Trash or treasure? Use of sagittal otoliths partially composed of vaterite for hatchery stock discrimination in steelhead

2020 ◽  
Vol 77 (2) ◽  
pp. 276-284
Author(s):  
Richard R. Budnik ◽  
John R. Farver ◽  
Joel E. Gagnon ◽  
Jeffrey G. Miner
Keyword(s):  
Classification Accuracy ◽  
Lake Erie ◽  
Juvenile Fish ◽  
Otolith Chemistry ◽  
Elemental Concentrations ◽  
Stock Discrimination ◽  
Quadratic Discriminant Function ◽  
Hatchery Stock ◽  
Similar Classification ◽  
Discriminant Function Analyses

Sagittal otoliths are normally deposited as the CaCO3 polymorph aragonite; however, a proportion of otoliths transitions to vaterite during growth. This transition can complicate otolith chemistry analyses, as differences in the crystalline structure (aragonite or vaterite) of otoliths causes variation in otolith chemistry signatures. To address this issue, we introduce a method to utilize sagittal otoliths partially composed of vaterite for stock discrimination. Using this method, we determined the hatchery origins of yearlings from five Lake Erie hatcheries by using Ba, Mg, Mn, and Sr concentrations in vaterite sections of steelhead (Oncorhynchus mykiss) otoliths. We then compared the classification accuracy of our vaterite method with a method in which otoliths composed entirely of aragonite were used. Overall, quadratic discriminant function analyses revealed similar classification success when elemental concentrations from vaterite (95% accuracy) and aragonite (94% accuracy) otolith regions were used. The methods introduced here could likely be used for other fish species that have otoliths that transition to vaterite as long as an adequate number of juvenile fish are available to develop vaterite otolith chemistry signatures.

Consistency of patterns between laboratory experiments and field collected fish in otolith chemistry: an example and applications for salinity reconstructions

2005 ◽  
Vol 56 (5) ◽  
pp. 609 ◽  
Author(s):  
T. S. Elsdon ◽  
B. M. Gillanders
Keyword(s):  
Environmental Variables ◽  
Laboratory Experiments ◽  
Juvenile Fish ◽  
Otolith Chemistry ◽  
Ambient Water ◽  
Elemental Concentrations ◽  
Adequate Knowledge ◽  
Black Bream ◽  
Environmental Histories ◽  
The Relationship

Elemental concentrations within fish otoliths can track movements and migrations of fish through gradients of environmental variables. Tracking the movements of fish relies on establishing links between environmental variables and otolith chemistry, with links commonly made using laboratory experiments that rear juvenile fish. However, laboratory experiments done on juvenile fish may not accurately reflect changes in wild fish, particularly adults. We tested the hypotheses that: (1) the relationship between ambient (water) and otolith chemistry is similar between laboratory-reared black bream (Acanthopagrus butcheri) and wild black bream; and (2) ontogeny does not influence otolith chemistry. Field-collected and laboratory-reared fish showed similar effects of ambient strontium : calcium (Sr : Ca) on otolith Sr : Ca concentrations. However, ambient and otolith barium : calcium concentrations (Ba : Ca) differed slightly between laboratory-reared and field-collected fish. Importantly, fish reared in stable environmental variables showed no influence of ontogeny on Sr : Ca or Ba : Ca concentrations. Natural distributions of ambient Sr : Ca showed no clear relationship to salinity, yet, ambient Ba : Ca was inversely related to salinity. The distribution of ambient Sr : Ca and Ba : Ca in estuaries inhabited by black bream, suggest that these elements can answer different questions regarding environmental histories of fish. Reconstructing salinity histories of black bream using otolith Ba : Ca concentrations seems plausible, if adequate knowledge of Ba : Ca gradients within estuaries is obtained.

Can otolith chemistry be used for identifying essential seagrass habitats for juvenile spotted seatrout, Cynoscion nebulosus, in Chesapeake Bay?

2005 ◽  
Vol 56 (5) ◽  
pp. 645 ◽  
Author(s):  
Emmanis Dorval ◽  
Cynthia M. Jones ◽  
Robyn Hannigan ◽  
Jacques van Montfrans
Keyword(s):  
Trace Elements ◽  
Chesapeake Bay ◽  
Classification Accuracy ◽  
Spatial Scales ◽  
Juvenile Fish ◽  
Spotted Seatrout ◽  
Otolith Chemistry ◽  
Seagrass Beds ◽  
Cynoscion Nebulosus ◽  
High Classification Accuracy

We investigated the variability of otolith chemistry in juvenile spotted seatrout from Chesapeake Bay seagrass habitats in 1998 and 2001, to assess whether otolith elemental and isotopic composition could be used to identify the most essential seagrass habitats for those juvenile fish. Otolith chemistry (Ca, Mn, Sr, Ba, and La; δ13C, δ18O) of juvenile fish collected in the five major seagrass habitats (Potomac, Rappahannock, York, Island, and Pocomoke Sound) showed significant variability within and between years. Although the ability of trace elements to allocate individual fish may vary between years, in combination with stable isotopes, they achieve high classification accuracy averaging 80–82% in the Pocomoke Sound and the Island, and 95–100% in the York and the Potomac habitats. The trace elements (Mn, Ba, and La) provided the best discrimination in 2001, a year of lower freshwater discharge than 1998. This is the first application of a rare earth element measured in otoliths (La) to discriminate habitats, and identify seagrass habitats for juvenile spotted seatrout at spatial scales of 15 km. Such fine spatial scale discrimination of habitats has not been previously achieved in estuaries and will distinguish fish born in individual seagrass beds in the Bay.

Corrigendum to: Identifying physiological and environmental influences on otolith chemistry in a coastal fishery species

2021 ◽  
Vol 72 (6) ◽  
pp. 922
Author(s):  
Jasmin C. Martino ◽  
Zoë A. Doubleday ◽  
Anthony J. Fowler ◽  
Bronwyn M. Gillanders
Keyword(s):  
Environmental Influences ◽  
Mixed Effects ◽  
Sea Surface ◽  
Physiological Age ◽  
Otolith Chemistry ◽  
Otolith Growth ◽  
Physiological Factors ◽  
Elemental Concentrations ◽  
Study Designs ◽  
Fishery Species

Otolith (ear stone) chemistry provides powerful insights into the lives of fish. Although frequently used to reconstruct past environments, the influence of physiology remains unsettled. As such, we investigated the relationships between otolith chemistry, physiological factors and environmental factors in an iconic fishery species, snapper (Chrysophrys auratus). Lifetime otolith profiles were analysed of carbon (δ13C) and oxygen (δ18O) isotopes, and elemental concentrations of lithium (Li:Ca), magnesium (Mg:Ca), manganese (Mn:Ca), strontium (Sr:Ca), and barium (Ba:Ca). Mixed-effects modelling alongside a detailed literature review was used to investigate physiological (age, otolith growth rate, fish size, sex) and environmental influences (sea-surface temperature and chlorophyll-a) on otolith chemistry. Carbon isotopes and magnesium related to physiological factors, suggesting their potential as physiological proxies. Physiology also weakly related to strontium and lithium. By contrast, oxygen isotopes, barium, and manganese (except for natal signatures) were suggested to provide insights into past environments. Our study stresses the importance of consistency in biological characteristics for study designs, and highlights the potential of physiological proxies for distinguishing between populations in uniform water bodies. This study has not only reinforced our confidence in field applications of otolith chemistry, but has furthered our understanding of the influence of physiology.

scholarly journals Recognition of Rare Low-Moral Actions Using Depth Data

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2758
Author(s):  
Kanghui Du ◽  
Thomas Kaczmarek ◽  
Dražen Brščić ◽  
Takayuki Kanda
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Public Spaces ◽  
Moral Action ◽  
Human Actions ◽  
Depth Data ◽  
Moral Behaviors ◽  
Small Dataset ◽  
Similar Classification

Detecting and recognizing low-moral actions in public spaces is important. But low-moral actions are rare, so in order to learn to recognize a new low-moral action in general we need to rely on a limited number of samples. In order to study the recognition of actions from a comparatively small dataset, in this work we introduced a new dataset of human actions consisting in large part of low-moral behaviors. In addition, we used this dataset to test the performance of a number of classifiers, which used either depth data or extracted skeletons. The results show that both depth data and skeleton based classifiers were able to achieve similar classification accuracy on this dataset (Top-1: around 55%, Top-5: around 90%). Also, using transfer learning in both cases improved the performance.

scholarly journals Rapid changes in age and size at maturity in Lake Erie yellow perch (Perca flavescens) are not explained by harvest

2018 ◽  
Vol 75 (2) ◽  
pp. 211-223 ◽  
Author(s):  
Davíð Gíslason ◽  
Robert L. McLaughlin ◽  
Beren W. Robinson ◽  
Andy Cook ◽  
Erin S. Dunlop
Keyword(s):  
Yellow Perch ◽  
Lake Erie ◽  
Juvenile Fish ◽  
Perca Flavescens ◽  
Individual Growth ◽  
Phenotypic Traits ◽  
Plasticity Model ◽  
Size At Maturity ◽  
Fish Stocks ◽  
Carry Over

Harvest can change phenotypic traits of populations through immediate demographic consequences, evolutionary responses to harvest selection, or developmental responses by individuals. This study investigated the plastic phenotypic effects of harvest on size and age at maturity in a commercially exploited freshwater fish. We tested an individual growth and life history plasticity model using lagged correlations incorporating how harvesting fish ages 2 and older influenced the abundance of juvenile fish, resource availability, individual growth rates, and carry-over responses in age and size at maturity. Our test used cohort data for Lake Erie yellow perch (Perca flavescens). Age and size at maturity fluctuated widely and rapidly across 23 cohorts between 1991 and 2013, suggesting phenotypic plasticity contributed strongly to maturation dynamics. The changes in maturity could not be explained by responses to harvest, as expected under the plasticity model. In Lake Erie, age and size at maturity in yellow perch appear to be responding to other drivers, such as harvest-induced dynamics of other fish stocks or ecosystem changes that are independent of harvest.

Stock-specific advection of larval walleye (Sander vitreus) in western Lake Erie: Implications for larval growth, mixing, and stock discrimination

2015 ◽  
Vol 41 (3) ◽  
pp. 830-845 ◽  
Author(s):  
Michael E. Fraker ◽  
Eric J. Anderson ◽  
Cassandra J. May ◽  
Kuan-Yu Chen ◽  
Jeremiah J. Davis ◽  
...  
Keyword(s):  
Lake Erie ◽  
Larval Growth ◽  
Sander Vitreus ◽  
Stock Discrimination ◽  
Western Lake ◽  
Western Lake Erie

Coupling genetic and otolith trace element analyses to identify river-born fish with hatchery pedigrees in stocked Atlantic salmon (Salmo salar) populations

2011 ◽  
Vol 68 (6) ◽  
pp. 977-987 ◽  
Author(s):  
Charles Perrier ◽  
Françoise Daverat ◽  
Guillaume Evanno ◽  
Christophe Pécheyran ◽  
Jean-Luc Bagliniere ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Trace Element ◽  
Salmo Salar ◽  
Inductively Coupled Plasma ◽  
Geographic Origin ◽  
Wild Populations ◽  
Inductively Coupled ◽  
Elemental Concentrations ◽  
Atlantic Salmon Salmo Salar ◽  
Hatchery Stock

This study combines otolith trace element and genetic analyses to explore the origin of individuals when hatchery-reared fish are released into wild populations. We sampled 90 juvenile Atlantic salmon ( Salmo salar ) in four rivers in Normandy (France) and in the hatchery stock. Individuals were analyzed at six microsatellite markers and their otolith elemental concentrations (14 elements) were measured using femto-second laser ablation inductively-coupled plasma mass spectrometry. Wild populations were genetically differentiated from the hatchery strain (FST ≈ 0.06). Significant differences in elemental concentrations were found among otoliths of juveniles from the four rivers and the hatchery, allowing the identification of their geographic origin (83%–100% correct assignment). Coupling genetic and trace element analyses on the same individuals provided formal evidence that hatchery-born juveniles released into the wild can migrate to the sea and return as adults to breed on natural spawning grounds. Their progeny have pure hatchery pedigrees but have otoliths typical of river-born juveniles, meaning that they can be mistaken for hatchery-raised juveniles if only genetic data are considered. The presence of hybrids also confirmed that individuals with hatchery pedigrees can breed with wild conspecifics.

scholarly journals Control the Information of the Image with Anisotropic Diffusion and Isotropic Diffusion for the Image Classification

2021 ◽  
Author(s):  
Hyun-Tae Choi ◽  
Nahyun Lee ◽  
Jewon No ◽  
Sangil Han ◽  
Jaeho Tak ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Anisotropic Diffusion ◽  
Diffusion Processes ◽  
Original Image ◽  
Learning Framework ◽  
Isotropic Diffusion ◽  
Using Data ◽  
Discriminative Parts ◽  
Similar Classification

Humans can recognize objects well even if they only show the shape of objects or an object is composed of several components. But, most of the classifiers in the deep learning framework are trained through original images without removing complex elements inside the object. And also, they do not remove things other than the object to be classified. So the classifiers are not as effective as the human classification of objects because they are trained with the original image which has many objects that the classifier does not want to classify. In this respect, we found out which pre-processing can improve the performance of the classifier the most by comparing the results of using data through other pre-processing. In this paper, we try to limit the amount of information in the object to a minimum. To restrict the information, we use anisotropic diffusion and isotropic diffusion, which are used for removing the noise in the images. By using the anisotropic diffusion and the isotropic diffusion for the pre-processing, only shapes of objects were passed to the classifier. With these diffusion processes, we can get similar classification accuracy compared to when using the original image, and we found out that although the original images are diffused too much, the classifier can classify the objects centered on discriminative parts of the objects.

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