Ecology

Confirmation of species, using DNA analysisBecause the DNA of our sphere samples matches that of adult squid identified as I. coindetii from Norwegian waters we infer that the spheres are from I. coindetii. Much has been written about taxonomic difficulties in Illex. The COI tree comprises four clades of Illex, one of which clearly pertains to Illex argentinus (Castellanos, 1960). There are three other described species: Illex coindetii, Illex illecebrosus (Lesueur, 1821), and I. oxygonius Roper, Lu & Mangold, 1969. We labelled our clades A, B, and C, to indicate their correspondence with the findings of Carlini et al.32, and assume that each pertains to one of the described species of Illex. Carlini was unable to match species to clades, but Clade A not only contains the adults identified in this project as I. coindetii, but also contains specimens from the Mediterranean (DQ373941). Since I. coindetii is the only species of Illex known from the Mediterranean, this is further confirmation of the identity of Clade A, and thus our spheres, as Illex coindetii.Using citizen science from roughly 200 divers secured observations of 90 spheres, including rare tissue samples of four of them, thus enabling a molecular approach towards the first confirmation of egg masses in situ as those of the broadtail shortfin squid, Illex coindetii. Illex coindetii was named in honour of Dr. Coindet from Geneva in 185137. It took 180 years from the description of the adult to identification of its egg mass in the wild. To our knowledge no whole egg mass of Illex spp. has previously been reported from the wild, except by Adolf Naef, who reported on live ommastrephid embryos and paralarvae from Naples, Italy2. The embryos were pulled out of a floating spawn or floating egg mass, or as he describes «Fig. 1 und 2 sind aus einem flottierenden Laich gezogene Larven von Ommatostrephiden». These illustrations were later identified as Illex coindetii by Boletzky et al.26, studying egg development of I. coindetii in the laboratory, claiming «The general characteristics of the embryonic developement observed by us match the figures given by Naef (1923 : plts 9–12) of an unidentified egg mass of a member of the Ommastrephidae (Naef 1921)». However, no drawing of the «laich» was provided.Challenges collecting in situ materialHuge gelatinous spheres from squid are difficult to study in situ. They are rarely reported, and hard to sample. We have collected 90 sphere observations from ~ 35 years back (~ 1985 to 2019), from an area stretching from the Mediterranean Sea north to the Norwegian Sea, which gives a good illustration on sphere findings of ~ 2.6 sphere observations per year. In addition, the spheres most likely have a short-life span. Life span of spheres spawned and reared in aquaria (between 40 and 120 cm in diameter) of Todarodes pacificus (Steenstrup, 1880) is 5–7 days, with the smallest disintegrating first38.Sphere shape and sizeGelatinous egg masses of cephalopods vary in size and form among species. Some egg masses are spherical, but there are also examples of oblong structures39,40,41. Sphere size may be up to 4 m in diameter1,5,42. Ringvold and Taite (op. cit.) collected information on a total of 27 spheres recorded in European waters varying from 0.3 to 2 m in diameter, as also for the additional spheres from this study. The four spheres in our study, confirmed to belong to I. coindetii, measured between 0.5 and 1 m in diameter.Egg mass of another ommastrephid squid, Todarodes sagittatus, has yet to be found in situ. The species is known to be larger than Illex species, and egg mass is also most probably larger. The largest spheres recorded in our study measured up to 2 m in diameter, but none of these were sampled for molecular analysis, nor were pictures taken. It is uncertain whether they could belong to other species e.g., Todaropsis eblanae (Ball, 1841), Todarodes sagittatus or Ommastrephes sp..Dark streak through coreAlmost 60% of the spheres had a dark streak through the center. This feature might be ink, one important characteristic of cephalopods, produced by most cephalopod orders. The ink sac with its ink glands produces black ink containing melanin43. During fertilization, sperm are released—as well as possibly some ink. Spheres with or without ink may be a result of spheres beeing at different maturity stages1, where spheres with ink are freshly spawned. After a while, when embryos starts developing, the whole sphere, including the streak, will start to disintegrate.Some of us speculate that one function of the streak through the center might serve as visual mimics e.g. of a large fish in order to scare off predators. Other possible functions discussed are also if the streak/structure can be caused by a sphere strengthening structure which is denser or having a higher optical density than the sourrounding structure. A disadvantage with the streak is that it might reveal the whole transparent sphere in the water, visible to e.g. scuba divers.Function of the gelatinous matrixObservations in captivity3,44 showed that species within the genus Illex produce gelatinous egg masses while swimming in open water. Gel functions as a buoyancy mechanism that prevents eggs from sinking, and complete density equilibration requires many days under most conditions44. Such a buoyancy mechanism keeps pelagically spawned eggs of Illex in areas where temperatures are most optimal for embryonic development. Optimal environmental conditions will likely have a positive effect on survival of both hatchlings and paralarvae. Despite consistency in where spawning areas are found, interannual variability has been recorded in the main recruitment areas, which could be related to e.g. mesoscale eddies and/or affecting post-hatching dispersal45.Huge spheres are formed of mucus produced by the nidamental glands, situated inside the mantle cavity of the female46,47. When fully developed, hatchlings emit an enzyme which starts to dissolve the mucus. Eggs and embryos from our four spheres were covered in sticky gelatinous mass, except for a few specimens (from Arendal, collected 7 August, and Søgne) laying in the petri dish outside the sticky gel, in the surrounding sea water following the tissue sample, and might have been old enough to start producing such enzymes.When at hatching, Illex coindetii eggs are about 2 mm long26,48, in line with other ommastrephids12. The longest of our embryos (from Arendal, collected 7 August) measured ~ 2 mm, a developed embryo with long proboscis, mantle about ½ of total body length, as well as chromatophores, large eyes and funnel visible (Fig. 3). It could possibly be a hatchling.Abiotic factors and locationsThe success and duration of embryonic development is related to water temperature. All observations available to date indicate that successful embryonic development for I. coindetii takes ca. 10–14 days at 15 °C; this temperature corresponds to the median temperature value reported for Mediterranean Sea midwater48. Boletzky et al.26 reports on a temperature minimum above 10 °C. Spheres in the Mediterranean were observed in temperatures ranging between 14 and 24 °C. Watermass temperature for one sphere with recently fertilized eggs (Ålesund sample, embryos stage ~ 3) from Norway was 8 °C. It was also observed north of the existing known distribution range for I. coindetii, in the Norwegian Sea, at 43 m depth. Most spheres from Norway were observed from July and August, in water mass 10–14 °C, with maximum temperature at 18 °C.It is unknown whether some of the observed spheres had drifted to water layers unsuitable for the development of the eggs, and, eventually, would have died due to unfavourable abiotic conditions (e.g. transport outside the optimal temperature- or depth range for that particular species), but most likely they were in an area where they would survive. Higher occurrence of sphere sightings from 2017 to 2019, could be a combination of higher abundance of these squid in the area as well as increased knowledge regarding our Citizen Science Sphere Project, and thereby increased reports of observations.Illex coindetii may be considered as an intermittent spawner with a spawning season extending throughout the year, reaching a peak in July–August18.Our sphere observations from all areas were made from March to October: The earliest sphere which can be documented (to month) in the North Sea to date was observed 27 May (2001), and the last sphere was reported on 20 October (2019), coinciding with a study on adult Illex condetii from the North Sea where the spawning season has been suggested to be between spring and autumn49. However, our data show a peak of sphere observations from July to September (all areas combined), from July to August in Norway and from August to September in the Mediterranean Sea. The two recordings from Galicia in Spain, and Seiano in Italy, were the earliest recordings of the year, observed 24 March (in 2017 and 2019, respectively). For all areas combined, no observations during wintertime (November to February) have been recorded.Embryonic development and consistencies of spheresWe collected tissue mass of four different spheres of I. coindetii, and embryos in each sphere were at different developmental stages, ~ 3 to 30, according to Sakai et al.36 based on I. argentinus. The sphere walls of the four spheres were also of different consistencies (Table 2); from Ålesund sphere with recently fertilized eggs and firm, transparent sphere wall to Søgne and Arendal spheres (the latter collected 7 August) with developed embryos and disintegrating sphere walls. The remains of the Arendal sphere was hanging as a long «scarf» in the water. Experienced divers, who previously had seen a few spherical spheres, recognized this disintegrating sphere.Function of spheresOmmastrephidae fecundity is extremely high, and a single sphere may contain thousands to several hundred thousands of eggs41,50,51,52. The function of the spheres is protection and transport of the offspring by sea currents for paralarval dispersal. Inside these gelatinous structures, the eggs and newly hatched paralarvae are protected from predation by e.g. fish, parasite infection and infestation by crustaceans and protozoans during a first relative short period of their lives5,51. Bottom trawlers operate in spawning areas of squids, exposing them to a risk of egg loss, as also for our fisherman at Askøy, Norway, who caught a sphere in his trawl1,5.Scientific cruises and fisheryThe Institute for Marine Research in Norway started identification of cephalopods on their regular scientific cruises in 2013, but no Illex coindetii was recorded that year. However, data show increasing catches from 2014 to 2019 (unpublished). No spheres are reported from Norway in 2013, but between 1995 to 2010, and from 2015 to 2019, observations were made. Most observations are between 2017 and 2019, indicating more frequent squid visits/spawnings. This coincides with more frequent sphere observations from 2017 to 2019.The broadtail shortfin squid, Illex coindetii, is probably the most widespread species found on both sides of the Atlantic and throughout the Mediterranean Sea12. In the NE Atlantic, it has been reported from Oslofjorden, Norway (59°N);53 and the Firth of Forth, east Scotland54, southwards along the European and African coasts to Namibia, including Hollam’s Bird Island (24°S) and Cape Frio (18°S)55. For example, I. coindetii is periodically very abundant in coastal waters of the eastern North Atlantic off Scotland, Ireland and Spain, where it supports opportunistic fisheries. However, the oceanographic and biological factors that drive this phenomenon, are still unknown12.Illex coindetii is widely distributed throughout the Mediterranean Sea11, where it is caught commercially mostly by Italian trawlers, usually as a by-catch, but also by recreational fishing, by means of squid jigging. Annual Italian landings during the last five years have varied between two and three thousand tonnes, but with historical landings reaching numbers of more than eight thousand tonnes during the 1980s and 1990s (FAO 2019)15.In the North Sea, studies show that inshore squids (Alloteuthis subulata (Lamarck, 1798) and Loligo forbesii Steenstrup, 1856) are more abundant than short-finned squid (Illex coindetii, Todaropsis eblanae and Todarodes sagittatus), and I. coindetii is among the rarest ommastrephid species caught49,56. However, two recent studies (1) on summer spawning stock of Illex coindetii in the North Sea57 and (2) I. coindetii recorded from the brackish Baltic Sea58 suggest more frequent visits to this area. Reports on Illex coindetii from Norwegian waters are scarce, but it has been reported from Oslofjorden53, and recently as by-catch from Stavanger area, and by divers from Oslofjorden and Bergen. More

Most problematic fishing methods based on ALDFG relative risksThis study presents the first quantitative assessment of gear-specific relative risks from ALDFG. Findings accounted for the: (a) derelict gear leakage rate; (b) fishing gear quantity indicators of catch and area of fishing grounds; and (c) adverse consequences from ALDFG. Maximum global conservation gains can be achieved through focusing ALDFG mitigation efforts on the fishing gears with the highest overall relative risk. Set and fixed gillnets and trammel nets, drift gillnets, gears using drifting and anchored FADs (tuna purse seines and pole-and-lines), and bottom trawls were the five most problematic gears on a global scale. This was followed by traps (fyke nets, pots, barriers, fences, weirs, corrals and pound nets).The overall RR score indicates a fishing gear’s relative degree of total adverse effects from ALDFG, accounting for the quantity of ALDFG produced by that gear (estimated from the ALDFG leakage rate and indices of fishing gear quantity of catch and area of fishing grounds), and the adverse consequences that result from ALDFG from that gear type relative to other gears. Globally, gillnets have the highest risks from ALDFG, while hand dredges and harpoons were least problematic.The focus of local management interventions to address problematic derelict fishing gear will be dictated by the specific context. Locally, adopting ALDFG controls following a sequential mitigation hierarchy and implementing effective monitoring, surveillance and enforcement systems are needed to curb derelict gear from these most problematic fisheries. This includes accounting for which fishing gears are predominant and the existing fisheries management framework. For example, a site may have pot and tuna purse seine anchored FAD fisheries. The purse seine fishery has a higher relative risk globally. However, a fisheries management system may have effective ALDFG preventive methods in place for this fishery, such as a high rate of detection and recovery of anchored FADs when they break from moorings, and minimization methods, such as prescribing the use of only non-entangling and biodegradable FAD designs to minimize adverse effects from derelict FADs35, 36. But there may be minimal measures in place to monitor and manage ALDFG from pots. In this hypothetical example, it would be a higher priority locally to improve ALDFG management for the pot fishery.Priority data quality improvementsThere are several priorities for data quality improvement to increase the certainty of future assessments. Given substantial deficits both in estimates of gear-specific quantity/effort and ALDFG rates, it is not yet possible to produce a robust contemporary estimate to replace the ca. five decades-old crude estimate of the magnitude of the annual quantity of leaked ALDFG4, 30. More robust estimates of ALDFG rates are needed for all gear types. Gear-specific estimates have low certainty due to small numbers of studies and sample sizes. Many compiled records estimate only one ALDFG component, typically only loss rates, and therefore may substantially underestimate total ALDFG rates. Most records are dated and may not accurately characterize contemporary rates. There is geographical sampling bias with estimates being primarily derived from the northern hemisphere. Furthermore, many estimates were derived from expert surveys (Supplementary Material Table S1), which have a higher risk of error and bias than approaches higher on the evidence hierarchy37. Substantially more primary studies with robust designs are needed.An expanded meta-analysis on gear-specific ALDFG rates is an additional priority, once sufficient sample sizes of robust studies accumulate. The statistical modeling approach used by Richardson et al.34 could be readily improved by using (1) a random-effects instead of a fixed effects structure to account for study-specific heterogeneity, and (2) a more appropriate model likelihood, such as zero-inflated Beta likelihood, to account for the zero values in the dataset38. Due to larger sample sizes and the number of independent studies, meta-analyses can produce estimates with increased accuracy, with increased statistical power to detect real effects. By synthesizing estimates from an assortment of independent, small and context-specific studies, pooled estimates from random-effects meta-analyses are generalizable and therefore relevant over diverse settings39. The strength of conclusions of hypotheses based on a single study can vary. This is because a single study can be context-specific, where true results may be affected by conditions specific to that single study, such as the species involved and environmental conditions, that cause the results from the single study to not be applicable under different conditions. A single study may also fail to find a meaningful result due to small sample sizes and low power. However, robust synthesis research, including meta-analysis, is more precise and powerful once a sufficient number of similar studies have accumulated, and therefore investing in more primary ALDFG studies is a high priority.For some gear types and fisheries, estimated ALDFG rates may overestimate adverse effects when gear that is abandoned, lost or even discarded does not become derelict because another fishing vessel continues to use the gear. For example, gear that is lost by theft remains in use. Macfadyen et al.4 explained that theft was likely a minor contribution to ALDFG, occurring, for instance, in inshore fishing grounds where static commercial fishing gear and recreational marine activities conflict. However, fishing gear theft may be prevalent in some developing country fisheries (e.g., Cambodian crab traps40). And, there is one gear type where theft has become a globally prevalent, routine and largely accepted practice: Tuna purse seine vessels routinely exchange satellite buoys attached to drifting FADs that they encounter at sea. The stolen FAD, lost by the previous vessel that had been tracking its position, remains in-use and not derelict, although it may eventually become derelict41, 42. Furthermore, because ALDFG leakage rates may be highest in illegal and unregulated fisheries4, if only legal fisheries are sampled, then this may produce underestimates. Thus, accounting for theft and illegal and unregulated fishing would increase the certainty of estimates of ALDFG leakage rates for some fishing gear types.The 20% ALDFG global production rate value used for anchored FADs by pole-and-line fisheries was likely an underestimate. We relied on a single value from the contemporary Maldives pole-and-line fishery’s government-owned and -managed network of anchored FADs. This fishery underwent a substantial reduction in anchored FAD loss rate, from 82 to 20%, by improving designs and a government incentive program that pays fishers to retrieve FADs when they break from their moorings35, 43. For comparison, describing Indonesia’s pole-and-line fishery’s anchored FADs, Widodo et al.44 stated: “Inaccuracy of number and position of FADs in the fishing ground are the outstanding issue facing by fisheries manager…This was largely the result of the current lack of effective systems of FAD registration and monitoring, and also because of the desire of fishing companies and vessel skippers to keep FADs position information confidential. [sic]”.Proctor et al.45, who estimated that between 5000 and 10,000 anchored FADs are used in Indonesian tuna fisheries, also reported a lack of accurate estimates of the numbers and locations of anchored FADs due to ineffective implementation of the government registration system and to high loss rates, including from storms, strong currents, vandalism, vessel collisions and wear and degradation of the FADs. Using the estimated rates of (1) Shainee and Leira43 that 82% of anchored FADs were lost per year prior to the Maldivian government’s incentives program, which might accurately characterize the Indonesian and other anchored FAD networks used by pole-and-line fisheries, and (2) the 20% loss rate value from Adam et al.35, the posterior mean = 0.506 (95% HDI: 0.15–0.84). Thus, 51% might have been a more appropriate estimate for a global ALDFG production rate for pole-and-line anchored FADs. The Maldivian and Indonesian pole-and-line fisheries, which combined supply over half of global pole-and-line catch, rely heavily on anchored FADs, as do several other smaller pole-and-line fisheries (e.g., Solomon Islands, segments of the Japanese pole-and-line fleet)35, 45,46,47,48.Units for ALDFG rates are highly variable. Records using different rates cannot be pooled for synthesis research29, 34. For example, some records reported rates of the percent of number of panels (sheets) or fleets (strings) of gillnets that were lost, while others reported the percent of the length or area of gillnets that were lost29. Similarly, for longline gear, some studies reported the percent of the length of the mainline, while others reported the percent of the number of branchlines/snoods that were lost34. Employment of agreed harmonized units for ALDFG rates are needed.Future assessments could use a ratio of ALDFG risk-to-seafood production to assess gear-specific relative risks locally and globally, similar to assessments of vulnerable fisheries bycatch by using bycatch-to-target catch ratios49. This would enable the assessment of risk from ALDFG to be balanced against meeting objectives of food security and nutritional health.Relationship between alternative indices with the quantity of fishing gearWe used gear-specific annual catch and area of fishing grounds as indicators of the relative global amount of each gear that is used annually as two terms in the model to assess gear-specific relative risks from ALDFG. However, the assumption of a linear relationship between these indices and gear quantity is questionable for similar reasons that have been raised with the relationship between various indices of effort (number of fishing hours, number of vessels, engine power, vessel length, gross tonnage, gear size, hold capacity, as well as kWh) and catch. For example, the ratio of catch from one set by an anchoveta purse seiner to the volume or weight of the gear is likely substantially different than for pots or driftnets. Not only is the relationship between catch and amount of gear variable by gear type and target species, there is also high variability within gear types—by fishery and within fisheries—due to the broad range of factors that significantly explain fishing efficiency per unit of nominal effort50, 51. Similarly, the relationship between catch weight and number of fishing operations varies substantially across gear types. For example, an industrial tropical tuna purse seine vessel might have a total catch of about 37 t per set on a drifting FAD27 while a tuna pole-and-line vessel catches about 1 t per fishing day52.Similarly, the relationship between the area of fishing grounds and amount of gear may vary substantially between gear types. A small number of vessels using a relatively small magnitude of active, mobile gear may have a much larger area of fishing grounds than a large number of vessels and shore-based fishers using a large amount of passive and static gears. For example, about 686 large-scale tuna purse seine vessels fish across the tropics53, while gillnets, which may be the most globally prevalent gear type, are used predominantly within 20 nm (37 km) of shore, most intensively in southeast Asia and the northwest Pacific54.Fishing effort has also been estimated using engine power as well as by using energy expended, such as in kilowatt-hours (kWh), the product of the fishing time and engine power of a fishing vessel, including non-motorized vessels55,56,57. We did not use these metrics for effort because the correlation between rate of production of ALDFG and vessel engine power or kWh, including of non-motorized vessels (1.70 million of the estimated global 4.56 million fishing vessels21), has not been explored. In general, vessel power and power per unit of fishing period largely distinguishes between mobile and passive gears, where the former (e.g., trawls, dredges), use substantially more vessel power per weight of catch than passive gears (traps, gillnets). Also, estimates using these fishing effort metrics used a small number of aggregated gear categories and extrapolated estimates primarily from sampled developed world fisheries (however, see56). These effort indices would also prevent inclusion of shore-based fishing methods.There have been recent gear specific estimates of effort, in units of time spent fishing and the estimated energy expended (fishing power * fishing time), using Automated Identification Systems (AIS) data, which are available for industrial fishing vessels, primarily using longlines, trawls and pelagic purse seines6, 58. AIS data provide coverage of the majority of large fishing vessels ≥ 24 m in overall length58. However, this accounts for only about 2% of the number of global fishing vessels (of an estimated 4.56 million global fishing vessels, about 67,800 are ≥ 24 m in length21).ALDFG monitoring, management and performance assessmentsA sequential mitigation hierarchy of avoidance, minimization, remediation and offsets can be applied to manage ALDFG29, 59. Referring to the three components of relative risk assessed by this study, avoidance and minimization of risks from ALDFG is achieved by reducing the ALDFG leakage rate, fishing effort, and/or adverse consequences from derelict gear. Remedial methods reduce adverse effects, such as reducing ghost fishing by reducing the duration that ALDFG remains in the marine environment1, 29, 60. In general, preventative methods are more cost effective than remedial methods—it is less expensive to prevent gear abandonment, loss and discarding than it is, for example, to detect and then disable or remove derelict gear61. Methods to prevent ALDFG include, for instance, spatially and temporally separating passive and mobile fishing gears, having bottom trawlers avoid features that could snag the net such as by using high-resolution seabed maps, tracking the real-time position of unattended fishing gears using various electronic technologies, and using gear marking to identify the owner and increase the visibility of passive gears. Furthermore, because some remedial methods, such as using less durable materials for fishing gear components, can reduce economic viability and practicality, preventative methods and remediation through quick recovery of ALDFG may be more effective as well as elicit broader stakeholder support29, 62.To assess the performance of global ALDFG management interventions against this study’s quantitative benchmark, substantial deficits in monitoring and surveillance of fisheries’ waste management practices must first be addressed1. Of 68 fisheries that catch marine resources managed by regional fisheries management organizations, 47 lack any observer coverage, half do not collect monitoring data on ALDFG, and surveillance and enforcement systems are rudimentary or nonexistent in many fisheries1, 63.Findings from this quantitative, global assessment of ALDFG risks guide the allocation of resources to achieve the largest improvements from preventing and remediating derelict gear from the world’s 4.6 million fishing vessels. With improved data quality and governance frameworks for fishing vessel waste management, including ALDFG, we can expect reductions in ecological and socioeconomic risks from derelict gear. More

pH distributions within sediment microcosms showed distinct spatial and temporal patterns. For the January 2019 experiment series, the strong pH maximum bands developed in the oxic surface sediment after 20 ~ 22 days of incubation. Development was not spatially uniform. Sediment surface pH maxima started to develop from isolated points covering horizontal lengths of 0.6–1 cm at the times of imaging (Fig. 1). Within a week, the pH maximum bands expanded laterally, covering the entire 6 cm long monitoring panel, and were sustained until the end of the experiment (106 days, data not shown). The pH maximum bands were about 2 mm in vertical extent with average pH ~ 8.5. Even after the electrogenic colonization was laterally complete (day 27), the activity intensities of cable bacteria, or the impacts of their activity as reflected by the magnitudes of pH elevations and associated reactions, were still spatially heterogeneous (Fig. 1C). pH values in the underlying anoxic sediment decreased from 7.0 to 6.5 gradually as surface pH maxima formed. However, in the experiment with the sediment collected in August 2019, sediment surface pH maxima started appearing on day 39 and expanded much more slowly, with only a 2.4 cm-long lateral coverage after a week of growth in one of the duplicate microcosms and no development at all in the other (Fig. 2A,B). At the same time, where pH maxima were present, a pH minimum band developed in the anodic zone ( > 2 mm depth) just below sediment surface pH maxima and expanded over time (Fig. 2A).Figure 12D pH distribution dynamics of duplicate microcosms starting from day 20 (January 2019 sediment). (A,B) For duplicate microcosms, sediment surface pH maximum bands started from isolated hotspots and quickly spread across the whole surface area with spreading speed ~ 1.2 cm/day. Anoxic sediment pH decreased from day 20–26. (C) The horizontal pH variations within the surficial pH maximum band on day 27 (microcosm B; vertical width ~ 1 mm). The pH ± standard deviations within the band are indicated by the blue dots.Full size imageFigure 22D pH and H2S distribution dynamics within microcosms during colonization. (A) 2D pH distribution dynamics starting from day 39 (August 2019 sediment) and corresponding 1D pH profiles. The sediment surface pH maximum band started from isolated hotspots and spread across sediment surface with an average rate of 0.3 cm/day, which is much slower compared with January 2019 sediment (1.2 cm/day). The pH in the cable bacteria anodic zone is lower (blue line, A2) compared with un-colonized sediment side (black line, A1) in the pH profile panel insert. (B) The duplicate microcosm (August 2019 sediment) did not show sediment surface pH maxima during the same time window. (C) 2D pH distribution dynamics starting from day 46 (October 2020 sediment). Both surface pH maxima and deep minima expand during electrogenic colonization. The pH minima (white arrows) are evident first on day 46 and 64. (D) Sediment 2D H2S distribution on day 71 (October 2020 sediment) with upper boundary showing the sediment water interface. The H2S distributions are consistent with pH patterns, but the sediment H2S concentrations are generally lower compared with other experiment series (Fig. 4).Full size imageThe October 2020 results (Fig. 2C) further resolved the cathodic and anodic zone development patterns. During colonization, the anoxic zone pH minima were evident earlier (day 46 and 64) and were distinctly wider than the sediment surface pH maxima (day 46–71). These differences in cathode and anode detection sensitivity might be caused by more rapid diffusion at the sediment–water boundary (free solution) and rapid neutralization by seawater CO2. They may also be related to the mode of colonization as discussed below. In addition to pH heterogeneity, the electrogenic activity also resulted in complex topographies of H2S distributions (Fig. 2D). In the locations where pH hotspots were found, the depths of detectable H2S are deeper compared with anywhere else, consistent with ongoing electrogenic sulfide oxidation metabolic activity. These data suggest that cable bacteria dynamics can be distinctly different in otherwise similar sediment (e.g. similar concentrations of dissolved H2S at depth), with variable development controlled by unknown factors.High resolution cable bacteria abundance data are not available in this study because of the design of the experiment. However, the vertical cable bacteria abundance dynamics, which were retrieved from random locations in the January 2019 microcosms during incubation from day 20 to 43, show that cable bacteria cell abundance in the oxic zone of the sediment did not vary significantly during the primary colonization period. In contrast, one of the microcosm series (Fig. 3B) showed a distinct trend of subsurface, anodic region (0.5–1.5 cm depth) enrichment of filament cells, and importantly, both microcosm series had similar terminal distributions when electrogenic activity had expanded across the entire surface (day 43) (Fig. 3). The first time sample in series A (day 20) (Fig. 3A) is similar in abundance distribution as at day 43. It is possible that the first sample taken in series A was located in an electrogenic colonization patch, that is, locally comparable to what would become the pattern across the entire surface at day 43. These abundance data together with the high resolution pH patterns allow inference of the colonization strategy of cable bacteria, as outlined subsequently.Figure 3Cable bacteria cell abundance dynamics in the duplicate January 2019 sediment microcosms. (A) and (B) represent duplication microcosms. From day 20 to 43, cable bacteria can be detected throughout the top 5 cm sediment with heterogeneous abundance patterns. There were depth intervals (e.g. B 2.5–3 cm) with cable bacteria cell abundance below the detection limit of the enumeration method. The sediment surface ( More

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DataPathosystemWMV is widespread in cucurbit crops, mostly in temperate and Mediterranean climatic regions of the world16. WMV has a wide host range including some legumes, orchids and many weeds that can be alternative hosts16. Like other potyviruses, it is non-persistently transmitted by at least 30 aphid species16. In temperate regions, WMV causes summer epidemics on cucurbit crops, and it can overwinter in several common non-cucurbit weeds when no crops are present16,34. WMV has been common in France for more than 40 years, causing mosaics on leaves and fruits in melon, but mostly mild symptoms on zucchini squash. Since 2000, new symptoms were observed in southeastern France on zucchini squash: leaf deformations and mosaics, as well as fruit discoloration and deformations that made them unmarketable. This new agronomic problem was correlated to the introduction of new molecular groups of WMV strains. At least four new groups have emerged since 2000 and they have rapidly replaced the native “classical” strains, causing important problems for the producers35. These new groups, hereafter “emerging strains” (ES) are significantly more related molecularly to worldwide strains than to any other isolates from the French populations36. As emphasised in35, this supports that the new group of emerging strains has arisen through introductions, mostly from Southeastern Asia, rather than through local differentiation.In this study, we focus on the pathosystem corresponding to a classical strain (CS) and four emerging strains (ESk, (k = 1, ldots ,4)) of WMV and their cucurbit hosts.Study area and samplingThe study area, located in Southeastern France, is included in a rectangle of about 25,000 km2 and is bounded on the South by the Mediterranean Sea. Between 2004 and 2008, the presence of WMV had been monitored in collaboration with farmers, farm advisers and seed companies. Each year, cultivated host plants were collected in different fields and at different dates between May 1st and September 30th. In total, more than two thousand plant samples were collected over the entire study area. All plant samples were analyzed in the INRAE Plant Pathology Unit to confirm the presence of WMV and determine the molecular type of the virus strain causing the infection (see35 for detail on field and laboratory protocols). All infected host plants were cucurbits, mostly melon and different squashes (e.g., zucchini, pumpkins).Observations In the absence of individual geographic coordinates, all infected host plants were attributed to the centroid of the municipality (French administrative unit, median size about 10 km2) where they have been collected. Then for one date, one observation corresponded to a municipality in which at least one infected host plant was sampled. Table 1 summarizes for each year, the number of observations (i.e. number of municipalities), the number of infected plants sampled and the proportion of each WMV strain (CS, and ES1 to ES4) found in the infected host plants. Errors in assignment of virus samples to the CS or ES strains was negligible because of the large genetic distance separating them: 5 to 10% nucleotide divergence both in the fragment used in the study and in complete genomes35, also precluding the possibility of local jumps between groups by accumulation of mutations.Table 1 Number of observations and corresponding proportions of classical and emerging strains.Full size tableLandscapeTo approximate the density of WMV host plants over the study area, we used 2006 land use data (i.e. BD Ocsol 2006 PACA and LR) produced by the CRIGE PACA (http://www.crige-paca.org/) and the Association SIG-LR (http://www.siglr.org/lassociation/la-structure.html). Based on satellite images, land use is determined at a spatial resolution of 1/50,000 using an improved three-level hierarchical typology derived from the European Corine Land Cover nomenclature. Here we used the third hierarchical level of the BD Ocsol typology (i.e. 42 land use classes) to classify the entire study area in three habitats: (1) WMV-susceptible crops, (2) habitats unfavorable to WMV host plants (e.g. forests, industrial and commercial units…) and, (3) non-terrestrial habitat (i.e. water). The proportion of WMV-susceptible crops was then computed within all cells of a raster covering the entire study area, with a spatial resolution of (1.4 times 1.4) km2. These proportions were used to approximate host plant density (zleft( {varvec{x}} right)), which was normalized, so that (zleft( {varvec{x}} right) = 0) corresponds to an absence of host plants and (zleft( {varvec{x}} right) = 1) to the maximum density of host plants (Fig. 1).Figure 1Approximated density (zleft( x right)) of the host plants in the study area. The density was normalized, so that (zleft( x right) = zleft( {x_{1} ,x_{2} } right) = 0) corresponds to an absence of cucurbit plants and (zleft( x right) = 1) to the maximum density. The axes (x_{1}) and (x_{2}) correspond to Lambert93 coordinates (in km). The white regions are non-terrestrial habitats (water). Land use data were not available in the gray regions; the host plant density was then computed by interpolation.Full size imageMechanistic-statistical modelThe general modeling strategy is based on a mechanistic-statistical approach12,22,33. This type of approach combines a mechanistic model describing the dynamics under investigation with a probabilistic model conditional on the dynamics, describing how the measurements have been collected. This method that has already proved its theoretical effectiveness in determining dispersal parameters using simulated genetic data12 aims at bridging the gap between the data and the model for the determination of virus dynamics.Here, the mechanistic part of the model describes the spatio-temporal dynamics of the virus strains, given the model parameters (demographic parameters, introduction dates/sites). This allows us to compute the expected proportions of the five types of virus strains (CS and ES1 to ES4) at each date and site of observation. The probabilistic part of the mechanistic-statistical model describes the conditional distribution of the observed proportions of the virus strains, given the expected proportions. Using this approach, it is then possible to derive a numerically tractable formula for the likelihood function associated with the model parameters.Population dynamicsThe model is segmented into two stages: (1) the intra-annual stage describes the dispersal and growth of the five virus strains during the summer epidemics on cucurbit crops, and the competition between them, during a period ranging from May 1st (noted (t = 0)) to September 30 (noted (t = t_{f}), (t_{f} = 153) days); (2) the inter-annual stage describes the winter decay of the different strains when no crops are present and the virus overwinters in weeds. We denote by (c^{n} left( {t,{varvec{x}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}} right)) the densities of classical strain (CS) and emerging strains (ESk, (k = 1, ldots ,4)), at position ({varvec{x}}) and at time (t) of year (n.)Dynamics of the classical strain before the first introduction eventsBefore the introduction of the first emerging strain, the intra-annual dynamics of the population CS is described by a standard diffusion model with logistic growth: (partial_{t} c^{n} = D{Delta }c^{n} + rc^{n} left( {zleft( {varvec{x}} right) – c^{n} } right)). Here, ({Delta }) is the Laplace 2D diffusion operator (sum of the second derivatives with respect to coordinate). This operator describes uncorrelated random walk movements of the viruses, with the coefficient (D) measuring the mobility of the viruses (e.g.,26,37). The term (r zleft( {varvec{x}} right)) is the intrinsic growth rate (i.e., growth rate in the absence of competition) of the population, which depends on the density of host plants (zleft( {varvec{x}} right)) and on a coefficient (r) (intrinsic growth rate at maximum host density). Under these assumptions, the carrying capacity at a position ({varvec{x}}) is equal to (zleft( {varvec{x}} right)), which means that the population densities are expressed in units of the maximum host population density. The model is initialized by setting (c^{1980} left( {0,{varvec{x}}} right) = (1 – m_{c} ) zleft( {varvec{x}} right)), where (m_{c}) is the winter decay rate of the CS (see the description of the inter-annual stage below). In other terms, we assume that the CS density is at the carrying capacity in 1979, i.e., 5 years after its first detection and 20 years before the first detections of ESs38.Introduction eventsThe ESs are introduced during years noted (n_{k} ge 1981), at the beginning of the intra-annual stage (other dates of introduction within the intra-annual stage would lead—at most—to a one-year lag in the dynamics). Their densities are (0) before introduction: (e_{k}^{n} = 0) for (n < n_{k}). Once introduced, the initial density of any ES is assumed to be 1/10th of the carrying capacity at the introduction point (other values have been tested without much effect, see Supplementary Fig. S1), with a decreasing density as the distance from this point increases:$$e_{k}^{{n_{k} }} left( {0,x} right) = frac{{zleft( {varvec{x}} right)}}{10}exp left( { - frac{|{{varvec{x}} - {varvec{X}}_{{varvec{k}}}|^{2} }}{{2sigma^{2} }}} right),$$where ({varvec{X}}_{{varvec{k}}}) is the location of introduction of the strain (k.) In our computations, we took (sigma = 5) km for the standard deviation.Intra-annual dynamics after the first introduction eventIntra-annual dynamics were described by a neutral competition model with diffusion (properties of this model have been analyzed in [54]):$$left{ {begin{array}{*{20}c} {partial_{t} c^{n} left( {t,x} right) = DDelta c^{n} + rc^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ {partial_{t} e_{k}^{n} left( {t,x} right) = DDelta e_{k}^{n} + re_{k}^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ end{array} } right.,$$for (t = 0 ldots t_{f}) and for all introduced emerging strains, i.e. all (k) such that (n ge n_{k} .) We assume reflecting boundary conditions, meaning that the population flows vanish at the boundary of the study area, due to truly reflecting boundaries (e.g., sea coast in the southern part of the site) or symmetric inward and outward fluxes26. In addition, in order to limit the number of unknown parameters, and in the absence of precise knowledge on the differences between the strains, we assume here that the diffusion, competition and growth coefficients are common to all the strains during the intra-annual stage (see the discussion for more details on this assumption).Inter-annual dynamicsThe population densities at time (t = 0) of year (n) are connected with those of year (n - 1,) at time (t = t_{f} ,) through the following formulas:$$left{ {begin{array}{*{20}c} {c^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{c} } right)c^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for } n ge 1981} \ {e_{k}^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{e} } right)e_{k}^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for }n ge n_{k} + 1} \ end{array} } right.$$with (m_{c}) and (m_{e}) the winter decay rates of the CS and ESs strains (note that (m_{e}) is common to all of the ESs). Estimation of CS and ES decay rates provides an assessment of the competitive advantage of one type of strain vs the other.Numerical computationsThe intra-annual dynamics were solved using Comsol Multiphysics time-dependent solver, which is based on a finite element method (FEM). The triangular mesh which was used for our computations is available as Supplementary Fig. S2.Probabilistic model for the observation processDuring the years (n = 2004, ldots ,2008), (I_{n}) observations were made (see Section Observations above and Table 1). They consist in counting data, that we denote by (C_{i}) and (E_{k,i}) for (k = 1, ldots ,4) and (i = 1, ldots ,I_{n}), corresponding to the number of samples infected by the CS and ESs strains, respectively, at each date of observation and location (left( {t_{i} ,{varvec{x}}_{i} } right)). Note that these dates and locations depend on the year of observation (n). More generally, the above quantities should be noted (C_{i}^{n} , E_{k,i}^{n} , t_{i}^{n} , {varvec{x}}_{i}^{n} ;) for simplicity, the index (n) is omitted in the sequel, unless necessary.We denote by (V_{i} = C_{i} + mathop sum nolimits_{k = 1}^{4} E_{k,i}) the total number of infected samples observed at (left( {t_{i} ,{varvec{x}}_{i} } right)). The conditional distribution of the vector (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)), given (V_{i}) can be described by a multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) with ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)) the vector of the respective proportions of each strain in the population at (left( {t_{i} ,{varvec{x}}_{i} } right)). We chose to work conditionally to (V_{i}) because the sample sizes are not related to the density of WMV.Statistical inferenceUnknown parametersWe denote by ({{varvec{Theta}}}) the vector of unknown parameters: the diffusion coefficient (D,) the intrinsic growth rate at maximum host density (r), the winter decay rates ((m_{c} , m_{e} )) and the locations ((x_{k} in {mathbb{R}}^{2})) and years ((n_{k})) of introduction, for (k = 1, ldots ,4.) Thus ({{varvec{Theta}}} in {mathbb{R}}^{16} .)Computation of a likelihoodGiven the set of parameters ({{varvec{Theta}}}), the densities (c^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) can be computed explicitly with the mechanistic model for population dynamics. Thus, at a given year (n), at (left( {t_{i} ,x_{i} } right)), the parameter ({varvec{p}}_{i}) of the multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) writes:$$p_{i}^{c} left( {{varvec{Theta}}} right) = frac{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} left( {t_{i} ,{varvec{x}}_{i} {|}{{varvec{Theta}}}} right)}}, p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right) = frac{{e_{k}^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} (t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}})}}.$$The probability (P(C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} |{{varvec{Theta}}},{text{V}}_{{text{i}}} )) of the observed outcome (C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i}) is then$$Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right) = frac{{left( {V_{i} } right)!}}{{C_{i} ! mathop prod nolimits_{k = 1}^{4} E_{k,i} !}}left( {p_{i}^{c} left( {{varvec{Theta}}} right)} right)^{{C_{i} }} mathop prod limits_{k = 1}^{4} (p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right))^{{E_{k,i} }} .$$Assuming that the observations during each year and at each date/location are independent from each other conditionally on the virus strain proportions, we get the following formula for the likelihood:$${mathcal{L}}left( {{varvec{Theta}}} right) = mathop prod limits_{n = 2004}^{2008} mathop prod limits_{{i = 1, ldots , I_{n} }} Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right).$$A priori constraints on the parameters By definition and for biological reasons, the parameter vector ({{varvec{Theta}}}) satisfies some constraints. First, (D in left( {10^{ - 4} ,10} right){text{ km}}^{2} /{text{day}}), (r in left( {0.1,1} right) {text{day}}^{ - 1} ,) and (m_{c} , m_{e} in left{ {0,0.1,0.2, ldots ,0.9} right},) (see Supplementary Note S7 for a biological interpretation of these values). Second, we assumed that the locations of introductions ({varvec{X}}_{{varvec{k}}}) belong to the study area. To facilitate the estimation procedure, the points ({varvec{X}}_{{varvec{k}}}) were searched in a regular grid with 20 points (see Supplementary Fig. S3), and the dates of introduction (n_{k}) were searched in (left{ {1985,1990,1995,2000} right}.)Inference procedureDue to the important computation time (4 min in average for one simulation of the model on an Intel(R) Core(R) CPU i7-4790 @ 3.60 GHz), we were not able to compute an a posteriori distribution of the parameters in a Bayesian framework. Rather, we used a simulated annealing algorithm to compute the maximum likelihood estimate (MLE), i.e., the parameter ({{varvec{Theta}}}^{*}) which leads to the highest log-likelihood. This is an iterative algorithm, which constructs a sequence (({{varvec{Theta}}}_{j} )_{j ge 1}) converging in probability towards a MLE. It is based on an acceptance-rejection procedure, where the acceptance rate depends on the current iteration (j) through a "cooling rate" ((alpha )). Empirically, a good trade-off between quality of optimization and time required for computation (number of iterations) is obtained with exponential cooling rates of the type (T_{0} alpha^{j}) with (0 < alpha < 1) and some constant (T_{0} gg 1) (this cooling schedule was first proposed in= 39 = 39). Too rapid cooling ((alpha ll 1)) results in a system frozen into a state far from the optimal one, whereas too slow cooling ((alpha approx 1)) leads to important computation times due to very slow convergence. Here, we ran (6) parallel sequences with cooling rates (alpha in left{ {0.995,0.999,0.9995} right}). For this type of algorithm, there are no general rules for the choice of the stopping criterion [HenJac03], which should be heuristically adapted to the considered optimization problem. Here, our stopping criterion was that ({{varvec{Theta}}}_{j}) remained unchanged during 500 iterations. The computations took about 100 days (CPU time).Confidence intervals and goodness-of-fitTo assess the model’s goodness-of-fit, 95% confidence regions were computed for the observations (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)) at each date/location (left( {t_{i} ,{varvec{x}}_{i} } right),) and for each year of observation. The confidence regions were computed by assessing the probability of each possible outcome of the observation process, at each date/location, based on the computed proportions ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)), corresponding to the output of the mechanistic model using the MLE ({{varvec{Theta}}}^{user2{*}}) and given the total number of infected samples (V_{i}). Then, we checked if the observations belonged to the 95% most probable outcomes. More

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