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    Sugar transporters enable a leaf beetle to accumulate plant defense compounds

    Insect culturePhyllotreta armoraciae beetles were reared on potted Brassica juncea cultivar “Bau Sin” plants or on potted Brassica rapa cultivar “Yo Tsai Sum” plants (Known-You Seed Co., Ltd) in mesh cages (Bugdorm, MegaView Science Co., Ltd) in a controlled environment chamber at 24 °C, 60% relative humidity, and a 16-h photoperiod. Food plants were cultivated in a growth chamber at 24 °C, 55% relative humidity, and a 14-h photoperiod. Beetles were provided with 3–4-week-old plants once per week, and plants with eggs were kept separately for larval development. Larvae were allowed to pupate in the soil, and after 3 weeks, the soil with pupae was transferred into plastic boxes (9 L, Lock&Lock) until the new generation of beetles emerged.RNA isolation, RNA sequencing, and de novo transcriptome assemblyTotal RNA was extracted from dissected foregut, midgut, hindgut, and Malpighian tubule tissue of newly emerged P. armoraciae beetles that were reared on B. juncea using the innuPREP RNA Mini Kit (Analytik Jena). Tissues from at least ten beetles were pooled per sample. RNA integrity was verified using an Agilent Technologies 2100 Bioanalyzer with the RNA 6000 Nano Kit (Agilent Technologies). RNA quantity was determined using a Nanodrop ND-1000 spectrophotometer (PEQlab Biotechnologie GmbH). One set of RNA samples was sequenced by GATC Biotech on the HiSeq 2500 System from Illumina in Rapid Run mode, using the paired-end (2 × 125 bp) read technology at a depth of 15–25 million reads for each sample. For a second set of samples consisting of four biological replicates per tissue, we additionally performed an on-column DNA digestion with the innuPREP DNase I Digest Kit (Analytik Jena) according to the manufacturer’s instructions. RNA samples were poly(A)-enriched, fragmented, and sequenced at the Max Planck Genome Centre Cologne on the HiSeq 3000 Sequencing System from Illumina, using the paired-end (2 × 150 bp) read technology at a depth of 22 million reads for each sample. Sequencing reads were filtered to remove bad-quality reads based on fastq file scores and trimmed based on read length using CLC Genomics Workbench software version 10.1. With a randomly sampled set of 420 million reads from the two sets of sequencing data, a transcriptome was assembled de novo with the following parameters: nucleotide mismatch cost = 1; insertion = deletion costs = 2; length fraction = 0.6; similarity = 0.9. Conflicts among the individual bases were resolved by voting for the base with the highest frequency. After removing contigs shorter than 250 bp, the final assembly contained 36,445 contigs with an N50 contig size of 2115 bp.Identification of coleopteran MFS transportersWe predicted a protein dataset for P. armoraciae by translating each contig of the gut and Malpighian tubule-specific transcriptome into all six reading frames. After removing sequences shorter than 50 amino acids, we submitted the protein dataset (267,568 sequences) to the TransAAP hosted at the TransportDB 2.0 web portal35. This initial annotation predicted a total of 1401 putative transporter sequences and revealed the MFS and the ABC transporters to be the largest transporter families (Supplementary Data 1). We focused on MFS transporters, which are classified into >80 families53. We used one protein sequence from each MFS family as a query to search candidate MFS transporters in the protein dataset from P. armoraciae using Blastp (E value threshold of 10−5), and assigned each candidate to an MFS family based on sequence similarity to transporter sequences deposited in TCDB. Additional candidates were identified by repeating the search procedure with an extended dataset including the candidate MFS transporters from P. armoraciae. The number of TMDs for each candidate was predicted using the TMHMM Server v.2.054. Partial sequences encoding less than six predicted TMDs were removed from the dataset. The same strategy was used to identify putative MFS transporters in protein datasets that were predicted from the genomes of Leptinotarsa decemlineata, genome annotations v.0.5.355, Anoplophora glabripennis, assembly Agla_1.056, and Tribolium castaneum, assembly Tcas3.057, respectively. The predicted protein sequences are provided in Supplementary Data 2.Digital gene expression analysisDigital gene expression analysis of putative MFS transporters identified in the P. armoraciae transcriptome was carried out using CLC Genomics Workbench v.10.1 by mapping the Illumina reads from the second set of samples onto the reference transcriptome, and counting the reads to estimate gene expression levels. For the cloned MFS genes, complete open-reading frames (ORFs) were used as reference sequences for mapping. For read alignment, we used the following parameters: nucleotide mismatch cost = 2; insertion = deletion costs = 3; length fraction = 0.6; similarity fraction = 0.9; maximum number of hits for a read = 15. Each pair of reads was counted as two. Biases in the sequence datasets and different transcript sizes were corrected using the TPM (transcripts per kilobase million) normalization method to obtain correct estimates for relative expression levels between samples.Phylogenetic analyses of coleopteran MFS transportersWe inferred the lineage-specific diversification patterns of putative MFS transporters from P. armoraciae, L. decemlineata, A. glabripennis, and T. castaneum in phylogenetic analyses with two different datasets, one containing all identified MFS transporters (867 sequences), the other containing a subset of putative sugar porters (120 sequences) from the above four species and 35 sugar porters from C. populi52. The corresponding protein sequences were aligned using the MUSCLE algorithm58 implemented in MEGA 7 with default parameters. The alignments were trimmed manually and the best substitution models were determined using ProtTest 3.4.259. Maximum-likelihood phylogenetic trees were constructed in IQ-TREE version 1.6.060 using the VT + G + F substitution model with 1000 ultrafast bootstrap replicates for the full dataset, and the LG + G + F substitution model with 1000 bootstrap replicates for the subset of putative sugar porters.Identification and sequencing of candidate transportersBased on our phylogenetic analysis, we selected the largest clade of putative MFS transporters that was specifically expanded in P. armoraciae for further studies. Transcriptome analyses revealed the presence of a pseudogene (PaMFS9-ps) that shares between 43 and 95% nucleotide sequence identity with members of the focal clade. The protein encoded by this pseudogene is predicted to possess only two transmembrane domains due to a premature stop codon caused by frameshift mutations in the coding sequence. To obtain the full-length ORFs of partial transcripts, we synthesized 5′- and 3′-rapid amplification of cDNA (complementary DNA) ends (RACE)–cDNA using the SMARTerRACE cDNA Amplification Kit (Clontech) and performed 5′- and 3′-RACE-PCR according to the manufacturer’s protocols (Clontech). All full-length ORFs were cloned into the pCR™4-TOPO® TA vector (Thermo Fisher Scientific) for sequence verification.Tissue-specific expression of candidate transportersWe used quantitative PCR (qPCR) to analyze the expression of candidate transporter genes in the foregut, midgut, hindgut, Malpighian tubules, and other tissues of 1-day-old adult P. armoraciae beetles (n = 4 biological replicates, each with two technical replicates), respectively. In addition, the expression of candidate transporter genes was analyzed in the proximal, central, and distal Malpighian tubule regions (Supplementary Fig. 2d) dissected from 4-day-old adult P. armoraciae beetles (n = 3 biological replicates, each with two technical replicates). Total RNA was extracted using the InnuPrep RNA Mini Kit (Analytik Jena), treated with TURBO DNase (Thermo Fisher Scientific), and purified using the RNeasy MinElute Cleanup Kit (Qiagen). First-strand cDNA was synthesized using the Verso cDNA Synthesis Kit (Thermo Fisher Scientific) using a 3:1 mixture (v/v) of random hexamer and oligo dT primers according to the manufacturer’s protocol. Quantitative PCR was performed in optical 96-well plates (Bio-Rad) on a Bio-Rad CFX Connect Real-Time System using the Absolute Blue qPCR SYBR Green Kit (Thermo Fisher Scientific). The PCR program was as follows: 95 °C for 15 min, then 40 cycles at 95 °C for 15 s, 57 °C for 30 s, and 72 °C for 30 s, followed by a melt cycle from 55 to 95 °C in 0.5 s increments. Primers (Supplementary Data 4) were designed using Primer3web version 4.1.0. We verified the amplification specificity by sequencing each PCR product after cloning into the pCR™4-TOPO® TA vector (Thermo Fisher Scientific) and by melting-curve analyses. Primer efficiencies were calculated using a cDNA template dilution series. Gene expression was normalized to the expression level of eukaryotic initiation factor 4A (eIF4A), which showed the lowest variability across tissues among four tested reference genes (Supplementary Table 4).Expression of candidate transporters in insect cellsFor protein expression, we cloned each ORF without stop codon into the pIEx-4 expression vector (Novagen) in frame with the vector-encoded carboxy-terminal 6× His-tag and sequenced the resulting constructs. Primer sequences are listed in Supplementary Data 4. One construct of each candidate gene was used for transfection of High Five™ insect cells (Gibco) cultured in Express Five® SFM medium (Gibco) supplemented with 20 mM glutamine (Gibco) and 50 μg/mL gentamicin (Gibco). Confluent insect cells were diluted 1:5, dispensed in 500 μL aliquots into 24-well plates, and incubated at 27 °C. The next day, we transfected the cells using FuGENE HD Transfection Reagent (Promega) according to the manufacturer’s protocol. Cells treated with transfection reagent only were used as a negative control. After 48 h, we harvested the cells for Western blotting and uptake assays.Western blottingTo confirm protein expression, transfected insect cells were washed twice with phosphate-buffered saline (PBS; pH 7.4), collected by centrifugation, and resuspended in hypotonic buffer (20 mM Tris-HCl (pH 7.5), 5 mM EDTA, 1 mM dithiothreitol, 0.1% benzonase nuclease (Merck Millipore) (v/v), and protease inhibitors (cOmplete Mini, ETDA-free, Roche Diagnostics GmbH)). After incubation on ice for 10 min, the samples were frozen in liquid nitrogen, thawed, and centrifuged (16,000 × g for 15 min at 4 °C). The resulting cell pellet was resuspended in hypotonic buffer and used for Western blotting using horseradish peroxidase-conjugated anti-His antibody (1:10,000; Novex, Life technologies).Glucoside uptake assays with transfected insect cellsCells were washed with PBS (pH 5.5) by pipetting and incubated with different glucoside substrates at 200 µM in PBS (pH 5.5) for 1 h at 27 °C. Assays were performed with substrate mixtures containing seven different glucosinolates (2-propenyl glucosinolate (Roth), 4-methylsulfinylbutyl glucosinolate (Phytoplan), 4-methylthiobutyl glucosinolate (Phytoplan), 2-phenylethyl glucosinolate (Phytoplan), benzyl glucosinolate (Phytoplan), 4-hydroxybenzyl glucosinolate (isolated from Sinapis alba seeds61), and I3M glucosinolate (Phytoplan)), or five other plant glucosides (salicin (Sigma-Aldrich), linamarin (BIOZOL), dhurrin (Roth), catalpol (Sigma-Aldrich), and aucubin (Sigma-Aldrich)). After incubation, cells were washed three times with ice-cold PBS (pH 5.5) by pipetting, collected in 300 μL 80% (v/v) methanol, frozen in liquid nitrogen, thawed, and centrifuged at 3220 × g for 10 min at 4 °C. The supernatant was dried by vacuum centrifugation, dissolved in ultrapure water, and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). All glucoside uptake assays were performed in triplicates. The amount of each substrate in transporter-expressing cells was compared with that detected in control cells. Transporters were considered active when the average glucoside amounts detected in transporter-expressing cells were at least twofold higher than those detected in control cells.Cloning of PaGTR1 into the pNB1u vector and cRNA synthesisWe amplified the ORF of PaGTR1 without stop codon by PCR using uracil-containing primers (Supplementary Data 4). The 3′ primer was designed to encode a human influenza hemagglutinin tag to enable the detection of recombinant protein by Western blotting if necessary. The pNB1u vector was digested overnight at 37 °C with PacI and Nt.BbvCI (New England Biolabs) to generate 8-nt overhangs. One microliter gel-purified PCR product (100 ng/µL) was combined with 1 µL gel-purified vector (50 ng/µL), 1 U USER enzyme, 2 μL 5× PCR reaction buffer and 5 μL H2O, incubated at 37 °C for 25 min, followed by 25 min at room temperature. After the transformation of chemically competent E. coli cells, colonies containing the appropriate insert were identified by Sanger sequencing. The DNA template for complementary RNA (cRNA) synthesis was amplified by PCR from the X. laevis expression construct using pNB1uf/r primers (Supplementary Data 4) and cRNA was synthesized using the mMESSAGE mMACHINE™ T7 Transcription Kit (Invitrogen) according to the manufacturer’s manual.Biochemical characterization of PaGTR1 in Xenopus oocytesThe cRNA concentration was adjusted to 800 ng/μL with RNase-free water for oocyte injection. Xenopus laevis oocytes (Ecocyte Bioscience) were injected with 50 nL containing 40 ng cRNA or with 50 nL pure water as a control using a Drummond NANOJECT II (Drummond Scientific Company) or a Nanoliter 2010 Injector (World Precision Instruments).Injected oocytes were incubated in Kulori buffer (90 mM NaCl, 1 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 5 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid, pH 7.4) supplemented with 50 μg/mL gentamicin at 16 °C for 3 days until assaying. All transport assays were performed at room temperature. Injected oocytes were pre-incubated for 5 min in Kulori buffer (90 mM NaCl, 1 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 5 mM 2-(N-morpholino)ethanesulfonic acid (MES), pH 6.0) before they were transferred into the same buffer containing the substrate(s). To determine the substrate preference of PaGTR1, we incubated oocytes in Kulori buffer (pH 6.0) containing an equimolar mixture of 2-propenyl glucosinolate, 4-methylsulfinylbutyl glucosinolate, 4-methylthiobutyl glucosinolate, 2-phenylethyl glucosinolate, benzyl glucosinolate, 4-hydroxybenzyl glucosinolate, and I3M glucosinolate, each at 200 µM, for 1 h.The pH dependency of glucosinolate transport was determined by incubating the injected oocytes with 100 μM I3M glucosinolate for 10 min in Kulori buffer adjusted to different pH values. The effects of different ionophores on glucosinolate transport was studied by incubating the PaGTR1-expressing oocytes in Kulori buffer at pH 6.0 containing either 20 μM carbonyl cyanide m-chlorophenyl hydrazone (H+ ionophore, Sigma-Aldrich), 20 μM nigericin (K+/H+ exchanger, Abcam), or 20 μM valinomycin (K+ ionophore, Abcam) for 15 min. Afterwards, we incubated the oocytes in Kulori buffer containing 100 μM I3M glucosinolate and the corresponding ionophore for 10 min. Assays performed with oocytes incubated in Kulori buffer without any ionophore served as a control.The time course of I3M glucosinolate uptake was analyzed by incubating oocytes with 100 μM I3M glucosinolate in Kulori buffer (pH 6.0) for 5, 10, 20, 30, 45, 60, 120, 180, and 240 min, respectively. The apparent Km value of PaGTR1 for I3M glucosinolate was determined by incubating injected oocytes in Kulori buffer (pH 6.0) for 10 min with different substrate concentrations. The Km value was calculated by nonlinear regression analysis in SigmaPlot 14.0 (Systat Software Inc.).Each assay consisted of 14–15 oocytes and was stopped by washing oocytes four times with Kulori buffer. Afterwards, 12 of the washed oocytes were distributed into four Eppendorf tubes, with three oocytes per tube and immediately homogenized in 100 µL of 50% (v/v) methanol. After centrifugation (21,380 × g or 16,000 × g for 15 min), the supernatant was incubated at −20 °C for at least 1 h to precipitate proteins, which were pelleted by centrifugation (21,380 × g or 16,000 × g for 15 min). Finally, 60 μL sample was diluted with 120 μL ultrapure water, filtered through a 0.22 μm PVDF-based filter plate (Merck Millipore), and analyzed by LC-MS/MS. The glucosinolate concentration in oocytes was calculated by assuming an oocyte volume of 1 μL62.LC-MS/MSGlucosinolates were quantified by LC-MS/MS using an Agilent 1200 HPLC system connected to an API3200 tandem mass spectrometer (AB SCIEX). Separation was achieved on an EC 250/4.6 NUCLEODUR Sphinx RP column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel) using a binary solvent system consisting of 0.2% (v/v) formic acid in water (A) and acetonitrile (B), with a flow rate of 1 mL/min at 25 °C. The elution gradient was as follows: 0–1 min, 1.5% B; 1–6 min, 1.5–5% B; 6–8 min, 5–7% B; 8–18 min, 7–21% B; 18–23 min, 21–29% B; 23–23.1 min, 29–100% B; 23.1–24 min, 100% B; 24–24.1 min, 100 to 1.5% B; 24.1–28 min, 1.5% B. Glucosinolates were detected in negative ionization mode. The ion spray voltage was set to −4500 V. Gas temperature was set to 700 °C, curtain gas to 20 psi, collision gas to 10, nebulizing gas to 70 psi, and drying gas to 60 psi. Nonhost glucosides were quantified using an Agilent 1200 HPLC system connected to an API5000 tandem mass spectrometer (AB SCIEX). Separation was achieved on an Agilent XDB-C18 column (5 cm × 4.6 mm, 1.8 μm) using a binary solvent system consisting of 0.05% (v/v) formic acid in water (A) and acetonitrile (B) with a flow rate of 1.1 mL/min at 25 °C. The elution gradient was as follows: 0–0.5 min, 5% B; 0.5–2.5 min, 5–31% B; 2.5–2.52 min, 31–100% B; 2.52–3.5 min, 100% B; 3.5–3.51 min, 100 to 5% B; 3.51–6 min, 5% B. Compounds were detected in negative ionization mode with ion spray voltage set to −4500 V. The gas temperature was set to 700 °C, curtain gas to 30 psi, collision gas to 6, and both nebulizing gas and drying gas to 60 psi. Multiple reaction monitoring (MRM) was used to monitor the transitions from precursor ion to product ion for each compound (Supplementary Table 5). Compounds were quantified using external standard curves. Analyst Software 1.6 Build 3773 (AB Sciex) was used for data acquisition and processing.Samples from the pH dependency experiment were analyzed by LC-MS/MS using an Advance UHPLC system (Bruker) connected to an EVOQ Elite TripleQuad mass spectrometer (Bruker) equipped with an electrospray ion source. Separation was achieved on a Kinetex 1.7u XB-C18 column (100 mm × 2.1 mm, 1.7 µm, 100 Å, Phenomenex) using a binary solvent system consisting of 0.05 % (v/v) formic acid in water (A) and acetonitrile with 0.05% (v/v) formic acid (B), with a flow rate of 0.4 mL/min at 40 °C. The elution gradient was as follows: 0–0.2 min, 2% B; 0.2–1.8 min, 2–30% B; 1.8–2.5 min, 30–100% B; 2.5–2.8 min, 100% B; 2.8–2.9 min, 100 to 2% B; and 2.9–4.0 min, 2% B. Glucosinolates were detected in negative ionization mode. The instrument parameters were optimized by infusion experiments with pure standards. The ion spray voltage was set to −4000 V. Cone temperature was set to 350 °C, cone gas to 20 psi, heated probe temperature to 200 °C, and probe gas flow to 50 psi. Nebulizing gas was set to 60 psi, and collision gas to 1.6 mTorr. MRM parameters are provided in Supplementary Table 5. Bruker MS Workstation software (Version 8.2.1, Bruker) was used for data acquisition and processing of glucosinolates. All other samples from experiments using the oocyte expression system were analyzed using the LC-MS/MS method described above for insect cell-based assays. The concentrations of all glucosinolates in the substrate preference assay were determined using external standard curves. Assays performed to characterize I3M glucosinolate transport were quantified using 2-propenyl glucosinolate as an internal standard.Double-stranded RNA synthesisWe synthesized seven different double-stranded RNAs (dsRNAs) between 120 and 298 bp in length, one specific for each PaGTR1/2/3/5/6/7/8, respectively, and a 223-bp fragment of the inducible metalloproteinase inhibitor (IMPI) from the greater wax moth Galleria mellonella (AY330624.1) (dsIMPI) using the T7 RiboMAX™ Express RNAi System (Promega). In silico off-target prediction was done by searches of all possible 21 mers of both RNA strands against the local P. armoraciae transcriptome database allowing for two mismatches. Except for PaGTR5, the prediction did not find any off-target towards putative transporter genes in the transcriptome (Supplementary Data 5).Function of PaGTR1 in vivoTo analyze the function of PaGTR1, we injected newly emerged adult P. armoraciae beetles (reared on B. juncea) with 100 nL ultrapure water containing 80 ng of dsPaGTR1 or 80 ng of dsIMPI, respectively, using a Nanoliter 2010 Injector (World Precision Instruments). Injected beetles were provided with detached leaves of 3–4-week-old B. juncea plants and kept in plastic containers with moistened tissue in the laboratory under ambient conditions. Four days after dsRNA injection, we collected dsIMPI- and dsPaGTR1-injected beetles for gene expression analysis (n = 5 replicates, three beetles per replicate) and glucosinolate analysis (n = 10 replicates, three beetles per replicate), respectively. The remaining beetles were used for a sequestration experiment with Arabidopsis thaliana Col-0 (Arabidopsis) plants that had been cultivated in a growth chamber at 21 °C, 55% relative humidity, and a 10-h photoperiod. To compare the accumulation and excretion of ingested glucosinolates in dsIMPI- and dsPaGTR1-injected beetles, we fed beetles with detached Arabidopsis leaves in Petri dishes (60 mm diameter) that contained 50 μL of ultrapure water and were sealed with parafilm to prevent leaf wilting (n = 10 replicates, five beetles per replicate). Feeding assays were performed in the laboratory under ambient conditions, and leaves were exchanged every day for five consecutive days. To estimate how much the beetles fed, we determined the weight of each leaf before and after feeding. Feces were collected every day using 100 µL of ultrapure water per replicate, combined with 300 μL of pure methanol in a 1.5 mL Eppendorf tube and dried by vacuum centrifugation. Feces samples were then homogenized in 200 μL of 80% (v/v) methanol using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz. After feeding, adults were starved for one day, weighed, frozen in liquid nitrogen, and stored at −20 °C until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL 80% (v/v) methanol. All samples were then extracted with 1 mL 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard. After centrifugation (16,000 × g for 10 min), glucosinolates were extracted from the supernatant, converted to desulfo-glucosinolates, and analyzed by high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) as described below. The glucosinolate content in adults or feces was calculated in nanomole per adult, respectively.To confirm the specificity of PaGTR1 knockdown, we analyzed the effect of dsPaGTR1 injection on the expression of PaGTR2, PaGTR3, PaGTR9, and PaGTR10. PaGTR9 and PaGTR10 share the highest nucleotide sequence similarity (69% sequence identity) with PaGTR1. PaGTR2 and PaGTR3 expression was analyzed because the recombinant transporters also used I3M glucosinolate as a substrate. RNA extraction, purification, cDNA synthesis, and qPCR were performed as described above.Function of PaGTR5/6/7/8 in vivoTo analyze the function of PaGTR5/6/7/8, we injected newly emerged adult P. armoraciae beetles that had fed for 2 days on B. juncea leaves with 100 nL ultrapure water containing 100 ng of dsIMPI or each 100 ng of dsPaGTR5/6/7/8 using a Nanoliter 2010 Injector (World Precision Instruments). A subset of the dsRNA-injected beetles was fed with detached leaves of Arabidopsis plants in plastic containers with moistened tissue for gene expression analysis (n = 6 replicates, two beetles per replicate). The remaining dsRNA-injected beetles were used for a sequestration experiment with Arabidopsis plants to compare the accumulation and excretion of previously stored and ingested glucosinolates in dsIMPI- and dsPaGTR5/6/7/8-injected beetles. We fed the injected beetles with detached Arabidopsis leaves in Petri dishes (60 mm diameter) that contained 30 μL of ultrapure water and were sealed with parafilm to prevent leaf wilting (n = 10 replicates, six beetles per replicate). Feeding assays were performed in the laboratory under ambient conditions, and leaves were exchanged every day for six consecutive days. To estimate how much the beetles fed, we determined the weight of each leaf before and after feeding. Starting from the second day, feces were collected as above for 5 days. After drying by vacuum centrifugation, feces were homogenized in 1 mL of 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz. Fed adults were starved for 1 day, weighed, and frozen in liquid nitrogen until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL of 80% (v/v) methanol and then extracted with 1 mL of 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard. After centrifugation (16,000 × g for 10 min), glucosinolates were extracted from the supernatant, converted to desulfo-glucosinolates, and analyzed by HPLC-DAD as described below.To confirm the specificity of PaGTR5/6/7/8 knockdown, we analyzed the effect of dsPaGTR5/6/7/8 injection on the expression of PaGTR9 and PaGTR10, which share the highest nucleotide sequence similarity (67–69% sequence identity) with PaGTR5/6/7/8. RNA extraction, purification, cDNA synthesis, and qPCR were performed as described above.Function of PaGTR2/3 in vivoTo analyze the functions of PaGTR2 and PaGTR3, we injected third instar larvae of P. armoraciae (reared on B. rapa) with 100 nL ultrapure water containing 80 ng of dsPaGTR2 and 80 ng of dsPaGTR3 (dsPaGTR2/3) or 80 ng of dsIMPI, respectively, using a Nanoliter 2010 Injector (World Precision Instruments). Injected larvae were provided with detached B. rapa petioles and kept in plastic tubes with moistened tissue in the laboratory under ambient conditions until pupation. Newly emerged adults were again injected with dsRNAs and provided with detached B. rapa leaves. Three days after the second dsRNA injection, we collected dsIMPI- and dsPaGTR2/3-injected beetles for gene expression analysis (n = 6 replicates, two beetles per replicate) and glucosinolate analysis (n = 10 replicates, three beetles per replicate), respectively. The remaining beetles were used for a feeding experiment with Arabidopsis. Each replicate consisted of one Arabidopsis leaf and three beetles that were placed in a Petri dish (60 mm diameter) with 50 μL of ultrapure water (n = 12–13 replicates). Each leaf was photographed before and after feeding to determine the consumed leaf area. Fed leaves were frozen in liquid nitrogen, freeze-dried, and homogenized using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 2 min at 30 Hz. Fed beetles were starved for 1 day, weighed, frozen in liquid nitrogen, and stored at −20 °C until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL 80% (v/v) methanol. All samples were extracted with 1 mL 80% (v/v) methanol containing 25 μM 1-methylethyl glucosinolate (extracted from Sisymbrium officinale seeds) as an internal standard. Extracts were applied to DEAE-Sephadex A-25 (Sigma-Aldrich) columns in 96-well filter plates (Nunc) that were preconditioned with 1 mL of ultrapure water and 2 × 500 μL of 80% (v/v) methanol. After loading 900 μL of extract, the columns were washed with 500 μL of 80% (v/v) methanol, followed by 2 × 1 mL of ultrapure water. To adjust the pH condition, 500 μL of 0.02 M MES buffer (pH 5.2) was added to each column. After adding 30 μL of Helix pomatia sulfatase solution to each column and overnight incubation at room temperature, desulfo-glucosinolates were eluted using 300 or 500 μL of ultrapure water into 96-deep well plates (Nunc). Samples were analyzed by desulfo-HPLC-DAD as described below. The glucosinolate content in adults and fed leaves was calculated in nanomole per adult and nanomole per cm2 leaf, respectively. The ingested glucosinolate amount was calculated based on the ingested leaf area and the corresponding leaf glucosinolate content. To elucidate which proportion of the ingested glucosinolates was sequestered, we expressed the glucosinolate amount detected in the beetles relative to the ingested glucosinolate amount from the leaves, which was set to 100%.HPLC-DADSamples were analyzed by HPLC on an Agilent Technologies HP1100 Series instrument equipped with a photodiode array detector. After injection of 100 μL of each sample, separation was achieved on an EC 250/4.6 NUCLEODUR Sphinx RP column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel; samples of the RNA interference (RNAi) experiments of PaGTR1, and PaGTR2/3) or an EC 250/4.6 NUCLEODUR 100-5 C18ec column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel; samples of the RNAi experiments of PaGTR5/6/7/8, and PaGTR2/3) using a binary solvent system consisting of ultrapure water (A) and acetonitrile (B), with a flow rate of 1 mL/min. The elution gradient was as follows: 0–1 min, 1.5% B; 1–6 min, 1.5–5% B; 6–8 min, 5–7% B; 8–18 min, 7–21% B; 18–23 min, 21–29% B; 23–23.5 min, 29–100% B; 23.5–26 min, 100% B; 26–26.1 min, 100 to 1.5% B; and 26.1–31 min, 1.5% B. The eluent was monitored by diode array detection between 190 and 360 nm. Desulfo-glucosinolates were identified based on a comparison of retention times and absorption spectra with those of known standards63. The glucosinolate content in each sample was calculated from the peak areas at 229 nm relative to the peak area of the internal standard using relative response factors64.Glucosinolate concentration in the hemolymph of adult P. armoraciae
    Hemolymph was collected from 7-day-old adult P. armoraciae reared on B. juncea by cutting off an abdominal leg and collecting the extruding droplet using glass capillaries (0.5 µL, Hirschmann® minicaps®) (n = 6 replicates, 50 beetles per replicate). The capillaries were marked with 1 mm intervals (corresponding to 15.6 nL) to estimate the volume of collected hemolymph. The hemolymph was diluted in 500 µL of 90% (v/v) methanol, homogenized using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz, and boiled for 5 min at 95 °C. After two centrifugation steps (13,000 × g for 10 min each), the supernatant was dried by vacuum centrifugation, dissolved in 50 µL 50% (v/v) methanol, diluted in ultrapure water, and analyzed by LC-MS/MS.Morphology of the Malpighian tubule system of P. armoraciae
    To investigate the structure of the Malpighian tubule system, we dissected the gut and Malpighian tubules of 4-day-old P. armoraciae adults in PBS (pH 6.0) under a stereomicroscope. The tracheae that attach Malpighian tubules to the gut were removed using fine forceps to release the tubules. Pictures were taken with a Canon EOS 600D camera.pH of hemolymph and excretion fluid of isolated Malpighian tubules of P. armoraciae
    The pH of the hemolymph and Malpighian tubule excretion fluid of 5-day-old P. armoraciae adults was assessed using the pH indicator bromothymol blue (Alfa Aesar). Hemolymph was collected by cutting off an abdominal leg and collecting the extruding droplet using a pipette (n = 3 replicates, six to ten beetles per replicate). Excretion fluid was collected from dissected Malpighian tubules that were incubated in saline A as described for the Ramsay assay (n = 4 replicates, one tubule per replicate). Hemolymph and excretion fluid were immediately mixed with the same volume of 0.16% (w/v) bromothymol blue dissolved in 10% (v/v) ethanol, respectively, under water-saturated paraffin oil in a Sylgard-coated petri dish. The resulting color of the droplet was compared with those of citric acid–Na2HPO4 buffer solutions ranging from pH 5.2 to 6.6 in 0.2 increments mixed with 0.16% (w/v) bromothymol blue.Fate of plant glucosides injected in P. armoraciae beetlesTo analyze the fate of plant glucosides in vivo, we injected 100 nL of an equimolar mixture of 2-propenyl glucosinolate, 4-hydroxybenzyl glucosinolate, linamarin, salicin, and catalpol, each at 10 mM, and 0.15% (w/v) amaranth into the hemolymph of 2-day-old adult P. armoraciae (reared on B. rapa). One group of beetles was sampled 30 min after injection by freezing beetles in liquid nitrogen (n = 10 replicates, five beetles per replicate). The remaining beetles were fed with detached leaves of Arabidopsis in Petri dishes (60 mm diameter) in the laboratory under ambient conditions (n = 10 replicates, five beetles per replicate). We added 30 μL of ultrapure water to each Petri dish and sealed them with parafilm to prevent leaf wilting. After 1 day, we sampled the beetles as described above (n = 10 replicates, five beetles per replicate). Feces were collected using 100 µL of ultrapure water per replicate and combined with 300 μL of pure methanol in a 1.5 mL Eppendorf tube. All samples were stored at −20 °C until extraction. Beetle and feces samples were homogenized as described in the RNAi experiment. After centrifugation (16,000 × g for 10 min), the supernatant was dried by vacuum centrifugation, dissolved in 100 µL of ultrapure water, and analyzed by LC-MS/MS. The glucoside content in adults or feces was calculated as nanomole per adult, respectively.Ramsay assayTo analyze the excretion of plant glucosides in situ, we performed Ramsay assays65 with dissected Malpighian tubules from 4- to 5-day-old P. armoraciae adults reared on B. rapa (Supplementary Fig. 6). Malpighian tubules were dissected in saline A (100 mM NaCl, 8.6 mM KCl, 2 mM CaCl2, 8.5 mM MgCl2, 4 mM NaH2PO4, 4 mM NaHCO3, 24 mM glucose, 10 mM proline, 25 mM 3-(N-morpholino)propanesulfonic acid (MOPS), pH 6.0)66. Single tubules were transferred into a 10 μL droplet of saline B (60 mM NaCl, 10.3 mM KCl, 2.4 mM CaCl2, 10.2 mM MgCl2, 4.8 mM NaH2PO4, 4.8 mM NaHCO3, 28.8 mM glucose, 12 mM proline, 30 mM MOPS, 1 mM cyclic AMP, pH 6.0) under water-saturated paraffin oil in a Sylgard-coated petri dish. The proximal end of the tubule was pulled out of the droplet, attached to a metal pin, and cut using a glass capillary to allow the collection of excretion fluid. To start the assay, we added 2 μL of an equimolar glucoside mixture consisting of 2-propenyl glucosinolate, 4-methylsulfinylbutyl glucosinolate, 4-hydroxybenzyl glucosinolate, 2-phenylethyl glucosinolate, I3M glucosinolate, linamarin, salicin, and catalpol, each at 40 mM, and 0.6% (w/v) amaranth) to saline B. After 2–3 h, we collected the excretion fluid and 2 μL of the bathing droplet in 300 μL of 80% (v/v) methanol, respectively. The Malpighian tubule was washed three times in ~15 mL of saline A and afterwards transferred into 300 μL of 80% (v/v) methanol. All samples were stored at −20 °C until extraction. Malpighian tubule samples were homogenized using a plastic pestle. After centrifugation (16,000 × g for 10 min), the supernatant was dried by vacuum centrifugation, dissolved in 70 µL ultrapure water, and analyzed by LC-MS/MS. Out of 36 assays, 25 assays were excluded because no excretion fluid was visible within 2–3 h.Statistical analysisNo statistical methods were used to predetermine sample size. Statistical analyses were conducted in R 3.5.167 or in SigmaPlot 14.0 or 14.5 (Systat Software Inc.). Two groups were compared by two-tailed Student’s t test, Mann–Whitney U test, or the method of generalized least squares68, depending on the variance homogeneity and the normality of residuals. Data of the Ramsay assays were compared by paired two-tailed Student’s t test using FDR (false discovery rate)-corrected P values69. Three or more groups were compared by one-way analysis of variance, followed by post hoc multiple comparisons test, or the method of generalized least squares68. If necessary, data were transformed prior to analysis. For comparisons using the method of generalized least squares, we applied the varIdent variance structure, which allows each group to have a different variance. The P value was obtained by comparing models with and without explanatory variables using a likelihood ratio test70. Significant differences between groups were revealed with factor level reductions71. Information about data transformation, statistical methods, and results of the statistical analyses are summarized in Supplementary Tables 2 and 3 and Supplementary Data 6.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Linking functional traits and demography to model species-rich communities

    1.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

    Google Scholar 
    3.Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. 111, 740–745 (2014).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Pierce, S., Brusa, G., Vagge, I. & Cerabolini, B. E. Allocating C. S. R. plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Funct. Ecol. 27, 1002–1010 (2013).5.Kattge, J. et al. TRY – a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).ADS 
    Article 

    Google Scholar 
    6.Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Connolly, S. R., Keith, S. A., Colwell, R. K. & Rahbek, C. Process, mechanism, and modeling in macroecology. Trends Ecol. Evol. 32, 835–844 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Dormann, C. F. et al. Correlation and process in species distribution models: bridging a dichotomy. J. Biogeogr. 39, 2119–2131 (2012).Article 

    Google Scholar 
    10.Thuiller, W., Pollock, L. J., Gueguen, M. & Münkemüller, T. From species distributions to meta-communities. Ecol. Lett. 18, 1321–1328 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Zurell, D. et al. Benchmarking novel approaches for modelling species range dynamics. Glob. Change Biol. 22, 2651–2664 (2016).ADS 
    Article 

    Google Scholar 
    12.Alexander, J. et al. Lags in the response of alpine plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).ADS 
    Article 

    Google Scholar 
    13.Evans, M. E., Merow, C., Record, S., McMahon, S. M. & Enquist, B. J. Towards process-based range modeling of many species. Trends Ecol. Evol. 31, 860–871 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    15.Paine, C. E. T., Deasey, Anna, Bradley, DuthieA. & Ken, Thompson Towards the general mechanistic prediction of community dynamics. Funct. Ecol. 32, 1681–1692 (2018).Article 

    Google Scholar 
    16.Hartig, F. et al. Connecting dynamic vegetation models to data–an inverse perspective. J. Biogeogr. 39, 2240–2252 (2012).Article 

    Google Scholar 
    17.Levins, R. The strategy of model building in population biology. Am. Sci. 54, 421–431 (1966).
    Google Scholar 
    18.Kraft, N. J., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. 112, 797–802 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Mayfield, M. M. & Stouffer, D. B. Higher-order interactions capture unexplained complexity in diverse communities. Nat. Ecol. Evol. 1, 0062 (2017).Article 

    Google Scholar 
    20.Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. Experimental evidence for strong stabilizing forces at high functional diversity of aquatic microbial communities. Ecology 96, 1340–1350 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Laughlin, D. C. & Messier, J. Fitness of multidimensional phenotypes in dynamic adaptive landscapes. Trends Ecol. Evol. 30, 487–496 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Curtsdotter, A. et al. Ecosystem function in predator–prey food webs—confronting dynamic models with empirical data. J. Anim. Ecol. 88, 196–210 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).24.Terborgh, J. W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. 112, 11415–11422 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Swenson, N. G. & Enquist, B. J. Ecological and evolutionary determinants of a key plant functional trait: wood density and its community-wide variation across latitude and elevation. Am. J. Bot. 94, 451–459 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Körner, C. Alpine Plant Life. (Springer, 2003).27.Westoby, M. A leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227 (1998).CAS 
    Article 

    Google Scholar 
    28.Adler, P. B. et al. Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition. Ecol. Lett. 21, 1319–1329 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Chalmandrier, L., Albouy, C. & Pellissier, L. Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Sci. Rep. 7, 15405 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    30.Nagelkerke, N. J. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    31.Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    32.Godínez-Alvarez, H., Herrick, J. E., Mattocks, M., Toledo, D. & Van Zee, J. Comparison of three vegetation monitoring methods: Their relative utility for ecological assessment and monitoring. Ecol. Indic. 9, 1001–1008 (2009).Article 

    Google Scholar 
    33.Grime, J. P. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 111, 1169–1194 (1977).Article 

    Google Scholar 
    34.Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

    Google Scholar 
    35.de Bello, Fde et al. Hierarchical effects of environmental filters on the functional structure of plant communities: a case study in the French Alps. Ecography 36, 393–402 (2013).Article 

    Google Scholar 
    36.Dray, S. et al. Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. Ecology 95, 14–21 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Violle, C. et al. Competition, traits and resource depletion in plant communities. Oecologia 160, 747–755 (2009).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Michelsen, A., Schmidt, I. K., Jonasson, S., Quarmby, C. & Sleep, D. Leaf 15N abundance of subarctic plants provides field evidence that ericoid, ectomycorrhizal and non-and arbuscular mycorrhizal species access different sources of soil nitrogen. Oecologia 105, 53–63 (1996).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Laughlin, D. C., Joshi, C., van Bodegom, P. M., Bastow, Z. A. & Fulé, P. Z. A predictive model of community assembly that incorporates intraspecific trait variation. Ecol. Lett. 15, 1291–1299 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Laughlin, D. C. The intrinsic dimensionality of plant traits and its relevance to community assembly. J. Ecol. 102, 186–193 (2014).Article 

    Google Scholar 
    41.Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    42.Letten, A. D., Ke, P.-J. & Fukami, T. Linking modern coexistence theory and contemporary niche theory. Ecol. Monogr. 87, 161–177 (2017).Article 

    Google Scholar 
    43.O’Dwyer, J. P. Whence Lotka-Volterra? Theor. Ecol. 11, 441–452 (2019).Article 

    Google Scholar 
    44.Pichler, M., Boreux, V., Klein, A.-M., Schleuning, M. & Hartig, F. Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks. Methods Ecol. Evol. 11, 281–293 (2020).Article 

    Google Scholar 
    45.Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).Article 

    Google Scholar 
    46.Damgaard, C. A critique of the space-for-time substitution practice in community ecology. Trends Ecol. Evol. 34, 416–421 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Arnoldi, J.-F., Loreau, M. & Haegeman, B. The inherent multidimensionality of temporal variability: how common and rare species shape stability patterns. Ecol. Lett. 22, 1557–1567 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.May, R. M. Patterns of species abundance and diversity. in Ecology and Evolution of Communities (eds Cody, M. L. & Diamond, J. M.) 81–120 (Harvard University Press, 1975).49.Falster, D. S., Brännström, Å., Westoby, M. & Dieckmann, U. Multitrait successional forest dynamics enable diverse competitive coexistence. Proc. Natl. Acad. Sci. 114, E2719–E2728 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Boulangeat, I., Georges, D. & Thuiller, W. FATE-HD: a spatially and temporally explicit integrated model for predicting vegetation structure and diversity at regional scale. Glob. Change Biol. 20, 2368–2378 (2014).Article 

    Google Scholar 
    51.Lischke, H. & Löffler, T. J. Finding all multiple stable fixpoints of n-species Lotka–Volterra competition models. Theor. Popul. Biol. 115, 24–34 (2017).PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    52.Jamil, T., Ozinga, W. A., Kleyer, M. & ter Braak, C. J. Selecting traits that explain species–environment relationships: a generalized linear mixed model approach. J. Veg. Sci. 24, 988–1000 (2013).Article 

    Google Scholar 
    53.ter Braak, C. J. New robust weighted averaging-and model-based methods for assessing trait–environment relationships. Methods Ecol. Evol. 10, 1962–1971 (2019).Article 

    Google Scholar 
    54.Pielou, E. C. Ecological Diversity. (Wiley, New York, 1975).55.Gibert, A., Gray, E. F., Westoby, M., Wright, I. J. & Falster, D. S. On the link between functional traits and growth rate: meta-analysis shows effects change with plant size, as predicted. J. Ecol. 104, 1488–1503 (2016).Article 

    Google Scholar 
    56.Durand, Y. et al. Reanalysis of 44 Yr of climate in the French Alps (1958–2002): methodology, model validation, climatology, and trends for air temperature and precipitation. J. Appl. Meteorol. Climatol. 48, 429–449 (2009).ADS 
    Article 

    Google Scholar 
    57.Chalmandrier, L. et al. Spatial scale and intraspecific trait variability mediate assembly rules in alpine grasslands. J. Ecol. 105, 277–287 (2017).Article 

    Google Scholar 
    58.Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

    Google Scholar 
    59.Reich, P. B. et al. Generality of leaf trait relationships: a test across six biomes. Ecology 80, 1955–1969 (1999).Article 

    Google Scholar 
    60.Poorter, H. & Bergkotte, M. Chemical composition of 24 wild species differing in relative growth rate. Plant Cell Environ. 15, 221–229 (1992).CAS 
    Article 

    Google Scholar 
    61.Farquhar, G. D., O’leary, M. H. & Berry, J. A. On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Funct. Plant Biol. 9, 121–137 (1982).CAS 
    Article 

    Google Scholar 
    62.Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4. (Springer, 2018).63.Rapisarda, F., Brigo, D. & Mercurio, F. Parameterizing correlations: a geometric interpretation. IMA J. Manag. Math. 18, 55–73 (2007).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    64.Blumenson, L. E. A derivation of n-dimensional spherical coordinates. Am. Math. Mon. 67, 63–66 (1960).MathSciNet 

    Google Scholar 
    65.Banner, K. M., Irvine, K. M. & Rodhouse, T. The use of bayesian priors in ecology: the good, the bad, and the not great. Methods Ecol. Evol. 00, 1–8 (2020).
    Google Scholar 
    66.Hartig, F., Minunno, F. & Paul, S. BayesianTools: General-Purpose MCMC and SMC samplers and tools for bayesian statistics. R package (2017).67.Warton, D. I. et al. So Many Variables: Joint Modeling in Community Ecology. Trends Ecol. Evol. 30, 766–779 (2015). 68.Pichler, M. & Hartig, F. A new method for faster and more accurate inference of species associations from novel community data. Preprint at https://arxiv.org/abs/2003.05331 (2020).69.Advanced Research Computing Center (ARCC). Teton Computing Environment. https://doi.org/10.15786/m2fy47 (2018). More

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    A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model

    Study areaChina is one of the countries with the poorest per capita water resources in the world while also having the largest water consumption in the world. In 2018, China’s total water consumption was 601.55 billion m3, with 369.31 billion m3 of water used in agriculture, accounting for 61.4% of the total water2. Agriculture is the most important industrial sector in water resource consumption. However, due to regional and climate differences, the distribution of agricultural water resources is uneven, and the shortage of water resources seriously affects agricultural development in water-deficient areas.Figure 1 shows the agricultural water consumption in China by province for 2007 and 2018. The agricultural water consumption includes farmland irrigation water consumption (classified as paddy field, irrigated land, vegetable field, groundwater exploitation), forest, animal husbandry, fishery, and livestock (classified as forest and fruit, grassland, fish pond, animal husbandry, groundwater exploitation), domestic water consumption of rural residents and rural ecological environment water consumption. Previous studies have mainly considered the irrigation water consumption of the planting industry as the research object at the provincial or regional levels (e.g., eastern, central, and western regions). Few were able to consider all 31 provinces in China and have comprehensively assessed water consumption and water use efficiency in the various types of agricultural production3,4,5,6,10,16,17,22,23,24,25,30. In this study, the agricultural water use efficiency and its influencing factors are assessed based on the agricultural water consumption of agriculture, forestry, animal husbandry, and fishery in China.Figure 1Agricultural water consumption in China by province for (a) 2007 and (b) 2018. Note: Map created using ArcGIS [10.2], (http://www.esri.com/software/arcgis).Full size imageResearch methodIn this study, the agricultural water use efficiency (under the common frontier and the group frontier) is calculated using the super-efficiency slacks-based measure (Super-SBM) model. The significant factors affecting water-use efficiency are then analyzed through the threshold regression model.Super-efficiency SBM modelData envelopment analysis (DEA) is an efficiency evaluation method proposed by Charnes31, a famous American operational research scientist. While traditional radial and angular DEA models do not require the specific form of the estimation function, they ignore the relaxation of variables and result in efficiency values in the range of 0 to 1. If there are multiple efficiency value of decision making units(DMUs) with an efficiency value of 1, these values cannot be compared. The efficiency of the super efficiency DEA model can be greater than 1, which means that the efficiency level of all decision-making units can be compared.To avoid the problem of slack variables, Tone (2001) proposed the SBM model, which is a non-radial and non-angular DEA analysis method based on the relaxation variable measure16,17,18,19,20,32. The SBM model of unexpected output solves the slack problem of input and output variables, minimizing deviations in the efficiency measurement. The super-efficiency SBM model combines the super-efficiency DEA model and the SBM model. It is also one of the methods based on data envelopment analysis, which can measure the efficiency of all decision-making units and the slack of input and output variables.Assume n to be the decision-making units, each of which has m inputs, expected output r1, and unexpected output r2. Let X (X ∈ Rm), Yd (Yd ∈ Rs1), and Yu (Yu ∈ Rs2) be matrices, such that (X=[{x}_{1},dots ,{x}_{n}]in {R}^{m*n}) and (Y=[{y}_{1}^{d}, dots ,{ y}_{n}^{d}in {R}^{{r}_{1}*n}). The form of the super-efficiency SBM model is as follows1,17,19,54:$$min=frac{frac{1}{m}sum_{i=1}^{m}(overline{x}/{x}_{ik})}{1/left({r}_{1}+{r}_{2}right)*(sum_{r=1}^{{r}_{1}}overline{{y}^{d}}/{y}_{rk}^{d}+sum_{q=1}^{{r}_{2}}overline{{y}^{u}}/{y}_{qk}^{u}}.$$
    (1)
    Among them,$$overline{x}ge sum_{j=1ne k}^{n}{x}_{ij}{lambda }_{j}, i=1,dots ,m;$$
    (2)
    $$overline{{y}^{d}}le sum_{j=1,ne k}^{n}{y}_{rj}^{d}{lambda }_{j}, r=1,dots ,{s}_{1};$$
    (3)
    $$overline{{y}^{d}}ge sum_{j=1,ne k}^{n}{y}_{qj}^{u}{lambda }_{j}, q=1,dots ,{s}_{2};$$
    (4)
    $${lambda }_{y}ge 0,j=1,dots ,n;jne 0;$$
    (5)
    $$overline{x}ge {x}_{k},k=1,dots ,m;$$
    (6)
    $$overline{{y}^{d}}le {y}_{k}^{d},d=1,dots ,{r}_{1};$$
    (7)
    $$overline{{y}^{u}}ge {y}_{k}^{u},b=1,dots ,{r}_{2}.$$
    (8)
    Based on the Super-SBM model (Eq. 1) and its constraint formula, the agricultural water use efficiency for the different provinces was calculated for the period 2007–2018 using Maxdea 8 ultra software.Threshold effectConsidering the differences in economic development and technical levels, the agricultural water use in different regions of China shows characteristics of time-series evolution, spatial heterogeneity, and unbalanced spatial distribution. There is a non-linear relationship between the influencing factors of agricultural water use efficiency, which suggests the existence of certain threshold characteristics33,34. This means that for a particular determinant, agricultural water use efficiency would be affected differently depending on whether the parameter has crossed the threshold. In this study, the threshold panel model proposed by Hansen is used. The threshold value of the threshold variable is taken as the critical point, and the regression equation is divided into different stage intervals to analyze the influence of threshold variables on the explained variables at different stages . Therefore, according to the relationship between agricultural water use efficiency and its influencing factors in different regions, the following single threshold regression model is set:$${Y}_{it}=alpha {X}_{it}+{beta }_{1}{T}_{it}Ileft({T}_{it}le {gamma }_{1}right)+{beta }_{2}{T}_{it}Ileft({T}_{it} >{gamma }_{1}right)+C+{varepsilon }_{it},$$
    (9)
    such that i is the province; t is the year; Yit and Tit are the explanatory variables and explained variables, respectively; Xit is the control variable that has a significant impact on the explained variables; Tit is threshold variable, which changes with the different explanatory variables; γ is a specific threshold value; α is the corresponding coefficient vector; β1 and β2 represent the influence coefficients of the threshold variable Tit on the explained variable Yit in the case of ({T}_{it}le {gamma }_{1}) and ({T}_{it} >{gamma }_{1}) , respectively; C is a constant; ε is random disturbance term, ({varepsilon }_{it}sim i.i.d.N(0,{sigma }^{2})); and, I (·) is an indicative function. After obtaining the estimated value of each parameter, two tests need to be carried out: (1) establish whether the threshold effect is significant; and (2) determine whether the estimated threshold value is equal to the true value. In addition, the above equation assumes that only one threshold exists. For two or more thresholds, the model would have to be adjusted according to the data.Based on the panel data of 31 provinces in China from 2007 to 201844,45,46, Stata15.0 software was used to perform threshold regression on seven variables: per capita water resources, rural labor force, disposable income, government’s attention, foreign trade dependence, industrial structure, and gross domestic product (GDP). The threshold effect of each factor can be analyzed, and the impact on agricultural water consumption can be assessed using the threshold value.Variable selection and data sourceThe super-efficiency SBM model was used in calculating the agricultural water use efficiency for the 31 provinces in China from 2007 to 2018. The input–output indicators were defined before the calculations, as shown in Extended Data Table 1.The selection of input–output factors to measure the utilization efficiency of agricultural water resources follows the principles of availability and operability. The input variables included: (1) agricultural water consumption, (2) the number of employees in agriculture, forestry, animal husbandry, and fishery, (3) the total power of agricultural machinery, and (4) the expenditure of local finance on agriculture, forestry, and water affairs. In terms of output, the added value in agriculture, forestry, animal husbandry, and fishery (based on 2007) was used as the expected output, while ammonia nitrogen emission, agricultural chemical oxygen demand emission, and agricultural carbon emission comprised the unexpected output.This study considered the scale of carbon emissions released by the agricultural system. According to existing research, agricultural carbon emissions are associated with rural environmental pollution35. The main consequence of agricultural pollutant emissions is soil pollution, which leads to rural groundwater pollution36,37,39,40,41,41. The deterioration of groundwater quality adversely affects the development of the agricultural economy and threatens the safety of the drinking water supply for rural residents.The threshold regression model was used to investigate the convergence of agricultural water use efficiency and observe the changes in agricultural water consumption under different influencing factors. The control variables include the following: water resource endowment, the number of agricultural labor, the income level of rural residents, industrial structure, the degree of government’s attention, the degree of dependence on foreign trade, and the level of economic development, as shown in Extended Data Table 2. For water resource endowment (WR), WR is expressed in per capita water resource (m3 / person). Zhang Lixiao45,46 and previous studies have shown a negative correlation between water resource endowment and water resource utilization. For agricultural labor (ah), the variable is expressed by the number of people engaged in agriculture, forestry, animal husbandry, and fishery (10,000 people). Past studies suggest rural population affects the consumption of agricultural water resources47,50,51,52,53,52. For income levels, rural residents’ income level is indicated by the per capita disposable income of rural households. Wang Xueyuan et al.3 and Han Qing et al.53 argue that the increase in the rural residents’ income would limit agricultural water consumption. For industrial structure (× 2), which is expressed by the proportion of industrial added value in GDP, research has shown water resource efficiency would vary under different industrial structures54,57,56. For the government’s attention degree (GA), the variable is expressed by the proportion of agriculture, water affairs, and forestry spending in the total financial expenditure. The government’s support for comprehensive agricultural development and infrastructure and technology upgrading for agricultural, forestry, and water conservation significantly affects water resource utilization efficiency16,56,59,58. For the degree of dependence on foreign trade (open), the parameter is indicated by the proportion of the total import and export of agricultural and sideline products in the GDP. Changes in import demand can reduce or increase the consumption and pollution of water resources. Likewise, export demand changes, especially in high water-consuming and high polluting products, can significantly improve or degrade water resource efficiency. And for the level of economic development, expressed in terms of GDP, the level of regional economic development plays a positive role in promoting the efficiency of water resource utilization59,62,61. More

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    Erosion of tropical bird diversity over a century is influenced by abundance, diet and subtle climatic tolerances

    1.Turner, I. M. Species loss in fragments of tropical rain forest: a review of the evidence. J. Appl. Ecol. 33, 200–209 (1996).Article 

    Google Scholar 
    2.Pimm, S. L. & Raven, P. Biodiversity: extinction by numbers. Nature 403, 843 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Robinson, W. D. et al. Distribution of bird diversity in a vulnerable Neotropical landscape. Conserv. Biol. 18, 510–518 (2004).Article 

    Google Scholar 
    4.Rompré, G., Robinson, W. D. & Desrochers, A. Causes of habitat loss in a Neotropical landscape: The Panama Canal corridor. Landsc. Urban Plan. 87, 129–139 (2008).Article 

    Google Scholar 
    5.Diamond, J. Dammed experiments. Science 294, 1847 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Şekercioḡlu, Ç. H. et al. Disappearance of insectivorous birds from tropical forest fragments. Proc. Natl. Acad. Sci. USA 99, 263 (2002).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    7.Henle, K., Davies, K. F., Kleyer, M., Margules, C. & Settele, J. Predictors of species sensitivity to fragmentation. Biodivers. Conserv. 13, 207–251 (2004).Article 

    Google Scholar 
    8.Stratford, J. A. & Robinson, W. D. Gulliver travels to the fragmented tropics: geographic variation in mechanisms of avian extinction. Front. Ecol. Environ. 3, 85–92 (2005).Article 

    Google Scholar 
    9.Robinson, W. D. & Sherry, T. W. Mechanisms of avian population decline and species loss in tropical forest fragments. J. Ornithol. 153, 141–152 (2012).Article 

    Google Scholar 
    10.Terborgh, J. Preservation of natural diversity: the problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

    Google Scholar 
    11.Karr, J. R. Population variability and extinction in the avifauna of a tropical land bridge island. Ecology 63, 1975–1978 (1982).Article 

    Google Scholar 
    12.Sieving, K. E. Nest predation and differential insular extinction among selected forest birds of central Panama. Ecology 73, 2310–2328 (1992).Article 

    Google Scholar 
    13.Bierregaard, R. O., Lovejoy, T. E., Kapos, V., dos Santos, A. A. & Hutchings, R. W. The biological dynamics of tropical rainforest fragments. Bioscience 42, 859–866 (1992).Article 

    Google Scholar 
    14.Laurance, W. F. Forest-climate interactions in fragmented tropical landscapes. Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 359, 345–352 (2004).Article 

    Google Scholar 
    15.Laurance, W. F. & Curran, T. J. Impacts of wind disturbance on fragmented tropical forests: a review and synthesis. Austral. Ecol. 33, 399–408 (2008).Article 

    Google Scholar 
    16.Stratford, J. A. & Stouffer, P. C. Forest fragmentation alters microhabitat availability for Neotropical terrestrial insectivorous birds. Biol. Conserv. 188, 109–115 (2015).Article 

    Google Scholar 
    17.Patten, M. A. & Smith-Patten, B. D. Testing the microclimate hypothesis: light environment and population trends of Neotropical birds. Biol. Conserv. 155, 85–93 (2012).Article 

    Google Scholar 
    18.Ausprey, I. J., Newell, F. L. & Robinson, S. K. Adaptations to light predict the foraging niche and disassembly of avian communities in tropical countrysides. Ecology 102, e03213 (2021).PubMed 
    Article 

    Google Scholar 
    19.Busch, D. S., Robinson, W. D., Robinson, T. R. & Wingfield, J. C. Influence of proximity to a geographical range limit on the physiology of a tropical bird. J. Anim. Ecol. 80, 640–649 (2011).PubMed 
    Article 

    Google Scholar 
    20.Stouffer, P. C. & Bierregaard, R. O. Use of Amazonian forest fragments by understory insectivorous birds. Ecology 76, 2429–2445 (1995).Article 

    Google Scholar 
    21.Ferraz, G. et al. Rates of species loss from Amazonian forest fragments. Proc. Natl. Acad. Sci. 100, 14069–14073 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Brooks, T. M., Pimm, S. L. & Oyugi, J. O. Time lag between deforestation and bird extinction in tropical forest fragments. Conserv. Biol. 13, 1140–1150 (1999).Article 

    Google Scholar 
    23.Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).PubMed 
    Article 

    Google Scholar 
    24.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).PubMed 
    Article 

    Google Scholar 
    25.Kattan, G. H., Alvarez-López, H. & Giraldo, M. Forest fragmentation and bird extinctions: San Antonio eighty years later. Conserv. Biol. 8, 138–146 (1994).Article 

    Google Scholar 
    26.Christiansen, M. B. & Pitter, E. Species loss in a forest bird community near Lagoa Santa in southeastern Brazil. Biol. Conserv. 80, 23–32 (1997).Article 

    Google Scholar 
    27.Laurance, W. F. et al. Ecosystem decay of Amazonian forest fragments: a 22-year investigation. Conserv. Biol. 16, 605–618 (2002).Article 

    Google Scholar 
    28.Sigel, B. J., Sherry, T. W. & Young, B. E. Avian community response to lowland tropical rainforest isolation: 40 years of change at La Selva biological station, Costa Rica. Conserv. Biol. 20, 111–121 (2006).PubMed 
    Article 

    Google Scholar 
    29.Stouffer, P. C., Bierregaard, R. O., Strong, C. & Lovejoy, T. E. Long-term landscape change and bird abundance in amazonian rainforest fragments. Conserv. Biol. 20, 1212–1223 (2006).PubMed 
    Article 

    Google Scholar 
    30.Moura, N. G. et al. Two hundred years of local avian extinctions in Eastern Amazonia. Conserv. Biol. 28, 1271–1281 (2014).PubMed 
    Article 

    Google Scholar 
    31.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Foster, R. B. & Brokaw, N. V. Structure and History of the Vegetation of Barro Colorado Island (1982).33.Leigh, E. G. Tropical Forest Ecology: A View from Barro Colorado Island (Oxford University Press, 1999).
    Google Scholar 
    34.Panama Canal Authority (ACP). Meteorology and Hydrology Branch. http://www.pancanal.com (2016).35.ANAM. Informe Final de Resultados de la Cobertura Boscosa y uso del Suelo de la Republica de Panamá 1992–2000 (La Autoridad Nacional para el Ambiente (ANAM) y The International Tropical Timber Organization Panamá, 2003).36.Paton, S. 2017 Meterological and Hydrological Summary for Barro Colorado Island (2018).37.Rompré, G., Robinson, W. D., Desrochers, A. & Angehr, G. Environmental correlates of avian diversity in lowland Panama rain forests. J. Biogeogr. 34, 802–815 (2007).Article 

    Google Scholar 
    38.Karr, J. R. Avian extinction on Barro Colorado island, Panama: a reassessment. Am. Nat. 119, 220–239 (1982).Article 

    Google Scholar 
    39.Willis, E. O. Populations and local extinctions of birds on Barro Colorado Island, Panama. Ecol. Monogr. 44, 153–169 (1974).Article 

    Google Scholar 
    40.Robinson, W. D. Long-term changes in the avifauna of Barro Colorado Island, Panama, a tropical forest isolate. Conserv. Biol. 13, 85–97 (1999).Article 

    Google Scholar 
    41.Robinson, W. D., Robinson, T. R., Robinson, S. K. & Brawn, J. D. Nesting success of understory forest birds in central Panama. J. Avian Biol. 31, 151–164 (2000).Article 

    Google Scholar 
    42.Robinson, W. D. & Robinson, T. R. Observations of predation events at bird nests in central Panama. J. Field Ornithol. 72, 43–48 (2001).Article 

    Google Scholar 
    43.Robinson, W. D., Rompré, G. & Robinson, T. R. Videography of Panama bird nests shows snakes are principal predators. Ornitol. Neotrop. 16, 187–195 (2005).
    Google Scholar 
    44.Chapman, F. M. My Tropical Air Castle (D. Appleton and Co., 1929).
    Google Scholar 
    45.Chapman, F. M. Life in an Air Castle: Nature Studies in the Tropics (D. Appleton-Century Company, Incorporated, 1938).
    Google Scholar 
    46.Eisenmann, E. Annotated List of Birds of Barro Colorado Island, Panama Canal Zone Vol. 117 (Smithsonian Institution, 1952).
    Google Scholar 
    47.Willis, E. O. & Eisenmann, E. A revised list of birds of Barro Colorado Island, Panamá. Smithson. Contrib. Zool. https://doi.org/10.5479/si.00810282.291 (1979).Article 

    Google Scholar 
    48.Robinson, W. D. Changes in abundance of birds in a Neotropical forest fragment over 25 years: a review. Anim. Biodivers. Conserv. 24, 51–65 (2001).
    Google Scholar 
    49.Robinson, W. D., Brawn, J. D. & Robinson, S. K. Forest bird community structure in central Panama: influence of spatial scale and biogeography. Ecol. Monogr. 70, 209–235 (2000).Article 

    Google Scholar 
    50.Sodhi, N. S., Liow, L. H. & Bazzaz, F. A. Avian extinctions from tropical and subtropical forests. Annu. Rev. Ecol. Evol. Syst. 35, 323–345 (2004).Article 

    Google Scholar 
    51.Dunning, J. B. Jr. CRC Handbook of Avian Body Masses (CRC Press, 2007).Book 

    Google Scholar 
    52.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
    Google Scholar 
    53.McCune, B. & Mefford, M. J. PC-ORD. Multivariate Analysis of Ecological Data (MjM Software, 2011).
    Google Scholar 
    54.Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).Article 

    Google Scholar 
    55.Chevan, A. & Sutherland, M. Hierarchical partitioning. Am. Stat. 45, 90–96 (1991).
    Google Scholar 
    56.Navarrete, C. B. & Soares, F. C. dominanceanalysis: Dominance Analysis. R package version 1.0.0. (2019).57.McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior (1973).58.Menard, S. Coefficients of determination for multiple logistic regression analysis. Am. Stat. 54, 17–24 (2000).
    Google Scholar 
    59.McFadden, D. Quantitative methods for analyzing travel behaviour of individuals: some recent developments, Cowles Foundation Discussion Papers No. 474 (Cowles Foundation for Research in Economics, Yale University, 1977).60.Clark, W. A. & Hosking, P. L. Statistical Methods for Geographers. (1986).61.Walsh, C. & MacNally, R. Hier.Part: Hierarchical Partitioning. R package version 1.0-4. (2013).62.Harrell Jr, F. E. RMS: Regression Modeling Strategies. R package version 5.1-3. City (2019).63.Le Cessie, S. & Van Houwelingen, J. C. A goodness-of-fit test for binary regression models, based on smoothing methods. Biometrics 47, 1267–1282 (1991).MATH 
    Article 

    Google Scholar 
    64.Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).Article 

    Google Scholar 
    65.Barbosa, A. M., Brown, J. A., Jimenez-Valverde, A. & Real, R. modEvA: Model Evaluation and Analysis. R package version 1.3.2. (2016).66.Suzuki, R., Shimodaira, H., Suzuki, M. R. & Suggests, M. Package ‘pvclust’. R Top. Doc. 14, 1540–1542 (2015).
    Google Scholar 
    67.Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    68.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
    Google Scholar 
    69.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    70.Oksanen, J. et al. Vegan: Community Ecology Package (2013).71.Moore, R. P. Biogeographic and Experimental Evidence for Local Scale Dispersal Limitation in Central Panamanian Forest Birds (Oregon State University, 2005).
    Google Scholar 
    72.Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).PubMed 
    Article 

    Google Scholar 
    73.Moore, R. P., Robinson, W. D., Lovette, I. J. & Robinson, T. R. Experimental evidence for extreme dispersal limitation in tropical forest birds. Ecol. Lett. 11, 960–968 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    74.Asquith, N. M. & Mejía-Chang, M. Mammals, edge effects, and the loss of tropical forest diversity. Ecology 86, 379–390 (2005).Article 

    Google Scholar 
    75.Wolda, H. Trends in abundance of tropical forest insects. Oecologia 89, 47–52 (1992).ADS 
    PubMed 
    Article 

    Google Scholar 
    76.Franks, N. R. A new method for censusing animal populations: the number of Eciton burchelli army ant colonies on Barro Colorado Island, Panama. Oecologia 52, 266–268 (1982).ADS 
    PubMed 
    Article 

    Google Scholar 
    77.Socolar, J. B. & Wilcove, D. S. Forest-type specialization strongly predicts avian responses to tropical agriculture. Proc. R. Soc. B 286, 20191724 (2019).PubMed 
    Article 

    Google Scholar 
    78.Şekercioğlu, Ç. H., Primack, R. B. & Wormworth, J. The effects of climate change on tropical birds. Biol. Conserv. 148, 1–18 (2012).Article 

    Google Scholar 
    79.Karr, J. R. & Freemark, K. E. Habitat selection and environmental gradients: dynamics in the” stable” tropics. Ecology 64, 1481–1494 (1983).Article 

    Google Scholar 
    80.Ibarra-Macias, A., Robinson, W. D. & Gaines, M. S. Experimental evaluation of bird movements in a fragmented Neotropical landscape. Biol. Conserv. 144, 703–712 (2011).Article 

    Google Scholar 
    81.Stouffer, P. C. et al. Long-term change in the avifauna of undisturbed Amazonian rainforest: ground-foraging birds disappear and the baseline shifts. Ecol. Lett. 24, 186–195 (2021).PubMed 
    Article 

    Google Scholar 
    82.Blake, J. G. & Loiselle, B. A. Enigmatic declines in bird numbers in lowland forest of eastern Ecuador may be a consequence of climate change. PeerJ 3, e1177 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Legendre, P. & Condit, R. Spatial and temporal analysis of beta diversity in the Barro Colorado Island forest dynamics plot, Panama. For. Ecosyst. 6, 7 (2019).Article 

    Google Scholar 
    84.Condit, R., Pérez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. 4, 17 (2017).Article 

    Google Scholar 
    85.Aguilar, E. et al. Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. J. Geophys. Res.: Atmos. 110, 2064–2082 (2005).Article 

    Google Scholar 
    86.Gonzalez, A. & Loreau, M. The causes and consequences of compensatory dynamics in ecological communities. Annu. Rev. Ecol. Evol. Syst. 40, 393–414 (2009).Article 

    Google Scholar 
    87.Kaspari, M. & Weiser, M. D. Ant activity along moisture gradients in a neotropical forest 1. Biotropica 32, 703–711 (2000).Article 

    Google Scholar 
    88.Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Change Biol. 14, 2661–2677 (2008).ADS 
    Article 

    Google Scholar 
    89.Levings, S. C. & Windsor, D. M. Litter moisture content as a determinant of litter arthropod distribution and abundance during the dry season on Barro Colorado Island, Panama. Biotropica 16, 125–131 (1984).Article 

    Google Scholar 
    90.Brawn, J. D., Benson, T. J., Stager, M., Sly, N. D. & Tarwater, C. E. Impacts of changing rainfall regime on the demography of tropical birds. Nat. Clim. Chang. 7, 133 (2017).ADS 
    Article 

    Google Scholar 
    91.Karp, D. S. et al. Agriculture erases climate-driven β-diversity in Neotropical bird communities. Glob. Change Biol. 24, 338–349 (2018).ADS 
    Article 

    Google Scholar 
    92.Frishkoff, L. O. et al. Loss of avian phylogenetic diversity in neotropical agricultural systems. Science 345, 1343 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    93.Wright, S. J. How isolation affects rates of turnover of species on islands. Oikos 44, 331–340 (1985).Article 

    Google Scholar 
    94.Chadwick, R., Good, P., Martin, G. & Rowell, D. P. Large rainfall changes consistently projected over substantial areas of tropical land. Nat. Clim. Chang. 6, 177–181 (2016).ADS 
    Article 

    Google Scholar 
    95.Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).ADS 
    Article 

    Google Scholar  More

  • in

    Inhibition of a nutritional endosymbiont by glyphosate abolishes mutualistic benefit on cuticle synthesis in Oryzaephilus surinamensis

    1.Sikorski, J. A. & Gruys, K. J. Understanding glyphosate’s molecular mode of action with EPSP synthase: evidence favoring an allosteric inhibitor model. Acc. Chem. Res. 30, 2–8 (1997).CAS 
    Article 

    Google Scholar 
    2.Duke, S. O. & Powles, S. B. Glyphosate: a once‐in‐a‐century herbicide. Pest Manag. Sci. 64, 319–325 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Siehl, D. L. Inhibitors of EPSP synthase, glutamine synthetase and histidine synthesis. In Herbicide Activity: Toxicology, Biochemistry and Molecular Biology, vol. 1 (eds. Michael Roe, R., Burton, J. D. & Kuhr, R. J.) 37 (IOS Press, 1997).4.Shilo, T., Zygier, L., Rubin, B., Wolf, S. & Eizenberg, H. Mechanism of glyphosate control of Phelipanche aegyptiaca. Planta 244, 1095–1107 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Tzin, V. & Galili, G. New Insights into the shikimate and aromatic amino acids biosynthesis pathways in plants. Mol. Plant 3, 956–972 (2010).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    7.Hacker, S. D. & Gaines, S. D. Some implications of direct positive interactions for community species diversity. Ecology 78, 1990–2003 (1997).Article 

    Google Scholar 
    8.van den Bosch, T. J. M. & Welte, C. U. Detoxifying symbionts in agriculturally important pest insects. Microb. Biotechnol. 10, 531–540 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    9.Lemoine, M. M., Engl, T. & Kaltenpoth, M. Microbial symbionts expanding or constraining abiotic niche space in insects. Curr. Opin. Insect Sci. 39, 14–20 (2020).PubMed 
    Article 

    Google Scholar 
    10.Feldhaar, H. Bacterial symbionts as mediators of ecologically important traits of insect hosts. Ecol. Entomol. 36, 533–543 (2011).Article 

    Google Scholar 
    11.Moran, N. A. Symbiosis. Curr. Biol. 16, R866–R871 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Moran, N. A. & Telang, A. Bacteriocyte-associated symbionts of insects. Bioscience 48, 295–304 (1998).Article 

    Google Scholar 
    13.Oliver, K. M. & Martinez, A. J. How resident microbes modulate ecologically-important traits of insects. Curr. Opin. Insect Sci. 4, 1–7 (2014).PubMed 
    Article 

    Google Scholar 
    14.Douglas, A. E. The microbial dimension in insect nutritional ecology. Funct. Ecol. 23, 38–47 (2009).Article 

    Google Scholar 
    15.Douglas, A. E. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr. Opin. Insect Sci. 23, 65–69 (2017).PubMed 
    Article 

    Google Scholar 
    16.Vigneron, A. et al. Insects recycle endosymbionts when the benefit is over. Curr. Biol. 24, 2267–2273 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Andersen, S. O. Cuticular sclerotization and tanning. In Insect Molecular Biology and Biochemistry (ed. Gilbert, L. I.) 167–192 (Elsevier, 2012).18.Anbutsu, H. & Fukatsu, T. Symbiosis for insect cuticle formation. In Cellular Dialogues in the Holobiont (eds. Bosch, T. C. G. & Hadfield, M. G.) 201–216 (CRC Press, 2020).19.Anbutsu, H. et al. Small genome symbiont underlies cuticle hardness in beetles. Proc. Natl. Acad. Sci. USA 114, E8382–E8391 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Li, A. P. & Long, T. J. An evaluation of the genotoxic potential of glyphosate. Fundam. Appl. Toxicol. 10, 537–546 (1988).CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Smith, E. A. & Oehme, F. W. The biological activity of glyphosate to plants and animals: a literature review. Vet. Hum. Toxicol. 34, 531–543 (1992).CAS 
    PubMed 

    Google Scholar 
    22.Smith, D. F. Q. et al. Glyphosate inhibits melanization and increases insect susceptibility to infection. bioRxiv (2020).23.Torretta, V., Katsoyiannis, I., Viotti, P. & Rada, E. Critical review of the effects of glyphosate exposure to the environment and humans through the food supply chain. Sustainability 10, 950 (2018).Article 
    CAS 

    Google Scholar 
    24.Snyder, A. K. & Rio, R. V. M. “Wigglesworthia morsitans” folate (Vitamin B 9) biosynthesis contributes to tsetse host fitness. Appl. Environ. Microbiol. 81, 5375–5386 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Motta, E. V. S., Raymann, K. & Moran, N. A. Glyphosate perturbs the gut microbiota of honey bees. Proc. Natl. Acad. Sci. USA 115, 10305–10310 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    26.Motta, E. V. S. et al. Oral or topical exposure to glyphosate in herbicide formulation impacts the gut microbiota and survival rates of honey bees. Appl. Environ. Microbiol. 86, e01150–20 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Klein, A. et al. A novel intracellular mutualistic bacterium in the invasive ant Cardiocondyla obscurior. ISME J 10, 376–388 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Wu, D. et al. Metabolic complementarity and genomics of the dual bacterial symbiosis of sharpshooters. PLoS Biol. 4, e188 (2006).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    29.Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. B Biol. Sci 364, 1711–1723 (2009).Article 

    Google Scholar 
    30.Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

    Google Scholar 
    31.Memmott, J. et al. Biodiversity loss and ecological network structure. In Ecological Networks: Linking Structure to Dynamics in Food Webs (eds Pascual, M. & Dunne, J. A.) 325–347 (Oxford University Press, 2005).32.Liao, C., Upadhyay, A., Liang, J., Han, Q. & Li, J. 3,4-Dihydroxyphenylacetaldehyde synthase and cuticle formation in insects. Dev. Comp. Immunol. 83, 44–50 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Muthukrishnan, S., Merzendorfer, H., Arakane, Y. & Kramer, K. J. Chitin metabolism in insects. In Insect Molecular Biology and Biochemistry (ed. Gilbert, L. I.) 193–235 (Elsevier, 2012).34.Wirtz, R. A. & Hopkins, T. L. Tyrosine and phenylalanine concentrations in haemolymph and tissues of the American cockroach, Periplaneta americana, during metamorphosis. J. Insect Physiol. 20, 1143–1154 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Gibbs, A. G. & Rajpurohit, S. Cuticular lipids and water balance. In Insect Hydrocarbons (eds Blomquist, G. J. & Bagneres, A. -G.) 100–120 (Cambridge University Press, 2010).36.Hackman, R. H. Chemistry of the insect cuticle. in The Physiology of Insecta (ed. Rodstein, M.) 215–270 (Academic Press, 1974).37.Mattson, W. J. Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

    Google Scholar 
    38.Kumar, V. et al. Amino acids distribution in economical important plants: a review. Biotechnol. Res. Innov 3, 197–207 (2019).Article 

    Google Scholar 
    39.Noh, M. Y., Muthukrishnan, S., Kramer, K. J. & Arakane, Y. Cuticle formation and pigmentation in beetles. Curr. Opin. Insect Sci. 17, 1–9 (2016).PubMed 
    Article 

    Google Scholar 
    40.Sterkel, M. et al. Tyrosine detoxification is an essential trait in the life history of blood-feeding arthropods. Curr. Biol. 26, 2188–2193 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Herrmann, K. M. & Weaver, L. M. The shikimate pathway. Annu. Rev. Plant Biol. 50, 473–503 (1999).CAS 
    Article 

    Google Scholar 
    42.Engl, T. et al. Ancient symbiosis confers desiccation resistance to stored grain pest beetles. Mol. Ecol. 27, 2095–2108 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Hirota, B. et al. A novel, extremely elongated, and endocellular bacterial symbiont supports cuticle formation of a grain pest beetle. MBio 8, 1–16 (2017).Article 

    Google Scholar 
    44.Boyer, S., Zhang, H. & Lempérière, G. A review of control methods and resistance mechanisms in stored-product insects. Bull. Entomol. Res. 102, 213 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    47.McCutcheon, J. P. & Moran, N. A. Extreme genome reduction in symbiotic bacteria. Nat. Rev. Microbiol. 10, 13–26 (2012).CAS 
    Article 

    Google Scholar 
    48.Van Leuven, J. T., Meister, R. C., Simon, C. & McCutcheon, J. P. Sympatric speciation in a bacterial endosymbiont results in two genomes with the functionality of one. Cell 158, 1270–1280 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    49.Campbell, M. A., Łukasik, P., Simon, C. & McCutcheon, J. P. Idiosyncratic genome degradation in a bacterial endosymbiont of periodical cicadas. Curr. Biol. 27, 3568–3575.e3 (2017).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    51.Chen, Y. C., Liu, T., Yu, C. H., Chiang, T. Y. & Hwang, C. C. Effects of GC bias in next-generation-sequencing data on de novo genome assembly. PLoS ONE 8, e62856 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Kozarewa, I. et al. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+ C)-biased genomes. Nat. Methods 6, 291–295 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Quail, M. A. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13, 1–13 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    54.Treangen, T. J. & Salzberg, S. L. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat. Rev. Genet. 13, 36–46 (2012).CAS 
    Article 

    Google Scholar 
    55.Sloan, D. B. et al. Parallel histories of horizontal gene transfer facilitated extreme reduction of endosymbiont genomes in sap-feeding insects. Mol. Biol. Evol. 31, 857–871 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Zucko, J. et al. Global genome analysis of the shikimic acid pathway reveals greater gene loss in host-associated than in free-living bacteria. BMC Genomics 11, 628 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Tokuda, G. et al. Maintenance of essential amino acid synthesis pathways in the Blattabacterium cuenoti symbiont of a wood-feeding cockroach. Biol. Lett. 9, 20121153 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Kinjo, Y. et al. Parallel and gradual genome erosion in the Blattabacterium endosymbionts of Mastotermes darwiniensis and Cryptocercus Wood Roaches. Genome Biol. Evol. 10, 1622–1630 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Menzel, R. & Roth, J. Purification of the putA gene product. A bifunctional membrane-bound protein from Salmonella typhimurium responsible for the two-step oxidation of proline to glutamate. J. Biol. Chem. 256, 9755–9761 (1981).CAS 
    PubMed 
    Article 

    Google Scholar 
    60.Zhou, Y., Zhu, W., Bellur, P. S., Rewinkel, D. & Becker, D. F. Direct linking of metabolism and gene expression in the proline utilization a protein from Escherichia coli. Amino Acids 35, 711–718 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Sabree, Z. L., Kambhampati, S. & Moran, N. A. Nitrogen recycling and nutritional provisioning by Blattabacterium, the cockroach endosymbiont. Proc. Natl. Acad. Sci. USA 106, 19521–19526 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.McCutcheon, J. P., McDonald, B. R. & Moran, N. A. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc. Natl. Acad. Sci. USA 106, 15394–15399 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    63.Sabree, Z. L., Huang, C. Y., Okusu, A., Moran, N. A. & Normark, B. B. The nutrient supplying capabilities of Uzinura, an endosymbiont of armoured scale insects. Environ. Microbiol. 15, 1988–1999 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Rosas-Pérez, T., Rosenblueth, M., Rincón-Rosales, R., Mora, J. & Martínez-Romero, E. Genome Sequence of “Candidatus Walczuchella monophlebidarum” the Flavobacterial endosymbiont of Llaveia axin axin (Hemiptera: Coccoidea: Monophlebidae). Genome Biol. Evol. 6, 714–726 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    65.Kuriwada, T. et al. Biological role of Nardonella endosymbiont in its weevil host. PLoS ONE 5, e13101 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    66.Okude, G. et al. Novel bacteriocyte-associated pleomorphic symbiont of the grain pest beetle Rhyzopertha dominica (Coleoptera: Bostrichidae). Zool. Lett. 3, 13 (2017).Article 

    Google Scholar 
    67.Hirota, B., Meng, X.-Y. & Fukatsu, T. Bacteriome-sssociated rndosymbiotic bacteria of Nosodendron tree sap beetles (Coleoptera: Nosodendridae). Front. Microbiol. 11, 2556 (2020).Article 

    Google Scholar 
    68.Hopkins, T. L. & Kramer, K. J. Insect cuticle sclerotization. Annu. Rev. Entomol. 37, 273–302 (1992).CAS 
    Article 

    Google Scholar 
    69.Andersen, S. O. Insect cuticular sclerotization: a review. Insect Biochem. Mol. Biol. 40, 166–178 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Cao, G. et al. A novel 5-enolpyruvylshikimate-3-phosphate synthase shows high glyphosate tolerance in Escherichia coli and tobacco plants. PLoS ONE 7, e38718 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Moran, N. A. & Bennett, G. M. The tiniest tiny genomes. Annu. Rev. Microbiol. 68, 195–215 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    72.McCutcheon, J. P., Boyd, B. M. & Dale, C. The life of an insect endosymbiont from the cradle to the grave. Curr. Biol. 29, R485–R495 (2019).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    74.Reis, F. et al. Bacterial symbionts support larval sap feeding and adult folivory in (semi-) aquatic reed beetles. Nat. Commun. 11, 1–15 (2020).
    Google Scholar 
    75.Salem, H., Florez, L., Gerardo, N. & Kaltenpoth, M. An out-of-body experience: the extracellular dimension for the transmission of mutualistic bacteria in insects. Proc. R. Soc. B Biol. Sci. 282, 20142957 (2015).Article 

    Google Scholar 
    76.Salem, H. et al. Symbiont digestive range reflects host plant breadth in herbivorous beetles. Curr. Biol. 30, 2875–2886 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Hansen, A. K., Pers, D. & Russell, J. A. Symbiotic solutions to nitrogen limitation and amino acid imbalance in insect diets. In Mechanisms Underlying Microbial Symbiosis, vol. 58 (ed. Kerry M. Oliver, J. A. R.) 161–205 (Academic Press, 2020).78.Tanner, J. J. Structural biology of proline catabolism. Amino Acids 35, 719–730 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Adams, E. & Frank, L. Metabolism of proline and the hydroxyprolines. Annu. Rev. Biochem. 49, 1005–61 (1980).CAS 
    PubMed 
    Article 

    Google Scholar 
    80.Bursell, E. The role of proline in energy metabolism.In Energy Metabolism in Insects (ed. Downer R.G.H.) 135–154 (Springer, Boston, 1981).81.Engl, T., Schmidt, T. H. P., Kanyile, S. N. & Klebsch, D. Metabolic cost of a nutritional symbiont manifests in delayed reproduction in a grain pest beetle. Insects 11, 717 (2020).PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    82.José de Souza, D., Devers, S. & Lenoir, A. Blochmannia endosymbionts and their host, the ant Camponotus fellah: cuticular hydrocarbons and melanization. C. R. Biol. 334, 737–741 (2011).PubMed 
    Article 
    CAS 

    Google Scholar 
    83.Zientz, E., Beyaert, I., Gross, R. & Feldhaar, H. Relevance of the endosymbiosis of Blochmannia floridanus and carpenter ants at different stages of the life cycle of the host. Appl. Environ. Microbiol. 72, 6027–6033 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    84.Oakeson, K. F. et al. Genome degeneration and adaptation in a nascent stage of symbiosis. Genome Biol. Evol. 6, 76–93 (2013).Article 

    Google Scholar 
    85.Chong, R. A. & Moran, N. A. Evolutionary loss and replacement of Buchnera, the obligate endosymbiont of aphids. ISME J 12, 898–908 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.McCutcheon, J. P. & Moran, N. A. Functional convergence in reduced genomes of bacterial symbionts spanning 200 My of evolution. Genome Biol. Evol. 2, 708–718 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Gerth, M., Gansauge, M. T., Weigert, A. & Bleidorn, C. Phylogenomic analyses uncover origin and spread of the Wolbachia pandemic. Nat. Commun. 5, 1–7 (2014).Article 
    CAS 

    Google Scholar 
    88.Santos-Garcia, D., Silva, F. J., Morin, S., Dettner, K. & Kuechler, S. M. The all-rounder Sodalis: a new bacteriome-associated endosymbiont of the Lygaeoid bug Henestaris halophilus (Heteroptera: Henestarinae) and a critical examination of its evolution. Genome Biol. Evol. 9, 2893–2910 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Motta, E. V. S. & Moran, N. A. Impact of glyphosate on the honey bee gut microbiota: effects of intensity, duration, and timing of exposure. Msystems 5, e00268–20 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Helander, M., Pauna, A., Saikkonen, K. & Saloniemi, I. Glyphosate residues in soil affect crop plant germination and growth. Sci. Rep. 9, 19653 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Kiers, E. T., Rousseau, R. A., West, S. A. & Denlson, R. F. Host sanctions and the legume-rhizobium mutualism. Nature 425, 78–81 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    92.Whiteside, M. D., Digman, M. A., Gratton, E. & Treseder, K. K. Organic nitrogen uptake by arbuscular mycorrhizal fungi in a boreal forest. Soil Biol. Biochem. 55, 7–13 (2012).CAS 
    Article 

    Google Scholar 
    93.Faita, M. R., Cardozo, M. M., Amandio, D. T. T., Orth, A. I. & Nodari, R. O. Glyphosate-based herbicides and Nosema sp. microsporidia reduce honey bee (Apis mellifera L.) survivability under laboratory conditions. J. Apic. Res. 59, 1–11 (2020).Article 

    Google Scholar 
    94.Wilson, A. C. C. et al. Genomic insight into the amino acid relations of the pea aphid, Acyrthosiphon pisum, with its symbiotic bacterium Buchnera aphidicola. Insect Mol. Biol. 19, 249–258 (2010).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    96.Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
    Article 

    Google Scholar 
    97.Desneux, N., Decourtye, A. & Delpuech, J.-M. The sublethal effects of pesticides on beneficial arthropods. Annu. Rev. Entomol. 52, 81–106 (2007).CAS 
    Article 

    Google Scholar 
    98.Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971 (2019).CAS 
    PubMed 
    Article 

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

    Google Scholar 
    100.Hayes, T. B. & Hansen, M. From silent spring to silent night: agrochemicals and the anthropocene. Elem. Sci. Anthropol. 5, (2017).101.Bowler, D. E., Heldbjerg, H., Fox, A. D., Jong, M. & Böhning‐Gaese, K. Long‐term declines of European insectivorous bird populations and potential causes. Conserv. Biol. 33, 1120–1130 (2019).PubMed 
    Article 

    Google Scholar 
    102.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    103.Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, 1–12 (2014).Article 

    Google Scholar 
    104.Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    105.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2015).Article 
    CAS 

    Google Scholar 
    106.Laczny, C. C. et al. BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation. Nucleic Acids Res. 45, W171–W179 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    107.Aziz, R. K. et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 1–15 (2008).Article 
    CAS 

    Google Scholar 
    108.Arkin, A. P. et al. KBase: The United States department of energy systems biology knowledgebase. Nat. Biotechnol. 36, 566 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    109.Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    110.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    111.Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    112.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    113.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    114.Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    115.Li, L., Stoeckert, C. J. & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–2189 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    117.Overbeek, R. et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 42, D206–14 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    118.Weiss, B. & Kaltenpoth, M. Bacteriome-localized intracellular symbionts in pollen-feeding beetles of the genus Dasytes (Coleoptera, Dasytidae). Front. Microbiol. 7, 1486 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    119.Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).Article 

    Google Scholar 
    120.Tanahashi, M. Natsumushi: Image measuring software for entomological studies. Entomol. Sci. 21, 347–360 (2018).Article 

    Google Scholar 
    121.Pérez-Palacios, T., Barroso, M. A., Ruiz, J. & Antequera, T. A rapid and accurate extraction procedure for analysing free amino acids in meat samples by GC–MS. Int. J. Anal. Chem. 2015, 209214 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    122.Miller, R. G. Simultaneous Statistical Inference (Springer, 1981).123.Engl, T., Kiefer, J.S.T. Data from: Inhibition of a nutritional endosymbiont by glyphosate abolishes mutualistic benefit on cuticle synthesis in Oryzaephilus surinamenis. Max Planck Soc. https://doi.org/10.17617/3.5l (2021). More

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    Viruses infecting a warm water picoeukaryote shed light on spatial co-occurrence dynamics of marine viruses and their hosts

    Isolation and characterisation of viruses infecting the picoeukaryote Bathycoccus Clade BIIBathycoccus BII isolates RCC716 and RCC715 used in our experiments were originally cultured from a nutrient-limited region in the Indian Ocean. Clade BII as a whole has been reported extensively in warm oligotrophic ocean gyres based on metagenome analyses [22,23,24]. Peak abundances occurr when well-developed deep chlorophyll maxima are present, or throughout the photic zone during mixing periods at Station ALOHA of the Hawaii Ocean Time-series [12]. We targeted BATS for viral isolation in springtime because Bathycoccus has been observed at relatively high abundance during this period using qPCR [74]. Here, three viruses were isolated against RCC716 [12] using seawater flown from BATS/Bermuda to the laboratory, obviating bringing this finicky strain into the field for use as a viral host. We then purified the viruses by serial dilutions and sequenced the partial PolB gene to determine whether they were evolutionarily different from other cultured viruses. BLASTn and preliminary phylogenetic analysis using GenBank nr sequences indicated they were distinct from described viruses with deposited sequences, with best BLASTn hits to Bathycoccus prasinos viruses (62–74% nucleotide identity). Transmission electron microscopy (TEM) revealed that all three viruses have similar morphology to other characterised prasinoviruses [75], with icosahedral capsids diameter ranging between 120 and 140 nm (Fig. 1A).Fig. 1: Morphology and evolutionary relationships of newly discovered Bathycoccus viruses.A Transmission electron micrographs of BII-V1, BII-V2 and BII-V3 (scale bar, 50 nm). The capsid diameters (n = 6 virions) measured 138 ± 2 nm (BII-V1), 150 ± 5 nm (BII-V2) and 152 ± 11 nm (BII-V3). B Maximum Likelihood (ML) phylogenetic reconstruction of green algal viruses inferred from a concatenated alignment of 22 core proteins shared among the viruses (7,001 positions) under the LG + G + F model. Node support was calculated from 1000 bootstrap (BS) replicates, with all branches acquired support values of 100% (white dots). Viruses infecting Chlorella were used as an outgroup and the branch connecting the prasinoviruses to the outgroup was truncated for display purpose. The new Bathycoccus viruses isolated against Bathycoccus Clade II (sensu [12]) isolate RCC716 (named as species Bathycoccus calidus herein, see below) are in bold. Colours reflect different host species within each genus. Letters alongside vertical lines (a and b) correspond to Bathycoccus viral clades. C Venn diagram of the shared and unique protein-encoding genes in the genome sequences of the new Bathycoccus viruses.Full size imageGenomic sequencing and multi-gene evolutionary analysesAssembly of DNA sequences from the viral isolates after deep sequencing by Illumina rendered one complete dsDNA genome sequence (BII-V3), and two others may still be partial (Table 1). The BII-V2 genome, which was in one contig, was similar in size (~208 kb) to that of BII-V3 (~212 kb). The BII-V1 genome assembly was ~174 kb and comprised of four linear dsDNA scaffolds. The viral concentrate was deeply sequenced ( >50x coverage) and minor fragmentation of the genome was partially related to repeats that were not resolved during assembly. The total number of putative open reading frames (ORFs) varied from 220 in BII-V1 to 235 in BII-V2 (Table 1). Gene synteny was globally well-conserved across the BII-Vs and the BpV1 and BpV2 viruses of B. prasinos (Fig. S1), with limited genomic rearrangements. Other genome characteristics such as the coding proportion (~90%) and G + C % (~36%) were similar to other described prasinoviruses infecting Mamiellophyceae [64, 75], for which the reported number of proteins range from 203 to 268 and G + C % from 37 to 45%. However, the full-length PolB gene from the genome assemblies differed for BII-V3 from the other two, in having a 329 amino acid intein (Supplementary information table S3). Likewise, inteins have been reported at the same PolB position in uncultivated prasinoviruses from the subtropical Pacific Ocean [76], where Bathycoccus BII is abundant [12].Table 1 Genomic characteristics of the three Bathycoccus viruses (BII-Vs) isolated against Clade BII isolate RCC716.Full size tableTo reconstruct a robust phylogeny for the new viruses, we employed 22 proteins previously identified as being shared across all available green algal virus genomes, including both prasinoviruses and chloroviruses [65]. We found all 22 in the predicted coding sequences of BII-V1; however, DNA helicase (SNF2) was not found in BII-V2 or -V3, FAD-dependent thymidylate synthase (thy1) and the topoisomerase IV were not found in BII-V2, nor was the prolyl 4-hydroxylase in the BII-V3 genome. Additional searches with tBLASTn did not recover these genes or fragments of them, suggesting they have been lost. Phylogenomic reconstruction grouped the three BII-Vs with the two BpVs [32], in a fully supported clade that branched adjacent to a large group of viruses that infect various species of Ostreococcus and Micromonas (Fig. 1B). The clade of Bathycoccus viruses was segregated in two subclades with BII-V2 and BII-V3 clustering together adjacent to BII-V1 and BpVs (Fig. 1B). While better resolution of the position of BII-V1 awaits greater taxonomic sampling, our results demonstrated that the three new viruses branch adjacent or basally to BpVs.Variation in prasinovirus gene content and functions encodedThe three Bathycoccus Clade BII viruses had 72–77% of their proteins held in common, and ~30 unique proteins as well as a few proteins shared by just two of the three viruses (Fig. 1C). The 170 shared proteins had higher amino acid identities between BII-V2 and BII-V3 (73% aa identity) than to BII-V1 (69% and 68%, respectively). Generally, only 19–21% of Bathycoccus viral genes could be assigned a functional category, based on EggNOG classification. Similar functional category distributions were observed across both prasinoviruses and chloroviruses, including lipid metabolism, RNA processing and modification, and nucleotide metabolism and transport (Fig. 2A). Other functional categories were more variable, such as cell wall/membrane/envelope biogenesis genes prevalent in chloroviruses (potentially related to their enveloped nature), as well as genes involved in modification of the capsid with compounds such as with chitin and hyaluronan [77, 78] that are absent from prasinoviruses sequenced to date (Fig. 2A). Within prasinoviruses, most of the unique proteins in the Bathycoccus viruses lack defined functional categories. Among those with functional assignments, all five Bathycoccus viruses encoded a P2X receptor in the intracellular trafficking and secretion category, and both BII-V2 and -V3 encode two proteins putatively involved in degrading the aromatic compound 4-hydroxy-2-oxopentanoate to acetyl-CoA (secondary metabolite category), that otherwise are only encoded by one other prasinovirus, MpV1 [32]. Similar to the phylogenetic relationships, the functional category distributions of BII-V1 were closer to those of BpVs than to BII-Vs. The primary difference was in carbohydrate metabolism, where BII-V2 and -V3 each encodes ribulose-phosphate 3-epimerase (involved in the pentose phosphate pathway and carbon fixation; not found in any other available virus genomes, but encoded by B. prasinos) and TDP-glucose 4,6-dehydratase (involved in biosynthesis of rhamnose and encoded by most other chloroviruses and prasinoviruses [79]). Notably, the putative high-affinity phosphate transporter (PHO4, also termed HAPT) was only present in BII-V1 and BpV1, as well as OtV2 (isolated against the Ostreococcus Clade OII ecotype), and most sequenced viruses of O. lucimarinus (Supplementary information table S3). This gene is hypothesised to enhance phosphate uptake during infection under phosphorus‐limited host growth [25], as observed for the PstS phosphate transport system expressed by cyanophages [80], mitigating limitation of this key component of viral genomes. However, most isolated prasinovirus genomes come from waters that are not considered phosphate-limited, hence presence of this gene may connect to poising the host for responding to sudden availability of other nutrients, such as nitrogen, which is often limiting in the ecosystems from which these viruses were isolated. Studies of virus-cell responses under various limiting nutrients are required to understand the retention of this host-derived HGT.Fig. 2: Distribution of functions and orthologous protein families across genome-sequenced prasinoviruses.A Functional category distributions across 21 genome-sequenced prasinoviruses and chloroviruses based on EggNOG categorisation. Viruses are clustered by similarity in their distribution of the functional categories on the y-axis and the frequency of each category across the viral genomes determines clustering along the x-axis ordering. Genes with homology to proteins in the EggNOG database but could not be assigned a function are in the “function unknown” category. B Orthogroups presence/absence patterns ordered along the x-axis by ranking according to the total number of genes in the orthogroup. For inclusion, the orthogroup was required to include protein sequences from at least two different viral genomes. Viruses are ordered along the vertical by their presence/absence pattern reconstructed by hierarchical clustering (topology on the left). Top histogram: frequency of each orthogroup in sequenced prasinoviruses. C Genes in each virus (number) not assigned to any orthogroup, with viruses in the same vertical order as B.Full size imageHierarchical clustering of orthologous proteins revealed patterns across prasinoviruses that generally corresponded with phylogenetic relationships. The BII- and Bp-viruses shared 130 orthologous proteins and hierarchical clustering (Fig. 2B) followed the clade structure of the phylogenomic reconstruction (Fig. 1B) with the exception of BII-V1 that grouped with BII-Vs, as well as OtV6, which grouped with Micromonas viruses. These orthologous proteins had on average 72% amino acid identity between BII-V2 and BII-V3, and 88% between the two B. prasinos viruses, but between 65 to 67% when comparing members of these two groups (Table 2). BII-V1 orthologs also had 67% and 66% amino acid identity to BII-V2 and BII-V3, respectively, while they had 83% and 80% identity to BpV1 and BpV2, respectively. Collectively, these results indicate that BII-V2 and -V3 diverged from BpVs prior to the divergence of BII-V1.Table 2 Average percent amino acid identity of the orthologous proteins between the five Bathycoccus viruses.Full size tableOf the 130 orthologous Bathycoccus virus proteins, 37% were assigned putative functions revealing core components of this viral group (Supplementary information table S3). These included genes involved in DNA replication and transcription, including PolB (type II), a DNA topoisomerase, a transcription factor S-II, mRNA capping enzymes, ribonucleases, a ribonucleotide reductase, and a dUTPase. Several others are necessary for viral particle synthesis, such as genes encoding structural elements for assembling the virion, including capsid proteins (5–6 copies per genome), as well as transcriptional regulators connected to the replication cycle. The BII viruses showed a number of differences among orthologous protein families. In addition to each having “unique” protein sets, there was a set of BII-V specific orthogroups, as well as some shared with BpVs, and/or other prasinoviruses (Fig. 1C and Supplementary information table S3). First, six predicted proteins showed orthologs across the three BII-Vs, but were not present in other prasinoviruses sequenced to date. Only one of these six was assigned putative function, belonging to the XRE family of transcriptional regulators. Additionally, all BII viruses harboured a tandem duplication of the FstH gene, while other sequenced prasinoviruses (including the two Clade BI viruses) have one copy (Supplementary information table S3). This ATP-dependent metalloprotease has been shown to be involved in photosystem II repair in cyanobacteria [81], and is present in genomes of photosynthetic eukaryotes, including all Mamiellophyceae [15, 16]. In Arabidopsis and Chlamydomonas it has been shown to be involved in protein quality control in the thylakoid membranes [82]. A gene of unknown function was also duplicated in the BII-virus genomes, that is a single copy in BpVs and absent from other sequenced prasinoviruses. Genes putatively encoding a glucose-1-phosphate adenylyltransferase, a glycosyltransferase and a thiamine pyrophosphate-requiring enzyme involved in amino acid biosynthesis were sporadically found in BII-viruses.Considering the two Bathycoccus virus subclades (Fig. 1B), there is one predicted protein of unknown function exclusive to BpV1, BpV2 and BII-V1 and six predicted proteins shared only by BII-V2 and BII-V3. Among the latter, one belonged to the Ribulose-5-Phosphate-3-Epimerase (RPE) family, which catalyses the interconversion of D-ribulose 5-phosphate (Ru5P) into d-xylulose 5-phosphate, as part of the Calvin cycle (although no transit peptide was detected using TargetP) and in the oxidative pentose phosphate pathway. The ortholog analyses further showed that among prasinoviruses, 9, 17 and 18 genes were unique to BII-V1, BII-V2 and BII-V3, respectively (Fig. 2B). Apart from one nucleotidyltransferase and one glycosyltransferase (group 1) in BII-V1, none of these unique genes had known functions.To study the evolutionary aspects of the shared prasinovirus proteins, we constructed and examined 130 phylogenies of orthogroups shared between Bathycoccus viruses. Nine showed a topology where all three BII-Vs grouped together with full support (100% bootstrap support), separate from the BpV orthologs, and in contrast to the multi-gene phylogeny where BII-V1 grouped with BpVs (Fig. 1B). The average amino acid similarities within each of these nine protein ortholog groups ranged from 85 to 88% between BII-Vs proteins, while they were 77 to 81% between BII-Vs and BpVs, different from overall amino acid similarity averages (Table 2). Interestingly, proteins from three of these nine ortholog groups, all lacking known functions, were adjacent to each other in the genome, or separated by only one gene. This synteny and co-location likely reflects the acquisition of these genes before co-infecting viruses diverged via viral recombination [83].Infection dynamics of Bathycoccus virusesGeneral host specificity of BII-viruses was assessed using two B. prasinos isolates (CCMP1898 and RCC4222, Clade BI), the two available Clade BII isolates (RCC715 and RCC716), four Ostreococcus species and one Micromonas species (Table 3). None were able to infect the B. prasinos, Ostreococcus or Micromonas isolates tested, suggesting BII-V specificity for Bathycoccus Clade BII. Similar host specificity has been observed in O. lucimarinus viruses, none of which infect O. tauri [64], and other viruses of eukaryotic and prokaryotic algae [84, 85]. Some other prasinoviruses appear to have broader host ranges [85,86,87], or their host species are less divergent than the two known Bathycoccus clades. For example, generalist viruses isolated against Micromonas commoda can infect M. bravo [85]. Further investigations are necessary to determine the extent to which the six shared proteins in BII-Vs (absent from BpVs), are responsible for the differences in host and virus specificity of interactions, versus variations in the shared Bathycoccus virus proteins (65–83% similarity). Importantly, host specificity tests for the new viruses described herein were limited by weak sampling of Bathycoccus diversity (in culture; all that we could acquire were tested).Table 3 Results of cross infectivity tests of BII-V1, BII-V2 and BII-V3 against isolates representing various picoprasinophyte species within the Class Mamiellophyceae.Full size tableAlthough specific for the BII clade, the three BII-Vs exhibited variations in infectivity of the two cultured BII strains available, despite their isolation from the same sample and having identical ITS1 and ITS2 sequences. BII-V1 lysed and cleared RCC715 and RCC716 cultures after four days (Table 3). The same was true for BII-V2 and BII-V3, when incubated with RCC716. Different from results for BII-V1, we found that while BII-V2 and -V3 initially lysed RCC715 cultures, resistant populations became evident at day 7 of infectivity tests, and measureable lysis of RCC715 could not be achieved thereafter. These results underscored the need to further examine host-virus interactions for the three new viruses.Infection dynamics over time course experiments further illuminated differences in BII-V impacts on hosts. In these experiments, growth rates of the uninfected (control) RCC715 and RCC716 cultures were 0.45 ± 0.04 day−1 and 0.49 ± 0.06 per day, respectively, similar to rates during the pre-experiment acclimation period (T-test, p  > 0.05). Host and virus dynamics were similar for RCC715 and RCC716 infected with BII-V1 (Fig. S2 and Fig. 3), with cell numbers starting to diverge from control abundances 10 h after inoculation (T-test, p  More

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    Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights

    1.Francis, R. C., Hare, S. R., Hollowed, A. B. & Wooster, W. S. Effects of interdecadal climate variability on the oceanic ecosystems of the NE Pacific. Fish. Oceanogr. 7, 1–21. https://doi.org/10.1046/j.1365-2419.1998.00052.x (1998).Article 

    Google Scholar 
    2.Hollowed, A. B. & Wooster, W. S. Variability of winter ocean conditions and strong year classes of Northeast Pacific groundfish. ICES Mar. Sci. Symp. 195, 433–444 (1992).
    Google Scholar 
    3.Mantua, N. J. & Hare, S. R. The Pacific decadal oscillation. J. Oceanogr. 58, 35–44. https://doi.org/10.1023/A:1015820616384 (2002).Article 

    Google Scholar 
    4.Di Lorenzo, E. et al. North Pacific gyre oscillation links ocean climate and ecosystem change. Geophys. Res. Lett.https://doi.org/10.1029/2007GL032838 (2008).Article 

    Google Scholar 
    5.Di Lorenzo, E. et al. Synthesis of pacific ocean climate and ecosystem dynamics. Oceanography 26, 68–81. https://doi.org/10.5670/oceanog.2013.76 (2013).Article 

    Google Scholar 
    6.Anderson, P. J. & Piatt, J. F. Community reorganization in the Gulf of Alaska following ocean climate regime shift. Mar. Ecol. Prog. Ser. 189, 117–123 (1999).ADS 
    Article 

    Google Scholar 
    7.Polovina, J. J., Mitchum, G. T. & Evans, G. T. Decadal and basin-scale variation in mixed layer depth and the impact on biological production in the Central and North Pacific, 1960–88. Deep Sea Res. Part I Oceanogr. Res. Pap. 42, 1701–1716 (1995).8.Litzow, M. A. & Mueter, F. J. Assessing the ecological importance of climate regime shifts: an approach from the North Pacific Ocean. Prog. Oceanogr. 120, 110–119. https://doi.org/10.1016/j.pocean.2013.08.003 (2014).ADS 
    Article 

    Google Scholar 
    9.Möllmann, C., Folke, C., Edwards, M. & Conversi, A. Marine regime shifts around the globe: theory, drivers and impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130260. https://doi.org/10.1098/rstb.2013.0260 (2015).10.Litzow, M. A., Mueter, F. J. & Hobday, A. J. Reassessing regime shifts in the North Pacific: incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability. Glob. Change Biol. 20, 38–50. https://doi.org/10.1111/gcb.12373 (2014).ADS 
    Article 

    Google Scholar 
    11.Goen, J., & Erikson, L. Fishery Statistics. Technical Report. IPHC-2018-AM094-05, International Pacific Halibut Commission (2017).12.Johnson, K. F. et al. Status of the U.S. Sablefish Resource in 2015. Technical Report. Pacific Fishery Management Council (2016).13.Pacific Fishery Management Council. Pacific Coast Groundfish Fishery Management Plan. Technical Report, NOAA (2016).14.NPFMC. Fishery Management Plan for Groundfish of the Gulf of Alaska. Technical Report, North Pacific Fishery Management Council (2017).15.Pennoyer, S. & Balsiger, J. Groundfish Total Allowable Catch Specifications and Prohibited Species Catch Limits Under the Authority of the Fishery Management Plans for the Groundfish Fishery of the Bering Sea and Aleutian Islands Area and Groundfish of the Gulf of Alaska: Final Supplemental Environmental Impact Statement. Technical Report, United States National Marine Fisheries Service Alaska Regional Office, Juneau, Alaska (1998).16.Rodgveller, C. J., Lunsford, C. R. & Fujioka, J. T. Evidence of hook competition in longline surveys. Fish. Bull. 106, 364–374 (2008).
    Google Scholar 
    17.Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885. https://doi.org/10.1038/35022565 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Moore, J. A. Deep-sea finfish fisheries: lessons from history. Fisheries 24, 16–21 (1999).Article 

    Google Scholar 
    19.Moore, J. & Mace, P. Challenges and prospects for deep-sea finfish fisheries. Fisheries 24, 22–23 (1999).Article 

    Google Scholar 
    20.Rijnsdorp, A. D., Peck, M. A., Engelhard, G. H., Möllmann, C. & Pinnegar, J. K. Resolving the effect of climate change on fish populations. ICES J. Mar. Sci. J. Conseil 66, 1570–1583. https://doi.org/10.1093/icesjms/fsp056 (2009).Article 

    Google Scholar 
    21.Xia, Y. & Li, W. K. On single-index coefficient regression models. J. Am. Stat. Assoc. 94, 1275–1285. https://doi.org/10.1080/01621459.1999.10473880 (1999).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    22.Kammann, E. E. & Wand, M. P. Geoadditive models. J. R. Stat. Soc. Ser. C (Appl. Stat.) 52, 1–18. https://doi.org/10.1111/1467-9876.00385 (2003).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    23.Lu, Z., Steinskog, D. J., Tjøstheim, D. & Yao, Q. Adaptively varying-coefficient spatiotemporal models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 71, 859–880. https://doi.org/10.1111/j.1467-9868.2009.00710.x (2009).24.Ruppert, D., Wand, M. P. & Carroll, R. J. Semiparametric Regression. Cambridge Series in Statistical and Probabilistic Mathematics (Cambridge University Press, 2003).25.Scheipl, F., Staicu, A.-M. & Greven, S. Functional additive mixed models. J. Comput. Graph. Stat. 24, 477–501. https://doi.org/10.1080/10618600.2014.901914 (2015).MathSciNet 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    26.Wood, S. N. Generalized Additive Models. Texts in Statistical Science Series (Chapman & Hall/CRC, 2006). An Introduction with (R).27.Wood, S. N., Scheipl, F. & Faraway, J. J. Straightforward intermediate rank tensor product smoothing in mixed models. Stat. Comput. 23, 341–360. https://doi.org/10.1007/s11222-012-9314-z (2013).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    28.Conn, P. B., Johnson, D. S. & Boveng, P. L. On extrapolating past the range of observed data when making statistical predictions in ecology. PLoS ONE 10, 1–16. https://doi.org/10.1371/journal.pone.0141416 (2015).CAS 
    Article 

    Google Scholar 
    29.Hodges, J. S. & Reich, B. J. Adding spatially-correlated errors can mess up the fixed effect you love. Am. Stat. 64, 325–334. https://doi.org/10.1198/tast.2010.10052 (2010).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    30.Kim, M. & Wang, L. Generalized spatially varying coefficient models. J. Comput. Graph. Stat.https://doi.org/10.1080/10618600.2020.1754225 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Mu, J., Wang, G. & Wang, L. Estimation and inference in spatially varying coefficient models. Environmetrics 29, e2485. https://doi.org/10.1002/env.2485 (2018).MathSciNet 
    Article 

    Google Scholar 
    32.Brumback, B. A. & Rice, J. A. Smoothing spline models for the analysis of nested and crossed samples of curves. J. Am. Stat. Assoc. 93, 961–976. https://doi.org/10.1080/01621459.1998.10473755 (1998).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    33.Augustin, N. H., Trenkel, V. M., Wood, S. N. & Lorance, P. Space-time modelling of blue ling for fisheries stock management. Environmetrics 24, 109–119. https://doi.org/10.1002/env.2196 (2013).MathSciNet 
    Article 

    Google Scholar 
    34.Finley, A. O. Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods Ecol. Evol. 2, 143–154. https://doi.org/10.1111/j.2041-210X.2010.00060.x (2011).Article 

    Google Scholar 
    35.Al-Sulami, D., Jiang, Z., Lu, Z. & Zhu, J. Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data. Econom. Stat. 2, 22–35. https://doi.org/10.1016/j.ecosta.2017.01.002 (2017).MathSciNet 
    Article 

    Google Scholar 
    36.Gelfand, A. E., Kim, H.-J., Sirmans, C. F. & Banerjee, S. Spatial modeling with spatially varying coefficient processes. J. Am. Stat. Assoc. 98, 387–396. https://doi.org/10.1198/016214503000170 (2003).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    37.Feng, S. & Xue, L. Model detection and estimation for single-index varying coefficient model. J. Multivariate Anal. 139, 227–244. https://doi.org/10.1016/j.jmva.2015.03.008 (2015).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    38.Zhao, P. & Xue, L. Variable selection for semiparametric varying coefficient partially linear models. Stat. Probab. Lett. 79, 2148–2157 (2009).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    39.Guisan, A. Jr. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model. 157, 89–100. https://doi.org/10.1016/S0304-3800(02)00204-1 (2002).Article 

    Google Scholar 
    40.Zhang, L. & Gove, J. H. Spatial assessment of model errors from four regression techniques. For. Sci. 51, 334–346. https://doi.org/10.1093/forestscience/51.4.334 (2005).Article 

    Google Scholar 
    41.Cai, A., Tsay, R. S. & Chen, R. Variable selection in linear regression with many predictors. J. Comput. Graph. Stat. 18, 573–591 (2009).MathSciNet 
    Article 

    Google Scholar 
    42.Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).43.Ledolter, J. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO), chap. 6, 71–82 (Wiley, 2013). https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118596289.ch6.44.Yuan, M. & Lin, Y. Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 68, 49–67. https://doi.org/10.1111/j.1467-9868.2005.00532.x (2006).45.Fan, J., Yao, Q. & Cai, Z. Adaptive varying-coefficient linear models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 65, 57–80 (2003).46.Matsui, H. & Misumi, T. Variable selection for varying-coefficient models with the sparse regularization. Comput. Stat. 30, 43–55 (2015).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    47.Wang, H. & Xia, Y. Shrinkage estimation of the varying coefficient model. J. Am. Stat. Assoc. 104, 747–757. https://doi.org/10.1198/jasa.2009.0138 (2009).MathSciNet 
    CAS 
    Article 
    MATH 

    Google Scholar 
    48.Xue, L. & Qu, A. Variable selection in high-dimensional varying-coefficient models with global optimality. J. Mach. Learn. Res. 13, 1973–1998 (2012).MathSciNet 
    MATH 

    Google Scholar 
    49.Feng, S. & Xue, L. Variable selection for single-index varying-coefficient model. Front. Math. China 8, 541–565. https://doi.org/10.1007/s11464-013-0284-z (2013).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    50.Song, Y., Jian, L. & Lin, L. Robust exponential squared loss-based variable selection for high-dimensional single-index varying-coefficient model. J. Comput. Appl. Math. 308, 330–345. https://doi.org/10.1016/j.cam.2016.05.030 (2016).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    51.Yang, J. & Yang, H. Robust modal estimation and variable selection for single-index varying-coefficient models. Commun. Stat. Simul. Comput 46, 2976–2997. https://doi.org/10.1080/03610918.2015.1069346 (2017).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    52.Wei, F., Huang, J. & Li, H. Variable selection and estimation in high-dimensional varying-coefficient models. Stat. Sin. 21, 1515–1540. https://doi.org/10.5705/ss.2009.316 (2011).MathSciNet 
    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 
    53.Sun, W., Bindele, H. F., Abebe, A. & Correia, H. E. Robust functional coefficient selection for the single-index varying coefficients regression model. J. Stat. Comput. Simul. 1, 17. https://doi.org/10.1080/00949655.2020.1867548 (2021).Article 

    Google Scholar 
    54.Alaska Fisheries Science Center. AFSC/ABL: Longline Sablefish Survey. https://noaa-fisheries-afsc.data.socrata.com/dataset/AFSC-ABL-Longline-Sablefish-Survey/itxd-qjvg/data (2019). Accessed 14 Apr 2014.55.Sigler, M. F. & Lunsford, C. R. Survey Protocol for the Alaska Sablefish Longline Survey. Technical Report, Alaska Fisheries Science Center (2009).56.National Data Buoy Center. Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys. https://accession.nodc.noaa.gov/NDBC-CMANWx (2018).57.Alaska Fisheries Science Center. AFSC/RACE/GAP: RACEBASE Database. Online: http://www.afsc.noaa.gov/RACE/groundfish/survey_data/default.htm (2019).58.O’Brien, T. D. COPEPOD: The Global Plankton Database. A Review of the 2007 Database Contents and New Quality Control Methodology. Technical Report. NOAA Tech. Memo. NMFS-F/ST-34, U.S. Dep. Commerce (2007).59.Boyer, T. P. et al. World Ocean Database 2013. Technical Report. National Oceanographic Data Center, Ocean Climate Laboratory, NOAA (2013). https://doi.org/10.7289/V5NZ85MT.60.Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. Applied Linear Statistical Models, vol. 4 (Irwin Chicago, 1996).61.O’Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).Article 

    Google Scholar 
    62.Li, L., Losser, T., Yorke, C. & Piltner, R. Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter pm2.5 in the contiguous U.S. using parallel programming and k–d tree. Int. J. Environ. Res. Public Health 11, 9101–9141. https://doi.org/10.3390/ijerph110909101 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Melo, C. & Melo, O. geosptdb: Spatio-Temporal Inverse Distance Weighting and Radial Basis Functions with Distance-Based Regression (2015). R package version 0.5-0.64.Whitney, F. A. Nutrient variability in the mixed layer of the subarctic Pacific Ocean, 1987–2010. J. Oceanogr. 67, 481–492. https://doi.org/10.1007/s10872-011-0051-2 (2011).CAS 
    Article 

    Google Scholar 
    65.Whitney, F. A., Bograd, S. J. & Ono, T. Nutrient enrichment of the subarctic Pacific Ocean pycnocline. Geophys. Res. Lett. 40, 2200–2205. https://doi.org/10.1002/grl.50439 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Brodeur, R. D. & Ware, D. M. Long-term variability in zooplankton biomass in the subarctic Pacific Ocean. Fish. Oceanogr. 1, 32–38. https://doi.org/10.1111/j.1365-2419.1992.tb00023.x (1992).Article 

    Google Scholar 
    67.Chiba, S., Tadokoro, K., Sugisaki, H. & Saino, T. Effects of decadal climate change on zooplankton over the last 50 years in the western subarctic North Pacific. Glob. Change Biol. 12, 907–920. https://doi.org/10.1111/j.1365-2486.2006.01136.x (2006).ADS 
    Article 

    Google Scholar 
    68.Childers, A. R., Whitledge, T. E. & Stockwell, D. A. Seasonal and interannual variability in the distribution of nutrients and chlorophyll a across the Gulf of Alaska shelf: 1998–2000. Deep Sea Res. Part II Top. Stud. Oceanogr. 52, 193–216. https://doi.org/10.1016/j.dsr2.2004.09.018 (2005). U.S. GLOBEC Biological and Physical Studies of Plankton, Fish and Higher Trophic Level Production, Distribution, and Variability in the Northeast Pacific.69.Sackmann, B., Mack, L., Logsdon, M. & Perry, M. J. Seasonal and inter-annual variability of SeaWiFS-derived chlorophyll a concentrations in waters off the Washington and Vancouver Island coasts, 1998–2002. Deep Sea Res. Part II Top. Stud. Oceanogr. 51, 945–965 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    70.Wong, C. et al. Seasonal cycles of nutrients and dissolved inorganic carbon at high and mid latitudes in the North Pacific Ocean during the Skaugran cruises: determination of new production and nutrient uptake ratios. Deep Sea Res. Part II Top. Stud. Oceanogr. 49, 5317–5338 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    71.Houde, E. D. chap. Recruitment variability. In Fish Reproductive Biology: Implications for Assessment and Management (Eds. Jakobsen, T., Fogarty, M. J., Megrey, B. A. & Moksness, E.) (Wiley, 2016).72.Mason, J. C., Beamish, R. J. & McFarlane, G. A. Sexual maturity, fecundity, spawning, and early life history of sablefish (Anoplopoma fimbria) off the Pacific Coast of Canada. Can. J. Fish. Aquat. Sci. 40, 2126–2134. https://doi.org/10.1139/f83-247 (1983).Article 

    Google Scholar 
    73.Stark, J. W. Geographic and seasonal variations in maturation and growth of female Pacific cod (Gadus macrocephalus) in the Gulf of Alaska and Bering Sea. Fish. Bull. 105, 396–407 (2007).
    Google Scholar 
    74.Clark, W. G., Hare, S. R., Parma, A. M., Sullivan, P. J. & Trumble, R. J. Decadal changes in growth and recruitment of Pacific halibut (Hippoglossus stenolepis). Can. J. Fish. Aquat. Sci. 56, 242–252. https://doi.org/10.1139/f98-163 (1999).Article 

    Google Scholar 
    75.Echeverria, T. W. Thirty-four species of California rockfishes: maturity and seasonality of reproduction. Fish. Bull. 85, 229–250 (1987).
    Google Scholar 
    76.Di Lorenzo, E. North Pacific Gyre Oscillation (2018). NPGO index.77.NOAA ESRL Physical Sciences Division. Multivariate ENSO Index Version 2 (MEI.v2) (2019). ENSO index.78.Mantua, N. J. & JISAU, University of Washington. The Pacific Decadal Oscillation. http://research.jisao.washington.edu/pdo/ (2016).79.Di Lorenzo, E. et al. Central Pacific El Niño and decadal climate change in the North Pacific Ocean. Nat. Geosc. 3, 762–765. https://doi.org/10.1038/ngeo984 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    80.Ladd, C. & Stabeno, P. J. Stratification on the Eastern Bering Sea shelf revisited. Deep Sea Res. Part II Top. Stud. Oceanogr. 65-70, 72–83. https://doi.org/10.1016/j.dsr2.2012.02.009 (2012). Understanding Ecosystem Processes in the Eastern Bering Sea.81.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).82.Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. 114, 12202–12207, https://doi.org/10.1073/pnas.1706080114 (2017).83.Hewitt, J. E., Ellis, J. I. & Thrush, S. F. Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems. Glob. Change Biol. 22, 2665–2675. https://doi.org/10.1111/gcb.13176 (2016).ADS 
    Article 

    Google Scholar 
    84.Liu, H. et al. Nonlinear dynamic features and co-predictability of the Georges Bank fish community. Mar. Ecol. Prog. Ser. 464, 195–207 (2012).ADS 
    Article 

    Google Scholar 
    85.Echave, K., Rodgveller, C. & Shotwell, S. K. Calculation of the Geographic Area Sizes Used to Create Population Indices for the Alaska Fisheries Science Center Longline Survey. Technical Report. NOAA Tech. Memo. NMFS-AFSC-253, U.S. Department of Commerce (2013).86.Webster, R. A., Soderlund, E., Dykstra, C. L. & Stewart, I. J. Monitoring change in a dynamic environment: spatio-temporal modelling of calibrated data from different types of fisheries surveys of Pacific halibut. Can. J. Fish. Aquat. Sci.https://doi.org/10.1139/cjfas-2019-0240 (2020).Article 

    Google Scholar 
    87.Spencer, P. D., Hollowed, A. B., Sigler, M. F., Hermann, A. J. & Nelson, M. W. Trait-based climate vulnerability assessments in data-rich systems: an application to eastern Bering Sea fish and invertebrate stocks. Glob. Change Biol. 25, 3954–3971. https://doi.org/10.1111/gcb.14763 (2019).ADS 
    Article 

    Google Scholar 
    88.Sogard, S. M. & Olla, B. L. Growth and behavioral responses to elevated temperatures by juvenile sablefish Anoplopoma fimbria and the interactive role of food availability. Mar. Ecol. Prog. Ser. 217, 121–134 (2001).ADS 
    Article 

    Google Scholar 
    89.Stoner, A. W. & Sturm, E. A. Temperature and hunger mediate sablefish (Anoplopoma fimbria) feeding motivation: implications for stock assessment. Can. J. Fish. Aquat. Sci. 61, 238–246. https://doi.org/10.1139/f03-170 (2004).Article 

    Google Scholar 
    90.Sogard, S. Interannual variability in growth rates of early juvenile sablefish and the role of environmental factors. Bull. Mar. Sci.https://doi.org/10.5343/bms.2010.1045 (2011).Article 

    Google Scholar 
    91.Shotwell, S. K., Hanselman, D. H. & Belkin, I. M. Toward biophysical synergy: investigating advection along the Polar Front to identify factors influencing Alaska sablefish recruitment. Deep Sea Res. Part II Top. Stud. Oceanogr. 107, 40–53. https://doi.org/10.1016/j.dsr2.2012.08.024 (2014). Fronts, Fish and Top Predators.92.Tolimieri, N., Haltuch, M. A., Lee, Q., Jacox, M. G. & Bograd, S. J. Oceanographic drivers of sablefish recruitment in the California current. Fish. Oceanogr. 27, 458–474. https://doi.org/10.1111/fog.12266 (2018).Article 

    Google Scholar 
    93.Harrison, P. J., Whitney, F. A., Tsuda, A., Saito, H. & Tadokoro, K. Nutrient and plankton dynamics in the NE and NW gyres of the subarctic Pacific Ocean. J. Oceanogr. 60, 93–117 (2004).CAS 
    Article 

    Google Scholar 
    94.Coffin, B. & Mueter, F. Environmental covariates of sablefish (Anoplopoma fimbria) and Pacific ocean perch (Sebastes alutus) recruitment in the Gulf of Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr. 132, 194–209. https://doi.org/10.1016/j.dsr2.2015.02.016 (2016). Understanding Ecosystem Processes in the Gulf of Alaska: Volume 1.95.Hagen, P. T. & Quinn, T. J. Long-term growth dynamics of young Pacific halibut: evidence of temperature-induced variation. Fish. Res. 11, 283–306. https://doi.org/10.1016/0165-7836(91)90006-2 (1991). Fish Population Dynamics: Solving Fishery Management Problems.96.Hurst, T. P., Spencer, M. L., Sogard, S. M. & Stoner, A. W. Compensatory growth, energy storage and behavior of juvenile Pacific halibut Hippoglossus stenolepis following thermally induced growth reduction. Mar. Ecol. Prog. Ser. 293, 233–240 (2005).ADS 
    Article 

    Google Scholar 
    97.Holsman, K. K., Aydin, K., Sullivan, J., Hurst, T. & Kruse, G. H. Climate effects and bottom-up controls on growth and size-at-age of Pacific halibut (Hippoglossus stenolepis) in Alaska (USA). Fish. Oceanogr. 28, 345–358. https://doi.org/10.1111/fog.12416 (2019).Article 

    Google Scholar 
    98.Lynam, C. P. et al. Interaction between top-down and bottom-up control in marine food webs. Proc. Natl. Acad. Sci. 114, 1952–1957. https://doi.org/10.1073/pnas.1621037114 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    99.Desmit, X., Ruddick, K. & Lacroix, G. Salinity predicts the distribution of chlorophyll a spring peak in the southern North Sea continental waters. J. Sea Res. 103, 59–74. https://doi.org/10.1016/j.seares.2015.02.007 (2015).ADS 
    Article 

    Google Scholar 
    100.Benson, A. J. & Trites, A. W. Ecological effects of regime shifts in the Bering Sea and eastern North Pacific Ocean. Fish Fish. 3, 95–113. https://doi.org/10.1046/j.1467-2979.2002.00078.x (2002).Article 

    Google Scholar 
    101.Feng, J. et al. Contrasting correlation patterns between environmental factors and chlorophyll levels in the global ocean. Glob. Biogeochem. Cycles 29, 2095–2107. https://doi.org/10.1002/2015GB005216 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    102.Kahru, M. et al. Global correlations between winds and ocean chlorophyll. J. Geophys. Res. Oceanshttps://doi.org/10.1029/2010JC006500 (2010).Article 

    Google Scholar 
    103.Sadorus, L. L., Mantua, N. J., Essington, T., Hickey, B. & Hare, S. Distribution patterns of Pacific halibut (Hippoglossus stenolepis) in relation to environmental variables along the continental shelf waters of the US West Coast and southern British Columbia. Fish. Oceanogr. 23, 225–241. https://doi.org/10.1111/fog.12057 (2014).Article 

    Google Scholar 
    104.Barbeaux, S. et al. Gulf of Alaska Stock Assessments. Technical Report, North Pacific Fishery Management Council, Anchorage, AK (2018).105.Barbeaux, S. J. & Hollowed, A. B. Ontogeny matters: climate variability and effects on fish distribution in the eastern Bering Sea. Fish. Oceanogr. 27, 1–15. https://doi.org/10.1111/fog.12229 (2018).Article 

    Google Scholar 
    106.Yang, Q. et al. How “The Blob” affected groundfish distributions in the Gulf of Alaska. Fish. Oceanogr. 28, 434–453. https://doi.org/10.1111/fog.12422 (2019).107.Hagens, M. & Middelburg, J. J. Attributing seasonal pH variability in surface ocean waters to governing factors. Geophys. Res. Lett. 43, 12528–12537. https://doi.org/10.1002/2016GL071719 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    108.Fry, C. H., Tyrrell, T., Hain, M. P., Bates, N. R. & Achterberg, E. P. Analysis of global surface ocean alkalinity to determine controlling processes. Mar. Chem. 174, 46–57 (2015).CAS 
    Article 

    Google Scholar 
    109.Bromhead, D. et al. The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna (Thunnus albacares). Deep Sea Res. Part II Top. Stud. Oceanogr. 113, 268–279, https://doi.org/10.1016/j.dsr2.2014.03.019 (2015). Impacts of climate on marine top predators.110.Doney, S. C. et al. Impact of anthropogenic atmospheric nitrogen and sulfur deposition on ocean acidification and the inorganic carbon system. Proc. Natl. Acad. Sci. 104, 14580–14585. https://doi.org/10.1073/pnas.0702218104 (2007).111.Napp, J. M. & Hunt, G. L. Anomalous conditions in the south-eastern Bering Sea 1997: linkages among climate, weather, ocean, and biology. Fish. Oceanogr. 10, 61–68. https://doi.org/10.1046/j.1365-2419.2001.00155.x (2001).Article 

    Google Scholar 
    112.Noakes, D. J. & Beamish, R. J. Synchrony of marine fish catches and climate and ocean regime shifts in the North Pacific Ocean. Mar. Coast. Fish. 1, 155–168. https://doi.org/10.1577/C08-001.1 (2009).Article 

    Google Scholar  More

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    Quantitative modeling of radioactive cesium concentrations in large omnivorous mammals after the Fukushima nuclear power plant accident

    Data setsRadioactivity measurement data for several species of wild game mammals and birds in Fukushima Prefecture from May 2011 to March 2018 were released to the public by the Fukushima Prefecture Government (https://emdb.jaea.go.jp/emdb/en/portals/1040501000/). We extracted the data for wild boar (Sus scrofa), 1404 samples, and Asian black bear (Ursus thibetanus), 422 samples. The resulting boar and bear data sets contained total radioactive cesium activity (134Cs + 137Cs isotopes) values (in Bq/kg) from animals captured at different times and locations within Fukushima Prefecture. The data were imported for analysis into R 4.0.3 software21.We ln-transformed the cesium activity values to bring their distribution closer to normal, creating the variable LnCsTot. To facilitate regression analyses (described below), we removed instances of missing data and cesium levels below detection: 20 samples (1.4%) for boar and 15 samples (3.3%) for bears. The time when each sample was taken (labeled “Day of collection” in the Fukushima Prefecture Government data set) was converted to years since the Fukushima accident (since March 11, 2011), assuming that 1 year = 365.25 days. This time of sample collection in years was called variable T.Since for each sample some time passed between sample collection and radioactivity measurement (labeled “Result found Date”, called Tr in our notation), we needed to correct the reported LnCsTot values for physical decay over this time, which was different for different samples. The procedure used to perform this correction is described in Supplementary methods. The data with corrected total cesium values (LnCsc) are provided in Supplementary data (Supplementary_Dataset_File_Full).Mathematical modelTo describe the data on ln-transformed total radioactive cesium levels (LnScc) in each species as function of time after the accident (T), we developed the following simple mathematical model (Eqs. 1A, 1B):$${LnCs}_{c}=X+Q-mu times {T}^{nu }+Atimes mathrm{sin}left[2times pi times left(T+Pright)right], $$
    (1A)
    $$X=mathrm{ln}left[mathrm{exp}left(LnCs{134}_{t{0}_{r}}right)times {2}^{-frac{T}{{Th}_{Cs134}}}+mathrm{exp}left(LnCs{137}_{t{0}_{r}}right)times {2}^{-frac{T}{{Th}_{Cs137}}}right]$$
    (1B)
    Here the term X represents the estimated average radioactive cesium level in the studied area, based on the intercepts (LnCs134t0r for 134Cs and LnCs137t0r for 137Cs, respectively) from robust regression discussed in Supplementary methods, and taking into account only physical decay for each isotope (with half-lives of ThCs134 for 134Cs and ThCs134 for 137Cs, respectively). The terms Q, µ, ν, A and P represent adjustable model parameters. Parameter Q represents the fitted relationship between radioactive cesium levels in the animal (Bq/kg), relative to the external environment (Bq/m2). Parameter µ represents the net rate of radioactive cesium reduction in animal tissues over time due to all processes except physical decay (e.g. decrease in bioavailability due to migration of cesium into deeper soil layers, human-mediated cleanup efforts, etc.). Parameter ν is a potential power dependence for these processes. By default, ν was set to ν = 1, but exploratory calculations using ν = 2 or treating ν as a freely adjustable parameter (≥ 0.1) were performed as well. Parameters A and P in the sine function represent a sinusoidal approximation for seasonal changes in radioactive cesium levels in animal tissues (e.g. due to seasonal variations in diet and life style), where A is the amplitude of the oscillations, P is the phase shift, and the period is set to 1 year. For simplicity, these parameters were assumed to be the same for both studied cesium isotopes. The descriptions of each parameter are also presented in Table 1.Table 1 The meanings of all parameters used in our mathematical model (Eq. 1A, 1B) for radioactive cesium levels in wild boar (Sus scrofa) and Asian black bear (Ursus thibetanus).Full size tableModel fitting approachesInitially, we used nonlinear ordinary least squares (OLS) regression (nls R function) to fit the model (Eq. 1A, 1B) to the data. To find the global optimum fit, we repeated the fitting procedure 2000 times with slightly different random initial parameter values and recorded the solution with the smallest root mean squared error (RMSE). Diagnostics on this regression included checking of convergence criteria and analyses of residuals (by scatter plot and histogram, regressing residuals as function of T, visualizing the QQ plot, autocorrelation and partial autocorrelation functions with 95% confidence intervals, performing the Shapiro–Wilk normality test, and calculating skewness and kurtosis). For boar data, diagnostics revealed problems with convergence (both X-convergence and relative convergence) and non-normality of residuals: e.g. Shapiro–Wilk p-value = 1.476 × 10–7, skewness = − 0.37, kurtosis = 3.50. For black bear data similar problems occurred with convergence, but residuals were closer to the normal distribution (perhaps due to smaller sample size): e.g. Shapiro–Wilk p-value = 0.0526, skewness = − 0.058, kurtosis = 2.45.Due to these issues, we used robust nonlinear regression (nlrob R package) to reduce the effects of “outlier” data points. To find the global optimum, we repeated the fitting procedure 2000 times with slightly different random initial parameter values and selected the solution with the smallest absolute value of median residuals. The best-fit parameters for OLS and robust regressions were somewhat different for both boar and bear data. For boar data, the minimum robustness weight was 0.339 and the median was 0.762, and the corresponding values for black bear data were 0.557 and 0.821, respectively.For each species, we compared the performances of model variants with different assumptions about parameter ν: (1) The default case with ν = 1, which represents an exponential rate of radioactive cesium decrease due to processes other than physical decay. (2) The case with ν = 2, which represents quadratic decay. (3) The case with ν being freely adjustable (≥ 0.1). The comparisons were based on Akaike information criterion (AIC)22,23. The purpose of these calculations was to better assess the shape of the time course for non-physical factors involved in radioactive cesium level decline in animal tissues over time after the accident.In addition to analyzing the full data set for each species, we also performed separate analyses on subsets of data from specific locations: from those districts of Fukushima Prefecture where the mean radioactive cesium levels in animal samples were the highest, and where a sufficiently large number of samples was present. For wild boar, the two selected districts for this subset analysis were Soso and Kenpoku (819 samples), and for black bear they were Kenpoku and Kenchu (163 samples).To further assess the sensitivity of model results to geographical and temporal factors, we also constructed a separate subset of data for each species. This subset excluded data from the Aizu and Minamiaizu districts, which are separated by mountains from the Fukushima Daiichi Nuclear Power Plant, and excluded data collected ≤ 6 months after the accident. These restrictions were intended to determine model performance on data collected in a more geographically contiguous area after the initial abrupt changes in contamination levels were completed and the system entered the phase of more stable kinetics. The purpose of all these analyses was to assess whether the time course of radioactive cesium levels in the bodies of each species differed between locations with high contamination vs. those with lower contamination, and as function of time after the accident.We were interested in quantifying not only the center of the distribution of radioactive cesium values in each species over time, but also in assessing the lower and upper tails of this distribution. For this purpose, we fitted the model (Eq. 1A, 1B) for each species using quantile regression (nlrq function in quantreg R package) for the median (50th percentile), and also for the 25th and 75th percentiles. Initial parameter estimates for the quantile regressions were taken from best-fit parameters from robust regression described above. The 25th and 75th percentiles were selected instead of more extreme values (e.g. 5th and 95th) because the latter resulted in poor quality fits due to limited amounts of data at the fringes of the distribution.To assess the variability of model parameters by location in more detail, we used mixed effects modeling (nlme R package) on the data from each species. Since original OLS fits suggested substantial deviations of residuals from the normality assumption, we performed mixed effects modeling on data with some outlier data points removed. The OutlierDetection package in R removed 43 boar samples and 5 bear samples. These outliers are listed in the Supplementary_outlier_data_points file. The remaining samples were used for mixed effects model fitting, but model performance metrics like coefficient of determination (R2) and RMSE were assessed on the full data set (with outliers included) for each species.Since the Fligner-Killeen test of homogeneity of variances by district generated low p-values for both species (4.6 × 10–14 for boar and 0.018 for black bear), we allowed modelled variances to differ by district (using the weights option in nlme). We investigated several random effects structures for some or all model parameters, with randomness by district only, or by district and municipality within district. Model diagnostics were the same as for fixed effects OLS modeling described above, and also included boxplots of model residuals by district. The mixed effects model variants with different random effects structures were compared using the anova function in R, and also by assessing convergence criteria, normality of residuals, skewness, and kurtosis. Consequently, preferred mixed effects model variants were selected for the full data as well as for the subset of two districts with high radioactive cesium levels, separately for each species.Model extrapolation from training to testing dataTo investigate how the robust and quantile regression fits of our model could extrapolate beyond the time range that was used for model fitting, we split the data for each species into “training” (early times) and “testing” (later times) parts. The split was done based on time since the accident (T variable), so that approximately ½ of the samples were assigned to the training and testing sets, respectively. For wild boar data, the training set included times between 0.20 and 3.45 years after the accident, and the testing set included times between 3.45 and 7.03 years. For black bear data, the training set included times between 0.42 and 3.46 years after the accident, and the testing set included times between 3.46 and 6.87 years.We also evaluated an alternative approach to splitting the data, where the split was done randomly instead of by time. In other words, any data point regardless of time had an equal probability of being assigned to either the training or the testing data set. Both the training and testing data subsets generated by this random split included the complete time range. This approach was implemented in context of the sensitivity analysis described above.For each species, robust and quantile regressions were fitted to training data, and their predictions were calculated for testing data. For robust regression, RMSE was calculated on testing data for two scenarios: (1) for the model fitted to training data only, and (2) for the model fitted over the entire data range (training + testing). These RMSE values for conditions 1 and 2 were compared to assess the quality of model extrapolation. Extrapolation performance for robust and quantile regressions was also assessed visually by plotting the model predictions and data.Application of the model to wild boar data from the Chernobyl accident areaTo compare the results of our analysis of wild boar contamination with radioactive cesium in the area affected by the Fukushima accident with data from another location, we also analyzed wild boar data from the Chernobyl accident area. These data were published by Gulakov14 and contain summaries of 137Cs contamination levels in the muscles of 188 boar collected between 1991 and 2008 (i.e. from 5 to 22 years after the 1986 accident). Sampling was carried out in three zones with different land contamination levels with 137Cs. This data set provides important information on radioactive cesium contamination in wild boar in the Chernobyl area. Unfortunately, 137Cs measurements in each sampled boar were not provided by Gulakov14, and only summary statistics are available for each zone and year after the accident (Tables 1–3 in reference14): number of animals, mean, minimum and maximum 137Cs levels.We could not apply the full model (Eq. 1A, 1B) to these summary data which lacked seasonality information and 134Cs data. However, we were able to perform a weighted linear regression to quantify the ecological half-life of 137Cs in Chernobyl boar and the relationship between 137Cs levels in the animals (Bq/kg), relative to the external environment (Bq/m2). The data used for this analysis, derived from Gulakov14, are provided in Supplementary data (Supplementary_Dataset_File_Full). They contain the following variables. Zone = location of sample collection (Alienation, Permanent control or Periodic control). Time = time in years after the Chernobyl accident. LnMeanCs = ln-transformed mean 137Cs level in boar muscle (Bq/kg). LnMeanCs_c = LnMeanCs − X, where X is ln-transformed 137Cs land contamination (Bq/m2) in the given zone, corrected for physical decay of 137Cs. Weight = weighting of each data point used for regression. Weight = number of animals/(ln[maximum 137Cs level] − ln[minimum 137Cs level])2. These approximately inverse-variance weights were normalized by the overall mean across all data points, so that the mean weight across all data points was set to 1.These data were analyzed by weighted linear regression in R, where LnMeanCs_c was allowed to depend on Time and Zone variables. Model variants containing all possible combinations and pairwise interactions between these predictor variables were fitted and their performances were compared using the Akaike information criterion with correction for small sample size (AICc). These calculations were performed using the glmulti R package. Multimodel inference (MMI) was performed on this collection of fitted model variants. It resulted in the calculation of model-averaged parameter estimates, 95% CIs and importance scores, corrected for model selection uncertainty. Of main interest here were the intercept parameter, which is analogous to parameter Q in the full model (Eq. 1A, 1B), and the Time parameter, which is analogous to parameter µ in the full model. The ecological half-life for 137Cs was calculated based on the Time parameter. More