ALBERT

Description: Using an innovative analytical approach, we ascertained concentration and composition of triglycerides (TAGs) in individual ova of New Zealand snapper Chrysophrys auratus during three consecutive spawning events in one season. C. auratus broodstock were wild caught several months prior to the commencement of the study and kept in captivity at NIWA's (National Institute of Water and Atmospheric research) Bream Bay aquaculture facility, New Zealand. Thirty-six fish were split equally between two 20 m3 tanks, within the same recirculating system at a water exchange rate of 130 L min−1. Filtered (10 µm) seawater from Bream Bay was allowed to naturally increase to and then maintained at a temperature of 18°C to provide for optimum spawning temperature (Parsons et al., 2014 (doi:10.1080/00288330.2014.892013)). The photoperiod mirrored normal day light hours and fish were hand-fed to satiation daily using a diet of pilchard and squid. Eggs of C. auratus were collected on three separate dates: 21st December 2017, 11th January 2018 and 21st January 2018. Using a net, floating eggs were collected directly from the tanks after spawning and transferred into a large container with the same seawater, dried using a fine plastic mesh (150 μm), sorted and transferred into Eppendorf tubes and stored at -80°C in preparation for subsequent biochemical analysis using Liquid Chromatography-Mass Spectrometry [LC-MS]. Individual eggs were prepared and placed in glass low volume autosampler inserts. 40 μL of LC-MS grade isopropanol (Thermo Fisher NZ Ltd) containing 100 mg L-1 glyceryl tripentadecenoate [TG(15:1(10Z)/15:1(10Z)/15:1(10Z))] [TPD] as internal standard was added to the insert before the eggs were crushed. 10 μL of ultrapure water was then added to the sample and the glass insert was placed into a 1.5 ml Eppendorf tube and centrifuged at 6000 rcf for five minutes. Glass inserts were placed into 1.8 ml amber glass autosampler vials and capped for injection to LC-MS. In total, 147 eggs were prepared for TAG analysis. Triglyceride profiles were acquired using an Agilent 1200 Series liquid chromatograph with an Agilent 6420 triple quadrupole tandem mass spectrometer. An Agilent Poroshell 120 EC-C8 column measuring 150 × 2.1 mm with 2.7 μm packing material was used to separate TAGs and the injection volume was 25 μL. Three mobile phases were used: A) 89.9% ultrapure water and 10% acetonitrile [MeCN] with 0.1% acetic acid, B) MeCN with 0.1% acetic acid and 10mM NH4, and C) 80% IPA, 19.9% MeCN and 0.1% acetic acid with 10mM NH4. All mobile phase solvents and modifiers were mass spectrometry grade. The results were first visualised in TOPPView, an open-source software that is an integrated data visualisation and analysis tool for mass spectrometric data (Sturm and Kohlbacher, 2009 (doi:10.1021/pr900171m)). The m/z and retention times of each TAG peak was recorded and LIPID MAPS online tools for lipid research were used to assign carbon numbers to them, as described in Fahy et al. (2009; doi:10.1194/jlr.R800095-JLR200). TAG peaks were quantified relative to TPD using Agilent MassHunter Quantitative Data Analysis software. Quality was controlled using procedural and carryover blanks.

Description: Submarine inflow of freshwater from land into the ocean, (fresh) submarine groundwater discharge (SGD), is increasingly recognized as an important source of local nutrient and pollutants influx to coastal ecosystems. Still, very little is known about the effects of SGD on ecosystem functioning. Especially the effects of fresh SGD on the productivity of higher trophic levels such as ichthyofaunal communities remain unclear. We recorded nutrient, radon, and chlorophyll concentrations and benthic composition in two tropical coral reef lagoons on Mauritius, one influenced by distinctive freshwater springs inside the lagoon (Trou aux Biches) and one influenced by freshwater seepage along the shoreline (Flic en Flac). In Trou aux Biches we additionally examined the load of total suspended solids (TSS) in the water column and recorded and analyzed fish abundance and fish community across nine different stations within three different sites (spring, spring influenced, and control) of the lagoon. Depth measurements were taken using a handheld depth sounder (Plastimo Echotest 2) without consideration of benthic cover. Actual depth might be underestimated, especially in coral rich areas of the lagoon. Water samples for nutrient analysis were taken from a depth of 50 cm using a peristaltic pump, filtered, and stored frozen until measurement. Conductivity, Temperature, and Salinity were measured in situ using a handheld probe (WTW Cond 3310, TetraCon325). Water pH was determined in the laboratory using a stationary pH probe (Ohaus Starter 2100). Additionally, water samples were obtained from two oceanic stations offshore the SGD influenced and control part of the lagoon. Nutrient concentrations were determined using standard methods with a discrete analyzer (Systea Easychem Plus) equipped with a 5 cm absorbance reading unit. 222Rn concentrations were determined using an electronic radon detector (RAD 7, Durridge Co.). Benthic coverage was determined using 50 m point intersect transects. All transects were video recorded (Canon Powershot G16) for subsequent analysis. Transect videos were evaluated in the laboratory, recording benthic makeup every 50 cm (n=100) using classifications by English, Wilkinson, & Baker (1997). Total suspended solids (TSS) were sampled in triplicates along 100 m horizontal transects at the springs, the spring influenced part of the lagoon, and at the control site using a 20 µm plankton net. Per transect,a total of 4.91 m³ water (net diameter 25 cm) were filtered. All samples were kept cold in a portable cooling box and frozen at -20°C in the laboratory for subsequent analyses. Frozen TSS samples were defrosted, filtered on a pre-weighted microfiber filter (Whatman GF/F), dried, and weighed again. For Chlorophyll a determination, 1 liter of seawater were taken from a depth of 50cm, filled into a dark PVP bottle and stored in a portable cooling box. In the laboratory the water was filtered onto a microfibre filter (Whatman GF/F), wrapped in tinfoil, stored at -20°C, and shipped to the ZMT in Bremen, Germany. At the laboratory in Bremen, filters where bisected and one half was used to extract Chlorophyll a, using 96% Ethanol. The Chlorophyll a / Ethanol solutions were measured using a fluorometer (TD10AU Flouru, Ex436, Em680). To evaluate fish abundances, GoPro Hero 4 video cameras were weighted and placed at two stations close to the springs, and at three stations at the spring influenced site as well as at the control site. Video data was evaluated using the MaxN/MIN count method (Cappo et al., 2003). Furthermore, videos were used to a) compose a list of all fish species occurring in the lagoon and b) determinate abundances. Diversity was determined using three commonly used metrics, species richness, Shannon's diversity index H', and Pielou evenness J'. Functional groups were used to evaluate changes in the fish community structuring. Groupings are based on similar ecosystem functioning disregarding taxonomic relationships (Bellwood et al., 2004). Fish species were classified into feeding groups indicating feeding behavior and dietary composition (Pratchett et al., 2011). Herbivores and corallivores were further classified using definitions by Green & Bellwood (2009) and Cole et al. (2008), respectively. Commercial species were determined using an open-source dataset provided by the Mauritius Oceanography Institute (Curpen et al., 2013).

Description: Submarine groundwater discharge (SGD) is an ubiquitous pathway for nutrient rich freshwater to coastal ecosystems, demonstrably altering their hydrography, hydrochemistry, and primary productivity. Yet only little is known about the effects of fresh SGD on the fitness of higher trophic levels such as teleost fish. We examined feeding intensities, somatic conditions, and growth rates in juvenile grey demoiselle Chrysiptera glauca sampled between October 2017 and January 2018 in two contrasting tide pools in Mauritius: One strictly marine tide pool at Flic en Flac and another one influenced by the influx of SGD (Albion). Specimens of C. glauca were caught with hand nets and immediately preserved in 30% ethanol/seawater solution. In the laboratory, fish were gradually transferred to 50% and 70% ethanol/freshwater solution. Further, fish were measured by standard length (SL, nearest mm), eviscerated, and weighed (EM, ± 0.001 g). Additionally, for each fish, stomach wet mass (SM, ± 0.001 g) was determined to calculate feeding intensity (FI): FI = SM × [E]^(-1) × 100. Further, a condition index (CI) was calculated for each individual by using b of the length-weight relationship: CI = EM × [SL]^(-b) × 100. We used the frequency of occurrence method for stomach content analysis (0: absence and 1: presence). Food organisms were identified to class level. The sagittae and lapilli otoliths were dissected under a stereomicroscope, cleaned with deionized water, and stored dry in FEMA-cells (26 x 76 mm). Sagittae otoliths were weighed (± 0.001 mg). Lapilli otoliths were fixed on glass slides using 2-component adhesive (Araldite 2020/A and Araldite 2020/B) and ground sequentially on glass plates by silicon carbide (SiC) powder with grit sizes of 400 and 800. After polishing with waterproof silicon carbide grinding paper (grain size of 5 µm), otoliths in immersion oil were examined with a digital microscope (Keyence VHX-5000) using transmitted light at a magnification of 400x - 800x. With the digital microscope it was possible to compose depth-stitchings of different focus levels. Increment counts were conducted and OR was measured using ImageJ 1.49 (Rasband, WS, US National Institutes of Health, Bethesda, MD, http://imagej.nih.gov/ij/). Daily deposition of increments on the lapillus otolith has been validated in a number of species from this family and it was therefore assumed that increments on the lapilli were deposited daily. The increment closest to the core of the otolith was assumed to be formed at the day of hatching, as is the case in many other species from the family Pomacentridae. The number of increments was determined from 3 replicate increment counts. The settlement mark was identified as the first increment of a transition zone, characterized by a rapid narrowing in increment width.

Description: We used remote underwater stereo-video footage and AI driven object tracking to assess the functional foraging traits and movement trajectories of benthic herbivorous fishes on a degraded model coral reef. Sampling took place on the reef in front of the Inter-University Institute for Marine Sciences (IUI) (29°30'7.0"N, 34°55'3.7"E) in Eilat (Israel, Gulf of Aqaba) between 8th and 14th of March 2018. In preparation for the surveys, calibrated stereo-video setups, each consisting of 2 GoPro (4 x Hero 5 and 2 x Hero 4) cameras, were mounted on a total of 3 racks (Neuswanger et al. 2016, doi:10.1139/cjfas-2016-0010). For each sampling day, racks were sequentially installed at a depth of between 2 to 3 m and set to record continuously. Setting up the cameras was the sole purpose of a dive to minimize the disturbance caused to the site. Sites were chosen based on the criteria that a variety of grazable substratum (not just live coral) must be present, as there are a range of micro-habitats within the grazable substrate for fishes that require specific categorisation (Green & Bellwood, 2009, https://repository.library.noaa.gov/view/noaa/926). Therefore, sites with a heterogenous mixture of available benthic substrate cover such as live coral and epilithic algal turf (EAT) on standing dead coral, bare rocks, coral rubble and sand were generally preferred. Because grazing rates in surgeonfishes are highest during midday, the majority of our filming was; conducted between 11:00 – 15:00 (Montgomery et al. 1989 (doi:10.1016/0022-0981(89)90127-5); Fouda and El-Sayed 1994). The analysable video was accumulated from 15 rack placements and comprised 22.9 hrs of footage in total. At the beginning of each recording, we placed a 1 x 1 m PVC quadrat in front of the cameras. We quantified the substrate cover of each quadrat by taking a long shot photograph. These images were uploaded to the program SketchAndCalc Version 1.1.2 (iCalc Inc), in which the 1 x 1 m quadrat was calibrated so each transformed image contained roughly the same number of cells. This equated to ~1000 cells per image, each being around 5 cm². The images with the canvas imprinted upon them were subsequently exported and annotated with each form of substratum having a corresponding colour. Annotated cells were counted and relative substrate cover (in %) was calculated. We then proceeded to measure fish total length (mm), bite rate (bites per min), and the distance between each consecutive bite (bite distance, in mm) only within the delimited quadrat area during the entirety of the recorded video footage. In total, we recorded 2,386 bites by 23 different fish species (from 11 families). We calculated individual fish mass according to the following formula: mass = aTL^b, where a and b for each species were informed from FishBase (www.fishbase.org). The initial 15-min of each video, however, were discarded to allow for the fishes to resume normal behaviour after the quadrat was removed and divers left the site. To standardize against time, only the subsequent 45-min of recording were used for analysis of feeding traits in all species (https://doi.org/10.1594/PANGAEA.932686). Foraging traits in the three most common surgeonfishes were determined in the entirety of the recorded footage after the initial acclimation period (https://doi.org/10.1594/PANGAEA.957631). The time at which a single fish entered the quadrat to take bites from substrates until the time when it exited constituted a feeding event. For each feeding event, all bites were collated and then standardized to obtain bites per minute. Further, for each feeding event we averaged the distances between consecutive bites to obtain bite distance. We conducted all measurements in VidSync Version 1.661 (Neuswanger et al. 2016, doi:10.1139/cjfas-2016-0010). For the two surgeonfish species we calculated Manly's feeding ratios (Manly et al. 2002), which illustrate an individual's use of each substrate category (number of bites) in relation to the availability of substrate type across the entire reef. We achieved AI driven fish detection, identification and tracking from stereo-video by performing several steps. Firstly, we calibrated the system in Matlab (TheMathWorks) using a checkboard pattern recorded with both cameras. Next, we performed stereorectification using OpenCV (Open Source Computer Vision Library) to locate pixels in both images and triangulate the depth of the scene. Using this method of calibration we obtained an overall mean [±SD] absolute reprojection error of 0.9 [±1.9] mm which corresponds to 0.45% of the true value. For object detection, we employed the You Only Look Once (YOLO) convolutional neural network (CNN) (Bochkovskiy et al. 2020), which we retrained with background images from the recorded videos to improve its performance. We then used the bounding boxes produced by the detection algorithm as input data for the classifier and stereo matching. To classify the detected fish species, we utilized science-grade location invariant images of identified fish species from iNaturalist (www.inaturalist.org) to train the CNN (Van Horn et al. 2018, doi:10.1109/CVPR.2018.00914; Shepley et al. 2021, doi:10.1002/ece3.7344). However, the iNaturalist dataset had limited images, and therefore we employed transfer learning using weights computed from a previously recorded dataset from Mayotte as a starting point (Villon et al. 2018, doi:10.1016/j.ecoinf.2018.09.007). Finally, we implemented the Deep SORT framework - an enhanced version of the Simple Online and Realtime Tracking (SORT) algorithm - for multi-object tracking (Wojke et al. 2017, doi:10.48550/arXiv.1703.07402). This framework tracked each bounding box in both the left and right videos. Triangulation was performed to retrieve the 3D coordinates of the fish relative to the left camera, and we applied de-noising to remove any erroneous data points. Overall, our approach enabled reliable and automatic object detection and tracking from stereo-video, providing valuable data for studying the behaviour and ecology of the two focal species in their natural habitats. We extracted XYZ coordinates from a subgroup of 16 Acanthurus nigrofuscus and 23 Zebrasoma xanthurum individuals whose automatically measured lengths fell within the manually determined length frequency distribution. These individuals had automatically generated tracks that were precisely cut down to 700 frames, ensuring a standardized and consistent observation period.