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59 results for “fish farming”
Fish Food on Floodplain Farm Fields, California Central Valley, Seasons 2019 and 2021
2019 Water Year (October 1, 2018 through September 30, 2019) In the winter and spring of 2018-2019, 5,000 acres of agricultural land in Yolo County, California was intentionally flooded. These “dry-side” rice fields, although on the former floodplain of the Sacramento River, are separated from the fish-bearing Sacramento River (the “wet-side”) by high flood levees. Today, levees cut off 95% of the Central Valley’s floodplains from river channels so that Central Valley aquatic ecosystems no longer recruit floodplain the food web resources needed to support robust aquatic food webs, create fish biomass and sustain abundant fish populations. In this experiment we asked whether floodplain food web resources “grown” in intentionally inundated “dry-side” agricultural fields could be exported back to the river via flood drainage infrastructure. If so, we were interested to know whether those resources could improve juvenile salmon foraging success and increase growth rates. In order to test these questions, we caged fish in the floodplain drainage canal, at the location where the floodplain drainage water entered the river and at locations both up- and downstream. We hypothesized that zooplankton abundance and fish growth rates would be elevated at the managed floodplain outfall location, relative to the upstream location. We measured water quality parameters, zooplankton species assemblage and abundance, and juvenile Chinook salmon growth rates with PIT tagged, hatchery-origin fish confined to enclosures at the study locations. The 5,000 acres of managed floodplain was drained over the coarse of 5 weeks in February and March, 2019 at a maximum rate of 1,000 cfs. The Sacramento River flow during the experiment ranged from 20,000-30,000 cfs. Fish growth rates at the floodplain outfall location were up to five times greater than growth rates upstream of the outfall and enclosure fish experienced growth rate benefits at least up to a mile downstream from the managed floodplai
The Gloria2 fish farm escapes identification dataset (v1.0)
<p>This is version 1.0 of the <strong>GLORiA<sup>2</sup> dataset</strong>, which can be used for the training of computer vision models aimed at <strong>detecting fish farm escapes caused by sea storms</strong>. Escaped fish can be recognized based on their appearance from pictures taken in lab conditions, when compared to farmed and wild specimens. Several images of fish specimens raised in <strong>captivity</strong> (C), farm <strong>escapes</strong> (E), and the <strong>sea</strong> (S) are provided for each of the <strong>three</strong> considered commercial <strong>species</strong>: Mediterranean <strong>sea bream</strong> (<em>S. aurata</em>), <strong>sea bass</strong> (<em>D. labrax</em>), and <strong>meagre</strong> (<em>A. regius</em>).</p> <p>The images provided are 224x224 crops of the original images, centred around the fish in the image, as <strong>preprocessed</strong> by the <strong>accompanying Python scripts</strong>.</p> <p><strong>Acknowledgement: </strong>GLORiA<sup>2</sup> is a project supported by the Biodiversity Foundation of the Ministry for the Ecological Transition, through the FEMP Pleamar Programme. It is also part of the LIFE IP INTEMARES project "Integrated, innovative and participatory management of the Natura 2000 Network in the Spanish marine environment", coordinated by the Ministry, through the Biodiversity Foundation.</p> <p> </p>
Plate 1 in Incidence of parasitic infection in adult and juvenile Clarias gariepinus in a private fish farm, Yola, Adamawa state
Plate 1: Adult Clarias gariepinus placed on adissecting board after measurement and weighing for dissection
Fig. 6 in Zooplankton Community Structure Of The Fish Farm Nagļi (Latvia)
Fig. 6. Mean (Zscore) of zooplankton (rotifers, cladocerans, copepods) abundance and biomass in the ponds, May.
Fig. 5 in Zooplankton Community Structure Of The Fish Farm Nagļi (Latvia)
Fig. 5. Mean (Zscore) of zooplankton taxa number, Shannon-Wiener index and total abundance in the ponds.
FIGURE 5 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 5 | Redundancy analysis (RDA). Relation of ichthyofauna to the significant environmental variable (quota) in the drought and wet seasons in an area without the influence of fish farming (Control) at the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. Ala = Acestrorhynchus lacustris, Acr = Astronotus crassipinnis, Cke = Cichla kelberi, Cpi = Cichla piquiti, Cbr = Cyphocharax gillii, Cgi = Crenicichla britskii, Gsv = Geophagus sveni, Hma = Hoplias aff. malabaricus, Mac = Megalancistrus parananus, Mli = Metynnis lippincottianus, Oni = Oreochromis niloticus, Ppl = Pimelodus platicirris, Ppi = Pinirampus pirinampu, Psq = Plagioscion squamosissimus, Pam = Pterygoplichthys ambrosettii, Rvu = Rhaphiodon vulpinus, Rde = Roeboides descalvadensis, Spa = Satanoperca pappaterra, Sin = Steindachnerina insculpta, Sit = Schizodon intermedius, Sna = Schizodon nasutus, Sma = Serrasalmus maculatus, Smg = Serrasalmus marginatus, Hbu = Heterotilapia buttikoferi, and Tne = Triportheus nematurus.
FIGURE 4 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 4 | Beta diversity (PERMDISP) in the drought and wet seasons in the control and fish farm areas at the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. Average distances of centroids with presence/absence of data. Asterisks indicates significant upper value between seasons within each area.
FIGURE 3 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 3 | Box plots (minimum and maximum value = vertical line ends, standard error = box, and mean = horizontal line) of the ichthyofauna attributes in the control area and fish farm area, in the drought and wet seasons at the Ilha Solteira reservoir, Upper Paraná River, SP, Brazil. A. Total abundance; B. Species richness; C. Shannon index; D. Pielou evenness. Asterisks indicates significant upper value between seasons within each area. Hash indicates significant upper value between areas within each season.
FIGURE 2 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 2 | Historical series of the variation in quota (m) between 1999 and 2019 in the Ilha Solteira reservoir, Upper Paraná River, São Paulo, Brazil. The dots indicate the sampling period.
FIGURE 1 in Structure and composition of ichthyofauna associated with cage fish farming and compared to a control area after severe drought in a Neotropical reservoir
FIGURE 1 | Map of South America showing the Ilha Solteira reservoir, with an indication of the sample areas (black circles). Adapted from Kliemann et al. (2018).
Figure 5 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 5. Vigtorniella spp. Haplotype network of cytochrome c oxidase subunit I (COI). Each large circle represents a sequence from an individual of Vigtorniella ardabilia that has been collected in Sweden (white) or Norway (grey). Black circles represent V. flokati, collected in the Pacific Ocean, three specimens sampled sharing the same haplotype. Letters in circles, A-G, represent haplotypes of V. ardabilia sampled in this study (Table 1). Each line represents a mutation and small empty circles are inferred haplotypes not present in the current study.
Figure 4 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 4. Vigtorniella ardabilia sp. nov., specimen from whale-fall in Sweden, light micrographs: (A) parapodia from mid-body region, (B) detail of neuropodial falcigers, (C) detail of notopodial spine. Scale bar in (A) is 100 Mm, in (B) and (C) 10 Mm.
Figure 3 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 3. Vigtorniella ardabilia sp. nov., specimen from whale-fall in Sweden. SEM micrographs of (A) head region dorsal view, (B) head region ventral view, (C) fine neuropodial falcigers from segment 2, (D) neuropodial falcigers from mid-body region, and (E) notopodial spine. Scale bars in (A) and (B) are 150 Mm, in (C) 7.5 Mm, in (D) 15 Mm and in (E) 6 Mm.
Figure 2 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 2. Vigtorniella ardabilia sp. nov., live photo of specimen from whale-fall in Sweden. The worm is 6 mm long.
Figure 3 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 3. Relationship between the number of chaetigers and total bodylength in 15 specimens of Vigtorniella ardabilia and 33 specimens of V. flokati.
Figure 6. Diagram showing Kimura 2 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 6. Diagram showing Kimura 2 Parameter (K2P)-distances between congeneric species in four chrysopetalid genera, for two nuclear genes (18S, 28S) and two mitochondrial genes (16S, cytochrome c oxidase subunit I (COI)).
Stable isotopes reveal intertidal fish and crabs use shellfish farms as foraging habitat in Puget Sound, Washington
<p>This is the dataset used for my second thesis chapter. The data consists of carbon and nitrogen stable isotope data collected in 3 locations in North Puget Sound in 2020-2022. Locations are Samish Bay, Padilla Bay, and Drayton Harbor. Stable isotope data is presented in standard permil format, with values referenced from the Vienna Pee Dee Belemnite and atmospheric nitrogen standards, which are the international reference standards commonly used as isotopic references for carbon and nitrogen. </p> <p>Primary productivity samples, snails, oysters, and clams were collected by hand at low tide. Fish and crabs were collected via beach seine and crab pots deployed concurrently with seining. </p> <p>Samples were collected either within bivalve farms, or within eelgrass meadow reference sites outside of farmed areas.</p> <p>All code and data necessary to reproduce the analysis is available via my Github page. </p> <p>The metadata spreadsheet contains descriptions of each column in the datasheet.</p> <p>For more information, see my chapter 2 thesis.</p>
Salmon on the lam: Drivers of escaped farmed fish abundance in rivers
<p>Abstract:</p> <ol> <li>The production of Atlantic salmon in aquaculture has grown substantially over the last 40 years. The unintentional release of domesticated salmon poses a significant risk to the long-term persistence of wild Atlantic salmon populations through ecological interactions and genetic introgression. Our ability to link aquaculture production to farmed escaped salmon in rivers is still limited and hinders identifying the appropriate production capacity of salmon aquaculture to reduce unwanted interactions between wild and escaped Atlantic salmon. </li> <li>Here, we use a 14-year dataset of farmed escapee abundance in rivers to model how the a priori selected covariables of wild salmon abundance, aquaculture intensity, river discharge, hydropower, and fjord placement of the river affects escapee abundance across 54 rivers in western Norway. Then, we evaluate the predictive strength of the model to provide context for its use to minimize escapees. </li> <li>We found that the abundance of farmed escaped Atlantic salmon in rivers is correlated to aquaculture intensity. Furthermore, the abundance of wild Atlantic salmon, mean yearly discharge, and the interaction between fjord placement and wild salmon abundance were important predictors of escapee abundance in rivers. </li> <li>The model was 40% accurate when predicting the abundance of farmed escaped salmon in rivers. However, the accuracy improved to 75% when using risk categories derived from modeled intrusion rates that induced long-term genetic changes to the wild population (low < 4%, medium 4 – 10%, and high > 10% escaped farmed salmon).</li> <li><em>Synthesis and applications: </em>This study links aquaculture production, at relevant spatiotemporal scales (75 km from rivers), to the abundance of escaped farmed Atlantic salmon in rivers, and provides governmental agencies with a tool to help regulate domesticated salmon production based on the carrying capacity of the system to buffer against introgression between conspecifics. Furthermore, understanding this relationship will be beneficial when establishing new aquaculture sites in pristine ecosystems where they would overlap with wild Atlantic salmon. Finally, future mitigation efforts should continue to focus on new technologies (e.g., triploid females) that can eliminate the risk of introgression without limiting aquaculture production.</li> </ol> <p> </p> <p> </p> <p> </p>
An Overview of Limnological Studies in Brazilian Fish Farms - Supplementary Material
<p>Este repositório contém uma planilha Excel com dados complementares para o artigo <strong>"Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras".</strong> A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.</p>
Supplementary Material - An overview of limnological studies in Brazilian fish farms
<p>Este repositório contém uma planilha Excel com dados complementares para o artigo <strong>"Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras".</strong> A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.</p>
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