Spatial Drift Dynamics of Shovelnose Sturgeon and Pallid Sturgeon Prelarvae in the Transition Zone of Ft. Peck Reservoir

Habitats in reservoir headwaters may cause high mortality of sturgeon prelarvae. Short inter-reservoir reaches export drifting prelarvae from hatch locations into reservoirs. However, flooded vegetation could entrain prelarvae. We used 2 day post hatch (dph) shovelnose sturgeon (Scaphirhynchus platorynchus) and 1-dph pallid sturgeon (Scaphirhynchus albus) to determine the spatial dynamics of drifting prelarvae.We released 220,000 2-dph shovelnose sturgeon 4 km upstream of Ft. Peck Reservoir and 135,000 1-dph pallid sturgeon 2.5 km upstream of the reservoir the following day. We recaptured shovelnose sturgeon prelarvae with nets deployed along three transects of the transition zone and within the headwaters of the reservoir.We sampled 5148.2 m3 of water and recaptured 323 prelarval shovelnose sturgeon for a recapture rate of 0.14 percent. Fifty-nine percent of recaptured prelarvae were recaptured from the thalweg, 12 percent from the flooded vegetation-main channel interface, 9 percent from the channel border, and 19 percent from the zero-velocity area of Ft. Peck Reservoir. We recaptured pallid sturgeon prelarvae with nets deployed along one transect of the transition zone and within the headwaters of the reservoir. We sampled 6608.5 m3 of water and recaptured 397 pallid sturgeon prelarvae for a recapture rate of 0.29 percent. Twenty one percent of prelarvae were recaptured within the thalweg, 0.25 percent were recaptured along the channel margins, and 79 percent from the zero-velocity area of Ft. Peck Reservoir. Although recapture rates were low, the majority of prelarvae were captured in the thalweg and transported to the headwaters of Ft. Peck Reservoir. The drift dynamics observed in this study provide a springboard for further research

Unconditional care in academic emergency departments

Recent news stories have explicitly stated that patients with symptoms of COVID-19 were "turned away" from emergency departments. This commentary addresses these serious allegations, with an attempt to provide the perspective of academic emergency departments (EDs) around the Nation. The overarching point we wish to make is that academic EDs never deny emergency care to any person

Influence of Feeding History on Metabolic Rates in Fishes: Evidence for Metabolic Compensation in Largemouth Bass (Micropterus salmoides)

Metabolism is a key component in fish energy budgets. Although primarily influenced by mass and water temperature, other factors are known to influence standard metabolic rate of fishes. Factors such as salinity, stress, water toxicity (e.g., heavy metals), diel periodicity, and stock differences can have positive and/or negative effects on oxygen consumption in fishes. Although they can be significant, these effects are generally not incorporated into fish bioenergetics models (BEM) to estimate metabolism. As a result, the accuracy of bioenergetics models can be hampered because of incomplete parameterization of the model. Moreover, efforts to synthesize information about factors affecting fish metabolism are lacking. To address this issue, I conducted a literature review to evaluate factors affecting fish metabolism. Results of this synthesis showed that environmental factors such as hypoxia, salinity, toxicity, and pH can influence metabolic rates in fishes, resulting in a -60% to +120% change in standard metabolic rate. Similarly, biological factors such as stress, stock differences, seasonal effects, and daily biorhythms can significantly influence standard metabolic rate in fishes. Incorporating environmental factors (e.g. hypoxia) into estimates of fish metabolism would improve the accuracy of energy budget calculations. Moreover, because of their potential to influence metabolism across a broad array of taxa, biological factors (e.g. seasonal variation) need to be considered in the development of fish energy budgets. Despite the popularity of bioenergetics models (BEMs), efforts to evaluate the accuracy of their predictions are often met with mixed success. Indeed, accuracy of BEM estimates varies with feeding rate of fishes and may be associated with error in measuring fish metabolism. Metabolic rate of fishes is a key parameter in energy budget formulations and can strongly influence accuracy of model predictions. In this study, I tested the hypothesis that metabolic rate of age-1 largemouth bass [mean (SE) = 105.9g (1.77); n = 24] varies with feeding history. Different feeding regimes were applied to two size classes of fish—i.e., maintenance rations for larger fish and ad libitum rations for smaller fish—over a nine week period such that body mass was similar prior to metabolic measurements. Fish that were fed maintenance rations had metabolic rates that were only 63% of those fed ad libitum (0.00230 vs. 0.00368 g O2/g/d). The percent difference between predicted versus observed food consumption for fish fed ad libitum was moderate (11%) indicating that the bioenergetics model provided a reasonable fit to observed values. For fish fed maintenance rations, differences between predicted and observed consumption values were significant and the error in the model was substantial (43%). After reducing the respiration parameter in the model by 37% (an experimentally determined amount), the percent difference dropped to 15%, suggesting that metabolism was overestimated for fish fed maintenance rations. These findings explain consumption-dependent error in the largemouth bass bioenergetics model and highlight the importance of feeding history in parameterizing fish metabolism. Incorporating feeding history into sub-models for metabolism in BEMs will improve their predicative accuracy and allow fisheries biologists to make better decisions regarding fish populations

Effect of Feeding–Fasting Cycles on Oxygen Consumption and Bioenergetics of Female Yellow Perch

We measured growth and oxygen consumption of age-1 yellow perch Perca flavescens subjected to ad libitum (control) or variable feeding cycles of 2 (i.e., 2 d of feed, 2 d of deprivation), 6, or 12 d for a 72-d period. Individual, female yellow perch (initial weight = 51.9 ± 0.9 g [mean ± SE]) were stocked in 110-L aquaria to provide six replicates per treatment and fed measured rations of live fathead minnow Pimephales promelas. Consumption, absolute growth rate, growth efficiency, and oxygen consumption were similar among feeding regimens. However, growth trajectories for fish on the 2-d cycle were significantly lower than other feed–fast cycles. Hyperphagia occurred in all treatments. Bioenergetics model simulations indicated that consumption was significantly underestimated (t = 5.4, df = 4, P = 0.006), while growth was overestimated (t = −5.5, df = 4, P = 0.005) for fish on the 12-d cycle. However, model errors detected between observed and predicted values were low, ranging from −10.1% to + 7.8%. We found that juvenile yellow perch exhibited compensatory growth (CG), but none of the feed–fast treatments resulted in growth overcompensation. Likewise, we found no evidence that respiration rates varied with CG, implying that yellow perch bioenergetics models could be used to predict the effects of feeding history and CG response on food consumption and fish growth