlei Xia

    Nanjing Forestry University, Nanjing, China.

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    Su Shiung Lam

    University Malaysia Terengganu, Terengganu, Malaysia.

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    Kongeegen, the King Oak, in Denmark could be up to 2,000 years old.Credit: Andreas Altenburger/Alamy

    Some of the oldest trees in Europe are in danger because they are not being given the necessary level of protection. Oak trees (Quercus robur) that are more than 1,000 years old are found in the United Kingdom and in Fennoscandia, which includes Denmark, Sweden and Norway.For example, Denmark’s King Oak (pictured) is one of the world’s oldest living trees, dating to around 1,900 years of age. The United Kingdom has the largest collection of ancient oaks, reflecting 1,500 years of ship-building.The trees contain rings that represent archives of historical climate fluctuations and levels of atmospheric gases, so they can help to answer pressing questions about climate change and ecosystem dynamics (P. M. Kelly et al. Nature 340, 57–60; 1989).Fennoscandia and the United Kingdom could better safeguard their oaks using mechanisms such as those offered by the European Union’s Natura 2000 network of protected areas, or the protections conferred by UNESCO World Heritage sites in the United Kingdom. Otherwise, unsustainable management practices, deforestation, air pollution and climate change could leave these ancient species vulnerable to disease and extinction, with the loss of irreplaceable scientific information and cultural heritage.

    Nature 594, 495 (2021)
    doi: https://doi.org/10.1038/d41586-021-01699-0

    Competing Interests
    The authors declare no competing interests.

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    A new galling insect model enhances photosynthetic activity in an obligate holoparasitic plant

    1.Redfern, M. Plant Galls. The New Naturalist Library (Harper Collins, 2011).
    Google Scholar 
    2.Stone, G. N. & Schönrogge, K. The adaptive significance of insect gall morphology. Trends Ecol Evol. 18, 512–522 (2003).Article 

    Google Scholar 
    3.Dawkins, R. The Extended Phenotype (Oxford University Press, 1982).
    Google Scholar 
    4.Raman, A. Morphogenesis of insect-induced plant galls: Facts and questions. Flora 206, 517–533 (2011).Article 

    Google Scholar 
    5.Gatjens-Boniche, O. The mechanism of plant gall induction by insects: Revealing clues, facts, and consequences in a cross-kingdom complex interaction. Rev. Biol. Trop. 67, 1359–1382 (2019).Article 

    Google Scholar 
    6.Gonçalves-Alvim, S. J. & Fernandes, G. W. Biodiversity of galling insects: Historical, community and habitat effects in four neotropical savannas. Biodivers. Conserv. 10, 79–98 (2001).Article 

    Google Scholar 
    7.Veldtman, R. & McGeoch, M. Gall-forming insect species richness along a non-scleromorphic vegetation rainfall gradient in South Africa: The importance of plant community composition. Austral. Ecol. 28, 1–13 (2003).Article 

    Google Scholar 
    8.Stuart, J., Chen, M.-S., Shukle, R. & Harris, M. Gall midges (Hessian flies) as plant pathogens. Annu. Rev. Phytopath. 50, 339–357 (2012).CAS 
    Article 

    Google Scholar 
    9.Kono, H. Langrüssler aus japanischen Reich. Insecta Matsumurana 4, 145–162 (1930).
    Google Scholar 
    10.Morimoto, K. & Kojima, H. Weevils of the genus Smicronyx in Japan (Coleoptera: Curculionidae). Entomol. Rev. Jpn. 62, 1–9 (2007).
    Google Scholar 
    11.Hayakawa, H., Fujii, S. & Yoshitake, H. Reexamination of the host plant of Smicronyx madaranus (Coleoptera, Curculionidae, Smicronycinae). SAYABANE 30, 51–55 (2018) (in Japanese).
    Google Scholar 
    12.Yukawa, J. Synchronization of gallers with host plant phenology. Popul. Ecol. 42, 105–113 (2000).Article 

    Google Scholar 
    13.Vitou, J., Skuhravá, M., SkuhravÝ, V., Scott, J. & Sheppard, A. The role of plant phenology in the host specificity of Gephyraulus raphanistri (Diptera: Cecidomyiidae) associated with Raphanus spp. (Brassicaceae). Eur. J. Entomol. 105, 113–119 (2008).
    Article 

    Google Scholar 
    14.Yamaguchi, H. et al. Phytohormones and willow gall induction by a gall-inducing sawfly. New Phytol. 196, 586–595 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Tanaka, Y., Okada, K., Asami, T. & Suzuki, Y. Phytohormones in Japanese mugwort gall induction by a gall-inducing gall midge. Biosci. Biotechnol. Biochem. 77, 1942–1948 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Liu, P., Yang, Z. X., Chen, X. M. & Foottit, R. G. The effect of the gall-forming aphid Schlechtendalia chinensis (Hemiptera: Aphididae) on leaf wing ontogenesis in Rhus chinensis (Sapindales: Anacardiaceae). Ann. Entomol. Soc. Am. 107, 242–250 (2014).Article 

    Google Scholar 
    17.Hirano, T. et al. Reprogramming of the developmental program of Rhus javanica during initial stage of gall induction by Schlechtendalia chinensis. Front. Plant Sci. 11, 471 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Kaiser, B., Vogg, G., Fürst, U. B. & Albert, M. Parasitic plants of the genus Cuscuta and their interaction with susceptible and resistant host plants. Front. Plant Sci. 6, 45 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Pattee, H. E., Allred, K. R. & Wiebe, H. H. Photosynthesis in dodder. Weeds 13, 193–195 (1965).CAS 
    Article 

    Google Scholar 
    20.van der Kooij, T. A. W., Krause, K., Dörr, I. & Krupinska, K. Molecular, functional and ultrastructural characterisation of plastids from six species of the parasitic flowering plant genus Cuscuta. Planta 210, 701–707 (2000).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Sherman, T. D., Pettigrew, W. T. & Vaughn, K. C. Structural and immunological characterization of the Cuscuta pentagona L. chloroplast. Plant Cell Physiol. 40, 592–603 (1999).CAS 
    Article 

    Google Scholar 
    22.Machado, M. A. & Zetsche, K. A structural, functional and molecular analysis of plastids of the holoparasites Cuscuta reflexa and Cuscuta europaea. Planta 181, 91–96 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Hibberd, J. M. et al. Localization of photosynthetic metabolism in the parasitic angiosperm Cuscuta reflexa. Planta 205, 506–513 (1998).CAS 
    Article 

    Google Scholar 
    24.Taiz, L., Zieiger, E., Max Moller, I. & Angus, M. Plant Physiology and Development 6th edn. (Sinauer Associates, 2015).
    Google Scholar 
    25.Bartlett, L. & Connor, E. F. Exogenous phytohormones and the induction of plant galls by insects. Arthropod Plant Interact. 8, 339–348 (2014).
    Google Scholar 
    26.Tooker, J. F. & Helms, A. M. Phytohormone dynamics associated with gall insects, and their potential role in the evolution of the gall-inducing habit. J. Chem. Ecol. 40, 742–753 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Tokuda, M. et al. Phytohormones related to host plant manipulation by a gall-inducing leafhopper. PLoS ONE 8, e62350 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Suzuki, H. et al. Biosynthetic pathway of the phytohormone auxin in insects and screening of its inhibitors. Insect Biochem. Mol. Biol. 53, 66–72 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Yokoyama, C., Takei, M., Kouzuma, Y., Nagata, S. & Suzuki, Y. Novel tryptophan metabolic pathways in auxin biosynthesis in silkworm. J. Insect Physiol. 101, 91–96 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Kaiser, W., Huguet, E., Casas, J., Commin, C. & Giron, D. Plant green-island phenotype induced by leaf-miners is mediated by bacterial symbionts. Proc. Biol. Sci. 277, 2311–2319 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Body, M., Kaiser, W., Dubreuil, G., Casas, J. & Giron, D. Leaf-miners co-opt microorganisms to enhance their nutritional environment. J. Chem. Ecol. 39, 969–977 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Giron, D. & Glevarec, G. Cytokinin-induced phenotypes in plant-insect interactions: Learning from the bacterial world. J. Chem. Ecol. 40, 826–835 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Gutzwiller, F., Dedeine, F., Kaiser, W., Giron, D. & Lopez-Vaamonde, C. Correlation between the green-island phenotype and Wolbachia infections during the evolutionary diversification of Gracillariidae leaf-mining moths. Ecol. Evol. 5, 4049–4062 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Giron, D., Huguet, E., Stone, G. N. & Body, M. Insect-induced effects on plants and possible effectors used by galling and leaf-mining insects to manipulate their host-plant. J. Insect Physiol. 84, 70–89 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Zhao, C. et al. A massive expansion of effector genes underlies gall-formation in the wheat pest Mayetiola destructor. Curr. Biol. 25, 613–620 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Lemus, L. P. et al. Salivary proteins of a gall-inducing aphid and their impact on early gene responses of susceptible and resistant poplar genotypes. bioRxiv https://doi.org/10.1101/504613 (2018).Article 

    Google Scholar 
    37.Vogel, A. et al. Footprints of parasitism in the genome of the parasitic flowering plant Cuscuta campestris. Nat. Commun. 9, 2515 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    38.Senthil-Kumar, M. & Mysore, K. S. Tobacco rattle virus–based virus-induced gene silencing in Nicotiana benthamiana. Nat. Protoc. 9, 1549–1562 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Norkunas, K., Harding, R., Dale, J. & Dugdale, B. Improving agroinfiltration-based transient gene expression in Nicotiana benthamiana. Plant Methods 14, 71 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    40.Christiaens, O. et al. RNA interference: A promising biopesticide strategy against the African Sweetpotato Weevil Cylas brunneus. Sci. Rep. 6, 38836 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Maire, J., Vincent-Monégat, C., Masson, F., Zaidman-Rémy, A. & Heddi, A. An IMD-like pathway mediates both endosymbiont control and host immunity in the cereal weevil Sitophilus spp. Microbiome. 6, 6 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Barnewall, E. C. & De Clerck-Floate, R. A. A preliminary histological investigation of gall induction in an unconventional galling system. Arthropod Plant Interact. 6, 449–459 (2012).Article 

    Google Scholar 
    43.Aistova, E. V. & Bezborodov, V. G. Weevils belonging to the genus Smicronyx Schönherr, 1843 (Coleoptera, Curculionidae) affecting dodders (Cuscuta Linnaeus, 1753) in the Russian Far East. Russ. J. Biol. Invasions. 8, 184–188 (2017).Article 

    Google Scholar 
    44.Dinelli, G., Bonetti, A. & Tibiletti, E. Photosynthetic and accessory pigments in Cuscuta-Campestris Yuncker and some host species. Weed Res. 33, 253–260 (1993).CAS 
    Article 

    Google Scholar 
    45.Anikin, V. V., Nikelshparg, M. I., Nikelshparg, E. I. & Konyukhov, I. V. Photosynthetic activity of the dodder Cuscuta campestris (Convolvulaceae) in case of plant inhabitation by the gallformed weevil Smicronyx smreczynskii (Coleoptera, Curculionidae). Chem. Biol. Ecol. 17, 42–47 (2017) (in Russian).
    Google Scholar 
    46.Zagorchev, L. I., Albanova, I. A., Tosheva, A. G., Li, J. & Teofanova, D. R. Metabolic and functional distinction of the Smicronyx sp. galls on Cuscuta campestris. Planta 248, 591–599 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods. 9, 676–682 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Carneiro, R. G. D. S. & Isaias, R. M. D. S. Gradients of metabolite accumulation and redifferentiation of nutritive cells associated with vascular tissues in galls induced by sucking insects. AoB Plants. 7, plv086 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    49.Porra, R. J., Thompson, W. A. & Kriedemann, P. E. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: Verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim. Biophys. Acta 975, 384–394 (1989).CAS 
    Article 

    Google Scholar 
    50.Kawase, M., Hanba, Y. T. & Katsuhara, M. The photosynthetic response of tobacco plants overexpressing ice plant aquaporin McMIPB to a soil water deficit and high vapor pressure deficit. J. Plant Res. 126, 517–527 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Ihaka, R. & Gentleman, R. R: A language for data analysis and graphics. J. Comput. Graph Stat. 5, 299–314 (1996).
    Google Scholar  More

  • in

    Shell shock: a biologist’s quest to save the endangered painted snail

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    In my laboratory at the University of Oriente, in Santiago de Cuba, we study the six species of Polymita, known as painted snails, which are endemic to eastern Cuba and are in danger of extinction. The shells’ vibrant swirls and stripes look as if they’ve been painted by hand. Unfortunately, you can find their shells for sale on eBay, and many are exported to places such as the United States, China and Spain for use in art and jewellery — despite laws banning such trade.Painted snails live in mangrove forests, in sandy and rocky coastal areas and in rainforests. Some species are important parts of agro-ecosystems, such as coffee and coconut plantations. In 1995, my team began a breeding laboratory. We needed a way to isolate individual snails in containers, and to provide them with food, such as a fig-tree branch covered with moss, lichens and sooty mould fungus. But getting enough of the right containers was a problem because the nation was in an economic depression then.My students realized that when tourists visited Cuba, they left behind plastic one-litre water bottles. Since then we’ve been using them as living spaces for the snails.We study the breeding behaviour, nesting, hatching and growth of these hermaphrodites. If we want to save Polymita, we need to know more about their reproduction patterns — why one species hatches only between July and December, for instance.When mating, Polymita use a protrusion called a dart to transfer hormones, but we know very little about it. We are studying how these hormones affect the reproductive tract and influence fertilization success.In Cuba, there is more support for medical research than for biodiversity research. So we look for collaborations around the world. My motto is a Cuban saying: “We have the ‘no’, and therefore always have to look for the ‘yes’.” In other words, there is always another way, if you keep looking.

    Nature 594, 606 (2021)
    doi: https://doi.org/10.1038/d41586-021-01683-8

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    Fear of large carnivores is tied to ungulate habitat use: evidence from a bifactorial experiment

    1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484. https://doi.org/10.1126/science.1241484 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Ford, A. T. & Goheen, J. R. Trophic cascades by large carnivores: A case for strong Inference and mechanism. Trend Ecol. Evol. 30, 725–735 (2015).Article 

    Google Scholar 
    4.Suraci, J. P., Clinchy, M., Dill, L. M., Roberts, D. & Zanette, L. Y. Fear of large carnivores causes a trophic cascade. Nat. Commun. 7, 10698. https://doi.org/10.1038/ncomms10698 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: Optimal foraging, game theory and trophic interactions. J. Mammal. 80, 385–399 (1999).Article 

    Google Scholar 
    7.Brown, J. S. Ecology of fear. In Encyclopedia of Animal Behaviour (ed. Chun, C.) (Academic Press, 2019).
    Google Scholar 
    8.Trussell, G. C., Ewanchuk, P. J. & Matassa, C. M. The fear of being eaten reduces energy transfer in a simple food chain. Ecology 87, 2979–2984 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: The primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).Article 

    Google Scholar 
    10.Say-Sallaz, E., Chamaillé-James, S., Fritz, H. & Valeix, M. Non-consumptive effects of predation in large terrestrial mammals: Mapping our knowledge and revealing the tip of the iceberg. Biol. Conserv. 235, 36–52 (2019).Article 

    Google Scholar 
    11.Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl. Acad. Sci. U.S.A. 113, 838–846 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl. Acad. Sci. USA 106, 4947–4952 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Ford, A. T. et al. Large carnivores make savanna tree communities less thorny. Science 346, 346–349 (2014).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Bernes, C. et al. Manipulating ungulate herbivory in temperate and boreal forests: effects on vegetation and invertebrates: A systematic review. Environ. Evid. 7, 13. https://doi.org/10.1186/s13750-018-0125-3 (2018).Article 

    Google Scholar 
    15.Creel, S. The control of risk hypothesis: Reactive vs proactive antipredator responses and stress-mediated vs food-mediated costs of response. Ecol. Lett. 21, 947–956 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Riginos, C. Climate and the landscape of fear in an African savanna. J. Anim. Ecol. 84, 124–133 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.le Roux, E. G., Kerley, I. H. & Cromsigt, J. P. G. M. Megaherbivores modify trophic cascades triggered by fear of predation in an African savanna ecosystem. Curr. Biol. 28, 2493–2499 (2018).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    18.Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis. Ecol. Lett. 14, 709–722 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Stanton, R. A. et al. Shrub encroachment and vertebrate diversity: A global meta-analysis. Glob. Ecol. Biogeogr. 27, 368–379 (2018).Article 

    Google Scholar 
    20.Soto-Shoender, J. R., McCleery, R. A., Monadjem, A. & Gwinn, D. C. The importance of grass cover for mammalian diversity and habitat associations in a bush encroached savanna. Biol. Conserv. 221, 127–136 (2018).Article 

    Google Scholar 
    21.Courbin, N. et al. Reactive responses of zebra to lion encounters shape their predator-prey space game at large scale. Oikos 125, 829–838 (2016).Article 

    Google Scholar 
    22.van Buskirk, J. Specific induced responses to different predator species in anuran larvae. J. Evol. Biol. 14, 482–489 (2001).Article 

    Google Scholar 
    23.Chalcraft, D. R. & Resetarits, W. J. Jr. Predator identity and ecological impacts: Functional redundancy or functional diversity?. Ecology 84, 2407–2418 (2003).Article 

    Google Scholar 
    24.Templeton, C. N., Greene, E. & Davis, K. Allometry of alarm calls: Black-capped chickadees encode information about predator size. Science 308, 1934–1937 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Cooper, W. E. Jr. & Frederick, W. G. Predator lethality, optimal escape behavior, and autonomy. Behav. Eco. 21, 91–96 (2009).Article 

    Google Scholar 
    26.Dröge, E., Creel, S., Becker, M. S. & Msoka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Davies, A. B., Tambling, C. J., Kerley, G. I. H. & Asner, G. P. Effects of vegetation structure on the location of lion kill sites in African thicket. PLoS ONE https://doi.org/10.1371/journal.pone.0149098 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Bertram, B. C. R. Serengeti Predators and their Social Systems in Serengeti: Dynamics of an Ecosystem, 221–285. (Sinclair, A. R. E. and Norton-Griffiths, M., Eds). (University of Chicago Press, Chicago, 1979).29.Bailey, T. N. The African Leopard: Ecology and Behavior of a Solitary Felid (Columbia University Press, 1993).Book 

    Google Scholar 
    30.Hayward, M. W. & Kerley, G. I. H. Prey preferences and dietary overlap amongst Africa’s large predators. S. Afr. J. Wildl. Res. 38, 93–108 (2008).Article 

    Google Scholar 
    31.McCleery, R. A. et al. Animal diversity declines with broad-scale homogenization of canopy cover in African savannas. Biol. Conserv. 226, 54–62 (2018).Article 

    Google Scholar 
    32.Roques, K. G., O’Connor, T. G. & Watkinson, A. R. Dynamics of shrub encroachment in an African savanna: Relative influences of fire, herbivory, rainfall and density dependence. J. Appl. Ecol. 38, 268–280 (2001).Article 

    Google Scholar 
    33.Sirami, C. & Monadjem, A. Changes in bird communities in Swaziland savannas between 1998 and 2008 owing to shrub encroachment. Divers. Distrib. 18, 390–400 (2012).Article 

    Google Scholar 
    34.Estes, R. D. The Behavior Guide to African Mammals: Including Hoofed Mammals, Carnivores, Primates (University of California Press, 2012).
    Google Scholar 
    35.Hayward, M. et al. Prey preferences of the leopard (Panthera pardus). J. Zool. 270, 298–313 (2006).Article 

    Google Scholar 
    36.Holekamp, K. E. & Dloniak, S. M. Intraspecific Variation in the Behavioral Ecology of a Tropical Carnivore, the Spotted Hyena in Advances in the Study of Behavior. Vol. 42 189–229 (Elsevier, 2010).37.Retief, F. The Ecology of Spotted Hyena, Crocuta crocuta, in Majete Wildlife Reserve, Malawi. Dissertation. (Stellenbosch University, 2016).38.Suraci, J. P. et al. A new automated behavioural response system to integrate playback experiments into camera trap studies. Methods Ecol. Evol. 8, 957–964 (2017).Article 

    Google Scholar 
    39.Smith, J. A. et al. Fear of the human ‘super predator’ reduces feeding time in large carnivores. Proc. R. Soc. Lond. Ser. B. https://doi.org/10.1098/rspb.2017.0433 (2017).Article 

    Google Scholar 
    40.Stankowich, T. & Blumstein, D. T. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. Lond. B. 272, 2627–2634 (2005).
    Google Scholar 
    41.Scogings, P. F. Large herbivores and season independently affect woody stem circumference increment in a semi-arid savanna. Plant Ecol. 215, 1433–1443 (2014).Article 

    Google Scholar 
    42.Skinner, J. D. & Chimimba, C. T. The Mammals of the Southern African Sub-region (Cambridge University Press, 2005).Book 

    Google Scholar 
    43.Canfield, R. H. Application of the line interception method in sampling range vegetation. J. For. 39, 388–394 (1941).
    Google Scholar 
    44.Favreau, F. R., Pays, O., Goldizen, A. W. & Fritz, H. Short-term behavioural responses of impalas in simulated antipredator and social contexts. PLoS ONE https://doi.org/10.1371/journal.pone.0084970 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Suraci, J. P., Clinchy, M. & Zanette, L. Y. Do large carnivores and mesocarnivores have redundant impacts on intertidal prey?. PLoS ONE https://doi.org/10.1371/journal.pone.0170255 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Chandler, R. B., Engebretsen, K., Cherry, M. J., Garrison, E. P. & Miller, K. V. Estimating recruitment from capture–recapture data by modelling spatio-temporal variation in birth and age-specific survival rates. Methods Ecol. Evol. 9, 2115–2130 (2018).Article 

    Google Scholar 
    47.Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Stud. Behav. 16, 229–249 (1986).Article 

    Google Scholar 
    48.Lind, J. & Cresswell, W. Determining the fitness consequences of anti-predation behavior. Behav. Ecol. 16, 945–956 (2005).Article 

    Google Scholar 
    49.Berger, J. Carnivore repatriation and holarctic prey: Narrowing the deficit in ecological effectiveness. Conserv. Biol. 21, 1105–1116 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Dalerum, F. & Belton, L. African ungulates recognize a locally extinct native predator. Behav. Ecol. 26, 215–222 (2015).Article 

    Google Scholar 
    51.Palmer, M. S. & Gross, A. Eavesdropping in an African large mammal community: Antipredator responses vary according to signaler reliability. Anim. Behav. 137, 1–9 (2018).Article 

    Google Scholar 
    52.Crawley, M. J. Statistical Computing: An Introduction to Data Analysis Using S-PLUS (Wiley, 2002).MATH 

    Google Scholar 
    53.Hodges, J. S. Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (CRC Press, 2016).MATH 
    Book 

    Google Scholar 
    54.Agresti, A. An Introduction to Categorical Data Analysis 2nd edn. (Wiley, 2002).MATH 
    Book 

    Google Scholar 
    55.Hopcraft, J. G. C., Sinclair, A. R. E. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).Article 

    Google Scholar 
    56.Gorini, L. et al. Habitat heterogeneity and mammalian predator-prey interactions. Mammal Rev. 42, 55–77 (2011).Article 

    Google Scholar 
    57.Creel, S. et al. What explains variation in the strength of behavioral responses to predation risk? A standardized test with large carnivore and ungulate guilds in three ecosystems. Biol. Conserv. 232, 164–172 (2019).Article 

    Google Scholar 
    58.Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Kohl, M. T. et al. Diel predator activity drives a dynamic landscape of fear. Ecol. Monogr. 88, 1–10. https://doi.org/10.1002/ecm.1313 (2018).Article 

    Google Scholar 
    60.Breitenmoser, U., Breitenmoser-Wursten, C., Carbyn, L. N. & Funk, S. M. Assessment of Carnivore Reintroduction in Carnivore Conservation (eds. J. L. Gittleman, S. M. Funk, D. W. Macdonald and R. K. Wayne) 241–280 (Cambridge University Press and Zoological Society of London, 2001).61.Hayward, M. W. et al. The reintroduction of large carnivores to the Eastern Cape, South Africa: an assement. Oryx 41, 205–214 (2007).Article 

    Google Scholar 
    62.Thaker, M. et al. Minimizing predation risk in a landscape of multiple predators: Effects on the spatial distribution of African ungulates. Ecology 92, 398–407 (2011).PubMed 
    Article 

    Google Scholar 
    63.Augustine, D. J. & Mcnaughton, S. J. Regulation of shrub dynamics by native browsing ungulates on East African rangeland. J. Appl. Ecol. 41, 45–58 (2004).Article 

    Google Scholar 
    64.Daskin, J. H., Stalmans, M. & Pringle, R. M. Ecological legacies of civil war: 35-year increase in savanna tree cover following wholesale large-mammal declines. J. Ecol. 104, 79–89 (2016).Article 

    Google Scholar 
    65.Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. https://doi.org/10.1038/s41598-019-53071-y (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Keesing, F. & Young, T. P. Cascading consequences of the loss of large mammals in an African savanna. Bioscience 64, 487–495 (2014).Article 

    Google Scholar  More

  • in

    Monitoring abundance of aggregated animals (Florida manatees) using an unmanned aerial system (UAS)

    1.Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations, Modeling, Estimation, and Decision Making (eds. Wood, J. M. & Tanner, G. W.) (Academic Press, 2002).2.Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).
    Google Scholar 
    3.Riipi, M. et al. Multiple benefits of gregariousness cover detectability costs in aposematic aggregations. Nature 413, 512–514. https://doi.org/10.1038/35097061 (2001).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Griffin, A. S., Savani, R. S., Hausmanis, K. & Lefebvre, L. Mixed-species aggregations in birds: Zenaida doves, Zenaida aurita, respond to alarm call of carib grackles, Quiscalus lugubris. Anim. Behav. 70, 507–515. https://doi.org/10.1016/j.anbehav.2004.11.023 (2005).Article 

    Google Scholar 
    5.Kunz, T. H. Roosting ecology of bats. In Ecology of Bats (ed. Kunz, T. H.) 1–55 (Springer, 1982).6.Dobson, A. & Poole, J. Conspecific aggregation and conservation biology. In Behavioral Ecology and Conservation Biology (ed. Caro, T. M.) 193–208 (Oxford University Press, 1998).7.Laist, D. W. & Reynolds, J. E. Influence of power plants and other warm-water refuges on Florida manatees. Mar. Mamm. Sci. 21, 739–764 (2005).Article 

    Google Scholar 
    8.Bossart, G. D. et al. Pathological features of the Florida manatee cold stress syndrome. Aquat. Mamm. 29, 9–17 (2002).Article 

    Google Scholar 
    9.Laist, D. W., Taylor, C. & Reynolds, J. E. III. Winter habitat preferences for Florida manatees and vulnerability to cold. PLoS One 8(3), e58978 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    10.Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Sys. 3, 137–155 (2015).Article 

    Google Scholar 
    11.Hodgson, A., Kelly, N. & Peel, D. Unmanned aerial vehicles (UAVs) for surveying marine fauna: A dugong case study. PLoS One 8, 1–15. https://doi.org/10.1371/journal.pone.0079556 (2013).CAS 
    Article 

    Google Scholar 
    12.Hodgson, A., Peel, D. & Kelly, N. Unmanned aerial vehicles for surveying marine fauna: Assessing detection probability. Ecol. Appl. 27, 1253–1267 (2017).Article 

    Google Scholar 
    13.Landeo-Yauri, S. S., Ramos, E. A., Castelblanco-Martínez, D. N., Niño-Torres, C. A. & Searle, L. Using small drones to photo-identify Antillean manatees: A novel method for monitoring an endangered marine mammal in the Caribbean Sea. Endanger. Species Res. 41, 79–90. https://doi.org/10.3354/esr01007 (2020).Article 

    Google Scholar 
    14.Linchant, J., Lisein, J., Smeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mamm. Rev. 45, 239–252. https://doi.org/10.1111/mam.12046 (2015).Article 

    Google Scholar 
    15.Martin, J. et al. Estimating distribution of hidden objects with drones: From tennis balls to manatees. PLoS One 7(6), 1–8. https://doi.org/10.1371/journal.pone.0038882 (2012).CAS 
    Article 

    Google Scholar 
    16.Fiori, L., Martinez, E., Bader, M. K. F., Orams, M. B. & Bollard, B. Insights into the use of an unmanned aerial vehicle (UAV) to investigate the behavior of humpback whales (Megaptera novaeangliae) in Vava’u, Kingdom of Tonga. Mar. Mamm. Sci. 36, 209–223 (2020).Article 

    Google Scholar 
    17.Hodgson, J. C. et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 9, 1160–1167 (2018).Article 

    Google Scholar 
    18.Edwards, H. H., Pollock, K. H., Ackerman, B. B., Reynolds, J. E. III. & Powell, J. A. Estimation of detection probability in manatee aerial surveys at a winter aggregation site. J. Wildl. Manag. 71, 2052–2060 (2007).Article 

    Google Scholar 
    19.Stith, B. M. et al. Passive thermal refugia provided warm water for Florida manatees during the severe winter of 2009–2010. Mar. Ecol. Prog. Ser. 462, 287–301. https://doi.org/10.3354/meps09732 (2012).ADS 
    Article 

    Google Scholar 
    20.Edwards, H. H. & Ackerman, B. B. (eds.) Aerial surveys of manatee distribution in Florida, 1984–2004. In Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, Fish and Wildlife Research Institute Technical Report, TR-19, 273 (2016).21.Hartman, D. S. Ecology and behavior of the manatee (Trichechus manatus) in Florida. Am. Soc. Mamm. Spec. Publ. 5, 1–153 (1979).
    Google Scholar 
    22.Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62, 1–133 (1978).MATH 

    Google Scholar 
    23.Kéry, M. & Schaub, M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective (Elsevier, Amsterdam, 2012).
    Google Scholar 
    24.Dorazio, R. M., Martin, J. & Edwards, H. H. Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts. Ecology 94, 1472–1478 (2013).Article 

    Google Scholar 
    25.Martin, J. et al. Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach. Methods Ecol. Evol. 2, 595–601 (2011).ADS 
    Article 

    Google Scholar 
    26.Hostetler, J. A., Edwards, H. H., Martin, J. & Schueller, P. Updated statewide abundance estimates for the Florida manatee. https://f50006a.eos-intl.net/F50006A/OPAC/Details/Record.aspx?BibCode=1864664. Accessed 12 June 2021 (2018).27.Craig, B. A. & Reynolds, J. E. III. Determination of manatee population trends along the Atlantic coast of Florida using a Bayesian approach with temperature adjusted aerial survey data. Mar. Mamm. Sci. 20, 386–400 (2004).Article 

    Google Scholar 
    28.Hisakado, M., Kitsukawa, K. & Mori, S. Correlated binomial models and correlation structures. J. Phys. A Math. Gen. 39, 15365–15378 (2006).MathSciNet 
    Article 

    Google Scholar 
    29.Royle, A. J., Dorazio, R. M. & Link, W. A. Analysis of multinomial models with unknown index using data augmentation. J. Comput. Graph. Stat. 16(1), 67–85. https://doi.org/10.1198/106186007X181425 (2007).MathSciNet 
    Article 

    Google Scholar 
    30.Royle, A. J. & Dorazio, R. M. Parameter-expanded data augmentation for Bayesian analysis of capture–recapture models. J. Ornithol. 152, 521–537 (2012).Article 

    Google Scholar 
    31.Kellner, K. jagsUI: a wrapper around “rjags” to streamline “JAGS” analyses. R package. version 1.4.4. (2016).32.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020) www.R-project.org/. Accessed 12 June 2021.33.Runge, M.C. et al. Status and threats analysis for the Florida manatee (Trichechus manatus latirostris), 2016. U.S. Geological Survey Scientific Investigations Report 2017–5030, Reston, VA, 2017. https://doi.org/10.3133/sir2017503034.U.S. Fish and Wildlife Service. Florida Manatee Recovery Plan, Trichechus manatus latirostris, Third Revision. (U.S. Fish and Wildlife Service, 2001).35.Flamm, R. O., Reynolds, J. E. III. & Harmak, C. Improving conservation of Florida manatees (Trichechus manatus latirostris): Conceptualization and contributions toward a regional warm-water network management strategy for sustainable winter habitat. Environ. Manag. 51, 154–166 (2013).ADS 
    Article 

    Google Scholar 
    36.Martin, J. et al. Combining information for monitoring at large spatial scales: First statewide abundance estimate of the Florida manatee. Biol. Conserv. 186, 44–51 (2015).Article 

    Google Scholar 
    37.Valade, J., Mezich, R., Smith, K., Merrill, M. & Calleson, T. Florida Manatee Warm-Water Habitat Action Plan. Florida Fish & Wildlife Service and Florida Fish and Wildlife Conservation Commission. 1–43 (2020).38.Wang, D., Shao, Q. & Yue, H. Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): A review. Remote Sens. 11(1308), 1–28 (2019).ADS 

    Google Scholar 
    39.Colefax, A. P., Butcher, P. A. & Kelaher, B. P. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft. ICES J. Mar. Sci. 75, 1–8 (2018).Article 

    Google Scholar 
    40.Linchant, et al. UAS imagery reveals new survey opportunities for counting hippos. PLoS One 13, 1–17. https://doi.org/10.1371/journal.pone.0206413 (2018).CAS 
    Article 

    Google Scholar 
    41.Ezat, M. A., Fritsch, C. J. & Downs, C. T. Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: A case study at Lake Nyamithi, Ndumo game reserve, South Africa. Biol. Conserv. 223, 76–81 (2018).Article 

    Google Scholar 
    42.Pӧysӓ, H., Kotilainen, J., Väänänen, V. & Kunnasranta, M. Estimating production in ducks: A comparison between ground surveys and unmanned aircraft surveys. Eur. J. Wildl. Res. 64(74), 1–4. https://doi.org/10.1007/s10344-018-1238-2 (2018).Article 

    Google Scholar 
    43.Ratcliffe, N. et al. A protocol for the aerial survey of penguin colonies using UAVs. J. Unmanned Veh. Syst. 3, 95–101. https://doi.org/10.1139/juvs-2015-0006 (2015).Article 

    Google Scholar 
    44.Brack, I. V., Kindel, A. & Oliveira, L. F. B. Detection error in wildlife abundance estimates from unmanned aerial systems (UAS) surveys: Synthesis, solutions, and challenges. Methods Ecol. Evol. 9, 1864–1873 (2018).Article 

    Google Scholar 
    45.Goebel, M. et al. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38, 619–630 (2015).Article 

    Google Scholar  More

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    Whimbrel populations differ in trans-atlantic pathways and cyclone encounters

    Field methodsWe captured 24 whimbrels between 2008 and 2018. Birds were captured on migration staging sites along the lower Delmarva Peninsula in Virginia, USA (n = 6) (37.398° N, 75.865° W), along the coast of Georgia, USA (n = 5) (31.148° N, 81.379° W), along the Acadian Peninsula in New Brunswick, Canada (n = 3) (47.973° N, 64.509° W) as well as on the nesting ground near the Mackenzie River, Northwest Territories, Canada (n = 10) (69.372° N, 134.894° W). All birds were aged as adults by plumage26, 27 and were banded with United States Geological Survey tarsal bands and coded leg flags. Sex of captured birds was not determined.We fitted all birds with satellite transmitters called Platform Transmitter Terminals (PTTs) using a modification of the leg-loop harness28, 29. Instead of elastic cord, we used Teflon® ribbon (Bally Ribbon Mills, Bally, Pennsylvania, USA) that was fastened with brass rivets or crimps30. We glued transmitters to a larger square of neoprene to elevate it above the body and prevent the bird from preening feathers over the solar panels. The transmitter package was below 3% of body mass (measured at the time of deployment,(bar{x}) = 484.5 ± 17.1) for all individuals tracked in this study. The PTTs used in this study were 9.5 g PTT-100 (n = 14) or 5.0 g PTT-100 (n = 10) solar-powered units produced by Microwave Telemetry, Inc. (Columbia, Maryland, USA).TrackingBirds were located using satellites of the National Oceanic and Atmospheric Administration and the European Organization for the Exploitation of Meteorological Satellites with onboard tracking equipment operated by Collecte Localisation Satellites (CLS America, Inc., Largo, Maryland, USA)31. Transmitters were programmed to operate with a duty cycle of 24 h off and 5 h on (n = 9) or 48 h off and 10 h on (n = 15) and collected 1–34 ((bar{x}) = 5.48 ± 0.07) locations per cycle. Locations in latitude and longitude decimal degrees, date, time, and location error were received from CLS America within 24 h of satellite contact with PTTs. Locations were estimated by the Advanced Research and Global Observation Satellite (ARGOS) system (www.Argos-system.org), which uses a Doppler shift in signal frequency and calculates a probability distribution within which the estimate lies. The standard deviation of this distribution gives an estimate of the location accuracy and assigns it to a “location class” (LC): LC3 =   1000 m, LCA = location based on 3 messages and has no accuracy estimate, LCB = location based on 2 messages and has no accuracy estimate, and LCZ = location process failed. We used LC classes 1–3 to determine whimbrel locations.Migration pathwaysWe used tracking data to delineate fall migration pathways and, though migration duration can include fueling at breeding territories32, we defined migration duration as the time between departure from the breeding grounds and arrival on winter territory. We identified the source population for all individuals included in this study either by capture on the breeding grounds (n = 10) or by capture within migratory staging sites and tracking birds to the breeding grounds (n = 14). Birds were either from the Mackenzie Delta (n = 13) or Hudson Bay (n = 11) breeding populations. We assessed departure and arrival when birds moved away from or settled into stationary breeding and winter territories respectively. Departure was abrupt and we recorded no “false starts” of birds leaving breeding areas and then returning before resuming migration. We present a stylized map of migration routes that was drawn by hand using the collection of flights recorded to provide a broad overview of routes relative to the distribution of storms.Trans-atlantic flightsWe used tracking data to delineate migration pathways across the Atlantic Ocean (from coast of North America to coast of South America). Most birds departed from coastal staging sites and we considered the last staging location prior to crossing the Atlantic the terminal staging area. Several birds departed from inland locations on James Bay. We only consider the segment of the latter flights that occur over the ocean. We consider the duration of transoceanic flights to be the time interval between emerging from the coast of North America and arriving along the coast of South America. In cases where departure and arrival times occurred outside the radio transmitter’s duty cycle, we drew a straight-line between the last known location on land for departures or the first known location on land for arrivals and the nearest location over water and measured the distance between the in-flight point and the coastline along the line. We then used the mean overall speed between in-flight points for all birds ((bar{x}) = 14.8 ± 0.4 m/s, n = 40) to interpolate the leaving or arrival times. We consider the flight length to be the sum of the distance between consecutive locations along the path taken between the site of emergence along the coast of North America and the site of landfall along the northern coast of South America.Exposure to tropical cyclonesWe examined the distribution of tropical cyclones throughout the Atlantic Ocean using position records (1961–2018) within the revised Atlantic hurricane best tracks from the National Hurricane Center (https://www.nhc.noaa.gov/data/#hurdat), known as the Atlantic HURDAT233. We restricted our analyses to storms classified as tropical depressions or above and HURDAT data collected since 1961, when satellites were first used to monitor tropical cyclone activity34. The database contains the storm category (Saffir Simpson Scale), wind speed (mph) and coordinates recorded for six-hour intervals during the period that each storm existed using standard six-hour intervals which allows for weighting of the storms according to their lifespans and estimating the distribution of probability density. We selected storms (N = 590) that were active between 15 July and 30 November to coincide with whimbrel migration through the region. We mapped all storm observation points (N = 17,637) using a kernel density estimator (KDE) method35 with the “ks” package36 in program R37. We used the normal (or Gaussian) kernel and a smooth cross-validation bandwidth selector38 to map 50% kernel densities. We considered the 50% KDE to be the area of highest storm occurrence and estimated exposure to this region by overlaying whimbrel tracks on the KDE polygon and measuring each whimbrel’s time within the area. Because the first and last points within the polygon occurred when the bird’s transmitter first transmitted the bird’s location within and outside the polygon, rather than when the bird first entered and exited the polygon, we measured the distance between the first point inside the polygon and the previous point outside the polygon and used the mean overall speed between in-flight points for all birds (,(bar{x}) = 14.8 ± 0.4 m/s, n = 40) to interpolate the time that the bird entered the polygon. We used the same method to calculate the time that the bird left the polygon using the last point within the polygon and next point outside the polygon.Encounters with tropical cyclonesWe documented encounters between whimbrels and tropical cyclones within the Atlantic Basin by overlaying migration tracks for individual birds on archives of storm tracks within HURDAT2 for the period (2008–2019) of the tracking study. We considered a whimbrel-storm encounter to have occurred when bird tracks intersected storm tracks during the same time period. For grounded birds, we considered an encounter to have occurred when a storm track moved over the ground position of a bird. For each encounter, we recorded the coordinate of the encounter and the storm intensity. Storm intensities were classified as tropical depressions, (≤ 38 mph), tropical storms (39–73 mph), category 1 hurricane (74–95 mph), category 2 hurricanes (96–110 mph), category 3 hurricanes (111–129 mph), category 4 hurricanes (130–156 mph), and category 5 hurricanes (≥ 157 mph) according to the Saffir–Simpson Hurricane Wind Scale39.We examined the post-encounter track of birds to categorize the response of birds including none, detour or grounding. We considered birds to exhibit no response to the storm encounter if the migration trajectory was unchanged during or shortly following a storm encounter. We considered birds to have taken a detour in response to a storm encounter if the migration trajectory followed over the previous day was deflected by  > 20° during or shortly following an encounter. We considered birds to have grounded if they landed on an island following a storm encounter.StatisticsWe used mixed-effects logistic regressions (R3.6.2: R Core Team 2019) to compare the likelihood of storm encounters between whimbrel populations using tracks as replicate samples. We initially fit models using whimbrel identity and year as random intercepts to account for potential lack of independence for journeys made by the same individuals and journeys made within the same year, but inclusion of bird identity as a random intercept resulted in a singular fit so this variable was excluded from further analysis. We then compared models with year as a random intercept and no fixed effects, year as a random intercept and breeding population (Mackenzie Delta vs Hudson Bay) as a fixed effect, year as a random intercept and journey number (1st, 2nd, or 3rd journey) as a fixed effect, and year as a random intercept with breeding population and journey number as fixed effects. We used Akaike’s information criterion for small sample size (AICc) and selected the model with the lowest AICc score as the best-supported model if no other model was within 2 ΔAICc after removing models with uninformative parameters40. Several birds made more than one transoceanic crossing in different years and we consider these to be independent samples. We used two-tailed t-tests to compare migration lengths and duration between routes. We used g-tests with Yates correction to make frequency comparisons.Data and ethics statementThis study was conducted in compliance with ARRIVE guidelines. Data used in this manuscript are unique and have not been submitted for publication elsewhere. The authors claim no conflict of interest. This project was reviewed and approved by the William & Mary Institutional Animal Care and Use Committee protocol IACUC-2017-04-18-12065 of The College of William and Mary, Environment Canada Animal Care Committee protocols EC-PN-12-006, EC-PN-13-006, EC-PN-14-006, Mount Allison University Animal Care Committee protocol 15-14, and the Government of the Northwest Territories Wildlife Care Committee protocol NWTWCC2014-007. All Methods were performed in accordance with the relevant guidelines and regulations. More