Philippa Kaur

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

CORRESPONDENCE
10 August 2021

Iran: drought must top new government’s agenda

Jamshid Parchizadeh

0
&

Jerrold L. Belant

1

Jamshid Parchizadeh

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Jerrold L. Belant

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

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

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.

Related Articles

See more letters to the editor

Subjects

Water resources

Climate sciences

Ecology

Politics

Latest on:

Water resources

Iran is draining its aquifers dry
Research Highlight 16 JUN 21

Most rivers and streams run dry every year
News & Views 16 JUN 21

Global prevalence of non-perennial rivers and streams
Article 16 JUN 21

Climate sciences

IPCC climate report: Earth is warmer than it’s been in 125,000 years
News 09 AUG 21

Diagnosing Earth: the science behind the IPCC’s upcoming climate report
News 05 AUG 21

The fraction of the global population at risk of floods is growing
News & Views 04 AUG 21

Ecology

COVID vaccine inequity, species swaps — the week in infographics
News 06 AUG 21

The world’s species are playing musical chairs: how will it end?
News Feature 04 AUG 21

Agrochemicals interact synergistically to increase bee mortality
Article 04 AUG 21

Jobs

Postdoctoral Position

The University of Texas MD Anderson Cancer Center
Houston, TX, United States

Bioinformatics Analyst II

Baylor College of Medicine (BCM)
Houston, TX, United States

FULL-TIME ACADEMIC POSITION IN MOLECULAR BIOLOGY

Université Libre de Bruxelles (ULB)
Gosselies, Belgium

Assistant Professor

Johns Hopkins University School of Medicine (JHUSOM), JHU
Baltimore, Maryland, MD, United States

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

The data collection protocol is described in Sánchez et al.19 and followed Reporting Standards for Systematic Evidence Syntheses (ROSES) guidelines20. Philibert et al.21, also proposed eight criteria for conducting high quality meta-analysis, which overlap to some extent with ROSES guidelines. Our methods fulfil the Philibert et al. requirement to use a repeatable procedure for paper selection, provide a list of references, and ensure availability of the dataset, while other quality criteria are only relevant at the meta-analysis stage.Search processThe literature search was conducted on 29 November 2019 and updated on 5 January 2021, and aimed to identify relevant English language articles published in peer-reviewed literature. We searched in titles, abstracts and keyword lists of literature in the Scopus and Web of Science databases, using the following search string (formatted for Scopus; see Dataset 1 for the equivalent string formatted for Web of Science): TITLE-ABS-KEY (“agricultur*” AND “biodiversity”) AND TITLE-ABS-KEY (“agro?ecology” OR “agro?biodivers*” OR “agroforestry” OR “border plant*” OR “riparian buffer” OR “woodlot” OR “hedgerow” OR “cover crop*” OR “crop rotation” OR “crop divers*” OR “inter?crop*” OR “mixed crop*” OR “cultivar mixture” OR “plant divers*” OR “polyculture” OR “tree divers*” OR “variet* diversity” OR “fallow” OR “field margin*” OR “grass strip*” OR “*flower strip*” OR “insect* strip” OR “conservation strip” OR “vegetation strip” OR “catch crop” OR “inter?crop*” OR “crop variety” OR “crop sequenc*” OR “mixed farming” OR “land sparing” OR “landscape heterogeneity” OR “heterogeneous landscape” OR “landscape diversi*” OR “divers* landscape” OR “homogeneous landscape” OR “landscape homogeneity” OR “landscape complexity” OR “simplif* landscape” OR “complex landscape” OR “multi?function* landscape” OR “integrated crop-livestock” OR “integrated crop-forest” OR “land sharing”) AND TITLE-ABS-KEY (“ richness” OR “ abundance” OR “species diversity” OR “functional diversity” OR “index”) AND TITLE-ABS-KEY (“crop yield” OR “crop production”) AND (LIMIT-TO (LANGUAGE, “English”)). We extracted the primary studies included in all relevant meta-analyses identified from the database search. In addition, we included a small number of peer-reviewed articles known to scientists consulted through the Sustainable Foods project and which were not retrieved by the search string or from previous meta-analyses. In total, 1590 articles with the potential to be included in the meta-analysis were identified (Fig. 1).Article screeningAll identified articles were screened at full-text level. We used the PICOC (Population, Intervention, Comparator, Outcomes, Context) framework to define the inclusion-exclusion criteria as described in Sánchez et al.19. These criteria required that, to be included: (i) the article presents a quantitative comparison of a diversified farming system (Intervention) compared to either a relatively simplified farming system (first Comparator) or to natural habitat (second Comparator), ii) the article reports quantitative outcomes for any terrestrial organism that is non-domesticated (Population), iii) the article provides the mean or median, variance and sample size for biodiversity outcomes, and outcome measures in comparator and intervention sites were collected using comparable sampling approaches (Outcome), iv) results are from primary field studies and not from experiments conducted in greenhouses or laboratories (Context).Diversified farming systems were defined as agricultural plots where: i) more than one plant species or variety is cultivated at multiple temporal and/or spatial scales, such as crop rotations, intercropping or agroforestry, or ii) semi-natural habitat such as hedgerows and flower strips is embedded into the system, or iii) crop production is integrated with livestock or fish production, such as aquaculture or integrated crop-livestock systems. Simplified farming systems were agricultural plots with less diversity than in eligible interventions, i.e., plots with relatively fewer plant species or varieties (usually monocultures), less semi-natural habitat embedded, or no mixed crop-animal production. Where natural habitat was used as a comparator, this was defined as habitat that is not actively used for human activities, such as primary and secondary forests, wetlands, unmanaged grasslands and shrublands.Suitable outcome metrics for biodiversity included any comparable quantified measure, such as richness, abundance, or Shannon’s diversity index. While studies only needed to report biodiversity outcomes to be considered for inclusion, we recorded harvested yield in all cases where this was reported and met our inclusion criteria. For yield outcomes to be included, the article must have provided means or medians, variance and sample sizes, and outcome measures at intervention and comparator sites must have been collected using comparable sampling approaches. Suitable outcome metrics for yields included the land equivalent ratio, weight of harvested produce per unit land area, or counts of harvested produce per standardized unit (e.g. grape bunch per plant, apples per branch). For comparisons comparing intercropped or agroforestry systems against simplified farming systems, the land equivalent ratio was prioritized as the outcome metric while other metrics were used only when the land equivalent ratio could not be calculated.In total, 237 (14.9%) of retrieved articles met our inclusion criteria (Fig. 1).Data extractionFrom each article that met our inclusion criteria, we extracted qualitative data on: the literature source (e.g. authors, publication year, title); crop type (common name, scientific name); agricultural system (e.g. intercropping, monoculture, agroforestry, integrated crop-livestock system, crop rotation, set aside); non-domesticated taxa sampled (common and scientific names); functional group of the non-domesticated taxa, if specified (e.g. pest, decomposer, predator); biodiversity outcome metric (e.g. species richness, abundance, Shannon’s diversity index); yield metric (e.g. kilogram per hectare, grams per plant, land equivalent ratio); sampling method used (e.g. transect, trap); pesticide use (yes or no, and kg/ha); fertilizer use (yes or no, and chemical fertiliser use yes or no); soil management (e.g. tillage, no tillage, slash and burn); landscape characteristics (e.g. % agricultural land use, climate); and study location (local name, country and geographic coordinates). Following initial data-entry, we classified several variables into categories to facilitate data exploration and analysis. This included categorizing crops by the Food and Agriculture Organisation of the United Nations commodity group, woodiness (e.g. tree, shrub, herb), and growth cycle (perennial, annual), and documenting the phylum, class, order and functional group of each non-domesticated taxon.We extracted quantitative data on: biodiversity outcome means or medians, variance and sample size; yield means or median, variance and sample size; farm size, if specified; length of time that the land has been in its current state, and; sampling duration (in days, from start to finish). Data on biodiversity outcomes and yield were extracted from figures using GetData Graph Digitizer 2.26 or WebPlotDigitizer v4.2. Where outcome values or units in an article were unclear or not provided, the corresponding author was contacted by email to request this information. If the author did not respond, the data entry was removed. We provide a dictionary of how the extracted data were recorded and coded in Dataset 2.Data were organized using R-4.0.0 (R-Core Team, 2013) such that each row contained a pair of biodiversity outcomes and, where provided, a pair of yield outcomes, for a single comparator-intervention pair. In total, 237 studies containing 4076 comparisons of biodiversity outcomes and 1214 comparisons of yield outcomes were retained for analysis (Fig. 1). More

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

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

1.Patrick, R., Binetti, V. P. & Halterman, S. G. Acid lakes from natural and anthropogenic causes. Science 211, 446–448 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Dokulil, M., Chen, W. & Cai, Q. Anthropogenic impacts to large lakes in China: The Tai Hu example. Aquat. Ecosyst. Health Manage. 3, 81–94 (2000).Article 

Google Scholar 
3.Woolway, R. I. et al. Global lake responses to climate change. Nat. Rev. Earth Environ. 1, 388–403 (2020).ADS 
Article 

Google Scholar 
4.Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Article 

Google Scholar 
5.Zhupankhan, A., Tussupova, K. & Berndtsson, R. Water in Kazakhstan, a key in Central Asian water management. Hydrol. Sci. J. 63, 752–762 (2018).Article 

Google Scholar 
6.Corell, D. L. The role of phosphorus in the euthrophication of receiving waters: A review. J. Environ. Qual. 27, 261–266 (1998).Article 

Google Scholar 
7.Hansson, L.-A. & Tranvik, L. A. Algal species composition and phosphorus recycling at contrasting grazing pressure: An experimental study in sub-Antarctic lakes with two trophic levels. Freshw. Biol. 37, 45–53 (1997).Article 

Google Scholar 
8.Gozlan, R., Karimov, B., Zadereev, E., Kuznetsova, D. & Brucet, S. Status, trends, and future dynamics of freshwater ecosystems in Europe and Central Asia. Inland Waters 9, 78–94 (2019).CAS 
Article 

Google Scholar 
9.WBGU (Wissenschaftliche Beirat der Bundesregierung Globale Umweltveränderungen; German Advisory Council on Global Change). Climate Change as a Security Risk (Earthscan, 2007).
Google Scholar 
10.Campbell, L. et al. Response of microbial community structure to environmental forcing in the Arabian Sea. Deep Sea Res II Top. Stud. Oceanogr. 45, 2301–2325 (1998).ADS 
Article 

Google Scholar 
11.Winder, M. & Sommer, U. Phytoplankton response to a changing climate. Hydrobiologia 698, 5–16 (2012).Article 

Google Scholar 
12.Bellinger, E. G. & Sigee, D. C. Freshwater Algae: Identification and Use as Bioindicators (Wiley, 2010).Book 

Google Scholar 
13.Zohary, T., Flaim, G. & Sommer, U. Temperature and the size of freshwater phytoplankton. Hydrobiologia 848, 143–155 (2021).Article 

Google Scholar 
14.Reynolds, C. S., Padisák, J. & Sommer, U. Intermediate disturbance in the ecology of phytoplankton and the maintenance of species diversity: A synthesis. Hydrobiologia 249, 183–188 (1993).Article 

Google Scholar 
15.Likens, G. E. Plankton of Inland Waters (Academic Press, 2010).
Google Scholar 
16.Hutchinson, G. E. A Treatise on Limnology. Volume 2. Introduction to Lake Biology and the Limnoplankton (Wiley, 1967).
Google Scholar 
17.Reynolds, C. S. The concept of ecological succession applied to seasonal periodicity of freshwater phytoplankton. Int. Ver. Limnol. 23, 683–691 (1988).
Google Scholar 
18.Bartram, J. & Ballance, R. (eds) Water Quality Monitoring—A Practical Guide to the Design and Implementation of Freshwater Quality Studies and Monitoring Programs (UNEP/WHO, 1996).
Google Scholar 
19.Lepistő, L., Holopainen, A.-L. & Vuorosto, H. Type-specific and indicator taxa of phytoplankton as a quality criterion for assessing the ecological status of Finnish boreal lakes. Limnologica 34, 236–248 (2004).Article 

Google Scholar 
20.Järvinen, M. et al. Phytoplankton indicator taxa for reference conditions in Northern and Central European lowland lakes. Hydrobiologia 704, 97–113 (2013).Article 

Google Scholar 
21.Soares, M. C. S. et al. Light microscopy in aquatic ecology: Methods for plankton communities studies. In Light Microscopy: Methods and Protocols (eds Chiarini-Garcia, H. & Melo, R. C. N.) 215–227 (Springer, 2011).Chapter 

Google Scholar 
22.Findlay, D. L. & Kling, H. J. Protocols for Measuring Biodiversity: Phytoplankton in Fresh Water Lakes (Department of Fisheries and Oceans, 1998).
Google Scholar 
23.Maurer, D. The dark side of taxonomic sufficiency. Mar. Pollut. Bull. 40, 98–101 (2000).CAS 
Article 

Google Scholar 
24.Bourlat, S. J. et al. Genomics in marine monitoring: New opportunities for assessing marine health status. Mar. Pollut. Bull. 74, 19–31 (2013).CAS 
PubMed 
Article 

Google Scholar 
25.Hering, D. et al. Implementation options for DNA-based identification into ecological status assessment under the European water framework directive. Water Res. 138, 192–205 (2018).CAS 
PubMed 
Article 

Google Scholar 
26.Ayaglas, E. et al. Translational molecular ecology in practice: Linking DNA-based methods to actionable marine environmental management. Sci. Total Environ. 744, 140780 (2020).ADS 
Article 
CAS 

Google Scholar 
27.Peperzak, L., Vrieling, E. G., Sandee, B. & Rutten, T. Immuno flow cytometry in marine phytoplankton research. Sci. Mar. 64, 165–181 (2000).Article 

Google Scholar 
28.Dashkova, V., Malashenkov, D., Poulton, N., Vorobjev, I. & Barteneva, N. S. Imaging flow cytometry for phytoplankton analysis. Methods 112, 188–200 (2017).CAS 
PubMed 
Article 

Google Scholar 
29.Dubelaar, G. & Jonker, R. R. Flow cytometry as a tool for the study of phytoplankton. Sci. Mar. 64, 135–156 (2000).Article 

Google Scholar 
30.Stockner, J. G. Phototrophic picoplankton: An overview from marine and freshwater ecosystems. Limnol. Oceanogr. 33, 765–775 (1988).ADS 
CAS 

Google Scholar 
31.Schmidt, T. M., DeLong, E. F. & Pace, N. R. Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. J. Bacteriol. 173, 4371–4378 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Diez, B., Pedros-Aliŏ, C. & Massana, R. Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and sequencing. Appl. Environ. Microbiol. 67, 2932–2941 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Stoeck, T., Hayward, B., Taylor, G. T., Varela, R. & Epstein, S. S. A multiple PCR-primer approach to access the microeukaryotic diversity in the anoxic Cariaco Basin (Caribbean Sea). Protist 157, 31–43 (2006).CAS 
PubMed 
Article 

Google Scholar 
34.Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
PubMed 
Article 

Google Scholar 
35.Szabó, A. et al. Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions. Extremophiles 21, 639–649 (2017).PubMed 
Article 

Google Scholar 
36.Bott, N. J. et al. Toward routine, DNA-based detection methods for marine pests. Biotechnol. Adv. 28, 706–714 (2010).CAS 
PubMed 
Article 

Google Scholar 
37.Tan, S. et al. An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics. J. Phycol. 51, 120–132 (2015).CAS 
PubMed 
Article 

Google Scholar 
38.Medlin, L. K. & Orozco, J. Molecular techniques for the detection of organisms in aquatic environments, with emphasis on harmful algal bloom species. Sensors 17, 1184 (2017).ADS 
PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
39.de Bruin, A., Ibelings, B. W. & Van Donk, E. Molecular techniques in phytoplankton research: From allozyme electrophoresis to genomics. Hydrobiologia 491, 47–63 (2003).Article 

Google Scholar 
40.Ebenezer, V., Medlin, L. K. & Ki, J. S. Molecular detection, quantification, and diversity evaluation of microalgae. Mar. Biotechnol. 14, 129–142 (2012).CAS 
Article 

Google Scholar 
41.Kim, J. et al. Microfluidic high-throughput selection of microalgal strains with superior photosynthetic productivity using competitive phototaxis. Sci. Rep. 6, 21155 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Xiao, X. et al. Use of high throughput sequencing and light microscopy show contrasting results in a study of phytoplankton occurrence in a freshwater environment. PLoS ONE 9, e106510 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. U.S.A. 103, 12115–12120 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Medinger, R. et al. Diversity in a hidden world: Potential and limitation of next-generation sequencing for surveys of molecular diversity of eukaryotic microorganisms. Mol. Ecol. 19, 32–40 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Andersson, A. F., Riemann, L. & Bertilsson, S. Pyrosequencing reveals contrasting seasonal dynamics of taxa within Baltic Sea bacterioplankton communities. ISME J. 4, 171–181 (2010).PubMed 
Article 

Google Scholar 
46.Filker, S., Gimmler, A., Dunthorn, M., Mahe, F. & Stoeck, T. Deep sequencing uncovers protistan plankton diversity in the Portuguese Ria Formosa solar saltern ponds. Extremophiles 19, 283–295 (2015).CAS 
PubMed 
Article 

Google Scholar 
47.Eiler, A. et al. Unveiling distribution patterns of freshwater phytoplankton by a next generation sequencing based approach. PLoS ONE 8, e53516 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Visco, J. A. et al. Environmental monitoring: Inferring the diatom index from next generation sequencing data. Environ. Sci. Technol. 49, 7597–7605 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Abad, D. et al. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy. Mar. Biol. 163, 149 (2016).Article 

Google Scholar 
50.Gao, W. et al. Bioassessment of a drinking water reservoir using plankton: High throughput sequencing vs. traditional morphological method. Water 10, 82 (2018).Article 
CAS 

Google Scholar 
51.Rimet, F., Vasselon, V., Barabar, A. & Bouchez, A. Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes. Org. Divers. Evol. 18, 51–62 (2018).Article 

Google Scholar 
52.Kazhydromet. Environmental Monitoring Bulletin of Republic of Kazakhstan for 2007 (Kazhydromet, 2007).
Google Scholar 
53.Lewis, W. M. Jr. A revised classification of lakes based on mixing. Can. J. Fish. Aquat. Sci. 40, 1779–1787 (1983).Article 

Google Scholar 
54.Welch, E. B. & Cooke, G. D. Internal phosphorus loading in shallow lakes: Importance and control. Lake Reserv. Manage. 11, 273–281 (1995).Article 

Google Scholar 
55.Kabiyeva, M. & Zubairov, B. Bathymetric measurements of Lake Shortandy, Burabay National Nature Park. In Proc. Central Asia GIS Conference—GISCA “Geospatial Management of Land, Water and Resources” ( May 14–16, Tashkent) 44–48 (2015).56.Plokhikh, R. V. Ecological state of regions: Northern Kazakhstan. In Republic of Kazakhstan: Environment and Ecology Vol. 3 (eds Budnikova, T. I. et al.) (Institute of Geography, 2010).
Google Scholar 
57.Kumanbayeva, A. S., Khusainov, A. T. & Zhumaj, E. Ecological state of Lake Burabay, National State Park Burabay. Sci. News Kazakhstan 3, 171–178 (2019).
Google Scholar 
58.Sadchikov, A. P. Methods of Studying Freshwater Phytoplankton: A Manual (Universitet i shkola, 2003).
Google Scholar 
59.Sukhanova, I. N. Settling without the inverted microscope. In Phytoplankton Manual (ed. Sourina, A.) 97 (UNESCO, 1978).
Google Scholar 
60.Schwoerbel, J. Methods of Hydrobiology (Freshwater Biology) (Elsevier, 1970).
Google Scholar 
61.Xia, S., Cheng, Y. Y., Zhu, H., Liu, G. X. & Hu, Z. Y. Improved methodology for identification of Cryptomonads: Combining light microscopy and PCR amplification. J. Microbiol. Biotechnol. 23, 289–296 (2013).CAS 
PubMed 
Article 

Google Scholar 
62.LeGresley, M. & McDermott, G. Counting chamber methods for quantitative phytoplankton—Haemocytometer, Palmer-Maloney cell and Sedgewick-Rafter cell. In Microscopic and Molecular Methods for Quantitative Phytoplankton Analysis (eds Karlson, B. et al.) 25–30 (UNESCO, 2010).
Google Scholar 
63.Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).Article 

Google Scholar 
64.Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankton Res. 25, 1331–1346 (2003).Article 

Google Scholar 
65.Konoplya, B. I. & Soares, F. S. New geometric models for calculation of microalgal biovolume. Braz. Arch. Biol. Technol. 54, 527–534 (2011).Article 

Google Scholar 
66.Vadrucci, M. R., Mazziotti, C. & Fiocca, A. Cell biovolume and surface area in phytoplankton of Mediterranean transitional water ecosystems: Methodological aspects. Transit. Water. Bull. 7, 100–123 (2013).
Google Scholar 
67.Saccà, A. A simple yet accurate method for the estimation of the biovolume of planktonic microorganisms. PLoS ONE 11, e0151955 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Mirasbekov, Y. et al. Semi-automated classification of colonial Microcystis by FlowCam imaging flow cytometry in mesocosm experiment reveals high heterogeneity during a seasonal bloom. Sci. Rep. 11, 9377 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
70.Aronesty, E. Comparison of sequencing utility programs. Open Bionforma J. 7, 1–8. https://doi.org/10.2174/1875036201307010001 (2013). (Accessed 6 May 2021)71.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).Article 

Google Scholar 
73.Lee, M. S. Y. A worrying systematic decline. Trends Ecol. Evol. 15, 346 (2000).CAS 
PubMed 
Article 

Google Scholar 
74.Kermarrec, L. et al. Next-generation sequencing to inventory taxonomic diversity in eukaryotic communities: A test for freshwater diatoms. Mol. Ecol. Resour. 13, 607–619 (2013).CAS 
PubMed 
Article 

Google Scholar 
75.Aylagas, E., Borja, Á., Irigoien, X. & Rodríguez-Ezpeleta, N. Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment. Front. Mar. Sci. 3, 96 (2016).
Google Scholar 
76.Bazin, P. et al. Phytoplankton diversity and community composition along the estuarine gradient of a temperate macrotidal ecosystem: Combined morphological and molecular approaches. PLoS ONE 9, e94110 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Edwards, D. L. & Knowles, L. L. Species detection and individual assignment in species delimitation: Can integrative data increase efficacy? Proc. R. Soc. B 281, 20132765 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
78.Guillot, G., Renaud, S., Ledevin, R., Michaux, J. & Claude, J. A unifying model for the analysis of phenotypic, genetic, and geographic data. Syst. Biol. 61, 897–911 (2012).PubMed 
Article 

Google Scholar 
79.Padial, J. M., Miralles, A., De la Riva, I. & Vences, M. The integrative future of taxonomy. Front. Zool. 7, 16 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Bickford, D. et al. Cryptic species as a window on diversity and conservation. Trends Ecol. Evol. 22, 148–155 (2007).PubMed 
Article 

Google Scholar 
81.Boopathi, T. & Ki, J.-S. Unresolved diversity and monthly dynamics of eukaryotic phytoplankton in a temperate freshwater reservoir explored by pyrosequencing. Mar. Freshw. Res. 67, 1680–1691 (2015).Article 

Google Scholar 
82.Kurmayer, R., Deng, L. & Entfellner, E. Role of toxic and bioactive secondary metabolites in colonization and bloom formation by filamentous cyanobacteria Planktothrix. Harmful Algae 54, 69–86 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Komárek, J. A polyphasic approach for the taxonomy of cyanobacteria: Principles and applications. Eur. J. Phycol. 51, 346–353 (2016).Article 
CAS 

Google Scholar 
84.Cellamare, M., Rolland, A. & Jacquet, S. Flow cytometry sorting of freshwater phytoplankton. J. Appl. Phycol. 22, 87–100 (2010).Article 

Google Scholar 
85.Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).Article 

Google Scholar 
86.Adl, S. M. et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J. Eukaryot. Microbiol. 52, 399–451 (2005).PubMed 
Article 

Google Scholar 
87.Adl, S. M. et al. The revised classification of eukaryotes. J. Eukaryot. Microbiol. 59, 429–493 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Komárek, J., Kaštovský, J., Mareš, J. & Johansen, J. R. Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia 86, 295–335 (2014).
Google Scholar 
89.Guiry, M. D. & Guiry, G. M. AlgaeBase (World-Wide Electronic Publication, National University of Ireland, 2019).
Google Scholar  More

Seed sourceSeeds of a segregating GR B. scoparia population identified from a wheat field (45°54′54.76″N; 108°14′44.15″W) in 2013 in Hill County, Montana, USA (designated as MT009) were used. The field was under a continuous no-till wheat-fallow rotation for  > 8 years and had a history of repeated glyphosate use (at least 3 applications per year) for weed control during the summer fallow phase prior to winter wheat planting. The permission of land owner was obtained prior to B. scoparia seed collection. All experimental research and field studies on plants, including the collection of plant material complied with the Montana State University guidelines and state/US legislation. Seeds of the field-collected population were used to generate GS and GR B. scoparia subpopulations through recurrent group selection procedure as described below.Development of GS and GR subpopulationsField collected seeds of MT009 population were sown on the surface of plastic trays (53 by 35 by 10 cm) filled with commercial potting soil (VERMISOIL, Vermicrop Organics, 4265 Duluth Avenue, Rocklin, CA, USA) in a greenhouse in the fall of 2013 at the Montana State University Southern Agricultural Research Center (MSU-SARC) near Huntley, MT, USA. Growth conditions in greenhouse were maintained at 25/22 ± 2 °C day/night temperatures and 16/8 h day/night photoperiods supplemented with metal halide lamps (450 μmol m-2 s-1). After emergence, approximately 200 uniform seedlings were individually transplanted in plastic pots (10-cm diam) containing the same potting mixture and grown for 6 weeks. A set of three clones (3 shoot cuttings) from each plant were then prepared and transplanted in plastic pots (10-cm diam) as described by Kumar and Jha22. At the 8- to 10-cm height, all cloned seedlings were separately treated with 435 (0.5×), 870 (1×), and 1740 (2×) g ae ha−1 of glyphosate (Roundup Powermax, Bayer Crop Science, Saint Louis, MO, USA) where 1× = field-use rate of glyphosate. All three glyphosate treatments included ammonium sulfate (2% w/v). Glyphosate applications were made using a cabinet spray chamber (Research Track Sprayer, De Vries Manufacturing, RR 1 Box 184, Hollandale, MN, USA) equipped with an even flat-fan nozzle tip (Teejet 8001EXR, Spraying System Co., Wheaton, IL, USA), calibrated to deliver 140 L ha−1 of spray solution at 276 kPa. Treated seedlings were returned to the greenhouse, watered as needed, and fertilized [Miracle-Gro water soluble fertilizer (24-8-16), Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH, USA] bi-weekly to maintain good plant growth. At 21 days after treatment, clones surviving the 2× rate of glyphosate were considered as ‘glyphosate-resistant (GR)’ and the clones that did not survive 1× rate of glyphosate were considered as ‘glyphosate-susceptible (GS)’. The parent B. scoparia plants corresponding to survived (resistant) or not-survived (susceptible) clones were transplanted separately in 20-L plastic pots (group of 3 to 4 plants pot−1) containing same potting soil for seed production. All 3- to 4 plants in each pot were collectively covered with a single pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flower initiation to restrict cross-pollination between GR and GS plants. At maturity, seeds from the respective GR and GS parent plants were collected and cleaned separately using an air column blower. The collected seeds from GR plants were subjected to three generations of recurrent group selection with the 2× rate of glyphosate in each generation. Seeds of GS plants were also subjected to recurrent group selection for three generations without glyphosate. Progenies of the GS plants were grown and sprayed with 1× rate of glyphosate to confirm the susceptibility to glyphosate in each generation23. This procedure allowed the development of relatively genetically homogenous GR and GS subpopulations from within a single B. scoparia population.Determination of EPSPS gene copy numberPreviously established protocols were adopted to estimate the relative EPSPS gene copy number in seedlings of GR and GS subpopulations through quantitative real-time polymerase chain reaction (qPCR)16,17,18. The ALS gene was used as reference since the relative ALS gene copy number and transcript abundance did not vary across B. scoparia samples17,18,29. Relative EPSPS:ALS gene copy number is a ratio of EPSPS to ALS PCR product fluorescence. Due to small differences in amplicon size, qPCR run conditions, and fluorescence detection, the values presented were estimates of relative gene copy number29.A total of 600 seedlings from the GR (450 seedlings) and GS (150 seedlings) B. scoparia subpopulations (developed by recurrent group selection) were grown in a greenhouse at MSU-SARC near Huntley, MT, USA in 2015 and 2016 to select enough plants for the field study each year. At 4-to 6-cm height, young leaf tissues (100 mg) from each seedling were sampled, frozen with liquid nitrogen and ground into powder using mortar and pestle. Genomic DNA were extracted from the tissue samples using the protocol from Qiagen Dneasy plant mini kit (Qiagen Inc., Valencia, CA, USA). Genomic DNA quantity and quality were determined using a Smartspec Plus spectrophotometer (Bio-Rad Company, CA, USA) and gel electrophoresis with 1% agarose, respectively. High quality genomic DNA (260/280 ratio of ≥ 1.8) were used to determine the relative EPSPS gene copy number. Two sets of primers to amplify the EPSPS and ALS genes, the final reaction volume and reagents used for each qPCR reaction, and the qPCR conditions used in this study were the same as previously described by Kumar and Jha22. Each qPCR reaction was performed on a Bio-Rad 96-well PCR plate in triplicates and fluorescence was detected using CFX Connect Real-Time PCR detection system. A negative control consisting of 250 nM of each forward and reverse primer, 1× Perfecta SYBR Green supermix, and deionized water with no DNA template was included. The EPSPS genomic copy number relative to ALS gene was estimated by ΔCT method (ΔCT = CT, ALS-CT, EPSPS)18,29. The relative increase in the EPSPS gene copy number was calculated as 2ΔCT.Survival and fecundity traits of GR and GS B. scoparia subpopulationsSeedlings (4- to 6-cm tall) of GR and GS B. scoparia subpopulations with known EPSPS gene copy numbers were transplanted into a fallow field in the summer of 2015 and 2016 at the MSU-SARC near Huntley, MT, USA. All transplanted B. scoparia seedlings were equally spaced at 1.5 m apart from each other and all plants were fertilized biweekly [2 to 3 g of MIRACLE-GRO water soluble fertilizer (24-8-16)] and irrigated as and when needed to avoid moisture stress. Experiments were conducted with a factorial arrangement of treatments (Factor A and Factor B) in a randomized complete block design, with 6 replications. Each transplanted B. scoparia seedling was an experimental unit. The factor A (4 levels) was comprised of B. scoparia plants with 1, 2–4, 5–6, and ≥ 8 EPSPS gene copy numbers, which were categorized as susceptible, low, moderate, and highly resistant plants, respectively based on their percent visible injury response to glyphosate. The factor B (ten levels) was comprised of increasing rates of glyphosate applied as single or sequential applications. Current labels of glyphosate allow a total of 3954 g ae ha−1 in split POST applications in GR sugar beet. As per the label, the maximum glyphosate rate of 2214 g ae ha−1 is allowed from crop emergence to 8-leaf stage of sugar beet and 1740 g ae ha−1 of glyphosate from 8-leaf stage to canopy closure or 30 days prior to sugar beet harvest. Hence, the tested total glyphosate rates were 0, 108, 217, 435, 870, 1265, 1740 [870 followed by ( +) 870], 2214 [1265 + 949], 3084 [1265 + 949 + 870], and 3954 [1265 + 949 + 870 + 870] g ae ha−1 along with ammonium sulfate (2% w/v). Sequential applications were made at 7- to 14-day intervals, with first application at 8- to 10-cm tall B. scoparia seedlings using a CO2-operated backpack sprayer fitted with a single AIXR 8001 flat-fan nozzle calibrated to deliver 94 L ha−1. Glyphosate rates and applications timings were selected to simulate the 2-leaf, 6-leaf, 8–10 leaf, and the canopy closure stage of GR sugar beet.Data collectionPercent visible control (relative to the non-treated) on a scale of 0 to 100 (0 means no control and 100 means complete plant death30) for each individual plant (240 plants total each year) were assessed at 7, 14, and 21 days after glyphosate treatment. Data on number of days from transplanting to 50% flowering (half of the inflorescences from each plant were covered with visible flowers) and seed set (seeds on half of the inflorescences from each plant were turned brown) were recorded for an individual plant. Each plant was covered with a pollination bag (DelStar Technologies, Inc., 601 Industrial drive, Middletown, DE, USA) prior to flowering to prevent any cross-pollination. At the time of flowering, pollens from each survived plant were collected in early morning hours (between 8 to 10 am). At maturity, each individual plant was harvested and threshed to determine 1000-seed weight and seeds plant−1.Pollen and progeny seed viabilityPollens and seeds collected from individual B. scoparia plants (240 plants total each year) were tested for viability using a tetrazolium test. Pollens were collected in petri dishes by shaking the whole plant at the time of flowering. Four sub-samples of pollens from each petri dish were transferred into glass slides. The pollens in the glass slides were soaked with a tetrazolium chloride solution (10 g L−1), sealed with a cover slip using a nail polish and were incubated at room temperature for an hour. Viable (red) and non-viable pollens (yellow/white) were counted using a simple microscope. The physical structure of viable and non-viable pollens was also checked for any deformity using a compound microscope. Pollen viability for individual plants (240 plants total each year) was calculated as percent viable pollens of the total number of pollens counted.For seed viability test, twenty-five intact seeds collected from each individual plant (240 plants total each year) from the field were evenly placed in between two layers of filter papers (WHATMAN Grade 2, SigmaAldrich, St Louis, MO, USA) inside a 10-cm-diameter petri dish. Seeds were soaked with a 5-ml of distilled water and the filter papers were kept moist for the entire duration of the germination test. Light is not required for B. scoparia seed germination31, so the petri dishes were wrapped with a thin aluminum foil and placed inside an incubator (VMR International, Sheldon Manufacturing, Cornelius, OR, USA) with alternating day/night temperatures set to 20/25 °C23. Seeds with a visible uncoiled radicle tip longer than the seed diameter was considered germinated32,33. Radicle length was measured from three randomly selected germinated seeds 24 h after incubation to test the seedling vigor. The number of germinated seeds in each petri dish were counted daily until no further germination was observed for 10 consecutive days. Non-germinated seeds were tested for viability by soaking the seeds with tetrazolium chloride solution (10 g L−1) for 24 h23,34. Seeds with a red-stained embryo examined under a dissecting microscope (tenfold magnification) were considered viable35. Seed viability was expressed as the percentage of total viable seeds.Relative fitness (w)Fitness is the evolutionary potential for success of a genotype based on survival, competitive ability, and reproduction. Individuals with the greatest number of offspring and with the most genes contributing to the gene pool of a population are considered most fit genotypes36. Fitness of a genotype is determined by comparison of its vigor, productivity or competitiveness relative to the other genotype by quantifying specific traits such as seed dormancy, flowering date, seedling vigor, seed production, and other factors that can possibly influence the survival and reproductive success of a genotype36,37. In this study, relative fitness (w) of GR B. scoparia was calculated as the reproductive rate (seed production plant−1) of a resistant genotype (B. scoparia plants with 2–4, 5–6, and ≥ 8 EPSPS gene copies) relative to the maximum reproductive rate of the susceptible genotype (B. scoparia plants with 1 EPSPS gene copy) in the population. The relative fitness (w) of susceptible plants was assumed to be one.Statistical analysesA natural logarithm transformation was performed on data for time to 50% flowering, time to seed set, seeds plant−1. An arcsine square root transformation was performed on data for pollen viability, visible control, seed viability, and relative fitness (w) before subjecting to analysis of variance, however all data were presented in their back-transformed values. No transformation was needed for 1000-seed weight and radicle length data. Experimental year, B. scoparia plants with different EPSPS copy number groups, glyphosate rate, and their interactions were considered fixed effects and replication nested within a year was considered as a random effect in the model. Data on percent visible control, time to 50% flowering, pollen viability, time to seed set, 1000-seed weight, and seeds plant−1, seed viability and radicle length were subjected to ANOVA using Proc Mixed in SAS (SAS version 9.4, SAS Institute, Cary, NC, USA) to test the significance of experimental run, treatment factors, and interactions. The ANOVA assumptions for normality of residuals and homogeneity of variance were tested using Proc Univariate and PROC GLM in SAS. Means were separated using Tukey–Kramer’s HSD with α = 0.05. Furthermore, data on percent visible control and seeds plant-1 for each group of B. scoparia plants with different EPSPS gene copy number were regressed against total glyphosate rates using a four-parameter log-logistic model Eq. (1)38,39:$$Y=c+{d-c/{1+mathrm{exp}[bleft(mathrm{log}left(xright)-mathrm{log}left(ED50right)right)]}$$
(1)
where Y is the percent visible control or seed production plant−1 (% of nontreated); d is the upper asymptote (the highest estimated % control or % seed reduction); c is the lower asymptote (the lowest estimated % control or % seed reduction); ED50 is the effective rate of glyphosate needed to achieve 50% control or 50% reduction in seed production; and b denotes the slope around the inflection point “ED50.” Slope parameter (b) indicates the response rate of each group of B. scoparia plants with different EPSPS gene copy number to glyphosate rates (i.e., a slope with a large negative value suggests a rapid response of selected B. scoparia group). The Akaike Information Criterion (AIC) was used to select the nonlinear four-parameter model. A lack-of-fit test (P  > 0.10) was used to confirm that the nonlinear regression model Eq. (1) described the response data for each B. scoparia group38. Parameter estimates, ED90, and SR99 values (i.e. effective rate required for 90% control or effective rate required for 99% reduction in seed production) for each group of B. scoparia plants with different EPSPS gene copy number were determined using the ‘drc’ package in R software37,39. Parameter estimates of B. scoparia groups were compared using the approximate t-test with the ‘compParm’ and ‘EDcomp’ functions in the ‘drc’ package of the R software39,40. More

1.Boyer, S., Zhang, H. & Lempérière, G. A review of control methods and resistance mechanisms in stored-product insects. Bull. Entomol. Res. 102, 213–229 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Perez-Mendoza, J., Campbell, J. & Throne, J. Influence of age, mating status, sex, quantity of food, and long-term food deprivation on red four beetle (Coleoptera: Tenebrionidae) fight initiation. J. Econ. Entomol. 104, 2078–2086 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Ahmad, F., Ridley, A., Daglish, G. J., Burrill, P. R. & Walter, G. H. Response of Tribolium castaneum and Rhyzopertha dominica to various resources, near and far from grain storage. J. Appl. Entomol. 137, 773–781 (2013).Article 

Google Scholar 
4.Ridley, A. W. et al. The spatiotemporal dynamics of Tribolium castaneum (Herbst): adult flight and gene flow. Mol. Ecol. 20, 1635–1646 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Suzuki, T. & Sugawara, R. Isolation of an aggregation pheromone from the flour beetles, Tribolium castaneum and T. confusum (Coleoptera: Tenebrionidae). J. Appl. Entomol. 14, 228–230 (1979).CAS 

Google Scholar 
6.Suzuki, T. 4,8-Dimethyldecanal: the aggregation pheromone of the flour beetles, Tribolium castaneum and T. confusum (Coleoptera: Tenebrionidae). Agric. Biol. Chem. 44, 2519–2520 (1980).CAS 

Google Scholar 
7.Suzuki, T. A facile synthesis of 4, 8-dimethyldecanal, aggregation pheromone of flour beetles and its analogues. Agric. Biol. Chem. 45, 2641–2643 (1981).CAS 

Google Scholar 
8.Suzuki, T., Kozaki, J., Sugawara, R. & Mori, K. Biological activities of the analogs of the aggregation pheromone of Tribolium castaneum (Coleoptera: Tenebrionidae). Appl. Entomol. Zool. 19, 15–20 (1984).CAS 
Article 

Google Scholar 
9.Oerke, E. C. & Dehne, H. W. Safeguarding production—losses in major crops and the role of crop protection. Crop. Protect. 23, 275–285 (2004).Article 

Google Scholar 
10.Fan, J., Zhang, T., Bai, S., Wang, Z. & He, K. Evaluation of Bt corn with pyramided genes on efficacy and Insect resistance management for the Asian corn borer in China. PLoS ONE 11, e0168442 (2016).Article 
CAS 

Google Scholar 
11.He, K. et al. Efficacy of transgenic Bt cotton for resistance to the Asian corn borer (Lepidoptera: Crambidae). Crop. Protect. 25, 167–173 (2006).CAS 
Article 

Google Scholar 
12.Koutroumpa, F. A. & Jacquin-Joly, E. Sex in the night: Fatty acid derived sex pheromones and corresponding membrane pheromone receptors in insects. Biochimie 107, 15–21 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Harari, A.R., Sharon, R. & Weintraub, P.G. Manipulation of insect reproductive systems as a tool in pest control. In Advances in insect control and resistance management. 93–119 Springer, Cham, (2016).14.Hussain, A. Chemical ecology of Tribolium castaneum Herbst (Coleoptera: Tenebrionidae): Factors affecting biology and application of pheromone. Dissertation, Oregon State University (1993).15.Liebhold, A. M. & Tobin, P. C. Population ecology of insect invasions and their management. Annu. Rev. Entomol. 53, 387–408 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Levinson, H. Z. & Mori, K. Chirality determines pheromone activity for flour beetles. Naturwissenschaften 70, 190–192 (1983).ADS 
CAS 
Article 

Google Scholar 
17.Olsson, P. O. C. et al. Male-produced sex pheromone in Tribolium confusum: Behavior and investigation of pheromone production locations. J. Stored. Prod. Res. 42, 173–182 (2006).Article 
CAS 

Google Scholar 
18.Duehl, A. J., Arbogast, R. T. & Teal, P. E. Age and sex related responsiveness of Tribolium castaneum (Coleoptera: Tenebrionidae) in novel behavioral bioassays. Environ. Entomol. 40, 82–87 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Verheggen, F. et al. Electrophysiological and behavioral activity of secondary metabolites in the confused flour beetle Tribolium confusum. J. Chem. Ecol. 33, 525–539 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Obeng-Ofori, D. & Coaker, T. H. Some factors affecting responses of four stored product beetles (Coleoptera: Tenebrionidae & Bostrichidae) to pheromones. Bull. Entomol. Res. 80, 433–441 (1990).Article 

Google Scholar 
21.Obeng-Ofori, D. & Coaker, T. Tribolium aggregation pheromone: Monitoring, range of attraction and orientation behavior of T. castaneum (Coleoptera: Tenebrionidae). Bull. Entomol. Res. 80, 443–451 (1990).Article 

Google Scholar 
22.Saunders, D. S. Insect circadian rhythms and photoperiodism. Invert. Neurosci. 3, 155–164 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Beer, K. & Helfrich-Förster, C. Model and non-model insects in chronobiology. Front. Behav. Neurosci. 14, 601676 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Li, C. J., Yun, X. P., Yu, X. J. & Li, B. Functional analysis of the circadian clock gene timeless in Tribolium castaneum. Insect Sci. 25, 418–428 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Yuan, Q., Metterville, D., Briscoe, A. D. & Reppert, S. M. Insect cryptochromes: gene duplication and loss define diverse ways to construct insect circadian clocks. Mol. Biol. Evol. 24, 948–955 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Hideharu, N., Yosuke, M. & Tomoko, I. Common features in diverse insect clocks. Zool. Lett. 1, 1–17 (2015).Article 

Google Scholar 
27.Yujie, L. et al. Anatomical localization and stereoisomeric composition of Tribolium castaneum aggregation pheromones. Naturwissenschaften 98, 755 (2011).Article 
CAS 

Google Scholar 
28.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2 − ΔΔCT method. Methods 25, 402–408 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Bates, D., Mächler, M., Bolker, S. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2014).
Google Scholar 
30.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Software. 82, 1–26 (2017).Article 

Google Scholar 
31.Barton, K. MuMIn: Multi-Model Inference. R package version 1.15.6. https://CRAN.R-project.org/package=MuMIn (2016).32.Hughes, M. E., Hogenesch, J. B. & Kornacker, K. JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J. Biol. Rhythms. 25, 372–380 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Swihart, B. et al. R package version 1.1.2. https://CRAN.R-project.org/package=repeated (2019).34.Levine, J. D., Funes, P., Dowse, H. B. & Hall, J. C. Resetting the circadian clock by social experience in Drosophila melanogaster. Science 298, 2010–2012 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Krupp, J. J. et al. Social experience modifies pheromone expression and mating behavior in male Drosophila melanogaster. Curr. Biol. 18, 1373–1383 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Holman, L., Trontti, K. & Helanterä, K. Queen pheromones modulate DNA methyltransferase activity in bee and ant workers. Biol. Lett. 12, 2015103 (2016).Article 
CAS 

Google Scholar 
37.Holman, L., Helanterä, H., Trontti, K. & Mikheyev, A. S. Comparative transcriptomics of social insect queen pheromones. Nat. Commun. 10, 159 (2019).ADS 
Article 
CAS 

Google Scholar 
38.Grozinger, C. M., Sharabash, N. M., Whitfield, C. W. & Robinson, G. E. Pheromone mediated gene expression in the honeybee brain. Proc. Natl. Acad. Sci. USA 100, 14519–14525 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Wanner, K. W. A honey bee odorant receptor for the queen substance 9-oxo-2- decenoic acid. Proc. Natl. Acad. Sci. USA 104, 14383–14388 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Beggs, K. T. et al. Queen pheromone modulates brain dopamine function in worker honey bees. Proc. Natl. Acad. Sci. USA 104, 2460–2464 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Ma, R., Rangel, J. & Grozinger, C. M. Honey bee (Apis mellifera) larval pheromones may regulate gene expression related to foraging task specialization. BMC Genom. 20, 592 (2019).Article 
CAS 

Google Scholar 
42.Alaux, C. & Robinson, G. E. Alarm pheromone induces immediate-early gene expression and slow behavioral response in honey bees. J. Chem. Ecol. 33, 1346–1350 (2007).CAS 
PubMed 
Article 

Google Scholar 
43.O’ceallachain, D. P. & Ryan, M. F. Production and perception of pheromones by the beetle Tribolium confusum. J. Insect Physiol. 23, 1303–1309 (1977).CAS 
Article 

Google Scholar 
44.Dunlap, J. C. Molecular bases for circadian clocks. Cell 96, 271–290 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Zhang, T. et al. Male- and female-biased gene expression of olfactory-related genes in the antennae of Asian corn borer Ostrinia furnacalis (Guenée) (Lepidoptera: Crambidae). PLoS ONE 10, 0128550 (2015).
Google Scholar 
46.Balakrishnan, K., Holighaus, G., Weißbecker, B. & Schütz, S. Electroantennographic responses of red flour beetle Tribolium castaneum Herbst (Coleoptera: Tenebrionidae) to volatile organic compounds. J. Appl. Entomol. 141, 477–486 (2017).CAS 
Article 

Google Scholar 
47.Webb, I. C., Antle, M. C. & Mistlberger, R. E. Regulation of circadian rhythms in mammals by behavioral arousal. Behav. Neurosci. 128, 304 (2014).PubMed 
Article 

Google Scholar 
48.Angelousi, A. et al. Clock genes alterations and endocrine disorders. Eur. J. Clin. Invest. 48, 12927 (2018).Article 
CAS 

Google Scholar 
49.Silvegren, G., Löfstedt, C. & Rosén, W. Q. Circadian mating activity and effect of pheromone pre-exposure on pheromone response rhythms in the moth Spodoptera littoralis. J. Insect. Phys. 51, 277–286 (2005).CAS 
Article 

Google Scholar 
50.Lam, V. H. & Chiu, V. C. Evolution and design of invertebrate circadian clocks. Oxford Handbook Invertebrate Neurobiol. https://doi.org/10.1093/oxfordhb/9780190456757.013.25 (2018).Article 

Google Scholar 
51.Chiba, Y., Cutkomp, L. K. & Halberg, F. Circadian oxygen consumption rhythm of the flour beetle Tribolium confusum. J. Insect. Physiol. 19, 2163–2172 (1973).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Rafter, M. A. Behavior in the presence of resource excess—flight of Tribolium castaneum around heavily-infested grain storage facilities. J. Pest. Sci. 92, 1227–1238 (2019).Article 

Google Scholar 
53.Harano, T. & Miyatake, T. Genetic basis of incidence and period length of circadian rhythm for locomotor activity in populations of a seed beetle. Heredity 105, 268–273 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Cheng, Y. & Hardin, P. E. Drosophila photoreceptors contain an autonomous circadian oscillator that can function without period mRNA cycling. J. Neurosci. 18, 741–750 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Short, C. A., Meuti, M. E., Zhang, Q. & Denlinger, D. L. Entrainment of eclosion and preliminary ontogeny of circadian clock gene expression in the flesh fly Sarcophaga crassipalpis. J. Insect Physiol. 93, 28–35 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
56.Wexler, Y. et al. Mating alters the link between movement activity and pattern in the red flour beetle: the effects of mating on behavior. Physiol. Entomol. 42, 299–306 (2017).ADS 
Article 

Google Scholar 
57.Gottlieb, D. Agro-chronobiology: Integrating circadian clocks/time biology into storage management. J. Stored. Prod. Res. 82, 9–16 (2019).Article 

Google Scholar  More