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Plant materialsAn ALS-inhibitor-resistant johnsongrass (resistant to nicosulfuron) obtained from the University of Nebraska-Lincoln (source credit: Dr. John Lindquist) was used as the pollen source (male parent), and the natural johnsongrass population present in the experimental field at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W) with no history of ALS-inhibitor resistance was used as the pollen recipient (female parent). Prior to the initiation of the field experiment, the susceptibility to nicosulfuron of the natural johnsongrass population was verified by spraying Accent Q at the labeled field rate of 63 g ai ha−1 [mixed with 0.25% v/v Crop Oil Concentrate (COC)] on 10 randomly selected 1 m2 johnsongrass patches across the field area at 15–30 cm tall seedling stage. For this purpose, a CO2 pressurized backpack sprayer was calibrated to deliver 140 L ha−1 of spray volume at an operating speed of 4.8 kmph. The natural johnsongrass population was determined to be completely susceptible to nicosulfuron.During spring 2018, the seeds of AR johnsongrass were planted in pots (14-cm diameter and 12-cm tall) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The environmental conditions were set at 26/22 °C day/night temperature regime and a 14-h photoperiod. In each pot, 5 seeds were planted and thinned to one healthy seedling at 1-leaf stage. Seedlings were supplied with sufficient water and nutrients (Miracle-Gro Water Soluble All Purpose Plant Food, Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH 43041). A total of 50 seedlings were established in the greenhouse and were maintained until they reached about 10 cm tall, at which point they were sprayed with 2× the field rate of nicosulfuron (63 × 2 = 126 g ai ha−1) (mixed with 0.25% v/v COC). The herbicide was applied using a track-sprayer (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, and at an operating speed of 4.8 kmph. All treated seedlings that survived the herbicide application at 21 days after treatment (DAT) were then used as the pollen donor in the field gene flow experiment. All plant materials were handled in accordance with relevant guidelines and regulations. No permissions or licenses were required for collecting the johnsongrass samples from the experimental fields.Dose–response assaysThe degree of resistance/susceptibility to nicosulfuron of the AR and AS johnsongrass biotypes were determined using a classical dose–response experiment. The assay consisted of seven rates (0, 0.0625, 0.125, 0.25, 0.5, 1, and 2×) for the AS population and nine rates (0, 0.25, 0.5, 1, 2, 4, 8, 16, and 32×) for the AR population [1 × (field recommended rate) = 63 g ai ha−1 of Accent Q]. The experimental units were arranged in a completely randomized design with four replications. Seeds of AR and AS plants were planted in plastic trays (25 × 25 cm) filled with commercial potting-soil mix (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) and maintained at 26/22 °C day/night cycle with a 14-h photoperiod in the greenhouse. Seedlings at 1–2 leaf stage were thinned to 20 seedlings per tray; four replications each of 20 seedlings per dose were considered. The seedlings were watered and fertilized as needed. The assay was conducted twice, thus a total of 160 seedlings were screened for each dose.The established seedlings were sprayed with the appropriate herbicide dose at the 10–15 cm tall seedling stage. The herbicide was applied using a track sprayer calibrated to deliver a spray volume of 140 L ha−1 at 4.8 kmph operating speed. Survival (%) and injury (%) were assessed at 28 DAT. Any plant that failed to grow out of the herbicide impact was considered dead. Plant injury was rated for each plot (i.e. on the 20 seedlings per rep) on a scale of 0–100%, where 0 indicates no visible impact compared to the nontreated control and 100 indicates complete death of all plants in the tray. Immediately after the visual ratings were completed, shoot biomass produced by the 20 plants from each tray was determined by harvesting all the tissues at the soil level and drying them in an oven at 60 °C for 72 h. Seedling mortality data were used for fitting dose–response curves that allowed for determining the lethal dose that caused 100% mortality of the susceptible biotype. This dose was used as a discriminant dose to distinguish between a hybrid (that confers resistance to nicosulfuron as a result of gene flow) and a selfed progeny (susceptible to nicosulfuron) in the field gene flow study.Field experimental location and set-upThe field experiment was conducted across two ENVs in 2018 (summer and fall) and one in 2019 (fall) at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W). The study site is characterized by silty clay loam soil with an average annual rainfall of 98.2 cm. The field experiment followed the Nelder-wheel design40, i.e. concentric donor-receptor design, a widely used method for gene flow studies, wherein the pollen-donors are surrounded by the pollen-receptors (Fig. 1). In this study, the AR plants (planted in the central block of the wheel) served as the pollen-donors, whereas the AS plants (present in the spokes) served as the pollen-receptors.Figure 1Aerial view of the experimental arrangement that was used to quantify pollen-mediated gene flow from ALS-inhibitor resistant (AR) to -susceptible (AS) johnsongrass at the Texas A&M University Research Farm near College Station, Texas. AR johnsongrass plants were transplanted in the pollen-donor block of 5 m diameter at the center of the field. The surrounding pollen-receptor area was divided into four cardinal (N, E, S, W) and four ordinal (NE, SE, SW, NW) directional blocks where naturally-existing AS johnsongrass plants were used as the pollen-recipients. AS panicles exhibiting flowering synchrony with AR plants were tagged at specific distances (5–50 m, at 5 m increments) along the eight directional arms. A tall-growing biomass sorghum border was established in the perimeter of the experimental site to prevent pollen inflow from outside areas.Full size imageThe center of the wheel was 5 m in diameter, and each spoke was 50 m long starting at the periphery of the central circular block. Thirty AR plants (pollen-donors) were transplanted in four concentric rings of 1, 5, 9, and 15 plants in the 5 m diameter central block, surrounded by the pollen-receptors (i.e. AS plants) (Fig. 1). The AR plants were contained within the central block during the 2 years of the field experiment by harvesting and removing all mature seeds and removing any expanding rhizomatous shoots. Further, field cultivation was completely avoided in the central block throughout the study period. Any newly emerging johnsongrass plants (seedling/rhizomatous) other than the transplanted AR plants in the central block were removed periodically by manual uprooting.The wheel consisted of eight spokes (i.e. directional blocks) arranged in four cardinal (N, E, S, W) and four ordinal (NE, SE, NW, SW) directions (Fig. 1). The plots to quantify gene flow frequency were arranged at 0 (border of the central block), 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 m distances from the central block in all eight directions (Fig. 1). Each plot measured 3 × 2 m and the area surrounding the plots was shredded prior to the booting stage with a Rhino® RC flail shredder (RHINOAG, INC., Gibson City, IL 60936).A tall-growing biomass sorghum border (6 m wide) was established surrounding the experimental area in all directions to prevent potential inflow of pollen from other Sorghum spp. in the nearby areas. Additionally, prevailing weather conditions, specifically wind direction, wind speed, relative humidity, and air temperature measured at 5-min intervals were obtained from a nearby weather station located within the Texas A&M research farm (http://afs102.tamu.edu/). The field did not require any specific agronomic management in terms of irrigation, fertilization, or pest management.Flowering synchrony, tagging, and seed harvestingAt peak flowering, when  > 50% of the plants in the AR block started anther dehiscence (i.e., pollen shedding), ten AS panicles (five random plants × 2 panicles per plant) that showed flowering synchrony with AR plants and displayed protruded, receptive stigma were tagged using colored ribbons at each distance and direction. At seed maturity, the tagged AS panicles were harvested separately for each distance and direction. Panicles were threshed, seeds were cleaned manually, and stored under room conditions until used in the herbicide resistance screening to facilitate after-ripening and dormancy release.Resistance screeningThe hybrid progeny produced on AS plants as a result of outcrossing with AR plants would be heterozygous for the allele harboring nicosulfuron resistance, and would exhibit survival upon exposure to the herbicide applied at the discriminant dose at which all wild type (AS) plants would die. The discriminant dose was determined using the dose–response study described above. Thus, the frequency of resistant plants in the progeny would represent outcrossing/gene flow (%).To effectively detect the levels of gene flow from AR to AS biotypes especially at low frequencies, the minimum sample size required for resistance screening was determined based on the following formula (Eq. 1)41:$${text{N }} = {text{ ln}}left( {{1} – P} right)/{text{ln}}left( {{1} – p} right),$$
(1)
where P is the probability of detecting a resistant progeny in the least frequent class and p is the probability of the least frequent class. Based on this formula, a minimum of 298 to as high as 916 plants were screened for each distance within each direction, allowing for a 1% detection level (p = 0.01) with a 95% (P = 0.95) confidence interval.Approximately one-year old progeny seeds harvested from the AS plants were scarified using a sandpaper for 15–20 s to release dormancy. The seeds for each distance within each direction were planted in four replicates of plastic trays (50 × 25 cm) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA). The plants were raised at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The greenhouse was maintained at 28/24 °C day/night temperature regime and a 14-h photoperiod. About 10–15 cm tall seedlings were sprayed with the discriminant dose of the ALS-inhibitor nicosulfuron (Accent Q, 95 g ai ha−1) using a spray chamber (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, operating at a speed of 4.8 kmph. At 28 DAT, percent seedling survival was determined based on the number of plants that survived the herbicide application out of the total number of plants sprayed. The number of plants in each tray was counted before spraying.Molecular confirmation of hybridsLeaf tissue samples were collected from thirty random surviving plants (putative resistant) in the herbicide resistance screening study for each of the three field ENVs, thus totaling 90 samples. Genomic DNA was extracted from 100 mg of young leaf tissue using the modified CTAB protocol42. The concentration of DNA was determined using a Nanodrop 1000 UV–Vis spectrophotometer (DeNovix DS-II spectrophotometer, DeNovix Inc., Wilmington, DE 19810, USA). DNA was then diluted to a concentration of 20 ng/µl for PCR assay. The nicosulfuron-resistant johnsongrass from Nebraska used in this study possessed the Trp574Leu mutation39. Hence, single nucleotide polymorphism (SNP) primers targeting a unique short-range haplotype of Inzen® sorghum (Val560Ile + Trp574Leu) were performed using the PCR Allele Competitive Extension (PACE) platform to confirm the resistant plants43. The SNP primers and the PACE genotyping master mix were obtained from Integrated DNA Technologies (IDT) Inc. (Coralville, IA) and 3CR Bioscience (Harlow CM20 2BU, United Kingdom), respectively. In addition to the two no-template controls (NTCs), two nicosulfuron-resistant johnsongrass, one wild-type johnsongrass, and one Inzen® sorghum were also used in the PCR.The PCR was performed according to the manufacturer’s protocol (Bio-Rad Laboratories, Inc., Hercules, CA), with denaturation for 15 min at 94 °C, followed by 10 cycles of denaturation at 94 °C for 20 s, annealing and extension at 65–57 °C for 60 s, 30 cycles of denaturation for 20 s at 94 °C, and annealing and extension for 60 s at 57 °C. Fluorescence of the reaction products were detected using a BMG PHERAStar plate reader that uses the FAM (fluorescein amidite) and HEX (hexachloro-fluorescein) fluorophores.Data analysisFor the dose–response assay, three-parameter sigmoidal curves (Eq. 2) were fit on the seedling mortality data for the AS and AR biotypes (with log of herbicide doses), using SigmaPlot version 14.0 (Systat Software Inc., San Jose, CA).$$y=b/[1+{exp}^{left(-(x-eright)/c)}],$$
(2)
where, y is the mortality (%), x is the herbicide dose (g ai ha−1), b is the slope around e, c is the lower limit (theoretical minimum for y normalized to 0%), and e = LD50 (inflection point, mid-point or estimated herbicide dose when y = 50%). Windrose plots that represented wind speed and frequency during the flowering window in each of the eight directions were created using a macro in Microsoft Excel. Progeny seedling survival (%) that represents gene flow (%) was determined using Eq. (3).$${text{PMGF }}left( {text{%}} right){ } = { }left( frac{X}{Y} right)_{{i,j{ }}} times { }100,$$
(3)
where, X is the number of plants that survived the herbicide application, Y is the total number of plants sprayed for ith distance in jth direction.To test whether gene flow frequencies varied among the directions, ANOVA was conducted using JMP PRO v.14 (SAS Institute, Cary, NC, USA), based on the average gene flow frequency values in each direction; ENVs were considered as replicates in this analysis. A non-linear regression analysis for gene flow rate, describing an exponential decay function (Eq. 4), was fit using SigmaPlot based on the gene flow frequencies observed at different distances pooled across the directions and ENVs.$$y=y0+left[atimes {exp}^{left(-btimes xright)}right],$$
(4)
where, y is the PMGF (%), x is the distance (m) from pollen source, y0 is the lower asymptote (theoretical minimum for y normalized to 0%), a is the inflection point, mid-point or estimated distance when y = 50%, and b is the slope around a.A Pearson correlation analysis was conducted to determine potential association between PMGF [overall PMGF, short-distance PMGF (5 m), and long-distance PMGF (50 m)] and the environmental parameters temperature, relative humidity, and dew point. Further, a correlation analysis was also conducted to understand the association between PMGF frequencies and specific wind parameters such as wind frequency, wind speed, and gust speed. The molecular data were analyzed using KlusterCaller 1.1 software (KBioscience). More

Diomedenema dinarctos, a parasitic worm that infests penguins, is named after the Greek deinos, meaning terrible, and arktos, or bear, because of its resemblance to a menacing teddy bear.Credit: Bronwen Presswell and Jerusha Bennett

What scientists choose to name parasitic worms could say more about the researchers than the organism they are studying.A study1 examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.The analysis uncovers ongoing biases in taxonomy — the classification of organisms — and could be used as a jumping-off point for rethinking how scientists name species, says study co-author Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand.“When you name something, it is now named forever. I think it’s worth giving some thought to what names we choose,” he says. The research was published on 11 May in Proceedings of the Royal Society B.As the worm turnsSpecies names often describe how an organism looks or where it was found. But since the nineteenth century, they have also been used to immortalize scientists. The parasite that causes the intestinal disease giardiasis, for instance, was named after French zoologist Alfred Giard.Wondering how naming practices had changed, Poulin and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm. The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.Many others were named after people, ranging from technical assistants to prominent politicians (Baracktrema obamai, a species found in Malaysian freshwater turtles, was named after former US president Barack Obama). But just 19% of the 596 species named after eminent scientists were named after women, a percentage that essentially didn’t budge over the decades (see ‘Parasite name game’).

Source: Ref. 1

This could be because of a historical dearth of female figures in the field, says Janine Caira, a parasite taxonomist at the University of Connecticut in Storrs. But another possibility is that the work of past female scientists often goes unrecognized, says Tanapan Sukee, a parasitologist at the University of Melbourne in Australia.Sukee has named two species of parasitic worm after now-deceased Australian biologist Patricia Mawson, who was a key player in the characterization of marsupial parasites. For most of her career, Mawson worked part-time as a technician, and she was often designated second author on papers describing species she had discovered, Sukee says. Similar situations could explain why so few parasites are named after women.Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends and family members of the scientists who formally described them. Some researchers even name species after pets: Rhinebothrium corbatai is a freshwater stingray parasite named after the first author’s Welsh terrier, Corbata.Poulin says this should be discouraged. Species are almost never named after the person who described them, and Poulin argues that names honouring parents, children or spouses could be seen as a way to get around this convention.And besides, “I don’t have any friends or family who want a parasite named after them!” says Sukee. More

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Our model focused on the trophic interactions among CoTS and two groups of coral within a feedback loop with natural and anthropogenic forcing. Our model draws on accepted features of the published dynamics described by Morello et al.37, Condie et al.28 and Condie et al.17, but is a substantial advance in terms of adding spatial structure and coupling with climate variables. Here we have resolved a fine spatiotemporal model structure, developed a novel recruitment formulation for CoTS, integrated tactical management control dynamics and incorporated the impact of broad-scale drivers upon the population dynamics of corals and CoTS at the local scale. Our model is formally fitted to a subset of the CoTS control program data described by Westcott et al.12. We operationalised our model as a tactical and strategic tool to inform how CoTS management strategies interact with alternative disturbance and ecological realisations at the sub-reef scale, the scale at which management operates.DataWe fitted our model to a subset of four reefs from the dataset described by Westcott et al.12, which were consistently and intensively managed (for a map with reef locations see Fig. 2). We restricted our focus to a subset to avoid parametrisation of reef and management site dynamics. Thus, ~39% of site visits were concentrated over the 13 management sites we considered, with a mean of 20.73 ± 5.5 (mean ± standard deviation) visits across the time series relative to a mean visitation rate of 12.23 ± 4.7 (mean ± standard deviation) for the rest of the sites. Each reef in the subset contained two or more management sites where each site was visited at least 18 times. The subset was used because it contained sufficient data for estimating the 11 model parameters for each management site. Across included sites were a range of CoTS densities, coral abundances and disturbance histories12,72,73. Given the intensity with which these sites were managed, they therefore provided us with a valuable opportunity to formally fit the interactions between management intervention, coral abundance and CoTS dynamics in the presence of regional sequential bleaching events.Model spatial structure and ecological componentsSpatially, we considered a circular 300 km region of the Great Barrier Reef centred between Cairns and Cape Tribulation, and resolved at a daily timescale and a sub-reef spatial scale, matching the scale at which observed data were resolved12,19. Reefs were randomly generated as points to capture possible spatial correlation in disturbance impacts between nearby reefs, as well as to allow variability in reef locations. Coral, CoTS and disturbance dynamics within the management sites of each reef were resolved relative to a 1 ha focal region. That is, each management site was captured as a 1 ha area representative of the whole site. In the Pacific, Acanthaster spp. disproportionally target faster-growing corals, predominantly Acropora, Pocillopora and Montipora22. Coral taxa characterised by slow growth rates and massive morphologies, such as Porites, are generally consumed less than expected based on their abundance22 and are thus non-preferred prey. The two modelled coral groups were the fast-growing favoured prey items of CoTS, and the slower-growing non-preferred prey. Processes resolved in the model included reproduction, density dependence, the effect of bleaching and cyclonic disturbances on corals and the impact of manual control (culling) upon CoTS and coral dynamics.CoTS population structureWe used an age-structured approach to model CoTS population dynamics. We defined our age classes to encapsulate plausible size-at-age variation due to plastic growth. This was achieved through linking catch size classes of the management control program19 to age classes through size-age relationships developed from observations spanning multiple environmental realisations, manipulated scenarios and methodologies55,70,74,75. Delayed growth in juvenile CoTS due to deferral of their switch to coral prey or composition of their pre-coral diet, may induce variability in the size-at-age of juveniles52,53. However, the population-level consequences of prolonged juvenile phases are not easily observed nor understood. For example, juveniles are subject to high mortality rates in situ, delayed growth may reduce lifetime fitness and there have been no observations of juveniles during spawning periods that would indicate protracted juvenile phases55,56,57. Consequently, suggests size-at-age is—due to an early life history mortality bottleneck or otherwise—predominantly concordant with growth curves of the literature55,70,74,75 and the size classes we have used here. Age classes comprised annual 0, 1, 2 and 3+ groups, with 3+ being an absorbing class – once there, they stay there. Age-0 ( ; 32.5)). This induced a slope change in the relationship between maximum wind velocity and its radius at a wind velocity of 32.5 m.s−1 (≥ category 3 intensities). However, whilst maximum wind velocity was modelled to determine ({d}_{{{{{{rm{m}}}}}}}), the overall size of the cyclone was uncorrelated with its intensity. The overall size was uniformly sampled from 130 to 460 km diameter which allowed for the potential of complete focal area coverage and for a range of intensity-size relationships to be captured. Given a cyclone footprint of radius ({d}_{0}) (km), wind velocity, (V) (m.s−1), at a distance, (d) (km), was interpolated104 through:$$Vleft(dright)=left{begin{array}{c}{V}_{0}+left({V}_{{{{{{rm{m}}}}}}}-{V}_{0}right){left(frac{sqrt{{d}_{0}}-sqrt{d}}{sqrt{{d}_{0}}-sqrt{{d}_{{{{{{rm{m}}}}}}}}}right)}^{alpha },,dge {d}_{{{{{{rm{m}}}}}}}\ {V}_{{{{{{rm{m}}}}}}}, , d ; < ; {d}_{{{{{{rm{m}}}}}}}end{array}right.$$ (32) The distance from the cyclone centre to the reef perimeter, (D) (km), is calculated through:$$D=sqrt{{left({x}_{{{{{{rm{rf}}}}}}}-{r}_{1}-{x}_{{{{{{rm{cyc}}}}}}}right)}^{2}+{left({x}_{{{rm{rf}}}}-{r}_{1}-{y}_{{{{{{rm{cyc}}}}}}}right)}^{2}}$$ (33) Thus, given a reef strike occurs ((sqrt{{d}_{0}}-sqrt{d}ge 0) required from non-integer (alpha)), the wind velocity experienced at said reef due to the tropical cyclone was calculated as (Vleft(Dright)). Wind velocity was subsequently categorised and damage to reef zone corals calculated as per Supplementary Table 4.We resolved stochasticity in cyclone dynamics in projection scenarios. In projected scenarios cyclone arrivals, locations and intensities were probabilistically sampled and their inflicted damage upon coral communities sampled from damage ranges. Cyclone locations, their footprints, intensity ranges and corresponding damage ranges were sampled from uniform distributions. Cyclone arrivals were sampled from a Poisson distribution and considered in scenarios from 2018 to 2029. Projections were averaged over 80 simulations to capture mean dynamics and bound trajectory uncertainty due to said stochasticity.Our cyclone model was calibrated to parameters sourced from the literature (Supplementary Tables 4-5). This was necessary since our data time series did not encompass a cyclone event and/or impacts upon a reef and cyclone-induced mortality is typically a key coral mortality source30. Consequently, we were unable to validate the impacts of cyclones through formal estimation in our model. However, our endeavours to source parameters from empirical and modelling studies in conjunction with our formulation allowed us to plausibly capture the cumulative outcomes of a cyclone event at discrete locations. Our cyclone model offers a limited complexity approach that is empirically grounded to simply resolve cyclone impacts in local-scale models without the need to be coupled to a regional-scale model.Cyclones, induced thermal stress and tactical managementThe occurrence of cyclone events was modelled to directly interact with both management interventions and thermal stress events. Cyclones were assumed to realistically preclude co-occurring co-located management interventions. This was such that a management site control visit was abandoned if a cyclone preceded or was forecast within five days of a control voyage. The later interaction of cyclones with thermal stress events operated through an induced thermal cooling of sea surface temperatures (SST) at impacted locations.In the case of the overlapping cyclone and thermally induced bleaching events, we first accounted for cyclone impacts. This was because, in addition to physical damage to corals, cyclones have the potential for regional-scale cooling of SST which can reduce coral bleaching43,107. To capture this interaction, we resolved the duration108,109 and amplitude107 of tropical cyclone-induced cooling. We captured this interaction through Degree Heating Weeks (DHW) which is a useful metric for the accumulated thermal stress experienced by corals94.The duration of tropical cyclone-induced cooling was modelled through a temporal-SST response curve consistent with the work of Lloyd and Vecchi108 and Vincent et al.109. Cooling rapidly occurs once a tropical cyclone arrives at a location and decays in an asymptotic manner over a period of ~40–60 days108,109. Temperatures however do not return to pre-cyclone levels and plateau at ~1/4 of the cooling signal amplitude below pre-cyclone levels108,109. We expressed this cooling response curve as it related to bleaching-induced coral mortality through DHWs.We based the average expected DHW cooling signal on the work of Carrigan and Puotinen107. This was achieved through scaling the difference in amplitude of overlapping thermal stress-tropical cyclone events and thermal stress only events—a cooling signal amplitude of ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}} sim 1.5) DHW. Consistent with the model of Carrigan and Puotinen107, we then resolved cooling within the radius of gale-force winds (category 1, 17 m.s−1) to model tropical cyclone-induced cooling. Depending on the size of the tropical cyclone, this meant that an individual cyclone would not necessarily cool all reefs within the model region. However, the culmination of multiple cyclones may have limited bleaching exposure for corals across the region107.We did not treat the cooling consequences of multiple cyclones additively nor the complex interplay of oceanic feedbacks upon cyclone intensity and cooling. Such processes were beyond the scope of our study and model. If multiple cyclones occurred within our model, then the cooling signal timeline was re-initialised at impacted reefs for the last tropical cyclone at said location. Non-impacted reefs maintained the timeline for the decay of the cooling signal originating from their previous tropical cyclone interaction.Once a tropical cyclone impacted a reef, the duration of the induced cooling signal was modelled. Price et al.110 found that cooling decays exponentially which is reflective of the recovery of SST following tropical cyclones as demonstrated by Lloyd and Vecchi108 and Vincent et al.109. We operationalised the exponential functional form in conjunction with the decay timelines of Lloyd and Vecchi108 and Vincent et al.109 and the DHW amplitude of Carrigan and Puotinen107. We modelled the level of cooling ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}) after ({d}_{{{{{{rm{postTC}}}}}}}) days post-cyclone event by:$${{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}left({d}_{{{{{{rm{postTC}}}}}}}right)=frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}+frac{frac{3}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}}{{e}^{{d}_{{{{{{rm{postTC}}}}}}}/10}}$$ (34) This ensured that once a reef experienced a tropical cyclone event, the cooling signal initialised at ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) and decayed to (sim frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) after 40–60 days108,109. The rate of decay was given by the e-folding time (days required for the cooling signal to be reduced by a factor of (e)) which we took to be 10. This is consistent with the results of Price et al.110, Lloyd and Vecchi108 and Vincent et al.109 who found e-folding times ranging from 5 through to 20 days. Thermally induced bleaching mortality of corals was computed after cyclone physical damage and cooling had been accounted for.Formal model fittingWe formally fitted our coral-CoTS model simultaneously to coral cover data, catch-per-unit-effort data and catch numbers obtained from the management control program with dive effort (minutes) treated as an input (visits summarised in Supplementary Table 7)12. Simultaneously fitting CoTS and coral dynamics at concurrent locations was useful here as it allowed for coral cover trajectories to help inform local CoTS abundance (sensu CoTS feeding vs. coral trajectories63,79 and local site fidelity24). Our model also used Long Term Monitoring Program (LTMP) data (based on manta tows and provided by the Australian Institute of Marine Science) which provides an independent index of relative abundance of CoTS. This was such that our model here was developed and parametrised based on an earlier version37,111 which did not use CPUE information but was fitted to the LTMP data on CoTS relative abundance, as well as the corresponding coral cover, to estimate a number of CoTS-coral interaction parameters used in the present model (Supplementary Table 3).Fitting and estimation of our model were achieved through Maximum Likelihood Estimation (MLE). Our objective function was the outcome of combining the negative log-likelihood contributions arising from fitting the model to multiple sets of location-specific data, across a range of environmental and ecological realisations, in conjunction with penalty terms. Specifically, we fitted coral cover (data series ({x}^{{{{{{rm{Coral}}}}}}})) and CoTS CPUEs (data series ({x}^{{{{{{rm{CoTS}}}}}}})) at each management site which contained ({n}_{{{{{{rm{Coral}}}}}}}) and ({n}_{{{{{{rm{CoTS}}}}}}}) data points respectively. This involved fitting parameters that were specific to management sites (e.g. thermal stress - DHW), reefs (e.g. recruitment variability) as well as those that were common amongst reefs (e.g. CoTS consumption rates). A parametrisation that optimised one contribution was unlikely to optimise all contributions and hence we obtained a parametrisation across all reefs and sub-regions. For a modelled catch of (N) (sum of catches across age classes), a catchability coefficient (a constant of proportionality) of ({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}), and data standard deviation of ({sigma }_{{{{{{rm{LL}}}}}}}) our likelihood contribution arising from a management site CPUEs was given by:$$-{{log }}{{{{{rm{L}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}N,{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{CoTS}}}}}}}right) = {n}_{{{{{{rm{CoTS}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{LL}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}frac{{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}{N}_{i}right)right)}^{2}}{2{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}}$$ (35) From which the data series variance and catchability coefficient were computed for the maximum likelihood estimate. The derived variance and the catchability were respectively computed as per:$${sigma }_{{{{{{rm{LL}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}right)right)}^{2}}$$ (36) and$${q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}=frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({N}_{i}right)right)$$ (37) Similarly, the likelihood contribution arising from fitting to a management site coral cover with standard deviation ({sigma }_{{Coral}}) was described by:$$-{{log }}{{{{{rm{L}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}}}+{C}_{y,d}^{{{{{{rm{s}}}}}}}}{{K}^{{{{{{rm{coral}}}}}}}},{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{Coral}}}}}}}right) = {n}_{{{{{{rm{Coral}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{Coral}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}frac{{left({ln}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}{2{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}}$$ (38) Where the standard deviation was given by:$${sigma }_{{{{{{rm{Coral}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{Coral}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-{{{{{rm{ln}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}$$ (39) We computed the negative log-likelihood objective function by summing the contributions from all management sites across considered reefs.Fitting was conducted through the modelling language Automatic Differentiation Model Builder (ADMB) which implements a Quasi-Newton optimisation algorithm for estimation of parameters and provides Hessian based estimation of standard errors112. Penalty terms were added to our likelihood function to integrate a prior understanding of system dynamics and to reduce model variability. Penalty terms encompassed recruitment variability and the magnitude of catches observed in the data.Recruitment was expressed through recruitment deviations, ({r}_{y}), given a standard deviation of ({sigma }_{{{{{{rm{R}}}}}}}) about underlying modelled recruitment (sum of self-recruitment and immigration sources described previously). The recruitment variability negative log-likelihood penalty contribution was given by:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{R}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{rec}}}}}}}right)={sum }_{y=1}^{{{{{{rm{#Years}}}}}}}{sum }_{{{{{{rm{reef}}}}}}=1}^{{{{{{rm{#Reefs}}}}}}}{r}_{y,{{{{{rm{reef}}}}}}}^{{rec}}/2{sigma }_{{{{{{rm{R}}}}}}}^{2}$$ (40) An additional penalty term for model deviations from the magnitude of observed catches was encompassed. This was such that a constant of proportionality relating modelled catches to observed catches tended to one. For an allowed standard deviation of ({sigma }_{{{{{{rm{CM}}}}}}}), the likelihood function was penalised for deviations from unity proportionality, ({r}^{{{{{{rm{CM}}}}}}}), through:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{CM}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{CM}}}}}}}right)={sum }_{{{{{{rm{zone}}}}}}=1}^{{{{{{rm{#Zones}}}}}}}{r}_{{{{{{rm{zone}}}}}}}^{{{{{{rm{CM}}}}}}}/2{sigma }_{{{{{{rm{CM}}}}}}}^{2}$$ (41) Model simulations were conducted in ADMB with output analysis and visualisation conducted in MATLAB.Sensitivity to CoTS controlTo test whether our projected scenarios were consistent with the period over which data were collected, we conducted a model-based before and after comparison to the impact of control. Specifically, we used the fitted trajectory for sites, including both the coral data and CoTS control data (voyages and time spent), and compared this to the model-suggested coral trajectories if CoTS control had not taken place. These were modelled over the fitted period (2013–2018) and, unlike the projected scenarios (2019–2029), were variable in terms of the timing of control (amount of time between visits was variable), the amount of time spent at sites (not a consistent number of dive minutes per visit), CoTS dynamics (recruitment was fitted and hence different annually and between reefs), and in the level of thermal stress they experienced (different sites experienced different effective levels and some sites experience back-to-back events).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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