scholarly journals Better boundaries: identifying the upper extent of fish distributions in forested streams using eDNA and electrofishing

Ecosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Brooke E. Penaluna ◽  
Jennifer M. Allen ◽  
Ivan Arismendi ◽  
Taal Levi ◽  
Tiffany S. Garcia ◽  
...  
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2003 ◽  
Vol 60 (1) ◽  
pp. 17-32 ◽  
Author(s):  
David G Argent ◽  
Joseph A Bishop ◽  
Jay R Stauffer ◽  
Robert F Carline ◽  
Wayne L Myers

Author(s):  
S. Acevedo ◽  
O. Dwane ◽  
J.M. Fives

Ichthyoplankton from an area in the Celtic Sea was studied to provide an insight into the fish larval community structure in March, May and June 1998. Three station groups were defined each month, Neritic, Transition and Oceanic. The Neritic assemblages included larval stages of coastal fish species and the Oceanic assemblages included mesopelagic and high-oceanic fish species which were not recorded from any of the Neritic stations. The Transition stations usually contained species characteristic of both the Neritic and Oceanic assemblages. It is suggested that these broad patterns of larval fish distributions are constant features of the Celtic Sea area, probably related to the spawning location of the adults. The area is, in general, species poor, with the Oceanic stations usually dominated by a single species, indicating the presence of a large spawning school of fish.


2011 ◽  
Vol 159 (6) ◽  
pp. 1499-1509 ◽  
Author(s):  
Andrew C. McKinley ◽  
Anthony Miskiewicz ◽  
Matthew D. Taylor ◽  
Emma L. Johnston

<i>Abstract.</i>—Scale influences the detection of relationships between landscape alterations and stream fishes and therefore has strong implications for conservation planning and implementation. This is especially true in riverscapes because terrestrial landscapes drain into riverscapes in a manner that can be measured at multiple scales. Two commonly employed scales in riverscape ecology and conservation include local catchment (i.e., the area of land draining directly into a segment of stream between two confluences) and network catchment (i.e., the total area of upstream land). We used a multispecies extension of species distribution modeling (i.e., gradient forest) to describe relationships between landscape alterations (measured at local catchment and network catchment scales) and stream fish occurrence patterns in portions of the Mississippi and Tennessee River basins in western Tennessee, USA. Landscape alterations included seven urban or agricultural classes, and densities of roads, road crossings, dams, human population, mines, and confined animal feeding operations. At the network catchment scale, the most influential landscape alterations affecting fish distributions were cultivated crops and pasture/hay land uses, but at the local catchment scale, open-space development, human population density, and road density were most important for describing multispecies fish distributions. Despite these differences, gradient forest model performance measured as explained variation at the species level was consistent between local catchment and network catchment scales. Furthermore, when predictions for unsampled stream segments were mapped across the region, both scales produced consistent patterns in fish assemblages affected by low, medium, and high development or cultivated crops. Our results provide direction to conservation practitioners by identifying regions where limited resources might be allocated to increase efficiencies within two highly altered and taxonomically diverse riverscapes. The framework described here provides a case study for application of new statistical innovations to address conservation challenges and can be used in other landscapes and riverscapes to identify locations where management efforts might be best allocated.


2020 ◽  
Vol 102 ◽  
pp. 102692
Author(s):  
João Marcelo S. Abreu ◽  
Ananda Carolina S. Saraiva ◽  
James S. Albert ◽  
Nivaldo M. Piorski

2020 ◽  
Vol 12 (2) ◽  
pp. 142-163 ◽  
Author(s):  
Derek G. Bolser ◽  
Jack P. Egerton ◽  
Arnaud Grüss ◽  
Tyler Loughran ◽  
Taylor Beyea ◽  
...  

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