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    Small pigmented eukaryote assemblages of the western tropical North Atlantic around the Amazon River plume during spring discharge

    Habitat typesThe sampled stations were classified into 5 habitat types (Fig. 1a,b) as described by Weber et al.35: young plume core (YPC), old plume core (OPC), west plume margin (WPM), east plume margin (EPM) and oceanic seawater (OSW). Each habitat was characterized by a unique combination of sea surface salinity, sea surface temperature, nitrate availability index, mixed layer depth and chlorophyll maximum depth35. Geographically, the different habitats were unevenly distributed along the transect (Fig. 1c), illustrating the dynamic and patchy nature of the ARP. At each station, the temperature and salinity profiles confirmed the stratification of the water column. Maximum Brunt–Väisälä buoyancy frequency was high (3–15 × 10–3 s−1) and close to the surface in the plume core (YPC and OPC), restricting turbulent mixing between the plume waters and the underlying ocean waters. The plume margin stations (WPM and EPM) showed deeper and more muted (1–2 × 10–3 s−1) maximum buoyancy frequency peaks while OSW stations exhibited turbulent mixing from the surface to ~ 100 m (Supplementary Fig. S1). Fluorescence profiles provided guidance to sample within the chlorophyll maximum (Supplementary Fig. S1). In the plume core, the chlorophyll peak was located above the halocline. At plume margin stations, multiple chlorophyll maxima were detected at the halocline or just below, while the oceanic seawater stations did not have haloclines, and chlorophyll peaks were far below the surface (deeper than 50 m). Surface samples from the core plume stations corresponded to high temperature-low salinity waters, with low density. These plume waters mixed with coastal waters at the surface of plume margin stations, but this was not the case at OSW stations (Supplementary Fig. S2).Figure 1Location of the study (A), distribution of sampling stations (B) and identification of the habitat types using a principal component analysis (C) and Ward’s hierarchical cluster analysis (D). The map in B shows the monthly composite surface chlorophyll concentration for May 2018 from satellite observations Reprocessed L4 (ESA-CCI: OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_093) downloaded from Copernicus Marine Service (https://resources.marine.copernicus.eu). The map was created using the NASA SeaDAS 7.5.3 software with land and exclusive economic zones boundaries (yellow lines) added with gmt v5.4.5 software. Note that all stations from EN614 were used to establish habitat types, but only the 10 stations highlighted in bold and shown on the map were used in this study. SSS, sea surface salinity; SST, sea surface temperature; NAI, nitrogen availability index; MLD, mixed layer depth; ChlMD, chlorophyll maximum depth.Full size imageSmall-sized pigmented eukaryote populations, size, abundance and biomass:Overall, the small pigmented eukaryote communities were composed of a variable combination of 3 to 4 populations per sample, with a total of 6 different populations (named P1, P2, P3, P4, P5 and P6) among all samples, identified by flow cytometry according to cell size range and pigment content (Fig. 2). Based on relative estimates from flow cytometry calibrations using beads of known sizes, most populations belonged to the picoplankton (≤ 2–3 µm). Cells in P1 were approx. 0.8 µm. P2 was a very diverse cluster resulting in a size range from ≤ 0.8 to 5 µm, with a majority of cells clustered around 2 µm, while P3 and P6 were characterized by cells of 0.8–2 µm and P4 by cells of 2–3.5 µm. Cells identified within P5 were larger, ranging from 3.5 to  > 5 µm, therefore encompassing small-sized members of the nanoplankton (3–20 µm). Studies that provide size calibrations for sorted picoeukaryote populations are rare37, making direct comparisons unreliable.Figure 2Example of a cytogram illustrating the gates used for small pigmented eukaryote population counts and sorting. Populations were first discriminated based on their position in the chlorophyll vs forward scatter cytogram (A, all events represented) and then redefined in the chlorophyll vs phycoerythrine (PE) autofluorescence cytogram (B, only events gated in A represented). In the later, we avoided Synechococcus overlapping with small pigmented eukaryote populations in A and cells exhibiting high PE fluorescence among the populations from cytogram B. Note that all 6 populations were never found present in the same sample. In particular, the sample represented here (S003 surface) did not contain P1 or P6, but the gates are represented nonetheless in panel A to provide an illustration for these populations. Note that the gating had to be adjusted between samples but the relative positions stayed similar to those illustrated here. The positions of standard size-calibrated non-fluorescent beads (dashed lines) along the x-axis were used to determine the size range of each gated population in cytogram A. Red ellipses mark the position of yellow-green reference beads of 1 and 2 µm (1-YG and 2-YG, respectively) used to maintain instrument alignment, although the bead clusters are not apparent in the sample since they were run separately (for details see “Methods”).Full size imageThe different small pigmented eukaryote populations had variable cell abundances relative to each other and varied with sampling location (Table 1). Surface communities were either dominated by population P3 (57–74% of small pigmented eukaryote abundance, hereafter counts) in the WPM (S003, S031 cast 03 (henceforth S031_03), and S031 cast 11 (henceforth S031_11)) as well as one station from EPM (S022) and one from OSW (S020), or by P2 (52–66% of counts) at the OPC (S024) and stations of the EPM (S025) and OSW (S027). All stations had lower abundances of P4 (5.7–32% of counts), and only four stations (S003, S020, S022, S031_11) also presented a small P5 population (3.1–6.3% of counts). The small pigmented eukaryote communities collected from chlorophyll maxima were all dominated by P2 (69–94% of counts) and accompanied by much less abundant P3 (5.6–23% of counts), except for station S022 whose chlorophyll maximum small pigmented eukaryote community was dominated by P3 (93% of counts). All chlorophyll maximum communities were characterized by a low contribution of P4 (0.6–6.2% of counts). The small pigmented eukaryote community collected from 40 m at S017 was characterized by the presence of population P6 (16% of counts), absent from the other stations. P1 was only present at the chlorophyll maximum of the OPC station (11% of counts). However, the amount of DNA extracted from P1 was too small to allow for sequencing of the 18S rDNA and it is therefore not part of the subsequent analyses.Table 1 Cell counts per population, as the proportion of the summed total cell density for all 6 gated populations. CM, chlorophyll maximum.Full size tableAt the surface, small pigmented eukaryotes contributed on average 1.3 ± 0.4% of the total small phytoplankton abundance (Supplementary Table S1), indicating that picocyanobacteria dominated all stations. Synechococcus dominated cell abundances at most stations (57–97%), except in the OSW (S020, S027) where Prochlorococcus dominated (92–97%). These results reflect the established paradigm that the eukaryotic component of small phytoplankton communities is less abundant than the prokaryotic component10,16,37. Nonetheless, in terms of biomass, small pigmented eukaryote dominated the small phytoplankton in all surface samples (11–44 × 103 µg C/m3; 47–71%), representing a biomass greater than or equal to the picocyanobacteria (Supplementary Table S2). The horizontal shift in surface nutrient concentrations among habitats was too modest to affect the relative contribution of small pigmented eukaryote to total small phytoplankton abundances, contrary to reports for much larger spatial scales involving greater differences in nutrient concentrations, ranging from coastal systems to the open ocean10,16. Small photosynthetic eukaryote abundance and biomass were not significantly correlated to nutrient concentrations, salinity or temperature (Spearman rho  0.05).At the chlorophyll maxima and in deeper waters, despite a consistent predominance of Prochlorococcus, small pigmented eukaryotes generally contributed more to the total small phytoplankton abundance than at the surface, similar to previous reports from the Indian Ocean7 and the south Pacific Ocean8,9. These samples showed decreased absolute abundances of ≤ 5 µm phytoplankton (Supplementary Table S1), with the plume core stations (S017, S024) exhibiting the lowest overall absolute abundances (8.2–32 × 103 cells/mL). This decrease in absolute numbers of the picocyanobacteria, concomitant with increased small pigmented eukaryote relative abundances (6–18%), indicated that eukaryotes fared better than the picocyanobacteria in the low light conditions of waters shaded by the plume. Dominance of the small phytoplankton biomass by small eukaryotes (5.9–50 µg C/m3; 53–95% of the total biomass), representing more than twice the picocyanobacterial biomass at the chlorophyll maxima and deeper water, is reminiscent of reports that small pigmented eukaryotes can contribute significantly to primary production in coastal regions12. Although this contrasts with findings from open oceans where Prochlorococcus dominates small phytoplankton biomass11,37, small pigmented eukaryotes were found to be similarly biomass-dominant and contributing up to a third of total primary production in surface seawater of the western subtropical North Atlantic under phosphorus depletion13.Taxonomic composition of small pigmented eukaryote populationsHigh-throughput sequencing of the small ribosomal subunit gene provided insights into the taxonomic composition of resident (live, inactive and recently dead) small pigmented eukaryote populations. A total of 234 operational taxonomic units (OTUs) were obtained, covering the full diversity of the populations in each sample (Supplementary Fig. S3) and after removal of metazoan OTUs and OTUs  1,000 OTUs38,39,40,41,42, the low OTU richness is a reminder that our cell sorting protocol allowed the focused targeting of small pigmented eukaryote populations. The low OTU counts (3–42) for each population (Table 2) further reflect the accuracy of the sorting method and the near taxonomic purity of some of the sorted populations.Table 2 Operational taxonomic units (OTUs) counts per population. CM, chlorophyll maximum.Full size tableMajor OTUs, constituting at least 20% of the total reads per population for at least one sample, represented 29 out of the 201 OTUs (Fig. 3; Supplementary Table S1). The most frequent OTU was a Chloropicophyceae (Chlorophyta), averaging 19.55% of total reads/sample. The second most frequent Chlorophyta OTU belonged to prasinophyte clade IX with an average of 4.36% of the total reads/sample. The Ochrophyta were represented by two Marine Ochrophyta clade 5 (MOCH-5) OTUs and five Bacillariophyceae OTUs ranging on average from 5.5 to 2.1%, and 5.3 to 1.6% of total reads per sample, respectively. Only one major OTU was associated with the prymnesiophytes, classified within the order Isochrysidales, representing 1.3% of the total reads/sample (Fig. 3). The rest of the major OTUs had lower average abundances throughout the samples ( 8 µm cells from our sorted populations. Furthermore, the consistently low abundance or absence of P5 throughout our samples suggests that this Isochrysidales OTU5 (Noelaerhabdaceae) did not dominate the small pigmented eukaryote communities of the ARP in the spring.Distinguishing the small pigmented eukaryote community composition between habitat typesA UniFrac unweighted paired group method with arithmetic mean analysis revealed stronger clustering among populations than among stations or depths, suggesting a consistency in the phylogenetic composition of the sorted populations (Supplementary Fig. S4). The only exceptions were S031_03 chlorophyll maximum and S031_11 surface samples for which the three populations clustered distinctly from the rest. A canonical correspondence analysis based on assemblages of major OTUs separated populations P2 and P4 of the OPC surface and subsurface and all four populations of S031_11 surface from the rest of the samples (Fig. 6a). The low abundance OTU composition of the surface and subsurface OPC populations and the deep YPC sample were distinct from the rest of the samples, the latter being strongly driven by salinity (Fig. 6b). The environmental variables used in the canonical correspondence analysis explain a sizable portion of the variability (33–50%), although it seems that an important driver of community composition was unaccounted for.Figure 6Canonical correspondence analysis with A major OTUs, and B low abundance OTUs.Full size imageThe YPC populations had low OTU richness (Table 2), and most of their major OTUs were shared with other stations, namely the Chloropicophyceae OTU192 and OTU165, detected in all 4 populations (16–86% of total reads/population). Notably, this sample was only distinguished from the rest by its low abundance OTU composition (Fig. 6b). Of the 15 low-abundance OTUs among the 4 populations detected, a few were shared with other samples, but only one or two at a time (Supplementary Table S3). The OPC surface was also characterized by a low OTU richness (Table 2), each population dominated by one or two major OTUs (Fig. 5). P2 was dominated by Bacillariophyta Nitzschia (OTU86), also found at other stations in lower abundance, and by a Syndiniales GrpI OTU108 unique to this station. P3 was composed of the ubiquitous Chloropicophyceae OTU192, classified as Chloropicon, and two MOCH-5 OTUs, which were also found in P4. The chlorophyll maximum sample was composed of a very similar small pigmented eukaryote community, albeit with a larger proportion of low abundance OTUs in P2. Interestingly, P3 at both surface and chlorophyll maximum was distinguished from other samples by the low abundance OTUs that accompanied the dominant Chloropicophyceae OTUs (Fig. 6b). The small pigmented eukaryote community of the sample below the chlorophyll maximum was characterized by an abundant P3 dominated by Chloropicon OTU192, accompanied by the Pelagophyceae Pelagomonas OTU232, which was also detected at S025 (EPM) and S027 (OSW). This sample collected below the halocline was distinct from the upper water column and more similar to the margin and oceanic samples. Such a pattern is consistent with the plume overriding the surrounding margin or oceanic waters and submerging the endemic communities that were there previously at the surface.In contrast to our first hypothesis regarding small pigmented eukaryote variability across the horizontal gradients of the ARP, the composition of small pigmented eukaryote communities was stable among the different habitat types. This is attributable to a combination of variability in OTU composition among samples from the same habitats and similarity of the small pigmented eukaryote assemblages between stations of different habitats. Indeed, the populations exhibited no significant differences between average UniFrac distances among habitats, stations of the same habitats and depths of the same stations (ANOVA, p  > 0.164 for P2, p  > 0.251 for P3 and p  > 0.735 for P4). The lack of statistical differences, particularly among the plume margins and oceanic waters, are indicative of the dynamic nature of large river plumes, such as reported for the Columbia River67. The meandering of the ARP creates a very dynamic system with a variable influence on local oligotrophic ocean waters68,69. It is possible that each station is too unique to establish a consensus small pigmented eukaryote community structure per habitat type, while abundant populations are shared between stations of different habitats limiting the detectable distinctions between the assemblages. For instance, the dominant Nitzschia OTU86 was shared between the OPC, one of the WPM stations and one of the EPM stations. Similarly, Chloropicon OTU192 dominated P3 at all stations, except in the surface waters of one WPM station (S031_11) and one OSW station (S027). Furthermore, our use of DNA as template for the taxonomic survey might have masked changes in the active communities among different habitats that would have been more apparent with RNA templates.The progressive mixing of oceanic waters into the plume is likely to exchange small pigmented eukaryote communities between the adjacent environments. This hydrodynamic phenomenon would allow the unrestrained dispersal of small pigmented eukaryotes between habitats, resulting in the observed similarities between the plume and surrounding ocean surface waters. In the dynamic environment of the ARP margins, the similarity between communities of different habitats is a function of time since the onset of the mixing event that exposed oceanic and plume small pigmented eukaryote communities to adjacent environments. Time-since-mixing might be the environmental parameter unaccounted for in our dataset that would explain the intra-habitat variability in major OTU composition, incidentally, obscuring the differences between habitats.Contrary to picocyanobacteria, which mostly use recycled, reduced forms of nitrogen (ammonium and urea), small pigmented eukaryotes rely more on nitrate70,71, making them more sensitive to the low nitrate concentrations in and around the ARP. While the uniformity of small pigmented eukaryote biomass between the oligotrophic ocean waters and the plume margins is likely the product of low nutrient concentrations in both environments, the variability of the OTU composition might be explained by a variable nitrate metabolism among small pigmented eukaryote taxa70. Alternatively, mixotrophy, the combination of photosynthesis and bacterivory common among small pigmented eukaryotes13,72,73,74, might confer a generalist advantage relative to picocyanobacteria by allowing maintenance of activity and abundance in rapidly varying habitats.Corroborating our second hypothesis that the small pigmented eukaryote diversity should vary with depth within the euphotic layer, the small pigmented eukaryote diversity and abundance varied vertically, with higher cell counts at the chlorophyll maximum. The taxonomic composition of chlorophyll maximum communities differed from those at the surface with populations characterized by high abundances of OTUs associated with Bacillariophecae, Pelagophyceae, radiolarians or Dinophyceae. The presence of Dinophyceae or Pelagophyceae OTUs at the chlorophyll maxima of plume stations (OPC, WPM and EPM), which were absent from surface waters, reflects the strong stratification at plume-influenced stations, reducing mixing between the surface and the bottom of the euphotic zone, the latter of which can be strongly influenced by oceanic waters. In particular at these stations (S024, S031 and S022), the chlorophyll maximum samples were collected below the halocline depth. Hence, these Dinophyceae and Pelagophyceae OTUs, uniquely shared with one of the oceanic stations, suggest that water masses under the plume-influenced surface might correspond to the oceanic water masses at the OSW stations.Station S031, a time-series station, showed a variation in major OTU assemblages between the cast conducted at 3 pm on May 26th (S031_03) and another cast carried out at 11am on May 27th (S031_11). Within this 19-h interval, in which environmental conditions remained consistent with the habitat type (Fig. 1), the OTU composition underwent a shift (Figs. 5, 6a). The relative cell abundances of each population remained similar, except for a P5 population appearing in samples from the second time point (Fig. 5). At the surface, the shift was characterized by the replacement of all OTUs from S031_03 with major OTUs assigned to Syndiniales in S031_11. The only common OTU, MAST-3A (OTU115), had low abundances (0.4–3.7%) in S031_03 and reached 14–24% in the S031_11 populations (Supplementary Table S3). Interestingly, the major Syndiniales OTUs in S031_11 were unique to this station, and different from the Syndiniales OTUs detected in the OPC and EPM stations (Fig. 5). This unexpected abundance in unpigmented Syndiniales OTUs in S031_11 might be due to the presence of dinospores in transitory free-living form, attached to or inside alveolate hosts or predators75,76. The large proportion of low abundance OTUs, which represented 70% of total reads in P3, were related to Syndiniales, ciliates and dinoflagellates (Supplementary Material SM2).Changes were also observed at the chlorophyll maximum where the unique radiolarian Collophidium OTU that dominated S031_03 disappeared in S031_11. This abundance of sequences related to the Radiolaria, large heterotrophic protozoa (≥ 100 µm), was unexpected among our targeted populations sorted by size and chlorophyll content. However, radiolarian sequences have been found among small size fractions before77,78, particularly at depth79,80 where they are suspected to descend and release small flagellate gametes called swarmers81. Hence, if attached to exopolymer-producing pigmented cells such as in the late stages of a phytoplankton bloom82, these swarmers could have been indiscriminately sorted into the three populations. In addition, three dinoflagellate OTUs appeared in P2 and P4 in cast S031_11, of which one was only found in the deep YPC, and one was shared with the chlorophyll maximum of S022 (EPM) and the surface of S027 (OSW).The radical shift in small pigmented eukaryote community composition between the two casts from station S031 reflects the dynamic nature of the ARP ecosystem and the multiple scales of heterogeneity within this system that is unlikely to be uncovered without the multiple approaches used in this study. It is unlikely that this interval of 19 h was sufficient for the resident small pigmented eukaryote community to change so radically as to completely replace the original taxa, as taxonomic turnover on daily time scales is usually very limited83,84. The salinity profiles indicated a stronger stratification at the time of cast 11, with a deeper mixing depth (22 m) compared to cast 03 (16 m), reflected in the chlorophyll profiles showing more homogenous concentrations in the top 22 m of cast 11 (Supplementary Fig. S1). In addition, the chlorophyll maximum peak sampled at 27 m was much smaller in cast 11 compared to cast 03, with a stronger secondary peak at 39 m. Satellite observations show the river plume defined as high surface chlorophyll, spreading north and eastward between the 25th and 29th of May (Supplementary Fig. S9—higher chlorophyll concentrations north of 17°N), suggesting a plume that was advecting past the ship during this time. This likely caused the deepening of the mixing depth, forcing the surface small pigmented eukaryote community northwards and the chlorophyll maximum community deeper below the mixing depth, effectively displacing the communities identified during cast 03.As a first study of the small pigmented eukaryotes and their response to the environmental habitats of the ARP, this work provides new insights into the detailed 18S rDNA-based taxonomy of an underexplored fraction of the phytoplankton. Our results illustrate that FACS is a reliable tool to enrich targeted taxonomic groups, such as Bacillariophyta, Chlorophyta and MOCH-5. The small pigmented eukaryote taxonomic composition was influenced by the ARP only at the plume core (OPC) where surface assemblages showed a strong dissimilarity with other stations, which were otherwise similar despite belonging to different habitat types. This result stands in apparent contrast to the drastic succession in community composition of the microphytoplankton driven by the nutrient gradients in the ARP1,3,4,6. The surprisingly limited influence of the ARP on surface small pigmented eukaryote communities warrants further inquiry. Sampling at different times of the year and using 18S rRNA as template for sequencing might reveal small pigmented eukaryotes to be more reactive to the habitat types earlier in the season, at the beginning of the massive discharge period from the Amazon River, or at the end of the summer when the ARP is entrained toward the east by the north equatorial countercurrent. More

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    Principles of seed banks and the emergence of complexity from dormancy

    1.Smith, B. D. Documenting plant domestication: The consilience of biological and archaeological approaches. Proc. Natl Acad. Sci. USA 98, 1324–1326 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Darwin, C. R. On the Origins of the Species. (John Murray, 1859).3.Venable, D. L. & Lawlor, L. Delayed germination and dispersal in desert annuals: escape in space and time. Oecologia 46, 272–282 (1980).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Ellner, S. ESS germination strategies in randomly varying environments.1. Logist.Type models Theor. Popul. Biol. 28, 50–79 (1985).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    5.Levin, D. A. Seed bank as a source of genetic novelty in plants. Am. Nat. 135, 563–572 (1990).Article 

    Google Scholar 
    6.Evans, M. E. K., Ferriere, R., Kane, M. J. & Venable, D. L. Bet hedging via seed banking in desert evening primroses (Oenothera, Onagraceae): demographic evidence from natural populations. Am. Nat. 169, 84–94 (2007). Simulations and field data support bet-hedging via dormancy.Article 

    Google Scholar 
    7.Kortessis, N. & Chesson, P. Germination variation facilitates the evolution of seed dormancy when coupled with seedling competition. Theor. Popul. Biol. 130, 60–73 (2019).PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    8.Peres, S. Saving the gene pool for the future: Seed banks as archives. Stud. Hist. Philos. Sci. Part C. Stud. Hist. Philos. Biol. Biomed. Sci. 55, 96–104 (2016).Article 

    Google Scholar 
    9.Tocheva, E. I., Ortega, D. R. & Jensen, G. J. Sporulation, bacterial cell envelopes and the origin of life. Nat. Rev. Microbiol. 14, 535–542 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Ginsburg, I., Lingam, M. & Loeb, A. Galactic Panspermia. Astrophys. J. Lett. 868 (2018).11.Maslov, S. & Sneppen, K. Well-temperate phage: optimal bet-hedging against local environmental collapses. Sci. Rep. 5, 10523 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Sriram, R., Shoff, M., Booton, G., Fuerst, P. & Visvesvara, G. S. Survival of Acanthamoeba cysts after desiccation for more than 20 years. J. Clin. Microbiol. 46, 4045–4048 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Storey, K. B. Life in the slow lane: molecular mechanisms of estivation. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 133, 733–754 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Hu, P. J. In WormBook (ed The C. elegans Research Community) (2007).16.Gilbert, J. J. Dormancy in rotifers. Trans. Am. Microsc. Soc. 93, 490–513 (1974).Article 

    Google Scholar 
    17.Kostal, V. Eco-physiological phases of insect diapause. J. Insect Physiol. 52, 113–127 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Schleucher, E. Torpor in birds: taxonomy, energetics, and ecology. Physiol. Biochem. Zool. 77, 942–949 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Cooke, S. J., Grant, E. C., Schreer, J. F., Philipp, D. P. & Devries, A. L. Low temperature cardiac response to exhaustive exercise in fish with different levels of winter quiescence. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 134, 159–167 (2003).Article 

    Google Scholar 
    20.Fenelon, J. C., Banerjee, A. & Murphy, B. D. Embryonic diapause: development on hold. Int. J. Dev. Biol. 58, 163–174 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Andrews, M. T. Advances in molecular biology of hibernation in mammals. Bioessays 29, 431–440 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Sottocornola, R. & Lo Celso, C. Dormancy in the stem cell niche. Stem Cell Res. Ther. 3, 10 (2012).23.Phan, T. G. & Croucher, P. I. The dormant cancer cell life cycle. Nat. Rev. Cancer 20, 398–411 (2020). Review discussing importance of dormancy for persistence and dispersal of cancer cells with clinical applications.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Darby, I. A. & Hewitson, T. D. Fibroblast differentiation in wound healing and fibrosis. Int Rev. Cytol. 257, 143–179 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Chapman, N. M., Boothby, M. R. & Chi, H. B. Metabolic coordination of T cell quiescence and activation. Nat. Rev. Immunol. 20, 55–70 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Shoham, S., O’Connor, D. H. & Segev, R. How silent is the brain: is there a “dark matter” problem in neuroscience? J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 192, 777–784 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Takahashi, T. M. et al. A discrete neuronal circuit induces a hibernation-like state in rodents. Nature 583, 109-114 (2020).28.Seger, J. & Brockmann, J. H. What is bet-hedging? In Oxford Surveys in Evolutionary Biology (eds Harvey P. H. & Partridge L.) Vol. 4, 182–211 (Oxford University Press, 1987). Comprehensive review of bet-hedging in population biology.29.Considine, M. J. & Considine, J. A. On the language and physiology of dormancy and quiescence in plants. J. Exp. Bot. 67, 3189–3203 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Cohen, D. Optimizing reproduction in a randomly varying environment. Theor. Biol. 12, 119–129 (1966). Among the first mathematical models describing the benefits of delayed seed germination.ADS 
    CAS 
    Article 

    Google Scholar 
    31.Amen, R. D. A model of seed dormancy. Bot. Rev. 34, 1–31 (1968).CAS 
    Article 

    Google Scholar 
    32.Bulmer, M. G. Delayed germination of seeds: Cohen’s model revisited. Theor. Popul. Biol. 26, 367–377 (1984).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    33.Philippi, T. Bet-hedging germination of desert annuals: beyond the 1st year. Am. Nat. 142, 474–487 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Rajon, E., Venner, S. & Menu, F. Spatially heterogeneous stochasticity and the adaptive diversification of dormancy. J. Evol. Biol. 22, 2094–2103 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Blath, J., González Casanova, A., Eldon, B., Kurt, N. & Wilke-Berenguer, M. Genetic variability under the seedbank coalescent. Genetics 200, 921–934 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Locey, K. J., Fisk, M. C. & Lennon, J. T. Microscale insight into microbial seed banks. Front. Microbiol. 7, 2040 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Yamamichi, M., Hairston, N. G., Rees, M. & Ellner, S. P. Rapid evolution with generation overlap: the double-edged effect of dormancy. Theor. Ecol. 12, 179–195 (2019). Models explore how dormancy and environmental fluctuations affect the rate of trait evolution and adaptation.Article 

    Google Scholar 
    38.Wörmer, L. et al. Microbial dormancy in the marine subsurface: Global endospore abundance and response to burial. Sci. Adv. 5, eaav1024 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    39.Baskin, C. C. & Baskin, J. Seeds: Ecology, Biogeography, and, Evolution of Dormancy and Germination. 1600 (Academic Press, 2014). Comprehensive book covering the causes and consequences of dormancy in plants.40.Magurran, A. E. Measuring Biological Diversity. (Blackwell Publishing, 2004).41.Hoyle, G. L. et al. Soil warming increases plant species richness but decreases germination from the alpine soil seed bank. Glob. Change Biol. 19, 1549–1561 (2013).ADS 
    Article 

    Google Scholar 
    42.Haaland, T. R., Wright, J. & Ratikainen, I. I. Bet-hedging across generations can affect the evolution of variance-sensitive strategies within generations. Proc. R. Soc. B Biol. Sci. 286, 20192070 (2019).Article 

    Google Scholar 
    43.Childs, D. Z., Metcalf, C. J. E. & Rees, M. Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. Proc. R. Soc. B Biol. Sci. 277, 3055–3064 (2010).Article 

    Google Scholar 
    44.Starrfelt, J. & Kokko, H. Bet-hedging – a triple trade-off between means, variances and correlations. Biol. Rev. 87, 742–755 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Cooper, W. S. & Kaplan, R. H. Adaptive coin-flipping: a decision-theoretic examination of natural selection for random individual variation. J. Theor. Biol. 94, 135–151 (1982).ADS 
    MathSciNet 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Kussell, E. & Leibler, S. Phenotypic diversity, population growth, and information in fluctuating environments. Science 309, 2075–2078 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Kussell, E., Kishony, R., Balaban, N. Q. & Leibler, S. Bacterial persistence: a model of survival in changing environments. Genetics 169, 1807–1814 (2005). Model showing that stochastic transitioning into dormancy is beneficial in fluctuating environments.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Beaumont, H. J. E., Gallie, J., Kost, C., Ferguson, G. C. & Rainey, P. B. Experimental evolution of bet hedging. Nature 462, 90–93 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Jost, J. & Wang, Y. Optimization and phenotype allocation. Bull. Math. Biol. 76, 184–200 (2014).MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    50.Lewis, K. Persister cells. Annu. Rev. Microbiol. 64, 357–372 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Epstein, S. S. Microbial awakenings. Nature 457, 1083–1083 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Buerger, S. et al. Microbial scout hypothesis, stochastic exit from dormancy, and the nature of slow growers. Appl. Environ. Microbiol. 78, 3221–3228 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Chevin, L. M. & Hoffman, A. A. Evolution of phenotypic plasticity in extreme environments. Philos. Trans. R. Soc. Lond. 372, 1723 (2017).Article 

    Google Scholar 
    54.Govern, C. C. & ten Wolde, P. R. Optimal resource allocation in cellular sensing systems. Proc. Natl Acad. Sci. USA 111, 17486–17491 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Baskin, J. M. & Baskin, C. C. The annual dormancy cycle in buried weed seeds: a continuum. Bioscience 35, 492–498 (1985).Article 

    Google Scholar 
    56.Tuan, P. A., Kumar, R., Rehal, P. K., Toora, P. K. & Ayele, B. T. Molecular mechanisms underlying abscisic acid/gibberellin balance in the control of seed dormancy and germination in cereals. Front. Plant Sci. 9, 668 (2018).57.Samuels, I. A. & Levey, D. J. Effects of gut passage on seed germination: do experiments answer the questions they ask? Funct. Ecol. 19, 365–368 (2005).Article 

    Google Scholar 
    58.Dworkin, J. & Losick, R. Developmental commitment in a bacterium. Cell 121, 401–409 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.McKenney, P. T., Driks, A. & Eichenberger, P. The Bacillus subtilis endospore: assembly and functions of the multilayered coat. Nat. Rev. Microbiol. 11, 33–44 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Locey, K. J. & Lennon, J. T. A residence time theory for biodiversity. Am. Nat. 194, 59–72 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Levin, B. R. et al. A numbers game: ribosome densities, bacterial growth, and antibiotic-mediated stasis and death. mBio. 8, e02253-16 (2017).62.Rambo, I. M., Marsh, A. & Biddle, J. F. Cytosine methylation within marine sediment microbial communities: potential epigenetic adaptation to the environment. Front. Microbiol. 10, 1291 (2019).63.Wisnoski, N. I., Leibold, M. A. & Lennon, J. T. Dormancy in metacommunities. Am. Nat. 194, 135–151 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Jones, S. E. & Lennon, J. T. Dormancy contributes to the maintenance of microbial diversity. Proc. Natl Acad. Sci. USA 107, 5881–5886 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Locey, K. J. et al. Dormancy dampens the microbial distance-decay relationship. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190243 (2020). Combined field and modeling approach demonstrating that dormancy can alter biogeographic patterns.66.Chihara, K., Matsumoto, S., Kagawa, Y. & Tsuneda, S. Mathematical modeling of dormant cell formation in growing biofilm. Front. Microbiol. 6, 534 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Frank, S. A. Metabolic heat in microbial conflict and cooperation. Front. Ecol. Evolution 8, 275 (2020).Article 

    Google Scholar 
    68.Maki, H. Origins of spontaneous mutations: specificity and directionality of base-substitution, frameshift, and sequence-substitution mutageneses. Annu. Rev. Genet. 36, 279–303 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Foster, P. L. Stress responses and genetic variation in bacteria. Mutat. Res. Fundam. Mol. Mech. Mutagen. 569, 3–11 (2005).CAS 
    Article 

    Google Scholar 
    70.Ryan, F. J. Spontaneous mutation in non-dividing bacteria. Genetics 40, 726–738 (1955).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Gangloff, S. et al. Quiescence unveils a novel mutational force in fission yeast. eLife 6, e27469 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Long, H. A. et al. Evolutionary determinants of genome-wide nucleotide composition. Nat. Ecol. Evol. 2, 237–240 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Shoemaker, W. R. & Lennon, J. T. Evolution with a seed bank: the population genetic consequences of microbial dormancy. Evol. Appl. 11, 60–75 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Tellier, A., Laurent, S. J. Y., Lainer, H., Pavllidis, P. & Stephan, W. Inference of seed bank parameters in two wild tomato species using ecological and genetic data. Proc. Natl. Acad. Sci. USA 108, 17052-17057 (2011). Infers seed bank quantities based on a coalescent theoretical model.75.Sellinger, T. P. P., Abu Awad, D., Moest, M. & Tellier, A. Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genet. 16, e1008698 (2020).76.Blath, J., Buzzoni, E., Koskela, J. & Berenguer, M. W. Statistical tools for seed bank detection. Theor. Popul. Biol. 132, 1–15 (2020).PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    77.Templeton, A. R. & Levin, D. A. Evolutionary consequences of seed pools. Am. Nat. 114, 232–249 (1979).Article 

    Google Scholar 
    78.Hairston, N. G. & Destasio, B. T. Rate of evolution slowed by dormant propagule pool. Nature 336, 239–242 (1988). Field evidence that dormancy and species interactions affect rates of evolution.ADS 
    Article 

    Google Scholar 
    79.Turelli, M., Schemske, D. W. & Bierzychudek, P. Stable two-allele polymorphisms maintained by fluctuating fitnesses and seed banks: Protecting the blues in Linanthus parryae. Evolution 55, 1283–1298 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Sundqvist, L., Godhe, A., Jonsson, P. R. & Sefbom, J. The anchoring effect-long-term dormancy and genetic population structure. ISME J. 12, 2929–2941 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Maughan, H. Rates of molecular evolution in bacteria are relatively constant despite spore dormancy. Evolution 61, 280–288 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Weller, C. & Wu, M. A generation-time effect on the rate of molecular evolution in bacteria. Evolution 69, 643–652 (2015). Phylogenetic comparative approach demonstrating that dormancy reduces rates of evolution.CAS 
    PubMed 
    Article 

    Google Scholar 
    83.Willis, C. G. et al. The evolution of seed dormancy: environmental cues, evolutionary hubs, and diversification of the seed plants. New Phytol. 203, 300–309 (2014).PubMed 
    Article 

    Google Scholar 
    84.Kalisz, S. & McPeek, M. A. Demography of an age-structured annual: resampled projection matrices, elasticity analyses, and seed bank effects. Ecology 73, 1082–1093 (1992).Article 

    Google Scholar 
    85.Morris, W. F. et al. Longevity can buffer plant and animal populations against changing climatic variability. Ecology 89, 19–25 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Moriuchi, K. S., Venable, D. L., Pake, C. E. & Lange, T. Direct measurement of the seed bank age structure of a Sonoran desert annual plant. Ecology 81, 1133–1138 (2000).Article 

    Google Scholar 
    87.Moger-Reischer, R. Z. & Lennon, J. T. Microbial ageing and longevity. Nat. Rev. Microbiol. 17, 679–690 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Dalling, J. W., Davis, A. S., Schutte, B. J. & Arnold, A. E. Seed survival in soil: interacting effects of predation, dormancy and the soil microbial community. J. Ecol. 99, 89–95 (2011).Article 

    Google Scholar 
    89.Hairston, N. G. & Kearns, C. M. Temporal dispersal: ecological and evolutionary aspects of zooplankton egg banks and the role of sediment mixing. Integr. Comp. Biol. 42, 481–491 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Morono, Y. et al. Aerobic microbial life persists in oxic marine sediment as old as 101.5 million years. Nat. Commun. 11, 3626 (2020).91.Wright, E. S. & Vetsigian, K. H. Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations. Mol. Ecol. 28, 3915–3928 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Cordero, F., Cassanova, A. G., Schweinsberg, J. & Wilke-Berenguer, M. Λ-coalescents arising in populations with dormancy. Preprint at https://arxiv.org/abs/2009.09418 (2020).93.Blath, J., Buzzoni, E., Gonzalez Casanova, A. & Wilke-Berenguer, M. Separation of time-scales for the seed bank diffusion and its jump-diffusion limit. J Math Biol. 82, 53 (2021).94.Rogalski, M. A. Maladaptation to acute metal exposure in resurrected Daphnia ambigua clones after decades of increasing contamination. Am. Nat. 189, 443–452 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Decaestecker, E. et al. Host-parasite ‘Red Queen’ dynamics archived in pond sediment. Nature 450, 870–873 (2007).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Warner, R. R. & Chesson, P. L. Coexistence mediated by recruitment fluctuation: a field guide to the storage effect. Am. Nat. 125, 769–787 (1985).Article 

    Google Scholar 
    97.Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994). Describes models of competition and coexistence, including the storage effect, which often involves dormancy in fluctuating environments.MATH 
    Article 

    Google Scholar 
    98.Pake, C. E. & Venable, D. L. Is coexistence of Sonoran Desert annuals mediated by temporal variability in reproductive success? Ecology 76, 246–261 (1995).Article 

    Google Scholar 
    99.Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q. F. & Levine, J. M. Climate variability has a stabilizing effect on the coexistence of prairie grasses. Proc. Natl Acad. Sci. USA 103, 12793–12798 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    100.Cáceres, C. E. Temporal variation, dormancy, and coexistence: a field test of the storage effect. Proc. Natl Acad. Sci. USA 94, 9171–9175 (1997). Dormancy in lake zooplankton contributes to maintenance of diversity via the storage effect.ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    101.Jiang, L. & Morin, P. J. Temperature fluctuation facilitates coexistence of competing species in experimental microbial communities. J. Anim. Ecol. 76, 660–668 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    102.Kuwamura, M., Nakazawa, T. & Ogawa, T. A minimum model of prey-predator system with dormancy of predators and the paradox of enrichment. J. Math. Biol. 58, 459–479 (2009).MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    103.Gulbudak, H. & Weitz, J. S. A touch of sleep: biophysical model of contact-mediated dormancy of archaea by viruses. Proc. R. Soc. B Biol. Sci. 283, 20161037 (2016).104.Kuwamura, M. & Nakazawa, T. Dormancy of predators dependent on the rate of variation in prey density. SIAM J. Appl. Math. 71, 169–179 (2011).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    105.McCauley, E., Nisbet, R. M., Murdoch, W. W., de Roos, A. M. & Gurney, W. S. C. Large-amplitude cycles of Daphnia and its algal prey in enriched environments. Nature 402, 653–656 (1999).106.Verin, M. & Tellier, A. Host-parasite coevolution can promote the evolution of seed banking as a bet-hedging strategy. Evolution 72, 1362–1372 (2018).CAS 
    Article 

    Google Scholar 
    107.Bautista, M. A., Zhang, C. Y. & Whitaker, R. J. Virus-induced dormancy in the archaeon Sulfolobus islandicus. mBio. 6, e02565–14 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    108.Rengefors, K., Karlsson, I. & Hansson, L. A. Algal cyst dormancy: a temporal escape from herbivory. Proc. R. Soc. B Biol. Sci. 265, 1353–1358 (1998).Article 

    Google Scholar 
    109.Dzialowski, A. R., Lennon, J. T., O’Brien, W. J. & Smith, V. H. Predator-induced phenotypic plasticity in the exotic cladoceran Daphnia lumholtzi. Freshwat. Biol. 48, 1593–1602 (2003).Article 

    Google Scholar 
    110.Sellinger, T., Muller, J., Hosel, V. & Tellier, A. Are the better cooperators dormant or quiescent? Math. Biosci. 318, 108272 (2019).MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    111.Honegger, R. The lichen symbiosis: what is so spectacular about it? Lichenologist 30, 193–212 (1998).Article 

    Google Scholar 
    112.Green, T. G. A., Pintado, A., Raggio, J. & Sancho, L. G. The lifestyle of lichens in soil crusts. Lichenologist 50, 397–410 (2018).Article 

    Google Scholar 
    113.Kuykendall, L. D., Hashem, F. M., Bauchan, G. R., Devine, T. E. & Dadson, R. B. Symbiotic competence of Sinorhizobium fredii on twenty alfalfa cultivars of diverse dormancy. Symbiosis 27, 1–16 (1999).
    Google Scholar 
    114.Vujanovic, V. & Vujanovic, J. Mycovitality and mycoheterotrophy: where lies dormancy in terrestrial orchid and plants with minute seeds? Symbiosis 44, 93–99 (2007).CAS 

    Google Scholar 
    115.Dittmer, J. & Brucker, R. M. When your host shuts down: larval diapause impacts host-microbiome interactions in Nasonia vitripennis. Microbiome 9, 85 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.Snyder, R. E. Multiple risk reduction mechanisms: can dormancy substitute for dispersal? Ecol. Lett. 9, 1106–1114 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    117.Vitalis, R., Rousset, F., Kobayashi, Y., Olivieri, I. & Gandon, S. The joint evolution of dispersal and dormancy in a metapopulation with local extinctions and kin competition. Evolution 67, 1676–1691 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    118.Horner-Devine, M. C., Lage, M., Hughes, J. B. & Bohannan, B. J. M. A taxa-area relationship for bacteria. Nature 432, 750–753 (2004).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.den Hollander, F. & Pederzani, G. Multi-colony Wright-Fisher with a seed bank. Indag. Math. 28, 637–669 (2017).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    120.Coates, A. R. M. Dormancy and Low Growth States in Microbial Disease. (Cambridge University Press, 2003). Book describing how dormancy is involved in many human diseases.121.Cohen, N. R., Lobritz, M. A. & Collins, J. J. Microbial persistence and the road to drug resistance. Cell Host Microbe 13, 632–642 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    122.Zhu, D. L., Sorg, J. A. & Sun, X. M. Clostridioides difficile biology: sporulation, germination, and corresponding therapies for C. difficile infection. Front. Cell. Infect. Microbiol. 8, 29 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    123.Wood, T. K., Knabel, S. J. & Kwan, B. W. Bacterial persister cell formation and dormancy. Appl. Environ. Microbiol. 79, 7116–7121 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    124.Manuse, S. et al. Bacterial persisters are a stochastically formed subpopulation of low-energy cells. PLoS Biol. 19, e3001194 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    125.Mukamolova, G. V., Turapov, O., Malkin, J., Woltmann, G. & Barer, M. R. Resuscitation-promoting factors reveal an occult population of tubercle bacilli in sputum. Am. J. Respir. Crit. Care Med. 181, 174–180 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    126.Shimizu, H. & Nakayama, K. Artificial intelligence in oncology. Cancer Sci. 111, 1452–1460 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    127.Aktipis, A. C., Boddy, A. M., Gatenby, R. A., Brown, J. S. & Maley, C. C. Life history trade-offs in cancer evolution. Nat. Rev. Cancer 13, 883–892 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    128.Gupta, P. B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    129.Miller, A. K., Brown, J. S., Basanta, D. & Huntly, N. What is the storage effect, why should it occur in cancers, and how can it inform cancer therapy? Cancer Control 27,1073274820941968 (2020).130.Park, S. Y. & Nam, J. S. The force awakens: metastatic dormant cancer cells. Exp. Mol. Med. 52, 569–581 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    131.Sorrell, I., White, A., Pedersen, A. B., Hails, R. S. & Boots, M. The evolution of covert, silent infection as a parasite strategy. Proc. R. Soc. B Biol. Sci. 276, 2217–2226 (2009).Article 

    Google Scholar 
    132.Boots, M. et al. The population dynamical implications of covert infections in host–microparasite interactions. J. Anim. Ecol. 72, 1064–1072 (2003).Article 

    Google Scholar 
    133.Gilbert, N. M., O’Brien, V. P. & Lewis, A. L. Transient microbiota exposures activate dormant Escherichia coli infection in the bladder and drive severe outcomes of recurrent disease. PLoS Pathog. 13, e1006238 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    134.Xu, R. Global dynamics of a delayed epidemic model with latency and relapse. Nonlinear Anal. Model Control 18, 250–263 (2013).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    135.Meeske, A. J., Nakandakari-Higa, S. & Marraffini, L. A. Cas13-induced cellular dormancy prevents the rise of CRISPR-resistant bacteriophage. Nature 570, 241–245 (2019). Hosts defend against parasites via dormancy with implications for herd immunity.ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    136.Lamont, B. B., Pausas, J. G., He, T. H., Witkowski, E. T. F. & Hanley, M. E. Fire as a selective agent for both serotiny and nonserotiny over space and time. Crit. Rev. Plant Sci. 39, 140–172 (2020).CAS 
    Article 

    Google Scholar 
    137.Alsos, I. G., Muller, E. & Eidesen, P. B. Germinating seeds or bulbils in 87 of 113 tested Arctic species indicate potential for ex situ seed bank storage. Polar Biol. 36, 819–830 (2013).Article 

    Google Scholar 
    138.Ooi, M. K. J., Auld, T. D. & Denham, A. J. Climate change and bet-hedging: interactions between increased soil temperatures and seed bank persistence. Glob. Change Biol. 15, 2375 – 2386 (2009).139.Gioria, M. & Pysek, P. The legacy of plant invasions: changes in the soil seed bank of invaded plant communities. Bioscience 66, 40–53 (2016).Article 

    Google Scholar 
    140.Kuo, V., Lehmkuhl, B. K. & Lennon, J. T. Resuscitation of the microbial seed bank alters plant‐soil interactions. Mol. Ecol. 30, 2905–2914 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    141.Gross, M. Permafrost thaw releases problems. Curr. Biol. 29, R39–R41 (2019).CAS 
    Article 

    Google Scholar 
    142.Kearns, P. J. et al. Nutrient enrichment induces dormancy and decreases diversity of active bacteria in salt marsh sediments. Nat. Commun. 7, 12881 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    143.Salazar, A., Lennon, J. T. & Dukes, J. S. Microbial dormancy improves predictability of soil respiration at the seasonal time scale. Biogeochemistry 144, 103–116 (2019).CAS 
    Article 

    Google Scholar 
    144.Zha, J. R. & Zhuang, Q. L. Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget. Biogeosciences 17, 4591–4610 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    145.Blath, J., Hermann, F. & Slowik, N. A branching process model for dormancy and seed banks in randomly fluctuating environments. J. Math. Biol. 83, 17 (2021).MathSciNet 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    146.Malik, T. & Smith, H. L. Does dormancy increase fitness of bacterial populations in time-varying environments? Bull. Math. Biol. 70, 1140–1162 (2008).MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    147.Dombry, C., Mazza, C. & Bansaye, V. Phenotypic diversity and population growth in a fluctuating environment. Adv. Appl. Prob. 43, 375–398 (2011). Mathematical model for assessing optimality of transitioning in random environments.MathSciNet 
    MATH 
    Article 

    Google Scholar 
    148.Wakeley, J. Coalescent Theory: An Introduction. (Greenwood Village: Roberts & Company Publishers, 2009). Concise introduction to the fundamentals of coalescent theory bridging mathematics and biology.149.Tellier, A. et al. Estimating parameters of speciation models based on refined summaries of the joint site-frequency spectrum. PLoS One 6, e18155 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    150.Tellier, A. Persistent seed banking as eco-evolutionary determinant of plant nucleotide diversity: novel population genetics insights. New Phytol. 221, 725–730 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    151.Kingman, J. F. C. The coalescent. Stoch. Process. Appl. 13, 235–248 (1982). Foundational paper that introduced the standard coalescent.MathSciNet 
    MATH 
    Article 

    Google Scholar 
    152.Kaj, I., Krone, S. M. & Lascoux, M. Coalescent theory for seed bank models. J. Appl. Probab. 38, 285–300 (2001). First paper to incorporate seed banks into coalescent theory.MathSciNet 
    MATH 
    Article 

    Google Scholar 
    153.Blath, J., Casanova, A. G., Kurt, N. & Wilke-Berenguer, M. A new coalescent for seed-bank models. Ann. Appl. Probab. 26, 857–891 (2016).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    154.Blath, J., Kurt, N., Gonzalez Casanova, A. & Wilke-Berenguer, M. The seed bank coalescent with simultaneous switching. Electron. J. Probab. 25, 1–21 (2020).155.Lalonde, R. G. & Roitberg, B. D. Chaotic dynamics can select for long-term dormancy. Am. Nat. 168, 127–131 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    156.Blath, J. & Tobias, A. Invasion and fixation of microbial dormancy traits under competitive pressure. Stoch. Proc. Appl. 130, 7363–7395 (2020).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    157.Tan, Z. X., Koh, J. M., Koonin, E. V. & Cheong, K. H. Predator dormancy is a stable adaptive strategy due to Parrondo’s paradox. Adv. Sci. 7, 1901559 (2020).Article 

    Google Scholar 
    158.McGill, B. J. et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).PubMed 
    Article 

    Google Scholar 
    159.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography. (Princeton University Press, 2001).160.Ewens, W. J. Sampling theory of selectively neutral alleles. Theor. Popul. Biol. 3, 87–112 (1972).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    161.Rosindell, J., Hubbell, S. P. & Etienne, R. S. The unified neutral theory of biodiversity and biogeography at age ten. Trends Ecol. Evol. 26, 340–348 (2011).PubMed 
    Article 

    Google Scholar 
    162.Rosindell, J., Wong, Y. & Etienne, R. S. A coalescence approach to spatial neutral ecology. Ecol. Inform. 3, 259–271 (2008).Article 

    Google Scholar 
    163.White, E. P., Thibault, K. M. & Xiao, X. Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology 93, 1772–1778 (2012).PubMed 
    Article 

    Google Scholar 
    164.Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 5 (2017).Article 

    Google Scholar 
    165.Greven, A., den Hollander, F. & Oomen, M. Spatial populations with seed-bank: well-posedness, duality and equilibrium. Preprint at https://arxiv.org/abs/2004.14137 (2020).166.Liggett, T. M. Interacting Particle Systems. 488 (Springer Science & Business Media, 1985). Overview of the mathematical theory of stochastic systems consisting of large numbers of interacting components.167.Kipnis, C. & Landim, C. Scaling Limits of Interacting Particle Systems. Vol. 320 (Springer, 1999).168.van der Hofstad, R. Random Graphs and Complex Networks. (Cambridge University Press, 2017).169.Levin, D. Z., Walter, J. & Murnighan, K. J. Dormant ties: the value of reconnecting. Organ. Sci. 22, 923–939 (2011).Article 

    Google Scholar 
    170.Marin, A. & Hampton, K. Network instability in times of stability. Sociol. Forum 34, 313–336 (2019).Article 

    Google Scholar 
    171.Crawford, D. C. & Mennerick, S. Presynaptically silent synapses: dormancy and awakening of presynaptic vesicle release. Neuroscientist 18, 216–223 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    172.Borsboom, D. A network theory of mental disorders. World Psychiatry 16, 5–13 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    173.Metz, J. A., Nisbet, R. M. & Geritz, S. A. How should we define ‘fitness’ for general ecological scenarios? Trends Ecol. Evol. 7, 198–202 (1992).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    174.Bansaye, V. & Meleard, S. Stochastic Models for Structured Populations: Scaling Limits and Long Time Behavior. (Springer, 2015).175.Champagnat, N., Ferrière, R. & Ben Arous, G. The canonical equation of adaptive dynamics: a mathematical view. Selection 2, 73–83 (2001).Article 

    Google Scholar 
    176.Champagnat, N. A microscopic interpretation for adaptive dynamics trait substitution sequence models. Stoch. Process. Their Appl. 116, 1127–1160 (2006).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    177.Champagnat, N. & Meleard, S. Polymorphic evolution sequence and evolutionary branching. Probab. Theory Relat. Field 151, 45–94 (2011).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    178.Kraut, A. & Bovier, A. From adaptive dynamics to adaptive walks. J. Math. Biol. 75, 1699–1747 (2019).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    179.Blath, J., Hammer, M. & Nie, F. The stochastic Fisher-KPP Equation with seed bank and on/off-branching-coalescing Brownian motion. Preprint at https://arxiv.org/abs/2005.01650 (2020). More

  • in

    Altitudinal gradient affect abundance, diversity and metabolic footprint of soil nematodes in Banihal-Pass of Pir-Panjal mountain range

    1.Bardgett, R. The Biology of Soil: A Community and Ecosystem Approach (Oxford University Press Inc, 2005).Book 

    Google Scholar 
    2.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Nat. Acad. Sci. 103, 626–631. https://doi.org/10.1073/pnas.0507535103 (2006).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Fitter, A. H. et al. Biodiversity and ecosystem function in soil. Funct. Ecol. 19, 369–377 (2005).Article 

    Google Scholar 
    4.Decaëns, T. Macroecological patterns in soil communities. Glob. Ecol. Biogeogr. 19, 287–302 (2010).Article 

    Google Scholar 
    5.Bardgett, R. D. & Van Der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Whitford, W. G. Pattern and Process in Desert Ecosystems 93–118 (University of New Mexico Press, 1986).
    Google Scholar 
    8.Fisher, F. M., Parker, L. W., Anderson, J. P. & Whitford, W. G. Nitrogen mineralization in a desert soil: Interacting effects of soil moisture and nitrogen fertilizer. Soil Sci. Soc. Am. J. 51, 1033–1041 (1987).ADS 
    Article 

    Google Scholar 
    9.Yeates, G. W. & Bongers, T. Nematode diversity in agroecosystems. Agric Ecosyst Environ. 74,113–135. https://doi.org/10.1016/S0167-8809(99)00033-X (1999).10.Ruess, L. Nematode soil faunal analysis of decomposition pathways in different ecosystems. Nematology 5, 179–181 (2003).Article 

    Google Scholar 
    11.Nielsen, U. N. et al. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties. Glob. Ecol. Biogeogr. 23, 968–978 (2014).Article 

    Google Scholar 
    12.Bhusal, D. R., Tsiafouli, M. A. & Sgardelis, S. P. Temperature-based bioclimatic parameters can predict nematode metabolic footprints. Oecologia 179, 187–199 (2015).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Bloemers, G. F., Hodda, M., Lambshead, P. J. D., Lawton, J. H. & Wanless, F. R. The effects of forest disturbance on diversity of tropical soil nematodes. Oecologia 111, 575–582 (1997).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Ferris, H. Form and function: Metabolic footprints of nematodes in the soil food web. Eur. J. Soil Biol. 46, 97–104 (2010).Article 

    Google Scholar 
    15.Tsiafouli, M. A., Bhusal, D. R. & Sgardelis, S. P. Nematode community indices for microhabitat type and large scale landscape properties. Ecol. Indic. 73, 472–479 (2017).Article 

    Google Scholar 
    16.Korner, C. Alpine plants: stressed or adapted? In Physiological Plant Ecology (Press, M.C. et al., eds), 297–311, (Blackwell, 1998).17.Rahbek, C. The role of spatial scale and the perception of large-scale species-richness patterns. Ecol. Lett. 8, 224–239 (2005).Article 

    Google Scholar 
    18.Loranger, G., Bandyopadhyaya, I., Razaka, B. & Ponge, J. F. Does soil acidity explain altitudinal sequences in collembolan communities?. Soil Biol. Biochem. 33, 381–393 (2001).CAS 
    Article 

    Google Scholar 
    19.Dong, K. et al. Soil nematodes show a mid-elevation diversity maximum and elevational zonation on Mt. Norikura, Japan. Sci. Rep. 7, 3028 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    20.Kergunteuil, A., Campos-Herrera, R., Sánchez-Moreno, S., Vittoz, P. & Rasmann, S. The abundance, diversity, and metabolic footprint of soil nematodes is highest in high elevation alpine grasslands. Front. Ecol. Evol. 4, 84 (2016).Article 

    Google Scholar 
    21.Liu, J., Yang, Q., Siemann, E., Huang, W. & Ding, J. Latitudinal and altitudinal patterns of soil nematode communities under tallow tree (Triadica sebifera) in China. Plant Ecol. 220, 965–976 (2019).Article 

    Google Scholar 
    22.Powers, L. E., Ho, M. C., Freckman, D. W. & Virginia, R. A. Distribution, community structure, and microhabitats of soil invertebrates along an elevational gradient in Taylor Valley, Antarctica. Arct. Alp. Res. 30, 133–141 (1998).Article 

    Google Scholar 
    23.Qing, X., Bert, W., Steel, H., Quisado, J. & de Ley, I. T. Soil and litter nematode diversity of Mount Hamiguitan, the Philippines, with description of Bicirronema hamiguitanense n. sp (Rhabditida: Bicirronematidae). Nematology 17, 325–344 (2015).Article 

    Google Scholar 
    24.Tong, F. C., Xiao, Y. H. & Wang, Q. L. Soil nematode community structure on the northern slope of Changbai Mountain, Northeast China. J. For. Res. 21, 93–98 (2010).Article 

    Google Scholar 
    25.Bokhorst, S. et al. Contrasting responses of springtails and mites to elevation and vegetation type in the sub-Arctic. Pedobiologia 67, 57–64 (2018).Article 

    Google Scholar 
    26.Cutz-Pool, L. Q., Palacios-Vargas, J. G., Cano-Santana, Z. & Castaño-Meneses, G. Diversity patterns of Collembola in an elevational gradient in the NW slope of Iztaccíhuatl volcano, state of Mexico, Mexico. Entomol. News 121, 249–261 (2010).Article 

    Google Scholar 
    27.Illig, J., Norton, R. A., Scheu, S. & Maraun, M. Density and community structure of soil- and bark-dwelling microarthropods along an altitudinal gradient in a tropical montane rainforest. Exp. Appl. Acarol. 52, 49–62 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Devetter, M., Háněl, L., Řeháková, K. & Doležal, J. Diversity and feeding strategies of soil microfauna along elevation gradients in Himalayan cold deserts. PLoS One 12, e0187646 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    29.Bird, A. F. & Wallace, H. R. The influence of temperature on Meloidogyne hapla and M. javanica. Nematologica 11, 581–589 (1965).Article 

    Google Scholar 
    30.Wang, Z. & Wu, H. Study towards the eco-geographic community of mountain soil nematode in the middle of Hunan. J. Nat. Sci. 15, 72–78 (1992).
    Google Scholar 
    31.Landesman, W. J., Treonis, A. M. & Dighton, J. Effects of a one-year rainfall manipulation on soil nematode abundances and community composition. Pedobiologia 54, 87–91 (2011).Article 

    Google Scholar 
    32.Luo, Y. & Zhou, X. Soil Respiration and the Environment (Academic Press, 2006).
    Google Scholar 
    33.Yan, D. et al. Community structure of soil nematodes under different drought conditions. Geoderma 325, 110–116 (2018).ADS 
    Article 

    Google Scholar 
    34.Quist, C. W. et al. Spatial distribution of soil nematodes relates to soil organic matter and life strategy. Soil Biol. Biochem. 136, 107542 (2019).CAS 
    Article 

    Google Scholar 
    35.Margesin, R., Minerbi, S. & Schinner, F. Litter decomposition at two forest sites in the Italian Alps: A field study. Arct. Antarct. Alp. Res. 48, 127–138 (2016).Article 

    Google Scholar 
    36.Kappes, H., Lay, R. & Topp, W. Changes in different trophic levels of litter-dwelling macrofauna associated with giant knotweed invasion. Ecosystems 10, 734–744 (2007).Article 

    Google Scholar 
    37.Veen, G. F. et al. Coordinated responses of soil communities to elevation in three subarctic vegetation types. Oikos 126, 1586–1599 (2017).Article 

    Google Scholar 
    38.Gerber, K. Nematodenfauna alpine Böden im Glocknergebiet (Hohe Tauern, Österreich). Veröffentlichungen des Österreichischen Mass-Hochgebirgsprogramms 4, 80–90 (1981).
    Google Scholar 
    39.Zhang, X. et al. Community composition, diversity and metabolic footprints of soil nematodes in differently-aged temperate forests. Soil Biol. Biochem. 80, 118–126 (2015).CAS 
    Article 

    Google Scholar 
    40.Ferris, H., Zheng, L. & Walker, M. A. Resistance of grape References [40] are given in list but not cited in text. Please cite in text or delete them from listrootstocks to plant-parasitic nematodes. J. Nematol. 44, 377–386 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Ferris, H., Bongers, T. & De Goede, R. G. M. A framework for soil food web diagnostics: Extension of the nematode faunal analysis concept. Appl. Soil Ecol. 18, 13–29 (2001).Article 

    Google Scholar 
    42.Sánchez-Moreno, S., Nicola, N. L., Ferris, H. & Zalom, F. G. Effects of agricultural management on nematode–mite assemblages: Soil food web indices as predictors of mite community composition. Appl. Soil Ecol. 41, 107–117 (2009).Article 

    Google Scholar 
    43.Dar, T. A., Uddin, M., Khan, M. M. A., Hakeem, K. R. & Jaleel, H. Jasmonates counter plant stress: A review. Environ. Exp. Bot. 115, 49–57 (2015).CAS 
    Article 

    Google Scholar 
    44.Davies, B. E. Loss-on-ignition as an estimate of soil organic matter. Soil Sci. Soc. Am. J. 38, 150–151 (1974).ADS 
    Article 

    Google Scholar 
    45.Van, B. J. Methods and Techniques for Nematology 20 (Wageningen University, 2006).
    Google Scholar 
    46.Goodey, T. Soil and Freshwater Nematodes (Methuen and Cooperation Limited, 1963).
    Google Scholar 
    47.Jairajpuri, M. S. & Ahmad, W. Dorylaimida: Free-Living, Predaceous and Plant-Parasitic Nematodes (Brill, 1992).
    Google Scholar 
    48.Ahmad, W. Plant Parasitic Nematodes of India (Litho Offset Printers, 1996).
    Google Scholar 
    49.Andrássy, I. Free-living nematodes of Hungary (Nematoda errantia), I. In Pedozoologica Hungarica No. 3 (eds Csuzdi, C. & Mahunka, S.) (Hungarian Natural History Museum, 2005).
    Google Scholar 
    50.Ahmad, W. & Jairajpuri, M. S. Mononchida: The Predaceous Nematodes. Nematology Monographs and Prespectives (Brill, 2010).Book 

    Google Scholar 
    51.Bongers, T. & Bongers, M. Functional diversity of nematodes. Appl. Soil Ecol. 10, 239–251 (1998).Article 

    Google Scholar 
    52.Hammer, O., Harper, D. & Ryan, P. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 
    53.Bongers, T. The maturity index: An ecological measure of environmental disturbance based on nematode species composition. Oecologia 83, 14–19 (1990).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Andrassy, I. The determination of volume and weight of nematodes. Acta Zool. Acad. Sci. Hung. 2, 1–15 (1956).
    Google Scholar 
    55.Sieriebriennikov, B., Ferris, H. & de Goede, R. G. NINJA: An automated calculation system for nematode-based biological monitoring. Eur. J. Soil Biol. 61, 90–93 (2014).Article 

    Google Scholar 
    56.Sperman’s correlation and linear regression was performed using GraphPad Prism version 8.0.2 for Windows, GraphPad Software, La Jolla California USA. www.graphpad.com. Accessed 20 Jan 2021. More

  • in

    Iran: drought must top new government’s agenda

    CORRESPONDENCE
    10 August 2021

    Iran: drought must top new government’s agenda

    Jamshid Parchizadeh

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    Jerrold L. Belant

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    Jamshid Parchizadeh

    Global Wildlife Conservation Center, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA.

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    Global Wildlife Conservation Center, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA.

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    We urge Iran’s incoming government to give priority to resolving the country’s worst drought in 50 years (see go.nature.com/2wkwyqn). In our view, the government needs to consult with international as well as domestic water experts to prevent the imposition of flawed agendas. It should also revise earlier policies that have contributed to the crisis.Outgoing president Hassan Rouhani blamed the drought on a 52% reduction in rainfall since last year. However, unregulated aquifer depletion and mismanagement of water resources by the authorities (see, for example, go.nature.com/3cce7or) have contributed.The drought and its associated dust haze is also severely affecting ecosystems in and around Iran (see go.nature.com/3jhauvc and http://pana.ir/news/1178597).

    Nature 596, 189 (2021)
    doi: https://doi.org/10.1038/d41586-021-02189-z

    Competing Interests
    The authors declare no competing interests.

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    From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s southern Murray-Darling Basin

    The JJAS river heights at Hay (Fig. 2a) have clearly reduced variability over the 27-year period 1965–1991 compared with 1992–2018 (p-value = 0.005, Table 4). Despite the apparent decrease in the mean and variance of JJAS Murrumbidgee River heights at Hay from the 1960s, as shown by the low p-values (0.309, 0.332), respectively, for the 27-year intervals 1938–1964 to 1965–1991 (Table 4), the decrease is not statistically significant. However, the decrease is consistent with the suggestion that a change point occurred from the late 1950s between unregulated to regulated flow at Hay11.While the mean JJAS precipitation of the three catchment locations of Burrinjuck Dam, Blowering Dam and Tumut indicate a slight decrease in percentile extremes from the 1990s, with 2016 (due to September) only above the 95th percentile (Fig. 3), they exhibit no significant change in mean or variance based on the bootstrapped intervals JJAS 1964–1991 to 1992–2018. Consequently, the question arises of why the significant decrease in mean and variance of the JJAS Murrumbidgee River height at Wagga Wagga and in variance at Hay does not match a similar significant decrease in mean or variance of JJAS catchment rainfall. A rainfall decline in recent decades was found to be most pronounced in late autumn20,21 and that without sufficient autumn rainfall to moisten catchments in southern Australia, follow-up rainfall in winter cannot be efficiently converted to run-off and catchment inflows22. There have been statistically significant decreases in April–May mean precipitation at the catchment locations of Blowering Dam, Burrinjuck Dam and Tumut from 1964 to 1991 to 1992–2018 and also for the mean inflows to the two Dams (Table 4). Furthermore, as a result of the Millennium Drought (1997–2009), modelling experiments indicate that, starting from very dry conditions, the run-off response to rainfall only will return to the normal pre-drought conditions after about 10–20 years of average rainfall23. Therefore, the significant decrease in variance of Murrumbidgee River heights at Hay and in mean and variance at Wagga Wagga, is most likely due to the April–May reduced dam inflows and precipitation, and from average JJAS catchment precipitation since 1991. Any role played by water extraction for irrigation between Wagga Wagga and Hay, where irrigation is concentrated, is likely to be small owing to the highly significant mean river height reduction at Wagga Wagga which is upstream from Hay. However, irrigation, and other water usage, is sourced from the dams, so there is a long-term impact of irrigation over the months preceding JJAS on flows at Wagga Wagga, due to the reduction in water stored in the upstream dams. The dams integrate the water extracted for irrigation and all other usage since the last spill event, and therefore the extractions over an extended period can have an impact on when the dam will fill, and hence on the flows downstream, including Wagga Wagga. The minimum water level in the dams, which typically occurs near the end of Autumn, is due to the reduction in inflows (impacted by climate change), and extractions from the dam. Coupled with the tendency for a slower fill rate due to reduced inflows, this results in fewer spill events. As a consequence, there is a change in the distribution between spill events and irrigation releases, changing the frequency distribution of flows. This will be particularly the case for the JJAS period, as a delayed dam filling will have a major impact in dam levels in that period. Before the 1990s the river at Wagga Wagga and Hay reached flood level height or close to flood level height regularly in JJAS from precipitation-driven inflows regardless of the amount of water that was extracted (see Fig. 2a,b). Since the mid-1990s less water reaching Wagga Wagga has significantly reduced the river height owing to significantly decreased April–May and JJAS precipitation-driven inflows at the upstream catchment dams of Burrinjuck and Blowering Dams and significantly decreased mean precipitation at Tumut, which also represents the catchment area of Blowering Dam (Fig. 2c,d; Table 4). Moreover, there has been no overallocation or hoarding of water found in the southern MDB24. In a different southern MDB catchment study of the Millennium Drought 1997–2008, factors for a disproportionate reduction in rainfall run-off were reduced mean annual rainfall, less interannual variability of rainfall, changed seasonality of rainfall and lastly increased potential evaporation25. However, the last two factors mentioned have since become well established in the last decade with reference to the work in this study. It was suggested that a rainfall reduction alone does not explain the observed inflow reduction trend26. Even after a major rain event, the soils are so dry that they absorb more water than before the rain event, and less reaches the dams and rivers than on a wet catchment. In the last three decades it is unknown what the effect on run-off into dams and the Murrumbidgee river has been in JJAS from major rain events because, apart from August–September 2016, there have been no major catchment net inflows since 1991 (Fig. 2c,d). There were significant precipitation-driven inflows during SON 2010 which led to flood level exceedances at Wagga Wagga and Hay in December 2010. In June and July 1991 there was a series of rain-producing cut-off low pressure systems over inland NSW and the adjacent coast influencing the catchment, interspersed with persistent, precipitation-producing frontal systems embedded in the westerly airflow during July and August. Rain producing inland cut-off low pressure systems over southeast Australia are the main influence on enhancing JJAS rainfall totals8.Decreased JJAS precipitation in continental southeast Australia has been evident for at least the last two decades, as anticipated by climate scientists. The naturally periodic La Niña phenomenon provided spring and summer precipitation during much of 2010 to 2012, which ended the Millennium Drought (1997–2009). The only other recent widespread significant rainfall in southeast continental Australia was in August–September 2016 due to a negative phase of the Indian Ocean Dipole (IOD). A negative IOD phase typically is associated with wetter than normal spring conditions for southeast Australia7,8.Although the SAM is an atmospheric index with a time scale typically of a few weeks, an annual average SAM reconstruction shows that since the 1970s it is in its most positive state over at least the past 1000 years27. Prior to the 1990s soil wetness would have been in phase with the annual cycle of winter/spring peak rainfall, dry summer/early autumn and without a long term trend in SAM. However, because SAM has trended positive since the 1970s, the annual cycle of soil wetness of the MDB has been increasingly disrupted particularly since the Millennium Drought23 and there is also a potential long-term impact from groundwater systems28. This is supported by the most recent available annual area-averaged actual evapotranspiration and soil moisture deciles in Fig. 7a,b. These figures show the anomalously dry MDB catchment area in the period 2018–2019. In the southeast corner of the MDB, actual evapotranspiration is below average and soil moisture is very much below average.Figure 7Available at: http://www.bom.gov.au/water/nwa/2019/mdb/climateandwater/climateandwater.shtml.Annual deciles of actual evapotranspiration and soil moisture 2018–2019. Map of southeast Australia showing for the MDB region deciles during the 2018–2019 year for, (a) annual area-averaged actual evapotranspiration. Note the below average decile in the southeast corner of the MDB, and (b) annual area-averaged soil moisture. Note the very much below average decile in the southeast corner of the MDB. (Reproduced with permission under Creative Commons Attribution Licence 3.0 from the Australian Bureau of Meteorology.Full size imageTwo Supplementary Tables showing historical April–May (S1) and JJAS (S2) maximum river heights above flood level at Hay (6.7 m) in IPO phases indicate, as expected, more in negative IPO phases than positive phases and importantly a dissociation with the IPO resulting from none in the most recent negative phase from 1998 and the preceding positive phase after the early 1990s. The implication is that accelerated global warming since the 1990s has overwhelmed the influence of negative IPO on precipitation.The MDB plan, introduced from 201329 provided, for the first time, regulated allocations to environmental flows for ecosystem sustainability of rivers in southeast Australia such as the Murrumbidgee. However, the plan requires that each year on 1 July a fixed amount of water is locked in for future consumption, split three ways with the highest priority for human consumption and irrigation for permanent crops (e.g., fruit trees and nuts). The remaining allocations are split between non-permanent crops (e.g., cotton, rice) and environmental flow. A major issue is that the forecast net inflows upon which the allocations are based are the minimum inflows experienced in the 120 years up to the end of the twentieth century. However, as shown, even lower inflows have been experienced in the past two decades. It is not surprising that there is a significant decrease in the JJAS variance of the Murrumbidgee River height at Hay (p-value = 0.005) and both the JJAS mean and variance at Wagga Wagga from the periods 1965–1991 to 1992–2018 (mean p-value = 0.0044, variance p-value = 0.095; Table 4) since this period corresponds with the significantly reduced mean April–May catchment precipitation and mean April–May dam net inflows. The fact that there has been no significant change in the mean Murrumbidgee River height since 1991 is an indication that there has been a lack of major April-September rain events. The lack of significant catchment rainfall events from April to September is the reason for the reduction in the mean and variance of river heights at Wagga Wagga. Floods in April–May are rare along the Murrumbidgee River and the six years since 1874 in which April–May floods occurred at Hay prior to 1991 (Table 3), were dominated by precipitation that occurred as a result of mid-latitude interaction with either tropical or subtropical moisture, whereas the last flood that occurred in March 2012, was the result of a rain-producing tropical low pressure trough in the easterly wind regime that extended from northwest Australia to a low pressure centre in southern New South Wales near the Murrumbidgee catchment. Moreover, given the significant decline in April–May, Murrumbidgee catchment rainfall, JJAS run-off into the dams and Murrumbidgee River height at Wagga Wagga since 1991, the implication for water allocations of irrigated agriculture downstream from Wagga Wagga and for flood plain environmental flows required for sustainable wetlands downstream from Hay, will continue to be a problem. More

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    Impact of elevation and slope aspect on floristic composition in wadi Elkor, Sarawat Mountain, Saudi Arabia

    1.Cunningham, S. C. et al. Balancing the environmental benefits of reforestation in agricultural regions. Perspect. Plant Ecol. Evol. Syst. 17, 301–317. https://doi.org/10.1016/j.ppees.2015.06.001 (2015).Article 

    Google Scholar 
    2.Pearse, I. S. & Hipp, A. L. Phylogenetic and trait similarity to a native speciespredict herbivory on non-native oaks. Proc. Natl. Acad. Sci. U. S. A. 106, 18097–18102 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Abdel Khalik, K., El-Sheikh, M. & El-Aidarous, A. Floristic diversity and vegetation analysisof wadi Al Noman, Holy Mecca, Saudi Arabia. Turk. J. Bot. 37, 894–907. https://doi.org/10.3906/bot-1209-56 (2013).Article 

    Google Scholar 
    4.Al-Sherif, E. A., Ayesh, A. M. & Rawi, S. M. Floristic composition, life form and chorology of plant life at Khulais region western Saudi Arabia. Pak. J. Bot. 45, 29–38 (2013).
    Google Scholar 
    5.Al-Sherif, E. A. & Fadl, M. A. Floristic study of the Al-Shafa Highlands in Taif, western Saudi Arabia. Flora 225, 20–29. https://doi.org/10.1016/j.flora.2016.09.004 (2016).Article 

    Google Scholar 
    6.Al-Nafie, A. H. Phytogeography of Saudi Arabia. Saudi J. Biol. Sci. 15, 159–176 (2008).
    Google Scholar 
    7.Mossa, J. S., Al-Yahya, M. A. & Al-Meshal, I. A. Medicinal Plants of Saudi Arabia (King Saud University Press, 1987).
    Google Scholar 
    8.Körner, C. Why are there global gradients in species richness? Mountains might hold the answer. Trends Ecol. Evol. 15, 513–514. https://doi.org/10.1016/S01695347(00)02004-8 (2000).Article 

    Google Scholar 
    9.Cano-Ortiz, A., Musarella, C. M., PiNar Fuentes, J. C., Gomes, C. J. P. & Cano, E. Distribution patterns of endemic flora to define hotspots on Hispaniola. Syst. Biodiv. 14, 261–275. https://doi.org/10.1080/14772000.2015.1135195 (2016).Article 

    Google Scholar 
    10.Hedberg, O. The flora of Ethiopia: a progress report. in Research in Ethiopia Flora (ed. Hedberg, I.). Symb. Bot. Ups. 26, 17–18 (1986).11.Cowling, R. M., Esler, K. J., Midgley, G. F. & Honing, M. A. Plant functional diversity, species diversity and climate in arid and semi-arid southern Africa. J. Arid Environ. 27, 141–158. https://doi.org/10.1006/jare.1994.1054 (1994).ADS 
    Article 

    Google Scholar 
    12.Montana, C. & Valientebanuet, A. Floristic and life-form diversity along an altitudinal gradient in an intertropical semiarid Mexican region. Southwest. Nat. 43, 25–39 (1998).
    Google Scholar 
    13.Pavón, N. P., Hernández-Trejo, H. & Rico-Gray, V. Distribution of plant lifeforms along an altitudinal gradient in the semi-arid valley of Zapotitlón, Mexico. J. Veg. Sci. 11, 39–42. https://doi.org/10.2307/3236773 (2000).Article 

    Google Scholar 
    14.Raunkiaer, C. Statistik der Lebensformen als Grundlage für die biologische Pflanzengeographie. Beih. Bot. Centralbl. 27, 171–206 (1910).
    Google Scholar 
    15.Sarmiento, G. & Monasterio, M. Life form and phenology. In Tropical Savannas (ed. Bourlièrre, F.) 79–108 (Elsevier, 1983).
    Google Scholar 
    16.Meher-Homji, V. M. Environmental implications of life-form spectra from India. J. Econ. Tax. Bot. 2, 23–30 (1981).
    Google Scholar 
    17.Campbell, B. M. & Werger, M. J. A. Plant form in mountains of the Cape, South Africa. J. Ecol. 76, 637–653 (1988).Article 

    Google Scholar 
    18.Komárková, V. & McKendrick, J.D. Patterns in vascular plant growth forms in arctic communities and environment at Atkasook, Alaska. in Plant Form and Vegetation Structure (eds. Werger, M. J. A., van der Aart, P. J. M., During, H. J. & Verhoeven, J. T. A.) 45–70 (SPB Academic Publishing BV, 1988).
    Google Scholar 
    19.Cody, M. L. Growth-form diversity and community structure in desert plants. J. Arid Environ. 17, 199–209 (1989).ADS 
    Article 

    Google Scholar 
    20.Danin, A. & Orshan, G. The distribution of Raunkiaer life forms in Israel in relation to the environment. J. Veg. Sci. 1, 41–48 (1990).Article 

    Google Scholar 
    21.Osman, A. K., Al-Ghamdi, F. & Bawadekji, A. Floristic diversity and vegetation analysis of Wadi Arar: a typical desert Wadi of the Northern Border region of Saudi Arabia. Saud. J. Biol. Sci. 21, 554–565. https://doi.org/10.1016/j.sjbs.2014.02.001 (2014).Article 

    Google Scholar 
    22.Grime, J. P. Plant Strategies and Vegetation Processes (John Wiley, 1979).
    Google Scholar 
    23.Palmer, M. W. The coexistence of species in fractal landscapes. Am. Nat. 139, 375–397 (1992).Article 

    Google Scholar 
    24.Huston, M. & DeAngelis, D. L. Competition and coexistence: the effects of resource transport and supply rates. Am. Nat. 144, 954–977. https://doi.org/10.1086/285720 (1994).Article 

    Google Scholar 
    25.Szaro, R. C. Riparian forest and scrubland communities of Arizona and New Mexico. Desert Plants 9, 69–138 (1989).
    Google Scholar 
    26.DeBano, L. F. & Schimdt, L. J. Potential for enhancing riparian habitat in the Southwestern United States with watershed practices. For. Ecol. Manag. 33(34), 385–403. https://doi.org/10.1016/0378-1127(90)90205-P (1990).Article 

    Google Scholar 
    27.Lieberman, D., Lieberman, M., Peralta, R. & Hartshorn, G. S. Tropical forest structure and composition on a large-scale altitudinal gradient in Costa Rica. J. Ecol. 84, 137–152. https://doi.org/10.2307/2261350 (1996).Article 

    Google Scholar 
    28.Zimmerman, J. C., DeWald, L. E. & Rowlands, P. G. Vegetation diversity in an interconnected ephemeral riparian system of north-central Arizona, USA. Biol. Conserv. 90, 217–228. https://doi.org/10.1016/S0006-3207(99)00035-X (1999).Article 

    Google Scholar 
    29.Brown, J. Mammals on mountainsides: elevational patterns of diversity. Glob. Ecol. Biogeogr. 10, 101–109. https://doi.org/10.1046/j.1466-822x.2001.00228.x (2001).Article 

    Google Scholar 
    30.Lomolino, M. V. Elevation gradients of species-density: historical and prospective views. Glob. Ecol. Biogeogr. 10, 3–13. https://doi.org/10.1046/j.1466822x.2001.00229.x (2001).Article 

    Google Scholar 
    31.Ahmed, M. J., Murtaza, G., Shaheen, H. & Habib, T. Distribution pattern and associated flora of Jurinea dolomiaea in the western Himalayan highlands of Kashmir: an indicator endemic plant of alpine phytodiversity. Ecol. Ind. 116, 106461. https://doi.org/10.1016/j.ecolind.2020.106461 (2020).Article 

    Google Scholar 
    32.Bhat, J. A. et al. Influence of altitude on the distribution pattern of flora in a protected area of Western Himalaya. Acta Ecol. Sin. 40, 30–43. https://doi.org/10.1016/j.chnaes.2018.10.006 (2020).Article 

    Google Scholar 
    33.Kutiel, P. & Lavee, H. Effect of slope aspect on soil and vegetation properties along an aridity transect. Isr. J. Plant Sci. 47, 169–178. https://doi.org/10.1080/07929978.1999.10676770 (1999).Article 

    Google Scholar 
    34.Cantlon, J. Vegetation and microclimates of north and south slopes of Cushetunk mountain. New Jersey. Ecol. Monogr. 23, 241–270 (1953).Article 

    Google Scholar 
    35.Vetaas, O. R. Gradients in field-layer vegetation on an arid misty mountain plateau in the Sudan. J. Veg. Sci. 3, 527–534 (1992).Article 

    Google Scholar 
    36.Kirkpatrick, J., Fensham, R., Nunez, M. & Bowman, D. Vegetation-radiation relation in the wet-dry tropics: granite hills in northern Australia. Vegetatio 76, 103–112 (1998).
    Google Scholar 
    37.Ady, J. The Taif escarpment, Saudi Arabia: a study for nature conservation and recreational development. Mt. Res. Dev. 15, 101–120 (1995).Article 

    Google Scholar 
    38.Almazroui, M., Nazrul Islam, M., Athar, H., Jones, P. D. & Rahman, M. A. Recent climate change in the Arabian Peninsula: annual rainfall and temperature analysis of Saudi Arabia for 1978–2009. Int. J. Climatol. https://doi.org/10.1002/joc.3446 (2012).Article 

    Google Scholar 
    39.Migahid, A. M. Flora of Saudi Arabia 4th edn. (King Saud University Press, 1996).
    Google Scholar 
    40.Collenette, S. Wild Flowers of Saudi Arabia (National Commission for Wildlife Conservation and Development, 1999).
    Google Scholar 
    41.Chaudhary, S. Flora of the Kingdom of Saudi Arabia (Ministry of Agriculture and Water, 2001).
    Google Scholar 
    42.Raunkiaer, C. Life Forms of Plants and Statistical Plant Geography (Collected Paper Translated into English) (University Press, 1934).
    Google Scholar 
    43.Wickens, G. E. The Flora of Jebel Morra (Sudan Republic) and Its Geographical Affinities. Kew Bulletin Additional Series V (HMSO, London, 1976).
    Google Scholar 
    44.Zohary, M. Geobotanical Foundations of the Middle East Vol. 2 (GustavFischer Verlag, 1973).
    Google Scholar 
    45.Broadbent, F. E. Organic matter. In Methods of Soil Analysis Part 1 (ed. Black, C. A.) 1397–1400 (American Society of Agronomy, Inc, 1965).
    Google Scholar 
    46.Bremmer, J. M. Total nitrogen. In Methods of Soil Analysis Part 1 (ed. Black, C. A.) 1149–1176 (American Society of Agronomy, Inc, 1965).
    Google Scholar 
    47.Ward, J. H. Hierarchical grouping to optimize an objective function. Am. Stat. Assoc. J. 58, 236–244 (1963).MathSciNet 
    Article 

    Google Scholar 
    48.Castro, S. A. & Jaksic, F. M. Patterns of turnover and floristic similarity show a non random distribution of naturalized flora in Chile. South America. Rev. Hist. Nat. 81, 111–121 (2008).
    Google Scholar 
    49.Magurran, A. E. Ecological Diversity and Its Measurements (Princeton University Press, 1988).Book 

    Google Scholar 
    50.Pielou, E. C. Ecological Diversity 1st edn. (Wiely Interscience, 1975).
    Google Scholar 
    51.Hosni, H. A. & Hegazy, A. K. Contribution to the flora of Asir, Saudi Arabia. Candollea 51, 169–202 (1996).
    Google Scholar 
    52.Al-Turki, T. A. & Al-Olayan, H. A. Contribution to the flora of Saudi Arabia: hail region. Saud. J. Biol. Sci. 10, 190–222 (2003).
    Google Scholar 
    53.Abd El-Ghani, M. M. & Abdel-Khalik, K. N. Floristic diversity and phytogeography of the gebel Elba national park South-East Egypt. Turk. J. Bot. 30, 121–136 (2006).
    Google Scholar 
    54.Panthi, M. P., Chaudhary, R. P. & Vetaas, O. R. Plant species richness and composition in a trans Himalayan inner valley of mananging district, Central Nepal. Himal. J. Sci. 4, 57–64. https://doi.org/10.3126/hjs.v4i6.983 (2007).Article 

    Google Scholar 
    55.Burke, A. Properties of soil pockets on arid Nama karoo inselbergsethe effect of geology and derived landforms. J. Arid Environ. 50, 219–234. https://doi.org/10.1006/jare.2001.0907 (2002).ADS 
    Article 

    Google Scholar 
    56.Måren, I. E., Karki, S., Prajapati, C., Yadav, R. K. & Shrestha, B. B. Facing north or south: does slope aspect impact forest standcharacteristics and soil properties in a semiarid trans-Himalayanvalley?. J. Arid Environ. 121, 112–123. https://doi.org/10.1016/j.jaridenv.2015.06.004 (2015).ADS 
    Article 

    Google Scholar 
    57.Boyko, H. On the role of plants as quantitative climate indicators and the geoecological law of distributions. J. Ecol. 25, 138–157 (1947).Article 

    Google Scholar 
    58.Andersen, G. L. & Krzywinski, K. Longevity and growth of Acacia tortilis; insights from 14C content and anatomy of wood. BMC Ecol. 7, 4. https://doi.org/10.1186/1472-6785-7-4 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Tiwari, N., Srivastava, N. & Sharma, V. Comparative analysis of total phenolic content and antioxidant activity of in vivo and in vitro grown plant parts of Carica papaya L. Ind. J. Plant Physiol. 19, 356–362 (2014).Article 

    Google Scholar 
    60.Daur, I. Plant flora in the rangeland of Western Saudi Arabia. Pak. J. Bot. 44, 23–26 (2012).
    Google Scholar 
    61.El-Demerdash, M. A., Hegazy, A. K. & Zilay, A. M. Distribution of plant communities in Tihamah coastal plains of Jazan region, Saudi Arabia. Vegetatio 112, 141–151 (1994).Article 

    Google Scholar 
    62.El-Ghanim, W. M., Hassan, L. M., Galal, T. M. & Badr, A. Floristic composition and vegetation analysis in Hail region north of Central Saudi Arabia. Saudi J. Biol. Sci. 17, 119–128. https://doi.org/10.1016/j.sjbs.2010.02.004 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Abd El-Ghani, M. M. Environmental correlates of species distribution in arid desert ecosystems of eastern Egypt. J. Arid Environ. 38, 297–313 (1998).ADS 
    Article 

    Google Scholar 
    64.Sharma, M. & Rajpal, K. Life-forms and biological spectrum of the flora of the Punjab state, India. Bull. Bot. Surv. India 33, 276–280. https://doi.org/10.1078/1439-1791-00163 (1991).Article 

    Google Scholar 
    65.Hegazy, A. K., El-Demerdash, M. A. & Hosni, H. A. Vegetation, species diversity and floristic relations along an altitudinal gradient in South-West Saudi Arabia. J. Arid Environ. 38, 3–13. https://doi.org/10.1006/jare.1997.0311 (1998).ADS 
    Article 

    Google Scholar 
    66.Kassas, M. & Girgis, W. A. Habitats and plant communities in the Egyptian deserts. V. The limestone plateau. J. Ecol. 52, 107–119 (1964).Article 

    Google Scholar 
    67.Orshan, G. The desert of the middle east. In Ecosystems of the World, 12B, Hot Desert and Arid Shrublands (eds Evenari, M. et al.) 1–28 (Elsevier, 1986).
    Google Scholar 
    68.Shaltout, K. H., Sheded, M. G. & Salem, A. M. Vegetation spatial heterogeneity in a hyper arid biosphere reserve area in North Africa. Act. Bot. Croat. 69, 31–46 (2010).
    Google Scholar 
    69.Stewart, L. et al. The regional species richness and genetic diversity of Arctic vegetation reflect both past glaciations and current climate. Ecol. Biogeogr. 25, 430–442. https://doi.org/10.1111/geb.12424 (2016).Article 

    Google Scholar 
    70.Cain, S. A. & Castro, M. O. Manual of Vegetation Analysis (Harper Brothers, 1959).
    Google Scholar 
    71.Dickoré, W. B. & Nüsser, M. Flora of Nanga Parbat (NW Himalaya, Pakistan): an annotated inventory of vascular plants with remarks on vegetation dynamics. Englera 19, 1–253. https://doi.org/10.2307/3776769 (2000).Article 

    Google Scholar 
    72.Hoffmann, A. J. & Hoffmann, A. E. Altitudinal ranges of phanerophytes and chamaephytes in central Chile. Vegetatio 48, 151–163. https://doi.org/10.1007/BF00726885 (1982).Article 

    Google Scholar 
    73.White, F. & Leonard, J. Phytogeographical links between Africa and Southwest Asia. Flora Veg. Mundi. 9, 229–246. https://doi.org/10.1007/BF01117080 (1991).Article 

    Google Scholar 
    74.König, P. Phytogeography of South-Western Saudi Arabia (Asir, Tihama). Erde 119, 75–89 (1988).
    Google Scholar 
    75.White, F. The vegetation of Africa: A descriptive memoir to accompany the UNSECO, AETFAT, UNSO vegetation map of Africa (United Nations Educational, Scientific and Cultural Organization, Paris, 1983).
    Google Scholar  More

  • in

    Double jeopardy for fish diversity

    1.Garilli, V. et al. Nat. Clim. Change 5, 678–682 (2015).CAS 
    Article 

    Google Scholar 
    2.Verberk, W. C. E. P. et al. Biol. Rev. 96, 247–268 (2020).Article 

    Google Scholar 
    3.Rhein, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 255–297 (Cambridge Univ. Press, 2013).4.Gardner, J. L. et al. Trends Ecol. Evol. 26, 285–291 (2011).Article 

    Google Scholar 
    5.Avaria-Llautureo, J. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01123-5 (2021).6.Burns, M. D. & Bloom, D. D. Proc. Biol. Sci. 287, 20192615 (2020).
    Google Scholar 
    7.Cheung, W. W. L. et al. Nat. Clim. Change 3, 254–258 (2013).Article 

    Google Scholar 
    8.Bernardi, G. Mol. Ecol. 22, 5487–5502 (2013).Article 

    Google Scholar 
    9.Lunt, D. et al. Clim. Past 12, 1181–1198 (2016).Article 

    Google Scholar 
    10.Warnock, R. et al. Paleobiology 46, 137–157 (2020).Article 

    Google Scholar 
    11.Zachos, J. et al. Science 292, 686–693 (2001).CAS 
    Article 

    Google Scholar 
    12.Marrama, G. & Carnevale, G. Hist. Biol. 29, 904–917 (2016).Article 

    Google Scholar 
    13.Marrama, G. & Carnevale, G. PalZ 92, 107–120 (2018).Article 

    Google Scholar 
    14.Burke, K. D. et al. Proc. Natl Acad. Sci. USA 115, 13288–13293 (2018).CAS 
    Article 

    Google Scholar  More

  • in

    Quantifying the dynamics of rocky intertidal sessile communities along the Pacific coast of Japan: implications for ecological resilience

    1.Holling, C. S. & Meffe, G. K. Command and control and the pathology of natural resource management. Conserv. Biol. 10, 328–337 (1996).Article 

    Google Scholar 
    2.Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

    Google Scholar 
    3.Gunderson, L. H. Ecological resilience—In theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).Article 

    Google Scholar 
    4.Thrush, S. F. et al. Forecasting the limits of resilience: Integrating empirical research with theory. Proc. R. Soc. B Biol. Sci. 276, 3209–3217 (2009).Article 

    Google Scholar 
    5.Bagchi, S. et al. Quantifying long-term plant community dynamics with movement models: Implications for ecological resilience. Ecol. Appl. 27, 1514–1528 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Hillebrand, H. et al. Decomposing multiple dimensions of stability in global change experiments. Ecol. Lett. 21, 21–30 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Radchuk, V. et al. The dimensionality of stability depends on disturbance type. Ecol. Lett. 22, 674–684 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).ADS 
    Article 

    Google Scholar 
    11.Donohue, I. et al. On the dimensionality of ecological stability. Ecol. Lett. 16, 421–429 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Pennekamp, F. et al. Biodiversity increases and decreases ecosystem stability. Nature 563, 109–112 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Kéfi, S. et al. Advancing our understanding of ecological stability. Ecol. Lett. 22, 1349–1356 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Raffaelli, D. & Hawkins, S. J. Intertidal Ecology (Chapman & Hall, 1996).Book 

    Google Scholar 
    15.Tsujino, M. et al. Distance decay of community dynamics in rocky intertidal sessile assemblages evaluated by transition matrix models. Popul. Ecol. 52, 171–180 (2010).Article 

    Google Scholar 
    16.Kanamori, Y., Fukaya, K. & Noda, T. Seasonal changes in community structure along a vertical gradient: Patterns and processes in rocky intertidal sessile assemblages. Popul. Ecol. 59, 301–313 (2017).Article 

    Google Scholar 
    17.Menge, B. A. et al. Benthic–pelagic links and rocky intertidal communities: Bottom-up effects on top-down control?. Proc. Natl. Acad. Sci. 94, 14530–14535 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Sanford, E. Regulation of keystone predation by small changes in ocean temperature. Science 283, 2095–2097 (1999).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Menge, B. A. Top-down and bottom-up community regulation in marine rocky intertidal habitats. J. Exp. Mar. Biol. Ecol. 250, 257–289 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Connolly, S. R., Menge, B. A. & Roughgarden, J. A. Latitudinal gradient in recruitment of intertidal invertebrates in the northeast Pacific Ocean. Ecology 82, 1799–1813 (2001).Article 

    Google Scholar 
    21.Menge, B. A. et al. Coastal oceanography sets the pace of rocky intertidal community dynamics. Proc. Natl. Acad. Sci. 100, 12229–12234 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Nielsen, K. J. & Navarrete, S. A. Mesoscale regulation comes from the bottom-up: Intertidal interactions between consumers and upwelling. Ecol. Lett. 7, 31–41 (2004).Article 

    Google Scholar 
    23.Schoch, G. C. et al. Fifteen degrees of separation: Latitudinal gradients of rocky intertidal biota along the California Current. Limnol. Oceanogr. 51, 2564–2585 (2006).ADS 
    Article 

    Google Scholar 
    24.Vinueza, L. R., Menge, B. A., Ruiz, D. & Palacios, D. M. Oceanographic and climatic variation drive top-down/bottom-up coupling in the Galápagos intertidal meta-ecosystem. Ecol. Monogr. 84, 411–434 (2014).Article 

    Google Scholar 
    25.Menge, B. A., Gouhier, T. C., Hacker, S. D., Chan, F. & Nielsen, K. J. Are meta-ecosystems organized hierarchically? A model and test in rocky intertidal habitats. Ecol. Monogr. 85, 213–233 (2015).Article 

    Google Scholar 
    26.Hacker, S. D., Menge, B. A., Nielsen, K. J., Chan, F. & Gouhier, T. C. Regional processes are stronger determinants of rocky intertidal community dynamics than local biotic interactions. Ecology https://doi.org/10.1002/ecy.2763 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Qiu, B. Kuroshio and Oyashio currents. In Ocean Currents: A Derivative of the Encyclopedia of Ocean Sciences (eds Steele, J. H. et al.) 1413–1425 (Academic Press, 2001).Chapter 

    Google Scholar 
    28.Qiu, B. Large-scale variability in the midlatitude subtropical and subpolar North Pacific Ocean: Observations and causes. J. Phys. Oceanogr. 32, 353–375 (2002).ADS 
    Article 

    Google Scholar 
    29.Sakurai, Y. An overview of the Oyashio ecosystem. Deep Sea Res. Pt. II 54, 2526–2542 (2007).ADS 
    Article 

    Google Scholar 
    30.Yatsu, A. et al. Climate forcing and the Kuroshio/Oyashio ecosystem. ICES J. Mar. Sci. 70, 922–933 (2013).Article 

    Google Scholar 
    31.Kawabe, M. Variations of the Kuroshio in the southern region of Japan: Conditions for large meander of the Kuroshio. J. Oceanogr. 61, 529–537 (2005).Article 

    Google Scholar 
    32.Okunishi, T. et al. Characteristics of oceanographic condition of Tohoku prefecture in 2018. in Bulletin of Liaison Conference of Tohoku Marine Surveys and Technology , Vol. 68, 4–5 (2018) (in Japanese).33.Japan Meteorological Agency. Fluctuations in the Kuroshio Current on a Scale of Months to Decades (Paths). http://www.data.jma.go.jp/gmd/kaiyou/data/shindan/b_2/kuroshio_stream/kuroshio_stream.html (in Japanese, accessed 11 March 2021).34.Taniguchi, K., Sato, M. & Owada, K. On the characteristics of the structural variation in the Eisenia bicyclis population on Joban coast, Japan. Bull Tohoku Natl. Fish. Res. Inst. 48, 49–57 (1986) (in Japanese with English abstract).
    Google Scholar 
    35.Nomura, K., & Hirabayashi, I. Mass mortality of coral communities caused by abnormality low water temperature observed at Kii peninsula west coast for winter season in 2018. Marine Pavilion. Supplement 7 (2018) (in Japanese).36.Yamaguchi, M. Acanthaster planci infestations of reefs and coral assemblages in Japan: A retrospective analysis of control efforts. Coral Reefs 5, 23–30 (1986).ADS 
    Article 

    Google Scholar 
    37.Ohgaki, S. I. et al. Effects of temperature and red tides on sea urchin abundance and species richness over 45 years in southern Japan. Ecol. Indic. 96, 684–693 (2019).Article 

    Google Scholar 
    38.Kawajiri, M., Sasaki, T. & Kageyama, Y. Extensive deterioration of Ecklonia kelp stands and death of the plants, and fluctuations in abundance of the abalone off Toji, southern Izu peninsula. Bull. Shizuoka Pref. Fish. Exp. Stn. 15, 19–30 (1981) (in Japanese).
    Google Scholar 
    39.Takami, H. et al. Overwinter mortality of young-of-the-year Ezo abalone in relation to seawater temperature on the North Pacific coast of Japan. Mar. Ecol. Prog. Ser. 367, 203–212 (2008).ADS 
    Article 

    Google Scholar 
    40.Okuda, T., Noda, T., Yamamoto, T., Ito, N. & Nakaoka, M. Latitudinal gradient of species diversity: Multi-scale variability in rocky intertidal sessile assemblages along the Northwestern Pacific coast. Popul. Ecol. 46, 159–170 (2004).Article 

    Google Scholar 
    41.Nakaoka, M., Ito, N., Yamamoto, T., Okuda, T. & Noda, T. Similarity of rocky intertidal assemblages along the Pacific coast of Japan: Effects of spatial scales and geographic distance. Ecol. Res. 21, 425–435 (2006).Article 

    Google Scholar 
    42.Gotelli, N. J. et al. Community-level regulation of temporal trends in biodiversity. Sci. Adv. 3, e1700315. https://doi.org/10.1126/sciadv.1700315 (2017).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Iwasaki, A., Fukaya, K. & Noda, T. Quantitative evaluation of the impact of the Great East Japan Earthquake and tsunami on the rocky intertidal community. In Ecological Impacts of Tsunamis on Coastal Ecosystems (eds Urabe, J. & Nakashizuka, T.) 35–46 (Springer Japan, 2016).Chapter 

    Google Scholar 
    45.Noda, T., Iwasaki, A. & Fukaya, K. Recovery of rocky intertidal zonation: Two years after the 2011 Great East Japan Earthquake. J. Mar. Biol. Assoc. UK 96, 1549–1555 (2016).Article 

    Google Scholar 
    46.Noda, T., Sakaguchi, M., Iwasaki, A. & Fukaya, K. Influence of the 2011 Tohoku Earthquake on population dynamics of a rocky intertidal barnacle: Cause and consequence of alternation in larval recruitment. Coast. Mar. Sci. 40, 35–43 (2017).
    Google Scholar 
    47.Nuvoloni, F. M., Feres, R. J. F. & Gilbert, B. Species turnover through time: Colonization and extinction dynamics across metacommunities. Am. Nat. 187, 786–796 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Clarke, A. Life in cold water: The physiological ecology of polar marine ectotherms. Oceanogr. Mar. Biol. A Rev. 21, 341–453 (1983).
    Google Scholar 
    49.Moss, D. K. et al. Lifespan, growth rate, and body size across latitude in marine Bivalvia, with implications for Phanerozoic evolution. Proc. R. Soc. B Biol. Sci. 283, 20161364. https://doi.org/10.1098/rspb.2016.1364 (2016).Article 

    Google Scholar 
    50.Bulleri, F. et al. Temporal stability of European rocky shore assemblages: Variation across a latitudinal gradient and the role of habitat-formers. Oikos 121, 1801–1809 (2012).Article 

    Google Scholar 
    51.Noda, T. Spatial hierarchical approach in community ecology: A way beyond high context-dependency and low predictability in local phenomena. Popul. Ecol. 46, 105–117 (2004).Article 

    Google Scholar 
    52.Sahara, R. et al. Larval dispersal dampens population fluctuation and shapes the interspecific spatial distribution patterns of rocky intertidal gastropods. Ecography 39, 487–495 (2015).Article 

    Google Scholar 
    53.Hanawa, K. & Mitsudera, H. Variation of water system distribution in the Sanriku coastal area. J. Oceanogr. 42, 435–446 (1987).Article 

    Google Scholar 
    54.Ohtani, K. Westward inflow of the coastal Oyashio Water into the Tsugaru Strait. Bull. Fac. Fish Hokkaido Univ. 38, 209–220 (1987) (in Japanese with English abstract).
    Google Scholar 
    55.Takasugi, S. Distribution of Tsugaru Warm Current water in the Iwate coastal area and their influence to sea surface temperature at coastal hydrographic station. Bull. Jpn. Soc. Fish. Oceanogr. 56, 434–448 (1992) (in Japanese with English abstract).
    Google Scholar 
    56.Takasugi, S. & Yasuda, I. Variation of the Oyashio water in the Iwate coastal region and in the vicinity of east coast of Japan. Bull. Jpn. Soc. Fish. Oceanogr. 58, 253–259 (1994) (in Japanese with English abstract).
    Google Scholar 
    57.Conlon, D. M. On the outflow modes of the Tsugaru Warm Current. La Mer. 20, 60–64 (1982).
    Google Scholar 
    58.Isoda, Y. & Suzuki, K. Interannual variations of the Tsugaru gyre. Bull. Fac. Fish. Hokkaido Univ. 55, 71–74 (2004) (in Japanese with English abstract).
    Google Scholar 
    59.Mrowicki, R. J., O’Connor, N. E. & Donohue, I. Temporal variability of a single population can determine the vulnerability of communities to perturbations. J. Ecol. 104, 887–897 (2016).Article 

    Google Scholar 
    60.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018). More