Modeling fish habitat: model tuning, fit metrics, and applications

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Jacob W. Brownscombe ◽  
Jonathan D. Midwood ◽  
Steven J. Cooke
2000 ◽  
Vol 11 (5) ◽  
pp. 541-552 ◽  
Author(s):  
Lynne C. Scott ◽  
John W. Boland ◽  
Karen S. Edyvane ◽  
G. K. Jones

1995 ◽  
Vol 82 (3) ◽  
pp. 211-224 ◽  
Author(s):  
Heinz G. Stefan ◽  
Midhat Hondzo ◽  
John G. Eaton ◽  
J.Howard McCormick
Keyword(s):  

2020 ◽  
Author(s):  
Brook Herman ◽  
Todd Swannack ◽  
Nathan Richards ◽  
Nancy Gleason ◽  
Safra Altman

2009 ◽  
Vol 60 (2) ◽  
pp. 97 ◽  
Author(s):  
Thomas S. Rayner ◽  
Bradley J. Pusey ◽  
Richard G. Pearson

Wet-season flooding causes dietary shifts in tropical freshwater fish by regulating instream productivity, habitat structure and food availability. These dynamics have been comprehensively documented worldwide, but data are limited for Australia’s Wet Tropics rivers. The aim of the present study was to extend our earlier fish–habitat model for these systems by examining the role of trophic dynamics in determining fish assemblage composition. Chlorophyll a and phaeophytin concentrations, benthic and littoral invertebrates and fish were collected at four sites in the lower Mulgrave River under a range of flow conditions. Wet-season flooding caused significant reductions in instream productivity, whereas habitat disturbance reduced densities and abundances of littoral and benthic invertebrates. However, volumetric gut contents of 1360 fish, from 36 species, revealed seasonal shifts in guild membership by only two species, with fish moving between sites to target their preferred prey items – largely irrespective of differences in habitat structure. As a result, the food consumed by the fish community present at each site closely reflected the seasonal availability of food resources. The present paper questions whether fish community composition in small tropical rivers can be accurately predicted from habitat surrogates alone and encourages consideration of constraints imposed by the trophic dynamics and reproductive ecology of fish.


Fisheries ◽  
2009 ◽  
Vol 34 (7) ◽  
pp. 330-339 ◽  
Author(s):  
E. Ashley Steel ◽  
Paul McElhany ◽  
Naomi J. Yoder ◽  
Michael D. Purser ◽  
Kevin Malone ◽  
...  

2018 ◽  
Vol 34 (8) ◽  
pp. 937-947 ◽  
Author(s):  
Peng Zhang ◽  
Lu Cai ◽  
Zhi Yang ◽  
Xiaojuan Chen ◽  
Ye Qiao ◽  
...  

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.


Author(s):  
Weiming Li ◽  
Xujiao Yao ◽  
Xia Yang

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