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Plant materials and genome sequencingFresh leaves of a wild P. koreana plant in the Changbai Mountains of Jilin province in China were collected, and the total genomic DNA was extracted using the CTAB method. For the Illumina short-read sequencing, paired-end libraries with insert sizes of 350 bp were constructed and sequenced using an Illumina HiSeq X Ten platform. For the long-read sequencing, the genomic libraries with 20-kbp insertions were constructed and sequenced using the PromethION platform of Oxford Nanopore Technologies (ONT). For the Hi-C experiment, approximately 3 g of fresh young leaves of the same P. koreana accession was ground to powder in liquid nitrogen. A sequencing library was then constructed by chromatin extraction and digestion, DNA ligation, purification, and fragmentation53 and was subsequently sequenced on an Illumina HiSeq X Ten platform.Genome assembly and scaffoldingThe quality-controlled reads were first corrected via a self-align method using the NextCorrect module in the software NextDenovo v2.0-beta.1 (https://github.com/Nextomics/NextDenovo) with parameters “reads_cutoff=1k (filter reads with length 20, percent of unqualified bases More

COVID variant family expandsSince the Omicron variant of SARS-CoV-2 emerged in late 2021, it has spawned a series of subvariants that have sparked global waves of infection. In the past few months, scientists have identified more than a dozen extra subvariants to watch. There are so many that they’re being called a swarm, or ‘variant soup’. BQ.1.1 (a descendant of BQ.1) and XBB seem to be rising to the top, possibly because they have many mutations in a key region of the viral spike protein called the receptor binding domain, which is required to infect cells.

Source: NextStrain

The variants near youIn Europe and North America, SARS-CoV-2 variants in the BQ.1 family are rising quickly and are likely to drive infection waves as these regions enter winter. They are also a common ingredient of the variant soup in South Africa, Nigeria and elsewhere in Africa. XBB, by contrast, looks likely to dominate infections in Asia; it recently drove a wave of infections in Singapore.

Source: Moritz Gerstung, Cov-Spectrum.org and GISAID

Money worries for science studentsEighty-five per cent of graduate students who responded to a Nature survey are worried about the increasing cost of living, and 25% are concerned about their growing student debt. Forty-five per cent said that rising inflation could cause them to reconsider whether to continue their science studies. The survey involved more than 3,200 self-selected PhD and master’s students from around the world.

How species suffer in heatwavesEven a small temperature rise has a severe effect on animal mortality, and understanding this relationship is important for predicting the effects of heatwaves caused by climate change. A paper in Nature used published data to examine how changes in temperature affect the rate of biological processes, such as movement or metabolism, at permissive temperatures — those at which species function normally. They also looked at how higher, stressful temperatures affect the rate of heat failure (irreversible heat injuries that result in death). This graph shows that rising temperatures drive a very rapid increase in heat-failure rates in frogs and molluscs. These high sensitivities suggest that when there is no way to escape hot conditions, species can quickly succumb. More

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GPP dataAs our primary productivity product we used the GOSIF GPP dataset21 which utilizes the linear relationship between GPP and remotely-sensed SIF34. GOSIF GPP is available globally at 0.05° spatial resolution for the period 2000–2021, with the period 2001–2015 selected here (for a short summary of all datasets used in this study see Supplementary Table 3). GOSIF GPP is based on a gridded SIF product (GOSIF)34 which uses MODIS enhanced vegetation index and meteorological data for spatial scaling and is trained with millions of SIF observations from the coarser-resolution Orbiting Carbon Observatory-235. The global coverage of GOSIF and the close relationship between SIF and GPP allow for an independent assessment of how land cover changes affect GPP in different regions around the world. For instance, SIF has been shown to capture the high GPP in the US Corn Belt derived from flux towers, while ecosystem models underestimated it36. While GPP can thus be empirically estimated from satellite SIF observations relatively reliably (even though some assumptions like the linear GPP–SIF relationship and its universality across biomes are still debated20,37,38,39), the calculation of NPP needs additional assumptions of autotrophic respiration. Therefore, we focused our study on GPP, but we included an NPP product in our uncertainty analysis. In addition to that, to account for the challenges and uncertainties in global GPP estimates we included four alternative GPP products in our sensitivity analysis (see below).Land cover mappingGridded land cover was derived from ESA-CCI22, a global land cover product designed for climate science. ESA-CCI is available at 300 m spatial resolution for the 1992–2020 period (https://cds.climate.copernicus.eu/). We first classified ESA-CCI land covers to forests, grasslands, and croplands according to IPCC classification: classes 50–100, 160, 170 forests (2,022,283 grid cells); classes 110 and 130 grasslands (509,297 grid cells); classes 10–40 croplands (950,025 grid cells). We focus on these three major land cover types to facilitate our analysis. We then converted the resulting map to 0.05° resolution by determining the prevalent (i.e., mode) land cover for each grid cell using the aggregate function from the raster package40 and only included grid cells in our training data in which the prevalent land cover was constant over the period 2001–2015. Other classes (e.g., cropland/natural vegetation mosaics) and grid cells where the land cover changed over the 2001–2015 period were not used for the RF training.Random forestsRF is a popular and efficient supervised machine learning technique which can be applied for classification and regression problems41. While complex, it is still easier to interpret compared to other machine learning methods such as Artificial Neural Networks. It has recently been applied to a wide range of ecological research questions, including the prediction of food42 and bioenergy43 crop yields, potential natural vegetation31, forest aboveground biomass44, soil respiration45, and soil carbon emissions from land-use change5 and is thus well suited for our approach. The “Forests” refer to a number of individual decision trees. For each tree, a random sample of the training data is selected and split multiple times based on a random subset of variables from which the one minimizing the weighted variance is selected by the algorithm. Model performance is computed directly on out-of-bag (OOB) data which is randomly omitted from the training data (36.8% of all grid cells). When RF is applied to new data, a weighted prediction of each individual decision tree contributes to the overall prediction. Variance in the individual trees, e.g., by selecting random subsets of the observations and random variables at each node improves the overall RF predictive skill. Model training and prediction were done using the R ranger package46. After initial testing (see Supplementary Fig. S11) we decided to set the number of individual decision trees to 800 and the number of variables to possibly split at in each node to 10. As the good evaluation measures of RF algorithms can be related to spatial autocorrelation24 we also tested a coordinate-only model and performed a leave-one-out cross validation including spatial buffers (Supplementary Discussion 2, Supplementary Fig. S3). Due to the large computational effort we reduced the number of decision trees to 100 for the buffered leave-one-out cross validation.Predictor variablesWe predicted forest, grassland, and cropland potential GPP using the following 20 predictor variables in our RF algorithm: mean annual surface temperature (Tmean), mean diurnal temperature range (Tdiurnal), temperature seasonality (Tseason; standard deviation), minimum temperature of the coldest month (Tmin), annual temperature range (Tannual), mean temperature of the warmest quarter (Twarmest), mean annual precipitation (Pmean), precipitation seasonality (Pseason; coefficient of variation), precipitation of the wettest quarter (Pwettest), precipitation of the driest quarter (Pdriest), precipitation of the warmest quarter (Pwarmest), mean annual solar radiation (SR), growing degree days (GDD), relative humidity (RH), soil clay content (Clay), elevation (EL), nitrogen deposition (Ndep), nitrogen fertilization (NF), pesticide application (Pest), and gross domestic product (GDP; a proxy for agricultural management input other than NF and Pest). Overall Tmean, Tannual, and Pmean were the most important predictor variables (see Supplementary Discussion 3 and Fig. S12). We also tested other predictors (including additional bioclimatic variables, soil pH, irrigation, or phosphate fertilization) but found only negligible improvements in RF evaluation metrics and hence decided to restrict our analysis to the 20 predictors mentioned above.Climate variables were taken from the CHELSA dataset47,48, remapped to 0.05° spatial resolution using the aggregate function from the raster package40. To only include years overlapping with our GPP data we used the CHELSA time-series data for the 2001–2013 period if available and 1979–2013 climatologies elsewise. Clay was derived from the Regridded Harmonized World Soil Database v1.249. Ndep was taken from ISIMIP2b50, bilinear remapped from 0.5° to 0.05° spatial resolution using Climate Data Operators33. Elevation was obtained from WorldClim51. NF and Pest were derived from country-specific FAO data (e.g., https://ourworldindata.org/grapher/pesticide-use-per-hectare-of-cropland), i.e., we used the same value for all grid cells in a country. GDP was obtained from ref.52.Suitable areaFor the comparison of potential forest, grassland, and cropland GPP in Fig. 1g–i we only included grid cells suitable for all three land cover types. For forests, we assumed forest cover possible if the grid cell is currently forested (e.g., all grid cells of our forest training data) or if the potential natural forest cover exceeds 36.3%. This threshold represents the 5th percentile of all currently forested grid cells. Potential natural forest cover was derived from a potential natural vegetation map, available for 20 biomes at 0.00833° spatial resolution31. To convert these biomes into potential natural forest cover we assumed 100% forest cover for the ten forest biomes and 30% forest cover for tropical savannah. Other biomes were not considered. We then aggregated the map to 0.05° spatial resolution by computing the mean of 36 grid cells using the aggregate function form the raster package40 (see Supplementary Fig. S5 for the resulting map). For grasslands and croplands, we computed the 5th percentile of Tmean and Pmean in the training data (− 9.9 °C and 165 mm for grasslands and 2.7 °C and 295 mm for croplands, respectively) and removed all grid cells below those thresholds, assuming these areas to be too cold or too dry for the respective land cover type. Finally, we calculated the land cover with the highest potential GPP for all overlapping grid cells.Sensitivity analysisTo explore the sensitivity and uncertainty of our RF approach we repeated our prediction using different input datasets, potential forest cover, and machine-learning approaches. The importance of the underlying potential forest map was estimated by replacing our potential forest map (Supplementary Fig. S5) by the LUH2 potential forest map (Supplementary Fig. S13)23. To explore the dependency on the land cover product we repeated our RF prediction using the spatially aggregated MODIS land cover map (MCD12C1; IGBP scheme), available at 0.05° spatial resolution53. We classified grid cells of classes 1, 2, 3, 4, 5, (all forests), 8 (woody savannahs) and 9 (savannahs) as forest. Classes 8 and 9 were included in forest because otherwise forest cover would be underestimated in the temperate and boreal zone. Class 10 was classified as grassland and class 12 as cropland. A comparison of ESA-CCI with MODIS reveals a substantially larger cropland area in ECA-CCI but a smaller grassland area (Supplementary Fig. S14).The sensitivity to the climate product was tested by repeating our analysis using predictor variables from the WorldClim climatologies (1970–2000)51, aggregated from 30 s to 0.05° spatial resolution using the aggregate function from the raster package40. In contrast to CHELSA, growing degree days and relative humidity were not available from WorldClim but we included water vapour pressure as additional predictor.We also tested four alternative global GPP products. The vegetation photosynthesis model (VPM) product, available for the period of interest at 0.05° spatial resolution, is based on improved light use efficiency theory and is driven by remotely sensed datasets and reanalysis climate data and land cover classification which also distinguishes C3 vs. C4 photosynthesis pathways54. The second product is derived from remote sensing considering radiation and canopy conductance limitations on GPP and is available at 0.05° resolution for the 2001–2012 period55. Land cover is not an input variable. The third product, FLUXCOM, uses machine learning to scale FLUXNET site GPP to the globe56,57. FLUXCOM is available at 0.0833° resolution and was conservative remapped to 0.05° using Climate Data Operators33 meaning that the GPP of different land cover types might be mixed in regions with heterogeneous land cover patterns. The forth product is the MODIS MOD17A3 GPP product58, available for the 2001–2013 period and aggregated to 0.05° resolution using the raster package40. It is derived from meteorological data, fraction of absorbed photosynthetic active radiation/leaf area index, and land cover. As there is also a MOD17A3 NPP product available we additionally conducted a prediction for potential NPP. The MOD17A3 NPP product is calculated as GPP minus maintenance and growth respiration estimated from allometric relationships linking daily biomass and annual growth of plant tissues to leaf area index58. This leads to additional uncertainty compared to the MOD17A3 GPP product.To test the effect of an alternative RF algorithm we repeated our prediction with the RF algorithm from the Python scikit-learn library59 using the same number of decision trees (800). Additionally, we tested another machine-learning technique, a deep neural network (DNN), using the PyTorch library60. We selected 10 linear layers with 5 times alternating 128 and 256 nodes and a sigmoid output function. All layers were connected using the rectified linear unit activation function. We used the adamW optimizer with 0.0003 learning rate and 2000 epochs of training. To prevent overfitting, we included a 10% dropout after the 7th layer. Lastly, we included a very simple estimate of the most productive land cover based on the nearest neighbour using scikit-learn’s BallTree implementation together with the Haversine formula. For each grid cell we searched for the nearest forest, grassland, and cropland grid cell and assigned the respective GPP also to this grid cell. We thus assumed that environmental conditions are more or less identical in these grid cells, which might be a reasonable assumption for many locations but less reliable in complex terrain or in large homogeneous regions like the central Amazon rainforest where the nearest cropland/grassland grid cell might be located far away.Land-use change scenariosTo estimate the effects of historical and potential future land cover changes on global GPP we applied LUH2 scenarios23 which also serve as input data for ESMs participating in CMIP6. Land-use changes over the historical period are based on the HYDE reconstruction3, while future projections were developed by different Integrated Assessment Models combining various assumptions of socio-economic behaviour (SSPs) with climate mitigation targets (RCPs). Annual fractions for the two land cover classes cropland (sum of 5 crop types) and managed grassland (sum of pasture and rangeland) were available for each scenario at 0.25° resolution (https://luh.umd.edu/). We converted to 0.05° resolution assuming the same land cover fractions for all 25 grid cells around the LUH2 grid cells. We considered the following land cover transitions: forest to managed grassland, forest to cropland, and natural grassland to cropland (and reverse transitions for future scenarios). Transitions in areas suitable for only two land cover types were also included. Conversions of natural grasslands to managed grasslands were assumed not to affect productivity. We assumed the original land cover of a grid cell to be either forest (i.e., potential forest cover  > 36.3%) or natural grassland and accordingly multiplied the converted areas by the differences in potential GPP derived from our RF approach. Our broad forest definition including open tree cover (see above) and the fact that we assumed a change from 100 to 0% forest area in deforested grid cells results in a total historical deforestation area substantially larger than estimated in a recent study (2.4 Mkm2 vs. 1.6 Mkm2)61. These assumptions, however, do not impair our GPP estimate as our approach implicitly accounts for gradients in forest productivity (open forests tend to have lower GPP than closed forests). To test the sensitivity of the resulting GPP reduction we also applied the potential GPP maps from our uncertainty analysis to historical land-use changes (Supplementary Fig. S6). For future land cover changes we investigated changes over the 2015–2100 period for all available LUH2 scenarios: SSP1-1.9, SSP2-2.6, SSP4-3.4, SSP5-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. Land-use activities in these scenarios range from large-scale deforestation (e.g., SSP3-7.0) to reforestation (e.g., SSP1-1.9) (Supplementary Fig. S7).Earth System ModelsWe compared the potential GPP estimated by our RF algorithm to simulations of eight ESMs participating in CMIP6 (CESM2-CLM562, CNRM-ESM2.1-Surfex 8.0c63, EC-Earth3-Veg-LPJ-GUESSv464, GFDL-ESM4-GFDL-LM4.165, IPSL-CM6A-LR-ORCHIDEEv2.066, MIROC-ES2L-MATSIRO6.0 + VISIT-e ver.1.067, MPI-ESM1-2-LR-JSBACH3.2068, UKESM1-0-LL-JULES-ES-1.069) with an explicit representation of natural vegetation and at least one agricultural land cover class (cropland or managed grassland) in their vegetation sub-model. We selected these ESMs so that all vegetation models implemented in more than one ESM were represented only once (e.g., the JSBACH vegetation model is a component of both MPI-ESM1-2-LR and AWI-ESM). For each ESM, the variable gppLut was downloaded from the CMIP6 archive (https://esgf-data.dkrz.de/search/cmip6-dkrz/) for the historical simulations. These files contain simulated GPP for natural vegetation, pasture, and cropland for which we calculated the 2001–2014 mean (2014 is the last year of the historical period). ESMs use fractional land covers for each grid cell, meaning that climatic drivers are inherently the same for all land cover types within a grid cell and simulated productivities can therefore be directly compared. As ESMs differ in their spatial resolution we bilinear remapped all output to 0.05° resolution using Climate Data Operators33 to allow for a fair comparison across models. To assess the sensitivity of our results to the interpolation method we also tested conservative remapping which results in slightly different maps (Supplementary Fig. S15) and usually larger model biases (Supplementary Table 2). In addition, ESMs differ in where they simulate forests in natural vegetation areas, and therefore we removed all grid cells from the comparison where at least one ESM simulated no tree productivity/cover/biomass in order to avoid comparing the GPP of natural grasslands to managed grasslands. We provide maps based on the original output for each ESM in Supplementary Fig. S10.FLUXNET dataWe compared our predictions of potential GPP to FLUXNET Tier 1 eddy covariance measurements (Supplementary Fig. S16)70. We included all forest, woody savannah (classified as forest), grassland and cropland sites21 which were located in suitable areas for the respective land cover. Mean GPP was calculated as the mean of the GPP estimates based on the night-time (GPP_NT_VUT_REF) and day-time (GPP_DT_VUT_REF) partitioning method. As some sites only had a few years of data, all available years were considered (i.e., site mean GPP was calculated for a different time period than 2001–2015). Comparisons were made with the potential GPP in the respective grid cell in which the site was located (i.e., not calibrated to site conditions). More

Ground-truth surveys of seagrass habitatTo obtain georeferenced field data on benthic cover levels from habitats of the Bahama Banks, we employed two similar, in-water survey and image approaches: (1) swimmer-based photo-transects; and (2) tow board photo transects (Supplementary Fig. 6), resulting in a total of 2542 surveys.For (1), free-divers swam over the bottom of the seafloor at a fixed height with a digital camera (Canon 5D mIV, GoPro Hero) set to capture images manually. Photographs were captured using automatic settings in a 1.0 m × 1.0 m footprint, 1.5 m above the seafloor following [39]. A center console vessel was used to run the transects at distances of 5–7 km, whereby the free-diver would capture successive photos at a horizontal distance of between 400–800 m, and the location was logged using either a handheld GPS (Garmin GPS 73) or a boat-mounted GPS with a depth sounder (Garmin EchoMap DV). Transect locations were chosen based on a priori local expert knowledge of varying benthic cover in the region. Surveyed areas included: southern New Providence (24.948862°, −77.387834°), southeast of New Providence (24.980265°, −77.229168°), south of Rose Island (25.066268°, −77.160063°), the middle Great Bahama Bank (24.735355°, −77.212998°), and the northern Exumas (24.729973°, −76.889488°). For (2), snorkeling observers were pulled from a research vessel on tow boards affixed with underwater action cameras (GoPro Hero 3+) traveling at ~1 m/s. The start and end of a tow were delineated with either a handheld GPS (Garmin eTrex 30) or a boat mounted GPS with depth-finder (Garmin EchoMap DV), and tows proceeded in a straight line recorded by the GPS. Cameras recorded images at 0.5 Hz throughout the tow, starting in conjunction with creating a waypoint. Samples (i.e., paired image and geolocated point) were sub-selected from the tow once movement began, at the midpoint of a tow, and immediately before movement stopped. Images were manually quality controlled such that if a selected image contained obstructions or was out of focus, the nearest clear image was selected to replace it. If no images within 10 s were clear (i.e., 10 m maximum spatial error), the sample was discarded. If the GPS track contained gaps or segments larger than 10 m, only images/point pairs at the start and end waypoints were sampled.Surveys focused on historical fishing grounds for queen conch (Lobatus gigas) between 2015 and 2018 following the sampling design and methods of ref. 32. A stratified random design was used to allocate 6000 m2 of observation effort into each cell of a 1’ by 1’ grid placed over each fishing ground. This effort was split into multiple tows between 200 and 1000 m in length, thus images were separated by at least 100 m.Fishing grounds extended from the edge of a deepwater sound to between 7 and 10 km up the bank and were limited to the depths used by freediving fishers. Surveyed fishing grounds included: the Exumas (24.382207°, −76.631058°), the southwestern Berry Islands (25.455529°, −78.014214°), south of Bimini (25.375592°, −79.187609°), the Grassy Cays (23.666864°, −77.383547°), the Joulter Cays (25.321297°, −78.109251°) and the southeast tip of the Tongue of the Ocean (23.376417°, −76.621943°). For details on image processing, see section on remote sensing below.Sediment coringTo gather the sediment cores analyzed for organic carbon content on the Bahama Banks, we collected samples from various benthic habitats that included varying densities of seagrass habitat (Thalassia testidinum and Syringodium filiforme). We percussed, via SCUBA, an acrylic cylinder tube perpendicular to the seafloor into marine sediment until rejection at various penetration depths up to 30 cm. The sample was then extracted vertically from the marine sediment and capped at the bottom to avoid loss of material. This sample was then transported vertically through the water column to a research vessel where it was removed from the coring device and immediately capped on top with an air-tight cap. Compression rates were negligible (~5 cm) across the first 5 cores, and as such were not subsequently measured. The samples were then labeled, photographed, geotagged, and the first 30 centimeters of each core was extruded. To complete the extrusion process, we placed each sample on top of a capped piston device in the same orientation as collection (deepest portion of collected sediment still on the bottom). The bottom cap was removed to thread the acrylic cylinder tube onto the piston device and then was lowered to various measured lengths to collect corresponding depth sections of the sediment core. These sections were sliced (every 1–5 centimeters), labeled, and placed into whirl pack bags to collect the wet weight of each sample. All samples were then frozen and stored for future laboratory analyses. All samples were dried in a laboratory oven at 55 °C for 48 h until constant dry weights were reached. The samples were then weighed to collect their corresponding dry weights. The dry bulk density (DBD) was calculated by diving the sample dry weight (g) by the sample volume (cm3). The samples were then further ground with a mortar and pestle until a homogeneous fine grain size was achieved. Sediment samples collected from the Exuma Cays (142 samples from 16 cores) were analyzed for Corg content. Sediment samples were weighed accurately into silver capsules and acidified with 4% HCl until no effervescence was detected in two consecutive cycles. The samples were then dried in a 60 °C oven overnight, encapsulated into tin capsules and analyzed using an Organic Elemental Analyzer Flash 2000 (Thermo Fisher Scientific, Massachusetts, USA). We then conducted a standard loss on ignition (LOI) methodology at our laboratory facility (Braintree, Massachusetts, USA) for all the samples. Each sample was subsequently sub sampled with 5–15 grams of representative material and placed into a ceramic crucible to collect its mass. The crucibles were then loaded into a separate muffle laboratory oven and heated at 550 °C for 6 h. Upon completion of this muffle, the crucibles were then immediately weighed to collect the LOI of organic material from each sample, defined as the weight lost in the muffle (g) divided by the subsample dry weight (g). A fitted regression between the Corg and LOI from the Exuma Cays cores was generated (Supplementary Fig. 7), and then used to predict the sediment Corg contents from LOI measurements in the Grand Bahama cores. Sediment Corg stocks were quantified by multiplying Corg and DBD data by soil depth increment (1–5 cm) of the sampled soil cores. The cores from the Exuma Cays (15 cm) and Grand Bahama (30 cm) were collected with different depths, we therefore fitted a regression between Corg stock in 15 cm-depth and Corg stock in 30 cm-depth for the Grand Bahama cores (Supplementary Fig. 8) and used this regression to extrapolate Corg stock of the Exuma Cays cores into 30 cm-depth. Moreover, to allow direct comparison among other studies27, the Corg stock per unit area was standardized to 1 m-thick deposits by multiplying 100/30.Tiger shark taggingThe research and protocols conducted in this study complies with relevant ethical regulations as approved by the Carleton University Animal Care Committee. The shark data used in this paper were collected as part of a multi-year, long-term research program evaluating the interannual behavior and physiology of large sharks throughout the coastal waters of The Commonwealth of The Bahamas23. All sharks were captured using standardized circle-hook drumlines33 on the Great and Little Bahama Banks throughout the country, focusing efforts in three primary locations: off New Providence Island, the Exuma Cays, and off West End, Grand Bahama, from 2011–2019. All sharks were secured alongside center console research vessels and local dive boats, where their sex, morphometric measurements, and blood samples were taken. A mark-recapture identification tag was applied to the shark at the base of the dorsal fin. Some of the sharks sampled in the present study were also tagged with a coded acoustic transmitter which was surgically implanted ventrally into the peritoneal cavity and then sutured, as part of a concurrent study on shark habitat use and residency within the region23.Pop-off archival satellite tags were affixed to eight tiger sharks (seven female, one male; 298 ± 28 cm total length; mean ± SD) in The Bahamas from 2011–2019, permitting measurements of swimming depth and water temperature recorded at either 4-min (Sea-Tag MODS, Desert Star Systems LCC, USA) or 10-s intervals (miniPAT tags, Wildlife Computers, USA). Pop-off satellite tags were inserted into the dorsal musculature of the sharks using stainless steel anchors and tethers. All pop-off satellite tags were either recovered manually, permitting access to the full time-series, or popped-off and transmitted their data to an Earth-orbiting Argos satellite, resulting in a subset of the full time-series (transmission frequencies: 2.5 min [miniPAT], 10 min [PSATGEO], daily average [Sea-Tag MOD]). Tiger shark positions were estimated from the satellite data using tag-specific proprietary state space algorithms from Wildlife Computers (GPE3; based on ref. 34) and Desert Star Systems35. With miniPAT tags, positions were further filtered to remove the least reliable positions ( More

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