Philippa Kaur

Epidemiological description of exhibition farm outbreakThe index farm where highly pathogenic avian influenza (HPAI) H5N1 virus in captive birds occurred was an exhibition farm in St. John’s, Province of Newfoundland and Labrador, Canada. The farm housed 409 birds of different species, namely chickens, guineafowl, peafowl, emus, domestic ducks, domestic geese, and domestic turkeys. On 9th December 2021, the farm owner first noticed mortality. On 13th December, the farm owner reported the increased mortality to a local veterinarian. Autopsies on four chickens showed swollen heads and cutaneous haemorrhages. Oropharyngeal and cloacal swabs from these chickens tested positive for H5 avian influenza virus at the Atlantic Veterinary College, University of Prince Edward Island, and the Canadian Food Inspection Agency (CFIA) was notified. On 16th December, by which time 306 birds (mostly chickens, turkeys and guineafowl) had died, staff of the CFIA collected tissue samples from dead chickens, as well as oropharyngeal and cloacal swabs and sera from different species of captive birds still present (Table 1), after which all remaining captive birds were culled. All oropharyngeal and cloacal swabs tested positive for HPAI virus of the subtype H5N1 by real-time RT-PCR, and all sera tested positive for influenza nucleoprotein antibodies by ELISA. On 20th December, the CFIA confirmed the diagnosis of HPAI of the subtype H5N1.Table 1 List of samples for virological and serological analysis collected by CFIA on 17 December 2021 from different species of captive birds still present at the farm.Full size tableWild birds were frequently observed co-mingling with the captive population. Captive birds except emus were allowed to move freely in and out of the open pens in which they were housed. At an on-site pond, domestic ducks were reported to mingle with free-living mallards (scientific names of wild birds in Table 2), and a snowy egret had been observed around 2nd to 6th December. Other wild birds reported on the farm were common starlings, feral pigeons, and unspecified gulls.Table 2 Common and scientific species names of the birds mentioned in the text.Full size tableRetrospectively, HPAI H5N1 virus also was identified in tissues of a great black-backed gull found at a nearby pond in St. John’s. The gull had been found ill on 26th November 2021 and taken to a local wildlife rehabilitation centre, where it died the following day.Phylogenetic analysisPhylogenetic analyses were performed to compare the genome sequences of the Newfoundland viruses from the exhibition farm birds and a great black-backed gull found nearby to other influenza viruses in the database. Based on BLAST analysis all eight gene segments of the virus had a Eurasian origin, and the virus was identified as a clade 2.3.4.4b H5N1 virus. Based on maximum likelihood and time-resolved trees inferred by use of whole genome sequences, the Newfoundland viruses had a shared common ancestor with European viruses circulating in early 2021 (Figs. 1, 2). The dates for the most recent common ancestor (MRCA) of all gene segments ranged from December 2019 to April 2021 (Table 3). There was no evidence that the Newfoundland viruses were genetically closely related to other current or recent viruses circulating in Europe. In contrast to currently circulating European viruses, the sequences of the Newfoundland viruses had no evidence of reassortment with other avian influenza viruses after ancestral emergence (Fig. 3). The virus from the great black-backed gull was highly similar to those from the exhibition farm, except for a small number of nucleotide differences in the neuraminidase (N) gene segment.Figure 1Maximum likelihood phylogenetic tree of the H5 HA gene. Relationships among the European 2021 H5 2.3.4.4b HPAI strains (magenta) and the Newfoundland wild bird and outbreak strains (red) are shown. The tree is rooted by the outgroup and nodes placed in descending order. Clades are collapsed for clarity.Full size imageFigure 2Maximum likelihood phylogenetic tree of the H5 gene segments. Relationships among the European 2021 H5 2.3.4.4b HPAI strains (magenta) and the Newfoundland wild bird and outbreak strains (red) are shown. The tree is rooted by the outgroup and nodes placed in descending order; order: HA, NA, PA, PB1, PB2, NP, MP, NS.Full size imageTable 3 Dates for the most recent common ancestor (MRCA) of all gene segments.Full size tableFigure 3Phylogenetic incongruence analyses. Maximum likelihood trees for the H and N gene segments and internal gene segments from equivalent strains were connected across the trees. Tips and connecting lines are coloured according to the legend.Full size imageAnalysis of avian migration and potential routes for HPAI H5 virus to be carried across the Atlantic with migrating birdsThere are several pathways for HPAI H5N1 virus to be carried across the Atlantic with migrating birds, based on the multitude of migration routes of wild birds and their overlapping ranges at breeding, stop-over, and wintering sites. Ring-recovery data confirm the regular movements of wild birds from Europe to Iceland and other North Atlantic islands, and from there to North America, and also provide evidence for direct movements of for example seabirds and gulls (Supplementary Table 1). Ringed individuals with a European origin have been found on Newfoundland for 10 of the 24 species in Supplementary Table 1: barnacle goose (1 ringed individual), Eurasian wigeon (5), Eurasian teal (1), great skua (13), European herring gull (1), black-headed gull (1), black-legged kittiwake (102), purple sandpiper (1), Brunnich’s guillemot (15), and Atlantic puffin (50). Given that the most likely ancestor of the virus detected in Newfoundland was circulating in Northwest Europe between the beginning of the 2020/2021 outbreak in Europe in October 2020 and April 2021 (see above), likely routes include spring migration of bird species moving to Icelandic, Greenlandic or Canadian High Arctic breeding grounds, or migration directly across the Atlantic Ocean (Fig. 4).Figure 4Maps of transatlantic migration. Putative virus transmission pathways between Europe and Newfoundland via migratory waterfowl/shorebirds (a) and pelagic seabirds (b). Many Icelandic waterfowl and shorebirds (a) winter in Northwest Europe and return to Iceland to breed in spring (1), including whooper swans, greylag geese, pink-footed geese, Eurasian wigeons, Eurasian teals, northern pintails, common ringed plovers and purple sandpipers. Some bird populations use Iceland as a stopover site, and continue to breeding grounds in East Greenland (2; barnacle geese and pink-footed geese), the East Canadian Arctic (3; light-bellied brent geese, red knots, ruddy turnstones) and West Greenland (4; greater white-fronted geese). Migratory birds from Europe share these breeding areas with species that winter in North America, including Canada geese and snow geese from East Greenland and the East Canadian Arctic (5), and some Iceland-breeding species of duck, including small numbers of Eurasian wigeons, Eurasian teals, and tufted ducks (6). Several seabird species (b), such as gulls, skuas, fulmars and auks, have large breeding ranges in the Arctic. After the breeding season many species become fully pelagic and can roam large parts of the northern Atlantic. The mid-Atlantic ridge outside Newfoundland is an important non-breeding area for seabirds, and is frequented by auks from Iceland (7), Svalbard (8) and Norway (9), including large numbers of Atlantic puffins and Brünnich guillemots, and by black-legged kittiwakes and northern fulmars originating from Iceland, Norway and the United Kingdom (7–8, 10). There these birds are joined by seabirds from Canadian and Greenlandic waters (11). Direct migratory links to Newfoundland occurs through greater and lesser-black backed gulls as well as black-headed gulls from Iceland and Greenland (12, 13), and gulls also link the pelagic and the coastal zone around Newfoundland (14). Thickness of the lines highlights the relative approximate population sizes. Dashed lines show where small numbers of individuals, or vagrants, provide a potential pathway. For more details on species and population numbers see Table 2. This figure was prepared using the software R (version 4.0.5, https://www.r-project.org/) and the following packages: -ggplot2 (version 3.3.5, https://cran.r-project.org/web/packages/ggplot2/index.html), -sf (version 1.0.5, https://cran.r-project.org/web/packages/sf/index.html).Full size imageThe first possible route via Iceland seems to be the strongest link between Newfoundland and Europe14,15,16,17, because it is a meeting point of breeding bird populations which winter in Europe as well as along the East coast of North America. Numerous species, totaling almost two million individual birds, migrate annually from northwestern Europe to breeding grounds in Iceland and beyond. Several populations breed on Iceland, including swans (whooper swan) (Supplementary Table 1), geese (greylag goose, pink-footed goose), ducks (Eurasian wigeon, Eurasian teal, Northern pintail), gulls (great- and lesser black-backed gull, black-headed gull, black-legged kittiwake) and shorebirds (common ringed plover, purple sandpiper, Supplementary Table 1). In addition, several species (e.g. barnacle geese and pink-footed geese) migrating to breeding grounds further away (Greenland and/or Canadian High Arctic) make spring and autumn stopovers in Iceland18,19. This creates potential for the virus to have been spread northwards to Iceland (or further northward) in spring, where it could have circulated among breeding birds, or transmitted during autumn migration by species returning from the Arctic. Several Iceland-breeding species of ducks (Eurasian wigeon, Eurasian teal, tufted duck), gulls (lesser black-backed gull, black-legged kittiwake, black-headed gull) and alcids (Brunnich’s guillemot, Atlantic puffin) winter along the Atlantic coast of North America in variable numbers (Supplementary Table 1). If the virus was transmitted to one of these populations during their stay on Iceland, it could have been spread to Newfoundland during the subsequent autumn migration. Importantly, Iceland-breeding Eurasian wigeons or Eurasian teals could be responsible for both the journey to Iceland from European wintering grounds, as well as the journey from Iceland to Newfoundland, where these species are frequently encountered as vagrants (Supplementary Table 1)20,21.The second possible route is via species that migrate from northwestern Europe to the Canadian High Arctic and/or Northwest Greenland. These include shorebirds (e.g. ruddy turnstone, red knot) and some geese (light-bellied brent goose and greater white-fronted goose). If the virus circulated in these breeding populations and then moved to other coastal marine bird populations bordering Baffin Bay, which include huge numbers of colonial seabirds and marine waterfowl22,23, the virus could have followed a coastal or even pelagic route south with the large autumn migration of Arctic marine birds (various sea ducks, auks and larids)24,25 to emerge in Newfoundland. Alternatively, shorebirds and waterfowl may have played a role: several European-wintering populations have overlapping breeding grounds with populations wintering along the east coast of North America. Regarding geese, greater white-fronted geese share breeding grounds in western Greenland with Canada geese26,27, which migrate south along the Canadian Atlantic coast. Also, brent geese have overlapping breeding grounds with snow geese18. In addition, individual barnacle geese and pink-footed geese breeding in Greenland could also have travelled south to Newfoundland carrying the virus, as these birds are regular vagrants to the North American Atlantic coast28. While geese occur only in small numbers on Newfoundland, two barnacle geese and four pink-footed geese, probably originating from Greenland breeding grounds, were observed in the autumn of 2021. St. John’s is the first major population center on a coastal route south from Baffin Bay/Davis Strait and along the Labrador Shelf, so emergence in eastern Newfoundland is consistent with this route.Three wild bird species involved in the Iceland and/or Greenland/High Canadian Arctic routes deserve particular attention. Eurasian wigeon have been prominently involved in outbreaks in Eurasia, and are considered prime candidates for carrying HPAI virus over long distances29. Also, during the first stages of an outbreak they are one of the first species to be detected HPAI virus positive, often without clinical signs. Barnacle geese and greylag geese, which congregate in Iceland, were in the top three most abundant species detected H5-positive in Europe in late winter and early spring 20215. Given that both greylag and barnacle geese have populations breeding on Iceland/Greenland and wintering in Europe (particularly the UK), these two species are high on the list of probable vectors that transported the virus to Iceland/Greenland and finally to Newfoundland. The high involvement of infected geese in the HPAI dynamics, which was not seen before October 2020, together with the unusually high levels of HPAI H5 virus presence in wild birds in Northwest Europe in spring 2021, might also explain why HPAI H5 virus spread to Newfoundland this winter (2021/2022), and not in the previous winters (2020/2021, 2016/2017, 2014/2015, 2005/2006). It is, however, striking that no cases of HPAI H5 virus have been recorded on Iceland in 2021.A third possible, pelagic, route is directly across the Atlantic Ocean. Such a route could have started with coastal and pelagic seabirds in Northwest Europe, where the virus may have remained undetected for much of the summer of 2021. A subsequent migration of seabirds to Newfoundland waters in the autumn of 2021 could have brought the virus to North America. The large populations of black-legged kittiwakes and northern fulmars that breed in the U.K. have long been known to frequent Newfoundland waters30, and these movements have been corroborated by recent telemetry studies31. Further, recent telemetry information reveals that millions of pelagic seabirds breeding all across the Atlantic congregate over the Mid-Atlantic Ridge in the central North Atlantic at all times of year32, making a pelagic transmission route a possibility. From the pelagic wintering grounds off Newfoundland, a species that uses both pelagic and coastal habitats, possibly a gull, may have brought the virus to shore in St. John’s. Trans-Atlantic transmission via seabirds has been suggested for LPAI viruses, including detection of mosaic Eurasian-North American viruses in gulls and alcids12,33,34,35.For the time period and geographical frame considered, HPAI-H5-positive species included ducks (Eurasian wigeon, mallard, common eider), geese (barnacle, greylag, brent, pink-footed and greater white-fronted goose), swans (whooper), gulls (black-headed, herring, lesser black-backed, great black-backed), and shorebirds (red knot, ruddy turnstone) (Supplementary Table 2). Of these 15 species, ringed individuals with a European origin have been recorded on Newfoundland for barnacle goose (1 ringed individual), Eurasian wigeon (5), great skua (13), and black-headed gull (1) (Supplementary Table 1). Ringed individuals with a European origin have been found on Newfoundland for 5 species which were found to be HPAI-H5-positive between October 2020 and April 2021, such as Barnacle Goose (1), Eurasian Wigeon (5), Great Skua (13), Black-headed Gull (1). These species might be considered to be possible carriers of HPAI H5 virus from Europe in late winter 2020/2021 or early spring 2021 partly or all the way to Newfoundland. However, given the incompleteness of sampling and the possibility of wild birds carrying HPAI virus subclinically, the involvement of other wild bird species in transatlantic virus transport cannot be ruled out.Having reached the Avalon Peninsula of Newfoundland via one of above routes, the virus may have spread further within the abundant local populations of ducks and gulls wintering in the city of St. John’s. The peridomestic populations of some of these species may be candidates for incursion of the virus into the farm in St John’s. More

Study population and song recordingsAll animal procedures were carefully reviewed by the Williams College IACUC (WH-D), the Bowdoin College Research and Oversight Committee (2009–18), and the University of Guelph Animal Care Committee (08R601), and were carried out as specified by the Canadian Wildlife Service (banding permit 10789D).We studied Savannah sparrows (Passerculus sandwichensis) at the Bowdoin Scientific Station on Kent Island, New Brunswick, Canada (44.5818°N, 66.7547°W). Since 1988, individuals nesting within a 10 ha study area in the middle of the island (30–70 pairs each year; part of a larger population of 350–500 males breeding on Kent Island and two adjacent islands) have been colour-banded to facilitate visual identification, and complete demographic information is available for birds on the study site (though not for the entire population) for the years 1989–2004 and 2009–2013. Because of strong natal and breeding philopatry51, birds hatched on the study site itself represent 40–80% of adult breeders in that area, and because of the systematic banding program, ages are known. Each year adds a new generation to the population, with yearlings making up approximately half of the adult breeding males. The birds banded and recorded on the study site are estimated to make up 10–20% of the Savannah sparrow population on Kent Island and two nearby islands.Details of the recording methods used in this study (covering the years 1980, 1982, 1988-9, 1993-8, and 2003–13) can be found elsewhere36,49. Using digitally generated sound spectrograms (using SoundEdit Pro and Audacity), birds were scored as having either a) high note cluster=a final introductory segment interval including at least two different note types, or b) a click train=one or more introductory segment intervals including at least two clicks and no other note types, or c) both features36 (see Supplementary Fig. 1 for a full description of note types). Although a small proportion of birds (mean = 8.3%) did not include either feature in their songs (such birds either had no feature in the introductory segment intervals or one non-click note type in the final interval), we did not include this option in the model and omitted these birds from summaries of the data. We did not include data after the breeding year 2013 because of we began an experimental field tutoring study in the summer of 201364.ModellingWe used a dynamic, discrete time model which allowed us to focus our analysis to specific time points within the year that are related to song learning (the beginning and end of the breeding season). These were: (1) the return of older birds between breeding seasons, (2) the recruitment of young birds singing newly crystallized songs in the spring, and (3) reproduction, resulting in the addition of juveniles during the summer breeding season.Because survival data were not available for every year during the time span we studied, we captured the variation in survival rates observed in the field57 by using a binomial distribution centered on the average historical survival rate for each age class (addressing the possibility that cultural drift resulting from random differences in survival rates was responsible for the shift in song features). The model incorporates stochasticity to capture the variation in population dynamics and return rates by assigning parameter values for survival and return rates from empirically generated probability distributions.We did not include spatial distribution of song variants in the model; although spatial patterns can be important for the dynamics of language loss58, territories with birds singing click trains and high note clusters were intermixed and no spatial structure was apparent (Fig. 3).The model assumes that males choose which features to incorporate into the introductory sections of their songs during song development. Individuals fall into one of six mutually exclusive classes of male Savannah sparrows. The classes are defined by (1) the bird’s developmental stage in the song learning process: juvenile (J, the first year, when the song is plastic) or adult (A, after the first spring, when the song is crystallized), and (2) the variant or variants sung as part of the bird’s introduction (high note clusters, click trains, or both). Denoting note high note clusters with X and click trains with C, the adult classes are therefore AX, AC, and AXC, and the juvenile classes are JX, JC, and JXC. The sum of the individuals in these classes is the total male population.We used two times during each year – late spring and late summer – to correspond to stages in song development (Fig. 5). At a given time t, when breeding is underway in the late spring, the male population consists entirely of adults singing crystallized song, and therefore each juvenile class is empty. At the end of the summer, the population of males has been augmented by juveniles, which are initially assigned to the same variant class as their fathers. To capture these dynamics, we define an intermediate time step, denoted ti. Time t + 1 then corresponds to the following breeding season (late spring), when juvenile males hatched the previous year have completed song development, crystallized their songs, and joined the adult class.Fig. 5: Model of song development.We used two age classes (J = juvenile and A = adult) and three classes of introductions (C = click trains, X = high note clusters, and  XC = both). In the late spring of a given year (time = t), only adult males are present. In late summer, those adults have bred and both they and juvenile males are present; at this intermediate time (ti) each male is initially allocated the same introduction type as his father (solid lines). Then, as song development progresses and juvenile males can be influenced by other tutors, they may retain their initial introduction type or switch to either of the other two types (dashed lines) before they crystallize their songs late in the following spring (time = t+1), and join the breeding cohort, which also includes adult males from the previous year who returned to breed again.Full size imageIn the late summer the male population increases with the addition of juveniles hatched that year, some of which will return to join the singing population the following year; survivors will return to breed within a few hundred meters of where they hatched51. To fit the observed historical decline in the Kent Island population57, the total number of returning juveniles, r (including both those hatched on site and those immigrating from nearby populations at time), follows a Poisson distribution where m = 33.6 – .182x and x is the number of years since 1980 (this function results in a decline of 5 males per decade; the initial number on the study site used in the model, 70, was extrapolated from historical data). The size of each returning juvenile class at time ti then takes the form:$${{{{{{rm{JY}}}}}}}_{{{{{{{rm{t}}}}}}}^{{{{{{rm{i}}}}}}}} sim {{{{{rm{Poisson}}}}}}left(mright)frac{{{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}}{{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{AX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}}$$
(1)
for each Y ∈ {X, C, XC}.After the following winter, the proportion of surviving adults at time t + 1 follows a binomial distribution where the mean survival rate s = 0.48 is derived from historical data. Therefore, each adult class takes the form:$${{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{rm{t}}}}}}+1} sim {{{{{rm{Binomial}}}}}}left({{{{{rm{AY}}}}}},{{{{{rm{s}}}}}}right)* {{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(2)
At the beginning of the next breeding season, juveniles complete song learning64, choosing which variant to crystallize as part of the song, and enter an adult song class; thus all of the juvenile classes disappear at t + 1. Which adult class juveniles join depends on separate learning functions for each of the two variants, ϕX for the high note cluster and ϕC for the click train. The ϕ function takes values between 0 and 1 and gives the probability of crystallizing a song form during the transition from natal year to breeding, depending upon the frequency-dependent bias and selection parameters (see below). These functions define the proportion of features that appear in the next generation as compared to that of the previous generation. Therefore we have:$${{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}+1}={left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(3)
$${{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}+1}={left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left({{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{upphi }}}_{{{{{{rm{C}}}}}}}{{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(4)
$${{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{rm{t}}}}}}+1}=2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+2{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+({{{upphi }}}_{{{{{{rm{X}}}}}}}{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}})+{{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(5)
The sum of probabilities defining all of song crystallization outcomes for the songs of fathers with song type X is:$${left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+{left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)=1$$
(6)
Learning curvesTo define how young males’ song learning is influenced by the songs they hear, we used learning curves based on type III Holling response curves59 which provide a means to numerically capture functional responses. In our model, the type III curve models the response of juvenile to the song form of adults in the population based on two variables: (1) frequency-dependent bias that favors one form based on its prevalence within the adult population, and (2) selection that favors a particular form of the song.The learning curves, ϕx for the high note cluster and ϕc for the click train, are modified forms of the type III Holling response curve):$${{{upphi }}}_{{{{{{rm{x}}}}}}}=frac{{x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}}}{{(1-x)}^{{{{{{rm{beta }}}}}}}+({x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}})}$$
(7)
and$${{{upphi }}}_{{{{{{rm{c}}}}}}}=frac{{{{{{rm{sigma }}}}}},{c}^{{{{{{rm{beta }}}}}}}}{{(1-c)}^{{{{{{rm{beta }}}}}}}+{{{{{rm{sigma }}}}}}{{c}}^{{{{{{rm{beta }}}}}}}}$$
(8)
where x is the proportion of the high note cluster within the population, c is the proportion of the click train within the population, β is frequency-dependent bias (favoring learning the novel or retaining the common variant), and σ is selection on the novel variant (a preference for learning the variant that is not dependent on frequency of the variant and includes factors such as prestige bias, success bias, status, and content bias). Note that the two learning curves do not have identical equations, because selection is not frequency-dependent. In these equations, β  > 1 corresponds to conformist selection, and when β  1 correspond to selection for a novel variant and values of σ  More

Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, DenmarkMorten Kam Dahl Dueholm, Marta Nierychlo, Kasper Skytte Andersen, Vibeke Rudkjøbing, Simon Knutsson, Per H. Nielsen, Mads Albertsen & Per Halkjær NielsenEnvironmental Science Department, The Institute for Scientific and Technological Research of San Luis Potosi (IPICYT), San Luis Potosí, MexicoSonia ArriagaDepartment of Process, Energy and Environmental Technology, University College of Southeast Norway, Porsgrunn, NorwayRune BakkeCenter for Microbial Ecology and Technology, Ghent University, Ghent, BelgiumNico BoonInstitute for Water and Wastewater Technology, Durban University of Technology, Durban, South AfricaFaizal Bux & Sheena KumariVeolia Water Technologies AB, AnoxKaldnes, Lund, SwedenMagnus ChristenssonDepartment Of Chemical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaAdeline Seak May ChuaEnvironmental Engineering, Newcastle University, Newcastle, EnglandThomas P. CurtisThe Cytryn Lab, Microbial Agroecology, Volcani Center, Agricultural Research Organization, Rishon Lezion, IsraelEddie CytrynINGEBI-CONICET, University of Buenos Aires, Buenos Aires, ArgentinaLeonardo ErijmanDepartment of Biochemistry and Microbial Genetics, Biological Research Institute “Clemente Estable”, Montevideo, UruguayClaudia EtchebehereNIREAS-International Water Research Center, University of Cyprus, Nicosia, CyprusDespo Fatta-KassinosEnvironmental Engineering, McGill University, Montreal, QC, CanadaDominic FrigonSchool of Microbiology, Universidad de Antioquia, Medellín, ColombiaMaria Carolina Garcia-ChavesSchool of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USAApril Z. GuWater Chemistry and Water Technology and DVGW Research Laboratories, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyHarald HornDavid Jenkins & Associates, Inc, Kensington, CA, USADavid JenkinsInstitute for Water Quality and Resource Management, TU Wien, Vienna, AustriaNorbert KreuzingerWater Innovation and Research Centre, University of Bath, Bath, EnglandAna LanhamSingapore Centre of Environmental Life Sciences Engineering (SCELSE) Nanyang Technological University, Singapore, SingaporeYingyu LawWater Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaTorOve LeiknesProcess Engineering in Urban Water Management, ETH Zürich, Zürich, SwitzerlandEberhard MorgenrothDepartment of Biology, Warsaw University of Technology, Warsaw, PolandAdam MuszyńskiEnvironmental Microbial Genetics Lab, La Trobe University, Melbourne, VIC, AustraliaSteve PetrovskiTechnologies and Evaluation Area, Catalan Institute for Water Research, ICRA, Girona, SpainMaite PijuanVA Tech Wabag Ltd, Chennai, IndiaSuraj Babu PillaiBiochemical Engineering Group, Universidade Nova de Lisboa, Lisboa, PortugalMaria A. M. ReisState Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, ChinaQi RongWater Research Institute IRSA – National Research Council (CNR), Rome, ItalySimona RossettiLa Trobe University, Melbourne, VIC, AustraliaRobert SeviourDepartment of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA, USANick TookerKemira Oyj, Espoo R&D Center, Espo, FinlandPirjo VainioEnvironmental Biotechnology, TU Delft, Delft, The NetherlandsMark van LoosdrechtVA Tech Wabag, Philippines Inc., Makati City, PhilippinesR. VikramanDepartment of Water Technology and Environmental Engineering, University of Chemistry and Technology, Prague, Czech RepublicJiří WannerEnvironmental Life Science Engineering, TU Delft, Delft, The NetherlandsDavid WeissbrodtSchool of Environment, Tsinghua University, Beijing, ChinaXianghua WenEnvironmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, Hong KongTong Zhang More

Chibuike, G. U. & Obiora, S. C. Heavy metal polluted soils: Effect on plants and bioremediation methods. Appl. Environ. Soil Sci. 2014, 1–12. https://doi.org/10.1155/2014/752708 (2014).CAS 
Article 

Google Scholar 
Baek, S. A. et al. Effects of heavy metals on plant growths and pigment contents in Arabidopsis thaliana. Plant Pathol. J. 28, 446–452. https://doi.org/10.5423/PPJ.NT.01.2012.0006 (2012).CAS 
Article 

Google Scholar 
Gong, Z. Z. et al. Plant abiotic stress response and nutrient use efficiency. Sci. China Life Sci. 63, 635–674. https://doi.org/10.1007/s11427-020-1683-x (2020).ADS 
Article 
PubMed 

Google Scholar 
Pravin, U. S., Manisha, P. T. & Ravindra, M. M. Sediment heavy metal contaminants in Vasai Creek of Mumbai: Pollution impacts. Am. Chem. Soc. 2(3), 171–180. https://doi.org/10.5923/j.chemistry.20120203.13 (2012).CAS 
Article 

Google Scholar 
Kim, Y. H. et al. Silicon mitigates heavy metal stress by regulating P-type heavy metal ATPases, Oryza sativa low silicon genes, and endogenous phytohormones. BMC Plant Biol. 14, 1–13. https://doi.org/10.1186/1471-2229-14-13 (2014).ADS 
CAS 
Article 

Google Scholar 
Mao, F. et al. The metal distribution and the change of physiological and biochemical process in soybean and mung bean plants under heavy metal stress. Int. J. Phytoremed. 20, 1113–1120. https://doi.org/10.1080/15226514.2017.1365346 (2018).CAS 
Article 

Google Scholar 
Reichman, S. M., Menzies, N. W., Asher, C. J. & Mulligan, D. R. Seedling responses of four Australian tree species to toxic concentrations of manganese in solution culture. Plant Soil. 258, 341–350. https://doi.org/10.1023/B:PLSO.0000016564.14512.eb (2004).CAS 
Article 

Google Scholar 
Driscoll, C. T., Mason, R. P., Chan, H. M., Jacob, D. J. & Pirrone, N. Mercury as a global pollutant: Sources, pathways, and effects. Environ. Sci. Technol. 47, 4967–4983. https://doi.org/10.1021/es305071v (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Z. C. et al. Effects of different concentrations of mercury on accumulation of mercury by five plant species. Ecol. Eng. 106, 273–278. https://doi.org/10.1016/j.ecoleng.2017.05.051 (2017).Article 

Google Scholar 
Hassan, M. J. et al. Effect of cadmium toxicity on growth, oxidative damage, antioxidant defense system and cadmium accumulation in two sorghum cultivars. Plants 9, 1575. https://doi.org/10.3390/plants9111575 (2020).CAS 
Article 

Google Scholar 
Patra, M., Bhowmik, N., Bandopadhyay, B. & Sharma, A. Comparison of mercury, lead and arsenic with respect to genotoxic effects on plant systems and the development of genetic tolerance. Environ. Exp. Bot. 52, 199–223. https://doi.org/10.1016/j.envexpbot.2004.02.009 (2004).CAS 
Article 

Google Scholar 
Zhou, Z. S., Wang, S. J. & Yang, Z. M. Biological detection and analysis of mercury toxicity to alfalfa (Medicago sativa) plants. Chemosphere 70, 1500–1509. https://doi.org/10.1016/j.chemosphere.2007.08.028 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Biczak, R. Quaternary ammonium salts with tetrafluoroborate anion: Phytotoxicity and oxidative stress in terrestrial plants. J. Hazard. Mater. 304, 173–185. https://doi.org/10.1016/j.jhazmat.2015.10.055 (2016).CAS 
Article 
PubMed 

Google Scholar 
Elbaz, A., Wei, Y. Y., Meng, Q., Zheng, Q. & Yang, Z. M. Mercury-induced oxidative stress and impact on antioxidant enzymes in Chlamydomonas reinhardtii. Ecotoxicology 19, 1285–1293. https://doi.org/10.1007/s10646-010-0514-z (2010).CAS 
Article 
PubMed 

Google Scholar 
Gao, S. et al. Growth and antioxidant responses in Jatropha curcas seedling exposed to mercury toxicity. J. Hazard. Mater. 182, 591–597. https://doi.org/10.1016/j.jhazmat.2010.06.073 (2010).CAS 
Article 
PubMed 

Google Scholar 
Warren, S. D. et al. Reproduction and dispersal of biological soil crust organisms. Front. Ecol. Evol. 7, 1–17. https://doi.org/10.3389/FEVO.2019.00344 (2019).MathSciNet 
Article 

Google Scholar 
Wu, L. & Zhang, Y. Precipitation and soil particle size co-determine spatial distribution of biological soil crusts in the Gurbantunggut Desert, China. J. Arid. Land. 10, 701–711. https://doi.org/10.1007/s40333-018-0065-3 (2018).Article 

Google Scholar 
Hu, R. et al. The mechanism of soil nitrogen transformation under different biocrusts to warming and reduced precipitation: From microbial functional genes to enzyme activity. Sci. Total Environ. 722, 137849. https://doi.org/10.1016/j.scitotenv.2020.137849 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Pan, Z. et al. The upside-down water collection system of Syntrichia caninervis. Nat. Plants. 2(7), 16076. https://doi.org/10.1038/nplants.2016.76 (2016).Article 
PubMed 

Google Scholar 
Coe, K. K. et al. Strategies of desiccation tolerance vary across life phases in the moss Syntrichia caninervis. Am. J. Bot. 108, 249–262. https://doi.org/10.1002/ajb2.1571 (2020).CAS 
Article 
PubMed 

Google Scholar 
Silva, A. T. et al. To dry perchance to live: Insights from the genome of the desiccation-tolerant biocrust moss Syntrichia caninervis. Plant J. 105, 1339–1356. https://doi.org/10.1111/tpj.15116 (2021).CAS 
Article 
PubMed 

Google Scholar 
Young, K. & Reed, S. Spectrally monitoring the response of the biocrust moss Syntrichia caninervis to altered precipitation regimes. Sci. Rep. 7, 41793. https://doi.org/10.1038/srep41793 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J. & Zhang, Y. M. Ecophysiological responses of the biocrust moss Syntrichia caninervis to experimental snow cover manipulations in a temperate desert of central Asia. Ecol. Res. 35, 198–207. https://doi.org/10.1111/1440-1703.12072 (2019).CAS 
Article 

Google Scholar 
Zheng, Y. P., Zhao, J. C., Zhang, B. C., Li, L. & Zhang, Y. M. Advances on ecological studies of algae and mosses in biological soil crusts. Chin. J. Bot. 44, 371–378 (2009).CAS 

Google Scholar 
Mei, L. et al. Mercury-induced phytotoxicity and responses in upland cotton (Gossypium hirsutum L.) seedlings. Plants 10, 1494. https://doi.org/10.3390/plants10081494 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, Z. S. et al. Metabolic adaptations to mercury-induced oxidative stress in roots of Medicago sativa L. J. Inorg. Biochem. 101, 1–9. https://doi.org/10.1016/j.jinorgbio.2006.05.011 (2007).CAS 
Article 

Google Scholar 
Yuniarti, R. & Yuniati, R. Mercury effects on the early seedling of Paraserianthes falcataria (L.) Nielsen grew in hydroponic culture. IOP Conf. Ser. Mater. Sci. Eng. 902, 012073. https://doi.org/10.1088/1757-899X/902/1/012073 (2020).CAS 
Article 

Google Scholar 
Li, Y. et al. Reorganization of photosystem II is involved in the rapid photosynthetic recovery of desert moss Syntrichia caninervis upon rehydration. J. Plant Physiol. 167, 1390–1397. https://doi.org/10.1016/j.jplph.2010.05.028 (2010).CAS 
Article 
PubMed 

Google Scholar 
Deng, B. L., Yang, K. J., Zhang, Y. F. & Li, Z. T. Can heavy metal pollution defend seed germination against heat stress? Effect of heavy metals (Cu2+, Cd2+ and Hg2+) on maize seed germination under high temperature. Environ. Pollut. 216, 46–52. https://doi.org/10.1016/j.envpol.2016.05.050 (2016).CAS 
Article 
PubMed 

Google Scholar 
Khan, K. Y. et al. Study amino acid contents, plant growth variables and cell ultrastructural changes induced by cadmium stress between two contrasting cadmiums accumulating cultivars of Brassica rapa ssp. chinensis L. (pak choi). Ecotoxicol. Environ. Saf. 200, 110748. https://doi.org/10.1016/j.ecoenv.2020.110748 (2020).CAS 
Article 
PubMed 

Google Scholar 
Arnon, D. L. Copper enzymes in isolated chloroplasts.Polyphenoloxidases in Beta vulgaris. Plant Physiol. 24, 1–15 (1949).CAS 
Article 

Google Scholar 
Bates, L. S., Waldren, R. P. & Teare, I. D. Rapid determination of free proline for water-stress studies. Plant Soil. 39, 205–207. https://doi.org/10.1007/BF00018060 (1973).CAS 
Article 

Google Scholar 
Luo, X. L. & Huang, Q. F. Relationships between leaf and stem soluble sugar content and tuberous root starch accumulation in Cassava. J. Agric. Sci. 3, 64–72. https://doi.org/10.5539/jas.v3n2p64 (2011).Article 

Google Scholar 
Choudhury, S. & Panda, S. K. Toxic effects, oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) broth under chromium and lead phytotoxicity. Water Air Soil Pollut. 167, 73–90. https://doi.org/10.1007/s11270-005-8682-9 (2005).ADS 
CAS 
Article 

Google Scholar 
Kumar, A., Dutt, S., Bagler, G., Ahuja, P. S. & Kumar, S. Engineering a thermo-stable superoxide dismutase functional at sub-zero to >50°C, which also tolerates autoclaving. Sci. Rep. 2, 347–351. https://doi.org/10.1038/srep00387 (2012).CAS 
Article 

Google Scholar 
Pasquariello, M. S. et al. Influence of postharvest chitosan treatment on enzymatic browning and antioxidant enzyme activity in sweet cherry fruit. Postharvest. Biol. Technol. 109, 45–46. https://doi.org/10.1016/j.postharvbio.2015.06.007 (2015).CAS 
Article 

Google Scholar 
Emamverdian, A., Ding, Y. L., Mokhberdoran, F. & Xie, Y. F. Growth responses and photosynthetic indices of bamboo plant (Indocalamus latifolius) under heavy metal stress. Sci. World J. 2018, 1–6. https://doi.org/10.1155/2018/1219364 (2018).CAS 
Article 

Google Scholar 
Sahu, G. K., Upadhyay, S. & Sahoo, B. B. Mercury induced phytotoxicity and oxidative stress in wheat (Triticum aestivum L.) plants. Physiol. Mol. Biol. Plants 18, 21–31. https://doi.org/10.1007/s12298-011-0090-6 (2012).CAS 
Article 
PubMed 

Google Scholar 
Wang, R. Y. et al. Effect of amendments on contaminated soil of multiple heavy metals and accumulation of heavy metals in plants. Environ. Sci. Pollut. Res. 25, 28695–28704. https://doi.org/10.1007/s11356-018-2918-x (2018).CAS 
Article 

Google Scholar 
Esposito, S. et al. In-field and in-vitro study of the moss Leptodictyum riparium as bioindicator of toxic metal pollution in the aquatic environment: Ultrastructural damage, oxidative stress and HSP70 induction. PLoS ONE 13, 1–16. https://doi.org/10.1371/journal.pone.0195717 (2018).CAS 
Article 

Google Scholar 
Qureshi, S. et al. Effect of microbial activity on trace element release from sewage sludge. Environ. Sci. Technol. 37, 3361–3366. https://doi.org/10.1021/es020970h (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lebeau, T., Bagot, D., Jézéquel, K. & Fabre, B. Cadmium biosorption by free and immobilised microorganisms cultivated in a liquid soil extract medium: Effects of Cd, pH and techniques of culture. Sci. Total Environ. 291, 73–83. https://doi.org/10.1016/S0048-9697(01)01093-2 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cho, U. H. & Park, J. O. Mercury-induced oxidative stress in tomato seedlings. Plant Sci. 156, 1–9. https://doi.org/10.1016/S0168-9452(00)00227-2 (2000).CAS 
Article 
PubMed 

Google Scholar 
Chen, J. et al. Bioaccumulation and physiological effects of mercury in Pteris vittata and Nephrolepis exaltata. Ecotoxicology 18, 110–121. https://doi.org/10.1007/s10646-008-0264-3 (2009).CAS 
Article 
PubMed 

Google Scholar 
Bellini, E. et al. The moss Leptodictyum riparium counteracts severe cadmium stress by activation of glutathione transferase and phytochelatin synthase, but slightly by phytochelatins. Int. J. Mol. Sci. 21, 1583. https://doi.org/10.3390/ijms21051583 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Altaf, M. A. et al. Melatonin mitigates nickel toxicity by improving nutrient uptake fluxes, root architecture system, photosynthesis, and antioxidant potential in tomato seedling. J. Soil Sci. Plant Nutr. 21, 1842–1855. https://doi.org/10.1007/s42729-021-00484-2 (2021).CAS 
Article 

Google Scholar 
Zhang, H. H. et al. Toxic effects of heavy metals Pb and Cd on mulberry (Morus alba L.) seedling leaves: Photosynthetic function and reactive oxygen species (ROS) metabolism responses. Ecotoxicol. Environ. Saf. 195, 110469. https://doi.org/10.1016/j.ecoenv.2020.110469 (2020).CAS 
Article 

Google Scholar 
Hoekstra, F. A., Golovina, E. A. & Buitink, J. Mechanisms of plant desiccation tolerance. Trends Plant Sci. 6, 431–438. https://doi.org/10.1016/S1360-1385(01)02052-0 (2001).CAS 
Article 
PubMed 

Google Scholar 
Xiong, A. S. et al. Expression and function of a modified AP2/ERF transcription factor from Brassica napus enhances cold tolerance in transgenic Arabidopsis. Mol. Biotechnol. 53, 198–206. https://doi.org/10.1007/s12033-012-9515-x (2013).CAS 
Article 
PubMed 

Google Scholar 
Hare, P. D., Cress, W. A. & Staden, J. V. Proline synthesis and degradation: A model system for elucidating stress-related signal transduction. J. Exp. Bot. 50, 413–434. https://doi.org/10.1093/jxb/50.333.413 (1999).CAS 
Article 

Google Scholar 
Székely, G. et al. Duplicated P5CS genes of Arabidopsis play distinct roles in stress regulation and developmental control of proline biosynthesis. Plant J. 53, 11–28. https://doi.org/10.1111/j.1365-313X.2007.03318.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Mishra, P., Bhoomika, K. & Dubey, R. S. Differential responses of antioxidative defense system to prolonged salinity stress in salt-tolerant and salt-sensitive Indica rice (Oryza sativa L.) seedlings. Protoplasma 250, 3–19. https://doi.org/10.1007/s00709-011-0365-3 (2013).CAS 
Article 
PubMed 

Google Scholar 
Mittler, R. ROS are good. Trends Plant Sci. 22, 11–19. https://doi.org/10.1016/j.tplants.2016.08.002 (2017).CAS 
Article 
PubMed 

Google Scholar 
Gill, S. S. & Tuteja, N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol. Biochem. 48, 909–930. https://doi.org/10.1016/j.plaphy.2010.08.016 (2010).CAS 
Article 
PubMed 

Google Scholar 
Kolahi, M., Kazemi, E. M., Yazdi, M. & Barnaby, A. G. Oxidative stress induced by cadmium in lettuce (Lactuca sativa Linn.): Oxidative stress indicators and prediction of their genes. Plant Physiol. Biochem. 146, 71–89. https://doi.org/10.1016/j.plaphy.2019.10.032 (2020).CAS 
Article 
PubMed 

Google Scholar 
Merwald, H. et al. UVA-induced oxidative damage and cytotoxicity depend on the mode of exposure. J. Photochem. Photobiol. B Biol. 79, 197–207. https://doi.org/10.1016/j.jphotobiol.2005.01.002 (2005).CAS 
Article 

Google Scholar 
Pazmiño, D. M. et al. Differential response of young and adult leaves to herbicide 2,4-dichlorophenoxyacetic acid in pea plants: Role of reactive oxygen species. Plant Cell Environ. 34, 1874–1889. https://doi.org/10.1111/j.1365-3040.2011.02383.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Ghori, N. H. et al. Heavy metal stress and responses in plants. Int. J. Environ. Sci. Technol. (Tehran) 16, 1807–1828. https://doi.org/10.1007/s13762-019-02215-8 (2019).CAS 
Article 

Google Scholar 
Vezza, M. E., Llanes, A., Travaglia, C., Agostini, E. & Talano, M. A. Arsenic stress effects on root water absorption in soybean plants: Physiological and morphological aspects. Plant Physiol. Biochem. 123, 8–17. https://doi.org/10.1016/j.plaphy.2017.11.020 (2018).CAS 
Article 
PubMed 

Google Scholar 
C, A., Tasdighi, H. & Gholamhoseini, M.,. Evaluation of proline, chlorophyll, soluble sugar content and uptake of nutrients in the German chamomile (Matricaria chamomilla L.) under drought stress and organic fertilizer treatments. Asian Pac. J. Trop. Biomed. 6(10), 886–891. https://doi.org/10.1016/j.apjtb.2016.08.009 (2016).CAS 
Article 

Google Scholar 
Sharma, A. et al. Phytohormones regulate accumulation of osmolytes under abiotic stress. Biomolecules 9(7), 285. https://doi.org/10.3390/biom9070285 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Zhang, S. S., Zhang, H. M., Qin, R., Jiang, W. S. & Liu, D. H. Cadmium induction of lipid peroxidation and effects on root tip cells and antioxidant enzyme activities in Vicia faba L. Ecotoxicology 18, 814–823. https://doi.org/10.1007/s10646-009-0324-3 (2009).CAS 
Article 
PubMed 

Google Scholar  More

Experimental designData collection took place in different sites disseminated along the mainland French coastline in sectors dedicated to Pacific oyster farming. Over the years, the number of sites monitored varied from 43 sites until 2009, to 13 between 2009 and 2013, and finally to 8 sites since 2015. Here, we focus on 13 sites (Fig. 1 & Table 1) that were almost continuously monitored since 1993. All these sites stand in tidal areas except Marseillan, located in the Mediterranean Thau lagoon, for which tidal variations are only tenuous and Men-er-Roué which is in subtidal deep-water oyster culture area in the Bay of Quiberon. Sentinel oysters were reared in plastic meshed bags fixed on iron tables, mimicking the oyster farmers practices. In Marseillan, half-grown oysters were cemented onto vertical ropes (from 1993 to 2007 and from 2015 to 2018), reared in Australian baskets (from 2008 to 2011), or put in bags fixed on iron tables (2012, 2013, 2014). As for spat oysters, they were reared in pearl-nets between 2008 and 2011 or put in bags since 2012.Fig. 1Site locations (coordinates in WGS84) along the French coastline. The site numbers refer to Table 1.Full size imageTable 1 Site identification and coordinates in WGS84.Full size tableDuring the 1993–2013 period, at the beginning of each annual campaign, one batch of diploid spat (three in 2012 and 2013) and one batch of diploid half-grown oysters were bought from an oyster farmer (i.e., wild-caught individuals) and then deployed simultaneously on all sites of the monitoring network. Here, the term “batch” designates a group of oysters born from the same reproductive event (spatfall or hatchery cohort), having experienced strictly the same zootechnical route. One batch could eventually be reared in several different bags (up to 3) deployed in the same site. Different batches were never mixed in the same bag.During the 2009–2013 period, up to three additional batches of triploid spat were bought in commercial hatcheries and included in the survey strategy (for a maximum of 6 batches of spat per site in 2012 and 2013). In 2009, the batches that were bought had already been exposed to a first wave of mortality before being followed by the network. Thus, the data collected this year should be interpreted with caution. Since 2014, the origin of spat and half-grown oysters has changed notably to better control the initial health status of oysters (no contact with the natural environment before deployment in all sites). The hatchery facility of Ifremer-Argenton now produces the sentinel diploid spat used in the monitoring network (one batch for all sites per campaign), whereas, the half-grown oysters was composed of spat reared on the same location the previous year but not monitored.Data collectionAfter the deployment of the different batches at the beginning of the campaign (seeding dates from February to April depending on the year), growth and mortality were longitudinally monitored yearly. Until 1999, annual campaigns usually ended in the winter of the year the monitoring began (i.e. in December), whereas, during the period 2000–2018, all sites frequently extended the campaign to end in the winter (February to March) of the following year.Observations were collected on each site quarterly until 2008 but then monthly to bimonthly depending on the season. At each sampling date, local operators carefully emptied each bag in separate baskets, counted the dead individuals (those with open or empty shells) and alive ones, and removed the dead individuals. Then local operators weighed all alive individuals in each basket (mass taken at the bag level, protocol mainly used between 1993 and 1998 and since 2004) and/or collected 30 individuals to individually weigh them in the laboratory (mass taken at the individual level, protocol used between 1995 and 2010 for spat and since 1996 for half-grown oysters).Data cleaningDuring the 2009–2013 period, several batches of spat were monitored per site and campaign. Some had a similar background to the batches monitored before 2009 (i.e., wild-caught spat from natural spatfall collected in the bay of Arcachon). To ensure the continuity of the time-series, we thus decided to remove all mass and mortality data of spat that did not originate from natural spatfall in the Bay of Arcachon, as well as triploid spat bought in hatcheries (see Table 2 for the origin and number of batches kept per site and campaign). To ensure that the life-cycle indicators are as comparable as possible between campaign and site (i.e. estimated in a common restricted time window), we removed data collected after December 31 of the year the monitoring began, as well as the site × campaign combinations when monitoring ended before October because the growth or mortality could still be in the exponential phase during this end-of-follow-up periods26. As the protocol of mass data collection changed over the years, we could not only use the mass data taken at the bag level or that at the individual level without greatly breaking the continuity of the time-series. We thus kept data taken at the individual level until 2008 and those taken at the bag level since 2009. We then checked for nonsense or missing data (e.g., the mass of a bag was equal to 0 or missing although they were still alive oysters in the bag), duplicated values and removed data for bags not part of the protocols or incorrectly identified. Finally, we removed site × campaign combinations for which we had fewer than four mass or mortality data because more data is necessary to study the temporal pattern of growth and mortality.Table 2 Origins of the different oyster batches retained after data cleaning.Full size tableData processing and analysisAt this point, the available data were, therefore, the number of living individuals per bag, the number of dead individuals since the last visit, the individual mass (g) of oysters (until 2008) and the total mass (g) of the living individuals per bag (since 2009).For mass data collected until 2008, we calculated the mean of the individual mass per date × site × age class combination by averaging the mass of the individuals. In other cases (mass data collected since 2009), we calculated the mean mass of individuals for each bag × date × site × age class combination by dividing the total mass of living oysters by the number of living individuals and then averaged data by date × site × age class combination. Our mass data, hereafter called mean mass data, is thus composed of the mean of the individual mass until 2008 and the mean mass of individuals since 2009.For mortality data, we could not calculate a cumulative mortality per bag × date × site × age class combination as (1-frac{number;of;alive;oysters;at;sampling;date}{number;of;oysters;at;previous;sampling;date}) because the total number of oysters (dead and alive) on a specific date often differed from the number of alive oysters at the previous date (e.g., because oysters were lost from the bags, or were sampled for complementary analyses such as pathogen detection). We thus took into account changes in oyster numbers between visits and calculated cumulative mortality using the following formula: CMt = 1 − ((1 − CMt-1) × (1 − IMt)). CMt = Cumulative mortality at time t; CMt-1 = Cumulative mortality at time t-1; IMt = Mortality rate at time t. IMt was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time t. When several bags were followed, we then averaged the cumulative mortality per date × site × age class combinations.We modeled the evolution of the mean mass and cumulative mortality data as a function of time to cope with changes in data frequency acquisition during annual monitoring campaigns. According to previous studies, annual mortality and growth curves in C. gigas follow a sigmoid curve11,26. Therefore, we fitted a logistic model, Eq. (1), and a Gompertz model, Eq. (2), which correspond to the most commonly used sigmoid models for growth and other data27, to describe Yt = mean mass (in grams) and cumulative mortality at time t.$${Y}_{t}=frac{a}{left(1+{e}^{left(-btimes left(t-cright)right)}right)}$$
(1)
$${Y}_{t}=atimes {e}^{left(-eleft(-btimes left(t-cright)right)right)}$$
(2)
These equations estimate three parameters: the upper asymptote (a), the slope at inflection (b), and the time of inflection (c).As the mean mass of half-grown individuals at the beginning of the campaign was higher than 0, we also fitted a four-parameter version of the logistic model, Eq. (3), and Gompertz model, Eq. (4), which is commonplace in the growth-curve analysis of bacterial counts27, and estimated (d) which represents the lowest asymptote of the curve. This parameter also moves the model curve vertically without changing its shape. The upper asymptote thus becomes equal to d + a.$${Y}_{t}=d+frac{a}{left(1+{e}^{left(-btimes left(t-cright)right)}right)}$$
(3)
$${Y}_{t}=d+atimes {e}^{left(-eleft(-btimes left(t-cright)right)right)}$$
(4)
Model fitting was carried out using non-linear least squares regressions (R package nls.multstrat28). This method allows running 5000 iterations of the fitting process with start parameters drawn from a uniform distribution and retaining the fit with the lowest score of Akaike Information Criterion (AIC). The sigmoid curve (i.e. logistic or Gompertz) with the lowest mean AIC of all models was selected as the best curve describing the data (see technical validation section). More

Tréguer PJ, Sutton JN, Brzezinski MA, Charette MA, Devries T, Dutkiewicz S, et al. Reviews and syntheses: the biogeochemical cycle of silicon in the modern ocean. Biogeosciences. 2021;18:1269–89.Article 
CAS 

Google Scholar 
Hutchins DA, Bruland KW. Iron-limited diatom growth and Si:N uptake ratios in a coastal upwelling regime. Nature. 1998;393:561–4.CAS 
Article 

Google Scholar 
King AL, Barbeau KA. Evidence for phytoplankton iron limitation in the southern California Current System. Mar Ecol Prog Ser. 2007;342:91–103.CAS 
Article 

Google Scholar 
Pichevin LE, Ganeshram RS, Geibert W, Thunell R, Hinton R. Silica burial enhanced by iron limitation in oceanic upwelling margins. Nat Geosci. 2014;7:541–6.CAS 
Article 

Google Scholar 
Brzezinski MA, Krause JW, Bundy RM, Barbeau M, Franks KA, Goericke RP, et al. Enhanced silica ballasting from iron stress sustains carbon export in a frontal zone within the California Current. J Geophys Res Ocean. 2015;120:4654–69.Article 

Google Scholar 
Baines SB, Twining BS, Vogt S, Balch WM, Fisher NS, Nelson DM. Elemental composition of equatorial Pacific diatoms exposed to additions of silicic acid and iron. Deep Res Part II Top Stud Oceanogr. 2011;58:512–23.CAS 
Article 

Google Scholar 
de Baar HJW, van Heuven SMAC, Middag R. Biochemical cycling and trace elements. Encycl Earth Sci Ser. 2017;14:1–21.Assmy P, Smetacek V, Montresor M, Klaas C, Henjes J, Strass VH, et al. Thick-shelled, grazer-protected diatoms decouple ocean carbon and silicon cycles in the iron-limited Antarctic Circumpolar Current. Proc Natl Acad Sci. 2013;110:20633–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leynaert A, Bucciarelli E, Claquin P, Dugdale RC, Martin-jézéquel V, Pondaven P, et al. Effect of iron deficiency on diatom cell size and silicic acid uptake kinetics. Limnol Oceanogr. 2004;49:1134–43.CAS 
Article 

Google Scholar 
Marchetti A, Harrison PJ. Coupled changes in the cell morphology and the elemental (C, N, and Si) composition of the pennate diatom Pseudo-nitzschia due to iron deficiency. Limnol Oceanogr. 2007;52:2270–84.CAS 
Article 

Google Scholar 
McNair HM, Brzezinski MA, Krause JW. Diatom populations in an upwelling environment decrease silica content to avoid growth limitation. Environ Microbiol. 2018;20:4184–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Glibert PM, McCarthy JK. Uptake and assimilation of ammonium and nitrate by phytoplankton: Indices of nutritional status for natural assemblages. J Plankton Res. 1984;6:677–97.CAS 
Article 

Google Scholar 
Takeda S. Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters. Nature. 1998;393:774–7.CAS 
Article 

Google Scholar 
Timmermans KR, Van Der Wagt B, De Baar HJW. Growth rates, half-saturation constants, and silicate, nitrate, and phosphate depletion in relation to iron availability of four large, open-ocean diatoms from the Southern Ocean. Limnol Oceanogr. 2004;49:2141–51.CAS 
Article 

Google Scholar 
Brzezinski MA, Olson R, Chisholm SW. Silicon availability and cell-cycle progression in marine diatoms. Mar Ecol Prog Ser. 1990;67:83–96.CAS 
Article 

Google Scholar 
Hildebrand M, Volcani BE, Gassmann W, Schroeder JI. A gene family of silicon transporters. Nature. 1997;385:688–9.CAS 
PubMed 
Article 

Google Scholar 
Durkin CA, Marchetti A, Bender SJ, Truong T, Morales RL, Mock T, et al. Frustule-related gene transcription and the influence of diatom community composition on silica precipitation in an iron-limited environment. Limnol Oceanogr. 2012;57:1619–33.CAS 
Article 

Google Scholar 
Allen AE, LaRoche J, Maheswari U, Lommer M, Schauer N, Lopez PJ, et al. Whole-cell response of the pennate diatom Phaeodactylum tricornutum to iron starvation. Proc Natl Acad Sci. 2008;105:10438–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meyerink SW, Ellwood MJ, Maher WA, Dean Price G, Strzepek RF. Effects of iron limitation on silicon uptake kinetics and elemental stoichiometry in two Southern Ocean diatoms, Eucampia antarctica and Proboscia inermis, and the temperate diatom Thalassiosira pseudonana. Limnol Oceanogr. 2017;62:2445–62.CAS 
Article 

Google Scholar 
Marchetti A, Schruth DM, Durkin CA, Parker MS, Kodner RB, Berthiaume CT, et al. Comparative metatranscriptomics identifies molecular bases for the physiological responses of phytoplankton to varying iron availability. Proc Natl Acad Sci USA. 2012;109:E317–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boyd PW, Muggli DL, Varela DE, Goldblatt RH, Chretien R, Orians KJ, et al. In vitro iron enrichment experiments in the NE subarctic pacific. Mar Ecol Prog Ser. 1996;136:179–93.CAS 
Article 

Google Scholar 
Marchetti A, Sherry ND, Kiyosawa H, Tsuda A, Harrison PJ. Phytoplankton processes during a mesoscale iron enrichment in the NE subarctic Pacific: Part I-Biomass and assemblage. Deep Res Part II Top Stud Oceanogr. 2006;53:2095–113.Article 

Google Scholar 
La Roche J, Geider RJ, Graziano LM, Murray H, Lewis K. Induction of specific proteins in eukaryotic algae grown under iron-, phosphorus-, or nitrogen-deficient conditions. J Phycol. 1993;29:767–77.Article 

Google Scholar 
Peers G, Price NM. Copper-containing plastocyanin used for electron transport by an oceanic diatom. Nature. 2006;441:341–4.CAS 
PubMed 
Article 

Google Scholar 
Boyd PW, Berges JA, Harrison PJ. In vitro iron enrichment experiments at iron-rich and -poor sites in the NE subarctic Pacific. J Exp Mar Bio Ecol. 1998;227:133–51.CAS 
Article 

Google Scholar 
Timmermans KR, Stolte W, de Baar HJW. Iron-mediated effects on nitrate reductase in marine phytoplankton. Mar Biol. 1994;121:389–96.CAS 
Article 

Google Scholar 
Jin X, Gruber N, Dune JP, Sarmiento JL, Armstrong RA. Diagnosing the contributions of phytoplankton functional groups to the production and export of particulate organic carbon, CaCO3, and opal from global nutrient and alkalinity distributions. Global Biogeochem Cycles. 2006;20:1–17.Article 
CAS 

Google Scholar 
McNair HM, Brzezinski MA, Till CP, Krause JW. Taxon-specific contributions to silica production in natural diatom assemblages. Limnol Oceanogr. 2018;63:1056–75.CAS 
PubMed 
Article 

Google Scholar 
Lampe RH, Cohen NR, Ellis KA, Bruland KW, Maldonado MT, Peterson TD, et al. Divergent gene expression among phytoplankton taxa in response to upwelling. Environ Microbiol. 2018;20:3069–82.CAS 
PubMed 
Article 

Google Scholar 
Lampe RH, Mann EL, Cohen NR, Till CP, Thamatrakoln K, Brzezinski MA, et al. Different iron storage strategies among bloom-forming diatoms. Proc Natl Acad Sci. 2018;115:E12275–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brembu T, Chauton MS, Winge P, Bones AM, Vadstein O. Dynamic responses to silicon in Thalasiossira pseudonana—identification, characterisation and classification of signature genes and their corresponding protein motifs. Sci Rep. 2017;7:4865.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kotzsch A, Gröger P, Pawolski D, Bomans PHH, Sommerdijk NAJM, Schlierf M, et al. Silicanin-1 is a conserved diatom membrane protein involved in silica biomineralization. BMC Biol. 2017;15:9–11.Fawcett SE, Ward BB. Phytoplankton succession and nitrogen utilization during the development of an upwelling bloom. Mar Ecol Prog Ser. 2011;428:13–31.CAS 
Article 

Google Scholar 
Lampe RH, Hernandez G, Lin YY, Marchetti A. Representative diatom and coccolithophore species exhibit divergent responses throughout simulated upwelling cycles. mSystems. 2021;6:e00188–21.Bruland KW, Rue EL, Smith GJ. Iron and macronutrients in California coastal upwelling regimes: Implications for diatom blooms. Limnol Oceanogr. 2001;46:1661–74.CAS 
Article 

Google Scholar 
Redfield AC, Ketchum BH, Richards FA. The influence of organisms on the composition of seawater. The Sea. 1963;2:26–77.White KK, Dugdale RC. Silicate and nitrate uptake in the Monterey Bay upwelling system. Cont Shelf Res. 1997;17:455–72.Article 

Google Scholar 
Chappell PD, Whitney LP, Wallace JR, Darer AI, Jean-Charles S, Jenkins BD. Genetic indicators of iron limitation in wild populations of Thalassiosira oceanica from the northeast Pacific Ocean. ISME J. 2015;9:592–602.CAS 
PubMed 
Article 

Google Scholar 
Marchetti A, Moreno CM, Cohen NR, Oleinikov I, deLong K, Twining BS, et al. Development of a molecular-based index for assessing iron status in bloom-forming pennate diatoms. J Phycol. 2017;53:820–32.CAS 
PubMed 
Article 

Google Scholar 
Thamatrakoln K, Korenovska O, Niheu AK, Bidle KD. Whole-genome expression analysis reveals a role for death-related genes in stress acclimation of the diatom Thalassiosira pseudonana. Environ Microbiol. 2012;14:67–81.CAS 
PubMed 
Article 

Google Scholar 
Cohen NR, Ellis KA, Lampe RH, McNair HM, Twining BS, Maldonado MT, et al. Diatom transcriptional and physiological responses to changes in iron bioavailability across ocean provinces. Front Mar Sci. 2017;4:1–20.CAS 
Article 

Google Scholar 
La Roche J, Boyd PW, McKay RML, Geider RJ. Flavodoxin as an in situ marker for iron stress in phytoplankton. Nature. 1996;382:802–5.Article 

Google Scholar 
Hervás M, Navarro JAJa, Diaz A, Bottin HH, De la Rosa MA, Díaz A, et al. Laser-flash kinetic analysis of the fast electron transfer from plastocyanin and cytochrome c6 to photosystem I. Experimental evidence on the evolution of the reaction mechanism. Biochemistry. 1995;34:11321–6.PubMed 
Article 

Google Scholar 
Franck VM, Bruland KW, Hutchins DA, Brzezinski MA. Iron and zinc effects on silicic acid and nitrate uptake kinetics in three high-nutrient, low-chlorophyll (HNLC) regions. Mar Ecol Prog Ser. 2003;252:15–33.CAS 
Article 

Google Scholar 
Brown KL, Twing KI, Robertson DL. Unraveling the regulation of nitrogen assimilation in the marine diatom Thalassiosira pseudonana (bacillariophyceae): Diurnal variations in transcript levels for five genes involved in nitrogen assimilation. J Phycol. 2009;45:413–26.CAS 
PubMed 
Article 

Google Scholar 
Allen AE, Dupont CL, Oborník M, Horák A, Nunes-Nesi A, McCrow JP, et al. Evolution and metabolic significance of the urea cycle in photosynthetic diatoms. Nature. 2011;473:203–7.CAS 
PubMed 
Article 

Google Scholar 
Görlich S, Pawolski D, Zlotnikov I, Kröger N. Control of biosilica morphology and mechanical performance by the conserved diatom gene Silicanin-1. Commun Biol. 2019;2:245.Durkin CA, Koester JA, Bender SJ, Armbrust VE. The evolution of silicon transporters in diatoms. J Phycol. 2016;52:716–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
King AL, Barbeau KA. Dissolved iron and macronutrient distributions in the southern California Current System. J Geophys Res Ocean. 2011;116:1–18.
Google Scholar 
Hoffmann LJ, Peeken I, Lochte K. Effects of iron on the elemental stoichiometry during EIFEX and in the diatoms Fragilariopsis kerguelensis and Chaetoceros dichaeta. Biogeosciences. 2007;4:569–79.CAS 
Article 

Google Scholar 
Matsumoto K, Sarmiento JL, Brzezinski MA. Silicic acid leakage from the Southern Ocean: a possible explanation for glacial atmospheric pCO2. Global Biogeochem Cycles. 2002;16:1–23.Geider RJ, Greene RM, Kolber ZS, MacIntyre HL, Falkowski PG. Fluorescence assessment of the maximum quantum efficiency of photosynthesis in the western North. Atlantic. Deep Res Part I. 1993;40:1205–24.CAS 
Article 

Google Scholar 
Durkin CA, Bender SJ, Chan KYK, Gaessner K, Grünbaum D, Armbrust VE. Silicic acid supplied to coastal diatom communities influences cellular silicification and the potential export of carbon. Limnol Oceanogr. 2013;58:1707–26.CAS 
Article 

Google Scholar 
Kudo I, Miyamoto M, Noiri Y, Maita Y. Combined effects of temperature and iron on the growth and physiology of the marine diatom Phaeodactylum tricornutum (Bacillariophyceae). J Phycol. 2000;36:1096–102.CAS 
Article 

Google Scholar 
Eldridge ML, Trick CG, Alm MB, DiTullio GR, Rue EL, Bruland KW, et al. Phytoplankton community response to a manipulation of bioavailable iron in HNLC waters of the subtropical Pacific Ocean. Aquat Microb Ecol. 2004;35:79–91.Article 

Google Scholar 
Sunda WG, Huntsman SA. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar Chem. 1995;50:189–206.CAS 
Article 

Google Scholar 
Morel FMM, Rueter JG, Price NM. Iron nutrition of phytoplankton and its possible importance in the ecology of ocean regions with high nutrient and low biomass. Oceanography. 1991;4:56–61.Article 

Google Scholar 
Coale TH, Moosburner M, Horák A, Oborník M, Barbeau KA, Allen AE. Reduction-dependent siderophore assimilation in a model pennate diatom. Proc Natl Acad Sci USA. 2019;116:23609–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De La Rocha CL, Hutchins DA, Brzezinski MA, Zhang Y. Effects of iron and zinc deficiency on elemental composition and silica production by diatoms. Mar Ecol Prog Ser. 2000;195:71–9.Article 

Google Scholar 
Claquin P, Martin-Jézéquel V, Kromkamp JC, Veldhuis MJW, Kraay GW. Uncoupling of silicon compared with carbon and nitrogen metabolisms and the role of the cell cycle in continuous cultures of Thalassiosira pseudonana (Bacillariophyceae) under light, nitrogen, and phosphorus control. J Phycol. 2002;38:922–30.CAS 
Article 

Google Scholar 
Wang WX, Dei RCH. Biological uptake and assimilation of iron by marine plankton: influences of macronutrients. Mar Chem. 2001;74:213–26.CAS 
Article 

Google Scholar 
Sapriel G, Quinet M, Heijde M, Jourdren L, Tanty V, Luo G, et al. Genome-wide transcriptome analyses of silicon metabolism in Phaeodactylum tricornutum reveal the multilevel regulation of silicic acid transporters. PLoS ONE. 2009;4:e7458–14.Kröger N, Deutzmann R, Sumper M. Polycationic peptides from diatom biosilica that direct silica nanosphere formation. Science. 1999;286:1129–32.PubMed 
Article 

Google Scholar 
Scheffel A, Poulsen N, Shian S, Kröger N. Nanopatterned protein microrings from a diatom that direct silica morphogenesis. Proc Natl Acad Sci USA. 2011;108:3175–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Knight MJ, Senior L, Nancolas B, Ratcliffe S, Curnow P. Direct evidence of the molecular basis for biological silicon transport. Nat Commun. 2016;7:1–11.Article 
CAS 

Google Scholar 
Shrestha RP, Hildebrand M. Evidence for a regulatory role of diatom silicon transporters in cellular silicon responses. Eukaryot Cell. 2015;14:29.PubMed 
Article 
CAS 

Google Scholar 
Thamatrakoln K, Hildebrand M. Silicon uptake in diatoms revisited: A model for saturable and nonsaturable uptake kinetics and the role of silicon transporters. Plant Physiol. 2008;146:1397–407.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Conway HL, Harrison PJ. Marine diatoms grown in chemostats under silicate or ammonium limitation. IV. Transient response of Chaetoceros debilis, Skeletonema costatum, and Thalassiosira gravida to a single addition of the limiting nutrient. Mar Biol. 1977;43:33–43.CAS 
Article 

Google Scholar 
Thamatrakoln K, Hildebrand M. Analysis of Thalassiosira pseudonana silicon transporters indicates distinct regulatory levels and transport activity through the cell cycle. Eukaryot Cell. 2007;6:271–9.CAS 
PubMed 
Article 

Google Scholar 
Ashworth J, Coesel SN, Lee A, Armbrust VE, Orellana MV, Baliga NS. Genome-wide diel growth state transitions in the diatom Thalassiosira pseudonana. Proc Natl Acad Sci USA. 2013;110:7518–23.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chauton MS, Winge P, Brembu T, Vadstein O, Bones AM. Gene regulation of carbon fixation, storage, and utilization in the diatom Phaeodactylum tricornutum acclimated to light/dark cycles. Plant Physiol. 2013;161:1034–48.CAS 
PubMed 
Article 

Google Scholar 
Chisholm SW, Costello JC. Influence of environmental factors and population composition on the timing of cell division in Thalassiosira fluviatilis (Bacillariophyceae) grown on light/dark cycles. J Phycol. 1980;16:375–83.Article 

Google Scholar 
Smith SR, Glé C, Abbriano RM, Traller JC, Davis AK, Trentacoste E, et al. Transcript level coordination of carbon pathways during silicon starvation-induced lipid accumulation in the diatom Thalassiosira pseudonana. New Phytol. 2016;210:890–904.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaulot D, Olson RJ, Chisholm SW. Light and dark control of the cell cycle in two marine phytoplankton species. Exp Cell Res. 1986;167:38–52.CAS 
PubMed 
Article 

Google Scholar 
Marchetti A, Parker MS, Moccia LP, Lin EO, Arrieta AL, Ribalet FF, et al. Ferritin is used for iron storage in bloom-forming marine pennate diatoms. Nature. 2009;457:467–70.CAS 
PubMed 
Article 

Google Scholar 
Goldman JAL, Schatz MJ, Berthiaume CT, Coesel SN, Orellana MV, Armbrust VE. Fe limitation decreases transcriptional regulation over the diel cycle in the model diatom Thalassiosira pseudonana. PLoS ONE. 2019;14:1–25.Article 
CAS 

Google Scholar 
Assmy P, Smetacek V, Montresor M, Klaas C, Henjes J, Strass VH, et al. Thick-shelled, grazer-protected diatoms decouple ocean carbon and silicon cycles in the iron-limited Antarctic Circumpolar Current. Proc Natl Acad Sci USA. 2013;110:20633–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kranzler CF, Brzezinski MA, Cohen NR, Lampe RH, Maniscalco M, Till CP, et al. Impaired viral infection and reduced mortality of diatoms in iron-limited oceanic regions. Nat Geosci. 2021;14:231–7.CAS 
Article 

Google Scholar 
Hutchins DA, Franck VM, Brzezinski MA, Bruland KW. Inducing phytoplankton iron limitation in iron-replete coastal waters with a strong chelating ligand. Limnol Oceanogr. 1999;44:1009–18.CAS 
Article 

Google Scholar 
Robertson G, Schein J, Chiu R, Corbett R, Field M, Jackman SD, et al. De novo assembly and analysis of RNA-seq data. Nat Methods. 2010;7:909–12.CAS 
PubMed 
Article 

Google Scholar 
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61.CAS 
PubMed 
Article 

Google Scholar 
Smith SR, Dupont CL, McCarthy JK, Broddrick JT, Oborník M, Horák A, et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat Commun. 2019;10:4552.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Morrissey J, Sutak R, Paz-Yepes J, Tanaka A, Moustafa A, Veluchamy A, et al. A novel protein, ubiquitous in marine phytoplankton, concentrates iron at the cell surface and facilitates uptake. Curr Biol. 2015;25:364–71.CAS 
PubMed 
Article 

Google Scholar 
Matsen FA, Kodner RB, Armbrust VE. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300. More

GRIIS and the Country CompendiumThe Global Register of Introduced and Invasive Species (GRIIS) arose following recognition of the need for a product of this nature in discussions on implementation of the Convention on Biological Diversity (CBD). In 2011, a joint work programme to strengthen information services on invasive alien species as a contribution towards Aichi Biodiversity Target 9 was developed19. The Global Invasive Alien Species Information Partnership (GIASI Partnership) was then established to assist Parties to the CBD, and others, to implement Article 8(h) and Target 9 of the Aichi Biodiversity Targets. The Conference of Parties (COP-11) welcomed the development of the GIASI Partnership and requested the Executive Secretary to facilitate its implementation (paragraph 22 of decision XI/28). In 2013, the development of GRIIS was identified as a key priority to be led by the IUCN ISSG and Partners built on a prototype initiated almost a decade earlier (Item 4, Report of the Global Invasive Alien Species Information Partnership, Steering Committee, 1st meeting Montreal, 15 October 2013)20.GRIIS is a database of discrete checklists of alien species that are present in specified geographic units (including not only countries, but also as yet unpublished checklists of islands, offshore territories, and protected areas) (Fig. 1). The GRIIS Country Compendium is a collation and key product that derives and is updatable from the working GRIIS Research Database that underpins this and other GRIIS products (Fig. 1). Individual checklists are published to GBIF through an installation of the Integrated Publishing Toolkit21 (IPT) and hosted by the GBIF Secretariat. Exceptions include the Belgium (hosted by the Research Institute for Nature and Forest) and U.S.A checklists (hosted by the United States Geological Survey). Data are published as Darwin Core (dwc namespace) Archive files and the terms and structure follow that standard exchange format22.The GRIIS Country Compendium is an aggregation of 196 GRIIS country checklists of which 82% have been verified by Country Editors (see13), along with revised and additional fields that enable global level analysis and country and taxon comparisons (Tables 2, 3). Checklists for the 196 countries were combined into a single file (Table 3). A field was added to indicate which country the checklist belonged to, and the ISO 3116-1 Alpha-2 and Alpha-3 country codes are included to facilitate dataset integration (see ‘Usage notes’) (Table 2). A field was also added to indicate the verification status of each checklist (Table 2). The ID field was renamed (originally ‘taxonID’ and now ‘recordID’), as the data now represent a country-level occurrence dataset containing multiple records per species, rather than checklist-type data that contains one record per species. In total, the data now include 18 fields as described in Table 2, encompassing taxonomic, location, habitat, occurrence, introduced and invasive alien status (see also Table 1). This publication represents a versioned, citable snapshot of the Compendium (Fig. 1) that is ready for analysis and integration with other data sources (e.g. workflow23 and ‘Example applications of the Compendium’ outlined further below).Table 2 Fields and field terms in the GRIIS Country Compendium.Full size tableTable 3 Countries in the GRIIS Country Compendium and their review status.Full size tablePopulation of data fields in GRIISThe methods by which GRIIS is populated were described in 201813 and are summarised in brief here. A systematic decision-making process is used for each geographic unit by species record to designate non-native origin and evidence of impact (see Fig. 2 in Pagad et al.13). Comprehensive searches are undertaken for each country. Records are included from the earliest documented to the most recent accessed record prior to the date of the latest published checklist version. Information sources include peer-reviewed scientific publications, national checklists and databases, reports containing results of surveys of alien and invasive alien species, general reports (including unpublished government reports), and datasets held by researchers and practitioners13. A log of the changes to each checklist is available on the GBIF IPT24, with the changes to the Belgium checklist available at the INBO IPT25. The most up to date version of each checklist is thus available via GBIF.org, as is a list of all GRIIS checklists at GBIF.org24.Fig. 2Summary of data in the GRIIS Country Compendium. Number of invasive alien species by major taxonomic group (a) and habitat (b). Number of records per major taxonomic groups (c) and habitat (d). The number of species and records associated with invasion impact (i.e. isInvasive) are shown in black. Note different y-axis scales in each case.Full size imageIntroduced species of all taxonomic groups are considered for inclusion in GRIIS. Habitats include terrestrial, freshwater, brackish, marine and also host (i.e. for species that are not free-living) (Table 2, Pagad et al.13). The habitat information in GRIIS (Table 2) is sourced from taxon and region-specific databases such as WoRMS (World Register of Marine Species), FishBase, Pacific Island Ecosystems at Risk, and the USDA Plants Database. Typically, GRIIS records are at the species level, but in some cases, other ranks are more appropriate including infraspecies (including forms, varieties and subspecies). A separate field is provided for hybrids (Table 2). Where species are present and both native to parts of a country and alien in other parts of the country, their introduction status (dwc:establishmentMeans) is included as Native|Alien (Tables 1, 2)26. If there is limited knowledge about the Origin of the species, its introduction status (dwc:establishmentMeans) is included as Cryptogenic|Uncertain (Tables 1, 2).Two types of evidence are considered to assign a species by country record as invasive (Table 1, see also Pagad et al.13): (i) when any authoritative source (e.g. from the primary literature or unpublished reports from country/species experts), describe an environmental impact, and/or (ii) when any source determines the species to be widespread, spreading rapidly or present in high abundance (based on the assumption that cover, abundance, high rates of population growth or spread are positively correlated with impact)27,28. Each record is assigned either invasive or null in the isInvasive field to reflect the presence of evidence of impact, or absence of evidence of impact (note, not ‘evidence of absence of impact’), for that species by country record (Table 2). In the future this information may be supplemented with impact scores29,30,31. Finally, a draft checklist is sent to Country Editors for validation and revision (see Technical Validation).Taxonomic harmonization and normalizationThe use of different synonyms across countries to refer to the same taxonomic concept is frequent32. The species in each Country Checklist were thus harmonised against the GBIF Backbone Taxonomy33. The names in each checklist were matched using a custom script that integrates with the GBIF API34, and the accepted name, taxon rank, status and higher taxonomy (Table 2) were obtained at this stage. Spelling and other errors in assigning species authorship were corrected where appropriate.To validate the taxonomic harmonisation, every name variant present in the GRIIS Country Compendium was checked against the GBIF Backbone Taxonomy using the API33. A unique list of names (i.e. acceptedName Usage) was thus produced and the source name retained as ‘scientificName’ (that can differ across countries) (Table 2). Over 95% of names across all kingdoms matched exactly at 98% or greater confidence (Table 4). All names that were below 98% confidence or had a match type other than ‘Exact’ were checked and modified if appropriate to do so. Of the non-matches (n = 253, those with a match type of ‘None’), most were formulaic hybrid names of plants and animals (~62%), which are not officially supported by GBIF35. The remaining non-matches were names of mostly plants (17%), but also animals (8%), viruses (8%) and chromists (3%).Table 4 Taxonomic matching results (percentages) by Kingdom using the GBIF Backbone Taxonomy33.Full size tableData summaryThere are currently ~23 700 species represented by 101 000 taxon-country combination records, across 196 countries in the GRIIS Country Compendium. All raw numbers are provided to the nearest order of magnitude to reflect the taxonomic uncertainty and dynamic nature of GRIIS (see ‘Known data gaps and uncertainties’). The vast majority of records are at the species level (97.6%), with the remaining present as subspecies (1.7%), varieties (0.6%), genera (0.1%) and forms ( More

Byrne, M. Impact of ocean warming and ocean acidification on marine invertebrate life history stages: vulnerabilities and potential for persistence in a changing ocean. Oceanogr. Mar. Biol. Annu. Rev. 49, 1–42 (2011).
Google Scholar 
Byrne, M. & Przeslawski, R. Multistressor impacts of warming and acidification of the ocean on marine invertebrates’ life histories. Integr. Comp. Biol. 53, 582–596 (2013).CAS 
Article 

Google Scholar 
Fitzer, S. C. et al. Ocean acidification and temperature increase impact mussel shell shape and thickness: problematic for protection? Ecol. Evol. 5, 4875–4884 (2015).Article 

Google Scholar 
Hofmann, G. E. et al. The effect of ocean acidification on calcifying organisms in marine ecosystems: an Organism-to-Ecosystem perspective. Annu. Rev. Ecol. Evol. Syst. 41, 127–147 (2010).Article 

Google Scholar 
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Chang. Biol. 19, 1884–1896 (2013).Article 

Google Scholar 
Kroeker, K. J. et al. Interacting environmental mosaics drive geographic variation in mussel performance and predation vulnerability. Ecol. Lett. 19, 771–779 (2016).Article 

Google Scholar 
Parker, L. M. et al. Predicting the response of molluscs to the impact of ocean acidification. Biology 2, 651–692 (2013).CAS 
Article 

Google Scholar 
Przeslawski, R., Byrne, M. & Mellin, C. A review and meta-analysis of the effects of multiple abiotic stressors on marine embryos and larvae. Glob. Chang. Biol. 21, 2122–2140 (2015).Article 

Google Scholar 
Suckling, C. C. et al. Adult acclimation to combined temperature and pH stressors significantly enhances reproductive outcomes compared to short-term exposures. J. Anim. Ecol. 84, 773–784 (2015).Article 

Google Scholar 
Thomsen, J., Haynert, K., Wegner, K. M. & Melzner, F. Impact of seawater carbonate chemistry on the calcification of marine bivalves. Biogeosciences 12, 4209–4220 (2015).Article 

Google Scholar 
Waldbusser, G. G. et al. Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nat. Clim. Chang. 5, 273–280 (2014).Article 
CAS 

Google Scholar 
Waldbusser, G. G. et al. Ocean acidification has multiple modes of action on bivalve larvae. PLoS ONE 10, e0128376 (2015).Article 
CAS 

Google Scholar 
Barclay, K. M. et al. Variation in the effects of ocean acidification on shell growth and strength in two intertidal gastropods. Mar. Ecol. Prog. Ser. 626, 109–121 (2019).CAS 
Article 

Google Scholar 
Byrne, M. & Fitzer, S. The impact of environmental acidification on the microstructure and mechanical integrity of marine invertebrate skeletons. Conserv. Physiol. 7, coz062 (2019).CAS 
Article 

Google Scholar 
Cross, E. L., Peck, L. S. & Harper, E. M. Ocean acidification does not impact shell growth or repair of the Antarctic brachiopod Liothyrella uva (Broderip, 1833). J. Exp. Mar. Biol. Ecol. 462, 29–35 (2015).CAS 
Article 

Google Scholar 
Cross, E. L., Peck, L. S., Lamare, M. D. & Harper, E. M. No ocean acidification effects on shell growth and repair in the New Zealand brachiopod Calloria inconspicua (Sowerby, 1846). ICES J. Mar. Sci. 73, 920–926 (2015).Article 

Google Scholar 
Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: The other CO2 problem. Annu. Rev. Mar. Sci. 1, 169–192 (2009).Article 

Google Scholar 
Watson, S.-A. et al. Marine invertebrate skeleton size varies with latitude, temperature and carbonate saturation: implications for global change and ocean acidification. Glob. Chang. Biol. 18, 3026–3038 (2012).Article 

Google Scholar 
Fitzer, S. C., Cusack, M., Phoenix, V. R. & Kamenos, N. A. Ocean acidification reduces the crystallographic control in juvenile mussel shells. J. Struct. Biol. 188, 39–45 (2014).CAS 
Article 

Google Scholar 
Gaylord, B. et al. Functional impacts of ocean acidification in an ecologically critical foundation species. J. Exp. Biol. 214, 2586–2594 (2011).CAS 
Article 

Google Scholar 
Gazeau, F. et al. Impacts of ocean acidification on marine shelled molluscs. Mar. Biol. 160, 2207–2245 (2013).CAS 
Article 

Google Scholar 
Bullard, E. M., Torres, I., Ren, T., Graeve, O. A. & Roy, K. Shell mineralogy of a foundational marine species, Mytilus californianus, over half a century in a changing ocean. Proc. Natl. Acad. Sci. USA 118, e2004769118 (2021).CAS 
Article 

Google Scholar 
Cross, E. L., Harper, E. M. & Peck, L. S. Thicker shells compensate extensive dissolution in brachiopods under future ocean acidification. Environ. Sci. Technol. 53, 5016–5026 (2019).CAS 
Article 

Google Scholar 
Harper, E. M. Are calcitic layers an effective adaptation against shell dissolution in the Bivalvia? J. Zool. 251, 179–186 (2000).Article 

Google Scholar 
Ashton, G. V., Morley, S. A., Barnes, D. K. A., Clark, M. S. & Peck, L. S. Warming by 1 °C drives species and assemblage level responses in Antarctica’s marine shallows. Curr. Biol. 27, 2698–2705.e3 (2017).Article 
CAS 

Google Scholar 
Cornwall, C. E. et al. A coralline alga gains tolerance to ocean acidification over multiple generations of exposure. Nat. Clim. Chang. 10, 143–146 (2020).CAS 
Article 

Google Scholar 
Donelson, J. M., Salinas, S., Munday, P. L. & Shama, L. N. S. Transgenerational plasticity and climate change experiments: where do we go from here? Glob. Chang. Biol. 24, 13–34 (2018).Article 

Google Scholar 
Gibbin, E. M., Massamba N’Siala, G., Chakravarti, L. J., Jarrold, M. D. & Calosi, P. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).Article 
CAS 

Google Scholar 
Peck, L. S. Organisms and responses to environmental change. Mar. Genom. 4, 237–243 (2011).Article 

Google Scholar 
Somero, G. N. The physiology of global change: linking patterns to mechanisms. Annu. Rev. Mar. Sci. 4, 39–61 (2012).Article 

Google Scholar 
Telesca, L., Peck, L. S., Backeljau, T., Heinig, M. F. & Harper, E. M. A century of coping with environmental and ecological changes via compensatory biomineralization in mussels. Glob. Chang. Biol. 27, 624–639 (2021).Article 

Google Scholar 
Kroeker, K. J. et al. Ecological change in dynamic environments: accounting for temporal environmental variability in studies of ocean change biology. Glob. Chang. Biol. 26, 54–67 (2020).Article 

Google Scholar 
Bernhardt, J. R., Sunday, J. M., Thompson, P. L. & O’Connor, M. I. Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment. Proc. Biol. Sci. 285, 20181076 (2018).
Google Scholar 
Harley, C. D. G. et al. Conceptualizing ecosystem tipping points within a physiological framework. Ecol. Evol. 7, 6035–6045 (2017).Article 

Google Scholar 
Griffiths, J. S., Pan, T.-C. F. & Kelly, M. W. Differential responses to ocean acidification between populations of Balanophyllia elegans corals from high and low upwelling environments. Mol. Ecol. 28, 2715–2730 (2019).CAS 

Google Scholar 
Telesca, L. et al. Biomineralization plasticity and environmental heterogeneity predict geographical resilience patterns of foundation species to future change. Glob. Chang. Biol. 25, 4179–4193 (2019).Article 

Google Scholar 
Barnes, D. K. A., Ashton, G. V., Morley, S. A. & Peck, L. S. 1 °C warming increases spatial competition frequency and complexity in antarctic marine macrofauna. Commun. Biol. 4, 208 (2021).Article 

Google Scholar 
Cross, E. L., Harper, E. M. & Peck, L. S. A 120-year record of resilience to environmental change in brachiopods. Glob. Chang. Biol. 24, 2262–2271 (2018).Article 

Google Scholar 
Kidwell, S. M. Biology in the anthropocene: challenges and insights from young fossil records. Proc. Natl Acad. Sci. USA 112, 4922–4929 (2015).CAS 
Article 

Google Scholar 
Pfister, C. A. et al. Historical baselines and the future of shell calcification for a foundation species in a changing ocean. Proc. Biol. Sci. 283, 20160392 (2016).
Google Scholar 
Angilletta, M. J., Jr Zelic, M. H., Adrian, G. J., Hurliman, A. M. & Smith, C. D. Heat tolerance during embryonic development has not diverged among populations of a widespread species (Sceloporus undulatus). Conserv. Physiol. 1, cot018 (2013).Article 

Google Scholar 
Hofmann, G. E. & Somero, G. Evidence for protein damage at environmental temperatures: Seasonal changes in levels of ubiquitin conjugates and hsp70 in the intertidal mussel Mytilus trossulus. J. Exp. Biol. 198, 1509–1518 (1995).CAS 
Article 

Google Scholar 
Roberts, D. A., Hofmann, G. E. & Somero, G. N. Heat-Shock protein expression in Mytilus californianus: Acclimatization (seasonal and Tidal-Height comparisons) and acclimation effects. Biol. Bull. 192, 309–320 (1997).CAS 
Article 

Google Scholar 
Easterling, D. R. et al. Climate extremes: observations, modeling, and impacts. Science 289, 2068–2074 (2000).CAS 
Article 

Google Scholar 
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).CAS 
Article 

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

Google Scholar 
Rummukainen, M. Changes in climate and weather extremes in the 21st century. Wiley Interdiscip. Rev. Clim. Change 3, 115–129 (2012).Article 

Google Scholar 
Nehls, G. & Thiel, M. Large-scale distribution patterns of the mussel Mytilus edulis in the Wadden Sea of Schleswig-Holstein: do storms structure the ecosystem? Neth. J. Sea Res. 31, 181–187 (1993).Article 

Google Scholar 
Sorte, C. J. B. et al. Thermal tolerance limits as indicators of current and future intertidal zonation patterns in a diverse mussel guild. Mar. Biol. 166, https://doi.org/10.1007/s00227-018-3452-6 (2019).Gao, Y. et al. Evolution of trace metal and organic pollutant concentrations in the Scheldt River Basin and the Belgian Coastal Zone over the last three decades. J. Mar. Syst. 128, 52–61 (2013).Article 

Google Scholar 
Camphuysen, K. & Vollaard, B. Oil pollution in the Dutch sector of the North Sea. In Oil Pollution in the North Sea (ed., Carpenter, A.) 117–140 (Springer International Publishing, 2016).Brion, N., Jans, S., Chou, L. & Rousseau, V. Nutrient loads to the Belgian coastal zone. In Current Status of Eutrophication in the Belgian Coastal Zone (eds Rousseau, V., Lancelot, C. & Cox, D.) 17–43 (Presses Universitaires de Bruxelles, Brussels, 2008).Gypens, N., Borges, A. V. & Lancelot, C. Effect of eutrophication on air-sea CO2 fluxes in the coastal Southern North Sea: a model study of the past 50 years. Glob. Chang. Biol. 15, 1040–1056 (2009).Article 

Google Scholar 
Mackenzie, F. T., Ver, L. M. & Lerman, A. Century-scale nitrogen and phosphorus controls of the carbon cycle. Chem. Geol. 190, 13–32 (2002).CAS 
Article 

Google Scholar 
Burrows, M. T. & Hughes, R. N. Natural foraging of the dogwhelk, Nucella lapillus (Linnaeus); the weather and whether to feed. J. Moll. Stud. 55, 285–295 (1989).Article 

Google Scholar 
Hughes, R. N. & Burrows, M. T. An interdisciplinary approach to the study of foraging behaviour in the predatory gastropod, Nucella lapillus (L.). Ethol. Ecol. Evol. 6, 75–85 (1994).Article 

Google Scholar 
Trussell, G. C., Ewanchuk, P. J. & Bertness, M. D. Trait-mediated effects in rocky intertidal food chains: predator risk cues alter prey feeding rates. Ecology 84, 629–640 (2003).Article 

Google Scholar 
Palmer, A. R. Effect of crab effluent and scent of damaged conspecifics on feeding, growth, and shell morphology of the Atlantic dogwhelk Nucella lapillus (L.). Hydrobiologia 193, 155–182 (1990).Article 

Google Scholar 
Pascoal, S., Carvalho, G., Creer, S., Mendo, S. & Hughes, R. N. Plastic and heritable variation in shell thickness of the intertidal gastropod Nucella lapillus associated with risks of crab predation and wave action, and sexual maturation. PLoS ONE 7, e52134 (2012).CAS 
Article 

Google Scholar 
Avery, R. & Etter, R. J. Microstructural differences in the reinforcement of a gastropod shell against predation. Mar. Ecol. Prog. Ser. 323, 159–170 (2006).Article 

Google Scholar 
Mayk, D. Transitional spherulitic layer in the muricid Nucella lapillus. J. Molluscan Stud. 87, https://doi.org/10.1093/mollus/eyaa035 (2020).Berry, R. J. & Crothers, J. H. Visible variation in the dog whelk, Nucella lapillus. J. Zool. 174, 123–148 (1974).Article 

Google Scholar 
Crothers, J. H. Two different patterns of shell-shape variation in the dog-whelk Nucella lapillus (L.). Biol. J. Linn. Soc. Lond. 25, 339–353 (1985).Article 

Google Scholar 
Galante-Oliveira, S., Marçal, R., Pacheco, M. & Barroso, C. M. Nucella lapillus ecotypes at the southern distributional limit in Europe: Variation in shell morphology is not correlated with chromosome counts on the Portuguese Atlantic coast. J. Mollusc. Stud. 78, 147–150 (2011).Article 

Google Scholar 
Appleton, R. D. & Palmer, A. R. Water-borne stimuli released by predatory crabs and damaged prey induce more predator-resistant shells in a marine gastropod. Proc. Natl Acad. Sci. USA 85, 4387–4391 (1988).CAS 
Article 

Google Scholar 
Cowell, E. B. & Crothers, J. H. On the occurrence of multiple rows of ‘teeth’ in the shell of the dog-whelk Nucella lapillus. J. Mar. Biol. Assoc. UK 50, 1101–1111 (1970).Article 

Google Scholar 
Currey, J. D. & Hughes, R. N. Strength of the dogwhelk Nucella lapillus and the winkle Littorina littorea from different habitats. J. Anim. Ecol. 51, 47–56 (1982).Article 

Google Scholar 
Hughes, R. N. & Elner, R. W. Tactics of a predator, Carcinus maenas, and morphological responses of the prey, Nucella lapillus. J. Anim. Ecol. 48, 65–78 (1979).Article 

Google Scholar 
Vermeij, G. J. & Currey, J. D. Geographical variation in the shell strength of thaidid snail shells. Biol. Bull. 158, 383–389 (1980).Article 

Google Scholar 
Benedetti-Cecchi, L. & Trussell, G. C. Rocky intertidal communities. In Marine Community Ecology and Conservation 203–225 (Sinauer Associates, Sunderland, MA, 2014).Telesca, L. et al. Blue mussel shell shape plasticity and natural environments: a quantitative approach. Sci. Rep. 8, 2865 (2018).Article 
CAS 

Google Scholar 
Cooke, A. H. & Reed, F. R. C. The Cambridge Natural History (Macmillan Company, 1895).Kitching, J. A. & Ebling, F. J. In Ecological Studies at Lough Ine Vol. 4 197–291 (ed. Cragg, J. B.) (Academic Press, 1967).Crothers, J. H. Dog-whelks: an introduction to the biology of Nucella lapillus (L.). Field Stud. 6, 291–360 (1985).Chadwick, M., Harper, E. M., Lemasson, A., Spicer, J. I. & Peck, L. S. Quantifying susceptibility of marine invertebrate biocomposites to dissolution in reduced pH. R. Soc. Open Sci. 6, 190252 (2019).CAS 
Article 

Google Scholar 
Laing, I. Effect of temperature and ration on growth and condition of king scallop (Pecten maximus) spat. Aquaculture 183, 325–334 (2000).Thouzeau, G. et al. Growth of Argopecten purpuratus (Mollusca: Bivalvia) on a natural bank in Northern Chile: sclerochronological record and environmental controls. Aquat. Living Resour. 21, 45–55 (2008).Article 

Google Scholar 
Kleinman, S., Hatcher, B. G., Scheibling, R. E., Taylor, L. H. & Hennigar, A. W. Shell and tissue growth of juvenile sea scallops (Placopecten magellanicus) in suspended and bottom culture in Lunenburg Bay, Nova Scotia. Aquaculture 142, 75–97 (1996).Article 

Google Scholar 
Doroudi, M. S., Southgate, P. C. & Mayer, R. J. The combined effects of temperature and salinity on embryos and larvae of the black lip pearl oyster, Pinctada margaritifera (L.). Aquacult. Res. 30, 271–277 (1999).Article 

Google Scholar 
Tomaru, Y., Kumatabara, Y., Kawabata, Z. & Nakano, S. Effect of water temperature and chlorophyll abundance on shell growth of the Japanese pearl oyster, Pinctada fucata martensii, in suspended culture at different depths and sites. Aquacult. Res. 33, 109–116 (2002).Article 

Google Scholar 
Schöne, B., Tanabe, K., Dettman, D. & Sato, S. Environmental controls on shell growth rates and δ18O of the shallow-marine bivalve mollusk Phacosoma japonicum in Japan. Mar. Biol. 142, 473–485 (2003).Article 

Google Scholar 
Ballesta-Artero, I., Witbaard, R., Carroll, M. L. & Meer, J van der Environmental factors regulating gaping activity of the bivalve Arctica islandica in northern Norway. Mar. Biol. 164, 116 (2017).Article 

Google Scholar 
Witbaard, R. Growth variations in Arctica islandica L. (Mollusca): a reflection of hydrography-related food supply. ICES J. Mar. Sci. 53, 981–987 (1996).Article 

Google Scholar 
Joubert, C. et al. Temperature and food influence shell growth and mantle gene expression of shell matrix proteins in the pearl oyster Pinctada margaritifera. PLoS ONE 9, e103944 (2014).Article 
CAS 

Google Scholar 
Fay, A. R. & McKinley, G. A. Global trends in surface ocean pCO2 from in situ data. Global Biogeochem. Cycles 27, 541–557 (2013).CAS 
Article 

Google Scholar 
Ostle, C. et al. Carbon Dioxide and Ocean Acidification Observations in UK Waters. Synthesis report with a focus on 2010–2015. 44 https://doi.org/10.13140/RG.2.1.4819.4164 (2016).Borges, A. V. & Gypens, N. Carbonate chemistry in the coastal zone responds more strongly to eutrophication than ocean acidification. Limnol. Oceanogr. 55, 346–353 (2010).CAS 
Article 

Google Scholar 
Clarke, A. & Beaumont, J. C. An extreme marine environment: a 14-month record of temperature in a polar tidepool. Polar Biol. 43, 2021–2030 (2020).Article 

Google Scholar 
Fisher, J. A. D., Rhile, E. C., Liu, H. & Petraitis, P. S. An intertidal snail shows a dramatic size increase over the past century. Proc. Natl Acad. Sci. USA 106, 5209–5212 (2009).CAS 
Article 

Google Scholar 
Clarke, A. Seasonal acclimatization and latitudinal compensation in metabolism: Do they exist? Funct. Ecol. 7, 139–149 (1993).Article 

Google Scholar 
Sanders, T., Thomsen, J., Müller, J. D., Rehder, G. & Melzner, F. Decoupling salinity and carbonate chemistry: low calcium ion concentration rather than salinity limits calcification in Baltic Sea mussels. Biogeosciences 18, 2573–2590 (2021).CAS 
Article 

Google Scholar 
Palmer, A. R. Relative cost of producing skeletal organic matrix versus calcification: evidence from marine gastropods. Mar. Biol. 75, 287–292 (1983).Article 

Google Scholar 
Palmer, A. R. Calcification in marine molluscs: how costly is it? Proc. Natl. Acad. Sci. USA 89, 1379–1382 (1992).Watson, S.-A., Morley, S. A. & Peck, L. S. Latitudinal trends in shell production cost from the tropics to the poles. Sci. Adv. 3, e1701362 (2017).Article 
CAS 

Google Scholar 
Burton, E. A. & Walter, L. M. Relative precipitation rates of aragonite and Mg calcite from seawater: temperature or carbonate ion control? Geology 15, 111–114 (1987).CAS 
Article 

Google Scholar 
Sanders, T., Schmittmann, L., Nascimento-Schulze, J. C. & Melzner, F. High calcification costs limit mussel growth at low salinity. Front. Mar. Sci. 5, 352 (2018).Article 

Google Scholar 
Etter, R. J. Assymmetrical development plasticity in an intertidal snail. Evolution 42, 322–334 (1988).Article 

Google Scholar 
Largen, M. J. The influence of water temperature upon the life of the dog-whelk Thais lapillus (Gastropoda: Prosobranchia). J. Anim. Ecol. 36, 207–214 (1967).Article 

Google Scholar 
Stickle, W. B., Moore, M. N. & Bayne, B. L. Effects of temperature, salinity and aerial exposure on predation and lysosomal stability of the dogwhelk Thais (Nucella) lapillus (L.). J. Exp. Mar. Biol. Ecol. 93, 235–258 (1985).Article 

Google Scholar 
Queirós, A. M. et al. Scaling up experimental ocean acidification and warming research: from individuals to the ecosystem. Glob. Chang. Biol. 21, 130–143 (2015).Article 

Google Scholar 
Hughes, R. N. Annual production of two Nova Scotian populations of Nucella lapillus (L.). Oecologia 8, 356–370 (1972).CAS 
Article 

Google Scholar 
Gattuso, J.-P., Frankignoulle, M., Bourge, I., Romaine, S. & Buddemeier, R. W. Effect of calcium carbonate saturation of seawater on coral calcification. Glob. Planet. Change 18, 37–46 (1998).Article 

Google Scholar 
Schneider, K. & Erez, J. The effect of carbonate chemistry on calcification and photosynthesis in the hermatypic coral Acropora eurystoma. Limnol. Oceanogr. 51, 1284–1293 (2006).CAS 
Article 

Google Scholar 
Kremling, K. & Wilhelm, G. Recent increase of the calcium concentrations in baltic sea waters. Mar. Pollut. Bull. 34, 763–767 (1997).CAS 
Article 

Google Scholar 
Riebesell, U., Fabry, V. J., Hansson, L. & Gattuso, J.-P. Guide to Best Practices for Ocean Acidification Research and Data Reporting (Office for Official Publications of the European Communities, 2011).Desmit, X. et al. Changes in chlorophyll concentration and phenology in the North Sea in relation to de eutrophication and sea surface warming. Limnol. Oceanogr. 65, 828–847 (2020).CAS 
Article 

Google Scholar 
Petraitis, P. S. & Dudgeon, S. R. Declines over the last two decades of five intertidal invertebrate species in the western North Atlantic. Commun. Biol. 3, 591 (2020).Article 

Google Scholar 
Mayk, D., Peck, L. S. & Harper, E. M. Evidence for carbonate system mediated shape shift in an intertidal predatory gastropod. Front. Mar. Sci. 9, 894182 (2022).Article 

Google Scholar 
Page, H. M. & Hubbard, D. M. Temporal and spatial patterns of growth in mussels Mytilus edulis on an offshore platform: relationships to water temperature and food availability. J. Exp. Mar. Biol. Ecol. 111, 159–179 (1987).Article 

Google Scholar 
Thomsen, J., Casties, I., Pansch, C., Körtzinger, A. & Melzner, F. Food availability outweighs ocean acidification effects in juvenile Mytilus edulis: laboratory and field experiments. Glob. Chang. Biol. 19, 1017–1027 (2013).Article 

Google Scholar 
Wołowicz, M., Sokołowski, A., Bawazir, A. S. & Lasota, R. Effect of eutrophication on the distribution and ecophysiology of the mussel Mytilus trossulus (Bivalvia) in southern Baltic Sea (the Gulf of Gdańsk). Limnol. Oceanogr. 51, 580–590 (2006).Article 

Google Scholar 
Moran, P. J. & Grant, T. R. The effects of industrial pollution on the development and succession of marine fouling communities I. Analysis of species richness and frequency data. Mar. Ecol. 10, 231–246 (1989).Article 

Google Scholar 
Moran, P. J. & Grant, T. R. Transference of marine fouling communities between polluted and unpolluted sites: impact on structure. Environ. Pollut. 72, 89–102 (1991).CAS 
Article 

Google Scholar 
Rastetter, E. B. & Cooke, W. J. Responses of marine fouling communities to sewage abatement in Kaneohe Bay, Oahu, Hawaii. Mar. Biol. 53, 271–280 (1979).Article 

Google Scholar 
Boschma, H. Elminius modestus in the Netherlands. Nature 161, 403–404 (1948).Article 

Google Scholar 
Wolff, W. J. Non-indigenous marine and estuarine species in the Netherlands. Zool. Meded. 79-1, 1–116 (2005).
Google Scholar 
Kerckhof, F. Barnacles (Cirripedia, Balanomorpha) in Belgian waters, an overview of the species and recent evolutions, with emphasis on exotic species. Bull. Inst. R. Sci. Nat. Belg. Biol./Bull. K. Belg. Inst. Natuurwet. Biol. 72, 93–104 (2002).
Google Scholar 
Gibbs, P. E., Bryan, G. W. & Pascoe, P. L. TBT-induced imposex in the dogwhelk, Nucella lapillus: geographical uniformity of the response and effects. Mar. Environ. Res. 32, 79–87 (1991).CAS 
Article 

Google Scholar 
Gibbs, P. E. Biological Effects of Contaminants: Use of Imposex in the Dogwhelk (Nucella lapillus) as a Bioindicator of Tributyltin Pollution. 37 https://doi.org/10.25607/OBP-272 (1999).Oehlmann, J. et al. Imposex in Nucella lapillus and intersex in Littorina littorea: Interspecific comparison of two TBT-induced effects and their geographical uniformity. In Aspects of Littorinid Biology (eds O’Riordan, R. M., Burnell, G. M., Davies, M. S. & Ramsay, N. F.) 199–213 (Springer Netherlands, 1998).Kerckhof, F. Over het verdwijnen van de purperslak Nucella lapillus (Linnaeus, 1758), langs onze kust. De Strandvlo 8, 82–85 (1988).
Google Scholar 
De Blauwe, H. & D’Udekem d’Acoz, C. Voortplantende populatie van de purperslak (Nucella lapillus) in België na meer dan 30 jaar afwezigheid (Mollusca, Gastropoda, Muricidae). De Strandvlo 32, 127–131 (2012).
Google Scholar 
Galante-Oliveira, S. et al. Factors affecting RPSI in imposex monitoring studies using Nucella lapillus (L.) as bioindicator. J. Environ. Monit. 12, 1055–1063 (2010).CAS 
Article 

Google Scholar 
National Centers for Environmental Information/NESDIS/NOAA/U.S. Department of Commerce et al. International Comprehensive Ocean–Atmosphere Data Set (ICOADS) Release 3, Monthly Summaries https://doi.org/10.5065/D6V40SFD (2016).ICES. Dataset on Ocean Hydrography (ICES, 2020).Giardina, C. R. & Kuhl, F. P. Accuracy of curve approximation by harmonically related vectors with elliptical loci. Comput. Graph. Image Process. 6, 277–285 (1977).Article 

Google Scholar 
Kuhl, F. P. & Giardina, C. R. Elliptic fourier features of a closed contour. Comput. Graph. Image Process. 18, 236–258 (1982).Article 

Google Scholar 
Bonhomme, V., Picq, S., Gaucherel, C. & Claude, J. Momocs: outline analysis using R. J. Stat. Softw. 56, 1–24 (2014).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2020).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).Article 

Google Scholar 
Bartoń, K. MuMIn: Multi-Model Inference https://cran.r-project.org/web/packages/MuMIn/index.html (2020).Akaike, H. Information theory and an extension of the maximum likelihood principle. Springer Ser. Stat. 610–624 https://doi.org/10.1007/978-1-4612-0919-5/_38 (1992). More