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ERROR: type should be string, got "https://doi.org/10.3390/s20205877 VoR (Version of Record) This item was submitted to Loughborough's Research Repository by the author. \nItems in Figshare are protected by copyright, with all rights reserved, unless otherwise indicated. This item was submitted to Loughborough's Research Repository by the author. Items in Figshare are protected by copyright, with all rights reserved, unless otherw PLEASE CITE THE PUBLISHED VERSION https://doi.org/10.3390/s20205877 PUBLISHER STATEMENT This article is an open access article distributed under the terms and conditions of the Creative Commons\nAttribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). LICENCE DNAzyme sensor for the detection of Ca2+ using resistive pulse sensing DNAzyme sensor for the detection of Ca2+ using resistive pulse sensing Received: 24 August 2020; Accepted: 10 October 2020; Published: 17 October 2020 Received: 24 August 2020; Accepted: 10 October 2020; Published: 17 October 2020 Abstract: DNAzymes are DNA oligonucleotides that can undergo a specific chemical reaction in the\npresence of a cofactor. Ribonucleases are a specific form of DNAzymes where a tertiary structure\nundergoes cleavage at a single ribonuclease site. The cleavage is highly specificity to co-factors, which\nmakes them excellent sensor recognition elements. Monitoring the change in structure upon cleavage\nhas given rise to many sensing strategies; here we present a simple and rapid method of following\nthe reaction using resistive pulse sensors, RPS. To demonstrate this methodology, we present a\nsensor for Ca2+ ions in solution. A nanoparticle was functionalised with a Ca2+ DNAzyme, and it\nwas possible to follow the cleavage and rearrangement of the DNA as the particles translocate the\nRPS. The binding of Ca2+ caused a conformation change in the DNAzyme, which was monitored\nas a change in translocation speed. A 30 min assay produced a linear response for Ca2+ between\n1–9 µm, and extending the incubation time to 60 min allowed for a concentration as low as 0.3 µm. We demonstrate that the signal is specific to Ca2+ in the presence of other metal ions, and we can\nquantify Ca2+ in tap and pond water samples. Keywords: DNAzyme; aptamer; nanopore; resistive pulse sensor; metal ion sensor REPOSITORY RECORD Heaton, Imogen, and Mark Platt. 2020. “Dnazyme Sensor for the Detection of Ca2+ Using Resistive Pulse\nSensing”. Loughborough University. https://hdl.handle.net/2134/13148729.v1. sensors sensors Imogen Heaton and Mark Platt * Department of Chemistry, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;\nI.Heaton@lboro.ac.uk\n* Correspondence: m.platt@lboro.ac.uk Department of Chemistry, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;\nI Heaton@lboro ac uk Department of Chemistry, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;\nI.Heaton@lboro.ac.uk\n* Correspondence: m platt@lboro ac uk * Correspondence: m.platt@lboro.ac.uk \u0001\u0002\u0003\u0001\u0004\u0005\u0006\u0007\b\u0001\n\u0001\u0002\u0003\u0004\u0005\u0006\u0007 Received: 24 August 2020; Accepted: 10 October 2020; Published: 17 October 2020 Sensors 2020, 20, 5877; doi:10.3390/s20205877 1. Introduction Calcium (Ca2+) is one of the most abundant metals in the human body, making up to 2%wt of\ntotal human body weight, and plays a fundamental role in biological processes including secondary\nmessengers critical for cell signalling, protein folding, and catalysis [1–4]. It has also been demonstrated\nthat monitoring Ca2+ concentrations is important within the environment. High or very low\nconcentrations within drinking water have been linked to problems with corrosion, scaling, and\npoor taste [5]. It also represents a hazard due to its high environmental mobility and bioavailability [6]. When unregulated, high concentrations of Ca2+ can cause various diseases such as hypercalcaemia [7]\nand heart disease, [8] while low levels can lead to deficiencies such as osteoporosis [9] and muscle and\nnerve tightening [10]. Many different methods have been developed for the detection of Ca2+ ions in solutions. Common methods include atomic absorption spectrometry [11,12], ion chromatography [13,14],\nand high-performance liquid chromatography [15]. While they are sensitive, e.g., the Limit of detection\n(LoD) for atomic absorption can be 0.005 µM [12], as well as selective and reliable, they are expensive,\nlabour intensive, and unable to be taken on-site. Fluorometric methods have been widely acknowledged\ndue to their advantageous short response times and high sensitivity; however, they too suffer complex\noperations, low detection throughput, and can also be problematic to apply on-site [16]. There remains\na need for a technology that is relatively inexpensive, has short analysis times, is sensitive, and can be\ndeployed in complex matrices. Sensors 2020, 20, 5877; doi:10.3390/s20205877 www.mdpi.com/journal/sensors www.mdpi.com/journal/sensors 2 of 11 Sensors 2020, 20, 5877 An important component for any sensor is the recognition element, and thus, there have been\ncomponents that have been developed to bind Ca2+ with high specificity that have been integrated\ninto various sensing platforms, such as binding proteins [7,17,18], magnetic nanoparticles [19],\nand glycosphingolipds [20]. A new category of recognition ligands to heavy metals include DNA\naptamers and DNAzymes. DNAzymes are DNA-based catalysts; all known DNAzymes have been\nselected through in vitro selection [8,21,22]. There are many DNAzymes that require divalent metal\nions such as Pb2+ [23–25], Zn2+ [26,27], Cu2+ [28,29], and UO22+ [30,31], for activity. DNAzymes have\nseveral advantages compared to enzymes commonly used within biosensing: they can easily be grafted\nonto surfaces, are more stable at ambient conditions, and can amplify detection due to their catalytic\nnature [32]. 1. Introduction DNAzymes have been widely used as they have simple reaction conditions and significant\nchanges in structure [33]. However, they usually require a tag or label to provide an analytical\nsignal [3,34,35]. Resistive pulse sensing (RPS) is a sensing technology capable of probing changes in\nDNA structure [36,37]. RPS has a broad range of applications including material characterisation [38–41],\nquantification of DNA/peptide analyte interactions [42,43], and biosensing [44–46]. RPS measures\nspeeds of nanocarriers as they translocate a nanopore, and through changes in nanocarrier speed,\nthey are able to infer carrier binding with target analyte [47–49]. The integration of DNAzymes onto nanocarriers presents a new method of analysis, as the\ncleavage of the DNAzymes could be identified and characterised. RPS has demonstrated the ability to\nmeasure speed changes between double and single stranded DNA [36]. There have also been other\nstudies that have utilised DNAzymes with a-hemolysin and solid-state nanopores [50–52]. Protein and\nsolid-state nanopores offer the ability to distinguish between the DNAzyme and its cleavage products\nthrough changes in blockade signal. However, protein nanopores suffer from issues with stability and\nlow reaction condition tolerance, while solid-state nanopores suffer from low signal-to-noise ratios and\nrequired large DNA strands to enable detection. To improve these limitations, carriers can be used to\nenable separation and preconcentration of samples, enhance the signal, and increase dynamic range\nand sensitivity of the assay [37,53]. Here, we present the use of a DNAzyme with RPS to detect Ca2+ ions in solution, using RPS’s\nability to identify changes in structure. Previous studies have reported the cleavage of the DNAzyme\nby Ca2+ [3,54,55]; here, we were able to measure and take this further by inferring the rearrangement\nof the DNAzyme structure post cleavage. The catalytic nature of the DNAzyme allowed for the assay\nlimits of detection to be tuned dependent on incubation times, as well as allowing quantification of\nCa2+ when incubation times are kept constant. We were also able to demonstrate our assay is specific\nto Ca2+ observing no interferences from a mix of divalent ions. Finally, we showed the ability of our\nassay to be deployed in real environmental samples through the quantification of Ca2+ in both tap and\npond water samples. 2.4. RPS Set Up A qNano (Izon Science Ltd., New Zealand) was used to complete all the measurements for this\nstudy. A qNano uses data capturing software (Izon Control Suite v3.3) to record the particles as\nthey traverse the pore. The lower fluid cell contained 80 µL of KCl solution and the upper fluid cell\ncontained 40 µL sample solution. After each measurement was taken, the nanopore was cleaned by\nfirst rinsing the upper fluid cell with background buffer before the buffer was removed and replaced\nmultiple times. Each time, a different pressure or vacuum was applied. The nanopore stretch was\nalso varied wider and narrower; this was done until there were not any residual particles observed in\nthe system, ensuring no cross contamination between the samples. The qNano was operated with a\npositive bias, i.e., the positive electrode in the lower fluid cell and the negative electrode in the upper\nfluid cell, so the particles traverse the pore towards the positive electrode unless otherwise stated. For all experiments, an NP200 nanopore was used to be able to analyse particles from 85–500 nm. More than 200 particles where measured for each sample, and a typical rate was 250–300 particles per\nmin. To account for any manufacturing variation between pores, the baseline current was kept constant\nthroughout all experiments, and the stretch was slightly changed to ensure this, with a maximum 10%\ndifference in baseline between sample runs. Each pore was first characterised using the calibration\nparticles so day-to-day differences could also be accounted for. A typical workflow was 30 min incubation of the particles in the test solution before being vortexed\nand circa 2 min to acquire the data. If required, a magnetic separation step was included. After the\nincubation period, the samples were placed next to the magnet for 5 min or until a clear cluster of\nparticles could be seen. The solution was then removed, and the particles resuspended before detection. 2.2. Custom DNA Oligonucleotides Two custom DNA oligonucleotides were purchased from Sigma-Aldrich, in lyophilised form, and\nwere purified using reverse-phase cartridge purification by the manufacturer. The two oligonucleotides\nordered were: GCCATCTTTTCTCACAGCGTACTCGCTAAGGTTGTTAGTGACTCGTGAC (enzyme\nstrand) and biotin-TCACGAGTCACTATRAGGAAGATGGCGAAA (substrate strand); they were\ndiluted to 100 µM stock solutions using deionised water. 2.1. Chemicals and Reagents Calcium(II) chloride and potassium chloride were purchased from Fisher Scientific, UK. Carboxylated polystyrene particles were purchased from Bangs Laboratories, USA, and are denoted\nas CPC200 (mode diameter 210 nm, measured concentration 1 × 1012 particles mL−1). Nanopores\nwere purchased from Izon Science Ltd., New Zealand. Reagents were prepared in deionised water\n(Elga PureLab), with a resistance of 15 MΩcm. 2-(N-Morpholino)ethanesulfonic acid (MES) and\n2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid (HEPES), Lithium Chloride, nickel sulphate\nhexahydrate, iron chloride, magnesium chloride, and TWEEN 20 were purchased from Sigma-Aldrich,\nUK. Streptavidin-modified magnetic particles, 120 nm, were purchased from Ademtech, France. 3 of 11 Sensors 2020, 20, 5877 2.3. Particle Preparation The DNAzyme complex was first formed by heating the substrate strand, 2.5 µL, with the enzyme\nstrand, 5 µL, in 50 mM MES buffer (pH 6 with 25 mM LiCl) with 0.05% TWEEN 20 added, at 70 ◦C\nfor 2 min followed by cooling. Once cool, streptavidin modified particles were added to the solution\nand left to bind for 30 min. Once bound, the particles were placed on a MagRack (Life Sciences) until\na clear cluster of particles was seen; the buffer was then removed and replaced with 10 mM HEPES,\npH 7.6, 50 mM LiCl, and a buffer with 0.05% TWEEN 20 was added. The concentration of the particles in each assay was 2.5 × 109 particles per mL. The suppliers data\nsheets give the binding capacity of 5665 pmol of biotin per mg of beads, which we used to calculate\nhow much DNA is 100% coverage. In each assay, the number of particles remained the same and the\nDNA was always added in excess to ensure complete coverage of the carrier surface. We have shown\nhow the number of the carriers and ligand density can affect the sensitivity in similar assays. This was\nnot the subject of investigation here [37,42,48,53,56]. 2.9. Particle Speed Particle speed through the pore was calculated from the pulse width. As particles translocate the\nnanopore, they produce a pulse. The maximum magnitude of this pulse is recorded as T1.0 as this\nmagnitude decreases multiple time points are recorded correlating to T0.9, T0.8, T0.7, etc. Here, we used\nthe reciprocal of the value at T0.5 (width of the pulse at 50% peak height) to determine relative particle\nspeed. These values were then normalised to either calibration particles (CPC200s) or blank peptide\nfunctionalised particles were run on the same day. Using the same nanopore and experimental\nconditions, normalisation was done by running multiple calibration runs, getting an average setting of\n1, and then dividing the sample data by this to get a ratio. This was done to account for any differences\nin measured speed between different pores and different days. 2.6. Binding Time DNAzyme particles where functionalised as above, and Ca2+ was added to make 1 µM solutions. The samples were the placed on a rotary wheel, and at 5, 10, 20, and 30 min, an aliquot was taken out\nand vortexed before being analysed. 2.8. Environmental Water Samples Water samples were collected from a lap tap and an outdoor pond. DNAzyme particles were\nfunctionalised as above and added to the water samples. The samples were also spiked with Ca2+\nto give a final concentration of 3 µM. They were then left on a rotary wheel for 30 min before being\nmagnetically separated and resuspended in buffer before analysis. 2.5. Saturation Point Testing Particles where functionalised with different amounts of the DNA substrate strand added to\ndetermine the saturation point. Heating the substrate and enzymes strands together formed the\nDNAzyme before different amounts were added to the particles to determine DNAzyme forming and\nsaturation point. Finally, post heating of the two DNA strands, they were left to cool in different places\nbefore being added to particles to determine the best cooling practices. 4 of 11 Sensors 2020, 20, 5877 2.7. Metal Ion Interferences Metal ion stocks were prepared from MgCl2, NiCl2, and FeCl3. These were added to DNAzyme\nfunctionalised particles to give 3 µM solutions. A mix was also prepared, which included Ca2+. Samples were left on a rotary wheel for 30 min before being vortexed and analysed. 3. Results and Discussion Within RPS experiments, each translocation of a carrier through the nanopore produces a pulse,\nsuch as in Figure 1a. The magnitude of the pulse, known as the pulse magnitude, ∆ip, is related to\nthe volume of the carrier, and the width or full width half maximum, FWHM, of the pulse relates to\nits velocity [36]. In the absence of convection, the velocity of the carrier can be proportional to the\nsurface charge or zeta potential of the carrier, assuming that electro osmosis remains constant [36,57]. Thus, RPS allows the quantity and structure of the DNA on the nanocarrier to be analysed [36]. The DNA sequences of the two DNA strands that make up the DNAzyme are shown in Table 1,\nand the two strands are labelled substrate and enzyme. The structure of the Ca2+ DNAzyme was\npreviously reported by Yu et al. after systematic mutation to identify the optimal sequence. This is\nshown schematically in Figure 1bi [3]. The substrate strand has a single RNA linkage (rA) that operates\nas the cleavage site. Upon binding with two Ca2+ ions [55], the substrate strand cleaves and rearranges,\nchanging from dsDNA to ssDNA, seen in Figure 1bii [3,4]. RPS can differentiate from dsDNA and\nssDNA through changes in the nanocarrier translocation speed [36]. 5 of 11 Sensors 2020, 20, 5877\nSensors 20 Figure 1. (a) Schematic of a particle traversing the RPS device and the signal produced. Blockade \nmagnitude (Δip) and full width half‐maximum (FWHM) are shown. (bi) Schematic of the DNAzyme \nonce  both  strands  have  bound  together,  with  the  substrate  strand  (blue)  and  the  enzyme  strand \n(orange);  (bii)  binding  with  two  Ca2+ ions  and  cleaving;  (biii)  rearrangement  of  DNAzyme  post \ncleavage. (c) Example of a baseline current and blockade events caused by the carriers translocating \nthe pore from 10 to 30 s, with the inset showing 17 to 21 s and two example pulses. The DNA sequences of the two DNA strands that make up the DNAzyme are shown in Table 1, \nand the two strands are labelled substrate and enzyme. The structure of the Ca2+ DNAzyme was \nFigure 1. (a) Schematic of a particle traversing the RPS device and the signal produced. Blockade\nmagnitude (∆ip) and full width half-maximum (FWHM) are shown. 3. Results and Discussion (bi) Schematic of the DNAzyme\nonce both strands have bound together, with the substrate strand (blue) and the enzyme strand (orange);\n(bii) binding with two Ca2+ ions and cleaving; (biii) rearrangement of DNAzyme post cleavage. (c) Example of a baseline current and blockade events caused by the carriers translocating the pore\nfrom 10 to 30 s, with the inset showing 17 to 21 s and two example pulses. Figure 1. (a) Schematic of a particle traversing the RPS device and the signal produced. Blockade \nmagnitude (Δip) and full width half‐maximum (FWHM) are shown. (bi) Schematic of the DNAzyme \nonce  both  strands  have  bound  together,  with  the  substrate  strand  (blue)  and  the  enzyme  strand \n(orange);  (bii)  binding  with  two  Ca2+ ions  and  cleaving;  (biii)  rearrangement  of  DNAzyme  post \ncleavage. (c) Example of a baseline current and blockade events caused by the carriers translocating \nthe pore from 10 to 30 s, with the inset showing 17 to 21 s and two example pulses. The DNA sequences of the two DNA strands that make up the DNAzyme are shown in Table 1, \nand the two strands are labelled substrate and enzyme The structure of the Ca2+ DNAzyme was\nFigure 1. (a) Schematic of a particle traversing the RPS device and the signal produced. Blockade\nmagnitude (∆ip) and full width half-maximum (FWHM) are shown. (bi) Schematic of the DNAzyme\nonce both strands have bound together, with the substrate strand (blue) and the enzyme strand (orange);\n(bii) binding with two Ca2+ ions and cleaving; (biii) rearrangement of DNAzyme post cleavage. (c) Example of a baseline current and blockade events caused by the carriers translocating the pore\nfrom 10 to 30 s, with the inset showing 17 to 21 s and two example pulses. p\ny\np\ny\ny\ny\np\nq\nshown  schematically  in  Figure  1bi  [3]. The  substrate  strand  has  a  single  RNA  linkage  (rA)  that \noperates as the cleavage site. Upon binding with two Ca2+ ions [55], the substrate strand cleaves and \nrearranges, changing from dsDNA to ssDNA, seen in Figure 1bii [3,4]. RPS can differentiate from \ndsDNA and ssDNA through changes in the nanocarrier translocation speed [36]. Table 1. Table of the two different DNAzyme strand sequences and lengths. The ribo-adenine is in the\nsubstrate strand denoted at rA, and it is here that the DNAzyme cleavages upon binding with calcium. 3. Results and Discussion (a) Determining the concentration of substrate strand DNA required to saturate the\nnanocarriers through changes in carrier speed. (b) Determining if the DNAzyme complex had formed\npost heating through the differing saturation points of the substrate strand vs. the DNAzyme complex. Studies have shown how the binding of Ca2+ to the DNAzyme complex cleaves a section of 15 base\npairs of DNA, as in Figure 1bi,bii [3]. If the reported mechanism is correct, it was hypothesised that\nas the DNA cleaves, changing from dsDNA to ssDNA, it would result in the decrease in nanocarrier\ntranslocation speeds. [36] This theory was tested by monitoring the DNAzyme modified nanocarriers\nspeeds at different time points after incubation with Ca2+. As can be seen in Figure 3ii, after the initial\n5 min, we measured a significant decrease in nanocarrier speed, from 1 to 0.84 ms−1, before a rapid\nincrease and plateauing after 10 min to 1.23 ms−1. We postulated that this increase in speed was due to\nthe DNAzyme rearranging post cleavage by Ca2+, as the remaining attached DNA strand is extended,\nseen in Figure 1biii [58]. This increase in DNA length would increase particle speed significantly, and\nit correlates to previous studies [36]. The speed of the nanocarriers in the presence/absence of Ca2+ is\nshown in Figure S3. The velocity of the uncoated and ssDNA carriers remains unchanged with the\naddition of Ca2+, which illustrates that the addition of divalent ions does not change the electroosmotic\nand electrophoretic forces acting on the particles and Nanopore channel. We also interpret this change\nin velocity for the DNAzyme coated carriers as being specific to the cleavage mechanism. Sensors 2020, 20, x \n7  of  12 \npost  heating  through  the  differing  saturation  points  of  the  substrate  strand  vs. the  DNAzyme \ncomplex. Studies have shown how the binding of Ca2+ to the DNAzyme complex cleaves a section of 15 \nbase pairs of DNA, as in Figure 1bi,bii [3]. If the reported mechanism is correct, it was hypothesised \nthat  as  the  DNA  cleaves,  changing  from  dsDNA  to  ssDNA,  it  would  result  in  the  decrease  in \nnanocarrier translocation speeds.[36] This theory was tested by monitoring the DNAzyme modified \nnanocarriers speeds at different time points after incubation with Ca2+. 3. Results and Discussion DNA Sequence\nSubstrate strand\n[Btn]GTCACGAGTCACTATrAGGAAGATGGCGAAA\n31 mer\nEnzyme Strand\nGCCATCTTTTCTCACAGCGTACTCGCTAAGGTTGTTAGTGACTCGTGAC\n49 mer p\ny\np\ny\ny\ny\np\nq\nshown  schematically  in  Figure  1bi  [3]. The  substrate  strand  has  a  single  RNA  linkage  (rA)  that \noperates as the cleavage site. Upon binding with two Ca2+ ions [55], the substrate strand cleaves and \nrearranges, changing from dsDNA to ssDNA, seen in Figure 1bii [3,4]. RPS can differentiate from \nTable 1. Table of the two different DNAzyme strand sequences and lengths. The ribo-adenine is in the\nsubstrate strand denoted at rA, and it is here that the DNAzyme cleavages upon binding with calcium. Previous work from our group has shown that the technique is cable of differentiating between\nthe location and position of dsDNA on a mixed ss/dsDNA strand [36]. Previous work from our group has shown that the technique is cable of differentiating between\nthe location and position of dsDNA on a mixed ss/dsDNA strand [36]. Here, the hypothesis was that the binding of Ca2+ ions to the DNA would cause a change in the\ntertiary structure that could be followed using RPS. The substrate strand binds to the nanocarrier\nvia the biotin-avidin interaction. To confirm the immobilisation of the substrate strand on the carrier,\nvarying concentrations were added to a consistent number of carriers, as in Figure 2a. At 120 nM,\nthe velocity remains unchanged; note that more DNA may attach to the carrier’s surface, but the\nsignal does not change above this concentration and we can infer that the particles are saturated\nwith DNA. Due to the double stranded nature of the DNAzyme and the lower packing density of\ndsDNA on the carrier, it was expected that a lower concentration of DNAzyme would be required to\ncover the same surface area of carriers. A comparable experiment was carried out and the data are\nshown in Figure 2b. This shows a decrease in saturation point from 120 to 60 nM for dsDNA. It is\nimportant to note that the binding of the DNAzyme to the carriers did not result in a change in the\npulse magnitude, indicating that changes in nanocarrier speed were down to differences in carrier\ncharge, not aggregation or disaggregation of the particles, Figure S1. Alternative processes were also\ntested to ensure a high grafting density of the dsDNA on the carrier used different cooling cycles. 3. Results and Discussion 6 of 11 Sensors 2020, 20, 5877\nsurfa\nThis When forming the DNAzyme complex, the two DNA strands are first added together, and the solution\nis heated to 70 ◦C before being cooled. To compare if the rate at which the solution cooled affected the\npacking, density samples were placed within the fridge (cold), room temperature (RT), heated (warm),\nand one was left in the dry bath (hot). The data presented in Figure S2 compare the different speeds of\nthe nanocarriers functionalised with the DNAzymes formed through the different cooling methods. As can be seen, although the differences are only slight, keeping the solution in a warm place to allow\nit to cool slowly led to the faster speed, indicating that more DNAzyme has been immobilised onto the\ncarrier surface. indicating  that  changes  in  nanocarrier  speed  were  down  to  differences  in  carrier  charge,  not \naggregation or disaggregation of the particles, Figure S1. Alternative processes were also tested to \nensure  a  high  grafting  density  of  the  dsDNA  on  the  carrier  used  different  cooling  cycles. When \nforming the DNAzyme complex, the two DNA strands are first added together, and the solution is \nheated to 70 °C before being cooled. To compare if the rate at which the solution cooled affected the \npacking,  density  samples  were  placed  within  the  fridge  (cold),  room  temperature  (RT),  heated \n(warm), and one was left in the dry bath (hot). The data presented in Figure S2 compare the different \nspeeds of the nanocarriers functionalised with the DNAzymes formed through the different cooling \nmethods. As can be seen, although the differences are only slight, keeping the solution in a warm \nplace to allow it to cool slowly led to the faster speed, indicating that more DNAzyme has been \nimmobilised onto the carrier surface. Comment Figure  2. (a)  Determining  the  concentration  of  substrate  strand  DNA  required  to  saturate  the \nnanocarriers through changes in carrier speed. (b) Determining if the DNAzyme complex had formed \nand\nFigure 2. (a) Determining the concentration of substrate strand DNA required to saturate the\nnanocarriers through changes in carrier speed. (b) Determining if the DNAzyme complex had formed\npost heating through the differing saturation points of the substrate strand vs. the DNAzyme complex. Figure  2. (a)  Determining  the  concentration  of  substrate  strand  DNA  required  to  saturate  the \nnanocarriers through changes in carrier speed. (b) Determining if the DNAzyme complex had formed \nFigure 2. 3. Results and Discussion Samples were run in triplicate, and between 250 and 300 particles were\nmeasured for each sample. Error bars are one standard deviation from the mean. Figure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus \nvarying amounts of Ca2+ ions added from 1–9 μM measured at 30 min (b) 0 3 μM Ca2+ ions added\nFigure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus Figure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus \nvarying amounts of Ca2+ ions added from 1–9 μM, measured at 30 min. (b) 0.3 μM Ca2+ ions added, \nincubated from 30 to 150 min. Samples were run in triplicate, and between 250 and 300 particles were \nmeasured for each sample. Error bars are one standard deviation from the mean. To demonstrate that the selectivity of our assay was dependent upon the Ca2+ binding to the\nFigure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus\nvarying amounts of Ca2+ ions added from 1–9 µM, measured at 30 min. (b) 0.3 µM Ca2+ ions added,\nincubated from 30 to 150 min. Samples were run in triplicate, and between 250 and 300 particles were\nmeasured for each sample. Error bars are one standard deviation from the mean. varying amounts of Ca2  ions added from 1–9 μM, measured at 30 min. (b) 0.3 μM Ca2  ions added, \nincubated from 30 to 150 min. Samples were run in triplicate, and between 250 and 300 particles were \nmeasured for each sample. Error bars are one standard deviation from the mean. To demonstrate that the selectivity of our assay was dependent upon the Ca2+ binding to the\np\np\ny\np\nvarying amounts of Ca2+ ions added from 1–9 µM, measured at 30 min. (b) 0.3 µM Ca2+ ions added,\nincubated from 30 to 150 min. Samples were run in triplicate, and between 250 and 300 particles were\nmeasured for each sample. Error bars are one standard deviation from the mean. DNAzyme, we incubated our DNAzyme functionalised particles with different metal ions. As the \nprevious study had reported some binding with Mg2+ [55], Mg2+ was added at 3 μM and incubated \nfor  30  min,  and  5  mM  was  tested every  hour  over 6  h  (Figure  S7)  to  confirm  that  there  was  no \ninterferences with our assay. 3. Results and Discussion 7 of 11 Sensors 2020, 20, 5877 Figure 4 demonstrates that the change in nanocarrier speed is proportional to the concentration\nof Ca2+ present in solution. Only the Ca2+ concentration was varied, as incubation times and DNA\nconcentration remained the same. As shown in Figure 4a, as the concentration of Ca2+ increases,\nthe speed of the nanocarriers increases. This constant translocation speed is stable over a large\nconcentration range, from 3 µM, 1.23 ms−1, to 3000 µM, 1.27 ms−1, demonstrating the end point of\nthe reaction, seen in Figure S4. The LOD for an assay run under these conditions, i.e., with 30 min\nincubation of DNAzyme particles in the Ca2+ solution, the LOD, calculated as 3 × STEY,X/gradient,\nis 1 µM. The saturation in velocity over 5 mm is due to the saturation of the binding sites. Whilst it\nmay be possible for more Ca2+ to bind over this concentration, we are unable to resolve it. Due to the\ncatalytic nature of DNAzymes, we also tested a lower concentration of Ca2+ ions, 0.3 µM, with varying\nincubation times from 30 to 150 min. As shown in Figure 4b, as incubation times increases, so did\nthe nanocarrier speed until after 90 min when the speed becomes constant at 1.25 ms−1 and 1.23 ms−1\nfor 90 and 150 min, respectively. The change in translocation speed in the presence of Ca2+ was not\ndue to particle aggregation or disaggregation, see Figure S5, which shows there was no measurable\nchange in particle size. The change in translocation speed was reproducible over different batches of\nnanocarriers such as in Figure S6. Sensors 2020, 20, x \n8  of  12 Figure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus \nvarying amounts of Ca2+ ions added from 1–9 μM, measured at 30 min. (b) 0.3 μM Ca2+ ions added, \nincubated from 30 to 150 min. Samples were run in triplicate, and between 250 and 300 particles were \nmeasured for each sample. Error bars are one standard deviation from the mean. To demonstrate that the selectivity of our assay was dependent upon the Ca2+ binding to the\nFigure 4. (a) Relative particle speeds normalised to blank DNAzyme functionalised particles, versus\nvarying amounts of Ca2+ ions added from 1–9 µM, measured at 30 min. (b) 0.3 µM Ca2+ ions added,\nincubated from 30 to 150 min. 3. Results and Discussion As can be seen in Figure 3ii, \nafter the initial 5 min, we measured a significant decrease in nanocarrier speed, from 1 to 0.84 ms−1, \nbefore a rapid increase and plateauing after 10 min to 1.23 ms−1. We postulated that this increase in \nspeed was due to the DNAzyme rearranging post cleavage by Ca2+, as the remaining attached DNA \nstrand is extended, seen in Figure 1biii [58]. This increase in DNA length would increase particle \nspeed significantly, and it correlates to previous studies [36]. The speed of the nanocarriers in the \npresence/absence of Ca2+ is shown in Figure S3. The velocity of the uncoated and ssDNA carriers \nremains unchanged with the addition of Ca2+, which illustrates that the addition of divalent ions does \nnot  change  the  electroosmotic  and  electrophoretic  forces  acting  on  the  particles  and  Nanopore \nchannel. We also interpret this change in velocity for the DNAzyme coated carriers as being specific \nto the cleavage mechanism. Figure 3. Measuring the change in the DNAzyme functionalised nanocarrier velocity versus different \nincubation  times  with  Ca2+  ions;  (a),  blank  DNAzyme  complex  on  the  bead,  (b)  cleavage  of  the \nDNAzyme from dsDNA to ssDNA, and (c) DNAzyme rearranging. Samples were run in triplicate. Figure 4 demonstrates that the change in nanocarrier speed is proportional to the concentration \nof Ca2+ present in solution. Only the Ca2+ concentration was varied, as incubation times and DNA\nCo\nnu\n(c\npi\nFigure 3. Measuring the change in the DNAzyme functionalised nanocarrier velocity versus different\nincubation times with Ca2+ ions; (a), blank DNAzyme complex on the bead, (b) cleavage of the\nDNAzyme from dsDNA to ssDNA, and (c) DNAzyme rearranging. Samples were run in triplicate. Figure 3. Measuring the change in the DNAzyme functionalised nanocarrier velocity versus different \nincubation  times  with  Ca2+  ions;  (a),  blank  DNAzyme  complex  on  the  bead,  (b)  cleavage  of  the \nDNAzyme from dsDNA to ssDNA, and (c) DNAzyme rearranging. Samples were run in triplicate. Figure 4 demonstrates that the change in nanocarrier speed is proportional to the concentration \nf C\ni\nl\ni\nO l\nh\nC\ni\ni d\ni\nb\ni\ni\nd DNA\nC\nn\n(\np\nFigure 3. Measuring the change in the DNAzyme functionalised nanocarrier velocity versus different\nincubation times with Ca2+ ions; (a), blank DNAzyme complex on the bead, (b) cleavage of the\nDNAzyme from dsDNA to ssDNA, and (c) DNAzyme rearranging. Samples were run in triplicate. 4. Conclusions Here we present RPS technologies inferring structure changes in the DNAzyme through differences\nin nanocarrier speed, enabling quantification of Ca2+ in solution. The cleavage of DNA post binding of\nthe Ca2+ to the DNAzyme is measured through the increase in nanocarrier speed as it traverses the\nnanopore. The method works across a large concentration range, is tuneable through incubation times,\nand can work in environmental samples. The assay work flow from nanocarrier functionalisation to\nincubation, extraction, and quantification can be done in under an hour. Supplementary Materials: The following are available online at http://www.mdpi.com/1424-8220/20/20/5877/s1. Figure S1. No change in particle size of substrate strand, orange, on the particles vs. DNAzyme complex, green,\non the particle. Size compared to CPC200s, known size 210 nm, run under the same conditions on the same day\nas the DNA samples; over 600 particles were counted. Figure S2. Varying the cooling process of the DNAzyme\nby placing it in the fridge, ~6 ◦C (cold), at room temperature, ~18 ◦C (RT), in a warm place, ~25 ◦C (warm) and\nleft cooling in the dry bath, <70 ◦C (hot). Relative speed normalised to CPC calibration beads ran on the same\nday under the same conditions. Samples were run in triplicate; more than 200 particles were measured each\ntime. Error bars are one standard deviation from the mean. Figure S3. Comparison of different controls run\nwith (red) and without (black) 3 µM Ca2+ present, incubated together for 30 min, CPC200s, 15T ssDNA strand,\nand DNAzyme complex. Relative speeds taken as 1/T0.5 normalised to the blank particles without Ca2+. Run\nunder the same conditions on the same day, each sample was run in triplicate and more than 200 particles were\nmeasured each time. Figure S4. Relative particle speeds normalised to blank DNAzyme functionalised particles,\nvs. varying amounts of Ca2+ ions added from 3–3000 µM. Samples were run in triplicate; more than 200 particles\nwere measured each time. Error bars are one standard deviation from the mean. Figure S5. Comparison of two\ndifferent batches of streptavidin particles, different blank particles run with (green) and without (orange) 3 µM\nCa2+ present. Relative speeds taken as 1/T0.5 normalised to the blank DNAzyme functionalised particles, without\nCa2+ present, ran under the same conditions on the same pore and day. Each sample was run in triplicate and\nmore than 200 particles were measured each time. Figure S6. 3. Results and Discussion As seen in Figure 5a, there were no inferences measured from any of \nthe metals tested at 3 μM, and when present in a mix with Ca, the measured speed was within the \nexpected  range. Although  some  slight  binding  was  overserved  with  5  mM  Mg2+,  this  was  only \nmeasured after 4 h and not measured to a comparable speed as with the Ca2+ present at 3 μM, 1.09 vs. 1.21 ms−1 respectively. Additional divalent ions were also tested in the sensor, shown in Figure 5a. No change in translocation velocity was recorded. In a final experiment, all the HMI including Ca2+ \nwas  added  to  the  DNAzyme  modified  particles,  termed  mix  in  Figure  5a. As  can  be  seen,  the \ntranslocation velocity is comparable to those recorded using Ca2+ alone. Commen\nand “b)” t\nTo demonstrate that the selectivity of our assay was dependent upon the Ca2+ binding to the\nDNAzyme, we incubated our DNAzyme functionalised particles with different metal ions. As the\nprevious study had reported some binding with Mg2+ [55], Mg2+ was added at 3 µM and incubated for\n30 min, and 5 mM was tested every hour over 6 h (Figure S7) to confirm that there was no interferences\nwith our assay. As seen in Figure 5a, there were no inferences measured from any of the metals tested\nat 3 µM, and when present in a mix with Ca, the measured speed was within the expected range. Although some slight binding was overserved with 5 mM Mg2+, this was only measured after 4 h and\nnot measured to a comparable speed as with the Ca2+ present at 3 µM, 1.09 vs. 1.21 ms−1 respectively. Additional divalent ions were also tested in the sensor, shown in Figure 5a. No change in translocation\nvelocity was recorded. In a final experiment, all the HMI including Ca2+ was added to the DNAzyme\nmodified particles, termed mix in Figure 5a. As can be seen, the translocation velocity is comparable to\nthose recorded using Ca2+ alone. 8 of 11 Sensors 2020, 20, 5877\nNo c\nwas Figure 5. (a) Relative speed of the DNAzyme functionalised particles when incubated with Ca2+, Mg2+, \nFe3+, and Ni2+ individually, and then when all present were mixed. All metals were present at 3 μM. (b) Environmental water samples, blank (orange), and spiked with 3 μM Ca2+ (green). 3. Results and Discussion Samples were run in triplicate, and between 250 and 300 particles were\nmeasured for each sample. Error bars are one standard deviation from the mean. sample, we added DNAzyme functionalised particles, and incubated for 30 min before magnetically \nFinally, to demonstrate the applicability of our assay in more complex sample matrices, we collected\nwater samples from a tap in the laboratory and from a local pond on campus. To each sample, we added\nDNAzyme functionalised particles, and incubated for 30 min before magnetically separating them\nand resuspending in buffer to analyse. We also incubated the nanocarriers in water samples spiked\nwith 3 µM Ca2+. As most calcium is present in water as calcium carbonate, we also incubated the\nnanocarriers in water samples spiked with 3 µM Ca2+ to show the ability of our assay to work in these\ndifferent matrices without experiencing any interference from other analytes present. As shown in\nFigure 5b, the nanocarriers increased significantly in speed when compared to the blanks, indicating\nthe ability of our assay to work in these different matrices without experiencing any interference from\nother analytes present. 3. Results and Discussion Relative particle \nspeeds normalised to blank DNAzyme functionalised particles, and they were run on the same day \nunder the same conditions. Samples were run in triplicate, and between 250 and 300 particles were \nmeasured for each sample. Error bars are one standard deviation from the mean. Finally,  to  demonstrate the  applicability  of  our assay  in  more  complex  sample  matrices,  we \ncollected water samples from a tap in the laboratory and from a local pond on campus To each\nand\nFigure 5. (a) Relative speed of the DNAzyme functionalised particles when incubated with Ca2+, Mg2+,\nFe3+, and Ni2+ individually, and then when all present were mixed. All metals were present at 3 µM. (b) Environmental water samples, blank (orange), and spiked with 3 µM Ca2+ (green). Relative particle\nspeeds normalised to blank DNAzyme functionalised particles, and they were run on the same day\nunder the same conditions. Samples were run in triplicate, and between 250 and 300 particles were\nmeasured for each sample. Error bars are one standard deviation from the mean. Figure 5. (a) Relative speed of the DNAzyme functionalised particles when incubated with Ca2+, Mg2+, \nFe3+ and Ni2+ individually and then when all present were mixed All metals were present at 3 μM\nFigure 5. (a) Relative speed of the DNAzyme functionalised particles when incubated with Ca2+, Mg2+, Fe3+, and Ni2+ individually, and then when all present were mixed. All metals were present at 3 μM. (b) Environmental water samples, blank (orange), and spiked with 3 μM Ca2+ (green). Relative particle \nspeeds normalised to blank DNAzyme functionalised particles, and they were run on the same day \nunder the same conditions. Samples were run in triplicate, and between 250 and 300 particles were \nmeasured for each sample. Error bars are one standard deviation from the mean. Finally,  to  demonstrate the  applicability  of  our assay  in  more  complex  sample  matrices,  we \nll\nt d\nt\nl\nf\nt\ni\nth\nl b\nt\nd f\nl\nl\nd\nT\nh\ng\n( )\np\ny\np\n,\ng\n,\nFe3+, and Ni2+ individually, and then when all present were mixed. All metals were present at 3 µM. (b) Environmental water samples, blank (orange), and spiked with 3 µM Ca2+ (green). Relative particle\nspeeds normalised to blank DNAzyme functionalised particles, and they were run on the same day\nunder the same conditions. 4. Conclusions No change in the blockade magnitude indicating\nthere is no particle aggregation when Ca2+ is present in concentrations from 1–9 µM. Figure S7. DNAzyme 9 of 11 Sensors 2020, 20, 5877 functionalised beads incubated with 5 mM Mg2+ over a period of 6 h with samples taken from the stock hourly. Relative speeds taken as 1/T0.5 normalised to the blank particles without Mg2+. Run under the same conditions\non the same day, each sample was run in triplicate and more than 200 particles were measured each time. Author Contributions: I.H. coordinated the experiments, ran the practical work, collected and analysed the data. M.P. supervised the project, analysed data and drafted the manuscript. All authors have read and agreed to the\npublished version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors would like to thank B Kralj for his guidance and support and the Nuc\nDecommissioning Authority for their funding. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest. References 1. Sui, B.; Liu, X.; Wang, M.; Belfield, K.D. A Highly Selective Fluorescence Turn-On Sensor for Extracellular\nCalcium Ion Detection. Chem. A Eur. J. 2016, 22, 10351–10354. [CrossRef] [PubMed] 1. Sui, B.; Liu, X.; Wang, M.; Belfield, K.D. A Highly Selective Fluorescence Turn-On Sensor for Extracellular\nCalcium Ion Detection. Chem. A Eur. J. 2016, 22, 10351–10354. [CrossRef] [PubMed] 2. He, H.Z.; Wang, M.; Chan, D.S.H.; Leung, C.H.; Lin, X.; Lin, J.M.; Ma, D.L. A parallel G-quadruplex-selective\nluminescent probe for the detection of nanomolar calcium(II) ion. Methods 2013, 64, 212–217. [CrossRef] 2. He, H.Z.; Wang, M.; Chan, D.S.H.; Leung, C.H.; Lin, X.; Lin, J.M.; Ma, D.L. A parallel G-quadruplex-selective\nluminescent probe for the detection of nanomolar calcium(II) ion. Methods 2013, 64, 212–217. [CrossRef] 3. Yu, T.; Zhou, W.; Liu, J. Screening of DNAzyme mutants for highly sensitive and selective detection of\ncalcium in milk. Anal. Methods 2018, 10, 1740–1746. [CrossRef] 3. Yu, T.; Zhou, W.; Liu, J. Screening of DNAzyme mutants for highly sensitive and selective detection of\ncalcium in milk. Anal. Methods 2018, 10, 1740–1746. [CrossRef] 4. Li, L.; Ma, X.; Dong, W.; Miao, P.; Tang, Y. Electrochemical Determination of Ca2+ Based On Recycling\nFormation of Highly Selective DNAzyme and Gold Nanoparticle-Mediated Amplification. Bioconjug. Chem. 2018, 29, 1021–1024. [CrossRef] 4. Li, L.; Ma, X.; Dong, W.; Miao, P.; Tang, Y. Electrochemical Determination of Ca2+ Based On Recycling\nFormation of Highly Selective DNAzyme and Gold Nanoparticle-Mediated Amplification. Bioconjug. Chem. 2018, 29, 1021–1024. [CrossRef] 5. Kozisek, F. Regulations for calcium, magnesium or hardness in drinking water in the European Union\nmember states. Regul. Toxicol. Pharmacol. 2020, 112, 104589. [CrossRef] [PubMed] 5. Kozisek, F. Regulations for calcium, magnesium or hardness in drinking water in the European Union\nmember states. Regul. Toxicol. Pharmacol. 2020, 112, 104589. [CrossRef] [PubMed] 6. Hampe, D.; Gleisberg, B.; Akhmadaliev, S.; Rugel, G.; Merchel, S. Determination of 41Ca with LSC and\nAMS: Method development, modifications and applications. J. Radioanal. Nucl. Chem. 2013, 296, 617–624. [CrossRef] 7. Kim, S.; Park, J.W.; Kim, D.; Kim, D.; Lee, I.H.; Jon, S. Bioinspired colorimetric detection of calcium(II) ions in\nserum using calsequestrin-functionalized gold nanoparticles. Angew. Chem. Int. Ed. 2009, 48, 4138–4141. [CrossRef] 8. Zhou, W.; Saran, R.; Liu, J. Metal Sensing by DNA. Chem. Rev. 2017, 117, 8272–8325. [CrossRef] 9. Ankireddy, S.R.; Kim, J. Highly selective and sensitive detection of calcium (II) ions in human serum using\nnovel fluorescent carbon dots. Sens. References Actuators B Chem. 2018, 255, 3425–3433. [CrossRef] 10. Yue, J.; Li, L.; Cao, L.; Zan, M.; Yang, D.; Wang, Z.; Chang, Z.; Mei, Q.; Miao, P.; Dong, W.F. Two-Step\nHydrothermal Preparation of Carbon Dots for Calcium Ion Detection. ACS Appl. Mater. Interfaces 2019, 11,\n44566–44572. [CrossRef] 11. Araújo, A.N.; Costa, R.C.C.; Lima, J.L.F.C.; Reis, B.F. Sequential injection system in flame atomic absorption\nspectrometry for the determination of calcium and magnesium in mineral waters. Anal. Chim. Acta 1998,\n358, 111–119. [CrossRef] 12. Szymczycha-Madeja, A.; Welna, M.; Pohl, P. Method Validation for Multi-Elemental Analysis of Dialyzable\nand Non-dialyzable Fractions of Coffee Brews by F AAS and ICP OES: A Bioaccessibility Study. Food Anal. Methods 2019, 12, 198–216. [CrossRef] 13. Smith, D.L.S.; Fritz, J. Rapid determination of magnesium and calcium hardness in water by ion\nchromatography. Anal. Chim. Acta 1988, 204, 87–93. [CrossRef] 14. García-Fernández, R.; García-Alonso, J.I.; Sanz-Medel, A. Simultaneous determination of inorganic anions,\ncalcium and magnesium by suppressed ion chromatography. J. Chromatogr. A 2004, 1033, 127–133. [CrossRef] 15. Paull, B.; MacKa, M.; Haddad, P.R. Determination of calcium and magnesium in water samples by high-\nperformance liquid chromatography on a graphitic stationary phase with a mobile phase containing\no-cresolphthalein complexone. J. Chromatogr. A 1997, 789, 329–337. [CrossRef] 16. Ding, Y.; Ling, J.; Qiao, Y.; Li, Z.; Sun, Z.; Cai, J.; Guo, Y.; Wang, H. A high-Throughput fluorimetric microarray\nwith enhanced fluorescence and suppressed “coffee-ring” effects for the detection of calcium ions in blood. Sci. Rep. 2016, 6, 1–9. [CrossRef] 10 of 11 10 of 11 Sensors 2020, 20, 5877 17. Atanasijevic, T.; Shusteff, M.; Fam, P.; Jasanoff, A. Calcium-sensitive MRI contrast agents based on\nsuperparamagnetic iron oxide nanoparticles and calmodulin. Natl. Acad. Sci. 2006, 103, 14707–14712. [CrossRef] 18. Miyawaki, A.; Llopis, J.; Heim, R.; Michael McCaffery, J.; Adams, J.A.; Ikura, M.; Tsien, R.Y. Fluorescent\nindicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 1997, 388, 882–887. [CrossRef]\n[PubMed] 19. Taktak, S.; Weissleder, R.; Josephson, L. Electrode chemistry yields a nanoparticle-based NMR sensor for\ncalcium. Langmuir 2008, 24, 7596–7598. [CrossRef] 20. Rojas, T.C.; Rojo, J.; Can, J.; Ferna, Â. Glycosphingolipid clustering and interactions at the cell membrane can\nbe modeled by gold glyconanoparticles prepared with biologically significant oligosaccharides. For more\ninformation see the following pages. Angew. Chemie Int. Ed. English 2001, 40, 2257–2261. [CrossRef] 21. Sigel, R.K.O.; Pyle, A.M. References Alternative roles for metal ions in enzyme catalysis and the implications for\nribozyme chemistry. Chem. Rev. 2007, 107, 97–113. [CrossRef] [PubMed] 22. Lu, Y. New transition-metal-dependent DNAzymes as efficient endonucleases and as selective metal\nbiosensors. Chem. A Eur. J. 2002, 8, 4588–4596. [CrossRef] 23. Lan, T.; Furuya, K.; Lu, Y. A highly selective lead sensor based on a classic lead DNAzyme. Chem. Commun. 2010, 46, 3896–3898. [CrossRef] [PubMed] 24. Li, J.; Lu, Y. A Highly Sensitive and Selective Catalytic DNA Biosensor for Lead Ions. J. Am. Chem. Soc. 2000,\n122, 10466–10467. [CrossRef] 25. Peng, D.; Li, Y.; Huang, Z.; Liang, R.P.; Qiu, J.D.; Liu, J. Efficient DNA-Catalyzed Porphyrin Metalation for\nFluorescent Ratiometric Pb2+ Detection. Anal. Chem. 2019, 91, 11403–11408. [CrossRef] 26. Li, J.; Zheng, W.; Kwon, A.; Lu, Y. In vitro selection and characterization of a highly efficient Zn(II)-dependent\nRNA-cleaving deoxyribozyme. Nucleic Acids Res. 2000, 28, 481–488. [CrossRef] 27. Zhu, L.; Li, G.; Shao, X.; Huang, K.; Luo, Y.; Xu, W. A colorimetric zinc(II) assay based on the use of hairpin\nDNAzyme recycling and a hemin/G-quadruplex lighted DNA nanoladder. Microchim. Acta 2020, 187, 26. [CrossRef] 28. Carmi, N.; Shultz, L.A.; Breaker, R.R. In vitro selection of self-cleaving DNAs. Chem. Biol. 1996, 3, 1039–1046. [CrossRef] 29. Liu, J.; Lu, Y. A DNAzyme catalytic beacon sensor for paramagnetic Cu2+ ions in aqueous solution with\nhigh sensitivity and selectivity. J. Am. Chem. Soc. 2007, 129, 9838–9839. [CrossRef] 30. Liu, J.; Brown, A.K.; Meng, X.; Cropek, D.M.; Istok, J.D.; Watson, D.B.; Lu, Y. A catalytic beacon sensor for\nuranium with parts-per-trillion sensitivity and millionfold selectivity. Proc. Natl. Acad. Sci. USA 2007, 104,\n2056–2061. [CrossRef] 31. He, Y.; Lu, Y. Metal-ion-dependent folding of a uranyl-specific DNAzyme: Insight into function from\nfluorescence resonance energy transfer studies. Chem. A Eur. J. 2011, 17, 13732–13742. [CrossRef] [PubMed] 31. He, Y.; Lu, Y. Metal-ion-dependent folding of a uranyl-specific DNAzyme: Insight into function from\nfluorescence resonance energy transfer studies. Chem. A Eur. J. 2011, 17, 13732–13742. [CrossRef] [PubMed]\n32. Willner, I.; Shlyahovsky, B.; Zayats, M.; Willner, B. DNAzymes for sensing, nanobiotechnology and logic gate fluorescence resonance energy transfer studies. Chem. A Eur. J. 2011, 17, 13732–13742. [CrossRef] [PubMed]\n32. Willner, I.; Shlyahovsky, B.; Zayats, M.; Willner, B. DNAzymes for sensing, nanobiotechnology and logic gate\napplications. Chem. Soc. Rev. 2008, 37, 1153–1165. [CrossRef] [PubMed] gy\n32. Willner, I.; Shlyahovsky, B.; Zayats, M.; Willner, B. References DNAzymes for sensing, nanobiotechnology and logic gate\napplications. Chem. Soc. Rev. 2008, 37, 1153–1165. [CrossRef] [PubMed] 33. Lu, Y.; Liu, J. Functional DNA nanotechnology: Emerging applications of DNAzymes and aptamers. Curr. Opin. Biotechnol. 2006, 17, 580–588. [CrossRef] 4. Zhou, W.; Ding, J.; Liu, J. Theranostic dnazymes. Theranostics 2017, 7, 1010–1025. [CrossRef] [PubMed] 5. Zhou, W.; Saran, R.; Chen, Q.; Ding, J.; Liu, J. A New Na + -Dependent RNA-Cleaving DNAzyme with\n1000-fold Rate Acceleration by Ethanol. ChemBioChem 2016, 17, 159–163. [CrossRef] [PubMed] 36. Blundell, E.L.C.J.; Vogel, R.; Platt, M. Particle-by-Particle Charge Analysis of DNA-Modified Nanoparticles\nUsing Tunable Resistive Pulse Sensing. Langmuir 2016, 32, 1082–1090. [CrossRef] 37. Mayne, L.; Lin, C.Y.; Christie, S.D.R.; Siwy, Z.S.; Platt, M. The Design and Characterization of Multifunctional\nAptamer Nanopore Sensors. ACS Nano 2018, 12, 4844–4852. [CrossRef] [PubMed] 38. Willmott, G.R.; Vogel, R.; Yu, S.S.C.; Groenewegen, L.G.; Roberts, G.S.; Kozak, D.; Anderson, W.; Trau, M. Use of tunable nanopore blockade rates to investigate colloidal dispersions. J. Phys. Condens. Matter 2010, 22,\n454116. [CrossRef] 39. Kozak, D.; Anderson, W.; Vogel, R.; Trau, M. Advances in resistive pulse sensors: Devices bridging the void\nbetween molecular and microscopic detection. Nano Today 2011, 6, 531–545. [CrossRef] 40. Luo, L.; German, S.R.; Lan, W.-J.; Holden, D.A.; Mega, T.L.; White, H.S. Resistive-Pulse Analysis of\nNanoparticles. Annu. Rev. Anal. Chem. 2014, 7, 513–535. [CrossRef] Sensors 2020, 20, 5877 11 of 11 41. Maugi, R.; Hauer, P.; Bowen, J.; Ashman, E.; Hunsicker, E.; Platt, M. A methodology for characterising\nnanoparticle size and shape using nanopores. Nanoscale 2020, 12, 262–270. [CrossRef] 42. Billinge, E.R.; Platt, M. Multiplexed, label-free detection of biomarkers using aptamers and Tunable Resistive\nPulse Sensing (AptaTRPS). Biosens. Bioelectron. 2015, 68, 741–748. [CrossRef] 43. Kohli, P.; Harrell, C.C.; Cao, Z.; Gasparac, R.; Tan, W.; Martin, C.R. DNA-functionalized nanotube, membranes\nwith single-base mismatch selectivity. Science 2004, 305, 984–986. [CrossRef] [PubMed] 44. Sexton, L.T.; Horen, L.P.; Martin, C.R. Biosensing with Nanopores and Nanotubes. In Molecular- and\nNano-Tubes SE-6; Hayden, O., Nielsch, K., Eds.; Springer: Boston, MA USA, 2011; pp. 165–207. 45. Blundell, E.L.C.J.; Mayne, L.J.; Billinge, E.R.; Platt, M. Emergence of tunable resistive pulse sensing as a\nbiosensor. Anal. Methods 2015, 7, 7055–7066. [CrossRef] 46. Bayley, H.; Martin, C.R. Resistive-pulse sensing—From microbes to molecules. Chem. Rev. 2000, 100,\n2575–2594. [CrossRef] 47. Mayne, L.J.; Christie, S.D.R.; Platt, M. A tunable nanopore sensor for the detection of metal ions using\ntranslocation velocity and biphasic pulses. References Nanoscale 2016, 8, 19139–19147. [CrossRef] 48. Billinge, E.R.; Broom, M.; Platt, M. Monitoring aptamer-protein interactions using tunable resistive pulse\nsensing. Anal. Chem. 2014, 86, 1030–1037. [CrossRef] 48. Billinge, E.R.; Broom, M.; Platt, M. Monitoring aptamer-protein interactions using tunable resistive pulse\nsensing. Anal. Chem. 2014, 86, 1030–1037. [CrossRef]\n49. Maugi, R.; Salkenova, Z.; Platt, M. Incorporating Peptide Aptamers into Resistive Pulse Sensing. Med. Devices 9. Maugi, R.; Salkenova, Z.; Platt, M. Incorporating Peptide Aptamers into Resistive Pulse Sensing. Med. De\nSens. 2020, 3, 1–7. [CrossRef] 50. Zhu, L.; Xu, Y.; Ali, I.; Liu, L.; Wu, H.; Lu, Z.; Liu, Q. Solid-State Nanopore Single-Molecule Sensing of\nDNAzyme Cleavage Reaction Assisted with Nucleic Acid Nanostructure. ACS Appl. Mater. Interfaces 2018,\n10, 26555–26565. [CrossRef] [PubMed] 51. Liu, G.; Zhang, L.; Dong, D.; Liu, Y.; Li, J. A label-free DNAzyme-based nanopore biosensor for highly\nsensitive and selective lead ion detection. Anal. Methods 2016, 8, 7040–7046. [CrossRef] 52. Liu, N.; Hou, R.; Gao, P.; Lou, X.; Xia, F. Sensitive Zn2+ sensor based on biofunctionalized nanopores:\nVia combination of DNAzyme and DNA supersandwich structures. Analyst 2016, 141, 3626–3629. [CrossRef]\n[PubMed] 53. Healey, M.J.; Sivakumaran, M.; Platt, M. Rapid quantification of prion proteins using resistive pulse sensing. Analyst 2020, 145, 2595–2601. [CrossRef] [PubMed] 54. Yu, T.; Zhou, W.; Liu, J. Ultrasensitive DNAzyme-Based Ca2+ Detection Boosted by Ethanol and a\nSolvent-Compatible Scaffold for Aptazyme Design. ChemBioChem 2018, 19, 31–36. [CrossRef] [PubMed] 55. Zhou, W.; Saran, R.; Huang, P.J.J.; Ding, J.; Liu, J. An Exceptionally Selective DNA Cooperatively Binding\nTwo Ca2+ Ions. ChemBioChem 2017, 18, 518–522. [CrossRef] 56. Healey, M.J.; Rowe, W.; Siati, S.; Sivakumaran, M.; Platt, M. Rapid Assessment of Site Specific DNA\nMethylation through Resistive Pulse Sensing. ACS Sens. 2018, 3, 655–660. [CrossRef] 57. Kozak, D.; Anderson, W.; Vogel, R.; Chen, S.; Antaw, F.; Trau, M. Simultaneous size and ζ-potential\nmeasurements of individual nanoparticles in dispersion using size-tunable pore sensors. ACS Nano 2012, 6,\n6990–6997. [CrossRef] [PubMed] 58. Zuker, M. Mfold web server for nucleic acid folding and hybridisation prediction. Nucleic Acids Res. 2003, 31,\n3406–3415. [CrossRef] Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional\naffiliations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access\narticle distributed under the terms and conditions of the Creative Commons Attribution\n(CC BY) license (http://creativecommons.org/licenses/by/4.0/). © 2020 by the authors. Licensee MDPI, Basel, Switzerland. References This article is an open access\narticle distributed under the terms and conditions of the Creative Commons Attribution\n(CC BY) license (http://creativecommons.org/licenses/by/4.0/)."

ERROR: type should be string, got "https://doi.org/10.1007/s10641-022-01246-4\nEnviron Biol Fish (2022) 105:461–476 https://doi.org/10.1007/s10641-022-01246-4\nEnviron Biol Fish (2022) 105:461–476 Demographic patterns of the tropical baitfish Spratelloides \ndelicatulus (Order: Clupeiformes) across the Great Barrier \nReef shelf and at multiple latitudes Reproductive development indicated \na size-based relationship. Males and females matured \nat similar sizes ranging from 36–38 mm, but fish from \nsouthern sites were 30–40 days older. Tropical clupei-\nforms live fast and die young, and patterns of abun-\ndance, composition and demography followed strong \nenvironmental gradients which conformed to some \nexisting models. least 9 months. Reproductive development indicated \na size-based relationship. Males and females matured \nat similar sizes ranging from 36–38 mm, but fish from \nsouthern sites were 30–40 days older. Tropical clupei-\nforms live fast and die young, and patterns of abun-\ndance, composition and demography followed strong \nenvironmental gradients which conformed to some \nexisting models. Keywords  Baitfish · Tropical Clupeiformes · \nOtolith · Latitudinal growth theory · Spratelloides · \nDelicate round herring Demographic patterns of the tropical baitfish Spratelloides \ndelicatulus (Order: Clupeiformes) across the Great Barrier \nReef shelf and at multiple latitudes Michael J. Kingsford   · \nKynan Hartog‑Burnett   · Emma J. Woodcock Received: 30 August 2021 / Accepted: 23 March 2022 \n© The Author(s) 2022\n/ Published online: 25 April 2022 Abstract  Clupeiformes are the most important \nfood fish in the world, and provide a key trophic \nlink in marine food chains. Here we describe broad \nscale patterns of clupeiform demographic charac-\nteristics of the delicate round herring sprat Spratel-\nloides delicatulus on the Great Barrier Reef (GBR). Sampling was conducted over 10° of latitude and two \nseasons at multiple distances across the GBR shelf. The oldest S. delicatulus sampled was 152 days and \nthe maximum standard length was 74 mm. Age and \nlength maxima increased with latitude conforming \nwith ‘counter gradient theory’ and these patterns \nwere consistent between years. von Bertalanffy rela-\ntionships showed that growth rates were highest at \nNorthern GBR sites; growth coefficients ranged from \n2–6  K  ­year−1, and were lowest on southern reefs, \ni.e. ‘tropical gradient of growth’. Daily survivorship \nranged from 91–97% ­day−1 at all sites. Hatching \ndates estimated from counts of daily otolith incre-\nments indicated a prolonged spawning season of at \nSupplementary Information  The online version \ncontains supplementary material available at https://​doi.​\norg/​10.​1007/​s10641-​022-​01246-4. M. J. Kingsford (*) · K. Hartog‑Burnett \nARC Centre of Excellence for Coral Reef Studies, JCU, \nTownsville, QLD 4811, Australia\ne-mail: Michael.kingsford@jcu.edu.au\nM. J. Kingsford · K. Hartog‑Burnett · E. J. Woodcock \nCollege of Science and Engineering, JCU, Townsville, \nQLD 4811, Australia Abstract  Clupeiformes are the most important \nfood fish in the world, and provide a key trophic \nlink in marine food chains. Here we describe broad \nscale patterns of clupeiform demographic charac-\nteristics of the delicate round herring sprat Spratel-\nloides delicatulus on the Great Barrier Reef (GBR). Sampling was conducted over 10° of latitude and two \nseasons at multiple distances across the GBR shelf. The oldest S. delicatulus sampled was 152 days and \nthe maximum standard length was 74 mm. Age and \nlength maxima increased with latitude conforming \nwith ‘counter gradient theory’ and these patterns \nwere consistent between years. von Bertalanffy rela-\ntionships showed that growth rates were highest at \nNorthern GBR sites; growth coefficients ranged from \n2–6  K  ­year−1, and were lowest on southern reefs, \ni.e. ‘tropical gradient of growth’. Daily survivorship \nranged from 91–97% ­day−1 at all sites. Hatching \ndates estimated from counts of daily otolith incre-\nments indicated a prolonged spawning season of at least 9 months. Introduction Temperate clupeiforms such as sardines, menha-\nden and herring provide some of the most abundant \nand valuable fisheries in the world (Gulland 1971). While tropical clupeiforms are not as valuable in \nlarge-scale fisheries, they sustain important artisa-\nnal fisheries along the equatorial Pacific and play \nan equally important role in the oceanic food chain. Clupeiforms worldwide provide a critical link in \nfood webs, maintaining the connection between the \nplankton and larger nekton. ‘Bait balls’ of schooling \nclupeiforms attract commercially important preda-\ntory fishes, oceanic birds and fishers alike (Cappo \nand Kelley 2001). It is well known that mortality \nrates of early life history stages of marine fishes \nare extremely high (Bailey and Houde 1989). For Supplementary Information  The online version \ncontains supplementary material available at https://​doi.​\norg/​10.​1007/​s10641-​022-​01246-4. Supplementary Information  The online version \ncontains supplementary material available at https://​doi.​\norg/​10.​1007/​s10641-​022-​01246-4. M. J. Kingsford (*) · K. Hartog‑Burnett \nARC Centre of Excellence for Coral Reef Studies, JCU, \nTownsville, QLD 4811, Australia\ne-mail: Michael.kingsford@jcu.edu.au M. J. Kingsford (*) · K. Hartog‑Burnett \nARC Centre of Excellence for Coral Reef Studies, JCU, \nTownsville, QLD 4811, Australia\ne-mail: Michael.kingsford@jcu.edu.au M. J. Kingsford · K. Hartog‑Burnett · E. J. Woodcock \nCollege of Science and Engineering, JCU, Townsville, \nQLD 4811, Australia M. J. Kingsford · K. Hartog‑Burnett · E. J. Woodcock \nCollege of Science and Engineering, JCU, Townsville, \nQLD 4811, Australia ol.: (011 123456789)\n3 123456789)\n3 Environ Biol Fish (2022) 105:461–476 462 climate change (Walther et al. 2002; Perry et al. 2005; \nMunday et al. 2008).fi tropical clupeiforms, however, a high mortality \nrate can extend throughout an individual’s life. The \navailable data suggests that tropical baitfish seldom \nlive for more than 5 months and become reproduc-\ntively viable at an early stage (Milton et  al. 1991, \n1993). Differences in the growth characteristics of fishes \nare influenced by temperature, food availability, \npredator and prey abundance (Jones 1986; Meekan \net al. 2003). Strong biological patterns and variation \nin physical factors both with latitude and across the \nGBR exist. Potential physical factors of influence \ninclude freshwater input, turbidity, temperature, abun-\ndance of planktonic food and upwelling (Wolanski \n2001; Fabricius et al. 2005; Udy et al. 2005). These \nfactors can influence the demography of a species \namong mesopopulations within a metapopulation. Abundance and growth of many fishes are known to \nvary across the GBR shelf (Williams 1982; Williams \nand Hatcher 1983; Williams et al. 1988; Gust et al. Introduction 2001; Kingsford and Hughes 2005; Kingsford et al. 2019), but there are few data on clupeiforms. The Great Barrier Reef spans ~ 2000 km along the \neast coast of Australia and consists of a diverse range \nof habitats including thousands of individual coral \nreefs that extend over 14° of latitude. Tropical fishes \nof the Order Clupeiformes are common baitfishes \nassociated with reefs. Existing studies on the demog-\nraphy of these fishes are relatively rare and cover \neither small spatial scales or very broad spatial scales \nthat do not consider environmental variation at spa-\ntial scales of less than tens to thousands of kilometres \n(Milton et al. 1991; Hatakeyama et al. 2005; Meekan \net al. 2006; Durieux et al. 2009). Furthermore, tropi-\ncal age-based demographic studies are few compared \nto those in temperate regions (Green et al. 2009) and, \ntherefore, form a weak basis for a tropical paradigm.i It was hypothesized that the demographic charac-\nteristics of S. delicatulus would vary across and along \nthe GBR. The broad objective of this study was to \ncompare age, growth, reproductive development and \nmeasures of instantaneous daily mortality and survi-\nvorship of the delicate round herring sprat (Spratel-\nloides delicatulus) across the GBR and at multiple \nlatitudes (from 14 to 23.8° S). p\np\ng\nPatterns of fish growth, and how they vary with \nlatitude, have been described and a number of casual \nmodels have been proposed. Inverse relationships \nbetween temperature and growth rate (i.e. ‘tropi-\ncal gradient’ of growth) have been described for reef \nfishes (Choat and Robertson 2002; Meekan et  al. 2003; Robertson et al. 2005) and fish larvae (Houde \n1989; Wilson and Meekan 2002). The opposite pat-\ntern of growth rate increasing with temperature (i.e. ‘counter gradient’) has been described for other \nfishes by some authors. For example, contrasting pat-\nterns for two species of Sebastes were observed; one \nof which showed a counter gradient in growth with \nlatitude and the other showed no difference in growth \nwith latitude (Boehlert and Kappenman 1980). Coun-\nter gradient trends in growth of Morone saxatalis \nhave been demonstrated experimentally (Conover \net  al. 1997). Furthermore, the genetic capacity for \ngrowth in this study was inversely related to length \nof the growing season and there was a strong positive \ncorrelation with growth and latitude of origin. Lon-\ngevity may also vary with latitude. Introduction In a review, it was \nconcluded that life span varies with temperature in a \nwide range of ectotherms, and there is also no pattern \nwith latitude if the effect of temperature is removed \n(Munch and Salinas 2009). A thorough understanding \nof these types of patterns has taken on new relevance \nwith changing temperature regimes brought about by Study sites and sampling design Study sites and sampling design Samples of clupeiforms were collected over 10° of \nlatitude on the GBR during the summer of 2009 \nto 2010. Additional samples were collected at One \nTree Island in winter of 2010 and at Lizard Island \nin winter of 2011. The focus of winter samples was \nto determine age max at different times of the year \nat the two latitudinal extremes of the study area, \nso only fish > 40 mm SL were aged. In summer of \n2009–2010, fish were collected at four latitudinal \ntransects of the GBR (Fig. 1). Water temperatures \nbetween L1 and L4 have been documented to differ \nby about 2 °C in summer and 4 °C in winter (King-\nsford et al. 2019). Temperatures measured at L1 to \nL4 in summers of 2009 to 2010 using a CTD ranged \nbetween 29.4 and 26.4  °C and the water columns \nwere well mixed (10–20 m deep). Additional sam-\nples for histological gonad analysis were collected 1 \nVol:. 3\n(1234567890) 463 Environ Biol Fish (2022) 105:461–476 Fig. 1   Map of the four \nlatitudinal transects off the \nQueensland coast, Great \nBarrier Reef, Australia. Containing 15 sites \ndistributed by strata inner, \nmid, and outer from the \nmainland (an example of \ndistance strata is provided \nin the inset). Specific \nsample sites at each latitude \nlisted in Table 1\n(L1)\n(L2)\n(L3)\n(L4) (L3) This time window of collection was consistent at all \nreefs.i This time window of collection was consistent at all \nreefs.i from the Capricorn Bunker and Lizard Island lati-\ntudes in summer of 2014–2015. In the summer of 2009 to 2010 and within each \nlatitudinal transect, samples were collected near \nreefs at inner, mid and outer shelf strata (except \nat the Capricorn Bunker latitude where only outer \nshelf sites were present). Care was taken to ensure \nthese traps were separated by sufficient distance \n(> 500  m) to maintain independence. Where pos-\nsible, two sites within a cross shelf distance were \nsampled, and this occurred at the mid-shelf distance \nfor the Lizard transect and the mid-shelf distance \nof the Cairns transect; n = 2 replicate traps per site \n(sites see Table 1). Fish were captured using light \ntraps (12  V, 8  W Fluoro tube), which fished at a \ndepth of approximately 1.5 to 2 m, in water columns \nof 10–15 m deep at each site. Study sites and sampling design The fish were fed wild zooplankton caught \ndaily with a 100-µm mesh net and were sacrificed \nafter either 3 or 10  days in the tank and stored in \nethanol. were counted under a compound microscope at \n400 × to 1000 × with transmitted light and immersion \noil. Digital images of otoliths were taken and pro-\ncessed using the Leica Application Suite (LAS), or \ncounts were made directly while viewing under the \nmicroscope. The sagittae of a small number of fish \nthat were less than 20 mm SL were mounted and read \nwhole.i Counts were made from the first clear increment, \nclosest to the primordium, and outwards along the \nlongest (ventral) axis of the otolith section. Counts of \nincrements were made separately without knowledge \nof fish length and slides were examined in random \norder. Each otolith was counted twice with a mini-\nmum of 48 h between each count. The quality control \ncriteria for age data were if sequential reads revealed \na > 5% age error then a third count was conducted. Any otoliths with continuing age discrepancy among \nreads were removed from further analysis. This \nmethod is accepted among fish biologists to give the Study sites and sampling design Collections of fish for \nmeasurements of size and age were from traps that \nfished for 3 to 4 h, depending on ease of pick up. Samples of fish were preserved immediately upon \ncapture in 80% ethanol to prevent dissolution of oto-\nliths. Voucher specimens were also sorted from traps \nto be stored in 10% formalin for later identification. Because it was possible that light traps would not \nsample largest fish, additional fish samples were col-\nlected at night from the boat for age-based analyses, \nusing 1000 W underwater lights and hand nets where \npossible.fi Validation of daily rings is notoriously difficult \nfor these fishes. Previous studies have validated daily \nrings for a total of four individual Spratelloides gra-\ncilis and 15 S. robustus over a maximum of 29 days \n(Milton et al. 1991; Rogers et al. 2003). In an experi-\nment to validate daily sagittal increments, S. delicatu-\nlus caught in light traps at One Tree Island Research \nStation were placed in a 10-L aerated container with 1 \nol.: (01 3\n123456789) 3\n123456789) Environ Biol Fish (2022) 105:461–476 464 Table 1   Study sites \nsampled during the summer \nof 2009 to 2010 (S), winter \nsamples (W) were collected \nin 2010 at One Tree Island \nand Lizard Island in 2011. Additional samples were \ncollected in 2014–2015 \n(*) at L1 and L4 were \nonly used for the study on \nmaturation\nLatitude\nDistance \n(cross \nshelf)\nSeason code Dates sampled\nSite name\nDistance \nfrom shore \n(km)\nL1 Lizard\nInner\nS\n14/12/2009\nLow Wooded\n12.3\nMid\nS\n17/12/2009\nRocky Islets B\n17.2\nS\nW\n16/12/2009\n1–8/06/2011\nLizard Island\n33\nOuter\nS\n15/12/2009\nYonge Reef\n57.9\nNorth Direction\n34.5\n*\n10/12/2014\nHicks Reef\n49.9\n*\n11/12/2014\nReef 14–149\n30.0\n*\n12/12/2014\nRibbon Reef 4\n50.8\nL2 Cairns\nInner\nS\n3/01/2010\nFitzroy Island\n6.8\nMid\nS\n4/01/2010\nGreen Island\n25.4\nS\n5/01/2010\nArlington Reef\n36.3\nOuter\nS\n6/01/2010\nMichaelmas Reef 43.4\nL3 Palms\nInner\nS\n9/02/2010\nOrpheus Island\n18.8\nMid\nS\n10/02/2010\nBritomart Reef\n51.8\nOuter\nS\n11/02/2010\nPith Reef\n80.4\nL4 Capricorn \nBunker \nGroup\nOuter\nS\nW\n*\n12 to 27/01/2010\n8–10-2010\n23 & 24/01/2015\nOne Tree\nOutside\nLagoon\nLagoon\n83.7\nS\n20/01/2010\nHeron Island\n78.3\nS\n*\n21 & 22/01/2010\n25/01/2015\nFitzroy Reef\n86.9\n*\n31/01/2015\nWistari Reef\n75.1 0.25 g ­L−1 tetracycline-treated seawater for 12 h. Sur-\nviving fish were then removed from the treatment \nand transferred to a 1000-L holding tank containing \nseawater. Table 1   Study sites \nsampled during the summer \nof 2009 to 2010 (S), winter \nsamples (W) were collected \nin 2010 at One Tree Island \nand Lizard Island in 2011. \nAdditional samples were \ncollected in 2014–2015 \n(*) at L1 and L4 were \nonly used for the study on \nmaturation Reproductive analysis Samples of S. delicatulus were collected for histo-\nlogical analysis of reproductive development at two \nlatitudes separated by over 1000  km in the Austral \nsummer of 2014–2015. Within each latitude, three \nouter reefs were sampled using light traps as per the \nprevious sampling. Once collected, individual fish \nwere selected at random, measured in length (SL) and \ntransversally cut with the posterior two-thirds pre-\nserved in 10% formalin to preserve the gonads. Two \nhundred samples were stained with hematoxylin and \neosin using standard techniques. A  minimum of 10 \nlongitudinal sections per fish were examined under a \nlight microscope and the relative percentage area of \neach stage of gametogenesis was used to define the \nreproductive stage, according to Milton and Blaber \n(1991) and Hatakeyama et  al. (2005). Mature fish \nwere defined as stage III or greater, with stages I and \nII individuals pooled as ‘immature’ and sexed where \npossible. A minimum of 30 ova were required for \nanalysis in females while males required the presence \nof at least 100 μm2 of male spermatogenic tissue. Data were based on 16 indeterminate fish, 25 imma-\nture females, 33 immature males, 63 mature males \nand 62 mature females, with a total of 100 fish from \neach of the Lizard (L1) and Capricorn Bunker (L4) \nregions. The distribution of lengths in samples from \nthe Lizard and Capricorn Bunker regions was similar \nwith means (± SE) of 42 ± 0.6 mm and 41 ± 0.7 mm, \nrespectively. Sex ratios of differentiated individuals \nwithin each latitude were tested for differences from \na 1:1 ratio of males to females using a two-tailed, \ntwo sample Z-tests of proportions. A single sample \nZ-test was used on individuals from both latitudes to \ntest overall if there was a difference in male to female \nratios. Comparisons of maturity and size were per-\nformed by fitting binomial logistic models in R using \nthe package ‘glm’ with the following factors: standard \nlength, region and sex. Only individuals which were \nable to be differentiated were used in this analysis and \nindividuals classified as mature were coded as 1 and \nimmature coded as 0 (Ruiz-Abierno et al. 2021). The \ndata were checked for assumptions by q-q plots, dis-\npersion and outlier tests in the package ‘DHARMa’ Analysis of growth age was estimated from counts \nof daily increments in sagittal otoliths of S. delica-\ntulus. Otolith processing Sagittal otoliths were dissected from each fish and \nstored in Eppendorf tubes. A sagittal otolith from \neach fish was then embedded in Crystal Bond (a ther-\nmoplastic glue) with the primordium located close to \nthe edge of the slide. The otolith was ground using \n3 µm lapping film on a grinding wheel down to the \nedge of the slide. The half otolith was then up-righted \nand fixed to the centre of another glass slide, and pol-\nished down to leave a thin transverse section contain-\ning the primordium (20–40  µm thick). Increments 1 \nVol:. 465 Environ Biol Fish (2022) 105:461–476 from 250 simulations. The data were then fitted using \na logit link function (Table S2, Eq. 1), which scored a \nbetter goodness of fit score (AIC score) than the alter-\nnative probit and complimentary log–log link func-\ntions when validating the model. The model used was \nselected by stepwise deletion of factors from the max-\nimal model (Boulcott and Wright 2008). To deter-\nmine the effect a change of a single unit in a param-\neter had on the probability of a fish being mature, the \ncoefficients were back transformed by calculating \ntheir exponential as an odds ratio. A  quasi-R2 was \ncalculated as per Table S2 Eq. 2 and the size at which \nan individual had a 50% chance of being mature ­(L50) \ncalculated as per Table S2 Eq. 3, with the procedures \nadapted from Chen and Paloheimo (1994), Boulcott \nand Wright (2008) and Hoffmann et al. (2017). best estimated of age (Cope and Punt 2007). A total \nof 1095 fish from multiple sites across the GBR were \naged and included in age analyses. best estimated of age (Cope and Punt 2007). A total \nof 1095 fish from multiple sites across the GBR were \naged and included in age analyses. Calculations of mortality The total instantaneous rate of mortality (Z) was \ncalculated using ­loge-linear regression analyses of \nage-frequency data of S. delicatulus; equal recruit-\nment among years was assumed. Age classes to the \nleft of the age-frequency distribution maxima were \nexcluded from the analysis to calculate the rate of the \nnegative slope only. These data are deleted as they are \nnot independent of sampling method bias (Hilborn \nand Walters 1992). The slope of the regression line \nbetween age classes was used to estimate the instanta-\nneous mortality rate (Z) following the equation: The age of S. delicatulus collected in this study \nranged from 9 to 152 days; the oldest fish was col-\nlected at One Tree Island in winter of 2011. The age \nmaximum of fish increased with latitude (Table  2) \nand there was a positive relationship between age \nmaximum and length maximum at all sites (regres-\nsion ANOVA ­df(1,11), y = 2.521x – 35.627, r2 = 0.66 s, \nP = 0.0004). Furthermore, the oldest 5% of S. delica-\ntulus increased with latitude (Table 2). Some S. deli-\ncatulus from the Southern GBR lived for at least a \nmonth longer than fish in the Northern GBR. Spra-\ntelloides delicatulus were consistently below 82 days \nold and 50 mm at all shelf strata at Lizard Island (L1), \nwhereas a greater range of ages were found from \nCairns and further south (L2 to L4, Fig. 2, Table 2). There was little evidence that age frequency varied \ncross shelf (Fig. 2). The age distribution was largely \nuni-modal at Lizard Island sites (i.e. L1, inner to \nouter shelf), and multi-modal at other sites indicat-\ning multiple age-cohorts of recruits (e.g. Orpheus and \nBritomart; Fig. 2). (2)\nZ = F + M (2) Z = F + M where F = fishing mortality rate and M = natural \nmortality rate (Hilborn and Walters 1992; Kingsford \nand Hughes 2005). Since there is no commercial or \nrecreational fishery for Spratelloides on the GBR, F \nequals zero and therefore Z is equivalent to M. From \nthe regression equations, daily mortality rates could \nbe calculated.f Tests for differences in instantaneous mortality \nwere made between distances at each latitude (n = 3 \ndistances); sites were pooled within a distance. Fur-\nthermore, because mortality rates were similar within \na latitude, instantaneous mortality rates were com-\npared among all latitudes (n = 4 latitudes), using indi-\nvidual values of Z from within latitudes. Age frequency and maxima Age frequency and maxima A short-term otolith marking experiment revealed that \nsectioned otoliths from fish treated with tetracycline \nhad a fluorescent marker close to the otolith edge \n(Suppl. Figure 1). Only eight fish survived the treat-\nment (89% fish mortality). There was close agree-\nment between the number of increments laid down \nafter the treatment and the number of days fish were \nheld after treatment. Fish that were sacrificed after \n3 days of treatment had a mean increment number of \n2.8 (n = 5, range = 2–3). Those otoliths removed after \n10  days had a mean increment number of 9.3  days \n(n = 3, range = 9–10). Along with two other studies, it \nwas concluded that increments were deposited daily \n(Milton et al. 1991; Rogers et al. 2003). Reproductive analysis To describe the relationship between age and \nlength over the complete size range of fish sampled, a \nconstrained form of the von Bertalanffy growth curve \nwas fitted using the formula: (1)\nL(t) = L∞[1 −e−K(t−t0)] (1) where L(t) = length of fish at age t. L∞ = asymptotic length.fi K = growth co-efficient. t0 = age at which L = (0), constrained to known \nlength of S. delicatulus at hatching (von Bertalanffy \n1938). The constrained version of this equation accounts \nfor fish length at time of hatching (t0) as L(0) = 4.3 mm \n(Leis and Carson-Ewart 2000). Other studies have \nused a re-parameterized form of the von Berta-\nlanffy equation, to ensure no extrapolation of L∞ \npast L (max). All sites contained large fish, so it \nwas assumed that these samples were adequate rep-\nresentations of the maximum size classes for S. delicatulus and a standard, constrained von Berta-\nlanffy equation was fitted. It should be noted that \nthe results from our study are comparable with the \nre-parameterized von Bertalanffy values given in the \nliterature for other Spratelloides species from differ-\nent regions (Milton et  al. 1991, 1993; Rogers et  al. 2003; Meekan et  al. 2006). Comparisons of K and \nL∞ were done among latitudes using regression with \nlatitude as the independent variable. Residuals were \nchecked for all regressions to meet the assumptions 1 \nol.: (01 3\n123456789) Environ Biol Fish (2022) 105:461–476 466 multiple estimates of Z and S at different spatial \nscales to get a broad overview of estimates of mortal-\nity and related survivorship. that the residuals were randomly distributed. Length \nat age data analysed using the von Bertalanffy model \nwere also compared with the Gompertz model. Although the Gompertz equations often resulted in \na little less variation (Table S1), we have presented \nthe von Bertalanffy equations for comparisons with \nother studies. Growth models were done in EXCEL \nand multiple iterations were run using Solver to vary \nparameters of the models with the objective of mini-\nmising variances. 1 3\nVol:. (1234567890) Calculations of mortality Daily survivorship rate (S) was calculated accord-\ning to Formula (3) as expressed as percentage sur-\nvival per day (see Hilborn and Walters 1992). (3)\nS = e−z (3) S = e−z S = e−z Spawning took place in multiple of months of the \nyear. When the birthdate of fish was back-calculated, \nour collections from December to February included \nbirthdates from September to January while fish col-\nlected at Lizard Island in June 2011 had birthdates Calculations based on total instantaneous rate of \nmortality (Z) assume equal recruitment for each age \ncohort, but this is of course questionable (Hilborn and \nWalters 1992). Our intent, therefore, was to provide 1 3\nVol:. (1234567890) Environ Biol Fish (2022) 105:461–476 467 Winter samples were collected in 2010 at One Tree Island \nand from Lizard in winter 2011; these fish were only aged if \nsize > 40 (mm) SL. Latitude L1 to L4 (North to South) Table 2   Age and length (standard length mm) maxima of \nS. delicatulus collected in the summer (2009–2010) at sites \nacross inner (I), mid (M), and outer (O) shelf distance strata. 2009–2010 (S)\n2010 2011 (W)\nLatitude\nSite\nn\nSize max \n(SL mm)\nAge max \n(days)\nMean age \ntop 5%\nn\nSize max \n(SL mm)\nAge max \n(days)\nMean age \ntop 5%\nL1\nLow Wooded (I)\n94\n31\n47.5\n42.7\nRocky Islets B (M)\n102\n49\n77.5\n67.7\nLizard Island (M)\n104\n43\n52.5\n51.6\n22\n54\n96\n77.4\nYonge Reef (O)\n109\n51\n74\n67\nL2\nGreen Island (M)\n106\n59\n125\n114.8\nArlington Reef (M)\n102\n58\n103.5\n95.8\nMichaelmas Reef (O)\n120\n50\n88\n75.7\nL3\nOrpheus Island (I)\n99\n74\n146\n138.3\nBritomart Reef (M)\n107\n68\n127.5\n115.4\nPith Reef (O)\n43\n66\n116\n93.8\nL4\nOne Tree Island (O)\n108\n53\n131\n126.2\n12\n58\n152\n109.3 from March to April. Fish collected at One Tree \nIsland in October 2010 had birthdates in July. from March to April. Fish collected at One Tree \nIsland in October 2010 had birthdates in July. Reproductive development Reproductive development Of the 200 S. delicatulus analysed, the smallest was \n24 mm standard length and the largest 55 mm. Sex \ndifferentiation was apparent in individuals as small as \n24 mm with no undifferentiated individuals observed \nlarger than 36 mm. Calculations of mortality Immature males were detected at \nup to 48 mm in the Capricorn Bunker (L4) but only \n42 mm in the Lizard Region (L1). Immature females \nwere observed up to 39  mm at both latitudes. The \nsex ratios for differentiated individuals were not sig-\nnificantly different to a 1:1 ratio in either latitude \n(Z  Ratio Test, df = 2, Z = 0.567, P = 0.753) nor for \ndifferentiated individuals pooled across latitudes (Z \nRatio Test, df = 1, Z = 0.440, P = 0.507). Growth NR, no reef; NF, \nfish caught note heading for L3 has not printed as it should \n-  L3 Palms\n468\nEnviron Biol Fish (2022) 105:461–4 0\n40\n80\n120\n160\n0\n5\n10\n15\nLow Wooded\nn=94\n0\n40\n80\n120\n160\n0\n1\n2\n3\n4\nOrpheus Island\nn=99\n0\n40\n80\n120\n160\n0\n5\n10\n15\nRocky Islets B\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nGreen Island\nn=106\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nBritomart Reef\nn=107\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\n10\nLizard Island\nn=104\n0\n40\n80\n120\n160\n0\n2\n4\n6\nArlington Reef\nn=102\nMi h\nl\nR\nf\nPith R\nf\nO\nT\nI l\nd\nInner shelf\nMid shel\nNF\nNR\nNR\nFrequency\nf\nL 1 Lizard L 2 Cairns L3 Palms L 4 Capricorn \nBunker 0\n40\n80\n120\n160\n0\n5\n10\n15\nLow Wooded\nn=94\n0\n40\n80\n120\n160\n0\n1\n2\n3\n4\nOrpheus Island\nn=99\nInner shelf\nNF\nNR\nL 1 Lizard L 2 Cairns L3 Palms L 4 Capricorn \nBunker 0\n40\n80\n120\n160\n0\n5\n10\n15\nLow Wooded\nn=94\n0\n40\n80\n120\n160\n0\n1\n2\n3\n4\nOrpheus Island\nn=99\n0\n40\n80\n120\n160\n0\n5\n10\n15\nRocky Islets B\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nGreen Island\nn=106\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nBritomart Reef\nn=107\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\n10\nLizard Island\nn=104\n0\n40\n80\n120\n160\n0\n2\n4\n6\nArlington Reef\nn=102\nInner shelf\nMid shel\nNF\nNR\nNR\nFrequency\nf Inner shelf 0\n40\n80\n120\n160\n0\n5\n10\n15\nRocky Islets B\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nGreen Island\nn=106\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nBritomart Reef\nn=107\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\n10\nLizard Island\nn=104\n0\n40\n80\n120\n160\n0\n2\n4\n6\nArlington Reef\nn=102\nMid shel\nNR\nFrequency\nf 0\n40\n80\n120\n160\n0\n5\n10\n15\nRocky Islets B\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nGreen Island\nn=106\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nBritomart Reef\nn=107\nequency Green Island\nn=106 NR Frequency Mid shelf 0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nYonge Reef\nn=109\n0\n40\n80\n120\n160\n0\n5\n10\n15\nMichaelmas Reef\nn=120\n0\n40\n80\n120\n160\n0\n2\n4\n6\nPith Reef\nn=43\n0\n40\n80\n120\n160\n0\n2\n4\n6\nOne Tree Island\nn=108\nOuter shelf\nAge (days) Outer shelf Fig. Growth There were great differences among latitudes in the \nparameters K, L∞ and ­t0 and the constrained von Ber-\ntalanffy growth curves (Fig. 3a-b, Fig. 4; Table  3). Contrasting patterns were found for the parameters of \ngrowth (L∞, K). K decreased with an increase in lati-\ntude while L∞ increased with latitude. Accordingly, \na significant inverse relationship was found between \nthe growth rate (K) and L∞ for S. delicatulus (regres-\nsion and ANOVA, ­df(1,9), y =  − 0.0002x + 0.0246, \n­r2 = 0.90, P = 0.000005. A comparison of age at \n40–50 mm provided further evidence that the rate of \ngrowth to L∞ varied with latitude (Fig. 3c). Age at \n40–50  mm SL increased with latitude as would be \nexpected with lower values of K. Furthermore, these \nfindings concurred with fish collected in two seasons. The 40–50 mm SL size class of fish at all outer sites \nwas compared among latitudes, and as for data pooled \nby latitude, a strong north–south pattern was detected \nwhere age at size increased with latitude; differences \namong latitude were significant (ANOVA ­df(1,3), \nF = 116.5, P = 0.001701; Fig. 3c). Patterns cross shelf \nwere minor compared to differences in K and L∞ \namong latitudes (Table 3). Mature males and females were detected at Liz-\nard from 36–37 mm SL, while in the Capricorn Bun-\nker, mature fish were detected from 38 mm SL. The \nbinomial logistic regression performed found that \nstandard length and latitude (Lat) were both signifi-\ncant factors in the maturity schedules of S. delicatulus \n(Table 3S). The ­L50 for samples from the Capricorn \nBunker (L4) was calculated at 40.1 mm SL, while the \n­L50 estimated for the Lizard (L1) was 38.3  mm SL \n(Fig. 5). Although this difference was less than 2 mm, \nit was statistically significant and fishes in the size \nrange 38–40 mm SL would mature at 30 to 40 days \nolder at L4 when compared with L1 (Fig. 4). 1 \nol.: (01 3\n123456789) 3\n123456789) 468 Environ Biol Fish (2022) 105:461–476 Mortality\nSpratelloides delicatulus had very high rates of \ndaily mortality at all latitudes and distances and \nsites within latitudes in the summer of 2009 to 20\n(Table 4, Fig. 6). Growth 2   Age frequency distributions of S. delicatulus collected \nfrom sites at three distance strata and four latitudes in the sum-\nmer of 2009 to 2010 (bin size 2 days), note that no fish were \ncaught at the inner L2 Cairns site and that inner and mid-shelf reefs are absent in the Capricorn Bunker. NR, no reef; NF, no \nfish caught note heading for L3 has not printed as it should be \n-  L3 Palms sites within latitudes in the summer of 2009 to 2010 \n(Table 4, Fig. 6). Instantaneous mortality rate Z for S. delicatulus ranged from 0.028 to 0.097 among sites \non the GBR and daily rates of survivorship varied Growth Instantaneous mortality rate Z for\ndelicatulus ranged from 0.028 to 0.097 among sit\non the GBR and daily rates of survivorship vari\n0\n40\n80\n120\n160\n0\n5\n10\n15\nLow Wooded\nn=94\n0\n40\n80\n120\n160\n0\n1\n2\n3\n4\nOrpheus Island\nn=99\n0\n40\n80\n120\n160\n0\n5\n10\n15\nRocky Islets B\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nGreen Island\nn=106\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nBritomart Reef\nn=107\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\n10\nLizard Island\nn=104\n0\n40\n80\n120\n160\n0\n2\n4\n6\nArlington Reef\nn=102\n0\n40\n80\n120\n160\n0\n2\n4\n6\n8\nYonge Reef\nn=109\n0\n40\n80\n120\n160\n0\n5\n10\n15\nMichaelmas Reef\nn=120\n0\n40\n80\n120\n160\n0\n2\n4\n6\nPith Reef\nn=43\n0\n40\n80\n120\n160\n0\n2\n4\n6\nOne Tree Island\nn=108\nInner shelf\nMid shel\nOuter shelf\nAge (days)\nNF\nNR\nNR\nFrequency\nf\nL 1 Lizard L 2 Cairns L3 Palms L 4 Capricorn\nBunker\nFig. 2   Age frequency distributions of S. delicatulus collected \nfrom sites at three distance strata and four latitudes in the sum-\nmer of 2009 to 2010 (bin size 2 days), note that no fish were \ncaught at the inner L2 Cairns site and that inner and mid-shelf \nreefs are absent in the Capricorn Bunker. Discussion Spratelloides delicatulus are very short-lived fish \nwith fast population turnover rates, and conformed \nto a ‘tropical gradient’ for growth (K) to L∞, and a \n‘counter gradient’ pattern for L∞ and age max both of \nwhich increased with latitude. No fish sampled in this \nstudy survived beyond 152 days and size maximum \nwas similar among latitudes (51 to 74 mm SL). Mil-\nton et al. (1991) found a similar age maximum for S. delicatulus in the Central Pacific to what we found in \nthe Northern GBR. In our study, age maxima was 2 to \n3 months less in the warmer waters of the Northern \nGBR which conforms with the counter gradient pat-\nterns for age max. Repeated sampling in the winters \nof 2010 and 2011 revealed similar differences in age \nmaximum at both the Lizard and Capricorn Bunker \nlatitudes, thus providing evidence that time of sam-\npling was not confounding the latitudinal patterns \nfound. It should be noted that the fish were collected \nat or close to the maximum recorded size of 70 mm \nSL on FishBase. Furthermore, this absolute size \nmaximum was rarely collected in our study or by oth-\ners (Whitehead et al. 1988; Froese and Pauly 2020). This was reassuring as by site fish rarely reached the \nasymptotic length (L∞) of the von Bertalanffy equa-\ntion. Although fish from the most northern and south-\nern sites matured at a similar size, fish from lowest \nlatitudes matured more than a month earlier than \nthose from the highest latitudes. Mean age 40-50 (mm) SL\nSE\n14°38'00\"\n16°41'00\"\n18°50'00\"\n23°30'00\"\n40\n60\n80\n100\n120\n140\nn=30 n=15\nn=62\nn=57\nn=39n=12\n(c)\nLatitude S\nS1\nS2 The shortest lived vertebrate is the goby Evi-\nota sigillata (Depczynski and Bellwood 2005) at \n59  days old, while orange roughy (Hoplostethus \natlanticus) may live to 149  years (Fenton et  al. 1991). Clearly the age maximum of Spratelloides \n(152 days) is close to the minimum age maximum \nfor vertebrates. A back calculation from age fre-\nquency data revealed that spawning takes place over \nan extended season. Given we found birthdays in \nwinter, spring and summer, it is highly likely that \nthey spawn all year which has been previously sug-\ngested (Dalzell 1993) and aligns with collections \nof large numbers of Spratelloides larvae in sum-\nmer (January/February) and winter at One Tree Fig. 3   Relationship between von Bertalanffy parameters for \nS. Mortality Spratelloides delicatulus had very high rates of \ndaily mortality at all latitudes and distances and 1 3\nVol:. (1234567890) Environ Biol Fish (2022) 105:461–476 469 K yr-1\n0\n2\n4\n6\n8\nL\n0\n40\n80\n120\n160\ny=-1.0065x+5.8969\nr2=0.5255\ny=-15.948x+51.172\nr2=0.4939\n(a)\n(b) Southern GBR (1.4% ­day−1). Pooled daily survival \n(S%) by latitude was about 1% less at low latitudes \n(Table 4). K yr-1\n0\n2\n4\n6\n8\nL\n0\n40\n80\n120\n160\nMean age 40-50 (mm) SL\nSE\n14°38'00\"\n16°41'00\"\n18°50'00\"\n23°30'00\"\n40\n60\n80\n100\n120\n140\ny=-1.0065x+5.8969\nr2=0.5255\ny=-15.948x+51.172\nr2=0.4939\nn=30 n=15\nn=62\nn=57\nn=39n=12\n(a)\n(b)\n(c)\nLatitude S\nS1\nS2\nFig. 3   Relationship between von Bertalanffy parameters for \nS. delicatulus a) K ­year−1 (growth coefficient) pooled by lati-\ntude, b) L∞ (mean asymptotic length mm) by latitude, and c) \nthe average age of fish, 40–50 mm SL, by latitude. Lizard and \nCapricorn Bunker latitudes show replicate age data from sum-\nmer and winter (Table 1) y=-1.0065x+5.8969\nr2=0.5255 Discussion delicatulus a) K ­year−1 (growth coefficient) pooled by lati-\ntude, b) L∞ (mean asymptotic length mm) by latitude, and c) \nthe average age of fish, 40–50 mm SL, by latitude. Lizard and \nCapricorn Bunker latitudes show replicate age data from sum-\nmer and winter (Table 1) from 90.8 to 97.3% (Table  4). The lowest survival \nrate was 90.8% ­day−1 at a northern site. The highest estimates of daily mortality, based \non pooled Z, were found at the Lizard transect on \nthe Northern GBR (2.5% ­day−1) and lowest in the 1 \nol.: (01 3\n123456789) 3\n123456789) Environ Biol Fish (2022) 105:461–476 470 0\n20\n40\n60\n0\n10\n20\n30\n40\nLow Wooded\n0\n50\n100\n150\n200\n0\n20\n40\n60\n80\nOrpheus Island\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nRocky Islets B\n0\n50\n100\n150\n0\n20\n40\n60\n80\nGreen Island\n0\n50\n100\n150\n0\n20\n40\n60\n80\nBritomart Reef\n0\n20\n40\n60\n0\n10\n20\n30\n40\nLizard Island\n0\n50\n100\n150\n0\n20\n40\n60\n80\nArlington Reef\n0\n20\n40\n60\n80\n0\n20\n40\n60\nYonge Reef\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nMichaelmas Reef\n0\n50\n100\n150\n0\n20\n40\n60\n80\nPith Reef\n0\n50\n100\n150\n0\n20\n40\n60\nOne Tree Island\nStandard Length (mm)\nAge (days)\n Lizard\n Cairns\n Palms\n Capricorn \nBunker\nMid shelf\nOuter shelf\nInner shelf\nNF\nNR\nNR\nFig. 4   von Bertalanffy growth curves for S. Discussion delicatulus col-\nlected from eleven sites within distance and latitude strata \n(North on the right and South on the left) during the summer \nof 2009–2010, note that no fish were caught at the inner L2 \nCairns site and that inner and mid-shelf reefs are absent in the \nCapricorn Bunker 0\n50\n100\n150\n200\n0\n20\n40\n60\n80\nOrpheus Island\n Palms\n Capricorn \nBunker\nNR 0\n20\n40\n60\n0\n10\n20\n30\n40\nLow Wooded\n Lizard\nInner shelf 0\n20\n40\n60\n0\n10\n20\n30\n40\nLow Wooded\n0\n50\n100\n150\n200\n0\n20\n40\n60\n80\nOrpheus Island\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nRocky Islets B\n0\n50\n100\n150\n0\n20\n40\n60\n80\nGreen Island\n0\n50\n100\n150\n0\n20\n40\n60\n80\nBritomart Reef\n0\n20\n40\n60\n0\n10\n20\n30\n40\nLizard Island\n0\n50\n100\n150\n0\n20\n40\n60\n80\nArlington Reef\nStandard Length (mm)\n Lizard\n Cairns\n Palms\n Capricorn\nBunker\nMid shelf\nInner shelf\nNF\nNR\nNR Inner shelf 0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nRocky Islets B\n0\n20\n40\n60\n0\n10\n20\n30\n40\nLizard Island\nStandard Length (mm) 00\n0\n50\n100\n150\n0\n20\n40\n60\n80\nGreen Island\n0\n50\n100\n150\n0\n20\n40\n60\n80\nBritomart Reef\n80\nArlington Reef\nNR Standard Length (mm) Mid shelf 80\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nMichaelmas Reef\n0\n50\n100\n150\n0\n20\n40\n60\n80\nPith Reef\n0\n50\n100\n150\n0\n20\n40\n60\nOne Tree Island\nAge (days) 0\n20\n40\n60\n80\n0\n20\n40\n60\nYonge Reef\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nMichaelmas Reef\n0\n50\n100\n150\n0\n20\n40\n60\n80\nPith Reef\n0\n50\n100\n150\n0\n20\n40\n60\nOne Tree Island\nAge (days)\nOuter shelf\nFig. 4   von Bertalanffy growth curves for S. Table 3   Values of the \nvon Bertalanffy growth \nparameters calculated \nfor S. delicatulus for all \nsites across inner (I), mid \n(M) and outer (O) shelf. \nLatitude L1 to 4 (North to \nSouth). K = a daily value. K \n­year−1 = K * 365 days Discussion delicatulus col-\nlected from eleven sites within distance and latitude strata \n(North on the right and South on the left) during the summer \nof 2009–2010, note that no fish were caught at the inner L2 \nCairns site and that inner and mid-shelf reefs are absent in the \nCapricorn Bunker 0\n20\n40\n60\n80\n0\n20\n40\n60\nYonge Reef\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\nMichaelmas Reef\n0\n50\n100\n150\n0\n20\n40\n60\n80\nPith Reef\n0\n50\n100\n150\n0\n20\n40\n60\nOne Tree Island\nAge (days)\nOuter shelf 100\n0\n50\n100\n150\n0\n20\n40\n60\n80\nPith Reef\n0\n50\n100\n150\n0\n20\n40\n60\nOne Tree Island\nge (days) Outer shelf Outer shelf Age (days) Fig. 4   von Bertalanffy growth curves for S. delicatulus col-\nlected from eleven sites within distance and latitude strata \n(North on the right and South on the left) during the summer of 2009–2010, note that no fish were caught at the inner L2 \nCairns site and that inner and mid-shelf reefs are absent in the \nCapricorn Bunker 1 3\nVol:. (1234567890)\nTable 3   Values of the \nvon Bertalanffy growth \nparameters calculated \nfor S. delicatulus for all \nsites across inner (I), mid \n(M) and outer (O) shelf. Latitude L1 to 4 (North to \nSouth). K = a daily value. K \n­year−1 = K * 365 days\nLatitude\nSite (distance)\nL∞\nK\nK ­year−1\nt0\nr2\nL1\nLow Wooded (I)\n57\n0.014\n5.11\n − 4.453\n0.595\nRocky Islets B (M)\n77\n0.011\n4.02\n − 6.433\n0.794\nLizard Island (M)\n54\n0.01677\n6.2\n − 5.000\n0.778\nYonge Reef (O)\n89\n0.0092\n3.63\n − 5.833\n0.785\nL2\nGreen Island (M)\n62\n0.01494\n5.48\n − 4.794\n0.848\nArlington Reef (M)\n86\n0.0107\n3.91\n − 4.741\n0.854\nMichaelmas Reef (O)\n68\n0.0141\n5.15\n − 4.543\n0.823\nL3\nOrpheus Island (I)\n122\n0.0047\n1.72\n − 9.175\n0.817\nBritomart Reef (M)\n96\n0.008\n2.92\n − 6.711\n0.906\nPith Reef (O)\n121\n0.0059\n2.15\n − 6.197\n0.962\nL4\nOne Tree Island (O)\n98\n0.0055\n2.00\n − 7.812\n0.752 Table 3   Values of the \nvon Bertalanffy growth \nparameters calculated \nfor S. delicatulus for all \nsites across inner (I), mid \n(M) and outer (O) shelf. Latitude L1 to 4 (North to \nSouth). K = a daily value. K \n­year−1 = K * 365 days 1 \nVol:. Discussion delicatulus \nreaches up to 6 K ­year−1 on the GBR (this study). mainland (Wenger et al 2012; Kingsford et al 2019). Genetic differences among latitudes are possible, but \nnot described. Temperature gradient is likely to be the \nkey driver of differences in growth patterns. This dif-\nference is known to fluctuate seasonally and is gener-\nally greater during the winter months with a dispro-\nportionate drop at higher latitudes (Wolanski 2001). Fish growth generally varies along latitudinal gradi-\nents and two main patterns have been observed as fol-\nlows: a ‘tropical gradient’ (an increase in growth with \na decrease in latitude) and the opposite ‘counter gra-\ndients’, though there are exceptions where some taxa \nshow no gradient in age max or growth with latitude \n(e.g. some Sebastes, Boehlert and Kappenman 1980). Tropical gradient models state that growth is slower \nin cold water (Houde 1989; Atkinson and Sibly 1997), \nwhile the counter gradient models propose that growth \nis slower in warm water (Yamahira and Conover 2002). These theories are principally driven by a gradient in \nwater temperature which has been clearly demonstrated \nin our study and by others in the over the same latitu-\ndinal range (2–4 °C, Kingsford et al. 2019). This can \nexplain why fish maximum length can be the same \nacross multiple latitudes, yet growth rates differ sig-\nnificantly. Our results for the growth of tropical Spra-\ntelloides conform to a tropical gradient model as sug-\ngested by Houde (1989) and Atkinson and Sibly (1997).f Density dependence and resource limitation is thought \nto be more intense in low latitudes (Atkinson and Sibly \n1997; Gust et al. 2002) but there were few data on den-\nsity effects and resource abundance in small planktivo-\nrous fishes. We predict that higher levels of competition \nwith other Spratelloides species such as S. gracilis and \nS. lewisi could occur at lower latitudes but there are few \ndata on abundance. However, we suggest that the impor-\ntance of these effects is likely to be highly site dependent \nand have little influence on latitudinal patterns. An extended spawning and growing season exists \nat lower latitudes with higher temperatures (Cushing \n1975). In the tropics, species generally experience a \nnarrower range of seasonal variation in sea tempera-\nture than temperate species (Munday et  al. 2008). Discussion ( 3\n(1234567890) 471 Environ Biol Fish (2022) 105:461–476 Fig. 5   Reproductive maturity development of S. delicatulus (classified as ripe fish with stage III oocytes or greater as mature) by \nstandard length (mm), sex, and latitude (L1 Lizard Region and L4 Capricorn Bunker); during summer of 2014–2015 Fig. 5   Reproductive maturity development of S. delicatulus (classified as ripe fish with stage III oocytes or greater as mature) by \nstandard length (mm), sex, and latitude (L1 Lizard Region and L4 Capricorn Bunker); during summer of 2014–2015 A result of the short life, high mortality and high \npopulation turnover rate of Spratelloides implies \nthat population bottlenecks would be frequent, and \nperiods of high larval abundance with few mature \nfish are likely to be common within and among \nreefs at different latitudes. Tropical Spratelloides \nhave the highest growth parameters of all clupei-\nforms recorded in the literature. Temperate and Island in June–July (i.e. Austral Winter; Kingsford \n2001)). However, more detailed temporal sampling \nwould be required to demonstrate seasonality and \npotential periodicity within such as lunar-related \npatterns (Johannes 1978). Such a short-lived organ-\nism experiences a biological limitation in life, and \na short window in which it can contribute to the \nnext generation (Depczynski and Bellwood 2005). Island in June–July (i.e. Austral Winter; Kingsford \n2001)). However, more detailed temporal sampling \nwould be required to demonstrate seasonality and \npotential periodicity within such as lunar-related \npatterns (Johannes 1978). Such a short-lived organ-\nism experiences a biological limitation in life, and \na short window in which it can contribute to the \nnext generation (Depczynski and Bellwood 2005). 1 \nol.: (0 3\n3 56789) Environ Biol Fish (2022) 105:461–476 472 Table 4   Mortality (Z) \nand daily survivorship (S) \nestimates for S. delicatulus \nat sites across the Great \nBarrier Reef and pooled by \nlatitude. Latitude L1 to 4 \n(North to South)\nLatitude\nPooled Z\nPooled S %\nSite\nZ\nS %\nr2\nLow Wooded\n0.097\n90.8\n0.493\nL1\n0.025\n97.5\nRocky Islets B\n0.074\n93.0\n0.670\nLizard Island\n0.078\n92.5\n0.472\nYonge Reef\n0.065\n93.7\n0.617\nGreen Island\n0.051\n95.1\n0.384\nL2\n0.014\n98.6\nArlington Reef\n0.052\n94.9\n0.39\nMichaelmas Reef\n0.076\n92.7\n0.703\nOrpheus Island\n0.028\n97.3\n0.534\nL3\n0.009\n99.1\nBritomart Reef\n0.037\n96.4\n0.708\nPith Reef\n0.055\n94.7\n0.612\nL4\n0.014\n98.6\nOne Tree Island\n0.062\n94.0\n0.763 sub-tropical species reach 0.5 to 1 K ­year−1 (Ahren-\nholz 1991; Emmett et al. 2005), while S. 1 3\nl:. (1234567890) Discussion Pooled daily mortality rate ranged from \n0.9 to 2.5% ­day−1 and site-specific mortality ranged \nfrom 9.2 to 2.7% ­day−1. Previous work showed that \nS. gracilis had a similar high daily mortality rate of \napproximately 3.7% at on the North West Shelf of \nWestern Australia (Meekan et al. 2006). y = -0.0137x + 1.4956\nr² = 0.9011\nn=328\n0 \n0.5 \n1 \n1.5 \n0 \n40\n80\n120 \n160 \nge Frequency\nL2 Cairns \nL3 P l Loge Frequency A fish reproductive strategy may shift in relation to \nenvironmental conditions, so breeding patterns are a \nconditional strategy (McBride et al. 2015). Reproductive \ndevelopment is fast with sexual differentiation observed \nfrom 24  mm and maturity beginning at 36–38  mm \nacross the two latitudes sampled. These values are simi-\nlar to those observed in previous studies which have also \nindicated the ability for S. delicatulus to repeat spawn \n(Milton et al. 1991, 1994). The shorter time frame avail-\nable to fishes at low latitudes as well as the onset of \nmaturity a month earlier than higher latitude individu-\nals suggest for S. delicatulus in a ‘tropical gradient of \ngrowth’ a higher reproductive allocation of energy at \nlow latitudes, perhaps resulting in a shorter lifespan. The \ndifferences in growth and age maxima could also reflect \ngenetic differences at large spatial scales. We cannot \nexclude the possibility for genetic variation driving dif-\nferences in the North and South GBR populations.f 0 \n40\n80\n120 \n160 \ny = -0.0141x + 1.7804\nr² = 0.916 \nn=108\n0 \n0.5 \n1 \n0 \n40\n80\n120 \n160 \nAge(days)\nL4 Capricorn \nBunker In conclusion, there were clear differences in age \nmaxima and growth characteristics for populations \nof S. delicatulus along the GBR. This species had \na short lifespan of age-max 48–152  days with fast \ngrowth. Furthermore, age maxima were up to 98 days \ngreater in high latitudes and concurred with a ‘coun-\nter gradient’ model. In contrast, growth to L∞ was \nhighest at low latitude and matched a ‘tropical gradi-\nent of growth’. Although fish matured at a similar size \nregardless of sex or latitude, they matured 30–40 days \nearlier at the lowest latitudes. Mortality was very high \namong all latitudes. The North–South axis of the \nGBR provides an excellent opportunity to test growth \ntheory and our data provide a baseline for potential \ndemographic variation with environmental change. Fig. 6   Daily age-based instantaneous mortality curves for S. Discussion Although most of the GBR is contained within the \ntropics, there is still a clear gradient in seasonality \nand variation in water temperatures over 10° of lati-\ntude; this in turn can be related to latitudinal growth \ntheories. Most theory is based on temperate to cold-\ntemperate environments where it is argued that fishes \nat low latitudes generally experience a longer grow-\ning season with short pulses of high productivity and \npeaks in hatching (Cushing 1975). In contrast, at high \nlatitudes, fishes have shorter growing seasons with, \non average, consistently high productivity and con-\ntinuous hatching (Fiedler et al. 1991). Therefore, fish Differences in growth curve trajectories between \ngeographic regions may be determined by both envi-\nronmental and genetic influences (Sebens 1987). Key factors driving the differences in growth are \npredation pressure, temperature, effects on metabo-\nlism, resource variation (abundance of plankton) and \nvariable water condition (turbidity). Wave exposure, \nsediment, depth and topographical complexity are all \nknown to correlate with growth patterns; however, \nthese factors are considered to be relatively consist-\nent across all latitudes for a given distance for the 1 3\n. (1234567890) 1 \nVol:. Environ Biol Fish (2022) 105:461–476 473 y = -0.0137x + 1.4956\nr² = 0.9011\nn=328\n0 \n0.5 \n1 \n1.5 \n0 \n40\n80\n120 \n160 \ny = -0.0088x + 1.1979\nr² = 0.7476\nn=249\n0 \n0.5 \n1 \n0 \n40\n80\n120 \n160 \ny = -0.0141x + 1.7804\nr² = 0.916 \nn=108\n0 \n0.5 \n1 \n0 \n40\n80\n120 \n160 \ny = -0.0251x + 1.9614\nr² = 0.8522\nn=409\n0 \n0.5 \n1 \n1.5 \n0 \n40\n80\n120 \n160 \nAge (days)\nLoge Frequency\nL1 Lizard \nL2 Cairns \nL3 Palms\nL4 Capricorn \nBunker\nFig. 6   Daily age-based instantaneous mortality curves for S. delicatulus pooled by latitude y = -0.0251x + 1.9614\nr² = 0.8522\nn=409\n0 \n0.5 \n1 \n1.5 \n0 \n40\n80\n120 \n160 \nL1 Lizard a reduction in the annual growth rate of an individual, \nas demonstrated for S. delicatulus in this study. Life history characteristics are a result of this \ntrade-off between survival and reproduction (Stearns \n1976). Fish with faster life history, such as clupei-\nforms (early maturity, high growth rate, small body \nsize), allocate more resources to reproductive output \nthan those with slower life histories (Denney et  al. 2002), resulting in extremely high rates of mortality. Mortality rates were high across all latitudes and sites \nof this study. Discussion delicatulus pooled by latitude potentially maximize their use of this short period \nresource at high latitudes. In contrast, the productivity \ncycle at low latitudes is less extreme and this resource \nis spread out more evenly throughout the season \n(Conover 1992). However, cold water is also known \nto slow metabolic and growth processes (Schmidt-\nNielsen 1997). The direct applicability of these \nassumptions to tropical to subtropical latitudes is yet \nto be tested. However, lower mean temperatures and \nshorter growing seasons at higher latitudes may cause 1 \nol.: (01 3\n123456789) 3\n123456789) 474 Environ Biol Fish (2022) 105:461–476 Acknowledgements  We \nwould \nlike \nto \nthank \nMark \nO’Callaghan, Emily Gerard of the Reef and Ocean Ecology \nLaboratory, JCU, and Kari Arbouin and all other volunteers \nfor field and lab assistance during this study. We also thank the \nOne Tree Island Research Station for field assistance and sup-\nport, and the crews from the following charter vessels: Phoe-\nnix of Bianca Charters, Kalinda and Melantre. Funding for this \nproject was awarded to MJK from the ARC Centre of Excel-\nlence of Coral Reef Studies and Marine and Tropical Sciences \nResearch Facility (MTSRF). otherwise in a credit line to the material. If material is not \nincluded in the article’s Creative Commons licence and your \nintended use is not permitted by statutory regulation or exceeds \nthe permitted use, you will need to obtain permission directly \nfrom the copyright holder. To view a copy of this licence, vis-\nithttp://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. Consent to participate  NA. Conover DO (1992) Seasonality and the scheduling of life his-\ntory at different latitudes. J Fish Biol 41:161–178 Consent for publication  The three authors agree that the \npaper is original and should be published. Conover DO, Brown JJ, Ehtisham A (1997) Countergradi-\nent variation in growth of young striped bass (Morone \nsaxatilis) from different latitudes. Can J Fish Aquat Sci \n54:2401–2409i Competing interests  The authors declare no competing inter-\nests. Data availability  Available upon reasonable request to the \nauthors. Data availability  Available upon reasonable request to the \nauthors. Bailey KM, Houde ED (1989) Predation on eggs and larvae of \nmarine fishes and the recruitment problem. Adv Mar Biol \n25:1–83 Code availability  The software SYSTAT Ver 13 is pub-\nlicly available, (https://​www.​statc​on.​de/​shop/​en/​softw​are/​stati​\nstics/​systat) and was used for most graphics. R version 4.1.2 \nwas used to test the maturity schedules of males and females \nbetween latitudes using the packages: tidyr, dplyr, DHARMa, \neffects, multcomp, emmeans, outliers, ggplot2 and ggiraph; the \ncode used is available on request. Growth models were done in \nEXCEL (Ver 2102; Build # 13,801.21004) and multiple itera-\ntions were run using Solver to vary parameters of the models \nwith the objective of minimising variances. Boehlert G, Kappenman R (1980) Variation of growth with \nlatitude in two species of rockfish (Sebastes pinniger and \nS. diploproa) from the Northeast Pacific Ocean. Mar Ecol \nProg Ser 3:1–10 Boulcott P, Wright PJ (2008) Critical timing for reproductive \nallocation in a capital breeder: evidence from sandeels. Aquat Biol 3:31–40 Cappo M, Kelley R (2001) Connectivity in the Great Barrier \nReef World Heritage Area: an overview of pathways and \nprocesses. In: Wolanski E (ed) Oceanographic Processes \nof Coral Reefs: Physical and Biological Links in the Great \nBarrier Reef. CRC Press, Boca Raton, pp 161–187i Declarations Chen Y, Paloheimo JE (1994) Estimating fish length and age \nat 50% maturity using a logistic type model. Aquat Sci \n56:206–219. https://​doi.​org/​10.​1007/​BF008​79965 Ethics approval  Animal Ethics: permit for samples collected \nfor aging fish A1487 and GBRMPA Permit numbers G06/202 \n34.1 and G10/332 39.1. Animal Ethics for reproductive samples \n(A1808) and GBRMPA permit number G13/35909.1. Choat JH, Robertson DR (2002) Age-based studies. In: Sale \nPF (ed) Coral reef fishes: dynamics and diversity in a \ncomplex ecosystem. Academic Press, San Diego, pp \n57–80 References Author contribution  All of the authors were involved in the \ncollection of fish, processing of data, and drafts of the paper. EW \naged the fish and KH-B assessed the reproductive status of fish. Ahrenholz DW (1991) Population biology and life history of \nthe North American menhadens, Brevoortia spp. Mar Fish \nRev 53:3–19 Atkinson D, Sibly RM (1997) Why are organisms usually big-\nger in colder environments? Making sense of a life history \npuzzle. Trends Ecol Evol 12:235–239i Funding  Open Access funding enabled and organized by \nCAUL and its Member Institutions This research was sup-\nported by funding to MJK from the ARC Center of Excellence \nfor Coral Reef Studies. Bagenal TB, Tesch FW (1978) Methods for assessment of fish \nproduction in fresh waters. In: Bagenal TB (ed) Age and \ngrowth. Blackwell Scientific Publications, Oxford, pp \n101–136 Competing interests  The authors declare no competing inter-\nests. Cope JM, Punt AE (2007) Admitting ageing error when fit-\nting growth curves: an example using the von Bertalanffy \ngrowth function with random effects. Can J Fish Aquat \nSci 64:205–218i Open Access  This article is licensed under a Creative Com-\nmons Attribution 4.0 International License, which permits \nuse, sharing, adaptation, distribution and reproduction in any \nmedium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Crea-\ntive Commons licence, and indicate if changes were made. The \nimages or other third party material in this article are included \nin the article’s Creative Commons licence, unless indicated Cushing DH (1975) Marine ecology and fisheries. Cambridge \nUniversity Press, Cambridgei Dalzell P (1993) Small pelagic fishes. In: Wright A, Hill L \n(eds) Nearshore marine resources of the South Pacific: \ninformation for fisheries development and management. Institute of Pacific Studies; Forum Fisheries Agency; 1 \nVol:. 3\n(\n) 475 Environ Biol Fish (2022) 105:461–476 International Centre for Ocean Development, Suva; Honi-\nara; Canada, pp 97–133 International Centre for Ocean Development, Suva; Honi-\nara; Canada, pp 97–133 Kingsford M, Hughes J (2005) Patterns of growth, mortal-\nity, and size of the tropical damselfish Acanthochromis \npolyacanthus across the continental shelf of the Great \nBarrier Reef. Fish Bull 103:561–573 Denney NH, Jennings S, Reynolds JD (2002) Life-history cor-\nrelates of maximum population growth rates in marine \nfishes. Proc R Soc B Biol Sci 269:2229–2237 Kingsford MJ (2001) Diel patterns of abundance of presettle-\nment reef fishes and pelagic larvae on a coral reef. Mar \nBiol 138:853–867 Depczynski M, Bellwood DR (2005) Wave energy and spatial \nvariability in community structure of small cryptic coral \nreef fishes. Mar Ecol Prog Ser 303:283–293 Kingsford MJ, Welch DJ, O’Callaghan M (2019) Latitudinal \nand cross-shelf patterns of size, age, growth, and mor-\ntality of a tropical damselfish Acanthochromis polyacan-\nthus on the Great Barrier Reef. Diversity 11:1–19i Durieux EDH, Meekan MG, Ponton D, Vigliola L (2009) \nTemperature, selective mortality and early growth in the \nshort-lived clupeid Spratelloides gracilis. J Fish Biol \n74:921–938i thus on the Great Barrier Reef. Diversity 11:1–19 Leis JM, Carson-Ewart M (2000) The Larvae of Indo-Pacific \nCoastal Fishes. In: Second Edition: An Identification \nGuide to Marine Fish Larvae (Fauna Malesiana Hand-\nbooks Fauna Malesiana Handbooks), 2nd edn. Competing interests  The authors declare no competing inter-\nests. Brill, \nLeiden, pp 850 Emmett RL, Brodeur RD, Miller TW et al (2005) Pacific sar-\ndine (Sardinops sagax) abundance, distribution, and eco-\nlogical relationships in the Pacific Northwest. Calif Coop \nOcean Fish Investig Reports 46:122–143 Fabricius K, De’ath G, McCook L et  al (2005) Changes in \nalgal, coral and fish assemblages along water quality gra-\ndients on the inshore Great Barrier Reef. Mar Pollut Bull \n51:384–398. https://​doi.​org/​10.​1016/j.​marpo​lbul.​2004.​10.​\n041 McBride RS, Somarakis S, Fitzhugh GR et al (2015) Energy \nacquisition and allocation to egg production in relation \nto fish reproductive strategies. Fish Fish 16:23–57 Meekan MG, Carleton JH, McKinnon AD et al (2003) What \ndetermines the growth of tropical reef fish larvae in \nthe plankton: Food or temperature? Mar Ecol Prog Ser \n256:193–204 Fenton GE, Short SA, Ritz DA (1991) Age determination of \norange roughy, Hoplostethus atlanticus (Pisces: Tra-\nchichthyidae) using210Pb:226Ra disequilibria. Mar Biol \n109:197–202 Meekan MG, Vigliola L, Hansen A et  al (2006) Bigger is \nbetter: size-selective mortality throughout the life his-\ntory of a fast-growing clupeid, Spratelloides gracilis. Mar Ecol Prog Ser 317:237–244 Fiedler PC, Philbrick V, Chavez FP (1991) Oceanic upwelling \nand productivity in the eastern tropical Pacific. Limnol \nOceanogr 36:1834–1850i Milton DA, Blaber SJM (1991) Maturation, spawning sea-\nsonality, and proximate spawning stimuli of six spe-\ncies of tuna baitfish in the Solomon Islands. Fish Bull \n89:221–237 Froese R, Pauly D (2020) www.​fishb​ase.​org. In: World Wide \nWedb Electron. Publ. www.​fishb​ase.​org. Accessed 16 Jul \n2020 Milton DA, Blaber SJM, Rawlinson NJF (1993) Age and \ngrowth of three species of clupeids from Kiribati, tropical \ncentral south Pacific. J Fish Biol 43:89–108 Green BS, Mapstone BD, Carlos G, Begg GA (2009) Tropical \nfish otoliths: information for the assessment, management \nand ecology. Springer Verlag, New Yorki Milton DA, Blaber SJM, Rawlinson NJF (1991) Age and \ngrowth of three species of tuna baitfish (genus: Spratel-\nloides) in the tropical Indo-Pacific. J Fish Biol 39:849–866 Gulland JA (1971) Ecological aspects of fishery research. In: \nCragg JB (ed) Advances in Ecological Research, 7th edn. Academic Press, London, pp 115–176 Gust N, Choat JH, Ackerman JL (2002) Demographic plastic-\nity in tropical reef fishes. Mar Biol 140:1039–1051 Milton DA, Blaber SJM, Rawlinson NJF (1994) Reproduc-\ntive biology and egg production of three species of Clu-\npeidae from Kiribati, tropical central Pacific. Fish Bull \n92:102–121 Gust N, Choat JH, McCormick MI (2001) Spatial vari-\nability in reef fish distribution, abundance, size and \nbiomass: a multi-scale analysis. Competing interests  The authors declare no competing inter-\nests. Mar Ecol Prog Ser \n214:237–251 Munch SB, Salinas S (2009) Latitudinal variation in lifespan \nwithin species is explained by the metabolic theory of \necology. PNAS 106:13860–13864 Hatakeyama R, Shirafuji N, Nishimura D et al (2005) Gonadal \ndevelopment in early life stages of Spratelloides gracilis. Fish Sci 71:1201–1208i Munday PL, Kingsford MJ, O’Callaghan M, Donelson JM \n(2008) Elevated temperature restricts growth potential of \nthe coral reef fish Acanthochromis polyacanthus. Coral \nReefs 27:927–931 Hilborn R, Walters CJ (1992) Quantitative fisheries stock \nassessment; choice, dynamics and uncertainty. Chapman \nand Hall, New Yorkf Perry AL, Low PJ, Ellis JR (1912) Reynolds JD (2005) Ecol-\nogy: Climate change and distribution shifts in marine \nfishes. Science 308(80):1915 Hoffmann GCS, Freitas MO, Moura RL et al (2017) Reproduc-\ntive biology of Haemulon plumierii in the south-western \nAtlantic Ocean’s most extensive reefs: implications for \nfisheries management. J Fish Biol 90:2111–2124 Robertson DR, Choat JH, Posada JM et  al (2005) Ocean \nsurgeonfish Acanthurus bahianus. II. Fishing effects \non longevity, size and abundance? Mar Ecol Prog Ser \n295:245–256 Houde ED (1989) Comparative growth, mortality, and energet-\nics of marine fish larvae: temperature and implied latitudi-\nnal effects. Fish Bull 87:471–495 Rogers PJ, Geddes M, Ward TM (2003) Blue sprat Spratel-\nloides robustus (Clupeidae: Dussumieriinae): a temperate \nclupeoid with a tropical life history strategy? Mar Biol \n142:809–824 Johannes RE (1978) Reproductive strategies of coastal marine \nfishes in the tropics. Environ Biol Fishes 3:65–84f Jones GP (1986) Food availability affects growth in a coral reef \nfish. Oecologia 70:136–139 Ruiz-Abierno A, Márquez-Fariás JF, Trápaga-Roig M, Hueter \nRE (2021) Length at maturity of two pelagic sharks Ruiz-Abierno A, Márquez-Fariás JF, Trápaga-Roig M, Hueter \nRE (2021) Length at maturity of two pelagic sharks 1 \nol.: (0 3\n123456789) 476 Environ Biol Fish (2022) 105:461–476 pilchards, sprats, shads, anchovies and wolf-herrings, vol \n7. Part 1 - Chirocentridae, Clupeidae and Pristigasteridae. FAO Fisheries Synopsis, Rome, pp 305–579i (Isurus paucus and Carcharhinus longimanus) found off \nnorthern Cuba. Bull Mar Sci 97:77–88 Schmidt-Nielsen K (1997) Animal physiology: adaptation \nand environment, 5th edn. Cambridge University Press, \nCambridge Williams DM, Hatcher A (1983) Structure of fish communities \non outer slopes of inshore, mid-shelf and outer shelf reefs \nof the Great Barrier Reef. Mar Ecol Prog Ser 10:239–250i g\nSebens KP (1987) The ecology of indeterminate growth in ani-\nmals. Annu Rev Ecol Syst 18:371–407 Williams DMB (1982) Patterns in the distribution of fish com-\nmunities across the Central Great Barrier Reef. Competing interests  The authors declare no competing inter-\nests. Coral \nReefs 1:35–43 Stearns SC (1976) Life history characteristics: a review of the \nideas. Q Rev Biol 51:3–47 Systat (2009) SYSTAT ver. 13 for Windowsf Williams DMB, Dixon P, English S (1988) Cross-shelf distri-\nbution of copepods and fish larvae across the central Great \nBarrier Reef. Mar Biol 99:577–589 Systat (2009) SYSTAT ver. 13 for Windowsf Udy J, Gall M, Longstaff B et al (2005) Water quality moni-\ntoring: a combined approach to investigate gradients of \nchange in the Great Barrier Reef, Australia. Mar Pollut \nBull 51:224–238f Barrier Reef. Mar Biol 99:577–589 Wilson DT, Meekan MG (2002) Growth-related advantages for \nsurvival to the point of replenishment in the coral reef fish \nStegastes partitus (Pomacentridae). Mar Ecol Prog Ser \n231:247–260 von Bertalanffy L (1938) A quantitative theory of organic \ngrowth (inquiries on growth laws II). Hum Biol \n10:181–213 Wolanski E (2001) Physical and biological links in the Great \nBarrier Reef. CRC Press, Australiai Walther GR, Post E, Convey P et  al (2002) Ecological \nresponses to recent climate change. Nature 416:389–395 Yamahira K, Conover DO (2002) Intra- vs interspecific lati-\ntudinal variation in growth: adaptation to temperature or \nseasonality. Ecology 83:1252–1262 Wenger AS, Johansen JL, Jones GP (2012) Increasing sus-\npended sediment reduces foraging, growth and condi-\ntion of a planktivorous damselfish. J Exp Mar Biol Ecol \n428:43–48 Publisher’s note  Springer Nature remains neutral with regard \nto jurisdictional claims in published maps and institutional \naffiliations. Whitehead PJP, Nelson GJ, Wongratana T (1988) FAO Spe-\ncies Catalogue. Clupeoid fishes of the world. In: An anno-\ntated and illustrated catalogue of the herrings, sardines, 1 \nVol:. ( 1 3\n(1234567890)"