Peer Review #2 of "The fishing and natural mortality of large, piscivorous Bull Trout and Rainbow Trout in Kootenay Lake, British Columbia (2008–2013) (v0.1)"

8 Background. Estimates of fishing and natural mortality are important for understanding, and ultimately managing, commercial and recreational fisheries. High reward tags with fixed station acoustic telemetry provides a promising approach to monitoring mortality rates in large lake recreational fisheries. Kootenay Lake is a large lake which supports an important recreational fishery for large Bull Trout and Rainbow Trout. 9

The fishing and natural mortality of large, INTRODUCTION 33 Since the late 1980s, the large piscivorous Rainbow Trout in Kootenay Lake have exhibited population  Here we document a combined tag-telemetry study to estimate the natural and fishing mortality of  (Cormack, 1964;Jolly, 1965;Seber, 1965)    The study was made possible by the pre-existence of an acoustic receiver array. The array, which was sup-87 plemented so that it included a total of 25 VR2(W) 81 kHz Vemco® acoustic receivers in Kootenay Lake 88 (Fig. 1), was originally designed to track juvenile White Sturgeon (Acipenser transmontanus) (Neufeld 89 and Rust, 2009). The array has also been used to track fluvial Bull Trout (Paragamian and Walters, 2011)  The acoustic receiver VRL download files were processed using Vemco® VUE software to calculate 151 the number of hourly detections of each transmitter by each acoustic receiver. The reported recaptures 152 were recorded by MFLNRO staff and the rewards administered by the Freshwater Fish Society of British 153 Columbia (FFSBC). Based on lake geomorphology and the locations of the receivers (Fig. 1) the study 154 area was divided into 30 sections (Fig. 2). 155 Hourly Receiver Data 156 The hourly detection, receiver deployment, fish capture and recapture data sets and sectional shapefiles 157 were manipulated using R version 3.3.2 (R Core Team, 2015). During data manipulation, two or less 158 detections of a fish at a receiver in an hour or any detections after the expected tag life were discarded.

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The resultant clean and tidy (Wickham, 2014) data sets were bundled together in an R data package called 160 klexdatr (Article S1).

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The hourly receiver detection data were then aggregated into daily sectional detection data using the lexr 163 R package (Article S1). During the aggregation process, acoustically tagged individuals that 30 days after 164 release were no longer detected or were only detected at a single section were classified as post-release   The probabilities of being recaught (and reported) by an angler versus dying of other causes were estimated from the seasonal data using an individual state-space (Royle, 2008; Kéry and Schaub, 2011) Cormack-Jolly-Seber (CJS) (Cormack, 1964;Jolly, 1965;Seber, 1965) survival model. In the individual state-space formulation of the CJS model, the ith individual is alive when it is initially tagged at time period f i , e.g. (1) Its latent state (alive versus dead) at subsequent periods is modelled as the outcome of a series of Bernoulli trials where φ i,t is the predicted survival for the ith fish in the tth time period. An individual that is alive at period t also has a probability ρ i,t of being recaptured, i.e.,

Base Model
In the base model of the current study, ρ i,t is the probability of being recaptured (and reported) by an angler. To reduce the number of necessary assumptions, reported recaptures are excluded from the analysis for all subsequent time periods, i.e., subsequent detections or recaptures of any released individuals were ignored. In addition, a living individual with an active transmitter (T i,t = 1) also has a probability (δ i,t ) of being detected moving (m i,t ) between sections by the receiver array Finally, in the spawning season (S i,t = 1) a fish has a probability κ i,t of entering the state of spawning (x i,t = 1) for the period In the base model, the terms φ i,t , ρ i,t , δ i,t and κ i,t , which represent probabilities, are specified by four parameters, i.e., logit(δ i,t ) = β δ 0 (8) and The full model extends the base model through the inclusion of seven additional parameters (β κL , β φ x , β δ S , β κY , β ρY , β φY and β δY ). The first parameter (β κL ) allows the log odds probability of spawning to vary with the calculated fork length (L i,t ) while the second (β φ x ) allows the log odds survival to vary with spawning The fork lengths were calculated based on the measured length at capture L i, f i plus the length increment expected under a Von Bertalanffy Growth Curve (Walters and Martell, 2004)  The third additional parameter (β δ S ) allows the log odds probability of being detected moving to vary with the spawning season while the last four parameters (β κY , β ρY , β φY and β δY ) allow the probability of spawning, recapture, survival, and detection moving between sections to vary with the standardised year (Y i,t ), i.e, The parameters in the full model are defined in Table 1.
In other words if the indicator is 1 then the parameter is drawn from the standard vague prior, otherwise 214 the parameter is drawn from a prior that is so constrained that its value is effectively zero. The values of 215 γ ρY and γ φY indicate the support for a change in F and M, respectively, over the course of the study.

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Software. The analyses were performed using R version 3.3.2 (R Core Team, 2015), JAGS 4.2.0 (Plum-217 mer, 2015) and the klexr R package (Article S1), which was developed specifically for this paper .   3. There is no loss of anchor or acoustic tags.   (Table 4). Of the 27 Bull Trout, seven (26%) were last 264 detected moving at section S07, one (4%) at S20 and zero (0%) at S32. For Rainbow Trout, the grand 265 total was 48 individuals (Table 5), of which seven (15%) were last detected at section S07, four (8%) at 266 S20 and three (6%) at S32.  The survival probability for Bull Trout was largely unaffected by spawning (γ φ κ = 0.29) but changed over There was little support (γ κY = 0.23) for a change in the probability of spawning for Bull Trout from 308 2009 to 2013. However, in contrast, there was strong support (γ κY = 1.00) for a decline in the probability 309 of spawning for Rainbow Trout over the course of the study (Fig. 8).

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The natural mortality is plotted by fork length in Figure 9.   in the spring at the top of the North Arm or at section S02 at the outflow of Trout Lake (Table 5)  lake other than temporary seasonal spawning migrations that are accounted for by the model.

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Recapture A key assumption of all mark-recapture studies is that there are no unmodelled individual 375 differences in the probability of (re)capture in each time interval (Biro, 2013). As is often the case, 376 the reliance on a single capture method means it is not possible to test this assumption (Biro, 2013).

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Depending on whether any individual differences were fixed or learned the estimated potential fishing  temporarily inactive individual or one that is subsequently recaptured will not be misidentified as a natural 397 mortality. Consequently any bias in M is expected to be small.

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Reporting To maximize the tag reporting rate, which was assumed to be 100%, at least one of the  Trout in Kootenay Lake are unknown but could include less efficient angling methods (anglers primarily 457 target Rainbow Trout) or more uniform fish distributions.

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Catchability is typically strongly negatively related to the population density -a phenomenon known as hyperstability (Ward et al., 2013). Based on a meta-analysis of Lake Trout (Salvelinus namaycush) in 12 lakes in Ontario, Canada, Shuter et al. (1998) modelled the relationship between catchability and density (D) using a modified form of the equation In Kootenay Lake in 2011, Bull Trout occured at a density of 0.75 fish.ha −1 and experienced an effort of  There was little to no support for a change in the fishing mortality for either Bull Trout or Rainbow Trout 471 over the course of the study. This finding is important because, as discussed below, it suggests that angler 472 effort is not the primary driver of any short-term fluctuations.

Spawning
For Bull Trout the CJS model did not identify an increase in the mortality rate associated with spawning. 510 However, as discussed above spawners which died prior to returning to Kootenay Lake would have 511 been misidentified as inlake mortalities. It is therefore likely that the natural mortality of spawners and 512 non-spawners are under and over-estimated, respectively.

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For Rainbow Trout, spawning increased the annual interval natural mortality rate from 23% to 59%.

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The high spawning mortality is consistent with the limited spawning area and high levels of antagonistic 515 interactions at the outflow of Trout Lake (Hartman, 1969;Hartman and Galbraith, 1970). The high 516 spawning mortality may also explain why the probability of spawning is low for Kootenay Lake Rainbow

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An additional key finding is that the probability of spawning declined dramatically for Rainbow 527 Trout over the course of the study. Given the high mortality cost of spawning, any reduction in the 528 energy available for gamete production associated with the decline in Kokanee abundance might cause 529 fish to delay spawning. An important implication of this potential relationship is that Rainbow Trout 530 escapement at the outflow to Trout Lake may be a less reliable index of population abundance than 531 previously assumed. Perhaps due to their density-dependent phenotypic plasticity the probability of 532 spawning remained constant for Bull Trout (Johnston and Post, 2009).

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It is important to note that the results of the current study do not preclude changes in angler effort or 534 lake productivity as drivers of longer-term trends in abundance. Indeed in a recent modelling study Kurota

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Based on the recapture rates and creel survey catch estimates, the current study found that in 2011 538 large Bull Trout were at least as abundant as large Rainbow Trout in Kootenay Lake. This finding is 539 important because it means that multi-species population models such as that developed by Kurota et al.

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(2016) need to be expanded to take Bull Trout into account.    The standardised year. FL The calculated fork length. L (FL -600) / 100 β δ 0 The log odds seasonal probability of being detected moving among sections. β δ S The effect of spawning season on β δ 0 . β δY The effect of Y on β δ 0 . β κ0 The log odds probability of spawning. β κL The effect of L on β κ0 . β κY The effect of Y on β κ0 .
The log odds seasonal probability of surviving.