- Email: [email protected]
Modeling submergence success of discarded yelloweye rockfish (Sebastes ruberrimus) and quillback rockfish (Sebastes maliger): Towards improved estimation of total fishery removals
Fisheries Research 127–128 (2012) 142–147
Contents lists available at SciVerse ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Modeling submergence success of discarded yelloweye rockfish (Sebastes ruberrimus) and quillback rockfish (Sebastes maliger): Towards improved estimation of total fishery removals Samuel J. Hochhalter ∗ Alaska Department of Fish and Game, Division of Sport Fish, 333 Raspberry Road, Anchorage, AK 99518, USA
a r t i c l e
i n f o
Article history: Received 24 October 2011 Received in revised form 23 February 2012 Accepted 25 March 2012 Keywords: Pacific rockfish Discard mortality Recreational fisheries Total fishery removals Catch-and-release
a b s t r a c t Identification of variables that can be used to predict discard mortality is an important step towards improving estimates of total fishery removals. I explored the utility of capture depth, six external signs of barotrauma, two behavioral responses, and an impairment index that summed the physical and behavioral impairment associated with rapid decompression at predicting the submergence success of hook-and-line captured yelloweye (n = 95) and quillback (n = 65) rockfish that were released at the water’s surface. Random forests classification models were used to identify the relative importance of predictor variables (n = 11) for each species and to explore the ability of these variables to accurately predict individual submergence success. Capture depth was identified as the most important variable in predicting yelloweye rockfish submergence but provided little improvement to the quillback rockfish model. The impairment index and the barotrauma sign associated with maximal gas retention were identified as important predictor variables for both yelloweye and quillback rockfish. These findings suggest that the impairment index, unlike capture depth, was able to account for individual variability in submergence success or failure of quillback rockfish. Published by Elsevier B.V.
1. Introduction Accurate accounting of total fishery removals has become an increasingly important goal in fisheries management (Pitcher et al., 2002; Harrington et al., 2005). The contribution of discard mortality to total fishery removals is often unknown due to the inherent difficulties in estimation of subsequent survival. Approximately one quarter of all fish caught worldwide are discarded (Alverson et al., 1994) with the average discard rate in recreational fisheries approaching 60% (Cooke and Cowx, 2004; Bartholomew and Bohnsack, 2005). Recreational fisheries are typically open access (no limit on the number of participants) which can exacerbate the impact of discarding on total fishery removals if regulatory actions to reduce directed harvest are not met with high survival of discarded fish and/or a reduction in total annual catch. The fully utilized or overexploited status of many fish stocks, coupled with potentially high discard rates as a result of restricted regulations on these stocks, necessitates development of improved methods to estimate and predict discard mortality rates for those stocks exposed to high rates of discarding.
Pacific rockfish Sebastes spp. are a diverse group of marine fishes found throughout the Northeast Pacific Ocean (Love et al., 2002). Rockfish, like many other physoclistic fish, frequently experience physical injury and positive buoyancy (collectively called barotrauma) due to rapid decompression when captured (Rummer and Bennett, 2005; Parker et al., 2006). Despite knowledge of species-specific susceptibility to barotrauma (Pribyl et al., 2009) and differences in the ability of species to submerge after release at the surface (Hannah et al., 2008a), submergence success of several commercially and recreationally important rockfish species (e.g., yelloweye rockfish S. ruberrimus) have not been explored. The objectives of this research were to (i) explore the utility of readily observable external signs of barotrauma, behavioral impairment, and capture depth to predict the submergence success of two commonly captured Pacific rockfish species (yelloweye and quillback (S. maliger) rockfish) in the Gulf of Alaska (GOA) and (ii) develop models of submergence probability for each species that can be applied by managers to improve estimation of the discard mortality rate and thus estimates of total fishery removals. 2. Methods
∗ Tel.: +1 907 267 2186; fax: +1 907 267 2401. E-mail address: [email protected] 0165-7836/$ – see front matter. Published by Elsevier B.V. http://dx.doi.org/10.1016/j.fishres.2012.03.009
Yelloweye and quillback rockfish (hereafter yelloweye and quillback) were captured with hook-and-line gear at four reefs in eastern Prince William Sound, AK, USA. Sampling was
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
conducted systematically throughout the peak recreational fishing season (early May to late August 2010) and used a variety of terminal tackle typical of the recreational marine fisheries in the GOA. In doing so, I attempted to encompass the full range of fishing conditions in order to provide the most applicable predictive models of submergence success. Sampling was conducted across the range of capture depths that these species are encountered at in the recreational fisheries in the GOA. Captured rockfish were measured for total length (mm) and assessed for physical and behavioral impairment related to rapid decompression with a modified reflex action mortality predictor (RAMP; sensu Davis and Ottmar, 2006) based on a series of external signs of barotrauma and behavioral responses. The modified RAMP, hereafter referred to as barotrauma-reflex (BtR) score (sensu Campbell et al., 2010a) is comprised of six external signs of barotrauma and two behavioral responses. External signs of barotrauma assessed were everted esophageal tissue/stomach (ES), distended abdomen (DA), prolapsed cloaca (PC), emphysemas in the pharyngo-cleithral membrane (PE), exophthalmia (EX), and corneal emphysemas (CE). Behavioral responses were the presence or absence of movement while on deck and the presence or absence of defense posturing (flared operculum and erect dorsal spines). All barotrauma signs and behavioral responses were assigned a categorical value of one for the healthy condition (e.g., barotrauma sign absent, behavior response present) or a zero for the unhealthy condition (e.g., barotrauma sign present, behavior absent). The presence or absence of defense posturing may be partially related to behavioral differences between species that are unrelated to the degree of impairment associated with rapid decompression. However, for purposes of this study and for ease of communication, I considered the presence and absence of defense posturing to be the healthy and unhealthy conditions, respectively. I used the criteria outlined by Hannah et al. (2008a) to assign presence or absence of external barotrauma signs. The final BtR score for each individual was calculated by summing across all barotrauma and behavior variables then dividing by the total number of variables assessed (n = 8). Upon release at the surface, rockfish were observed for submergence attempts for at least 30 min; binoculars were used to assist with observations when necessary. While the majority of rockfish able to successfully submerge typically do so within 5 min of release (Hannah et al., 2008a), Hochhalter and Reed (2011) found that successful submergence of yelloweye can occur up to 24 min after release. Time till submergence or total time of observation was recorded for each individual. I used two modeling approaches to predict the submergence success of yelloweye and quillback. First, random forests classification models (Breiman, 2001) were used to fully explore the predictive capacity of the dataset and to determine the relative importance of predictor variables for each species. Briefly, the random forests algorithm generates many (e.g., 500) bootstrapped samples of the dataset with approximately 63% of the original observations occurring at least once in each sample. The original observations that do not occur in a given bootstrapped sample are termed the “out-of-bag” observations. The algorithm then fits a classification tree to each bootstrapped sample by using a subset of randomly selected (e.g., 3) predictor variables at each node of the tree. Each tree is then used to predict the out-of-bag observations and the final predicted class of each observation determined by majority vote of the predictions for that observation from each of the trees. Model error rates are computed by averaging across the out-of-bag misclassification rates. An advantage of random forests over other classification models is its ability to discriminate variable importance among correlated variables (Cutler et al., 2007). Variable importance is measured by randomly permuting values of a given predictor variable for each out-of-bag observation and then running the permuted observations through each tree. The average
143
change in misclassification of the permuted observations relative to the original out-of-bag observations is calculated for each predictor variable in the dataset. By randomly permuting a variable, any relationship between this variable and the dependent variable, and any correlation with other predictor variables is removed (Genuer et al., 2010). The use of random forests allowed me to compare the importance of the individual variables that comprise the BtR score with the importance of the BtR score itself. Random forests classification was performed for each species with classes (submergence success or submergence failure) weighted equally to compensate for unequal distribution of observations across the two classes. Equal weight of classes increased model sensitivity for the underrepresented class at the cost of decreased specificity. Therefore, the overall misclassification rates were increased for each model. Partial dependence plots were used to explore the relationship between the logit of probability of submergence success and individual predictor variables: Logit = log
(X) 1 − (X)
where is the probability of submergence success. Positive logit values indicate that the probability of submergence success exceeds the probability of submergence failure. The second modeling approach used logistic regression to fit submergence probabilities with the predictor variable(s) that had the greatest influence on the accuracy of the random forests models. The goal of the logistic models is to provide more applicable models (relative to the random forests classification models) of submergence probability to allow for improved estimation of total fishery removals. Because an independent data set was not available to test the generality of these models, I used cross-validation and subsequent comparison of the odds ratios from each of the training and validation data sets to evaluate the expected applicability or “generality” of the models. Due to the relatively small size of the data sets, I used 5-fold cross-validation such that each of the five training and validation sets were comprised of 76 and 19, and 52 and 13 observations for the yelloweye and quillback rockfish data, respectively. Logistic models were considered general if the 95% CI around the odds ratios overlapped one another (i.e., the effect of the dependent variable was similar between the validation and test sets). Simultaneous inference bounds (i.e., 95% Working-Hotelling bounds) were calculated for each logistic model and were used to assess the precision of the entire model. All analyses were performed with R statistical software version 2.13.0 (R Development Core Team, 2004). 3. Results Yelloweye and quillback were captured at depths that ranged from 18 to 74 m (Table 1). The majority of captured yelloweye (98%) and quillback (75%) exhibited some level of physical and/or behavioral impairment due to capture and rapid decompression. However, the frequency of barotrauma signs and behavioral responses differed markedly between the two species (Fig. 1). In general, yelloweye were more behaviorally impaired than quillback and exhibited a higher frequency of ES and DA. Conversely, quillback suffered higher frequencies of EX and CE. Despite most individuals of both species showing some level of impairment, BtR scores were typically much lower for yelloweye than for quillback. For example, 73% of captured yelloweye had a BtR score ≤0.5 while 95% of captured quillback had a BtR score >0.5 (Fig. 2). There was no correlation between BtR scores and capture depth for quillback (r2 < 0.01; p-value = 0.95) and a weak but significant negative relationship for yelloweye (r2 = 0.15; p-value = 0.0001). Overall, 22% of yelloweye and 83% of quillback successfully submerged within the 30 min observation time. Maximum times to submergence for
144
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
Table 1 Sample sizes and the percent of individuals that successfully submerged by species and capture depth. Depth (m)
Yelloweye
Quillback
N
Submerged (%)
N
Submerged (%)
15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–74
2 4 6 19 27 21 8 5 3
100 75 17 37 22 10 0 0 0
0 0 3 11 16 12 16 4 3
100 100 88 58 75 100 100
Total
95
Fig. 1. Relative frequency of external barotrauma signs and behavioral responses in hook-and-line captured yelloweye and quillback rockfish in Prince William Sound, AK. External signs of barotrauma included the presence or absence of movement while on deck (Move), defense posturing (DP), everted esophageal tissue/stomach (ES), exophthalmia (EX), corneal emphysemas (CE), distended abdomen (DA), prolapsed cloaca (PC), and emphysemas in the pharyngo-cleithral membrane (PE).
yelloweye and quillback were 24 and 12 min, respectively. The majority of successful yelloweye (62%) and quillback (81%) submerged immediately upon release. Out-of-bag misclassification rates for the yelloweye and quillback random forests models were 26.3% and 21.5%, respectively. Although classes were weighted evenly (i.e., increased classification sensitivity for the least represented class), the misclassification
65
rate for the least represented class remained disproportionately higher. For example, the models were better at predicting the class with the most observations and relatively poor at predicting the class with the fewest observations (Table 2). This resulted in high misclassification rates for yelloweye that successfully submerged. The misclassification rate for quillback that failed to submerge was nominally higher than for quillback that submerged. The relative importance of individual predictor variables varied considerably between the two species although two similarities were evident. Random forests models found that the presence of the barotrauma signs that are associated with maximal retention of excess gases (e.g., ES and PE for yelloweye and CE for quillback) were important in the prediction of submergence success for each species. The proportion of yelloweye that successfully submerged decreased from 0.54 (SE = 0.14) for individuals without ES to 0.17 (SE = 0.04) for individuals with ES. For quillback, the proportion of individuals that successfully submerged decreased from 0.89 (SE = 0.04) when CE was absent to 0.38 (SE = 0.17) when CE was present. Of the 11 predictor variables considered, BtR score was the only variable that had high importance for both species suggesting that the impairment index is a useful predictor of submergence success for both species (Fig. 3). A positive trend in the partial dependence plots of yelloweye and quillback submergence on BtR score indicates that submergence probability increases as individual condition increases (Fig. 4). However, for yelloweye, the probability of submergence failure exceeded the probability of submergence success (negative logit values) for all levels of the BtR score including individuals that showed no physical or behavioral impairment (BtR = 1.0). Partial dependence plots for each species showed an inverse relationship between capture depth and the logit of probability of submergence (Fig. 5). The probability of submergence success exceeded the probability of submergence failure for yelloweye at depths <30 m while the opposite occurred for depths >35 m. A similar pattern is evident for quillback except that across all depths surveyed, the probability of submergence success exceeded the probability of submergence failure. Similar to yelloweye, the logit of probability of submergence for quillback declined between depths of 30–40 m but unlike yelloweye, the trend increased sharply at depths >40 m (Fig. 5). Table 2 Predicted classification of yelloweye and quillback rockfish that failed to submerge (Fail) and successfully submerged (Success) and the associated misclassification rates (MR) of random forests classification models.
Fig. 2. Relative frequency of barotrauma-reflex (BtR) scores for hook-and-line captured yelloweye and quillback rockfish. Higher scores indicate better condition.
Species
Actual
Yelloweye Yelloweye Quillback Quillback
Fail Success Fail Success
Predicted
MR
Fail
Success
58 9 8 11
16 12 3 43
0.22 0.43 0.27 0.20
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
145
Fig. 3. Variable importance plots for predictor variables in random forests classification models of yelloweye and quillback rockfish submergence success in Prince William Sound, AK. Higher values indicate greater importance. Predictor variables included capture depth (Depth); individual total length (Length); barotrauma-reflex scores (BtR); and the presence or absence of individual variables that comprise the BtR: everted esophageal tissue/stomach (ES), distended abdomen (DA), prolapsed cloaca (PC), emphysemas in the pharyngo-cleithral membrane (PE), exophthalmia (EX), corneal emphysemas (CE), movement while on deck (Move), and defense posturing (DP).
Based on the variable importance output from the random forests models, univariate logistic regression was used to model submergence probability as a function of capture depth for yelloweye (Fig. 6) and BtR score for quillback (Fig. 7). Cross-validation of Fig. 5. The partial dependence of yelloweye (A) and quillback (B) rockfish random forests classification models on capture depth for prediction of submergence success of discarded individuals.
the yelloweye model indicated general applicability of the model; odds ratios from the five training and validation sets were similar (range = 0.79–0.92; Fig. 8). This indicates that the odds of yelloweye submergence decreased 1.27–1.09 times with every 1 m increase in capture depth. Conversely, cross-validation of the quillback BtR model revealed highly variable odds ratios for both the training (331–3477) and validation sets (range = 0.99–265,186) suggesting the generality of the model was poor, and/or validation and training sets did not have adequate representation of both classes
Fig. 4. The partial dependence of yelloweye (A) and quillback (B) rockfish random forests classification models on barotrauma-reflex (BtR) scores for prediction of submergence success of discarded individuals.
Fig. 6. Submergence probability of discarded yelloweye rockfish as a function of capture depth.
146
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
Fig. 7. Submergence probability of discarded quillback rockfish as a function of barotrauma-reflex (BtR) score.
(e.g., training set comprised of only individuals that successfully submerged). 4. Discussion The need for improved understanding and estimation of the impact of fishery discards has increased as many fish stocks are fully utilized or overexploited. Consequently, identification of variables that can be used to predict post-release performance is essential for accurate accounting of the impact of discarding on the sustainability of fisheries (Davis, 2007). For Pacific rockfish, submergence success is considered to be the most critical step in surviving the catch-and-release process (Hannah et al., 2008a) and, to varying extents, decreases with increased capture depth as a result of the inverse relationship between pressure and volume as described by Boyle’s law. To date, discard mortality rates for several species of rockfish have been generated from depth specific estimates of submergence success combined with assumptions about delayed mortality (Pacific Fishery Management Council and National Marine Fisheries Service, 2009). Data presented here indicate that such an approach to approximation of discard mortality is appropriate for yelloweye given that submergence success of yelloweye is highly dependent on capture depth. However, capture
Fig. 8. Odds ratios from 5-fold cross-validation of a logistic model of yelloweye rockfish submergence probability as a function of capture depth. Vertical bars are 95% confidence intervals.
depth may not be a useful predictor of submergence success for species such as quillback. Random forests classification models provided a robust investigation into the ability of selected variables to predict individual submergence success of yelloweye and quillback rockfish. The range of misclassification rates indicates that the predictor variables used in this study were sufficient to accurately predict the individual submergence success of 74% of yelloweye and 78% of quillback rockfish. Conversely, the observed misclassification rates indicate that other factors that influence submergence success of yelloweye and quillback were not accounted for in this study. A range of behavioral responses have been used to develop impairment indices for other species of fish including non-induced responses such as tail movement and active gilling, and induced responses such as gag reflex, eye rotation, and startle responses (Davis and Ottmar, 2006; Campbell et al., 2010a). Inclusion of additional behavioral responses in the BtR score used in this study may have improved the misclassification rates of the random forests models. The extent to which submergence success correlates with capture depth varies markedly between species (Hannah et al., 2008a) and may be explained, in part, by inter-species differences in the physiological response to forced decompression. For example, excess gases from a ruptured or perforated swim bladder can escape the abdominal cavity of some species of rockfish (e.g., quillback and yellowtail S. flavidus) through ruptures in the pharyngo-cleithral membrane (Pribyl et al., 2009). The loss of excess gases acts to reduce positive buoyancy and improve the probability of submergence success (Hannah et al., 2008a). The increase in the logit of probability of quillback submergence at capture depths >40 m may be explained by excess gases in fish decompressed from these depths reaching pressures sufficient to rupture the pharyngo-cleithral membrane. Yelloweye and quillback rockfish captured in this study differed markedly in their physical and behavioral responses to capture and forced decompression and these differences were associated with disparate levels of submergence success. In general, I found that yelloweye were physically and behaviorally more impaired than quillback. As such, the importance of selected variables on the prediction of submergence success largely differed among the two species. Similar to the findings of Hannah et al. (2008a), the barotrauma signs that are associated with maximal retention of excess gases were identified as being highly important for both yelloweye and quillback but these signs differed between the two species. External signs of barotrauma develop as excess gases travel in a dorsal-anterior direction along the path of least resistance and when in sufficient quantities, invade other body cavities such as ocular orbits and/or force esophageal tissue and the stomach into the buccal cavity (Hannah et al., 2008b). When barotrauma signs associated with maximal gas retention were present, the probability of failure to submerge exceeded the probability of successful submergence for both species. Hannah et al. (2008a) found that the occurrence of severe esophageal eversion and eye related injuries was negatively correlated with submergence success for black (S. melanops), blue (S. mystinus) and canary (S. pinniger) rockfish, and canary and blue rockfish, respectively. Post-release performance of discarded fish is driven by the cumulative impact of stressors that are associated with the catchand-release process (Davis, 2002). The BtR score was used in an attempt to account for the readily identifiable effects of capture stressors on the physical and behavioral impairment of rockfish at the time of capture. Towards this end, the BtR score proved successful in that it was the only variable identified as important for the prediction of submergence success for both species under consideration in this study. Impairment indices have been used to model discard mortality rates for several species of fish (Trumble
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
et al., 2000; Davis and Ottmar, 2006; Davis, 2007; Campbell et al., 2010b) suggesting universal utility of impairment indices for such purposes. While I found the BtR score to be an important predictor variable of individual submergence success for yelloweye and quillback rockfish, the condition of an individual at the surface may not be representative of the condition of that individual once recompressed at depth. BtR scores are expected to improve relative to the score while at the surface due to recompression and reversal of many external signs of barotrauma (Hannah and Matteson, 2007). Hannah and Matteson (2007) found that behavioral impairment of yelloweye returned to depth of capture was among the lowest for nine species of Pacific rockfish surveyed. Low impairment of yelloweye returned to depth likely contributes to the high survival of yelloweye released at depth (Hochhalter and Reed, 2011). With this in mind, observations of physical and behavioral condition at the surface may not be an accurate predictor of delayed mortality for individuals that successfully submerge. I found that an impairment index based on the physical and behavioral responses of yelloweye and quillback rockfish to rapid decompression proved useful in the prediction of individual submergence success for both species. However, the utility of such a metric at improving the estimation of the discard mortality rates of Pacific rockfish remains to be shown. High variability in the odds ratios from the training and validation sets of the quillback BtR logistic model suggests that the generality of the model is questionable. However, the extreme values in the odds ratios from both the training and validation sets of the quillback BtR model may be the result of the relatively small number of quillback that failed to submerge in this study (8 of 65 individuals). The small sample size of quillback that failed to submerge likely increased the chance of the covariate (BtR score) leading to complete separation of the response variable (submergence probability). When such a scenario occurs, numerical problems such as an extreme value in the odds ratio and/or an odds ratio with a large standard error are common (Hosmer and Lemeshow, 2000). Recent research has shown that the discard mortality rate of yelloweye rockfish can be dramatically reduced with the use of deepwater release devices (Hochhalter and Reed, 2011). However, a large proportion of yelloweye discarded in the GOA recreational fisheries are released at the water’s surface (Alaska Department of Fish and Game unpublished data). Until deepwater release can be widely implemented, models that provide reasonable estimates of submergence success can serve as close approximations of the discard mortality rate of yelloweye. The modeling approach used here is a viable means by which influential variables that drive post-release performance of fish can be identified and simplified models applicable to management agencies can be developed and validated. Cross-validation of the yelloweye logistic model suggests the model can be used to approximate the discard mortality rate in the recreational fisheries of the GOA which will allow for more accurate accounting of total fishery removals. Acknowledgments I would like to thank Brittany Blain, Chuck Brazil, Dan Bosch, and Scott Meyer for valuable assistance in the field. I would also
147
like to thank Scott Meyer, Dan Reed, Bob Clark, and Jack Erickson for helpful edits on an earlier draft. Funding for this project was provided in part by Federal Aid in Sport Fish Restoration Grant Number F-10-25. References Alverson, D.L., Freeberg, M.H., Pope, J.G., Murawski, S.A., 1994. A global assessment of fisheries by catch and discards. FAO Fisheries Technical Paper No. 339. Bartholomew, A., Bohnsack, J.A., 2005. A review of catch-and-release angling mortality with implications for no-take reserves. Rev. Fish Biol. Fish. 15, 129–154. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32. Campbell, M.D., Patino, R., Tolan, J., Straus, R., Diamond, S.L., 2010a. Sublethal effects of catch-and-release fishing: measuring capture stress fish impairment, and predation risk using a condition index. ICES J. Mar. Sci. 67, 513–521. Campbell, M.D., Tolan, J., Strauss, R., Diamond, S.L., 2010b. Relating anglingdependent fish impairment to immediate release mortality of red snapper (Lutjanus campechamus). Fish. Res. 106, 64–70. Cooke, S.J., Cowx, I.G., 2004. The role of recreational fisheries in global fish crises. Bioscience 128, 857–859. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J., 2007. Random forests for classification in ecology. Ecology 88, 2783–2792. Davis, M.W., 2002. Key principles for understanding fish bycatch discard mortality. Can. J. Fish. Aquat. Sci. 59, 1834–1843. Davis, M.W., Ottmar, M.L., 2006. Wounding and reflex impairment may be predictors for mortality in discarded or escaped fish. Fish. Res. 82, 1–6. Davis, M.W., 2007. Simulated fishing experiments for predicting delayed mortality rates using reflex impairment in restrained fish. ICES J. Mar. Sci. 64, 1535–1542. Genuer, R., Poggi, J.M., Tuleau-Malot, C., 2010. Variable selection using random forests. Pattern Recogn. Lett. 31, 2225–2236. Hannah, R.W., Matteson, K.M., 2007. Behavior of nine species of Pacific rockfish after hook-and-line capture recompression, and release. Trans. Am. Fish. Soc. 136, 24–33. Hannah, R.W., Parker, S.J., Matteson, K.M., 2008a. Escaping the surface: the effects of capture depth on submergence success of surface-released Pacific rockfish. North Am. J. Fish. Manage. 28, 694–700. Hannah, R.W., Rankin, P.S., Penny, A.N., Parker, S.J., 2008b. Physical model of the development of external signs of barotrauma in Pacific rockfish. Aquat. Biol. 3, 291–296. Harrington, J.M., Myers, R.A., Rosenberg, A.A., 2005. Wasted fishery resources: discarded bycatch in the USA. Fish Fish. 6, 350–361. Hochhalter, S.J., Reed, D.J., 2011. The effectiveness of deepwater release at improving the survival of discarded yelloweye rockfish. North Am. J. Fish. Manage. 31, 852–860. Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression, 2nd ed. John Wiley and Sons, Inc., New York, NY. Love, M.S., Yoklavich, M., Thornsteinson, L., 2002. The Rockfish of the Northeast Pacific. University of California Press, Berkeley. Pacific Fishery Management Council and National Marine Fisheries Service, 2009. Proposed acceptable catch and optimum yield specifications and management measures for the 2009–2010 Pacific coast groundfish fishery final environmental impact statement including regulatory impact review and initial regulatory flexibility analysis. Pacific Fishery Management Council, Portland, OR. Parker, S.J., McElderry, H.I., Rankin, P.S., Hannah, R.W., 2006. Buoyancy regulation and barotrauma in two species of nearshore rockfish. Trans. Am. Fish. Soc. 135, 1213–1223. Pitcher, T.J., Watson, R., Forrest, R., Valtysson, H., Guenette, S., 2002. Estimating illegal and unreported catches from marine ecosystems: a basis for change. Fish Fish. 3, 317–339. Pribyl, A.L., Schrek, C.B., Kent, M.L., Parker, S.J., 2009. The differential response to decompression of three species of nearshore Pacific rockfish. North Am. J. Fish. Manage. 29, 1479–1486. R Development Core Team, 2004. R: A Language and Environment for Statistical Computing. R Found. for Stat. Comp., Vienna, Austria, Available at: www.Rproject.org. Rummer, J.L., Bennett, W.A., 2005. Physiological effects of swim bladder overexpansion and catastrophic decompression on red snapper. Trans. Am. Fish. Soc. 134, 1457–1470. Trumble, R.J., Kaimmer, S.M., Williams, G.H., 2000. Estimation of discard mortality rates for Pacific halibut bycatch in groundfish longline fisheries. North Am. J. Fish. Manage. 20, 931–939.
Contents lists available at SciVerse ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Modeling submergence success of discarded yelloweye rockfish (Sebastes ruberrimus) and quillback rockfish (Sebastes maliger): Towards improved estimation of total fishery removals Samuel J. Hochhalter ∗ Alaska Department of Fish and Game, Division of Sport Fish, 333 Raspberry Road, Anchorage, AK 99518, USA
a r t i c l e
i n f o
Article history: Received 24 October 2011 Received in revised form 23 February 2012 Accepted 25 March 2012 Keywords: Pacific rockfish Discard mortality Recreational fisheries Total fishery removals Catch-and-release
a b s t r a c t Identification of variables that can be used to predict discard mortality is an important step towards improving estimates of total fishery removals. I explored the utility of capture depth, six external signs of barotrauma, two behavioral responses, and an impairment index that summed the physical and behavioral impairment associated with rapid decompression at predicting the submergence success of hook-and-line captured yelloweye (n = 95) and quillback (n = 65) rockfish that were released at the water’s surface. Random forests classification models were used to identify the relative importance of predictor variables (n = 11) for each species and to explore the ability of these variables to accurately predict individual submergence success. Capture depth was identified as the most important variable in predicting yelloweye rockfish submergence but provided little improvement to the quillback rockfish model. The impairment index and the barotrauma sign associated with maximal gas retention were identified as important predictor variables for both yelloweye and quillback rockfish. These findings suggest that the impairment index, unlike capture depth, was able to account for individual variability in submergence success or failure of quillback rockfish. Published by Elsevier B.V.
1. Introduction Accurate accounting of total fishery removals has become an increasingly important goal in fisheries management (Pitcher et al., 2002; Harrington et al., 2005). The contribution of discard mortality to total fishery removals is often unknown due to the inherent difficulties in estimation of subsequent survival. Approximately one quarter of all fish caught worldwide are discarded (Alverson et al., 1994) with the average discard rate in recreational fisheries approaching 60% (Cooke and Cowx, 2004; Bartholomew and Bohnsack, 2005). Recreational fisheries are typically open access (no limit on the number of participants) which can exacerbate the impact of discarding on total fishery removals if regulatory actions to reduce directed harvest are not met with high survival of discarded fish and/or a reduction in total annual catch. The fully utilized or overexploited status of many fish stocks, coupled with potentially high discard rates as a result of restricted regulations on these stocks, necessitates development of improved methods to estimate and predict discard mortality rates for those stocks exposed to high rates of discarding.
Pacific rockfish Sebastes spp. are a diverse group of marine fishes found throughout the Northeast Pacific Ocean (Love et al., 2002). Rockfish, like many other physoclistic fish, frequently experience physical injury and positive buoyancy (collectively called barotrauma) due to rapid decompression when captured (Rummer and Bennett, 2005; Parker et al., 2006). Despite knowledge of species-specific susceptibility to barotrauma (Pribyl et al., 2009) and differences in the ability of species to submerge after release at the surface (Hannah et al., 2008a), submergence success of several commercially and recreationally important rockfish species (e.g., yelloweye rockfish S. ruberrimus) have not been explored. The objectives of this research were to (i) explore the utility of readily observable external signs of barotrauma, behavioral impairment, and capture depth to predict the submergence success of two commonly captured Pacific rockfish species (yelloweye and quillback (S. maliger) rockfish) in the Gulf of Alaska (GOA) and (ii) develop models of submergence probability for each species that can be applied by managers to improve estimation of the discard mortality rate and thus estimates of total fishery removals. 2. Methods
∗ Tel.: +1 907 267 2186; fax: +1 907 267 2401. E-mail address: [email protected] 0165-7836/$ – see front matter. Published by Elsevier B.V. http://dx.doi.org/10.1016/j.fishres.2012.03.009
Yelloweye and quillback rockfish (hereafter yelloweye and quillback) were captured with hook-and-line gear at four reefs in eastern Prince William Sound, AK, USA. Sampling was
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
conducted systematically throughout the peak recreational fishing season (early May to late August 2010) and used a variety of terminal tackle typical of the recreational marine fisheries in the GOA. In doing so, I attempted to encompass the full range of fishing conditions in order to provide the most applicable predictive models of submergence success. Sampling was conducted across the range of capture depths that these species are encountered at in the recreational fisheries in the GOA. Captured rockfish were measured for total length (mm) and assessed for physical and behavioral impairment related to rapid decompression with a modified reflex action mortality predictor (RAMP; sensu Davis and Ottmar, 2006) based on a series of external signs of barotrauma and behavioral responses. The modified RAMP, hereafter referred to as barotrauma-reflex (BtR) score (sensu Campbell et al., 2010a) is comprised of six external signs of barotrauma and two behavioral responses. External signs of barotrauma assessed were everted esophageal tissue/stomach (ES), distended abdomen (DA), prolapsed cloaca (PC), emphysemas in the pharyngo-cleithral membrane (PE), exophthalmia (EX), and corneal emphysemas (CE). Behavioral responses were the presence or absence of movement while on deck and the presence or absence of defense posturing (flared operculum and erect dorsal spines). All barotrauma signs and behavioral responses were assigned a categorical value of one for the healthy condition (e.g., barotrauma sign absent, behavior response present) or a zero for the unhealthy condition (e.g., barotrauma sign present, behavior absent). The presence or absence of defense posturing may be partially related to behavioral differences between species that are unrelated to the degree of impairment associated with rapid decompression. However, for purposes of this study and for ease of communication, I considered the presence and absence of defense posturing to be the healthy and unhealthy conditions, respectively. I used the criteria outlined by Hannah et al. (2008a) to assign presence or absence of external barotrauma signs. The final BtR score for each individual was calculated by summing across all barotrauma and behavior variables then dividing by the total number of variables assessed (n = 8). Upon release at the surface, rockfish were observed for submergence attempts for at least 30 min; binoculars were used to assist with observations when necessary. While the majority of rockfish able to successfully submerge typically do so within 5 min of release (Hannah et al., 2008a), Hochhalter and Reed (2011) found that successful submergence of yelloweye can occur up to 24 min after release. Time till submergence or total time of observation was recorded for each individual. I used two modeling approaches to predict the submergence success of yelloweye and quillback. First, random forests classification models (Breiman, 2001) were used to fully explore the predictive capacity of the dataset and to determine the relative importance of predictor variables for each species. Briefly, the random forests algorithm generates many (e.g., 500) bootstrapped samples of the dataset with approximately 63% of the original observations occurring at least once in each sample. The original observations that do not occur in a given bootstrapped sample are termed the “out-of-bag” observations. The algorithm then fits a classification tree to each bootstrapped sample by using a subset of randomly selected (e.g., 3) predictor variables at each node of the tree. Each tree is then used to predict the out-of-bag observations and the final predicted class of each observation determined by majority vote of the predictions for that observation from each of the trees. Model error rates are computed by averaging across the out-of-bag misclassification rates. An advantage of random forests over other classification models is its ability to discriminate variable importance among correlated variables (Cutler et al., 2007). Variable importance is measured by randomly permuting values of a given predictor variable for each out-of-bag observation and then running the permuted observations through each tree. The average
143
change in misclassification of the permuted observations relative to the original out-of-bag observations is calculated for each predictor variable in the dataset. By randomly permuting a variable, any relationship between this variable and the dependent variable, and any correlation with other predictor variables is removed (Genuer et al., 2010). The use of random forests allowed me to compare the importance of the individual variables that comprise the BtR score with the importance of the BtR score itself. Random forests classification was performed for each species with classes (submergence success or submergence failure) weighted equally to compensate for unequal distribution of observations across the two classes. Equal weight of classes increased model sensitivity for the underrepresented class at the cost of decreased specificity. Therefore, the overall misclassification rates were increased for each model. Partial dependence plots were used to explore the relationship between the logit of probability of submergence success and individual predictor variables: Logit = log
(X) 1 − (X)
where is the probability of submergence success. Positive logit values indicate that the probability of submergence success exceeds the probability of submergence failure. The second modeling approach used logistic regression to fit submergence probabilities with the predictor variable(s) that had the greatest influence on the accuracy of the random forests models. The goal of the logistic models is to provide more applicable models (relative to the random forests classification models) of submergence probability to allow for improved estimation of total fishery removals. Because an independent data set was not available to test the generality of these models, I used cross-validation and subsequent comparison of the odds ratios from each of the training and validation data sets to evaluate the expected applicability or “generality” of the models. Due to the relatively small size of the data sets, I used 5-fold cross-validation such that each of the five training and validation sets were comprised of 76 and 19, and 52 and 13 observations for the yelloweye and quillback rockfish data, respectively. Logistic models were considered general if the 95% CI around the odds ratios overlapped one another (i.e., the effect of the dependent variable was similar between the validation and test sets). Simultaneous inference bounds (i.e., 95% Working-Hotelling bounds) were calculated for each logistic model and were used to assess the precision of the entire model. All analyses were performed with R statistical software version 2.13.0 (R Development Core Team, 2004). 3. Results Yelloweye and quillback were captured at depths that ranged from 18 to 74 m (Table 1). The majority of captured yelloweye (98%) and quillback (75%) exhibited some level of physical and/or behavioral impairment due to capture and rapid decompression. However, the frequency of barotrauma signs and behavioral responses differed markedly between the two species (Fig. 1). In general, yelloweye were more behaviorally impaired than quillback and exhibited a higher frequency of ES and DA. Conversely, quillback suffered higher frequencies of EX and CE. Despite most individuals of both species showing some level of impairment, BtR scores were typically much lower for yelloweye than for quillback. For example, 73% of captured yelloweye had a BtR score ≤0.5 while 95% of captured quillback had a BtR score >0.5 (Fig. 2). There was no correlation between BtR scores and capture depth for quillback (r2 < 0.01; p-value = 0.95) and a weak but significant negative relationship for yelloweye (r2 = 0.15; p-value = 0.0001). Overall, 22% of yelloweye and 83% of quillback successfully submerged within the 30 min observation time. Maximum times to submergence for
144
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
Table 1 Sample sizes and the percent of individuals that successfully submerged by species and capture depth. Depth (m)
Yelloweye
Quillback
N
Submerged (%)
N
Submerged (%)
15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–74
2 4 6 19 27 21 8 5 3
100 75 17 37 22 10 0 0 0
0 0 3 11 16 12 16 4 3
100 100 88 58 75 100 100
Total
95
Fig. 1. Relative frequency of external barotrauma signs and behavioral responses in hook-and-line captured yelloweye and quillback rockfish in Prince William Sound, AK. External signs of barotrauma included the presence or absence of movement while on deck (Move), defense posturing (DP), everted esophageal tissue/stomach (ES), exophthalmia (EX), corneal emphysemas (CE), distended abdomen (DA), prolapsed cloaca (PC), and emphysemas in the pharyngo-cleithral membrane (PE).
yelloweye and quillback were 24 and 12 min, respectively. The majority of successful yelloweye (62%) and quillback (81%) submerged immediately upon release. Out-of-bag misclassification rates for the yelloweye and quillback random forests models were 26.3% and 21.5%, respectively. Although classes were weighted evenly (i.e., increased classification sensitivity for the least represented class), the misclassification
65
rate for the least represented class remained disproportionately higher. For example, the models were better at predicting the class with the most observations and relatively poor at predicting the class with the fewest observations (Table 2). This resulted in high misclassification rates for yelloweye that successfully submerged. The misclassification rate for quillback that failed to submerge was nominally higher than for quillback that submerged. The relative importance of individual predictor variables varied considerably between the two species although two similarities were evident. Random forests models found that the presence of the barotrauma signs that are associated with maximal retention of excess gases (e.g., ES and PE for yelloweye and CE for quillback) were important in the prediction of submergence success for each species. The proportion of yelloweye that successfully submerged decreased from 0.54 (SE = 0.14) for individuals without ES to 0.17 (SE = 0.04) for individuals with ES. For quillback, the proportion of individuals that successfully submerged decreased from 0.89 (SE = 0.04) when CE was absent to 0.38 (SE = 0.17) when CE was present. Of the 11 predictor variables considered, BtR score was the only variable that had high importance for both species suggesting that the impairment index is a useful predictor of submergence success for both species (Fig. 3). A positive trend in the partial dependence plots of yelloweye and quillback submergence on BtR score indicates that submergence probability increases as individual condition increases (Fig. 4). However, for yelloweye, the probability of submergence failure exceeded the probability of submergence success (negative logit values) for all levels of the BtR score including individuals that showed no physical or behavioral impairment (BtR = 1.0). Partial dependence plots for each species showed an inverse relationship between capture depth and the logit of probability of submergence (Fig. 5). The probability of submergence success exceeded the probability of submergence failure for yelloweye at depths <30 m while the opposite occurred for depths >35 m. A similar pattern is evident for quillback except that across all depths surveyed, the probability of submergence success exceeded the probability of submergence failure. Similar to yelloweye, the logit of probability of submergence for quillback declined between depths of 30–40 m but unlike yelloweye, the trend increased sharply at depths >40 m (Fig. 5). Table 2 Predicted classification of yelloweye and quillback rockfish that failed to submerge (Fail) and successfully submerged (Success) and the associated misclassification rates (MR) of random forests classification models.
Fig. 2. Relative frequency of barotrauma-reflex (BtR) scores for hook-and-line captured yelloweye and quillback rockfish. Higher scores indicate better condition.
Species
Actual
Yelloweye Yelloweye Quillback Quillback
Fail Success Fail Success
Predicted
MR
Fail
Success
58 9 8 11
16 12 3 43
0.22 0.43 0.27 0.20
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
145
Fig. 3. Variable importance plots for predictor variables in random forests classification models of yelloweye and quillback rockfish submergence success in Prince William Sound, AK. Higher values indicate greater importance. Predictor variables included capture depth (Depth); individual total length (Length); barotrauma-reflex scores (BtR); and the presence or absence of individual variables that comprise the BtR: everted esophageal tissue/stomach (ES), distended abdomen (DA), prolapsed cloaca (PC), emphysemas in the pharyngo-cleithral membrane (PE), exophthalmia (EX), corneal emphysemas (CE), movement while on deck (Move), and defense posturing (DP).
Based on the variable importance output from the random forests models, univariate logistic regression was used to model submergence probability as a function of capture depth for yelloweye (Fig. 6) and BtR score for quillback (Fig. 7). Cross-validation of Fig. 5. The partial dependence of yelloweye (A) and quillback (B) rockfish random forests classification models on capture depth for prediction of submergence success of discarded individuals.
the yelloweye model indicated general applicability of the model; odds ratios from the five training and validation sets were similar (range = 0.79–0.92; Fig. 8). This indicates that the odds of yelloweye submergence decreased 1.27–1.09 times with every 1 m increase in capture depth. Conversely, cross-validation of the quillback BtR model revealed highly variable odds ratios for both the training (331–3477) and validation sets (range = 0.99–265,186) suggesting the generality of the model was poor, and/or validation and training sets did not have adequate representation of both classes
Fig. 4. The partial dependence of yelloweye (A) and quillback (B) rockfish random forests classification models on barotrauma-reflex (BtR) scores for prediction of submergence success of discarded individuals.
Fig. 6. Submergence probability of discarded yelloweye rockfish as a function of capture depth.
146
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
Fig. 7. Submergence probability of discarded quillback rockfish as a function of barotrauma-reflex (BtR) score.
(e.g., training set comprised of only individuals that successfully submerged). 4. Discussion The need for improved understanding and estimation of the impact of fishery discards has increased as many fish stocks are fully utilized or overexploited. Consequently, identification of variables that can be used to predict post-release performance is essential for accurate accounting of the impact of discarding on the sustainability of fisheries (Davis, 2007). For Pacific rockfish, submergence success is considered to be the most critical step in surviving the catch-and-release process (Hannah et al., 2008a) and, to varying extents, decreases with increased capture depth as a result of the inverse relationship between pressure and volume as described by Boyle’s law. To date, discard mortality rates for several species of rockfish have been generated from depth specific estimates of submergence success combined with assumptions about delayed mortality (Pacific Fishery Management Council and National Marine Fisheries Service, 2009). Data presented here indicate that such an approach to approximation of discard mortality is appropriate for yelloweye given that submergence success of yelloweye is highly dependent on capture depth. However, capture
Fig. 8. Odds ratios from 5-fold cross-validation of a logistic model of yelloweye rockfish submergence probability as a function of capture depth. Vertical bars are 95% confidence intervals.
depth may not be a useful predictor of submergence success for species such as quillback. Random forests classification models provided a robust investigation into the ability of selected variables to predict individual submergence success of yelloweye and quillback rockfish. The range of misclassification rates indicates that the predictor variables used in this study were sufficient to accurately predict the individual submergence success of 74% of yelloweye and 78% of quillback rockfish. Conversely, the observed misclassification rates indicate that other factors that influence submergence success of yelloweye and quillback were not accounted for in this study. A range of behavioral responses have been used to develop impairment indices for other species of fish including non-induced responses such as tail movement and active gilling, and induced responses such as gag reflex, eye rotation, and startle responses (Davis and Ottmar, 2006; Campbell et al., 2010a). Inclusion of additional behavioral responses in the BtR score used in this study may have improved the misclassification rates of the random forests models. The extent to which submergence success correlates with capture depth varies markedly between species (Hannah et al., 2008a) and may be explained, in part, by inter-species differences in the physiological response to forced decompression. For example, excess gases from a ruptured or perforated swim bladder can escape the abdominal cavity of some species of rockfish (e.g., quillback and yellowtail S. flavidus) through ruptures in the pharyngo-cleithral membrane (Pribyl et al., 2009). The loss of excess gases acts to reduce positive buoyancy and improve the probability of submergence success (Hannah et al., 2008a). The increase in the logit of probability of quillback submergence at capture depths >40 m may be explained by excess gases in fish decompressed from these depths reaching pressures sufficient to rupture the pharyngo-cleithral membrane. Yelloweye and quillback rockfish captured in this study differed markedly in their physical and behavioral responses to capture and forced decompression and these differences were associated with disparate levels of submergence success. In general, I found that yelloweye were physically and behaviorally more impaired than quillback. As such, the importance of selected variables on the prediction of submergence success largely differed among the two species. Similar to the findings of Hannah et al. (2008a), the barotrauma signs that are associated with maximal retention of excess gases were identified as being highly important for both yelloweye and quillback but these signs differed between the two species. External signs of barotrauma develop as excess gases travel in a dorsal-anterior direction along the path of least resistance and when in sufficient quantities, invade other body cavities such as ocular orbits and/or force esophageal tissue and the stomach into the buccal cavity (Hannah et al., 2008b). When barotrauma signs associated with maximal gas retention were present, the probability of failure to submerge exceeded the probability of successful submergence for both species. Hannah et al. (2008a) found that the occurrence of severe esophageal eversion and eye related injuries was negatively correlated with submergence success for black (S. melanops), blue (S. mystinus) and canary (S. pinniger) rockfish, and canary and blue rockfish, respectively. Post-release performance of discarded fish is driven by the cumulative impact of stressors that are associated with the catchand-release process (Davis, 2002). The BtR score was used in an attempt to account for the readily identifiable effects of capture stressors on the physical and behavioral impairment of rockfish at the time of capture. Towards this end, the BtR score proved successful in that it was the only variable identified as important for the prediction of submergence success for both species under consideration in this study. Impairment indices have been used to model discard mortality rates for several species of fish (Trumble
S.J. Hochhalter / Fisheries Research 127–128 (2012) 142–147
et al., 2000; Davis and Ottmar, 2006; Davis, 2007; Campbell et al., 2010b) suggesting universal utility of impairment indices for such purposes. While I found the BtR score to be an important predictor variable of individual submergence success for yelloweye and quillback rockfish, the condition of an individual at the surface may not be representative of the condition of that individual once recompressed at depth. BtR scores are expected to improve relative to the score while at the surface due to recompression and reversal of many external signs of barotrauma (Hannah and Matteson, 2007). Hannah and Matteson (2007) found that behavioral impairment of yelloweye returned to depth of capture was among the lowest for nine species of Pacific rockfish surveyed. Low impairment of yelloweye returned to depth likely contributes to the high survival of yelloweye released at depth (Hochhalter and Reed, 2011). With this in mind, observations of physical and behavioral condition at the surface may not be an accurate predictor of delayed mortality for individuals that successfully submerge. I found that an impairment index based on the physical and behavioral responses of yelloweye and quillback rockfish to rapid decompression proved useful in the prediction of individual submergence success for both species. However, the utility of such a metric at improving the estimation of the discard mortality rates of Pacific rockfish remains to be shown. High variability in the odds ratios from the training and validation sets of the quillback BtR logistic model suggests that the generality of the model is questionable. However, the extreme values in the odds ratios from both the training and validation sets of the quillback BtR model may be the result of the relatively small number of quillback that failed to submerge in this study (8 of 65 individuals). The small sample size of quillback that failed to submerge likely increased the chance of the covariate (BtR score) leading to complete separation of the response variable (submergence probability). When such a scenario occurs, numerical problems such as an extreme value in the odds ratio and/or an odds ratio with a large standard error are common (Hosmer and Lemeshow, 2000). Recent research has shown that the discard mortality rate of yelloweye rockfish can be dramatically reduced with the use of deepwater release devices (Hochhalter and Reed, 2011). However, a large proportion of yelloweye discarded in the GOA recreational fisheries are released at the water’s surface (Alaska Department of Fish and Game unpublished data). Until deepwater release can be widely implemented, models that provide reasonable estimates of submergence success can serve as close approximations of the discard mortality rate of yelloweye. The modeling approach used here is a viable means by which influential variables that drive post-release performance of fish can be identified and simplified models applicable to management agencies can be developed and validated. Cross-validation of the yelloweye logistic model suggests the model can be used to approximate the discard mortality rate in the recreational fisheries of the GOA which will allow for more accurate accounting of total fishery removals. Acknowledgments I would like to thank Brittany Blain, Chuck Brazil, Dan Bosch, and Scott Meyer for valuable assistance in the field. I would also
147
like to thank Scott Meyer, Dan Reed, Bob Clark, and Jack Erickson for helpful edits on an earlier draft. Funding for this project was provided in part by Federal Aid in Sport Fish Restoration Grant Number F-10-25. References Alverson, D.L., Freeberg, M.H., Pope, J.G., Murawski, S.A., 1994. A global assessment of fisheries by catch and discards. FAO Fisheries Technical Paper No. 339. Bartholomew, A., Bohnsack, J.A., 2005. A review of catch-and-release angling mortality with implications for no-take reserves. Rev. Fish Biol. Fish. 15, 129–154. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32. Campbell, M.D., Patino, R., Tolan, J., Straus, R., Diamond, S.L., 2010a. Sublethal effects of catch-and-release fishing: measuring capture stress fish impairment, and predation risk using a condition index. ICES J. Mar. Sci. 67, 513–521. Campbell, M.D., Tolan, J., Strauss, R., Diamond, S.L., 2010b. Relating anglingdependent fish impairment to immediate release mortality of red snapper (Lutjanus campechamus). Fish. Res. 106, 64–70. Cooke, S.J., Cowx, I.G., 2004. The role of recreational fisheries in global fish crises. Bioscience 128, 857–859. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J., 2007. Random forests for classification in ecology. Ecology 88, 2783–2792. Davis, M.W., 2002. Key principles for understanding fish bycatch discard mortality. Can. J. Fish. Aquat. Sci. 59, 1834–1843. Davis, M.W., Ottmar, M.L., 2006. Wounding and reflex impairment may be predictors for mortality in discarded or escaped fish. Fish. Res. 82, 1–6. Davis, M.W., 2007. Simulated fishing experiments for predicting delayed mortality rates using reflex impairment in restrained fish. ICES J. Mar. Sci. 64, 1535–1542. Genuer, R., Poggi, J.M., Tuleau-Malot, C., 2010. Variable selection using random forests. Pattern Recogn. Lett. 31, 2225–2236. Hannah, R.W., Matteson, K.M., 2007. Behavior of nine species of Pacific rockfish after hook-and-line capture recompression, and release. Trans. Am. Fish. Soc. 136, 24–33. Hannah, R.W., Parker, S.J., Matteson, K.M., 2008a. Escaping the surface: the effects of capture depth on submergence success of surface-released Pacific rockfish. North Am. J. Fish. Manage. 28, 694–700. Hannah, R.W., Rankin, P.S., Penny, A.N., Parker, S.J., 2008b. Physical model of the development of external signs of barotrauma in Pacific rockfish. Aquat. Biol. 3, 291–296. Harrington, J.M., Myers, R.A., Rosenberg, A.A., 2005. Wasted fishery resources: discarded bycatch in the USA. Fish Fish. 6, 350–361. Hochhalter, S.J., Reed, D.J., 2011. The effectiveness of deepwater release at improving the survival of discarded yelloweye rockfish. North Am. J. Fish. Manage. 31, 852–860. Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression, 2nd ed. John Wiley and Sons, Inc., New York, NY. Love, M.S., Yoklavich, M., Thornsteinson, L., 2002. The Rockfish of the Northeast Pacific. University of California Press, Berkeley. Pacific Fishery Management Council and National Marine Fisheries Service, 2009. Proposed acceptable catch and optimum yield specifications and management measures for the 2009–2010 Pacific coast groundfish fishery final environmental impact statement including regulatory impact review and initial regulatory flexibility analysis. Pacific Fishery Management Council, Portland, OR. Parker, S.J., McElderry, H.I., Rankin, P.S., Hannah, R.W., 2006. Buoyancy regulation and barotrauma in two species of nearshore rockfish. Trans. Am. Fish. Soc. 135, 1213–1223. Pitcher, T.J., Watson, R., Forrest, R., Valtysson, H., Guenette, S., 2002. Estimating illegal and unreported catches from marine ecosystems: a basis for change. Fish Fish. 3, 317–339. Pribyl, A.L., Schrek, C.B., Kent, M.L., Parker, S.J., 2009. The differential response to decompression of three species of nearshore Pacific rockfish. North Am. J. Fish. Manage. 29, 1479–1486. R Development Core Team, 2004. R: A Language and Environment for Statistical Computing. R Found. for Stat. Comp., Vienna, Austria, Available at: www.Rproject.org. Rummer, J.L., Bennett, W.A., 2005. Physiological effects of swim bladder overexpansion and catastrophic decompression on red snapper. Trans. Am. Fish. Soc. 134, 1457–1470. Trumble, R.J., Kaimmer, S.M., Williams, G.H., 2000. Estimation of discard mortality rates for Pacific halibut bycatch in groundfish longline fisheries. North Am. J. Fish. Manage. 20, 931–939.