Philippa Kaur

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

Photosynthetic pigmentsBased on the analysis of variance, data of Photosynthetic pigments as presented in Table 1 indicate that photosynthetic pigments as chlorophyll a (Chl. a) had non-significant for three Canola genotypes AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), but chlorophyll b (Chl. b) and chlorophyll a/b ratio (Chl. a/b) had significant difference for three genotypes. Chl. a, Chl. b and Chl. a/b were positively responded to different N application i.e. without nitrogen fertilization (control F0), 95 kg N ha−1 (F1), 120 kg N ha−1 (F2) and 142 kg N ha−1 (F3) (without yeast); and integrated between nitrogen fertilization and yeast extract (YE) treatments as follows: 95 kg N ha−1 + YE (F4), 120 kg N ha−1 + YE (F5) and 142 kg N ha−1 (F6) (with yeast), data indicated that F5 and F6 gave the highest values of Chl. a and Chl. a/b ratio and lowest values of Chl. b Table 1. Interaction data showed that three Canola genotypes that were fertilized with N without yeast or with yeast had a slight difference with statistically significant in chl. a. The highest values of Chl. an obtained by G2 under F5 treatment followed by G1 under F6 treatments. In respect to Chl. a/b ratio, statistical analysis showed that Interaction between Canola genotypes treated with N applications without or with yeast had a significant difference whereas the highest values were recorded when Canola genotypes G3 and G2 fertilized with F6 and F5 with slight differences. While the interaction was significant between N treatments and Canola genotypes for Chl. b. and Canola genotype (G1) gave the highest value when treated with F1. Generally, F6 and F5 improve the contents of chl. a and chl. a/b ratio for three Canola genotypes Table 1. Chl. contents were increased in plants grown under middle and high N conditions as compared with plants grown under low N conditions, which significantly affected photochemical processes20. N is a fundamental element for leaf plants, insufficient N supply lead to decreased photosynthetic rate in plants21, this occurs to many factors such as a decrease in pigment degradation22, reduction in stomatal conductance23 and a decline in the light and dark reaction of photosynthesis. Canola is a nitrophilous plant, wherein a high concentration of NO3 in the culture media results in higher Chl. contents in the plant leave compared with controls20. The Chl. a/b ratio can be a valuable indicator of N element within a leaf because this ratio must be positively related to the ratio of PSII cores to light-harvesting chlorophyll-protein complex (LHCII)24. LHCII contains the majority of Chl. b, consequently it has a lower Chl a/b ratio than other Chl. binding proteins associated with PSII25. Thus, Chl. a/b ratios should increase with decreasing N availability, especially under high light conditions26, the Chl. a/b ratio and the ratio of PSII to Chl. are independent of N availability for spinach27, and lower Chl. a/b ratios were noticed when plants were subjected to low N28, while Kitajima and Hogan29 revealed that the Chl. a/b ratio increased when Chl. content decreased in response to N restriction in photosynthetic cotyledons in leaves of seedlings of four tropical woody species in the Bignoniaceae, and Bungard et al.30 demonstrated that there is a tiny response in Chl. a/b ratios to light or N. The yeast includes bio-regulators i.e. plant growth regulators and endogenous plant hormones, which enhance photosynthesis, also it produces 5-Aminolevulinic acid which is vital to tetrapyrrole biosynthesis and biochemical processes in plants, including heme and Chl. biosynthesis25.Table 1 Photosynthetic pigments for the three Canola genotypes under different N applications without and with yeast extract.Full size tableYield and its attributesComparing of mean data through the Duncan Multiple Range Test in the probability level of 5%, data showed significant differences among the Canola genotypes for the highest plant (cm), branches number/plant, and pods number/plant. On contrary, there wasn’t a significant difference for seed number/pods, seed yield (t ha−1), biological yield (t ha−1), and harvest index, wherein G2 gave the highest value for the highest plant (cm). In the same trend, G2 gave the highest values of branches No./plant and pods No./plant followed by G3 for the previous two treats Table 2. All examined N without or with yeast caused a significant difference in yield and its attributes, wherein F6 positively affected on abovementioned traits and gave the highest values on the highest plant (cm), branches No./plant, pods No./plant, seed No./pods, seed yield (t ha−1), and harvest index. While the highest values of biological yield (t ha−1) were obtained with F3, F6, and F5, respectively Table 2.Table 2 Growth, yield and its attributes for the three Canola genotypes under different N applications without and with yeast extract.Full size tableThe interaction between the Canola genotype and different N rates without or with yeast extract as shown in Table 2, demonstrated a significant difference. Data showed that the highest values of plant height and pods No./plant were recorded by G2 under F6 and the highest values of branches No./plant, seed No./pods, and seed yield (t ha−1) got by G3 and G2 under F6. There was a slight difference with statistically significant biological yield (t ha−1) and highest values established by G1 under F3 and F6; and G2 and G3 under F3, F5, and F6 respectively; and the highest values of harvest index recorded by G1, G2 and G3. under F6. Generally, data proved that 142 kg N/ h−1 + YE (F6) was enhanced the yield and its components of three Canola genotypes i.e. AD201 (G1), Topaz (G2), and SemuDNK 234/84 (G3). Many researchers reported that there are significant differences among Canola varieties and growth and yield traits are significantly increased by increasing N rates11. Increasing N fertilizer rates significantly increased most of the yield and its components31, N enhances metabolites synthesized by the plant which leads to more transformation of photosynthesis to reproductive parts, and induces different physiological mechanisms to access the nutrient32. Yeast extract as bio-fertilizer had a significant and positive effect on plant height and yield traits of Canola. The role of bread yeast in increasing the growth and yield traits; may be due to the content of yeast to many important nutrients elements i.e. N, Mg, Ca, Zn, Cu, and Fe, and the production of some growth regulators such as Auxin and Gibberellin and cytokinin which is necessary for plant biological processers especially photosynthesis and cell division and elongation33. Also, Yeast extract had stimulatory effects on cell division and enlargement, protein and nucleic acid synthesis, and chlorophyll formation34, in addition to its content of cryoprotective agent, i.e. sugars, protein, amino acids, and also several vitamins35. Consequently, it improves growth, flowering, and fruit set and formation and increases yield34.Correlation of Canola seed yield and chlorophyll a/b ratioPartial correlation coefficients of Canola seed yield and Chl. a/b ratio is given in Fig. 1. This result showed that seed yield was positively correlated with Chl. a/b ratio when the amount of N applied without or with yeast extract is increased. Chl. a/b ratio can be an important indicator of N within a leaf, this ratio must be positively related to photosynthesis and biological processers which reflect on seed yield.Figure 1Correlation of Canola seed yield (t/h) and chlorophyll a/b ratio as affected by different nitrogen rates without and with yeast extract.Full size imageCorrelation of Canola seed yield and its attributesCorrelations of seed yield and yield components of Canola are a function of the plant height, number of branches/plant, number of pods/plant, and number of seeds/pod as shown in Fig. 2a–d. These results proved that grain yield was strongly positively correlated with some of the abovementioned traits when N fertilization increased without or with yeast extract. Sufficient N contributes to enhance physiological processes, improves growth, flowering, seed formation, and the seed yield finally.Figure 2(a) Correlation of Canola seed yield (t/h) and plant height (cm) as affected by different nitrogen rates without and with yeast extract, (b) Correlation of Canola seed yield (t/h) and branch No/plant as affected by different nitrogen rates without and with yeast extract, (c) Correlation of Canola seed yield (t/h) and pods No/ plant as affected by different nitrogen rates without and with yeast extract, and (d) Correlation of Canola seed yield (t/h) and seeds No/ pod as affected by different nitrogen rates without and with yeast extract.Full size imageChemical propertiesRegarding results of the oil yield (t ha−1), seed oil %, protein %, N % in seed, and N% in straw as presented in Table 3, data showed significant differences among three Canola genotypes; AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), excepted oil yield had non-significant difference. G1 was surpassed in oil %; G2, G3 surpassed in protein % and N % in seed, and G3 surpassed in N% in straw. Different N fertilization applies without or with yeast extract had a significant effect on the abovementioned traits, wherein F6 treatment gave the highest oil yield, protein %, N % in seed, and N% in straw, while seed oil % significantly increased with F1 and F4 treatments. There was significant interaction concerning with abovementioned traits, Table 3, as well as the highest values of seed oil yield (t ha−1), protein % in seeds, and nitrogen % in seeds were obtained with G1, G2, and G3 when treated with F6. Wherein the highest values of oil % were obtained by G1 under F1 and F4 treatments. Concerning N% in straw was increased by increasing the rate of N fertilizer application and the highest value was recorded by adding F6 to G336. Seed oil percentage was decreased by increasing nitrogen rates; the effect of interaction between Canola cultivars and nitrogen fertilization treatments was significant on seed oil. % High rates of N led to decreases in seed oil % and increase in protein concentrations in Canola seed37, the increase in seed protein % because N is an integral part of protein and the protein of Canola.Table 3 Effect of different N applications without and with yeast extract on oil yield, oil %, protein %, N % in seed and N% in straw for the three Canola genotypes.Full size tableCorrelation of Canola seed yield and seed oil percentageA strong negative correlation was detected between seed oil percentage as shown in Fig. 3. The result indicates that seed oil percentage decreases with increasing in different N fertilization rates without or with yeast extract. That’s a negative correlation between seed yield and seed oil %; it might be due to N application which results in delaying maturity leading to poor seed filling and a greater proportion of green seed38.Figure 3Correlation of Canola seed yield (t h−1) and oil % as affected by different nitrogen rates without and with yeast extract.Full size imagePhysico-chemical properties of Canola oilThe effects of different N application rates without or with yeast extract on Canola genotypes on physico-chemical properties i.e. Acid value (mg g−1), saponification number (mg g−1) and peroxide value (mg kg−1) were shown in Table 4. Data of chemical properties of Canola oil showed significant differences among Canola genotypes, the highest acid value and peroxide value were obtained from G2 followed by G1 and G3, respectively, while the highest saponification number was obtained by G3 followed by G1 and G2, respectively.Table 4 Oil properties for three Canola genotypes under different N applications without and with yeast extract.Full size tableData had significant differences among different N application rates without or with yeast extract, by increasing the N rated from F0 to F6 caused decreases in Acid value, Saponification number, and peroxide value. Also, data showed a significant interaction between Canola genotypes and different N application rates without or with yeast extract for all abovementioned traits, wherein the highest values of saponification number were obtained by G1 and G3 under F0 treatment. In addition, the highest values of peroxide value and the acid value were obtained by G2 with F0. The acid value is a physicochemical indicator38, wherein oils which have higher acid value posse poor quality39, on another hand, Low acid value of Canola genotype shows their higher oil quality. The peroxide value varied between 7.1 and 9.06 meq. O2/kg indicates that the tested vegetable oils are fresh, and the lowest initial peroxide value is suitable for consumption40. High saponification value indicated that Canola oil possesses normal triglycerides and may be useful in the production of liquid soap and shampoo41. Saponification number was significantly different among genotypes and a higher nitrogen rate resulted in an increase in the unsaponifiable matter and led to a decrease in oil acid value and saponification value42.Fatty acids composition percentages in Canola oilThe main values of fatty acids composition percentages in Canola oil were determined and calculated in the second season Table 5. Gas–liquid chromatographic analysis showed that, saturated fatty acids (Palmitic, 16:0, Stearic, 18:0, Arachidic, 20:0, and Behenic, 22:0) represent about 9.1 of the total fatty acids. Palmitic was the dominant acid among the saturated ones. In respect of unsaturated fatty acids i.e., Oleic acid (18:1), Linoleic (18:2), Linolenic (18:3), and Erucic (22:1), they all represent about 90.9% of total fatty acids. Therefore, Oleic acid (18:1) was the major fatty acid in Canola oil (59.43%) followed by Linoleic (20.80%) and Linolenic (9.02%). Erucic acid was less than 2%.Table 5 Saturated and unsaturated fatty acids (%) in seeds of the three Canola genotypes and different N applications without and with yeast extract.Full size tableData in Table 5, showed slight differences in saturated fatty acids between Canola varieties. AD201(G1) variety contained more amount of Palmitic (4.78%) and Stearic (1.52%) acids followed by Topaz (G2) for Palmitic and SemuDNK 234/84 (V3) for Stearic. However, Behenic acid (1.20%) was higher in G3 than G2 (1.17%), while G2 was the highest in Arachidic acid than G3 variety. These results are in line with those obtained by El Habbasha et al.43. They reported that AD 201, Silvo, and Topas (G2) were different in their oil contents of saturated and unsaturated fatty acids. Canola varieties were also slightly differed in their content of the unsaturated fatty acids Table 5, G3 variety contained more amounts of Oleic (60.36%) acid followed by the G2 variety. G1 recorded the lowest amount of Oleic acid (58.36%) in comparison with the other two varieties. On the other hand, G1 showed a high increment in Linoleic and Linolenic acids followed by G3 for Linoleic and Linolenic acids. The second oil quality breeding objective is to reduce the percentage of Linolenic acid from the percent 8–10% to less than 3% while maintaining or increasing the level of Linoleic acid44. Lower Linolenic acid is desired to improve the storage characteristics of the oil, while higher Linolenic acid content may be nutritionally desirable. Similar observations were reported by Ref.45. Topaz variety recorded the highest value for Erucic acid (1.77%) followed by AD201 variety, whereas Semu DNK gave the lowest value (1.45%). The increase in Erucic acid content in the Topaz variety may be due to the decrease in Oleic acid content46. Stated that the concentrations of Oleic and Erucic acids were negatively correlated and a high Oleic acid concentration ( > 50%) was always associated with a low Erucic acid concentration ( More

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

Beniston, M., Diaz, H. F. & Bradley, R. S. Climatic change at high elevation sites: An overview. Clim. Change 36, 233–251 (1997).Article 

Google Scholar 
Chape, S., Spalding, M. & Jenkins, M. The world’s protected areas: Status, values, and prospects in the twenty-first century. Bioscience 59(7), 623–624 (2009).
Google Scholar 
Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11–17 (2004).Article 

Google Scholar 
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

Google Scholar 
Forero-Medina, G., Joppa, L. & Pimm, S. L. Constraints to species’ elevational range shifts as climate changes. Conserv. Biol. 25, 163–171 (2011).Article 
PubMed 

Google Scholar 
Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: Competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B 279, 2072–2080 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl. Acad. Sci. 115, 11982–11987 (2018).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024 (2011).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Lenoir, J. & Svenning, J. C. Climate-related range shifts: A global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. 117, 4211–4217 (2020).Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e200114 (2016).Article 

Google Scholar 
Orians, G. H. & Milewski, A. V. Ecology of Australia: The effects of nutrient-poor soils and intense fires. Biol. Rev. 82, 393–423 (2007).Article 
PubMed 

Google Scholar 
Laurance, W. F. et al. The 10 Australian ecosystems most vulnerable to tipping points. Biol. Cons. 144, 1472–1480 (2011).Article 

Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: An impending environmental catastrophe. Proc. R. Soc. Lond. B 270, 1887–1892 (2003).Article 

Google Scholar 
Mahony, M.J. The amphibians. in Remnants of Gondwana: A Natural and Social History of the Gondwana Rainforests of Australia. (eds. Kitching, R.L., Braithwaite, R., & Cavanaugh, J.) (Surrey Beatty & Sons, 2010).Kooyman, R. M., Watson, J. & Wilf, P. Protect Australia’s gondwana rainforests. Science 367, 1083–1083 (2020).Article 
PubMed 
ADS 

Google Scholar 
Narsey, S. et al. (2020). Impact of climate change on cloud forests in the Gondwana Rainforests of Australia World Heritage Area. Earth Systems and Climate Change Hub Report.Newell, D. An update on frog declines from the forests of subtropical eastern Australia in Status of Conservation and Decline of Amphibians: Australia, New Zealand, and Pacific Islands (eds. Heatwole H. and Rowley J. L.) 29–37 (CSIRO, 2018).DAWE. Bushfire Impacts Vol. 2021 (Commonwealth Department of Agriculture Water and Environment, 2020).
Google Scholar 
Collins, L. et al. The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire. Environ. Res. Lett. 16, 044029 (2021).Article 
ADS 

Google Scholar 
Filkov, A. I., Ngo, T., Matthews, S., Telfer, S. & Penman, T. D. Impact of Australia’s catastrophic 2019/20 bushfire season on communities and environment: Retrospective analysis and current trends. J. Saf. Sci. Resil. 1, 44–56 (2020).
Google Scholar 
Blunden, J. & Arndt, D. S. State of the climate in 2019. Bull. Am. Meteor. Soc. 101, S1–S429 (2020).Article 

Google Scholar 
Zhongming, Z., Linong, L., Wangqiang, Z. & Wei, L. AR6 Climate Change 2021: The Physical Science Basis (Springer, 2021).
Google Scholar 
Laidlaw, M. J., McDonald, W. J. F., Hunter, R. J., Putland, D. A. & Kitching, R. L. The potential impacts of climate change on Australian subtropical rainforest. Aust. J. Bot. 59, 440–449 (2011).Article 

Google Scholar 
Blaustein, A. R. et al. Direct and indirect effects of climate change on amphibian populations. Diversity 2, 281–313 (2010).Article 

Google Scholar 
Li, Y., Cohen, J. M. & Rohr, J. R. Review and synthesis of the effects of climate change on amphibians. Integr. Zool. 8, 145–161 (2013).Article 
PubMed 

Google Scholar 
Carey, C. & Alexander, M. A. Climate change and amphibian declines: Is there a link?. Divers. Distrib. 9, 111–121 (2003).Article 

Google Scholar 
Cohen, J. M., Civitello, D. J., Venesky, M. D., McMahon, T. A. & Rohr, J. R. An interaction between climate change and infectious disease drove widespread amphibian declines. Glob. Change Biol. 25, 927–937 (2019).Article 
ADS 

Google Scholar 
Geyle, H. M. et al. Red hot frogs: Identifying the Australian frogs most at risk of extinction. Pac. Conserv. Biol. 28, 211–223 (2021).Article 

Google Scholar 
Gillespie, G. R. et al. Status and priority conservation actions for Australian frog species. Biol. Conserv. 247, 108543 (2020).Article 

Google Scholar 
Almeida, A. M. et al. Prediction scenarios of past, present, and future environmental suitability for the Mediterranean species Arbutus unedo L. Sci. Rep. 12, 1–15 (2022).Article 

Google Scholar 
Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 1–14 (2022).Article 
ADS 

Google Scholar 
Tiwari, S. et al. Modelling the potential risk zone of Lantana camara invasion and response to climate change in eastern India. Ecol. Process. 11(1), 1–13 (2022).Article 

Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography 41, 726–736 (2018).Article 

Google Scholar 
Li, J. et al. Climate refugia of snow leopards in High Asia. Biol. Conserv. 203, 188–196 (2016).Article 

Google Scholar 
Searcy, C. A. & Shaffer, B. H. Do ecological niche models accurately identify climatic determinants of species ranges?. Am. Nat. 187, 423–435 (2016).Article 
PubMed 

Google Scholar 
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108857 (2020).Article 

Google Scholar 
Anstis, M. Tadpoles and Frogs of Australia (New Holland Publishers Pty Limited, 2017).
Google Scholar 
Knowles, R., Mahony, M., Armstrong, J. & Donnellan, S. Systematics of sphagnum frogs of the Genus Philoria (Anura: Myobatrachidae) in Eastern Australia, with the description of two new species. Rec. Aust. Mus. 56, 57–74 (2004).Article 

Google Scholar 
Mahony, M. J. et al. A new species of Philoria (Anura: Limnodynastidae) from the uplands of the Gondwana Rainforests world heritage area of eastern Australia. Zootaxa 5104, 209–241 (2022).Article 
PubMed 

Google Scholar 
Bolitho, L. J., Rowley, J. J. L., Hines, H. B. & Newell, D. Occupancy modelling reveals a highly restricted and fragmented distribution in a threatened montane frog (Philoria kundagungan) in subtropical Australian rainforests. Aust. J. Zool. 67, 231–240 (2021).Article 

Google Scholar 
Heard, G. et al. Post-fire impact assessment for priority frogs: northern Philoria. (NESP Threatened Species Recovery Hub Project 8.1.3 report, Brisbane, 2021).Vanderwal, J. All Future Climate Layers for Australia: 1 km Resolution (James Cook University, 2012).
Google Scholar 
Torkkola, J. J., Chauvenet, A. L. M., Hines, H. & Oliver, P. M. Distributional modelling, megafires and data gaps highlight probable underestimation of climate change risk for two lizards from Australia’s montane rainforests. Austral Ecol. 47(2), 365–379 (2021).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Geoscience, A. Digital Elevation Model (DEM) 25 Metre Grid of Australia derived from LiDAR. (Geoscience Australia, 2015).Thuiller, W., Georges, D., Engler, R. & Breiner, F. (2014). biomod2: Ensemble platform for species distribution modeling. R package version 3.1-64. http://CRANR-project.org/package=biomod2. Accessed Feb 2021.Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. 9, 10365–10376 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Thuiller, W. BIOMOD: Optimising predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).Article 
ADS 

Google Scholar 
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonisation, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207 (2003).Article 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci. 117, 19656–19657 (2020).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
CAS 
PubMed 
ADS 

Google Scholar 
Campos-Cerqueira, M. & Mitchell Aide, T. Lowland extirpation of anuran populations on a tropical mountain. PeerJ 2017, 1–10 (2017).
Google Scholar 
Pounds, J. A., Fogden, M. P. L. & Campbell, J. H. Biological response to climate change on a tropical mountain. Nature 398, 611–615 (1999).Article 
CAS 
ADS 

Google Scholar 
Raxworthy, C. J. et al. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: A preliminary appraisal for the highest massif in Madagascar. Glob. Change Biol. 14, 1703–1720 (2008).Article 
ADS 

Google Scholar 
Fordham, D. A. et al. Extinction debt from climate change for frogs in the wet tropics. Biol. Lett. 12, 20160236 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, E. P., Williams, K., Hipsey, M. R. & Mitchell, N. J. Drying microclimates threaten persistence of natural and translocated populations of threatened frogs. Biodivers. Conserv. 30(1), 15–34 (2020).Article 

Google Scholar 
Scheele, B. C., Driscoll, D. A., Fischer, J. & Hunter, D. A. Decline of an endangered amphibian during an extreme climatic event. Ecosphere 3, 101 (2012).Article 

Google Scholar 
Legge, S. et al. Rapid assessment of the biodiversity impacts of the 2019–2020 Australian megafires to guide urgent management intervention and recovery and lessons for other regions. Divers. Distrib. 28, 571–591 (2022).Article 

Google Scholar 
Canadell, J. G. et al. Multi-decadal increase of forest burned area in Australia is linked to climate change. Nat. Commun. 12, 6921 (2021).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).Article 
PubMed 

Google Scholar 
Holz, A., Wood, S. W., Veblen, T. T. & Bowman, D. M. J. S. Effects of high-severity fire drove the population collapse of the subalpine Tasmanian endemic conifer Athrotaxis cupressoides. Glob. Change Biol. 21, 445–458 (2015).Article 
ADS 

Google Scholar 
Hutley, L. B., Doley, D., Yates, D. J. & Boonsaner, A. Water balance of an australian subtropical rainforest at altitude: The ecological and physiological significance of intercepted cloud and fog. Aust. J. Bot. 45, 311–329 (1997).Article 

Google Scholar 
Godfree, R. C. et al. Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation. Nat. Commun. 12, 1023 (2021).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Hennessy, K. et al. Climate Change Impacts on Fire-Weather in South-East Australia (Commonwealth Scientific and Industrial Research Organisation, 2005).
Google Scholar 
Moriondo, M. et al. Potential impact of climate change on fire risk in the Mediterranean area. Clim. Res. 31, 85–95 (2006).Article 

Google Scholar 
Pitman, A. J., Narisma, G. T. & McAneney, J. The impact of climate change on the risk of forest and grassland fires in Australia. Clim. Change 84, 383–401 (2007).Article 
ADS 

Google Scholar 
Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Article 

Google Scholar 
Scheele, B. C. et al. Conservation translocations for amphibian species threatened by chytrid fungus: A review, conceptual framework, and recommendations. Conserv. Sci. Pract. 3, e524 (2021).
Google Scholar 
Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Geographic variation in adult and embryonic desiccation tolerance in a terrestrial-breeding frog. Evolution 74, 1186–1199 (2020).Article 
CAS 
PubMed 

Google Scholar 
Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
Google Scholar 
Keppel, G. et al. Refugia: Identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).Article 

Google Scholar 
Selwood, K. E. & Zimmer, H. C. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

Google Scholar  More

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

Decolonizing science

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

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

Petronio, C. Les cervidés endémiques des îles méditerranéennes. Quaternaire 3–4, 259–264 (1990).
Google Scholar 
Liouville, M. Variabilité du Cerf élaphe (Cervus elaphus LINNE 1758) au cours du pléistocène moyen et supérieur en Europe occidentale : Approches morphométrique, paléoécologique et cynégétique (Museum National d’Histoire Naturelle, Paris, 2007).
Google Scholar 
van der Made, J., Stefaniak, K. & Marciszak, A. The polish fossil record of the wolf canis and the deer alces, capreolus, megaloceros, dama and cervus in an evolutionary perspective. Quatern. Int. 326–327, 406–430 (2014).
Google Scholar 
Guadelli, J.-L. Contribution à l’étude des zoocénoses préhistoriques en Aquitaine (Würm ancien et interstade würmiem. Universite de Bordeaux, Talence, 1987).
Google Scholar 
Guadelli, J.-L. Les cerfs du würm ancien en Aquitaine. Paléo 8, 99–108 (1996).
Google Scholar 
Defleur, A. et al. Le niveau moustérien de la grotte de l’Adaouste (Jouques, Bouches-du-Rhône): Approche culturelle et paléoenvironnements. Bull. Mus. anthropol. préhist. Monaco 37, 11–48 (1994).
Google Scholar 
Tournepiche, J.-F. Les grands mammifères pléistocènes de Poitou-Charente. Paléo 8, 109–141 (1996).
Google Scholar 
Delagnes, A. et al. Le gisement Pléistocène moyen et supérieur d’artenac (Saint-Mary, Charente): Premier bilan interdisciplinaire. Bull. Soc. Prehist. Fr. 96, 469–496 (1999).
Google Scholar 
Valensi, P., Psathi, E. & Lacombat, F. Le cerf élaphe dans les sites du Paléolithique moyen du Sud-Est de la France et de la Ligurie. Intérêts biostratigraphique, environnemental et taphonomique. In Acts of the XIVth UISPP Congress, Session 3: Paleoecology, General Sessions and Posters, 2–8 september 2001 97–105 (BAR International Series, 2004).Steele, T. E. Variation in mortality profiles of red deer (Cervus elaphus) in middle palaeolithic assemblages from western Europe. Int. J. Osteoarchaeol. 14, 307–320 (2004).
Google Scholar 
Croitor, R. A new form of wapiti cervus canadensis Erxleben, 1777 (Cervidae, Mammalia) from the late pleistocene of France. Palaeoworld 29, 789–806 (2020).
Google Scholar 
Meiri, M. et al. Subspecies dynamics in space and time: A study of the red deer complex using ancient and modern DNA and morphology. J. Biogeogr. 45, 367–380 (2018).
Google Scholar 
Queirós, J. et al. Red deer in Iberia: Molecular ecological studies in a southern refugium and inferences on European postglacial colonization history. PLoS ONE 14, e0210282 (2019).PubMed 
PubMed Central 

Google Scholar 
Carranza, J., Salinas, M., de Andrés, D. & Pérez-González, J. Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol. Evol. 6, 905–922 (2016).PubMed 
PubMed Central 

Google Scholar 
Rey-Iglesia, A., Grandal-d’Anglade, A., Campos, P. F. & Hansen, A. J. Mitochondrial DNA of pre-last glacial maximum red deer from NW Spain suggests a more complex phylogeographical history for the species. Ecol. Evol. 7, 10690–10700 (2017).PubMed 
PubMed Central 

Google Scholar 
Geist, V. Deer of the world: Their evolution, behaviour, and ecology (Stackpole Books, Pennsylvania, 1998).
Google Scholar 
Rivals, F. & Lister, A. M. Dietary flexibility and niche partitioning of large herbivores through the pleistocene of Britain. Quatern. Sci. Rev. 146, 116–133 (2016).ADS 

Google Scholar 
Berlioz, E. Ecologie alimentaire et paléoenvironnements des cervidés européens du Pleistocène inférieur: le message des texutures de micro-usure dentaire (University of Poitiers, Poitiers, 2017).
Google Scholar 
Saarinen, J., Eronen, J., Fortelius, M., Seppä, H. & Lister, A. M. Patterns of diet and body mass of large ungulates from the pleistocene of Western Europe, and their relation to vegetation. Palaeontol. Electron. 19.3.32A, 1–58 (2016).
Google Scholar 
Stefano, G. D., Pandolfi, L., Petronio, C. & Salari, L. The morphometry and the occurrence of cervus elaphus (Mammalia, Cervidae) from the late Pleistocene of the Italian peninsula. Riv. Ital. Paleontol. Stratigr. 121, 103–120 (2015).
Google Scholar 
Terada, C., Tatsuzawa, S. & Saitoh, T. Ecological correlates and determinants in the geographical variation of deer morphology. Oecologia 169, 981–994 (2012).PubMed 
ADS 

Google Scholar 
Sommer, R. S., Fahlke, J. M., Schmölcke, U., Benecke, N. & Zachos, F. E. Quaternary history of the European roe deer capreolus capreolus. Mammal Rev. 39, 1–16 (2009).
Google Scholar 
Lorenzini, R. et al. European Roe Deer Capreolus capreolus (Linnaeus, 1758). In Handbook of the Mammals of Europe (eds Hackländer, F. & Zachos, F. E.) 1–32 (Springer, Cham, 2022).
Google Scholar 
Lorenzini, R., Garofalo, L., Qin, X., Voloshina, I. & Lovari, S. Global phylogeography of the genus capreolus (Artiodactyla: Cervidae), a palaearctic meso-mammal. Zool. J. Linn. Soc. 170, 209–221 (2014).
Google Scholar 
Tixier, H. & Duncan, P. Are European roe deer browsers? A review of variations in the composition of their diets. Rev. Ecol. 51, 3–17 (1996).
Google Scholar 
Merceron, G., Viriot, L. & Blondel, C. Tooth microwear pattern in roe deer (Capreolus capreolus L.) from Chizé (Western France) and relation to food composition. Small Rumin. Res. 53, 125–132 (2004).
Google Scholar 
Delibes, J. R. Ecología y comportamiento del corzo (Capreolus capreolus L. 1758) en la Sierra de Grazalema (Cádiz) (Universidad Complutense, Complutense, 1996).
Google Scholar 
Hewitt, G. M. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
ADS 

Google Scholar 
Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. Lond. B. Biol. Sci. 277, 661–671 (2010).
Google Scholar 
Álvarez-Lao, D. J. & García, N. Geographical distribution of pleistocene cold-adapted large mammal faunas in the Iberian peninsula. Quatern. Int. 233, 159–170 (2011).
Google Scholar 
Lumley, H. de. Le Paléolithique inférieur et moyen du Midi méditerranéen dans son cadre géologique. Tome I. Ligurie—Provence. Gall. Préhist. 5, (1969).Texier, P.-J. L’industrie moustérienne de l’abri pié-lombard (Tourettes-sur-Loup, Alpes-Maritimes). Bull. Soc. Préhist. Fr. 71, 429–448 (1974).
Google Scholar 
Texier, P.-J. et al. L’abri pié lombard à tourrettes-sur-loup (Alpes-Maritimes): Anciennes fouilles (1971–1985), nouvelles données. Bull. Mus.Anthropol.e Préhistor. Monaco 51, 19–49 (2011).
Google Scholar 
Tomasso, A. Territoires, systèmes de mobilité et systèmes de production : La fin du Paléolithique supérieur dans l’arc liguro-provençal (University of Nice Sophia Antipolis Nice, and University of Pisa, 2014).
Google Scholar 
Pelletier, M., Desclaux, E., Brugal, J.-P. & Texier, P.-J. The exploitation of rabbits for food and pelts by last interglacial neandertals. Quatern. Sci. Rev. 224, 105972 (2019).
Google Scholar 
Valladas, H. et al. Datations par la thermoluminescence de gisements moustériens du sud de la France. L’Anthropologie 91, 211–226 (1987).
Google Scholar 
Yokoyama, Y. et al. ESR dating of stalagmites of the Caune de l’Arago, the Grotte du Lazaret, the Grotte du Vallonnet and the abri Pié Lombard : a comparison with the U-Th method. In Third Specialist Seminar on TL and ESR Dating (eds. Hackens, T., Mejdahl, V., Bowman, S. G. E., Wintle, A. G. & Aitken, M. J.) 381–389 (1983).Romero, A. J., Fernández-Lomana, J. C. D. & Brugal, J.-P. Aves de caza. Estudio tafonómico y zooarqueológico de los restos avianos de los niveles musterienses de pié lombard (Alpes-Maritimes, Francia). Munibe Antropol. Arkeol. 68, 73–84 (2017).
Google Scholar 
Lumley (de), M.-A. Les néandertaliens dans le midi méditerranéen. In La Préhistoire française vol. T. 1 (Editions du CNRS, 1976).Porraz, G. En marge du milieu alpin. Dynamiques de formation des ensembles lithiques et modes d’occupation des territoires au paléolithique moyen (Université de Provence, Marseille, 2005).
Google Scholar 
Porraz, G. Middle Paleolithic mobile toolkits in shor-tterm human occupations: Two case studies. Eur. Prehist. 6, 33–55 (2009).
Google Scholar 
Roussel, A., Gourichon, L., Valensi, P. & Brugal, J.-P. Homme, gibier et environnement au Paléolithique moyen. Regards sur la gestion territoriale de l’espace semi-montagnard du Midi de la France. In Biodiversités, environnements et sociétés depuis la Préhistoire : nouveaux marqueurs et approches intégrées 87–99 (Éditions APDCA, 2021).Renault-Miskovsky, J. & Texier, J. Intérêt de l’analyse pollinique détaillée dans les concrétions de grotte .Application à l’abri pié-lombard (Tourettes-sur-Loup, Alpes maritimes). Quaternaire 17, 129–134 (1980).
Google Scholar 
Rosell, J. et al. A resilient landscape at teixoneres cave (MIS 3; Moià, Barcelona, Spain): The Neanderthals as disrupting agent. Quatern. Int. 435, 195–210 (2017).
Google Scholar 
Rosell, J. et al. Mossegades i Levallois: les noves intervencionsa la cova de les teixoneres (Moià, Bages). Trib d’Arqueologia 29–43 (2008).Rosell, J. et al. Los ocupaciones en la Cova de les Teixoneres (Moià, Barcelona): relaciones espaciales y grado de competencia entre hienas, osos y neandertales durante el Pleistoceno Superior. In Actas de la 1a Reunión de Científicos sobre Cubiles de Hiena (y Otros Grandes Carnívoros) en los Yacimientos Arqueológicos de la Península Ibérica (392–402) (eds Arriaza, M. C. et al.) (Museo Arqueológico Regional, 2010).
Google Scholar 
Rosell, J. et al. A stop along the way: The role of Neanderthal groups at level III of teixoneres cave (Moià, Barcelona, Spain). Quaternaire 21, 139–154 (2010).
Google Scholar 
Rosell, J. et al. Cova del toll y cova de les Teixoneres (Moià, Barcelona). In Los cazadores recolectores del Pleistoceno y del Holoceno en Iberia y el estrecho de Gibraltar (eds. Sala, R., Carbonell, E., Bermudez de Castro, J. M. & Arsuaga, J. L.) 302–307 (2014).Zilio, L. et al. Examining Neanderthal and carnivore occupations of teixoneres cave (Moià, Barcelona, Spain) using archaeostratigraphic and intra-site spatial analysis. Sci. Rep. 11, 4339 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Tissoux, H. et al. Datation par les séries de l’uranium des occupations moustériennes de la grotte de teixoneres (Moia, Province de Barcelone, Espagne). Quaternaire 17, 27–33 (2006).
Google Scholar 
Talamo, S. et al. The radiocarbon approach to Neanderthals in a carnivore den site: A well-defined chronology for teixoneres cave (Moià, Barcelona, Spain). Radiocarbon 58, 247–265 (2016).CAS 

Google Scholar 
Álvarez-Lao, D. J., Rivals, F., Sánchez-Hernández, C., Blasco, R. & Rosell, J. Ungulates from teixoneres cave (Moià, Barcelona, Spain): Presence of cold-adapted elements in NE Iberia during the MIS 3. Palaeogeogr. Palaeoclimatol. Palaeoecol. 466, 287–302 (2017).
Google Scholar 
Rufà, A., Blasco, R., Rivals, F. & Rosell, J. Leporids as a potential resource for predators (hominins, mammalian carnivores, raptors): An example of mixed contribution from level III of teixoneres cave (MIS 3, Barcelona, Spain). C.R. Palevol. 13, 665–680 (2014).
Google Scholar 
Rufà, A., Blasco, R., Rivals, F. & Rosell, J. Who eats whom? Taphonomic analysis of the avian record from the middle paleolithic site of teixoneres cave (Moià, Barcelona, Spain). Quatern. Int. 421, 103–115 (2016).
Google Scholar 
Sánchez-Hernández, C., Rivals, F., Blasco, R. & Rosell, J. Short, but repeated Neanderthal visits to teixoneres cave (MIS 3, Barcelona, Spain): A combined analysis of tooth microwear patterns and seasonality. J. Archaeol. Sci. 49, 317–325 (2014).
Google Scholar 
Sánchez-Hernández, C., Rivals, F., Blasco, R. & Rosell, J. Tale of two timescales: Combining tooth wear methods with different temporal resolutions to detect seasonality of Palaeolithic hominin occupational patterns. J. Archaeol. Sci. Rep. 6, 790–797 (2016).
Google Scholar 
Picin, A. et al. Neanderthal mobile toolkit in short-term occupations at teixoneres cave (Moia, Spain). J. Archaeol. Sci. Rep. 29, 102165 (2020).
Google Scholar 
Fernández-García, M. et al. New insights in Neanderthal palaeoecology using stable oxygen isotopes preserved in small mammals as palaeoclimatic tracers in teixoneres cave (Moià, northeastern Iberia). Archaeol. Anthropol. Sci. 14, 106 (2022).
Google Scholar 
Ochando, J. et al. Neanderthals in a highly diverse, mediterranean-Eurosiberian forest ecotone: The pleistocene pollen record of teixoneres cave, Northeastern Spain. Quatern. Sci. Rev. 241, 106429 (2020).
Google Scholar 
López-García, J. M. et al. A multidisciplinary approach to reconstructing the chronology and environment of Southwestern European Neanderthals: The contribution of teixoneres cave (Moià, Barcelona, Spain). Quatern. Sci. Rev. 43, 33–44 (2012).ADS 

Google Scholar 
Sánchez-Hernández, C. et al. Dietary traits of ungulates in northeastern Iberian Peninsula: Did these Neanderthal preys show adaptive behaviour to local habitats during the middle palaeolithic?. Quatern. Int. 557, 47–62 (2020).
Google Scholar 
Fortelius, M. & Solounias, N. Functional characterization of ungulate molars using the abrasion-attrition wear gradient: A new method for reconstructing paleodiets. Am. Mus. Novit. 3301, 1–36 (2000).
Google Scholar 
Rivals, F., Solounias, N. & Mihlbachler, M. C. Evidence for geographic variation in the diets of late pleistocene and early holocene bison in North America, and differences from the diets of recent bison. Quatern. Res. 68, 338–346 (2007).ADS 

Google Scholar 
King, T., Andrews, P. & Boz, B. Effect of taphonomic processes on dental microwear. Am. J. Phys. Anthropol. 108, 359–373 (1999).CAS 
PubMed 

Google Scholar 
Uzunidis, A. et al. The impact of sediment abrasion on tooth microwear analysis: An experimental study. Archaeol. Anthropol. Sci. 13, 134 (2021).
Google Scholar 
Kaiser, T. M. & Solounias, N. Extending the tooth mesowear method to extinct and extant equids. Geodiversitas 25, 321–345 (2003).
Google Scholar 
Xafis, A., Nagel, D. & Bastl, K. Which tooth to sample? A methodological study of the utility of premolar/non-carnassial teeth in the microwear analysis of mammals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 229–240 (2017).
Google Scholar 
Meadow, R. H. Early animal domestication in South Asia a first report of the faunal remains from mehrgarh Pakistan. In South Asian Archaeology (ed. Härtel, H.) 143–179 (Dietrich Reimer, Berlin, 1979).
Google Scholar 
Meadow, R. H. The use of size index scaling techniques for research on archaeozoological collections from the Middle East. In Historici Animalium ex. Ossibus Festschrift Angela Von Den Driesch Zum 65 Geburtstag (eds Becker, C. et al.) 285–300 (Verlag Marie Leidorf, Rahden, 1999).
Google Scholar 
Simpson, G. G. Large pleistocene felines of North America. Pleistocene felines North Am. 1136, 1–28 (1941).
Google Scholar 
Valli, A. M. F. & Guérin, C. L. gisement pléistocène supérieur de la grotte de Jaurens à Nespouls, Corrèze, France: Les cervidae (Mammalia, Artiodactyla). Publ. mus. Conflu. 1, 41–81 (2000).
Google Scholar 
Janis, C. M. The correlation between diet and dental wear in herbivorous mammals and its relationship to the determination of diets of extinct species. In Evolutionary Paleobiology of Behavior and Coevolution (ed. Boucot, A. J.) 241–259 (Elsevier, Amsterdam, 1990).
Google Scholar 
Heintz, E. Les Cervidés villafranchiens de France et d’Espagne (Museum National d’Histoire Naturelle, Parise, 1970).
Google Scholar 
Magniez, P. Etude paléontologique des artiodactyles de la grotte Tournal (Bize-Minervois, Aude, France) étude taphonomique, archéozoologique et paléoécologique des grands Mammifères dans leur cadre biostratigraphique et paléoenvironnemental (Université de Perpignan, Perpignan, 2010).
Google Scholar 
Cucchi, T., Hulme-Beaman, A., Yuan, J. & Dobney, K. Early neolithic pig domestication at Jiahu, Henan Province, China: clues from molar shape analyses using geometric morphometric approaches. J. Archaeol. Sci. 38, 11–22 (2011).
Google Scholar 
Evin, A. et al. The long and winding road: Identifying pig domestication through molar size and shape. J. Archaeol. Sci. 40, 735–743 (2013).
Google Scholar 
Pelletier, M., Kotiaho, A., Niinimäki, S. & Salmi, A.-K. Identifying early stages of reindeer domestication in the archaeological record: A 3D morphological investigation on forelimb bones of modern populations from Fennoscandia. Archaeol. Anthropol. Sci. 12, 169 (2020).PubMed 
PubMed Central 

Google Scholar 
Bignon, O., Baylac, M., Vigne, J.-D. & Eisenmann, V. Geometric morphometrics and the population diversity of late glacial horses in Western Europe (Equus caballus arcelini): Phylogeographic and anthropological implications. J. Archaeol. Sci. 32, 375–391 (2005).
Google Scholar 
Pelletier, M. Morphological diversity, evolution and biogeography of early pleistocene rabbits (Genus Oryctolagus). Palaeontology 64, 817–838 (2021).
Google Scholar 
Curran, S. C. Expanding ecomorphological methods: Geometric morphometric analysis of cervidae post-crania. J. Archaeol. Sci. 39, 1172–1182 (2012).
Google Scholar 
Curran, S. C. Exploring eucladoceros ecomorphology using geometric morphometrics. Anat. Rec. 298, 291–313 (2015).
Google Scholar 
Cucchi, T. et al. Taxonomic and phylogenetic signals in bovini cheek teeth: Towards new biosystematic markers to explore the history of wild and domestic cattle. J. Archaeol. Sci. 109, 104993 (2019).
Google Scholar 
Jeanjean, M. et al. Sorting the flock: Quantitative identification of sheep and goat from isolated third lower molars and mandibles through geometric morphometrics. J. Archaeol. Sci. 141, 105580 (2022).
Google Scholar 
Herrera, P. L. Différences entre les dents jugales deciduales du cerf elaphe (Cervus Elaphus L.) et du boeuf domestique (Bos Taurus L.). Rev. Paléobiol. 8, 77 (1989).
Google Scholar 
Pfeiffer, T. Die stellung von dama (Cervidae, Mammalia) im system plesiometacarpaler hirsche des pleistozäns. Phylogenetische reconstruktion-metrische analyse. Cour Forsch. Senckenberg. 211, 1–218 (1999).
Google Scholar 
Rohlf, F. J. TPSDig, version 2.17 (Stony Brook, NY: Department of Ecology and Evolution, State University of New York, 2013).Bookstein, F. L. Morphometric Tools for Landmark Data: Geometry and Biology (Cambridge University Press, Cambridge, 1992).MATH 

Google Scholar 
Schlager, S. Morpho: Calculations and visualizations related to geometric morphometrics. (2013).Bookstein, F. L. Size and shape spaces for landmark data in two dimensions. Stat. Sci. 1, 181–222 (1986).MATH 

Google Scholar 
Kaiser, T. M. & Schulz, E. Tooth wear gradients in zebras as an environmental proxy—a pilot study. Mitt. Hambg. Zool. Mus. Inst. 103, 187–210 (2006).
Google Scholar 
Louys, J., Ditchfield, P., Meloro, C., Elton, S. & Bishop, L. C. Stable isotopes provide independent support for the use of mesowear variables for inferring diets in African antelopes. Proc. R. Soc. B. Biol. Sci. 279, 4441–4446 (2012).CAS 

Google Scholar 
Schulz, E. & Kaiser, T. M. Historical distribution, habitat requirements and feeding ecology of the genus equus (Perissodactyla). Mammal Rev. 43, 111–123 (2013).
Google Scholar 
Ulbricht, A., Maul, L. C. & Schulz, E. Can mesowear analysis be applied to small mammals? A pilot-study on leporines and murines. Mamm. Biol. 80, 14–20 (2015).
Google Scholar 
Danowitz, M., Hou, S., Mihlbachler, M., Hastings, V. & Solounias, N. A combined-mesowear analysis of late miocene giraffids from North Chinese and Greek localities of the pikermian biome. Palaeogeogr. Palaeoclimatol. Palaeoecol. 449, 194–204 (2016).
Google Scholar 
Marom, N., Garfinkel, Y. & Bar-Oz, G. Times in between: A zooarchaeological analysis of ritual in Neolithic Sha’ar Hagolan. Quatern. Int. 464, 216–225 (2018).
Google Scholar 
Ackermans, N. L. et al. Mesowear represents a lifetime signal in sheep (Ovis aries) within a long-term feeding experiment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 553, 109793 (2020).
Google Scholar 
Mihlbachler, M. C., Rivals, F., Solounias, N. & Semprebon, G. M. Dietary change and evolution of horses in North America. Science 331, 1178–1181 (2011).CAS 
PubMed 
ADS 

Google Scholar 
Rivals, F., Rindel, D. & Belardi, J. B. Dietary ecology of extant guanaco (Lama guanicoe) from Southern Patagonia: Seasonal leaf browsing and its archaeological implications. J. Archaeol. Sci. 40, 2971–2980 (2013).
Google Scholar 
Rivals, F., Uzunidis, A., Sanz, M. & Daura, J. Faunal dietary response to the heinrich event 4 in southwestern Europe. Palaeogeogr. Palaeoclimatol. Palaeoecol. 473, 123–130 (2017).
Google Scholar 
Uzunidis, A., Rivals, F. & Brugal, J.-P. Relation between morphology and dietary traits in horse jugal upper teeth during the middle pleistocene in Southern France. Quat. Rev. Assoc. franc. l’étude Quat. 28, 303–312 (2017).
Google Scholar 
Uzunidis, A. Dental wear analyses of middle pleistocene site of Lunel-Viel (Hérault, France): Did equus and bos live in a wetland?. Quatern. Int. 557, 39–46 (2020).
Google Scholar 
Solounias, N. & Semprebon, G. Advances in the reconstruction of ungulate ecomorphology with application to early fossil equids. Am. Mus. Novit. 3366, 49 (2002).
Google Scholar 
Semprebon, G., Godfrey, L. R., Solounias, N., Sutherland, M. R. & Jungers, W. L. Can low-magnification stereomicroscopy reveal diet?. J. Hum. Evol. 47, 115–144 (2004).PubMed 

Google Scholar 
Grine, F. E. Dental evidence for dietary differences in australopithecus and paranthropus: A quantitative analysis of permanent molar microwear. J. Hum. Evol. 15, 783–822 (1986).
Google Scholar 
Teaford, M. F. & Oyen, O. J. In vivo and in vitro turnover in dental microwear. Am. J. Phys. Anthropol. 80, 447–460 (1989).CAS 
PubMed 

Google Scholar 
Winkler, D. E. et al. The turnover of dental microwear texture: Testing the” last supper” effect in small mammals in a controlled feeding experiment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 557, 109930 (2020).
Google Scholar 
Walker, A., Hoeck, H. N. & Perez, L. Microwear of mammalian teeth as an indicator of diet. Science 201, 908–910 (1978).CAS 
PubMed 
ADS 

Google Scholar 
Janis, C. M. & Lister, A. M. The morphology of the lower fourth premolaras a taxonomic character in the ruminantia (Mammalia; Artiodactyla), and the systematic position of triceromeryx. J. Paleontol. 59, 405–410 (1985).
Google Scholar 
Croitor, R. Animal husbandry and hunting. Bone material use ineconomic activities. In Kravchenko, E. A. (eds.) From Bronze to Iron: Pale-oeconomy of the Habitants of the Inkerman Valley (According the Materialof Excavations in Uch-Bash and Saharnaya Golovka Settlements). 191–222 (Institute of Archaeology of National Academy of Sciences of Ukraine, 2016).Geist, V. & Bayer, M. Sexual dimorphism in the cervidae and its relation to habitat. J. Zool. 214, 45–53 (1988).
Google Scholar 
Fichant, R. Le cerf: Biologie, comportement, gestion (Gerfaut Editions, 2003).
Google Scholar 
Arellano-Moullé, A. Les cervidés des niveaux moustériens de la grotte du Prince (Grimaldi, Vintimille, Italie) Etude paléontologique. Bull. Mus. Anthropol. Préhist. Monaco 39, 53–58 (1997).
Google Scholar 
Brugal, J. .-P. . La. faune des grands mammifères de l’abri des Canalettes – matériel 1980–1986. In L’abri des Canalettes Un habitat moustérien sur les grands Causses Nant Aveyron, 89–137 (ed. Meignen, L.) (CNRS Éditions, Paris, 1993).
Google Scholar 
La Gerber, J. P. faune des grands mammifères du Würm ancien dans le sud-est de la France (Université de Provence, Marseille, 1973).
Google Scholar 
Alonso, D. A. Analisis paleobiologico de los ungulados del pleistoceno superior de la meseta norte (Universidad de Salamanca, Salamanca, 2015).
Google Scholar 
Sanchez, B. La fauna de mamíferos del pleistoceno superior del Abric Romani (Capellades, Barcelona). Adas de Paleontol. 331–347 (1989).Clot, A. Le chevreuil, Capreolus capreolus (L.) (Ceervidae, Artiodactyla) dans le pléistocène de Ge$$rde (H.-P.) et des pyrénées. Bull. Soc. Hist. Nat. Toulouse 125, 83–86 (1989).
Google Scholar 
Vanpé, C. Mating systems and sexual selection in ungulates. New insights from a territorial species with low sexual size dimorphism: the European roe deer (Capreolus capreolus). (Université Paul Sabatier, Toulouse III and Swedish University of Agricultural Sciences, 2007).Horcajada-Sánchez, F. & Barja, I. Local ecotypes of roe deer populations (Capreolus capreolus L.) in relation to morphometric features and fur colouration in the centre of the Iberian Peninsula. Pol. J Ecol. 64, 113–124 (2016).
Google Scholar 
Semprebon, G. M., Sise, P. J. & Coombs, M. C. Potential bark and fruit browsing as revealed by Stereomicrowear analysis of the peculiar clawed herbivores known as Chalicotheres (Perissodactyla, Chalicotherioidea). J. Mammal. Evol. 18, 33–55 (2011).
Google Scholar 
Rivals, F. et al. Palaeoecology of the mammoth steppe fauna from the late pleistocene of the North Sea and Alaska: Separating species preferences from geographic influence in paleoecological dental wear analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 286, 42–54 (2010).
Google Scholar 
Rivals, F., Takatsuki, S., Albert, R. M. & Macià, L. Bamboo feeding and tooth wear of three sika deer (Cervus nippon) populations from northern Japan. J. Mammal. 95, 1043–1053 (2014).
Google Scholar 
Lister, A. M. Evolutionary and ecological origins of British deer. Proc. R. Soc. Edinb. Sect. B. Biol. Sci. 82, 205–229 (1984).
Google Scholar 
Coulson, T., Guinness, F., Pemberton, J. & Clutton-Brock, T. The demographic consequences of releasing a population of red deer from culling. Ecology 85, 411–422 (2004).
Google Scholar 
Nussey, D. H., Clutton-Brock, T. H., Elston, D. A., Albon, S. D. & Kruuk, L. E. B. Phenotypic plasticity in a maternal trait in red deer. J. Anim. Ecol. 74, 387–396 (2005).
Google Scholar 
Frevert, W. Rominten (BLV Bayerischer Landwirtschaftsverlag, 1977).
Google Scholar 
Clutton-Brock, T. H. & Albon, S. D. Winter mortality in red deer (Cervus elaphus). J. Zool. 198, 515–519 (1982).
Google Scholar 
Loison, A. & Langvatn, R. Short- and long-term effects of winter and spring weather on growth and survival of red deer in Norway. Oecologia 116, 489–500 (1998).PubMed 
ADS 

Google Scholar 
Torres-Porras, J., Carranza, J. & Pérez-González, J. Combined effects of drought and density on body and antler size of male iberian red deer cervus elaphus hispanicus: Climate change implications. Wildl. Biol. 15, 213–221 (2009).
Google Scholar 
Bugalho, M. N., Milne, J. A. & Racey, P. A. The foraging ecology of red deer (Cervus elaphus) in a mediterranean environment: Is a larger body size advantageous?. J. Zool. 255, 285–289 (2001).
Google Scholar 
Köhler, M. Skeleton and habitat of recent and fossil ruminants (F. Pfeil, Germany, 1993).
Google Scholar 
Boessneck, J. Zur grosse des mitteleuropaischen Rehes Capreolus capreolus L. in alluvial-vorgeschichtlicher und friiher historischer zeit. Z. f. Siiugetierkunde 21, 121–131 (1958).
Google Scholar 
Jensen, P. Body size trends of roe deer (Capreolus capreolus) from danish mesolithic sites. J. Dan. Archaeol. 10, 51–58 (1991).
Google Scholar 
Braza, F., San José, C., Aragon, S. & Delibes, J. R. El corzo andaluz. (Junta de Andalucía, 1994).Fandos, P. Skull biometry of spanish roe deer (Capreolus capreolus). Folia Zool. 43, 11–20 (1994).
Google Scholar 
Costa, L. First data on the size of north-Iberian roe bucks (Capreolus capreolus). Mammalia 59, 447–451 (1995).
Google Scholar 
Klein, D. R. & Strandgaard, H. Factors affecting growth and body size of roe deer. J. Wildl. Manag. 36, 64–79 (1972).
Google Scholar  More

Changes of straw degradation characteristics at different culture stagesCorn straw degradation ratioCorn straw weight loss in M44 at F1 reached 35.90% at 15 ℃ for 21 days, which was greater than that at F5, F8, and F11 by 2.33%, 3.01%, and 3.35%, respectively. There were no significant differences between F8 and F11(Fig. 1).Figure 1Corn straw degradation ratio was measured at different culture stages. The same small letter means there was no significant difference, and different small letters indicate significant differences at p  More

McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hammer TJ, Sanders JG, Fierer N. Not all animals need a microbiome. FEMS Microbiol Lett. 2019;366:fnz117.Article 
CAS 
PubMed 

Google Scholar 
Bagge LE, Osborn KJ, Johnsen S. Nanostructures and monolayers of spheres reduce surface reflections in hyperiid amphipods. Curr Biol. 2016;26:3071–6.Article 
CAS 
PubMed 

Google Scholar 
Apprill A. Marine animal microbiomes: toward understanding host–microbiome interactions in a changing ocean. Front Mar Sci. 2017;4:222.Article 

Google Scholar 
Wilkins LGE, Leray M, O’Dea A, Yuen B, Peixoto RS, Pereira TJ, et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 2019;17:e3000533.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva L, Nielsen S, Amir A, Gonzalez A, Ackermann GL, Cerrano C, et al. The sponge microbiome project. GigaScience. 2017;6:gix077.Article 
PubMed 
PubMed Central 

Google Scholar 
van Oppen MJH, Blackall LL. Coral microbiome dynamics, functions and design in a changing world. Nat Rev Microbiol. 2019;17:557–67.Article 
PubMed 

Google Scholar 
Henehan MJ, Hull PM, Penman DE, Rae JWB, Schmidt DN. Biogeochemical significance of pelagic ecosystem function: an end-Cretaceous case study. Phil Trans R Soc B. 2016;371:20150510.Article 
PubMed 
PubMed Central 

Google Scholar 
De Corte D, Srivastava A, Koski M, Garcia JAL, Takaki Y, Yokokawa T, et al. Metagenomic insights into zooplankton-associated bacterial communities. Environ Microbiol. 2018;20:492–505.Article 
PubMed 

Google Scholar 
Egerton S, Culloty S, Whooley J, Stanton C, Ross RP. The gut microbiota of marine fish. Front Microbiol. 2018;9:873.Article 
PubMed 
PubMed Central 

Google Scholar 
Scott JJ, Adam TC, Duran A, Burkepile DE, Rasher DB. Intestinal microbes: an axis of functional diversity among large marine consumers. Proc R Soc B. 2020;287:20192367.Article 
PubMed 
PubMed Central 

Google Scholar 
Sanders JG, Beichman AC, Roman J, Scott JJ, Emerson D, McCarthy JJ, et al. Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat Commun. 2015;6:8285.Article 
CAS 
PubMed 

Google Scholar 
Preheim SP, Boucher Y, Wildschutte H, David LA, Veneziano D, Alm EJ, et al. Metapopulation structure of Vibrionaceae among coastal marine invertebrates. Environ Microbiol. 2011;13:265–75.Article 
CAS 
PubMed 

Google Scholar 
Sullam KE, Essinger SD, Lozupone CA, O’Connor MP, Rosen GL, Knight R, et al. Environmental and ecological factors that shape the gut bacterial communities of fish: a meta-analysis. Mol Ecol. 2012;21:3363–78.Article 
PubMed 

Google Scholar 
Huang Q, Sham RC, Deng Y, Mao Y, Wang C, Zhang T, et al. Diversity of gut microbiomes in marine fishes is shaped by host‐related factors. Mol Ecol. 2020;29:5019–34.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webb TJ, Vanden Berghe E, O’Dor R. Biodiversity’s big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean. PLoS ONE. 2010;5:e10223.Article 
PubMed 
PubMed Central 

Google Scholar 
Irigoien X, Klevjer TA, Røstad A, Martinez U, Boyra G, Acuña JL, et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat Commun. 2014;5:3271.Article 
PubMed 

Google Scholar 
Drazen JC, Sutton TT. Dining in the deep: the feeding ecology of deep-sea fishes. Annu Rev Mar Sci. 2017;9:337–66.Article 

Google Scholar 
Boyd PW, Claustre H, Levy M, Siegel DA, Weber T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature. 2019;568:327–35.Article 
CAS 
PubMed 

Google Scholar 
Klevjer TA, Irigoien X, Røstad A, Fraile-Nuez E, Benítez-Barrios VM, Kaartvedt S. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci Rep. 2016;6:19873.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davison PC, Checkley DM, Koslow JA, Barlow J. Carbon export mediated by mesopelagic fishes in the northeast Pacific Ocean. Prog Oceanogr. 2013;116:14–30.Article 

Google Scholar 
Steinberg DK, Landry MR. Zooplankton and the ocean carbon cycle. Annu Rev Mar Sci. 2017;9:413–44.Article 

Google Scholar 
Stenvers VI, Hauss H, Osborn KJ, Neitzel P, Merten V, Scheer S, et al. Distribution, associations and role in the biological carbon pump of Pyrosoma atlanticum (Tunicata, Thaliacea) off Cabo Verde, NE Atlantic. Sci Rep. 2021;11:9231.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Robinson C, Steinberg DK, Anderson TR, Aristegui J, Carlson CA, Frost JR, et al. Mesopelagic zone ecology and biogeochemistry–a synthesis. Deep Sea Res II. 2010;57:1504–18.Article 
CAS 

Google Scholar 
Iacuaniello CM. An examination of intestinal microbiota of mesopelagic fish reveals microbial community diversity across fish families. Master’s Thesis, University of California San Diego. 2019.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 
PubMed 

Google Scholar 
Bernal A, Olivar MP, Maynou F, Fernández de Puelles ML. Diet and feeding strategies of mesopelagic fishes in the western Mediterranean. Prog Oceanogr. 2015;135:1–17.Article 

Google Scholar 
Bollens SM, Frost BW, Lin TS. Recruitment, growth, and diel vertical migration of Euphausia pacifica in a temperate fjord. Mar Biol. 1992;114:219–28.Article 

Google Scholar 
Hoving HJT, Neitzel P, Hauss H, Christiansen S, Kiko R, Robison BH, et al. In situ observations show vertical community structure of pelagic fauna in the eastern tropical North Atlantic off Cape Verde. Sci Rep. 2020;10:21798.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Judkins H, Vecchione M. Vertical distribution patterns of cephalopods in the northern Gulf of Mexico. Front Mar Sci. 2020;7:47.Article 

Google Scholar 
Miya M, Nemoto T. Reproduction, growth and vertical distribution of the meso- and bathypelagic fish Cyclothone atraria (Pisces: Gonostomatidae) in Sagami Bay, Central Japan. Deep Sea Res I. 1987;34:1565–77.Article 

Google Scholar 
Osborn KJ. Phylogenetics and ecology of pelagic munnopsid isopods (Crustacea, Asellota). Dissertation, University of California Berkeley. 2007.Pearcy WG, Forss CA. Depth distribution of oceanic shrimps (Decapoda; Natantia) off Oregon. J Fish Res Bd Can. 1966;23:1135–43.Article 

Google Scholar 
Russell FS. The vertical distribution of marine macroplankton. An observation on diurnal changes. J Mar Biol Ass. 1925;13:769–809.Article 

Google Scholar 
Watanabe H, Moku M, Kawaguchi K, Ishimaru K, Ohno A. Diel vertical migration of myctophid fishes (family Myctophidae) in the transitional waters of the western North Pacific. Fish Oceanogr. 1999;8:115–27.Article 

Google Scholar 
Madin LP. Gelatinous grazers: an underestimated force in ocean carbon cycles. 4th International Zooplankton Production Symposium, May 28–June 1. Hiroshima, Japan. 2007.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.Article 
CAS 
PubMed 

Google Scholar 
Ivanova NV, Zemlak TS, Hanner RH, Hebert PDN. Universal primer cocktails for fish DNA barcoding. Mol Ecol Notes. 2007;7:544–8.Article 
CAS 

Google Scholar 
Geller J, Meyer C, Parker M, Hawk H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all‐taxa biotic surveys. Mol Ecol Resour. 2013;13:851–61.Article 
CAS 
PubMed 

Google Scholar 
Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:34.Article 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.Article 
CAS 
PubMed 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res. 2013;41:D590–6.Article 
CAS 
PubMed 

Google Scholar 
Robeson MS II, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17:e1009581.Article 
PubMed 
PubMed Central 

Google Scholar 
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.Article 
PubMed 
PubMed Central 

Google Scholar 
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.Article 
PubMed 
PubMed Central 

Google Scholar 
Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3:e00021–18.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria; 2021.Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Knight R, Knights D. SHOGUN: a modular, accurate and scalable framework for microbiome quantification. Bioinformatics. 2020;36:4088–90.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Al-Ghalith G, Knights D. Faster and lower-memory metagenomic profiling with UTree. https://doi.org/10.5281/zenodo.998252.Jovel J, Patterson J, Wang W, Hotte N, O’Keefe S, Mitchel T, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol. 2016;7:459.Article 
PubMed 
PubMed Central 

Google Scholar 
DeWitt FA, Cailliet GM. Feeding habits of two bristlemouth fishes, Cyclothone acclinidens and C. signata (Gonostomatidae). Copeia. 1972;1972:868.Article 

Google Scholar 
Fauchald K, Jumars PA. The diet of worms: a study of polychaete feeding guilds. Oceanogr Mar Biol Annu Rev. 1979;17:193–284.
Google Scholar 
Flock ME, Hopkins TL. Species composition, vertical distribution, and food habits of the sergestid shrimp assemblage in the eastern Gulf of Mexico. J Crustacean Biol. 1992;12:210–23.Article 

Google Scholar 
Uttal L, Buck KR. Dietary study of the midwater polychaete Poeobius meseres in Monterey Bay, California. Mar Biol. 1996;125:333–43.Article 

Google Scholar 
Tanimata N, Yamamura O, Sakurai Y, Azumaya T. Dietary shift and feeding intensity of Stenobrachius leucopsarus in the Bering Sea. J Oceanogr. 2008;64:185–94.Article 

Google Scholar 
Hoving HJT, Robison BH. Vampire squid: detritivores in the oxygen minimum zone. Proc R Soc B. 2012;279:4559–67.Article 
PubMed 
PubMed Central 

Google Scholar 
Berning M. The feeding ecology of two species of holopelagic munnopsid isopods from the North Pacific (Acanthamunnopsis milleri and Munneurycope murrayi) using SEM analysis. Honors Thesis, Florida State University. 2014.Du X, Peterson W. Feeding rates and selectivity of adult Euphausia pacifica on natural particle assemblages in the coastal upwelling zone off Oregon, USA, 2010. J Plankton Res. 2014;36:1031–46.Article 

Google Scholar 
Henschke N, Everett JD, Richardson AJ, Suthers IM. Rethinking the role of salps in the ocean. Trends Ecol Evol. 2016;31:720–33.Article 
PubMed 

Google Scholar 
Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:e00920–20.Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Karlicki M, Antonowicz S, Karnkowska A. Tiara: deep learning-based classification system for eukaryotic sequences. Bioinformatics. 2022;38:344–50.Article 
CAS 

Google Scholar 
Levy Karin E, Mirdita M, Söding J. MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome. 2020;8:48Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH.UniProt Consortium UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2015;31:926–32.Article 
CAS 
PubMed 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.Article 
PubMed 
PubMed Central 

Google Scholar 
Chamberlain SA, Szöcs E. taxize: taxonomic search and retrieval in R. F1000Res. 2013;2:191.Article 
PubMed 
PubMed Central 

Google Scholar 
Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.Article 
CAS 
PubMed 

Google Scholar 
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.Article 
CAS 
PubMed 

Google Scholar 
Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.Article 
PubMed 
PubMed Central 

Google Scholar 
McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA, Zenger KR. Methods for normalizing microbiome data: an ecological perspective. Methods Ecol Evol. 2019;10:389–400.Article 

Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2020.Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200–2.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Molnár K, Ostoros G, Dunams-Morel D, Rosenthal B. Eimeria that infect fish are diverse and are related to, but distinct from, those that infect terrestrial vertebrates. Infect Genet Evol. 2012;12:1810–5.Article 
PubMed 

Google Scholar 
Domozych D. Algal cell walls. In: John Wiley & Sons, Ltd, editor. eLS. 1st ed. Wiley, Hoboken, NJ; 2019. p. 1–11.Gallet A, Koubbi P, Léger N, Scheifler M, Ruiz-Rodriguez M, Suzuki MT, et al. Low-diversity bacterial microbiota in Southern Ocean representatives of lanternfish genera Electrona, Protomyctophum and Gymnoscopelus (family Myctophidae). PLoS ONE. 2019;14:e0226159.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reji L, Tolar BB, Chavez FP, Francis CA. Depth-differentiation and seasonality of planktonic microbial assemblages in the Monterey Bay upwelling system. Front Microbiol. 2020;11:1075.Article 
PubMed 
PubMed Central 

Google Scholar 
De Corte D, Lekunberri I, Sintes E, Garcia J, Gonzales S, Herndl G. Linkage between copepods and bacteria in the North Atlantic Ocean. Aquat Microb Ecol. 2014;72:215–25.Article 

Google Scholar 
Russell SL. Transmission mode is associated with environment type and taxa across bacteria-eukaryote symbioses: a systematic review and meta-analysis. FEMS Microbiol Lett. 2019;366:fnz013.Article 
CAS 
PubMed 

Google Scholar 
Akbar S, Li X, Ding Z, Liu Q, Huang J, Zhou Q, et al. Disentangling diet- and medium-associated microbes in shaping Daphnia gut microbiome. Microb Ecol. 2022;84:911–21. https://doi.org/10.1007/s00248-021-01900-x.Eckert EM, Anicic N, Fontaneto D. Freshwater zooplankton microbiome composition is highly flexible and strongly influenced by the environment. Mol Ecol. 2021;30:1545–58.Article 
PubMed 

Google Scholar 
Rakusa-Suszczewski S. Predation of chaetognatha by Tomopteris helgolandica Greff. ICES J Mar Sci. 1968;32:226–31.Article 

Google Scholar 
Aldredge AL, Silver MW. Characteristics, dynamics and significance of marine snow. Prog Oceanogr. 1988;20:41–82.Article 

Google Scholar 
Jumars PA, Dorgan KM, Lindsay SM. Diet of worms emended: an update of polychaete feeding guilds. Ann Rev Mar Sci. 2015;7:497–520.Article 
PubMed 

Google Scholar 
Pfenning-Butterworth A, Cooper RO, Cressler CE. Daily feeding rhythm linked to microbiome composition in two zooplankton species. PLoS ONE. 2022;17:e0263538.Pappalardo P, Collins AG, Pagenkopp Lohan KM, Hanson KM, Truskey SB, et al. The role of taxonomic expertise in interpretation of metabarcoding studies. ICES J Mar Sci. 2021;78:3397–410.Article 

Google Scholar 
Hunt DE, Gevers D, Vahora NM, Polz MF. Conservation of the chitin utilization pathway in the Vibrionaceae. Appl Environ Microbiol. 2008;74:44–51.Article 
CAS 
PubMed 

Google Scholar 
Turner JT. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Prog Oceanogr. 2015;130:205–48.Article 

Google Scholar 
Chavez FP. Forcing and biological impact of onset of the 1992 El Niño in central California. Geophys Res Lett. 1996;23:265–8.Article 

Google Scholar 
Pennington TJ, Chavez FP. Seasonal fluctuations of temperature, salinity, nitrate, chlorophyll and primary production at station H3/M1 over 1989-96 in Monterey Bay, California. Deep Sea Res II. 2000;47:947–73.Article 
CAS 

Google Scholar  More