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435 results for “Stream fish”
Data from: Habitat-based polymorphism is common in stream fishes
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Data from: Accounting for groundwater in stream fish thermal habitat responses to climate change
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Relationship between eDNA concentration from metabarcoding method and stream fish density under field conditions
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Thermal tolerance of fish to heatwaves in agricultural streams: What does not kill you makes you stronger?
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Fig. 4 in Organization of fish assemblages in blackwater Atlantic Forest streams
Fig. 4. RDA ordination biplot of the first and second RDA axes based on fish biomass (g.m-2) of 31 blackwater mesohabitats. Vector lines in bold type indicate the relationship of the environmental variables to the ordination axis; the line's length is proportional to its relative significance. For species codes, see Tab. 3.
Fig. 1 in Organization of fish assemblages in blackwater Atlantic Forest streams
Fig. 1. Location of the study area in São Paulo State, showing the distribution of the sampling sites in the Itapanhaú, Itaguaré, Guaratuba and Una River subbasins located in the Municipalities of Bertioga and São Sebastião.
Fig. 5 in Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia
Fig. 5. Diagram of Canonical Correspondence Analysis (CCA) relating 29 fish species (blue letters), 11 environmental variables (black vectors), and 9 sampling sites each in first-order forest streams (solid green circles) and deforested stream reaches (open red circles) in the Upper Xingu River Basin. Species that had only one (singletons), two (doubletons), or three individuals collected were included in the analysis but are not displayed for improved visualization. Environmental variable marked with * was the strongest predictor of fish assemblage's structure. FBOM = fine benthic organic matter; CBOM = coarse benthic organic matter; Ami = Aequidens michaeli; Amu = Astyanax multidens; Api = Apistogramma sp.; Bra = Brachyglanis sp.; Cro = Crenicichla rosemariae; Etr = Eigenmannia trilineata; Gro = Gymnorhamphichthys rondoni; Gca = Gymnotus cf. carapo; Hac = Hisonotus acuen; Hel = Helogenes marmoratus; Hle = Hypopygus lepturus; Hlo = Hyphessobrycon loweae; Hma = Hoplias malabaricus; Hmu = Hyphessobrycon mutabilis; Hsp = Hyphessobrycon sp.; Hun = Hoplerythrinus unitaeniatus; Mme = Melanorivulus megaroni; Mco = Moenkhausia collettii; Mph = Moenkhausia phaeonota; Pam = Pamphorichthys sp.; Pau = Pyrrhulina australis.
Fig. 4 in Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia
Fig. 4. NMDS diagram of the taxonomic structure of fish assemblages in forest (n=9; green solid circles and polygon) and deforested (n=9; red open circles and polygon) stream reaches in the Upper Xingu River Basin. Analysis based on the Bray– Curtis distances of 29 fish species log(x+1)-transformed catch-per-unit-effort. Vectors are species with significant loadings on NMDS axes 1 and 2, and point in the direction of increasing abundance (number of individuals). A. michaeli = Aequidens michaeli; A. multidens = Astyanax multidens; H. marmoratus = Helogenes marmoratus; M. megaroni = Melanorivulus megaroni; M. phaeonota = Moenkhausia phaeonota.
Fig. 1 in Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia
Fig. 1. Location of study area and sampling sites. The inset shows the location of Tanguro Ranch in the southeastern region of the Amazonian Arc of Deforestation, with green representing closed canopy forests, yellow representing deforested areas and uncolored areas representing native savannas. From South to North, streams sampled are APPM, APP2A, APP2, TAN1, TAN2, and TAN3. See the text for details.
Fig. 3 in Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia
Fig. 3. Individual-based rarefaction curves calculated for fish assemblages collected in forest (n=9) and deforested (n=9) stream reaches in the Upper Xingu River Basin. Colored areas represent 95% confidence intervals.
Fig. 2 in Effects of deforestation on headwater stream fish assemblages in the Upper Xingu River Basin, Southeastern Amazonia
Fig. 2. Catch-per-unit-effort for fishes in forest (n=9) and deforested (n=9) first-order stream reaches in the Upper Xingu River Basin. a. Abundance; b. Biomass; c. Richness. Boxes define the 25th, 50th (median) and 75th percentiles, whiskers represent the 10th and 90th percentiles. P-values calculated using univariate PerMANOVA.
Fig. 5 in Organization of fish assemblages in blackwater Atlantic Forest streams
Fig. 5. RDA ordination biplot of the first and second RDA axes based on the fish community attributes of 31 blackwater mesohabitats. Vector lines in bold type indicate the relationship of the environmental variables to the ordination axis; the line's length is proportional to its relative significance.
Fig. 4 in Fish movement in an Atlantic Forest stream
Fig. 4. Relationship between distance (m) moved and body size of the moving species from Ubatiba stream, Southeast, Brazil.
Fig. 3 in Fish movement in an Atlantic Forest stream
Fig. 3. Distance moved by fish species from Ubatiba stream. Zero means that the individuals did not move, negative and positive values means down- and upstream movements, respectively. a. Astyanax janeiroensis; b. Astyanax hastatus; c. Awaous tajasica; d. Characidium sp.; e. Geophagus brasiliensis; f. Hoplias malabaricus; g. Hypostomus punctatus; h. Parotocinclus maculicauda; i. Pimelodella lateristriga; j. Rineloriacaria sp.
Fish diversity and discharge rates in Bornean rainforest streams
<b>Description: </b><p>TFE Field course 2020 group project. Recorded the fish communities and discharge rates along 100m transects on 7 rainforest streams in and around the SAFE project site in Sabah, Borneo. <br>- Stream discharge: this was estimated using gulp-injection dilution gauging using table salt as the tracer. An Omega CDH-SD conductivity logger was used to record stream conductivity at 1 second intervals. The conductivity meter was set up at 0m on the stream's transect, it was left to stabilise and then 50m upstream 200g of salt was injected. Conductivity was monitored for a peak followed by a return to original levels.<br>- Electrofishing: For each stream fishes were caught and released using three-pass electrofishing (model EFGI 650 electrofisher). The fishing occurred along a 100m transect with fish net blockades set up at each end to prevent immigration and emigration of fish during the sampling period. Electrofishing occurred in 3 phases, the same transect line was repeated with different voltages. Voltage varied per stream based on its conductivity and the recommended guidelines provided by SAFE. Fishes caught were identified to species level following the fish identification guide provided by SAFE and length of fishes was measured using a ruler.<br>The data collected was used to estimate fish biodiversity and stream discharge rates in R. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><b>MRes Tropical Forest Ecology Field Course</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3926374">here</a></p><p><b>Files: </b>This dataset consists of 4 files: SAFE_Hydrology_2020.xlsx, Wilkinson2018.pdf, Fish_ID_Sheet.pdf, Electrofishing_discharge.csv</p><p><b>SAFE_Hydrology_2020.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Discharge measurements for streams at SAFE</b> (described in worksheet Stream_discharge)</p><p>Description: Stream discharge rates were measured using gulp injection dilution gauging using salt as the tracer. Recordings were taken automatically by an Omega CDH-SD1 conductivity logger</p><p>Number of fields: 6</p><p>Number of data rows: 20699</p><p>Fields: </p><ul><li><b>Site ID</b>: Location at SAFE, river name (Field type: location)</li><li><b>Date</b>: date conductivity was recorded (Field type: date)</li><li><b>Time</b>: time of recorded ch1 and temperature measurements (Field type: time)</li><li><b>Ch1_Value</b>: conductivity measure value from logger (Field type: numeric)</li><li><b>Ch1_Unit</b>: unit Ch1 value is measured in on logger (Field type: categorical)</li><li><b>Temperature</b>: temperatue recording from logger (Field type: numeric)</li></ul></li><li><p><b>Surrounding forest types for the streams at SAFE</b> (described in worksheet Sites)</p><p>Description: For each stream, the surrounding forest type was determined based on Wilkinson et al. 2018 paper</p><p>Number of fields: 2</p><p>Number of data rows: 7</p><p>Fields: </p><ul><li><b>Location</b>: Location at SAFE, river name (Field type: location)</li><li><b>vegetation_type</b>: Logging category of surrounding forest (Field type: categorical)</li></ul></li><li><p><b>Electrofishing runs</b> (described in worksheet Runs)</p><p>Description: Number and d</p><p>Number of fields: 13</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Location</b>: Location at SAFE, river name (Field type: location)</li><li><b>run</b>: electrofishing run number (Field type: numeric)</li><li><b>date</b>: date electrofishing occurred (Field type: date)</li><li><b>time</b>: time of electrofishing (Field type: time)</li><li><b>conductivity</b>: conductivity of stream (Field type: numeric)</li><li><b>voltage</b>: voltage of electrofishing run (Field type: numeric)</li><li><b>water temperature</b>: temperature of stream (Field type: numeric)</li><li><b>electrofisher 1</b>: person electrofishing (Field type: comments)</li><li><b>electrofisher 2</b>: person electrofishing (Field type: comments)</li><li><b>electrofisher 3</b>: person electrofishing (Field type: comments)</li><li><b>RA 1</b>: Research assistant (RA) helping with species identification and measurement (Field type: comments)</li><li><b>RA 2</b>: Research assistant (RA) helping with species identification and measurement (Field type: comments)</li><li><b>RA 3</b>: Research assistant (RA) helping with species identification and measurement (Field type: comments)</li></ul></li><li><p><b>Fish biodiversity recorded from electrofishing at rivers at SAFE</b> (described in worksheet Fish_diversity)</p><p>Description: An EFGI 650 electrofisher was used to measure fish biodiveristy following local protocols</p><p>Number of fields: 5</p><p>Number of data rows: 977</p><p>Fields: </p><ul><li><b>Location</b>: Location at SAFE, river name (Field type: location)</li><li><b>run</b>: electrofishing run number (Field type: numeric)</li><li><b>species</b>: species of fishes caught (Field type: taxa)</li><li><b>length</b>: length of fishes caught (Field type: numeric)</li><li><b>notes</b>: comments (Field type: comments)</li></ul></li></ol><p><b>Wilkinson2018.pdf</b></p><p>Description: Referenced in Fish_diversity for classifying vegetation type of surrounding forests of streams</p><p><b>Fish_ID_Sheet.pdf</b></p><p>Description: Fish identifcation guide used for Fish_diversity data collection</p><p><b>Electrofishing_discharge.csv</b></p><p>Description: Voltage guidelines for electrofishing in Fish_diversity</p><p><b>Date range: </b>2020-02-04 to 2020-02-13</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Actinopterygii <br> -  -  -  -  Anguilliformes <br> -  -  -  -  -  Anguillidae <br> -  -  -  -  -  -  <i>Anguilla</i> <br> -  -  -  -  -  -  -  <i>Anguilla borneensis</i> <br> -  -  -  -  -  -  -  <i>Anguilla marmorata</i> <br> -  -  -  -  Siluriformes <br> -  -  -  -  -  Bagridae <br> -  -  -  -  -  -  <i>Hemibagrus</i> <br> -  -  -  -  -  -  -  <i>Hemibagrus baramensis</i> <br> -  -  -  -  Cypriniformes <br> -  -  -  -  -  Cobitidae <br> -  -  -  -  -  -  <i>Pangio</i> <br> -  -  -  -  -  -  -  <i>Pangio mariarum</i> <br> -  -  -  -  -  Balitoridae <br> -  -  -  -  -  -  <i>Protomyzon</i> <br> -  -  -  -  -  -  -  <i>Protomyzon griswoldi</i> <br> -  -  -  -  -  -  <i>Gastromyzon</i> <br> -  -  -  -  -  -  -  <i>Gastromyzon lepidogaster</i> <br> -  -  -  -  -  Nemacheilidae <br> -  -  -  -  -  -  <i>Nemacheilus</i> <br> -  -  -  -  -  -  -  <i>Nemacheilus olivaceus</i> (as synonym: <i>Nemachilus olivaceus</i>)<br> -  -  -  -  -  Cyprinidae <br> -  -  -  -  -  -  <i>Hampala</i> <br> -  -  -  -  -  -  -  <i>Hampala sabana</i> <br> -  -  -  -  -  -  <i>Rasbora</i> <br> -  -  -  -  -  -  -  <i>Rasbora hubbsi</i> <br> -  -  -  -  -  -  -  <i>Rasbora sumatrana</i> <br> -  -  -  -  -  -  <i>Nematabramis</i> <br> -  -  -  -  -  -  -  <i>Nematabramis everetti</i> <br> -  -  -  -  -  -  <i>Tor</i> <br> -  -  -  -  -  -  -  <i>Tor tambra</i> <br> -  -  -  -  -  -  <i>Puntius</i> <br> -  -  -  -  -  -  -  <i>Puntius sealei</i> (as synonym: <i>Barbodes sealei</i>)<br> -  -  -  -  -  -  <i>Osteochilus</i> <br> -  -  -  -  -  -  -  <i>Osteochilus chini</i> <br> -  -  -  -  -  -  -  <i>Osteochilus ingeri</i> <br></div><p></p>
Multi-species models reveal that eDNA metabarcoding is more sensitive than backpack electrofishing for conducting fish surveys in freshwater streams
Environmental DNA (eDNA) sampling can provide accurate, cost-effective, landscape-level data on species distributions. Previous studies have compared the sensitivity of eDNA sampling to traditional sampling methods for single species, but similar comparative studies on multi-species eDNA metabarcoding are rare. Using hierarchical species occupancy-detection models, we examined whether key choices associated with eDNA metabarcoding (primer selection, low-abundance read filtering, and the number of positive water samples used to classify a species as present at a site) affect the sensitivity of metabarcoding, relative to backpack electrofishing for fish in freshwater streams. Under all scenarios (teleostei and vertebrate primers; 0%, 0.1% and 1% read filtering thresholds; 1 or 2 positive samples required to classify species as present), we found that eDNA metabarcoding is, on average, more sensitive than electrofishing. Combining vertebrate and teleostei markers resulted in higher detection probabilities relative to the use of either marker in isolation. Increasing the threshold used to filter low abundance reads decreased species detection probabilities but did not change our overall finding that eDNA metabarcoding was more sensitive than electrofishing. Using a threshold of two positive water samples (out of 5) to classify a species as present typically had negligible effects on detection probabilities compared to using one positive water sample. Our findings demonstrate that eDNA metabarcoding is generally more sensitive than electrofishing for conducting fish surveys in freshwater streams, and that this outcome is not sensitive to methodological decisions associated with metabarcoding.
Data from: Phenotype-dependent selection underlies patterns of sorting across habitats: the case of stream-fishes
Spatial and temporal heterogeneity within landscapes influences the distribution and phenotypic diversity of individuals both within and across populations. Phenotype-habitat correlations arise either through phenotypes within an environment altering through the process of natural selection or plasticity, or phenotypes remaining constant but individuals altering their distribution across environments. The mechanisms of non-random movement and phenotype-dependent habitat choice may account for associations within highly heterogeneous systems, such as streams, where local adaptation may be negated, plasticity too costly and movement is particularly important. Despite growing attention, however, few empirical tests have yet to be conducted. Here we provide a test of phenotype-dependent habitat choice and ask: 1) if individuals collected from a single habitat type continue to select original habitat; 2) if decisions are phenotype-dependent and functionally related to habitat requirements; and 3) if phenotypic-sorting continues despite increasing population density. To do so we both conducted experimental trials manipulating the density of four stream-fish species collected from either a single riffle or pool and developed a game-theoretical model exploring the influence of individuals' growth rate, sampling and competitive abilities as well as interference on distribution across two habitats as a function of density. Our experimental trials show individuals selecting original versus alternative habitats differed in their morphologies, that morphologies were functionally related to habitat-type swimming demands, and that phenotypic-sorting remained significant (although decreased) as density increased. According to our model this only occurs when phenotypes have contrasting habitat preferences and only one phenotype disperses (i.e. selects alternatives) in response to density pressures. This supports our explanation that empirical habitat selection was due to a combination of collecting a fraction of mobile individuals with different habitat preferences and the exclusion of individuals via scramble competition at increased densities. Phenotype-dependent habitat choice can thereby account for observed patterns of natural stream-fish distribution.
Data from: Population size is weakly related to quantitative genetic variation and trait differentiation in a stream fish
How population size influences quantitative genetic variation and differentiation among natural, fragmented populations remains unresolved. Small, isolated populations might occupy poor quality habitats and lose genetic variation more rapidly due to genetic drift than large populations. Genetic drift might furthermore overcome selection as population size decreases. Collectively, this might result in directional changes in additive genetic variation (VA) and trait differentiation (QST) from small to large population size. Alternatively, small populations might exhibit larger variation in VA and QST if habitat fragmentation increases variability in habitat types. We explored these alternatives by investigating VA and QST using nine fragmented populations of brook trout varying 50-fold in census size N (179-8416) and 10-fold in effective number of breeders, Nb (18-135). Across 15 traits, no evidence was found for consistent differences in VA and QST with population size and almost no evidence for increased variability of VA or QST estimates at small population size. This suggests that (i) small populations of some species may retain adaptive potential according to commonly adopted quantitative genetic measures and (ii) populations of varying sizes experience a variety of environmental conditions in nature, however extremely large studies are likely required before any firm conclusions can be made.
Freshwater fishes dataset–Fish diversity of post-conflict Colombian Andes-Amazon streams
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Figure 4 from: Lazzarini Wolff L, Segatti Hahn N (2017) Fish habitat associations along a longitudinal gradient in a preserved coastal Atlantic stream, Brazil. Zoologia 34: 1-13. https://doi.org/10.3897/zoologia.34.12975
Figure 4 Species rarefaction curves for reaches in the Vermelho River, Paraná state, Brazil. E(Sn) denotes the expected number of species according to abundance.
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