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48 results for “Aquatic animals”
Data from: Animal-mediated organic matter transformation: aquatic insects as a source of microbially bioavailable organic nutrients and energy
1. Animal communities are essential drivers of energy and elemental flow in ecosystems. However, few studies have investigated the functional role of animals as sources of dissolved organic matter (DOM) and the subsequent utilization of that DOM by the microbial community. 2. In a small forested headwater stream, we tested the effects of taxonomy, feeding traits, and body size on the quality and quantity of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) excreted by aquatic insects. In addition, we conducted steady-state solute additions to estimate instream demand for labile C and compared it to the C excreted by invertebrates. 3. Individual excretion rates and excretion composition varied with body size, taxonomy, and feeding guild. The estimated average community excretion rate was 1.31 μg DOC· per mg insect dry weight (DW)-1·h-1 and 0.33 μg DON·mg DW-1·h-1 and individuals excreted DON at nearly twice the rate of 〖"NH" 〗_"4" ^"+" . This DOM was 2-5 times more bioavailable to microbial heterotrophs than ambient stream water DOM. 4. We estimated that the insect community, conservatively, excreted 1.62 mg of bioavailable DOC·m-2·h-1 and through steady-state additions measured an ambient labile C demand as 3.97±0.67 mg C·m-2·h-1. This suggests that insect-mediated transformation and excretion of labile DOC could satisfy a significant fraction (40±7%) of labile C demand in this small stream. 5. Collectively, our results suggest that animal excretion plays an essential functional role in transforming organic matter into microbially bioavailable forms and may satisfy a variable but significant portion of microbial demand for labile C and N.
Aquatic animal antimicrobial resistance trends dataset and R code
<p>First release of aquatic animal AMR R scripts and dataset accompanying "Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia" (doi: 10.1038/s41467-021-25655-8)</p>
Predicting the effects of climate change on the fertility of aquatic animals using a meta-analytic approach
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Data from: Long-term stoichiometry and fates highlight animal egestion as nutrient repackaging, not recycling, in aquatic ecosystems
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Data from: Animal-mediated organic matter transformation: aquatic insects as a source of microbially bioavailable organic nutrients and energy
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Data from: A snapping shrimp has the fastest vision of any aquatic animal
<p>Animals use their sensory systems to sample information from their environments. The physiological properties of sensory systems differ, leading animals to perceive their environments in different ways. For example, eyes have different temporal sampling rates, with faster-sampling eyes able to resolve faster-moving scenes. Eyes can also have different dynamic ranges. For every eye, there is a light level below which vision is unreliable because of an insufficient signal-to-noise ratio and a light level above which the photoreceptors are saturated. Here, we report the eyes of the snapping shrimp <i>Alpheus heterochaelis</i> have a temporal sampling rate of at least 160 Hz, making them the fastest-sampling eyes ever described in an aquatic animal. Fast-sampling eyes help flying animals detect objects moving across their retinas at high angular velocities. <i>A. heterochaelis </i>are fast-moving animals that live in turbid, structurally-complex oyster reefs and their fast-sampling eyes, like those of flying animals, may help them detect objects moving rapidly across their retinas. We also report the eyes of <i>A. heterochaelis </i>have a broad dynamic range that spans conditions from late twilight (~1 lux) to direct sunlight (~100,000 lux), a finding consistent with the circatidal activity patterns of this shallow-dwelling species.</p>
Data from: Aquatic animals promote antibiotic resistance gene dissemination in water via conjugation: role of different regions within the zebra fish intestinal tract, and impact on fish intestinal microbiota
The aqueous environment is one of many reservoirs of antibiotic resistance genes (ARGs). Fish, as important aquatic animals which possess ideal intestinal niches for bacteria to grow and multiply, may ingest antibiotic resistance bacteria from aqueous environment. The fish gut would be a suitable environment for conjugal gene transfer including those encoding antibiotic resistance. However, little is known in relation to the impact of ingested ARGs or antibiotic resistance bacteria (ARB) on gut microbiota. Here, we applied the cultivation method, qPCR, nuclear molecular genetic marker and 16S rDNA amplicon sequencing technologies to develop a plasmid-mediated ARG transfer model of zebrafish. Furthermore, we aimed to investigate the dissemination of ARGs in microbial communities of zebrafish guts after donors carrying self-transferring plasmids that encode ARGs were introduced in aquaria. On average, 15% of faecal bacteria obtained ARGs through RP4-mediated conjugal transfer. The hindgut was the most important intestinal region supporting ARG dissemination, with concentrations of donor and transconjugant cells almost 25 times higher than those of other intestinal segments. Furthermore, in the hindgut where conjugal transfer occurred most actively, there was remarkable upregulation of the mRNA expression of the RP4 plasmid regulatory genes, trbBp and trfAp. Exogenous bacteria seem to alter bacterial communities by increasing Escherichia and Bacteroides species, while decreasing Aeromonas compared with control groups. We identified the composition of transconjugants and abundance of both cultivable and uncultivable bacteria (the latter accounted for 90.4%–97.2% of total transconjugants). Our study suggests that aquatic animal guts contribute to the spread of ARGs in water environments.
Data from: Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival
1. Acoustic telemetry studies have frequently prioritized linear configurations of hydrophone receivers, such as perpendicular from shorelines or across rivers, to detect the presence of tagged aquatic animals. This approach introduces unknown bias when receivers are stationed for convenience at geographic bottlenecks (e.g., at the mouth of an embayment or between islands) as opposed to deployments following a statistical sampling design. 2. We evaluated two-dimensional acoustic receiver arrays (grids: receivers spread uniformly across space) as an alternative approach to provide estimates of survival, movement, and habitat use. Performance of variably-spaced receiver grids (5–25 km spacing) was evaluated by simulating (1) animal tracks as correlated random walks (speed: 0.1–0.9 m/s; turning angle standard deviation: 5–30 degrees); (2) variable tag transmission intervals along each track (nominal delay: 15–300 seconds); and (3) probability of detection of each transmission based on logistic detection range curves (midpoint: 200–1500 m). From simulations, we quantified i) time between successive detections on any receiver (detection time), ii) time between successive detections on different receivers (transit time), and iii) distance between successive detections on different receivers (transit distance). 3. In the most restrictive detection range scenario (200 m), the 95th percentile of transit time was 3.2 days at 5 km grid spacing, 5.7 days at 7 km, and 15.2 days at 25 km; for the 1500 m detection range scenario, it was 0.1 days at 5 km, 0.5 days at 7 km, and 10.8 days at 25 km. These values represented upper bounds on the expected maximum time that an animal could go undetected. Comparison of the simulations with pilot studies on three fishes (walleye Sander vitreus, common carp Cyprinus carpio, and channel catfish Ictalurus punctatus) from two independent large lake ecosystems (lakes Erie and Winnipeg) revealed shorter detection and transit times than what simulations predicted. 4. By spreading effort uniformly across space, grids can improve understanding of fish migration over the commonly employed receiver line approach, but at increased time cost for maintaining grids.
Aquatic Animal Species (AAS)
<p>Our newly constructed Aquatic Animal Species (AAS) dataset contains 4,239 images of aquatic animals in 46 categories. There are 5,041 instances in our dataset, and each image has 1.2 instances on average and a maximum of 22 instances. Fish takes the largest ratio with 33 species and 72% number of images. Non-fish animals include 13 species taking 28% number of images. </p>
Data from: Harvest selection on multiple traits in the wild revealed by aquatic animal telemetry
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Data from: Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival
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Data from: Aquatic animals promote antibiotic resistance gene dissemination in water via conjugation: role of different regions within the zebra fish intestinal tract, and impact on fish intestinal microbiota
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Data from: A snapping shrimp has the fastest vision of any aquatic animal
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Data from: Calculating the ecological impacts of animal-borne instruments on aquatic organisms
1. Animal-borne instruments provide researchers with valuable data to address important questions on wildlife ecology and conservation. However, these devices have known impacts on animal behaviour and energetics. Tags deployed on migrating animals may reduce reproductive output through increased energy demands or cause phenological mismatches of foraging and nesting events. For marine organisms, the only tagging guidelines that exist are based on lift and thrust impacts on birds – concepts that do not translate well to aquatic animals. Herein, we provide guidelines on assessing drag from animal-borne instruments and discuss the ecological impacts on marine organisms. Of particular concern is the effect of drag from instruments to the welfare of the animals and for the applicability of collected data to wild populations. 2. To help understand how drag from electronic tags affects marine animals in the wild, we used marine turtles as model aquatic organisms and conducted wind tunnel experiments to measure the fluid drag of various marine turtle body types with and without commercially available electronic tags (e.g. satellite, TDR, video cameras). We quantified the drag associated with carrying biotelemetry devices of varying frontal area and design (squared or tear drop shaped) and generated contour plots depicting percentage drag increase as a framework for evaluating tag drag by scientists and wildlife managers. Then, using concepts of fluid dynamics, we derived a universal equation estimating drag impacts from instruments across marine taxa. 3. The drag of the marine turtle casts was measured in wind speeds from 2 to 30 m s−1 (Re 3·0 × 104–1·9 × 106), equivalent to 0·1–1·9 m s−1 in seawater. The drag coefficient (CD) of the marine turtles ranged from 0·11 to 0·22, which is typical of other large, air-breathing, marine vertebrates (0·08–0·26). The CD of tags in reference to the turtle casts was 0·91 ± 0·18 and most tags caused minimal additional drag (<5%) to adult animals, but the same devices increased the drag for juveniles significantly (>100%). The sensitivity of aquatic animals to instrument drag is a dynamic relationship between the fluid flow patterns, or CD, and the frontal area ratio of the animal and tag. 4. In this paper, we have outlined methods for quantifying the drag costs from animal-borne instrumentation considering the instrument retention time (time to release from the animal) and the activity of the instrumented animal. With this valuable tool, researchers can quantify the drag costs from animal-borne instrumentation and choose appropriate tags for their intended study organism and question. Reducing drag will ultimately reduce the impact on the instrumented animals and lead to greater biological realism in the collected data.
Data from: Two-current choice flumes for testing avoidance and preference in aquatic animals
Aquatic chemical ecology is an important and growing field of research that involves understanding how organisms perceive and respond to chemical cues in their environment. Research assessing the preference or avoidance of a water source containing specific chemical cues has increased in popularity in recent years, and a variety of methods have been described in the scientific literature. Two-current choice flumes have seen the greatest increase in popularity, perhaps because of their potential to address the broadest range of research questions. Here, we review the literature on two-current choice flumes and show that there is a clear absence of standardized methodologies that make comparisons across studies difficult. Some of the main issues include turbulent flows that cause mixing of cues, inappropriate size of choice arenas for the animals, short experiments with stressed animals, failure to report how experiment and researcher biases were eliminated, general underreporting of methodological details, underutilization of collected data and inappropriate data analyses. In this review, we present best practice guidelines on how to build, test and use two-current choice flumes to measure the behavioural responses of aquatic animals to chemical cues, and provide blueprints for flume construction. The guidelines include steps that can be taken to avoid problems commonly encountered when using two-current choice flumes and analysing the resulting data. This review provides a set of standards that should be followed to ensure data quality, transparency and replicability in future studies in this field.
FIG. 8 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 8. — The Roman folk-taxonomy of piscis1 (true fish). Abbreviations: LF, life-form; LF1+, sublife-form; INT, intermediate; FG, folk-generic; FS, folk-specific. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources; + (superscript), multiple ethnotaxonomic ascription due to different statements in the sources;?, presumed folk taxon. See each entry in Appendix 1 for details.
FIG. 6 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 6. — The two traditional polysemous denotations of Lat. perca, i.e., from top to bottom, perca as a marine fish (the comber, see Appendix 1: perca1) and as a river fish (the European perch, see Appendix 1: perca2). Engraving from Salviani (1554: pl. 90). Salviani (1554: 226) is clear about "the similarity between them".
Data from: Overall dynamic body acceleration measures activity differently on large vs small aquatic animals
<p>Acceleration-based proxies for activity and energy expenditure are widely used in bio-logging studies of animal movement and locomotion to explore biomechanical strategies, energetic costs of behaviour, habitat use and the impact of anthropogenic disturbance. The foremost such proxy is Overall Dynamic Body Acceleration (ODBA) along with variants VeDBA and PDBA. This technique, which involves summing the magnitude of high-pass-filtered acceleration signals (the so-called dynamic acceleration) over a reference interval, has been applied to animals as diverse as shags, lobsters, humans and whales. The relationship between ODBA and energy use has been tested empirically on animals small enough to house in laboratory facilities and arguments have been offered for why the method should be generally applicable, however validations on larger animals are scant.</p> <p><span>Here, we examine how body size impacts ODBA and its variants under steady locomotion in large aquatic animals, using cetaceans as model species. To do this, we first develop a simplified mathematical model for the acceleration signals that would be measured by a tag on a swimming animal. We then test this model with empirical data gathered using bio-logging tags on whale species covering nearly an order of magnitude difference in body length from 1.3 m harbour porpoises to 12 m sperm whales.</span></p> <p><span>We show that the motions measured by ODBA can be fundamentally different in small compared to large aquatic animals. Whereas dynamic acceleration in small animals is predominantly due to specific acceleration (i.e., actual accelerative motions generated by muscle action), in larger aquatic animals body rotations (i.e., changes in orientation that accompany swimming and manoeuvring) can dominate the measured acceleration. </span></p> <p><span>As body rotations do not necessarily increase in magnitude as swimming speed increases, ODBA may under-estimate the relative cost of behaviours or responses to disturbance in large aquatic animals. This does not lessen the value of ODBA for small animals, but it raises a caution against uncritical use on larger animals. For large aquatic animals, activity proxies that specifically remove body rotations using gyroscopes or magnetometers may provide more consistent estimates of energy use although these methods are yet to be validated.</span></p>
Figure 1 from: Vinarski M (2015) Conceptual shifts in animal systematics as reflected in the taxonomic history of a common aquatic snail species (Lymnaea stagnalis). Zoosystematics and Evolution 91(2): 91-103. https://doi.org/10.3897/zse.91.4509
Figure 1 - A great pond snail in its natural environment. 12.08.2014. Russia, Western Siberia, "Malaya Sos'va" Nature Reserve, Kopanoye Lake (photo: M. Vinarski).
Figure 4 from: Vinarski M (2015) Conceptual shifts in animal systematics as reflected in the taxonomic history of a common aquatic snail species (Lymnaea stagnalis). Zoosystematics and Evolution 91(2): 91-103. https://doi.org/10.3897/zse.91.4509
Figure 4 - Varieties of Lymnaea stagnalis as they were identified by malacologists of the 19th century. A. Lymnaea stagnalis var. typica (det. S. Clessin; ZIN). B. Lymnaea stagnalis var. media (det. C.A. Westerlund; GNM). C. Lymnaea stagnalis var. producta (det. C.A. Westerlund; GNM). D. Lymnaea stagnalis var. rosea (identified by a unknown person; ZMUC). E. Lymnaea stagnalis var. colpodia (det. C.A. Westerlund; GNM). F. Lymnaea stagnalis var. variegata (det. C.A. Westerlund; GNM). G. Lymnaea stagnalis var. turgida (det. C.A. Westerlund; GNM). H. Lymnaea stagnalis var. raphidia (det. C.A. Westerlund; GNM). I. Lymnaea stagnalis var. palustriformis (det. A. Fuchs, NHMW). Scale bars 5 mm.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.