Philippa Kaur

Mehbub MF, Lei J, Franco C, Zhang W. Marine sponge derived natural products between 2001 and 2010: trends and opportunities for discovery of bioactives. Mar Drugs. 2014;12:4539–77.PubMed 
PubMed Central 

Google Scholar 
Sipkema D, Franssen MCR, Osinga R, Tramper J, Wijffels RH. Marine sponges as pharmacy. Mar Biotechnol. 2005;7:142–62.CAS 

Google Scholar 
Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.CAS 
PubMed 

Google Scholar 
Indraningrat AAG, Micheller S, Runderkamp M, Sauerland I, Becking LE, Smidt H, et al. Cultivation of sponge-associated bacteria from Agelas sventres and Xestospongia muta collected from different depths. Mar Drugs. 2019;17:578.CAS 
PubMed Central 

Google Scholar 
Piel J. Metabolites from symbiotic bacteria. Nat Prod Rep. 2009;26:338–62.CAS 
PubMed 

Google Scholar 
Webster NS, Thomas T. The sponge hologenome. mBio. 2016;7:e00135–16.PubMed 
PubMed Central 

Google Scholar 
de Oliveira MRF, de Maringá UE, da Costa C, Benedito E. Trends and gaps in scientific production on freshwater sponges. Oecologia Austrlis. 2020;24:61–75.
Google Scholar 
Manconi R, Pronzato R. How to survive and persist in temporary freshwater? Adaptive traits of sponges (Porifera: Spongillida): a review. Hydrobiologia. 2016;782:11–22.
Google Scholar 
Manconi R, Pronzato R. Chapter 8 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Ecology and general biology. Thorp and Covich’s freshwater invertebrates. vol 1 (4th ed.) New York: Academic Press; 2015. p. 133–157.Manconi R, Pronzato R. Chapter 3 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Keys to Nearctic fauna. Thorp and Covich’s freshwater invertebrates vol 2(4th ed.) San Diego: Academic Press, Elsevier; 2016. p. 39–83.Leidy J. On Spongilla. In: Proceedings of the Academy of Natural Sciences of Philadelphia. Philadelphia: Academy of Natural Sciences of Philadelphia; 1850. p. 278.Smith F. Distribution of the fresh-water sponges of North America. INHS Bull. 1921;14:9–22.
Google Scholar 
Old MC. Environmental selection of the fresh-water sponges (Spongillidae) of Michigan. Trans Am Microsc Soc. 1932;51:129–36.CAS 

Google Scholar 
Ashley JM. Fresh water sponges of Illinois and Michigan. Urbana-Champaign: Master of Arts, University of Illinois; 1913.Jewell ME. An ecological study of the fresh-water sponges of northeastern Wisconsin. Ecol Monogr. 1935;5:461–504.CAS 

Google Scholar 
Kolomyjec SH, Willford RA. The fall 2019 genetics class. Phylogenetic analysis of Michigan’s freshwater sponges (Porifera, Spongillidae) using extended COI mtDNA sequences. bioRxiv. 2020; https://doi.org/10.1101/2020.04.26.062448.Copeland J, Kunigelis S, Tussing J, Jett T, Rich C. Freshwater sponges (Porifera: Spongillida) of Tennessee. Am Midl Nat. 2019;181:310–26.
Google Scholar 
Lauer TE, Spacie A. An association between freshwater sponges and the zebra mussel in a southern Lake Michigan harbor. J Freshw Ecol. 2004;19:631–7.
Google Scholar 
Skelton J, Strand M. Trophic ecology of a freshwater sponge (Spongilla lacustris) revealed by stable isotope analysis. Hydrobiologia. 2013;709:227–35.CAS 

Google Scholar 
Early TA, Glonek T. Zebra mussel destruction by a Lake Michigan sponge: populations, in vivo 31P nuclear magnetic resonance, and phospholipid profiling. Environ Sci Technol. 1999;33:1957–62.CAS 

Google Scholar 
Early TA, Kundrat JT, Schorp T, Glonek T. Lake Michigan sponge phospholipid variations with habitat: A 31P nuclear magnetic resonance study. Comp Biochem Physiol. 1996;114:77–89.
Google Scholar 
Dembitsky VM, Rezanka T, Srebnik M. Lipid compounds of freshwater sponges: family Spongillidae, class Demospongiae. Chem Phys Lipids. 2003;123:117–55.CAS 
PubMed 

Google Scholar 
Řezanka T, Sigler K, Dembitsky VM. Syriacin, a novel unusual sulfated ceramide glycoside from the freshwater sponge Ephydatia syriaca (Porifera, Demospongiae, Spongillidae). Tetrahedron. 2006;62:5937–43.
Google Scholar 
Radnaeva LD, Bazarsadueva SV, Taraskin VV, Tulokhonov AK. First data on lipids and microorganisms of deepwater endemic sponge Baikalospongia intermedia and sediments from hydrothermal discharge area of the Frolikha Bay (North Baikal, Siberia). J Great Lakes Res. 2020;46:67–74.CAS 

Google Scholar 
Manconi R, Piccialli V, Pronzato R, Sica D. Steroids in porifera, sterols from freshwater sponges Ephydatia fluviatilis (L.) and Spongilla lacustris (L.). Comp Biochem Physiol. 1988;91:237–45.
Google Scholar 
Belikov S, Belkova N, Butina T, Chernogor L, Kley AM-V, Nalian A, et al. Diversity and shifts of the bacterial community associated with Baikal sponge mass mortalities. PLoS ONE. 2019;14:e0213926.CAS 
PubMed 
PubMed Central 

Google Scholar 
Costa R, Keller-Costa T, Gomes NCM, da Rocha UN, van Overbeek L, van Elsas JD. Evidence for selective bacterial community structuring in the freshwater sponge Ephydatia fluviatilis. Microb Ecol. 2013;65:232–44.PubMed 

Google Scholar 
Laport MS, Pinheiro U, Rachid CTCC. Freshwater sponge Tubella variabilis presents richer microbiota than marine sponge species. Front Microbiol. 2019;10:2799.PubMed 
PubMed Central 

Google Scholar 
Kenny NJ, Plese B, Riesgo A, Itskovich VB. Symbiosis, selection, and novelty: freshwater adaptation in the unique sponges of Lake Baikal. Mol Biol Evol. 2019;36:2462–80.CAS 
PubMed Central 

Google Scholar 
Gaikwad S, Shouche YS, Gade WN. Microbial community structure of two freshwater sponges using Illumina MiSeq sequencing revealed high microbial diversity. AMB Express. 2016;6:40.PubMed 
PubMed Central 

Google Scholar 
Gernert C, Glöckner FO, Krohne G, Hentschel U. Microbial diversity of the freshwater sponge Spongilla lacustris. Microb Ecol. 2005;50:206–12.CAS 
PubMed 

Google Scholar 
Hernandez A, Nguyen LT, Dhakal R, Murphy BT. The need to innovate sample collection and library generation in microbial drug discovery: a focus on academia. Nat Prod Rep. 2021;38:292–300.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li C-Q, Liu W-C, Zhu P, Yang J-L, Cheng K-D. Phylogenetic diversity of bacteria associated with the marine sponge Gelliodes carnosa collected from the Hainan Island coastal waters of the South China Sea. Microb Ecol. 2011;62:800–12.PubMed 

Google Scholar 
Sipkema D, Schippers K, Maalcke WJ, Yang Y, Salim S, Blanch HW. Multiple approaches to enhance the cultivability of bacteria associated with the marine sponge Haliclona (gellius) sp. Appl Environ Microbiol. 2011;77:2130–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Montalvo NF, Davis J, Vicente J, Pittiglio R, Ravel J, Hill RT. Integration of culture-based and molecular analysis of a complex sponge-associated bacterial community. PLoS ONE. 2014;9:e90517.PubMed 
PubMed Central 

Google Scholar 
Elfeki M, Alanjary M, Green SJ, Ziemert N, Murphy BT. Assessing the efficiency of cultivation techniques to recover natural product biosynthetic gene populations from sediment. ACS Chem Biol. 2018;13:2074–81.CAS 
PubMed 

Google Scholar 
Dieckmann R, Graeber I, Kaesler I, Szewzyk U, von Döhren H. Rapid screening and dereplication of bacterial isolates from marine sponges of the Sula Ridge by intact-cell-MALDI-TOF mass spectrometry (ICM-MS). Appl Microbiol Biotechnol. 2005;67:539–48.CAS 
PubMed 

Google Scholar 
Costa MS, Clark CM, Ómarsdóttir S, Sanchez LM, Murphy BT. Minimizing taxonomic and natural product redundancy in microbial libraries using MALDI-TOF MS and the bioinformatics pipeline IDBac. J Nat Prod. 2019;82:2167–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Clark CM, Costa MS, Sanchez LM, Murphy BT. Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function. Proc Natl Acad Sci USA. 2018;115:4981–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Clark CM, Costa MS, Conley E, Li E, Sanchez LM, Murphy BT. Using the open-source MALDI TOF-MS IDBac pipeline for analysis of microbial protein and specialized metabolite data. J Vis Exp. 2019;147:e59219.
Google Scholar 
Ryzhov V, Fenselau C. Characterization of the protein subset desorbed by MALDI from whole bacterial cells. Anal Chem. 2001;73:746–50.CAS 
PubMed 

Google Scholar 
Welker M, Moore ERB. Applications of whole-cell matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry in systematic microbiology. Syst Appl Microbiol. 2011;34:2–11.CAS 
PubMed 

Google Scholar 
Sandrin TR, Goldstein JE, Schumaker S. MALDI TOF MS profiling of bacteria at the strain level: a review. Mass Spectrom Rev. 2013;32:188–217.CAS 
PubMed 

Google Scholar 
Seuylemezian A, Aronson HS, Tan J, Lin M, Schubert W, Vaishampayan P. Development of a custom MALDI-TOF MS database for species-level identification of bacterial isolates collected from spacecraft and associated surfaces. Front Microbiol. 2018;9:780.PubMed 
PubMed Central 

Google Scholar 
Strejcek M, Smrhova T, Junkova P, Uhlik O. Whole-cell MALDI-TOF MS versus 16S rRNA gene analysis for identification and dereplication of recurrent bacterial isolates. Front Microbiol. 2018;9:1294.PubMed 
PubMed Central 

Google Scholar 
Giraud-Gatineau A, Texier G, Garnotel E, Raoult D, Chaudet H. Insights into subspecies discrimination potentiality from bacteria MALDI-TOF mass spectra by using data mining and diversity studies. Front Microbiol. 2020;11:1931.PubMed 
PubMed Central 

Google Scholar 
LaMontagne MG, Tran PL, Benavidez A, Morano LD. Development of an inexpensive matrix-assisted laser desorption-time of flight mass spectrometry method for the identification of endophytes and rhizobacteria cultured from the microbiome associated with maize. PeerJ. 2021;9:e11359.PubMed 
PubMed Central 

Google Scholar 
Freiwald A, Sauer S. Phylogenetic classification and identification of bacteria by mass spectrometry. Nat Protoc. 2009;4:732–42.CAS 
PubMed 

Google Scholar 
Croxatto A, Prod’hom G, Greub G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev. 2012;36:380–407.CAS 
PubMed 

Google Scholar 
Rodríguez-Sánchez B, Cercenado E, Coste AT, Greub G. Review of the impact of MALDI-TOF MS in public health and hospital hygiene, 2018. Eurosurveillance. 2019;24:1800193. PubMed Central 

Google Scholar 
Rahi P, Vaishampayan P. MALDI-TOF MS application in microbial ecology studies. Front Microbiol. 2019;10:2954.PubMed 

Google Scholar 
Popović NT, Kazazić SP, Strunjak-Perović I, Čož-Rakovac R. Differentiation of environmental aquatic bacterial isolates by MALDI-TOF MS. Environ Res. 2017;152:7–16.PubMed 

Google Scholar 
Rahi P, Prakash O, Shouche YS. Matrix-assisted laser desorption/ionization Time-of-Flight mass-spectrometry (MALDI-TOF MS) based microbial identifications: challenges and scopes for microbial ecologists. Front Microbiol. 2016;7:1359.PubMed 
PubMed Central 

Google Scholar 
Schumann P, Maier T. Chapter 13 – MALDI-TOF mass spectrometry applied to classification and identification of bacteria. In: Methods in microbiology, vol 41, ISSN 0580-9517. Goodfellow M, Sutcliffe I, Chun J, editors. Academic Press; 2014. p. 275–306.Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif. 2014;31:274–95.
Google Scholar 
Batagelj V. Generalized Ward and related clustering problems. In: Bock HH, editor. North Holland, Amsterdam: Proceedings of the First Conference of the International Federation of Classification Societies; 1988. p. 67–74.van Santen JA, Jacob G, Singh AL, Aniebok V, Balunas MJ, Bunsko D, et al. The natural products atlas: an open access knowledge base for microbial natural products discovery. ACS Cent Sci. 2019;5:1824–33.PubMed 
PubMed Central 

Google Scholar 
Ghyselinck J, Van Hoorde K, Hoste B, Heylen K, De Vos P. Evaluation of MALDI-TOF MS as a tool for high-throughput dereplication. J Microbiol Meth. 2011;86:327–36.CAS 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Henson MW, Lanclos VC, Pitre DM, Weckhorst JL, Lucchesi AM, Cheng C, et al. Expanding the diversity of bacterioplankton isolates and modeling isolation efficacy with large-scale dilution-to-extinction cultivation. Appl Environ Microbiol. 2020;86:e00943–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann T, Krug D, Bozkurt N, Duddela S, Jansen R, Garcia R, et al. Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria. Nat Commun. 2018;9:1–10.CAS 

Google Scholar 
Jensen PR, Williams PG, Oh D-C, Zeigler L, Fenical W. Species-specific secondary metabolite production in marine actinomycetes of the genus Salinispora. Appl Environ Microbiol. 2007;73:1146–52.CAS 
PubMed 

Google Scholar 
Ziemert N, Lechner A, Wietz M, Millán-Aguiñaga N, Chavarria KL, Jensen PR. Diversity and evolution of secondary metabolism in the marine actinomycete genus Salinispora. Proc Natl Acad Sci USA. 2014;111:E1130–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruns H, Crüsemann M, Letzel A-C, Alanjary M, McInerney JO, Jensen PR, et al. Function-related replacement of bacterial siderophore pathways. ISME J. 2018;12:320–9.CAS 
PubMed 

Google Scholar 
Chase AB, Sweeney D, Muskat MN, Guillén-Matus DG, Jensen PR. Vertical inheritance facilitates interspecies diversification in biosynthetic gene clusters and specialized metabolites. MBio. 2021;12:e0270021.PubMed 

Google Scholar 
Covington BC, Xu F, Seyedsayamdost MR. A natural product chemist’s guide to unlocking silent biosynthetic gene clusters. Annu Rev Biochem. 2021;90:763–88.CAS 
PubMed 

Google Scholar 
Adamek M, Alanjary M, Sales-Ortells H, Goodfellow M, Bull AT, Winkler A, et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics. 2018;19:426.PubMed 
PubMed Central 

Google Scholar 
Chevrette MG, Currie CR. Emerging evolutionary paradigms in antibiotic discovery. J Ind Microbiol Biotechnol. 2019;46:257–71.CAS 
PubMed 

Google Scholar 
Zdouc MM, Iorio M, Maffioli SI, Crüsemann M, Donadio S, Sosio M. Planomonospora: a metabolomics perspective on an underexplored Actinobacteria genus. J Nat Prod. 2021;84:204–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang D, Shoaie S, Jacquiod S, Sørensen SJ, Ledesma-Amaro R. Comparative genomics analysis of keratin-degrading Chryseobacterium species reveals their keratinolytic potential for secondary metabolite production. Microorganisms. 2021;9:1042.CAS 
PubMed 
PubMed Central 

Google Scholar 
Han S, Van Treuren W, Fischer CR, Merrill BD, DeFelice BC, Sanchez JM, et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature. 2021;595:415–20.CAS 
PubMed 

Google Scholar 
Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83:770–803.CAS 
PubMed 

Google Scholar 
Demain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot. 2009;62:5–16.CAS 

Google Scholar 
Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gibb S, Strimmer K. Mass spectrometry analysis using MALDIquant. In: Datta S, Mertens BJA, editors. Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry. Cham: Springer International Publishing; 2017. p. 101–24.Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics. 2012;28:2270–1.CAS 
PubMed 

Google Scholar 
Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Lawrence, J.M. Sea urchins: biology and ecology. Amsterdam, The Netherlands: Elsevier B.V. (2020)Purcell, S.W., Samyn, Y. & Conand, C. Commercially important sea cucumbers of the world. Rome, Italy: FAO. (2012)Yorke, C. E., Page, H. M. & Miller, R. J. Sea urchins mediate the availability of kelp detritus to benthic consumers. Proc. R. Soc. B. 286(1906), 20190846 (2019).CAS 
Article 

Google Scholar 
Dethier, M. N. et al. Feces as food: The nutritional value of urchin feces and implications for benthic food webs. J. Exp. Mar. Biol. Ecol. 514, 95–102 (2019).Article 

Google Scholar 
Purcell, S. W. et al. Ecological roles of exploited sea cucumbers. Oceanogr. Mar. Biol. 54, 367–386 (2017).
Google Scholar 
Hamel, J. F. & Mercier, A. Early development, settlement, growth, and spatial distribution of the sea cucumber Cucumaria frondosa (Echinodermata: Holothuroidea). Can. J. Fish. Aquat. Sci. 53(2), 253–271 (1996).Article 

Google Scholar 
Grosso, L. et al. Integrated Multi-Trophic Aquaculture (IMTA) system combining the sea urchin Paracentrotus lividus, as primary species, and the sea cucumber Holothuria tubulosa as extractive species. Aquaculture 534, 736268 (2021)Gabara, S.S., Konar, B.H. & Edwards, M.S. Biodiversity loss leads to reductions in community-wide trophic complexity. Ecosphere 12(2), e03361 (2021)Duffy, J. E. et al. The functional role of biodiversity in ecosystems: Incorporating trophic complexity. Ecol. Lett. 10(6), 522–538 (2010).ADS 
Article 

Google Scholar 
Miller, R. J. et al. Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering. Proc. R. Soc. B. 285(1874), 20172571 (2018).Article 

Google Scholar 
Soulsby, P. G., Lowthion, D. & Houston, M. Effects of macroalgal mats on the ecology of intertidal mudflats. Mar. Pollut. Bull. 13(5), 162–166 (1982).Article 

Google Scholar 
Filbee-Dexter, K. & Scheibling, R.E. Sea urchin barrens as alternative stable states of collapsed kelp ecosystems. Mar. Ecol.: Prog. Ser. 495(1), 1–25 (2014)Hendler, G., Miller, J. E., Pawson, D. L. & Kier, P. M. Sea stars, sea urchins and allies: echinoderms of Florida and the Caribbean (Smithsonian Institution Press, 1995).
Google Scholar 
James, D. B. Sea cucumber and sea urchin resources. CMFRI Bull. 34, 85–93 (1983).
Google Scholar 
Muthiga, N.A. & Kawaka, J.A. The effects of temperature and light on the gametogenesis and spawning of four sea urchin and one sea cucumber species on coral reefs in Kenya. Proceedings of the 11th international coral reef symposium. Fort Lauderdale, Florida pp 356–360 (2008)Byrnes, J., Cardinale, B. & Reed, D. Interactions between sea urchin grazing and prey diversity on temperate rocky reef communities. Ecology 94(7), 1636–1646 (2013).Article 

Google Scholar 
Vanderklift, M.A. & Kendrick, G.A. Contrasting influence of sea urchins on attached and drift macroalgae. Mar. Ecol.: Prog. Ser. 299, 101–110 (2005)Duggins, D. O. Interspecific facilitation in a guild of benthic marine herbivores. Oecologia 48(2), 157–163 (1981).ADS 
Article 

Google Scholar 
Bonaviri, C. et al. Fish versus starfish predation in controlling sea urchin populations in Mediterranean rocky shores. Mar. Ecol.: Prog. Ser. 382(1), 129–138 (2009)Purcell, S. W. & Simutoga, M. Spatio-temporal and size-dependent variation in the success of releasing cultured sea cucumbers in the wild. Rev. Fish. Sci. 16, 204–214 (2008).Article 

Google Scholar 
Scheibling, R. E. & Robinson, M. C. Settlement behaviour and early post-settlement predation of the sea urchin Strongylocentrotus droebachiensis. J. Exp. Mar. Biol. Ecol. 365(1), 59–66 (2008).Article 

Google Scholar 
Francour, P. Predation on holothurians: a literature review. Invertebr. Biol. 116(1), 52–60 (1997).Article 

Google Scholar 
Scheibling, R. E. & Hamm, J. Interactions between sea urchins (Strongylocentrotus droebachiensis) and their predators in field and laboratory experiments. Mar. Biol. 110(1), 105–116 (1991).Article 

Google Scholar 
Bartumeus, F., Romero, J. & Alcoverro, T. The scent of fear makes sea urchins go ballistic. Mov. Ecol. 9(1), 1–12 (2021).Article 

Google Scholar 
Campbell, A.C. & Coppard, S., Tudor-Thomas CD. Escape and aggregation responses of three echinoderms to conspecific stimuli. Biol. Bull. 201(2), 175–185 (2001)Chi, X. et al. Conspecific alarm cues are a potential effective barrier to regulate foraging behavior of the sea urchin Mesocentrotus nudus. Mar. Environ. Res. 171(8), 105476 (2021)Chi, X. et al. Foraging behavior of the sea urchin Mesocentrotus nudus exposed to conspecific alarm cues in various conditions. Sci. Rep. 11(1), 1–6 (2021).Article 

Google Scholar 
Zhadan, P.M. & Vaschenko, M.A. Long-term study of behaviors of two cohabiting sea urchin species, Mesocentrotus nudus and Strongylocentrotus intermedius, under conditions of high food quantity and predation risk in situ. PeerJ 7(1), e8087 (2019)Bshary, R. & Noë, R. Red colobus and Diana monkeys provide mutual protection against predators. Anim. Behav. 54(6), 1461–1474 (1997).CAS 
Article 

Google Scholar 
Peres, C. A. Anti-predation benefits in a mixed-species group of Amazonian tamarins. Folia Primatol. 61(2), 61–76 (1993).CAS 
Article 

Google Scholar 
Fuji, A. Ecological studies on the growth and food consumption of Japanese common littoral sea urchin, Strongylocentrotus intermedius (A. Agassiz). Mem. Fac. Fish. Hokkaido Univ. 15(2), 83–160 (1967)Chang, Y., Ding, J., Song, J. & Yang, W. Biology and aquaculture of sea cucumbers and sea urchins (Ocean Press, 2004).
Google Scholar 
Yang, H., Hamel, J. F. & Mercier, A. The sea cucumber Apostichopus japonicus: history, biology and aquaculture (Elsevier Inc., 2015).
Google Scholar 
Zhao, C. et al. Carryover effects of short-term UV-B radiation on fitness related traits of the sea urchin Strongylocentrotus intermedius. Ecotoxicol. Environ. Saf. 164, 659–664 (2018).CAS 
Article 

Google Scholar 
Zhang, L. et al. Effects of long-term elevated temperature on covering, sheltering and righting behaviors of the sea urchin Strongylocentrotus intermedius. PeerJ 5, e3122 (2017)Zhao, C. et al. Effects of covering behavior and exposure to a predatory crab Charybdis japonica on survival and HSP70 expression of juvenile sea urchins Strongylocentrotus intermedius. PloS One 9(5), e97840 (2014)Kawai, T. & Agatsuma, Y. Predators on released seed of the sea urchin Strongylocentrotus intermedius at Shiribeshi, Hokkaido, Japan. Fish. Sci. (Tokyo, Jpn.) 62(2), 317–318 (1996)Hatanaka, H. Experimental studies on the predation of juvenile sea cucumber, Stichopus japonicus by sea star Asterina pectinifera. Aquacult. Sci. 42(4), 563–566 (1994).
Google Scholar 
Guidetti, P. & Mori, M. Morpho-functional defences of Mediterranean sea urchins, Paracentrotus lividus and Arbacia lixula, against fish predators. Mar. Biol. 147(3), 797–802 (2005).Article 

Google Scholar 
Moitoza, D.J & Phillips, D.W. Prey defense, predator preference, and nonrandom diet: the interactions between Pycnopodia helianthoides and two species of sea urchins. Mar. Biol. 53(4), 299–304 (1979)Williams, J.P. et al. Sea urchin mass mortality rapidly restores kelp forest communities. Mar. Ecol.: Prog. Ser. 664, 117–131 (2021)Pearse, J. Ecological role of purple sea urchins. Science 314(5801), 940–941 (2006).ADS 
CAS 
Article 

Google Scholar 
Vadas, R. L. Preferential feeding: an optimization strategy in sea urchins. Ecol. Monogr. 47(4), 337–371 (1977).Article 

Google Scholar 
Lowe, A. T. et al. Sedentary urchins influence benthic community composition below the macroalgal zone. Mar. Biol. 36(2), 129–140 (2015).
Google Scholar 
Layton, C. et al. Kelp Forest Restoration in Australia. Front. Mar. Sci. 7(74) (2020)Eger, A.M. et al. Global Kelp forest restoration: Past lessons, status, and future goals. Preprint. EcoEvoRxiv. https://doi.org/10.32942/osf.io/emaz2 (2021)Ritson-Williams, R. & Paul, V. J. Marine benthic invertebrates use multimodal cues for defense against reef fish. Mar. Ecol. Prog. Ser. 340, 29–39 (2007).ADS 
Article 

Google Scholar 
Hu, F. et al. Effects of artificial reefs on selectivity and behaviors of the sea cucumber Apostichopus japonicas: New insights into the pond culture. Aquacult. Rep. 21(3), 100842 (2021)Sun, J. et al. Light intensity regulates phototaxis, foraging and righting behaviors of the sea urchin Strongylocentrotus intermedius. PeerJ 7, e8001 (2019)Bi, S., Shi, J. & Liu, A. Exploitation and utilization of Ulva lactuca L. Mod. Fish. Inf. 11, 21–23 (1993).
Google Scholar 
Chang, Y. Q., Wang, Z. C. & Wang, G. J. Effect of temperature and algae on feeding and growth in sea urchin Strongylocentrotus intermedius. J. Fish. China 23(1), 69–76 (1999).
Google Scholar 
Dumont, C., Himmelman, J.H. & Russell, M.P. Size-specific movement of green sea urchins Strongylocentrotus droebachiensis on urchin barrens in eastern Canada. Mar. Ecol.: Prog. Ser. 276, 93–101 (2004)Sun, J. et al. Interaction among sea urchins in response to food cues. Sci. Rep. 11(1), 1–9 (2021).ADS 
Article 

Google Scholar 
Węglarczyk, S. Kernel density estimation and its application. ITM Web Conf. 23(2), 00037 (2018).Article 

Google Scholar  More

Kumar, N. et al. Peste des petits ruminants virus infection of small ruminants: A comprehensive review. Viruses 6, 2287–2327. https://doi.org/10.3390/v6062287 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Zientara, S., Weyer, C. T. & Lecollinet, S. African horse sickness. Rev. Sci. Tech. 34, 315–327. https://doi.org/10.20506/rst.34.2.2359 (2015).CAS 
Article 
PubMed 

Google Scholar 
Rutkowska, D. A., Mokoena, N. B., Tsekoa, T. L., Dibakwane, V. S. & O’Kennedy, M. M. Plant-produced chimeric virus-like particles—A new generation vaccine against African horse sickness. BMC Vet. Res. 15, 1. https://doi.org/10.1016/j.rvsc.2010.05.031 (2019).CAS 
Article 

Google Scholar 
Barnard, B. J. H. Epidemiology of African horse sickness and the role of zebra in South Africa. Arch. Virol. Suppl. 14, 13–19. https://doi.org/10.1007/978-3-7091-6823-3_3 (1998).CAS 
Article 
PubMed 

Google Scholar 
Hamblin, C., Salt, J. S., Mellor, P. S., Graham, S. D. & Wohlsein, P. Donkeys as reservoirs of African horse sickness virus. Arch. Virol. Suppl. 14, 37–47. https://doi.org/10.1007/978-3-7091-6823-3_5 (1998).CAS 
Article 
PubMed 

Google Scholar 
Mellor, P. S., Boorman, J. P. T. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
Article 

Google Scholar 
Redmond, E. F., Jones, D. & Rushton, J. Economic assessment of african horse sickness vaccine impact. Equine Vet. J. https://doi.org/10.1111/j.2042-3306.1982.tb02404.x (2021).Article 
PubMed 

Google Scholar 
Venter, G. J., Wright, I. M., Linde, T. C. V. D. & Paweska, J. T. The oral susceptibility of South African field populations of Culicoides to African horse sickness virus. Med. Vet. Entomol. 23, 367–378. https://doi.org/10.1111/j.1365-2915.2009.00829.x (2010).Article 

Google Scholar 
Mellor, P. S., Boned, J., Hamblin, C. & Graham, S. D. Isolations of African horse sickness virus from vector insects made during the 1988 epizootic in Spain. Epidemiol. Infect. 105, 447–454. https://doi.org/10.1017/s0950268800048020 (1990).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meiswinkel, R. & Paweska, J. T. Evidence for a new field Culicoides vector of African horse sickness in South Africa. Prev. Vet. Med. 60, 243–253. https://doi.org/10.1016/s0167-5877(02)00231-3 (2003).CAS 
Article 
PubMed 

Google Scholar 
Howell, P. G. The isolation and identification of further antigenic types of African horsesickness virus. Onderstepoort. J. Vet. Res. 29, 139–149 (1962).
Google Scholar 
Calisher, C. H. & Mertens, P. P. C. Taxonomy of African horse sickness viruses. Arch. Virol. Suppl. 14, 3 (1998).CAS 
PubMed 

Google Scholar 
Rodriguez, M., Hooghuis, H. & Castaño, M. African horse sickness in Spain. Vet. Microbiol. 33, 129–142. https://doi.org/10.1016/0378-1135(92)90041-q (1992).CAS 
Article 
PubMed 

Google Scholar 
Howell, P. G. The 1960 epizootic of African Horsesickness in the Middle East and S.W. Asia (268KB) (268KB). J. South Afr. Vet. Med. Assoc. (1960).King, S., RajkoEnow, P., Ashby, M., Frost, L. & Batten, C. Outbreak of African Horse Sickness in Thailand, 2020. Transbound. Emerg. Dis. (2020).OIE. World Animal Health Information System. https://www.oie.int/wahis_2/public/wahid.php/Reviewreport/Review?page_refer=MapFullEventReport&reportid=33768 (2020).Castillo-Olivares, J. African horse sickness in Thailand: Challenges of Controlling an outbreak by vaccination. Equine Vet. J. (2020).Gibbens, N. Schmallenberg virus: a novel viral disease in northern Europe. Vet. Rec. 170, 58. https://doi.org/10.1136/vr.e292 (2012).Article 
PubMed 

Google Scholar 
Purse, B. V., Brown, H. E., Harrup, L., Mertens, P. & Rogers, D. J. Invasion of bluetongue and other orbivirus infections into Europe: the role of biological and climatic processes. Rev. Sci. Tech. 27, 427–442 (2008).CAS 
Article 

Google Scholar 
Leta, S., Fetene, E., Mulatu, T., Amenu, K. & Revie, C. W. Modeling the global distribution of Culicoides imicola: an Ensemble approach. Sci. Rep. 9, 1 (2019).CAS 
Article 

Google Scholar 
Thepparat, A., Bellis, G., Ketavan, C., Ruangsittichai, J. & Apiwathnasorn, C. T. species of Culicoides Latreille (Diptera: Ceratopogonidae) newly recorded from Thailand. Zootaxa 4033, 48–56. https://doi.org/10.11646/zootaxa.4033.1.2 (2015).Article 
PubMed 

Google Scholar 
Raksakoon, C. & Potiwat, R. Current arboviral threats and their potential vectors in Thailand. Pathogens 10, 80 (2021).CAS 
Article 

Google Scholar 
Gao, S. et al. Transboundary spread of peste des petits ruminants virus in western China: A prediction model. PLoS ONE 16, e0257898–e0257898. https://doi.org/10.1371/journal.pone.0257898 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joka, F. R., Van Gils, H., Huang, L. & Wang, X. High probability areas for ASF infection in china along the russian and korean borders. Transbound. Emerg. Dis. https://doi.org/10.1016/j.watres.2015.05.061.Steven et al. Opening the black box: an open-source release of Maxent. Ecography (2017).Gils, H. V., Westinga, E., Carafa, M., Antonucci, A. & Ciaschetti, G. Where the bears roam in Majella National Park, Italy. J. Nat. Conser. 22, 23–34. https://doi.org/10.1016/j.jnc.2013.08.001 (2014).Article 

Google Scholar 
Duque-Lazo, J., Navarro-Cerrillo, R. M., Van Gils, H. & Groen, T. A. Forecasting oak decline caused by Phytophthora cinnamomi in Andalusia : identification of priority areas for intervention. For. Ecol. Manage. 417, 122–136 (2018).Article 

Google Scholar 
Duque-Lazo, J., Gils, H. V., Groen, T. A. & Cerrillo, R. M. N. Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. Ecol. Model. 320, 62–70 (2016).Article 

Google Scholar 
Zeng, Z., Gao, S., Wang, H.-N., Huang, L.-Y. & Wang, X.-L. A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths. PLoS ONE 16, e0257094–e0257094. https://doi.org/10.1371/journal.pone.0257094 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joka, F. R., Wang, H., van Gils, H. & Wang, X. Could wild boar be the Trans-Siberian transmitter of African swine fever?. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.13814 (2020).Article 
PubMed 

Google Scholar 
Robin, M., Page, P., Archer, D. & Baylis, M. African horse sickness: The potential for an outbreak in disease-free regions and current disease control and elimination techniques. Equine Vet. J. 48, 659–669. https://doi.org/10.1111/evj.12600 (2016).CAS 
Article 
PubMed 

Google Scholar 
Maclachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. 41, 35. https://doi.org/10.1051/vetres/2010007 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
M. et al. African horse sickness: The potential for an outbreak in disease-free regions and current disease control and elimination techniques. Equine Vet. J. https://doi.org/10.1111/evj.12600 (2016).Eagles, D., Melville, L., Weir, R. & Davis, S. Long-distance aerial dispersal modelling of Culicoides biting midges: case studies of incursions into Australia. BMC Vet. Res. 10, 1. https://doi.org/10.1186/1746-6148-10-135 (2014).Article 

Google Scholar 
Pedgley, D. E. & Tucker, M. R. Possible spread of African horse sickness on the wind. J. Hygiene 79, 279–298 (1977).CAS 
Article 

Google Scholar 
Riddin, M. A., Venter, G. J., Labuschagne, K. & Villet, M. H. Culicoides species as potential vectors of African horse sickness virus in the southern regions of South Africa. Med. Vet. Entomol. 33, 1 (2019).Article 

Google Scholar 
Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African horse sickness Virus: History, transmission, and current status. Annu. Rev. Entomol. 62, 343–358. https://doi.org/10.1146/annurev-ento-031616-035010 (2017).CAS 
Article 
PubMed 

Google Scholar 
https://www.oie.int/wahis_2/public/wahid.php/Countryinformation/Countryreports. (Accessed 12 August 2020).OIE. African horse sickness(updated April 2013). OIE Technical Disease Cards, Paris, France: World Organisation for Animal Health. (2013).Ciss, M. et al. Ecological niche modelling to estimate the distribution of Culicoides, potential vectors of bluetongue virus in Senegal. BMC Ecology 19, doi:https://doi.org/10.1186/s12898-019-0261-9 (2019).Harrup, L. E. et al. Does covering of farm-associated Culicoides larval habitat reduce adult populations in the United Kingdom?. Vet. Parasitol. 201, 137–145. https://doi.org/10.1016/j.vetpar.2013.11.028 (2013).Article 
PubMed 

Google Scholar 
Hoch, A. L., Roberts, D. R. & Pinheiro, F. P. Host-seeking behavior and seasonal abundance of Culicoides paraensis (Diptera: Ceratopogonidae) in Brazil. J. Am. Mosq. Control Assoc. 6, 110–114 (1990).CAS 
PubMed 

Google Scholar 
Carpenter, S., Groschup, M. H., Garros, C., Felippe-Bauer, M. L. & Purse, B. V. Culicoides biting midges, arboviruses and public health in Europe. Antiviral Res. 100, 102–113. https://doi.org/10.1016/j.antiviral.2013.07.020 (2013).CAS 
Article 
PubMed 

Google Scholar 
Carpenter, S., Wilson, A., Barber, J., Veronesi, E. & Gubbins, S. Temperature Dependence of the Extrinsic Incubation Period of Orbiviruses in Culicoides Biting Midges. PLoS ONE 6, e27987. https://doi.org/10.1371/journal.pone.0027987 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yanase, T. et al. Molecular Identification of Field-CollectedCulicoidesLarvae in the Southern Part of Japan. J. Med. Entomol. (2013).Meiswinkel, R. Afrotropical Culicoides: C (Avaritia) miombo sp. nov., a widespread species closely allied to C. (A.) imicola Kieffer, 1913 (Diptera: Ceratopogonidae). Onderstepoort. J. Vet. Res. 58, 155–170 (1991).Sloyer, K. E. et al. Ecological niche modeling the potential geographic distribution of four Culicoides species of veterinary significance in Florida, USA. PLoS ONE 14, 1 (2019).Article 

Google Scholar 
Reynolds, D. R., Chapman, J. W. & Harrington, R. The migration of insect vectors of plant and animal viruses. Adv. Virus Res. 67, 453–517 (2006).CAS 
Article 

Google Scholar 
L. et al. Investigating Incursions of Bluetongue Virus Using a Model of Long-Distance Culicoides Biting Midge Dispersal. Transbound. Emerg. Dis. https://doi.org/10.1111/j.1865-1682.2012.01345.x (2013).Notice of the general office of the Ministry of agriculture and rural areas and the general office of the State General Administration of sports on printing and distributing the national horse industry development plan (2020–2025). (Animal Husbandry and Veterinary Bureau, 2020.09.29). More

Beaulieu, J. et al. Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits. Evol. Appl. 13, 2704–2722 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Multi-trait genomic selection for weevil resistance, growth and wood quality in Norway spruce. Evol. Appl. 13, 76–94 (2020).CAS 
PubMed 

Google Scholar 
Lebedev, V. G., Lebedeva, T. N., Chernodubov, A. I. & Shestibratov, K. A. Genomic selection for forest tree improvement: Methods, achievements and perspectives. Forests 11, 1190 (2020).
Google Scholar 
Mullin, T. J. et al. Economic importance, breeding objectives and achievements. In Genetics, Genomics and Breeding of Conifers (eds Plomion, C. et al.) (Science Publishers & CRC Press, 2011).
Google Scholar 
Zhang, J., Peter, G. F., Powell, G. L., White, T. L. & Gezan, S. A. Comparison of breeding values estimated between single-tree and multiple-tree plots for a slash pine population. Tree Genet. Genomes 11, 48 (2015).CAS 

Google Scholar 
Martínez-García, P. J. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genet. Genomes 13, 109 (2017).
Google Scholar 
Weng, Y., Ford, R., Tong, Z. & Krasowski, M. Genetic parameters for bole straightness and branch angle in Jack pine estimated using linear and generalized linear mixed models. For. Sci. 63, 111–117 (2017).
Google Scholar 
Mrode, R. A. Linear Models for the Prediction of Animal Breeding Values 2nd edn. (CAB International, 2005).
Google Scholar 
Henderson, C. R. Theoretical bias and computational methods for a number of different animal models. J. Dairy Sci. 71, 1–16 (1988).
Google Scholar 
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn. (Longman Publishing Group, 1996).
Google Scholar 
Henderson, C. R. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32, 69–83 (1976).MATH 

Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).
Google Scholar 
Hill, W. G. & Weir, B. S. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet. Res. 93, 47–64 (2011).CAS 

Google Scholar 
Doerksen, T. K. & Herbinger, C. M. Male reproductive success and pedigree error in red spruce open-pollinated and polycross mating systems. Can. J. For. Res. 38, 1742–1749 (2008).
Google Scholar 
Godbout, J. et al. Development of a traceability system based on SNP array for the large-scale production of high-value white spruce (Picea glauca). Front. Plant Sci. 8, 1264 (2017).PubMed 
PubMed Central 

Google Scholar 
Galeano, E., Bousquet, J. & Thomas, B. R. SNP-based analysis reveals unexpected features of genetic diversity, parental contributions and pollen contamination in a white spruce breeding program. Sci. Rep. 11, 4990 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: An example from white spruce. Heredity 124, 562–578 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Askew, G. R. & El-Kassaby, Y. A. Estimation of relationship coefficients among progeny derived from wind-pollinated orchard seeds. Theor. Appl. Genet. 88, 267–272 (1994).CAS 
PubMed 

Google Scholar 
Doerksen, T. K., Bousquet, J. & Beaulieu, J. Inbreeding depression in intra-provenance crosses driven by founder relatedness in white spruce. Tree Genet. Genomes 10, 203–212 (2014).
Google Scholar 
Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Heffner, E. L., Lorenz, A. J., Jannink, J.-L. & Sorrels, M. E. Plant breeding with genomic selection: Gain per unit time and cost. Crop Sci. 50, 1681–1690 (2010).
Google Scholar 
Grattapaglia, D. & Resende, M. D. V. Genomic selection in forest tree breeding. Tree Genet. Genomes 7, 241–255 (2011).
Google Scholar 
Beaulieu, J., Doerksen, T., Clément, S., MacKay, J. & Bousquet, J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 113, 342–352 (2014).
Google Scholar 
Habier, D., Tetens, J., Seefried, F.-R., Lichtner, P. & Thaller, G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Gen. Select. Evol. 42, 5 (2010).
Google Scholar 
Perkel, J. SNP genotyping: six technologies that keyed a revolution. Nat. Methods 5, 447–454 (2008).CAS 

Google Scholar 
Pavy, N. et al. Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners. Mol. Ecol. Res. 13, 324–336 (2013).CAS 

Google Scholar 
Thomson, M. J. High-throughput genotyping to accelerate crop improvement. Plant Breed. Biotechnol. 2, 195–212 (2014).
Google Scholar 
Beaulieu, J., Doerksen, T., MacKay, J., Rainville, A. & Bousquet, J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics 15, 1048 (2014).PubMed 
PubMed Central 

Google Scholar 
Liu, L., Chen, R., Fugina, C. J., Siegel, B. & Jackson, D. High-throughput and low-cost genotyping method for plant genome editing. Curr. Prot. 1, e100 (2021).CAS 

Google Scholar 
Lenz, P. et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics 18, 335 (2017).PubMed 
PubMed Central 

Google Scholar 
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D. & Calus, M. P. L. Whole-genome regression and prediction models applied to plant and animal breeding. Genetics 193, 327–345 (2013).PubMed Central 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for non-orthogonal problems. Technometrics 12, 55–67 (1970).MATH 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Series B. 58, 267–288 (1996).MathSciNet 
MATH 

Google Scholar 
VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).CAS 
PubMed 

Google Scholar 
Legarra, A., Aguilar, I. & Misztal, I. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92, 4656–4663 (2009).CAS 
PubMed 

Google Scholar 
Zapata-Valenzuela, J., Whetten, R. W., Neale, D., McKeand, S. & Isik, F. Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. Genes Genomes Genet. 3, 909–916 (2013).
Google Scholar 
Muñoz, P. R. et al. Unraveling additive from non-additive effects using genomic relationship matrices. Genetics 198, 1759–1768 (2014).PubMed 
PubMed Central 

Google Scholar 
Ratcliffe, B. et al. Single-step BLUP with varying genotyping effort in open-pollinated Picea glauca. Genes Genomes Genet. 7, 935–942 (2017).
Google Scholar 
Gamal El-Dien, O. et al. Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Picea glauca x engelmannii). Mol. Breed. 38, 26 (2018).
Google Scholar 
Zobel, B. J. & Sprague, J. R. Juvenile Wood in Forest Trees (Springer, 1988).
Google Scholar 
Osorio, L. F., White, T. L. & Huber, D. A. Age trends of heritabilities and genotype-by-environment interactions for growth traits and wood density from clonal trials of Eucalyptus grandis Hill ex Maiden. Silv. Genet. 50, 108–117 (2000).
Google Scholar 
Baltunis, B. S., Gapare, W. J. & Wu, H. X. Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silv. Genet. 59, 113–124 (2010).
Google Scholar 
Gamal El-Dien, O. et al. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16, 370 (2015).PubMed 
PubMed Central 

Google Scholar 
Resende, M. D. V. et al. Genomic selection for growth and wood quality in Eucalyptus: Capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194, 116–128 (2012).PubMed 

Google Scholar 
Chen, Z.-Q. et al. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics 19, 946 (2018).PubMed 
PubMed Central 

Google Scholar 
Beaulieu, J., Perron, M. & Bousquet, J. Multivariate patterns of adaptive genetic variation and seed source transfer in Picea mariana. Can. J. For. Res. 34, 531–545 (2004).
Google Scholar 
Li, P., Beaulieu, J. & Bousquet, J. Genetic structure and patterns of genetic variation among populations in eastern white spruce (Picea glauca). Can. J. For. Res. 27, 189–198 (1997).
Google Scholar 
Namkoong, G. Inbreeding effects on estimation of genetic additive variance. For. Sci. 12, 8–13 (1966).
Google Scholar 
Squillace, A. E. Average genetic correlations among offspring from open-pollinated forest trees. Silv. Genet. 23, 149–156 (1974).
Google Scholar 
Muñoz, P. R. et al. Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Sci. 53, 1115–1123 (2014).
Google Scholar 
Tan, B. et al. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 17, 110 (2017).PubMed 
PubMed Central 

Google Scholar 
Weigel, K. A., VanRaden, P. M., Norman, H. D. & Grosu, H. A 100-year review: Methods and impact of genetic selection in dairy cattle—From daughter-dam comparisons to deep learning algorithms. J. Dairy Sci. 100, 10234–10250 (2017).CAS 
PubMed 

Google Scholar 
Grattapaglia, D. et al. Quantitative genetics and genomics converge to accelerate forest tree breeding. Front. Plant Sci. 9, 1693 (2018).PubMed 
PubMed Central 

Google Scholar 
Park, Y.-S., Beaulieu, J. & Bousquet, J. Multi-varietal forestry integrating genomic selection and somatic embryogenesis. In Vegetative Propagation of Forest Trees (eds Park, Y.-S. et al.) 302–322 (National Institute of Forest Science, 2016).
Google Scholar 
Bousquet, J. et al. Spruce population genomics. In Population Genomics: Forest Trees (ed. Rajora, O. P.) (Springer Nature, 2021).
Google Scholar 
Chamberland, V. et al. Conventional versus genomic selection for white spruce improvement: A comparison of costs and benefits of plantations on Quebec public lands. Tree Genet. Genomes 16, 17 (2020).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
MacFarland, T. W. & Yates, J. M. Wilcoxon matched-pairs signed-ranks test. In Introduction to Nonparametric Statistics for the Biological Sciences Using R 133–175 (Springer, 2016) https://doi.org/10.1007/978-3-319-30634-6_5.Li, Y. et al. Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 20, 1026 (2019).PubMed 
PubMed Central 

Google Scholar 
Calleja-Rodriguez, A. et al. Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. BMC Genomics 21, 796 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet. Genomes 16, 14 (2020).
Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Prediction accuracy of single-step BLUP for growth and wood quality traits in the lodgepole pine breeding program in British Columbia. Tree Genet. Genomes 16, 64 (2020).
Google Scholar 
Thistlethwaite, F. R. et al. Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genomics 18, 930 (2017).PubMed 
PubMed Central 

Google Scholar 
Suontama, M. et al. Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity 122, 370–379 (2019).CAS 
PubMed 

Google Scholar 
Müller, B. S. F. et al. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics 18, 524 (2017).PubMed 
PubMed Central 

Google Scholar 
Thavamanikumar, S., Arnold, R. J., Luo, J. & Thumma, B. R. Genomic studies reveal substantial dominant effects and improved genomic predictions in an open-pollinated breeding population of Eucalyptus pellita. Genes Genomes Genet. 10, 3751–3763 (2020).CAS 

Google Scholar 
Resende, R. T. et al. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity 119, 245–255 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Marco de Lima, B. et al. Quantitative genetic parameters for growth and wood properties in Eucalyptus “urograndis” hybrid using near-infrared phenotyping and genome-wide SNP-based relationships. PLoS ONE 14, e0218747 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouvet, J.-M., Makouanzi, G., Cros, D. & Vigneron, Ph. Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications. Heredity 116, 146–157 (2016).CAS 
PubMed 

Google Scholar 
Pégard, M. et al. Favorable conditions for genomic evaluation to outperform classical pedigree evaluation highlighted by a proof-of-concept study in poplar. Front. Plant Sci. 11, 581954 (2020).PubMed 
PubMed Central 

Google Scholar  More

Lampert, W. Zooplankton research: The contribution of limnology to general ecological paradigms. Aquat. Ecol. 31, 19–27. https://doi.org/10.1023/A:1009943402621 (1997).Article 

Google Scholar 
Sotton, B. et al. Trophic transfer of microcystins through the lake pelagic food web: Evidence for the role of zooplankton as a vector in fish contamination. Sci. Total Environ. 466–467, 152–163. https://doi.org/10.1016/j.scitotenv.2013.07.020 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
St-Gelais, F. N., Sastri, A. R., del Giorgio, P. A. & Beisner, B. E. Magnitude and regulation of zooplankton community production across boreal lakes. Limnol. Oceanogr. Lett. 2(6), 210–217. https://doi.org/10.1002/lol2.10050 (2017).Article 

Google Scholar 
Dejen, E., Vijverberg, J., Nagelkerke, L. A. J. & Sibbing, F. A. Temporal and spatial distribution of microcrustacean zooplankton in relation to turbidity and other environmental factors in a large tropical lake (L. Tana, Ethiopia). Hydrobiologia 513(1), 39–49. https://doi.org/10.1023/b:hydr.0000018163.60503.b8 (2004).Article 

Google Scholar 
Arendt, K. E. et al. Effects of suspended sediments on copepods feeding in a glacial influenced sub-Arctic fjord. J. Plankton Res. 33, 1526–1537. https://doi.org/10.1093/plankt/fbr054 (2011).CAS 
Article 

Google Scholar 
Carrasco, N. K., Perissinotto, R. & Jones, S. Turbidity effects on feeding and mortality of the copepod Acartiella natalensis (Connell and Grindley, 1974) in the St Lucia Estuary, South Africa. J. Exp. Mar. Biol. Ecol. 446, 45–51. https://doi.org/10.1016/j.jembe.2013.04.016 (2013).Article 

Google Scholar 
Goździejewska, A. et al. Effects of lateral connectivity on zooplankton community structure in floodplain lakes. Hydrobiologia 774, 7–21. https://doi.org/10.1007/s10750-016-2724-8 (2016).CAS 
Article 

Google Scholar 
Zhou, J., Qin, B. & Han, X. The synergetic effects of turbulence and turbidity on the zooplankton community structure in large, shallow Lake Taihu. Environ. Sci. Pollut. Res. 25, 1168–1175. https://doi.org/10.1007/s11356-017-0262-1 (2018).CAS 
Article 

Google Scholar 
Chou, W.-R., Fang, L.-S., Wang, W.-H. & Tew, K. S. Environmental influence on coastal phytoplankton and zooplankton diversity: A multivariate statistical model analysis. Environ. Monit. Assess. 184(9), 5679–5688. https://doi.org/10.1007/s10661-011-2373-3 (2011).CAS 
Article 
PubMed 

Google Scholar 
Du, X. et al. Analyzing the importance of top-down and bottom-up controls in food webs of Chinese lakes through structural equation modeling. Aquat. Ecol. 49(2), 199–210. https://doi.org/10.1007/s10452-015-9518-3 (2015).CAS 
Article 

Google Scholar 
Feitosa, I. B. et al. Plankton community interactions in an Amazonian floodplain lake, from bacteria to zooplankton. Hydrobiologia 831, 55–70. https://doi.org/10.1007/s10750-018-3855-x (2019).CAS 
Article 

Google Scholar 
Kruk, M. & Paturej, E. Indices of trophic and competitive relations in a planktonic network of a shallow, temperate lagoon. A graph and structural equation modeling approach. Ecol. Indic. 112, 106007. https://doi.org/10.1016/j.ecolind.2019.106007 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Artiemjew, P. From explanatory to predictive network modeling of relationships among ecological indicators in the shallow temperate lagoon. Ecol. Indic. 117, 106637. https://doi.org/10.1016/j.ecolind.2020.106637 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Obolewski, K. Zooplankton predator–prey network relationships indicates the saline gradient of coastal lakes. Machine learning and meta-network approach. Ecol. Indic. 125, 107550. https://doi.org/10.1016/j.ecolind.2021.107550 (2021).Article 

Google Scholar 
Oh, H.-J. et al. Comparison of taxon-based and trophi-based response patterns of rotifer community to water quality: Applicability of the rotifer functional group as an indicator of water quality. Anim. Cells Syst. 21, 133–140. https://doi.org/10.1080/19768354.2017.1292952 (2017).Article 

Google Scholar 
Sodré, E. D. O. & Bozelli, R. L. How planktonic microcrustaceans respond to environment and affect ecosystem: A functional trait perspective. Int. Aquat. Res. 11, 207–223. https://doi.org/10.1007/s40071-019-0233-x (2019).Article 

Google Scholar 
Simões, N. R. et al. Changing taxonomic and functional β-diversity of cladoceran communities in Northeastern and South Brazil. Hydrobiologia 847, 3845–3856. https://doi.org/10.1007/s10750-020-04234-w (2020).Article 

Google Scholar 
Goździejewska, A. M., Koszałka, J., Tandyrak, R., Grochowska, J. & Parszuto, K. Functional responses of zooplankton communities to depth, trophic status, and ion content in mine pit lakes. Hydrobiologia 848, 2699–2719. https://doi.org/10.1007/s10750-021-04590-1 (2021).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton feeding rates in relation to suspended sediment content: Potential influences on community structure in a turbid reservoir. Fresh. Biol. 19, 123–139. https://doi.org/10.1111/j.1365-2427.1988.tb00334.x (1988).Article 

Google Scholar 
Gliwicz, Z. M. & Pijanowska, J. The role of predation in zooplankton succession. In Plankton Ecology. Succession in Plankton Communities (ed. Sommer, U.) 253–296 (Springer Verlag, 1989).Chapter 

Google Scholar 
Gardner, M. B. Effects of turbidity on feeding rates and selectivity of bluegills. Trans. Am. Fish. Soc. 110(3), 446–450. https://doi.org/10.1577/1548-8659(1981)110%3c446:EOTOFR%3e2.0.CO;2 (1981).Article 

Google Scholar 
Zettler, E. R. & Carter, J. C. H. Zooplankton community and species responses to a natural turbidity gradient in Lake Temiskaming, Ontario-Quebec. Can. J. Fish. Aquat. Sci. 43, 665–673. https://doi.org/10.1139/f86-080 (1986).Article 

Google Scholar 
APHA. Standard Methods for the Examination of Water and Wastewater 20th edn. (American Public Health Association, 1999).
Google Scholar 
Lind, O. T., Chrzanowski, T. H. & D’avalos-Lind, L. Clay turbidity and the relative production of bacterioplankton and phytoplankton. Hydrobiologia 353, 1–18. https://doi.org/10.1023/A:1003039932699 (1997).CAS 
Article 

Google Scholar 
Boenigk, J. & Novarino, G. Effect of suspended clay on the feeding and growth of bacterivorous flagellates and ciliates. Aquat. Microb. Ecol. 34, 181–192. https://doi.org/10.3354/ame034181 (2004).Article 

Google Scholar 
Noe, G. B., Harvey, J. W. & Saiers, J. E. Characterization of suspended particles in Everglades wetlands. Limnol. Oceanogr. 52, 1166–1178. https://doi.org/10.4319/lo.2007.52.3.1166 (2007).ADS 
CAS 
Article 

Google Scholar 
Bilotta, G. S. & Brazier, R. E. Understanding the influence of suspended solids on water quality and aquatic biota. Water Res. 42, 2849–2861. https://doi.org/10.1016/j.watres.2008.03.018 (2008).CAS 
Article 
PubMed 

Google Scholar 
Fernandez-Severini, M. D., Hoffmeyer, M. S. & Marcovecchio, J. E. Heavy metals concentrations in zooplankton and suspended particulate matter in a southwestern Atlantic temperate estuary (Argentina). Environ. Monit. Assess. 185, 1495–1513. https://doi.org/10.1007/s10661-012-3023-0 (2013).CAS 
Article 
PubMed 

Google Scholar 
Paaijmans, K. P., Takken, W., Githeko, A. K. & Jacobs, A. F. G. The effect of water turbidity on the near-surface water temperature of larval habitats of the malaria mosquito Anopheles gambiae. Int. J. Biometeorol. 52(8), 747–753. https://doi.org/10.1007/s00484-008-0167-2 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Asrafuzzaman, M., Fakhruddin, A. N. M. & Hossain, M. A. Reduction of turbidity of water using locally available natural coagulants. ISRN Microbiol. 1–6, 2011. https://doi.org/10.5402/2011/632189 (2011).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic rotifers and cladocerans. Ecology 71(5), 1741–1755. https://doi.org/10.2307/1937582 (1990).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshwater Biol. 28, 103–109. https://doi.org/10.1111/j.1365-2427.1992.tb00566.x (1992).Article 

Google Scholar 
Levine, S. N., Zehrer, R. F. & Burns, C. W. Impact of resuspended sediment on zooplankton feeding in Lake Waihola, New Zealand. Freshw. Biol. 50, 1515–1536. https://doi.org/10.1111/j.1365-2427.2005.01420 (2005).Article 

Google Scholar 
Moreira, F. W. A. et al. Assessing the impacts of mining activities on zooplankton functional diversity. Acta Limn. Bras. 28, e7. https://doi.org/10.1590/S2179-975X0816 (2016).Article 

Google Scholar 
Kerfoot, W. C. & Sih, A. Predation. Direct and Indirect Impacts on Aquatic Communities Vol. 160 (University Press of New England, 1987).
Google Scholar 
Schou, M. O. et al. Restoring lakes by using artificial plant beds: Habitat selection of zooplankton in a clear and a turbid shallow lake. Freshw. Biol. 54(7), 1520–1531. https://doi.org/10.1111/j.1365-2427.2009.02189.x (2009).Article 

Google Scholar 
Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9, 16113. https://doi.org/10.1038/s41598-019-52542-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ribeiro, F. et al. Silver nanoparticles and silver nitrate induce high toxicity to Pseudokirchneriella subcapitata, Daphnia magna and Danio rerio. Sci. Total Environ. 466–467, 232–241. https://doi.org/10.1016/j.scitotenv.2013.06.101 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vallotton, P., Angel, B., Mccall, M., Osmond, M. & Kirby, J. Imaging nanoparticle-algae interactions in three dimensions using Cytoviva microscopy. J. Microsc. 257(2), 166–169. https://doi.org/10.1111/jmi.12199 (2015).CAS 
Article 
PubMed 

Google Scholar 
Shanthi, S. et al. Biosynthesis of silver nanoparticles using a probiotic Bacillus licheniformis Dahb1 and their antibiofilm activity and toxicity effects in Ceriodaphnia cornuta. Microb. Pathogenesis 93, 70e77. https://doi.org/10.1016/j.micpath.2016.01.014 (2016).CAS 
Article 

Google Scholar 
Vijayakumar, S. et al. Ecotoxicity of Musa paradisiaca leaf extract-coated ZnO nanoparticles to the freshwater microcrustacean Ceriodaphnia cornuta. Limnologica 67, 1–6. https://doi.org/10.1016/j.limno.2017.09.004 (2017).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton distribution in relation to turbidity and related environmental gradients in a large subtropical reservoir: Patterns and implications. Freshw. Biol. 24(2), 241–263. https://doi.org/10.1111/j.1365-2427.1990.tb00706.x (1990).Article 

Google Scholar 
Pollard, A. I., González, M. J., Vanni, M. J. & Headworth, J. L. Effects of turbidity and biotic factors on the rotifer community in an Ohio reservoir. In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387388 (eds Wurdak, E. et al.) 215–223 (Springer, 1998).
Google Scholar 
Roman, M. R., Holliday, D. V. & Sanford, L. P. Temporal and spatial patterns of zooplankton in the Chesapeake Bay turbidity maximum. Mar. Ecol. Prog. Ser. 213, 215–227. https://doi.org/10.3354/meps213215 (2001).ADS 
Article 

Google Scholar 
Young, I. R. & Ribal, A. Multiplatform evaluation of global trends in wind speed and wave height. Science 364(6440), 548–552. https://doi.org/10.1126/science.aav9527 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 419, 33. https://doi.org/10.1051/kmae/2018020 (2018).Article 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Koszałka, J. & Bowszys, M. Effects of recreational fishing on zooplankton communities of drainage system reservoirs at an open-pit mine. Fish. Manag. Ecol. 00, 1–13. https://doi.org/10.1111/fme.12411 (2020).Article 

Google Scholar 
Allesina, S., Bodini, A. & Bondavalli, C. Ecological subsystems via graph theory: The role of strongly connected components. Oikos 110, 164–176. https://doi.org/10.1111/j.0030-1299.2005.13082.x (2005).Article 

Google Scholar 
D’Alelio, D. et al. Ecological-network models link diversity, structure and function in the plankton food-web. Sci. Rep. 6, 21806. https://doi.org/10.1038/srep21806 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krebs, C. J. Ecology: The Experimental Analysis of Distribution and Abundance 6th edn. (Benjamin Cummings, 2009).
Google Scholar 
Ejsmont-Karabin, J., Radwan, S. & Bielańska-Grajner, I. Rotifers. Monogononta–Atlas of Species. Polish Freshwater Fauna (Univ of Łódź, 2004).
Google Scholar 
Streble, H. & Krauter, D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des Süβwassers (Kosmos Gesellschaft der Naturfreunde Franckhsche Verlagshandlung Stuttgart, 1978).
Google Scholar 
Ejsmont-Karabin, J. The usefulness of zooplankton as lake ecosystem indicators: Rotifer trophic state index. Pol. J. Ecol. 60, 339–350 (2012).
Google Scholar 
Gutkowska, A., Paturej, E. & Kowalska, E. Rotifer trophic state indices as ecosystem indicators in brackish coastal waters. Oceanologia 55(4), 887–899. https://doi.org/10.5697/oc.55-4.887 (2013).Article 

Google Scholar 
Dembowska, E. A., Napiórkowski, P., Mieszczankin, T. & Józefowicz, S. Planktonic indices in the evaluation of the ecological status and the trophic state of the longest lake in Poland. Ecol. Indic. 56, 15–22. https://doi.org/10.1016/j.ecolind.2015.03.019 (2015).Article 

Google Scholar 
Sousa, W., Attayde, J. L., Rocha, E. D. S. & Eskinazi-Sant’Anna, E. M. The response of zooplankton assemblages to variations in the water quality of four man-made lakes in semi-arid northeastern Brazil. J. Plankton Res. 30(6), 699–708. https://doi.org/10.1093/plankt/fbn032 (2008).Article 

Google Scholar 
Kak, A. & Rao, R. Does the evasive behavior of H. exarthra influence its competition with cladocerans? In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387/388 (eds Wurdak, E. et al.) 409–419 (Springer, 1998).
Google Scholar 
Hochberg, R., Yang, H. & Moore, J. The ultrastructure of escape organs: Setose arms and crossstriated muscles in Hexarthra mira (Rotifera: Gnesiotrocha: Flosculariaceae). Zoomorphology 136, 159–173. https://doi.org/10.1007/s00435-016-0339-2 (2017).Article 

Google Scholar 
Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton. Science 150, 28–35 (1965).ADS 
CAS 
Article 

Google Scholar 
Connell, J. H. Intermediate-disturbance hypothesis. Science 204(4399), 1345 (1979).CAS 
Article 

Google Scholar 
Martín González, A. M., Dalsgaard, B. & Olesen, J. M. Centrality measures and the importance of generalist species in pollination networks. Ecol. Complex. 7(1), 36–43. https://doi.org/10.1016/j.ecocom.2009.03.008 (2010).Article 

Google Scholar 
Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 104, 91–93 (1969).Article 

Google Scholar 
Schmitz, O. J. & Trussell, G. C. Multiple stressors, state-dependence and predation risk—Foraging trade-offs: Toward a modern concept of trait-mediated indirect effects in communities and ecosystems. Curr. Opin. Behav. Sci. 12, 6–11. https://doi.org/10.1016/j.cobeha.2016.08.003 (2016).Article 

Google Scholar 
Burns, C. W. & Gilbert, J. J. Effects of daphnid size and density on interference between Daphnia and Keratella cochlearis. Limnol. Oceanogr. 31(4), 848–858. https://doi.org/10.4319/lo.1986.31.4.0848 (1986).ADS 
Article 

Google Scholar 
Gilbert, J. J. Suppression of rotifer populations by Daphnia: A review of the evidence, the mechanisms, and the effects on zooplankton community structure. Limnol. Oceanogr. 33(6), 1286–1303. https://doi.org/10.4319/lo.1988.33.6.1286 (1988).ADS 
Article 

Google Scholar 
Conde-Porcuna, J. M., Morales-Baquero, R. & Cruz-Pizarro, L. Effects of Daphnia longispina on rotifer populations in a natural environment: Relative importance of food limitation and interference competition. J. Plankton Res. 16(6), 691–706. https://doi.org/10.1093/plankt/16.6.691 (1994).Article 

Google Scholar 
Ladle, R. J. & Whittaker, R. J. (eds) Conservation Biogeography (Wiley–Blackwell, 2011).
Google Scholar 
Cottee-Jones, H. E. W. & Whittaker, R. J. The keystone species concept: A critical appraisal. Front. Biogeogr. 4(3), 117–127. https://doi.org/10.21425/F5FBG12533 (2012).Article 

Google Scholar 
Remane, A. Die Brackwasserfauna. Verhandlungen Der Deutschen Zoologischen Gesellschaft 36, 34–74 (1934).
Google Scholar 
Skrzypczak, A. R. & Napiórkowska-Krzebietke, A. Identification of hydrochemical and hydrobiological properties of mine waters for use in aquaculture. Aquac. Rep. 18, 100460. https://doi.org/10.1016/j.aqrep.2020.100460 (2020).Article 

Google Scholar 
von Flössner, D. & Krebstiere, C. Kiemen-und Blattfüsser, Branchiopoda, Fischläuse, Branchiura Vol. 382 (VEB Gustav Fischer Verlag, 1972).
Google Scholar 
Koste, W. Rotatoria. Die Rädertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband 52–570 (Gebrüder Borntraeger, 1978).
Google Scholar 
Rybak, J. I. & Błędzki, L. A. Freshwater Planktonic Crustaceans (Warsaw University Press, 2010).
Google Scholar 
Błędzki, L. A. & Rybak, J. I. Freshwater Crustacean Zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida). Key to Species Identification with Notes on Ecology, Distribution, Methods and Introduction to Data Analysis (Springer, 2016).Book 

Google Scholar 
Bottrell, H. H. et al. A review of some problems in zooplankton production studies. Norw. J. Zool. 24, 419–456 (1976).
Google Scholar 
Ejsmont-Karabin, J. Empirical equations for biomass calculation of planktonic rotifers. Pol. Arch. Hydr. 45, 513–522 (1998).
Google Scholar 
Kovach, W. L. MVSP—A Multivariate Statistical Package for Windows, ver. 3.2 (Kovach Computing Services Pentraeth, 2015).
Google Scholar 
Borgatti, S. P. Centrality and network flow. Soc. Netw. 27, 55–71. https://doi.org/10.1016/j.socnet.2004.11.008 (2005).Article 

Google Scholar 
Kamada, T. & Kawai, S. An algorithm for drawing general undirected graphs—Inform. Process Lett. 31, 7–15 (1989).MathSciNet 
Article 

Google Scholar 
Pavlopoulos, G. A. et al. Using graph theory to analyze biological networks. BioData Min 4, 10 (2011).Article 

Google Scholar 
Newman, M. E. J. A measure of betweenness centrality based on random walks. Soc. Netw. 27, 39–54. https://doi.org/10.1016/j.socnet.2004.11.009 (2005).Article 

Google Scholar 
Brandes, U. A. faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177. https://doi.org/10.1080/0022250X.2001.9990249 (2001).Article 
MATH 

Google Scholar  More

Seasonality of human–bear conflictOn the Deccan Plateau and Gujarat, most sloth bear attacks occurred in winter, which differs significantly from the seasonality of attacks reported by other studies. Unlike other study areas, people on the Deccan Plateau and in Gujarat are more active in the forest in winter when monsoons and crop harvests have ended. The higher incidence of attacks during monsoons in central India correlates with the increased presence of people farming and protecting crops from cattle depredation, as well as from bears and other wildlife species grazing in nearby forested areas5, 16,17,18. The Kanha–Pench Corridor study was the only one which documented an increase in sloth bear attacks during summer. This increase is concurrent with an increase of people in the forest that collect mahua flower (Madhuca spp) and tendu leaf (Diospyros spp)19. In Sri Lanka, most attacks occurred in the dry season, coincident with the highest levels of human activity in forested areas. People in Sri Lanka enter forests for alternative sources of income as agriculture activity declines during the dry season4.Across all studies, the majority of sloth bear attacks are correlated with the time of year when human activity is greatest in bear habitat. However, the time of year that the peak of human activity occurs in sloth bear habitat varies by region. We conclude that the seasonal activity of bears plays a much smaller role on attack rates than the seasonal activity of humans. Consistent with findings in other studies, human incursion into bear habitat is the primary factor responsible for precipitating conflict21.Time of day influences on human–bear conflictMost studies attributed the time of day that attacks occurred to when most humans were active in the forest4, 17,18,19,20. However, the Deccan plateau differed in that the majority of attacks occurred after dark when fewer people were active in or near the forest. Working in agricultural areas after dark is a more common practice on the Deccan Plateau than for the other study areas due to the availability of electricity and artificial lighting, though even with artificial lighting human activity after dark on the Deccan Plateau is still substantially less than during daytime. While a contributing factor, we do not feel that the increase in nighttime activity on the Deccan Plateau fully explains the significant increase in attacks during that time period as compared to other areas. We suspect that sloth bear activity patterns on the Deccan Plateau, and how bears use their environment, accounts for the shift in attack timing.Sloth bears, though potentially active throughout the day, are predominately crepuscular and nocturnal17, 22,23,24. During daytime, sloth bears seek shelter in naturally occurring caves, crevices between big boulders, the spaces between tree roots, beneath fallen trees, or under bushes1, 25,26,27,28. On the Deccan Plateau, however, sloth bears utilize rocky caves almost exclusively for daytime denning29. A cave reduces chance encounters with people and predators while providing a modicum of security, hence the lower incident rate for areas with naturally occurring caves.Conversely, studies conducted in Sri Lanka, Maharashtra and the Kanha-Pench corridor documented more attacks during daytime when people are more active but sloth bears are less active4, 5, 19. Large areas where sloth bears are located in Sri Lanka do not have caves for resting, though they do have dense vegetation and tree cavities (S. Ratnayeke, personal communication July 28, 2020). The Dnyanganga Wildlife Sanctuary, in the state of Maharastra, is mostly lower plains forest without rocky caves (N. Dharaiya, personal communication June 25, 2020). The Kanha-Pench corridor landscape is largely comprised of sal (Shorea spp) and teak (Tectona spp) forests largely devoid of caves30. The role of caves in minimizing daylight sloth bear attacks may be best exemplified by an attack in Sri Lanka as quoted in Ratnayeke et al.4:
“I was following two of my companions and saw a black form lying at the foot of a clump bushes, about 10 m from me. I called out to my companions. Before I knew it, the impact of the charging bear knocked me off my feet. It happened so fast, I didn’t see the bear coming… just dust, flying leaves, and the screams and roars of the bear.”
Had this bear been in a cave rather than the shade of a bush, it likely would not have felt threatened and reacted defensively. We speculate that during daylight on the Deccan Plateau, sloth bears rest securely within a cave and are not threatened by humans passing nearby. We know that farmers and livestock herders work in relatively close proximity to known den locations without fear of being attacked (S. Shanmugavelu, pers. observation). Clearly, caves afford a level of protection and separation that benefits both bears and humans. Consequently, we suggest this is the most likely explanation as to why there are relatively few attacks on the Deccan Plateau during daytime.Season and sloth bear safety messagingBear attack research and safety messaging often recognizes a seasonal component17,18,19,20, 31 (e.g., more sloth bear attacks occur during the monsoon season than during other seasons). Sloth bears are active year-round, and the rate of attacks is strongly correlated with the level of human activity in the forest. Similarly, in Alaska, Smith and Herrero32 reported that human-brown bear conflicts were strongly seasonal in their occurrence. Additionally, they reported that attacks occurred most often when both people and bears vied for the same resource, such as salmon or ungulates. Farther north, human-polar bear conflict peaks when bears are on land awaiting freeze up in the fall33. Not infrequently, sloth bear safety messaging amounts to little more than general statements such as “when in the forest or in sloth bear country be aware”. In other words, an individual’s odds of being attacked by a sloth bear while in the woods may not significantly vary regardless of season. But, where it has been found to vary by season, this information should be conveyed to the public.Time of day and sloth bear safety messagingSloth bear research and safety messaging often reports and warns of the “most dangerous” time or times of the day to be active in the forest17,18,19,20, 31, 34. Sloth bear attacks, like grizzly bear or American black bear attacks33, can occur anytime, day or night6. However, due to an abundance of naturally occurring caves on the Deccan Plateau, stumbling across a sleeping sloth bear mid-day is much less likely to occur than it is in Sri Lanka or in the Kanha-Pench corridor. Therefore, regional sloth bear safety messaging should acknowledge this significant difference which will promote bear safety.The Corbett Foundation31 and Dharaiya et al.34 do an admirable job of focusing their safety messaging to a specific regional group of people in their respective publications. This type of regional messaging is necessary for optimizing sloth bear safety messaging efficacy. However, there is also value to non-site-specific sloth bear safety messaging. The short film “Living with Sloth Bears”35 intentionally addresses general safety messaging that applies to sloth bears across their entire range. Consequently, in the making of this film, we purposely avoided referring to the timing of attacks, seasons or time of day, or other aspects of human-bear conflict because we were aware of significant differences with respect to these variables between locations.Yet another aspect of bear safety messaging is to keep it simple so that a person, under duress, will remember what to do in the event of a bear encounter Attempting to recall the details of an extended message, especially when being threatened by a bear, can be difficult, if not impossible. Therefore, the trend has been to keep bear messaging as simple as possible and we agree with it. However, teaching people that work in bear habitat the most likely times of day encounters occur can be beneficial. In summary, there is a time and place to provide detailed information that is regionally specific, and other situations in which to keep messaging simple.Sloth bear denning ecology on the Deccan Plateau and its role in human–bear conflictThe Deccan Plateau is known as high quality sloth bear habitat, as evidenced by the relatively high density of bears in this area (S. Shanmugavelu, pers. observation). While there is ample food on the Deccan Plateau, the abundance of caves there sets it apart from other areas within the specie’s range. Sloth bears use only caves or cave-like structures on the Deccan Plateau for resting (Shanmugavelu et al. In Print). Caves provide protection from the elements, such as the heat of the day or severe storms, as well as protection from potential predators. Sloth bears do not have many predators and while a cub or very young bear may be at risk from leopards (Panthera pardus) or wolves (Canis lupes pallipes), the only natural predator of adult sloth bears is the Bengal tiger (Panthera tigris tigris). Tiger scat studies revealed that sloth bears can comprise up to 2% of a their diet36,37,38,39. Tigers no longer occur on the Deccan Plateau, but the abundance of caves in the area undoubtedly historically benefited sloth bears, perhaps facilitating a higher density than would have been otherwise attainable. Presently, however, an increase in human population and habitat loss represents greater threat to the species. More

Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. USA 108, 17905–17909 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Change 3, 979–984 (2013).ADS 
CAS 

Google Scholar 
Doney, S. C. Plankton in a warmer world. Nature 444, 695–696 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Harley, C. D. G. et al. The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
PubMed 

Google Scholar 
Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).CAS 

Google Scholar 
Zingone, A., Phlips, E. J. & Harrison, P. J. Multiscale variability of twenty-two coastal phytoplankton time series: A global scale comparison. Estuaries Coasts 33, 224–229 (2010).CAS 

Google Scholar 
Cloern, J. E. et al. Human activities and climate variability drive fast-paced change across the world’s estuarine-coastal ecosystems. Glob. Change Biol. 22, 513–529 (2016).ADS 

Google Scholar 
Cloern, J. E. & Jassby, A. D. Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems. Estuaries Coasts 33, 230–241 (2010).CAS 

Google Scholar 
Romagnan, J.-B. et al. Comprehensive model of annual plankton succession based on the whole-plankton time series approach. PLoS ONE 10, e0119219 (2015).PubMed 
PubMed Central 

Google Scholar 
Guadayol, Ò. et al. Responses of coastal osmotrophic planktonic communities to simulated events of turbulence and nutrient load throughout a year. J. Plankton Res. 31, 583–600 (2009).CAS 

Google Scholar 
Totti, C. et al. Phytoplankton communities in the northwestern Adriatic Sea: Interdecadal variability over a 30-years period (1988–2016) and relationships with meteoclimatic drivers. J. Mar. Syst. 193, 137–153 (2019).
Google Scholar 
Zingone, A. et al. Coastal phytoplankton do not rest in winter. Estuaries Coasts 33, 342–361 (2010).CAS 

Google Scholar 
Widdicombe, C. E., Eloire, D., Harbour, D., Harris, R. P. & Somerfield, P. J. Long-term phytoplankton community dynamics in the Western English Channel. J. Plankton Res. 32, 643–655 (2010).
Google Scholar 
Harding, L. W. et al. Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay. Sci. Rep. 6, 1–16 (2016).
Google Scholar 
Suikkanen, S., Laamanen, M. & Huttunen, M. Long-term changes in summer phytoplankton communities of the open northern Baltic Sea. Estuar. Coast. Shelf Sci. 71, 580–592 (2007).ADS 

Google Scholar 
Wasmund, N., Tuimala, J., Suikkanen, S., Vandepitte, L. & Kraberg, A. Long-term trends in phytoplankton composition in the western and central Baltic Sea. J. Mar. Syst. 87, 145–159 (2011).
Google Scholar 
Cloern, J. E. Turbidity as a control on phytoplankton biomass and productivity in estuaries. Cont. Shelf Res. 7, 1367–1381 (1987).ADS 

Google Scholar 
Barbosa, A. B., Domingues, R. B. & Galvão, H. M. Environmental forcing of phytoplankton in a Mediterranean estuary (Guadiana Estuary, South-western Iberia): A decadal study of anthropogenic and climatic influences. Estuaries Coasts 33, 324–341 (2010).CAS 

Google Scholar 
Barrera-Alba, J. J., Abreu, P. C. & Tenenbaum, D. R. Seasonal and inter-annual variability in phytoplankton over a 22-year period in a tropical coastal region in the southwestern Atlantic Ocean. Cont. Shelf Res. 176, 51–63 (2019).ADS 

Google Scholar 
Brito, A. C. et al. Changes in the phytoplankton composition in a temperate estuarine system (1960 to 2010). Estuaries Coasts 38, 1678–1691 (2015).CAS 

Google Scholar 
Zingone, A. et al. Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data. Estuar. Coast. Shelf Sci. 162, 151–160 (2015).ADS 

Google Scholar 
Smayda, T. J. Phytoplankton species succession. In The Physiological Ecology of Phytoplankton 493–570 (Blackwell Scientific Publications, 1980).
Google Scholar 
Kremer, C. T. & Klausmeier, C. A. Species packing in eco-evolutionary models of seasonally fluctuating environments. Ecol. Lett. 20, 1158–1168 (2017).PubMed 

Google Scholar 
Sakavara, A., Tsirtsis, G., Roelke, D. L., Mancy, R. & Spatharis, S. Lumpy species coexistence arises robustly in fluctuating resource environments. Proc. Natl. Acad. Sci. USA 115, 738–743 (2018).CAS 
PubMed 

Google Scholar 
Wiltshire, K. H. et al. Resilience of North Sea phytoplankton spring bloom dynamics: An analysis of long-term data at Helgoland Roads. Limnol. Oceanogr. 53, 1294–1302 (2008).ADS 

Google Scholar 
Tsakalakis, I., Pahlow, M., Oschlies, A., Blasius, B. & Ryabov, A. B. Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity. Ecol. Model. 384, 241–248 (2018).
Google Scholar 
Platt, T., Fuentes-Yaco, C. & Frank, K. T. Spring algal bloom and larval fish survival. Nature 423, 398–399 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Vantrepotte, V. & Melin, F. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration. ICES J. Mar. Sci. 66, 1547–1556 (2009).
Google Scholar 
McQuatters-Gollop, A. et al. From microscope to management: The critical value of plankton taxonomy to marine policy and biodiversity conservation. Mar. Policy 83, 1–10 (2017).
Google Scholar 
Edwards, K. F., Litchman, E. & Klausmeier, C. A. Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol. Lett. 16, 56–63 (2013).PubMed 

Google Scholar 
Wentzky, V. C., Tittel, J., Jäger, C. G., Bruggeman, J. & Rinke, K. Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state. J. Ecol. 108, 1649–1663 (2020).CAS 

Google Scholar 
Karl, D. M. Oceanic ecosystem time-series programs: Ten lessons learned. Oceanography 23, 104–125 (2010).
Google Scholar 
d’Alcalà, M. R. et al. Seasonal patterns in plankton communities in a pluriannual time series at a coastal Mediterranean site (Gulf of Naples): An attempt to discern recurrences and trends. Sci. Mar. 68, 65–83 (2004).
Google Scholar 
Mazzocchi, M. G., Dubroca, L., García-Comas, C., Capua, I. D. & Ribera d’Alcalà, M. Stability and resilience in coastal copepod assemblages: The case of the Mediterranean long-term ecological research at Station MC (LTER-MC). Prog. Oceanogr. 97–100, 135–151 (2012).ADS 

Google Scholar 
Thioulouse, J., Simier, M. & Chessel, D. Simultaneous analysis of a sequence of paired ecological tables. Ecology 85, 272–283 (2004).
Google Scholar 
Lindeman, R. H., Merenda, P. F. & Gold, R. Z. Introduction to bivariate and multivariate analysis 119 (Scott Foresman Co, 1980).MATH 

Google Scholar 
Longobardi, L. From Data to Knowledge: Integrating Observational Data to Trace Phytoplankton Dynamics in a Changing World (Open Univ, 2021).
Google Scholar 
Pisano, A. et al. New evidence of mediterranean climate change and variability from sea surface temperature observations. Remote Sens. 12, 132 (2020).ADS 

Google Scholar 
Zingone, A. et al. Time series and beyond: multifaceted plankton research at a marine Mediterranean LTER site. Nat. Conserv. 34, 273–310 (2019).
Google Scholar 
Zingone, A., Licandro, P. & Sarno, D. Revising paradigms and myths of phytoplankton ecology using biological time series. In Mediterranean Biological Time Series. CIESM Workshop Monographs 109–114 (2003).Cianelli, D. et al. Disentangling physical and biological drivers of phytoplankton dynamics in a coastal system. Sci. Rep. 7, 1–15 (2017).CAS 

Google Scholar 
Zingone, A., Casotti, R., d’Alcalà, M. R., Scardi, M. & Marino, D. ‘St Martin’s Summer’: The case of an autumn phytoplankton bloom in the Gulf of Naples (Mediterranean Sea). J. Plankton Res. 17, 575–593 (1995).
Google Scholar 
Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanol. Acta 1, 493–509 (1978).
Google Scholar 
Sommer, U. et al. Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession. Annu. Rev. Ecol. Evol. Syst. 43, 429–448 (2012).
Google Scholar 
Reynolds, C. S. What factors influence the species composition of phytoplankton in lakes of different trophic status? In Phytoplankton and Trophic Gradients (eds Alvarez-Cobelas, M. et al.) 11–26 (Springer, 1998).
Google Scholar 
Zingone, A., Montresor, M. & Marino, D. Summer phytoplankton physiognomy in coastal waters of the Gulf of Naples. Mar. Ecol. 11, 157–172 (1990).ADS 

Google Scholar 
Harding, L. W. et al. Long-term trends of nutrients and phytoplankton in Chesapeake Bay. Estuaries Coasts 39, 664–681 (2016).CAS 

Google Scholar 
Andersen, J. H. et al. Long-term temporal and spatial trends in eutrophication status of the Baltic Sea. Biol. Rev. 92, 135–149 (2017).PubMed 

Google Scholar 
Giner, C. R. et al. Quantifying long-term recurrence in planktonic microbial eukaryotes. Mol. Ecol. https://doi.org/10.1111/mec.14929 (2019).Article 
PubMed 

Google Scholar 
Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).PubMed 
PubMed Central 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).CAS 
PubMed 

Google Scholar 
Beaugrand, G. et al. Synchronous marine pelagic regime shifts in the Northern Hemisphere. Philos. Trans. R. Soc. B 370, 20130272 (2015).
Google Scholar 
Conversi, A. et al. The Mediterranean Sea Regime Shift at the End of the 1980s, and intriguing parallelisms with other European Basins. PLoS ONE 5, e10633 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Eilertsen, H., Sandberg, S. & Tøllefsen, H. Photoperiodic control of diatom spore growth; a theory to explain the onset of phytoplankton blooms. Mar. Ecol. Prog. Ser. 116, 303–307 (1995).ADS 

Google Scholar 
Hensen, V. Ueber die Bestimmung des Plankton’s oder des im Meere treibenden Materials an Pflanzen und Thieren (Kiel Publishers, 1887).
Google Scholar 
Andersen, D. M. & Keafer, B. A. An endogenous annual clock in the toxic marine dinoflagellate Gonyaulax tamarensis. Nature 325, 616–617 (1987).ADS 

Google Scholar 
Kremp, A. & Anderson, D. M. Factors regulating germination of resting cysts of the spring bloom dinoflagellate Scrippsiella hangoei from the northern Baltic Sea. J. Plankton Res. 22, 1311–1327 (2000).
Google Scholar 
Aubry, F. B. et al. Plankton communities in the northern Adriatic Sea: Patterns and changes over the last 30 years. Estuar. Coast. Shelf Sci. 115, 125–137 (2012).ADS 

Google Scholar 
Gutiérrez-Rodríguez, A. et al. Growth and grazing rate dynamics of major phytoplankton groups in an oligotrophic coastal site. Estuar. Coast. Shelf Sci. 95, 77–87 (2011).ADS 

Google Scholar 
Brannock, P. M., Ortmann, A. C., Moss, A. G. & Halanych, K. M. Metabarcoding reveals environmental factors influencing spatio-temporal variation in pelagic micro-eukaryotes. Mol. Ecol. 25, 3593–3604 (2016).PubMed 

Google Scholar 
Piredda, R. et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol. Ecol. 93, fiw200 (2017).PubMed 

Google Scholar 
Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 

Google Scholar 
Hiltz, M., Bates, S. S. & Kaczmarska, I. Effect of light: Dark cycles and cell apical length on the sexual reproduction of the pennate diatom Pseudo-nitzschia multiseries (Bacillariophyceae) in culture. Phycologia 39, 59–66 (2000).
Google Scholar 
Mouget, J.-L., Gastineau, R., Davidovich, O., Gaudin, P. & Davidovich, N. A. Light is a key factor in triggering sexual reproduction in the pennate diatom Haslea ostrearia. FEMS Microbiol. Ecol. 69, 194–201 (2009).CAS 
PubMed 

Google Scholar 
Montresor, M., Vitale, L., D’Alelio, D. & Ferrante, M. I. Sex in marine planktonic diatoms: Insights and challenges. Perspect. Phycol. 3, 61–75 (2016).
Google Scholar 
Rost, B., Riebesell, U. & Sültemeyer, D. Carbon acquisition of marine phytoplankton: Effect of photoperiod length. Limnol. Oceanogr. 51, 12–20 (2006).ADS 
CAS 

Google Scholar 
Edwards, K. F. Community trait structure in phytoplankton: Seasonal dynamics from a method for sparse trait data. Ecology 97, 3441–3451 (2016).PubMed 

Google Scholar 
Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B 365, 3101–3112 (2010).
Google Scholar 
Margiotta, F. et al. Do plankton reflect the environmental quality status? The case of a post-industrial Mediterranean Bay. Mar. Environ. Res. 160, 104980 (2020).CAS 
PubMed 

Google Scholar 
Ferrera, I. et al. Assessment of microbial plankton diversity as an ecological indicator in the NW Mediterranean coast. Mar. Pollut. Bull. 160, 111691 (2020).CAS 
PubMed 

Google Scholar 
Cloern, J. E., Jassby, A. D., Thompson, J. K. & Hieb, K. A. A cold phase of the East Pacific triggers new phytoplankton blooms in San Francisco Bay. Proc. Natl. Acad. Sci. USA 104, 18561–18565 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scotto di Carlo, B. et al. Uno studio integrato dell’ecosistema pelagico costiero del Golfo di Napoli. Nova Thalass 7, 99–128 (1985).
Google Scholar 
Carrada, G. C., Fresi, E., Marino, D., Modigh, M. & D’Alcalà, M. R. Structural analysis of winter phytoplankton in the Gulf of Naples. J. Plankton Res. 3, 291–314 (1981).CAS 

Google Scholar 
Marino, D., Modigh, M. & Zingone, A. General features of phytoplankton communities and primary production in the Gulf of Naples and adjacent waters. In Marine Phytoplankton and Productivity (Springer, 1984).
Google Scholar 
Hansen, H. P. & Grasshoff, K. Automated chemical analysis. Methods Seawater Anal. 49, 347–395 (1983).
Google Scholar 
Sabia, L. et al. Assessing the quality of biogeochemical coastal data: A step-wise procedure. Mediterr. Mar. Sci. 20, 56–73 (2019).
Google Scholar 
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).MathSciNet 
MATH 

Google Scholar 
Kendall, M. G. Kendall Rank Correlation Methods (Griffin, 1975).
Google Scholar 
Jassby, A. D. & Cloern, J. E. wq: Exploring water quality monitoring data. R Package Version 04 5, (2015).Lomb, N. R. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462 (1976).ADS 

Google Scholar 
Scargle, J. D. Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1982).ADS 

Google Scholar 
Linnell Nemec, A. F. & Nemec, J. M. A test of significance for periods derived using phase-dispersion-minimization techniques. Astron. J. 90, 2317–2320 (1985).ADS 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

Google Scholar 
Escoufier, Y. Le traitement des variables vectorielles. Biometrics 29, 751–760 (1973).MathSciNet 

Google Scholar 
Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4 (Springer, 2018).
Google Scholar 
Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl. Acad. Sci. USA 103, 13104–13109 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
Google Scholar 
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).
Google Scholar 
Bi, J. A review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking. J. Sens. Stud. 27, 87–101 (2012).
Google Scholar  More

Literature search and study selectionA systematic literature search was conducted in the ISI Web of Science database for observational and experimental studies published from 1975 to 13 January 2020 using the following search terms: TOPIC: (grassland* OR prairie* OR steppe* OR shrubland* OR scrubland* OR bushland*) AND TOPIC: (drought* OR ‘dry period*’ OR ‘dry condition*’ OR ‘dry year*’ OR ‘dry spell*’) AND TOPIC: (product* OR biomass OR cover OR abundance* OR phytomass). The search was refined to include the subject categories Ecology, Environmental Sciences, Plant Sciences, Biodiversity Conservation, Multidisciplinary Sciences and Biology, and the document types Article, Review and Letter. This yielded a total of 2,187 peer-reviewed papers (Supplementary Fig. 1). At first, these papers were screened by title and abstract, which resulted in 197 potentially relevant full-text articles. We then examined the full text of these papers for eligibility and selected 87 studies (43 experimental, 43 observational and 1 that included both types) on the basis of the following criteria:

(1)

The research was conducted in the field, in natural or semi-natural grasslands or shrublands (for example, artificially constructed (seeded or planted) plant communities or studies using monolith transplants were excluded). We used this restriction because most reports on observational droughts are from intact ecosystems, and experiments in disturbed sites or using artificial communities would thus not be comparable to observational drought studies.

(2)

In the case of observational studies, the drought year or a multi-year drought was clearly specified by the authors (that is, we did not arbitrarily extract dry years from a long-term dataset). Please note that some observational data points are from control plots of experiments (of any kind), where the authors reported that a drought had occurred during the study period. We did not involve gradient studies that compare sites of different climates, which are sometimes referred to as ‘observational studies’.

(3)

The paper reported the amount or proportion of change in annual or growing-season precipitation (GSP) compared with control conditions. We consistently use the term ‘control’ for normal precipitation (non-drought) year or years in observational studies and for ambient precipitation (no treatment) in experimental studies hereafter. Similarly, we use the term ‘drought’ for both drought year or years in observational studies and drought treatment in experimental studies. In the case of multi-factor experiments, where precipitation reduction was combined with any other treatment (for example, warming), data from the plots receiving drought only and data from the control plots were used.

(4)

The paper contained raw data on plant production under both control and drought conditions, expressed in any of the following variables: ANPP, aboveground plant biomass (in grassland studies only) or percentage plant cover. In 79% of the studies that used ANPP as a production variable, ANPP was estimated by harvesting peak or end-of-season AGB. We therefore did not distinguish between ANPP and AGB, which are referred to as ‘biomass’ hereafter. We included the papers that reported the production of the whole plant community, or at least that of the dominant species or functional groups approximating the abundance of the whole community.

(5)

When multiple papers were published on the same experiment or natural drought event at the same study site, the most long-term study including the largest number of drought years was chosen.

In addition to the systematic literature search, we included 27 studies (9 experimental, 17 observational and 1 that included both types) meeting the above criteria from the cited references of the Web of Science records selected for our meta-analyses, and from previous meta-analyses and reviews on the topic. In total, this resulted in 114 studies (52 experimental, 60 observational and 2 that included both types; Supplementary Note 9, Supplementary Fig. 2 and ref. 25).Data compilationData were extracted from the text or tables, or were read from the figures using Web Plot Digitizer26. For each study, we collected the study site, latitude, longitude, mean annual temperature (MAT) and precipitation (MAP), study type (experimental or observational), and drought length (the number of consecutive drought years). When MAT or MAP was not documented in the paper, it was extracted from another published study conducted at the same study site (identified by site names and geographic coordinates) or from an online climate database cited in the respective paper. We also collected vegetation type—that is, grassland when it was dominated by grasses, or shrubland when the dominant species included one or more shrub species (involving communities co-dominated by grasses and shrubs). Data from the same study (that is, paper) but from different geographic locations or environmental conditions (for example, soil types, land uses or multiple levels of experimental drought) were collected as distinct data points (but see ‘Statistical analysis’ for how these points were handled). As a result, the 114 published papers provided 239 data points (112 experimental and 127 observational)25.For the observational studies, normal precipitation year or years specified by the authors was used as the control. If it was not specified in the paper, the year immediately preceding the drought year(s) was chosen as the control. When no data from the pre-drought year were available, the year immediately following the drought year(s) (14 data points) or a multi-year period given in the paper (22 data points) was used as the control. For the experimental studies, we also collected treatment size (that is, rainout shelter area or, if it was not reported in the paper, the experimental plot size).For the calculation of drought severity, we used yearly precipitation (YP), which was reported in a much higher number of studies than GSP. We extracted YP for both control (YPcontrol) and drought (YPdrought). For the observational studies, when a multi-year period was used as the control or the natural drought lasted for more than one year, precipitation values were averaged across the control or drought years, respectively. Consistently, in the case of multi-year drought experiments, YPcontrol and YPdrought were averaged across the treatment years. When only GSP was published in the paper (63 of 239 data points), we used this to obtain YP data as follows: we regarded MAP as YPcontrol, and YPdrought was calculated as YPdrought = MAP − (GSPcontrol − GSPdrought). From YPcontrol and YPdrought data, we calculated drought severity as follows: (YPdrought − YPcontrol)/YPcontrol × 100.For production, we compiled the mean, replication (N) and, if the study reported it, a variance estimate (s.d., s.e.m. or 95% CI) for both control and drought. In the case of multi-year droughts, data only from the last drought year were extracted, except in five studies (17 data points) where production data were given as an average for the drought years. When both biomass and cover data were presented in the paper, we chose biomass. For each study, we consistently considered replication as the number of the smallest independent study unit. When only the range of replications was reported in a study, we chose the smallest number.To quantify climatic aridity for each study site, we used an aridity index (AI), calculated as the ratio of MAP and mean annual PET (AI = MAP/PET). This is a frequently used index in recent climate change research27,28. AI values were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2 for the period of 1970–2000 (aggregated on annual basis)29.Because we wanted to prevent our analysis from being distorted by a strongly unequal distribution of studies between the two study types regarding some potentially important explanatory variables, we left out studies from our focal meta-analysis in three steps. First, we left out studies that were conducted at wet sites—that is, where site AI exceeded 1. The value of 1 was chosen for two reasons: above this value, the distribution of studies between the two study types was extremely uneven (22 experimental versus 2 observational data points with AI  > 1)25, and the AI value of 1 is a bioclimatically meaningful threshold, where MAP equals PET. Second, we left out shrublands, because we had only 14 shrubland studies (out of 105 studies with AI  More