Recruitment Variation of Crappies in Response to Hydrology of Tennessee Reservoirs

Sammons, Steven M.; Bettoli, Phillip W.; Isermann, Daniel A.; Churchill, Timothy N.

doi:10.1577/1548-8675(2002)022<1393:rvocir>2.0.co;2

Cited by 35 publications

(41 citation statements)

References 15 publications

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“…The water level (m) and flow data (m 3 /s) were separated into four periods: (1) January through March was considered winter: (2) April through June was considered spring; (3) July through September was considered summer; and (4) October through December was considered fall. Previous researchers (Maceina & Stimpert, 1998;Sammons et al, 2002) have found hydrological conditions before spawning was related to crappie year-class strength, so the fall period before the spawn were also evaluated in the regression analyses for each sportfish species and was referred to as pre-spawn fall.…”

Section: Methodsmentioning

confidence: 99%

Relations between hydrological variables and year-class strength of sportfish in eight Florida waterbodies

Bonvechio

Allen

2005

Hydrobiologia

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Hydrological variables have influenced fish recruitment in lakes, reservoirs and rivers. We evaluated how annual and seasonal hydrological variables were related to year-class strength (i.e., residuals from catch curves) of sportfish across eight Florida waterbodies (four rivers and four lakes). Multiple regression equations computed for black bass Micropterus spp. were combined across rivers and year-class strength was negatively related to spring median flow rates and in some cases positively related to winter median flow rates (all p £ 0.10). Conversely, Lepomis spp. residuals combined from the rivers indicated that year-class strength was positively related to median flow rates in the fall prior to spawning and negatively related to post-spawn fall median flow rates (all p £ 0.10). Fish recruitment combined across lakes were not related to water levels in this study, although within lake relationships did occur in some instances. Ecological implications of this work include regulations such as minimum flows and levels (MFLs) regarding sportfish species. Impacts of hydrology on year-class strength of sportfish were stronger in rivers than in lakes for these Florida systems. High flows at least once every 3 years in the fall may allow inundation of floodplain habitat, providing favorable environmental conditions for Lepomis spp. reproduction. Setting MFLs during periods of drought (i.e., 3 years or more) should consider impacts to short-lived species such as Lepomis spp.

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Section: Methodsmentioning

confidence: 99%

Relations between hydrological variables and year-class strength of sportfish in eight Florida waterbodies

Bonvechio

Allen

2005

Hydrobiologia

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“…False predictions have been applied elsewhere to successfully answer questions of biological processes. For example, residual analysis of linear models is used as a fisheries management tool to assess year class variability for some species of game fish (Maceina 1997;Sammons et al 2002). In the case of the models presented in this paper, residuals were categorical misclassifications of observed versus predicted category.…”

Section: Discussionmentioning

confidence: 99%

Predicting biological impairment from habitat assessments

Doll¹

2011

Environ Monit Assess

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The goal of biological monitoring programs is to determine impairment classification and identify local stressors. Biological monitoring performs well at detecting impairment but when used alone falls short of determining the cause of the impairment. Following detection a more thorough survey is often conducted using extensive biological, chemical, and physical analysis coupled with exhaustive statistical treatments. These methods can be prohibitive for small programs that are limited by time and budget. The objective of this study was to develop a simple and useful model to predict the probability of biological impairment based on routinely collected habitat assessments. Biological communities were assessed with the Index of Biotic Integrity (IBI), and habitat was assessed with the Qualitative Habitat Evaluation Index. Two models were constructed from a validation dataset. The first predicted a binary outcome of impaired (IBI < 35) or non-impaired (IBI ≥ 35) and the second predicted a categorical gradient of impairment. Categories include very poor, poor, fair, good, and excellent. The models were then validated with an independently collected dataset. Both models successfully predicted biological integrity of the validation dataset with an accuracy of 0.84 (binary) and 0.75 (categorical). Based on the binary outcome model, 22 sites were observed to be impaired while the model predicted them to not be impaired. The categorical model misclassified 47 samples while only seven of those were misclassified by two or more categories. The impairment source was subsequently identified by known stressors. The models developed here can be easily applied to other datasets from the Eastern Corn Belt Plain to aid in stressor identification by predicting the probability of observing an impaired fish community based on habitat. Predicted probabilities from the models can also be used to support conclusions that have already been determined.

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“…More recently, hydrological factors (both pre-and postspawning) have been linked to recruitment in southeastern reservoirs. Strong year-classes are associated with years with short retention time or high discharge before spawning (Maceina and Stimpert 1998;Sammons and Bettoli 2000;Sammons et al 2002;Maceina 2003) or years with long retention time after spawning (Maceina and Stimpert 1998;Maceina 2003). Speculation regarding the mechanism centers on the possibility that increased precipitation (and resultant short retention time) during winter may increase primary production and food for first-feeding crappies (Maceina 2003).…”

Section: Discussionmentioning

confidence: 99%

“…Via modeling, Allen and Miranda (2001) demonstrated that environmental fluctuation modulates densitydependent recruitment responses. In empirical studies, recruitment to age 1 has been strongly correlated with reservoir hydrology (i.e., discharge, retention) either before (Maceina and Stimpert 1998;Sammons and Bettoli 2000;Sammons et al 2002;Maceina 2003) or during (Mitzner 1981;Beam 1983;McDonough and Buchanan 1991) spawning. High discharge and short retention before spawning are hypothesized to either cue enhanced crappie reproduction or spur primary production to enhance food resources for young crappies (Maceina and Stimpert 1998;Maceina 2003).…”

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confidence: 99%

Predicting Crappie Recruitment in Ohio Reservoirs with Spawning Stock Size, Larval Density, and Chlorophyll Concentrations

Bunnell

Hale

Vanni

et al. 2006

N American J Fish Manag

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Abstract.-Stock-recruit models typically use only spawning stock size as a predictor of recruitment to a fishery. In this paper, however, we used spawning stock size as well as larval density and key environmental variables to predict recruitment of white crappies Pomoxis annularis and black crappies P. nigromaculatus, a genus notorious for variable recruitment. We sampled adults and recruits from 11 Ohio reservoirs and larvae from 9 reservoirs during 1998-2001. We sampled chlorophyll as an index of reservoir productivity and obtained daily estimates of water elevation to determine the impact of hydrology on recruitment. Akaike's information criterion (AIC) revealed that Ricker and Beverton-Holt stock-recruit models that included chlorophyll best explained the variation in larval density and age-2 recruits. Specifically, spawning stock catch per effort (CPE) and chlorophyll explained 63-64% of the variation in larval density. In turn, larval density and chlorophyll explained 43-49% of the variation in age-2 recruit CPE. Finally, spawning stock CPE and chlorophyll were the best predictors of recruit CPE (i.e., 74-86%). Although larval density and recruitment increased with chlorophyll, neither was related to seasonal water elevation. Also, the AIC generally did not distinguish between Ricker and Beverton-Holt models. From these relationships, we concluded that crappie recruitment can be limited by spawning stock CPE and larval production when spawning stock sizes are low (i.e., CPE , 5 crappies/net-night). At higher levels of spawning stock sizes, spawning stock CPE and recruitment were less clearly related. To predict recruitment in Ohio reservoirs, managers should assess spawning stock CPE with trap nets and estimate chlorophyll concentrations. To increase crappie recruitment in reservoirs where recruitment is consistently poor, managers should use regulations to increase spawning stock size, which, in turn, should increase larval production and recruits to the fishery.

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Recruitment Variation of Crappies in Response to Hydrology of Tennessee Reservoirs

Cited by 35 publications

References 15 publications

Relations between hydrological variables and year-class strength of sportfish in eight Florida waterbodies

Relations between hydrological variables and year-class strength of sportfish in eight Florida waterbodies

Predicting biological impairment from habitat assessments

Predicting Crappie Recruitment in Ohio Reservoirs with Spawning Stock Size, Larval Density, and Chlorophyll Concentrations

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