NOAA FISHERIES Pinniped-Fishery Interaction Task Force

Interim Questions of the States, NOAA and COE

1) Additional threshold information is needed for all task force members to agree whether or not pinnipeds are having a ‘significant negative impact’ on ESA listed fish. The following information is needed about fish that are likely to interact with pinnipeds:

During the time of pinniped predation… • What proportion of fish passing Bonneville are ESA listed? • What is the status and status trends of each of these stocks individually?

o What are the most critical life history time frames for these stocks? • How does delayed mortality factor in to concerns about the interaction? • What is the percentage of pinniped predation on hatchery vs. wild fish? • What is the age distribution of the fish taken? • Do we have a sense if predation is on one stock more than others?

o Characterize them by status and susceptibility to predation. o Break them into smaller timeframes.

Other related threshold questions: • What is the proportion of the other threats, impacts and limiting factors on the listed fish

stocks (e.g. commercial, recreational & tribal harvest, hydro, habitat?) • What actions are underway to alleviate these ‘other’ threats? • What is the status of litigation underway in response to those threats (i.e. is it possible

that other discussions will be impacting or changing the current threats to a degree that there is a likely impact on the pinniped-fishery interaction issue?)?

• How does the decline of fall stocks compare to those spring stocks impacted by pinniped predation? (note: graphic depiction was requested)

• http://marineresearch.oregonstate.edu/assets/page_folders/faculty_page/horning_hp.htmHow much take is allowed for tribal ceremonial harvest as compared with the amount of take by pinniped predation?

2) More information is needed on the following questions to assist future discussions and task force decision making:

• What will it take to deter the animals at Bonneville? As this is a recent phenomenon, what is the potential of impacting behaviors with lethal take?

o What is the expected behavioral impact of the proposed action on naïve animals? • What are other non-lethal alternatives that may be funded and/or implemented in the next

few years? (note: information on the Smith-Root info was sent to Task Force members on 9/6/07)

• More trend analysis is requested on Stansell’s data: o What impact might a total of 500-1000 sea lions at Bonneville have on the

salmonid population?

• What information is available on marine mammals in the Willamette?

o What can be said generally? Information on specific individuals? Are they also visiting Bonneville?

3) Further information was requested on the marked animals identified each year:

• How many animals are marked per year? • How many are new each year? • In terms of any decision to select individual animals to remove, the task force would like

to see data on which marked animals were present during which years to determine from data sets which animals are the real ‘culprits’ (e.g. dossiers).

4) Is there any quantifiable data about the impacts to public safety that might assist the task force in answering questions related to this issue, given the increasing numbers of reported ‘aggressive’ sea lions? 5) What are the underlying objectives for what the proposed action is trying to accomplish?

• Answer given at TF meeting: To ensure that predation on listed fish does not increase; to reduce predation back to a background level; and to do so with a minimal impact on the sea lion population.

Question 2.3. More trend analysis is requested on Stansell’s data: What impact might a total of 500-1000 sea lions at Bonneville have on the salmonid population? We attempted to answer this question in several ways. First, we fit a simple linear regression model to estimate daily per capita salmon consumption. Second, we estimated daily energy requirements based on a biogenetics model of sea lions on a 100% salmon diet. Lastly, we compared the total estimated salmonid consumption for 2007 against the adjusted and unadjusted estimates of the total number of California sea lion days (CSL-days) for that year. The results from all of these methods were then scaled up to a population size of 500 CSLs feeding for 32 days (the bias-adjusted mean residency time for 2007; see Question 3, Tables 3B and 3C). Regression analysis The data used for this approach comes from ACOE observations of 151 highly identifiable CSLs seen at Bonneville Dam from 2002-2007 (Tables 3.2, 3.3). The response variable is the total observed number of salmon consumed and the explanatory variable is the total number of days observed. A simple linear regression model through the origin (i.e., no intercept parameter) shows that a straight line relationship is reasonable (Fig. 2.3a) but that there is some indication of non-normal (Fig. 2.3b) and heterogeneous residuals (Fig. 2.3c). Given the robustness of least squares estimates to these assumption violations, and the nature of this exercise, we chose to use this model for interpretation rather than fit an alternative model (e.g., one with log transformations of both the response and explanatory variables). It is estimated that the per capita salmon predation rate is 0.76 salmon/day. A 95% confidence interval is approximately 0.72 to 0.81 salmon/day. The percentage of the total variation in salmon consumption explained by the number of days observed (R2) was 86.7%; the P-value associated with the slope coefficient was <0.0001. It should be noted that the estimated relationship is probably biased low since not all predation by a given individual on a given day is likely to be observed. Other potential explanatory variables to explore might include relative size, number of years observed, and possibly reproductive status of the branded subset of animals seen on the breeding grounds. Multiplying the estimated daily predation rate of 0.76 salmon/day by 500 individual CSLs with an average residency of 32 days equals 12,160 salmon. A 95% confidence interval is 11,520 to 12,960 salmon (uncertainty in residency time not included). Bioenergetics analysis A bioenergetic analysis of CSL energy requirements was conducted in fall 2006; an updated version of that analysis is provided in Appendix 2.3. The objective was to estimate the energy requirements of CSLs based on a 100% salmon diet. Under this model, the mean individual food requirement was estimated to be 9.8 kg/day (21.6 lbs/day). A 95% confidence interval was 5.2 to 16.4 kg/day (11.46 to 36.15 lbs/day). The results from this model were consistent with data from captive CSLs (Kastelein et al. 2000) that showed adult (age 10) males consumed

approximately 4,000 kg/year or 10.9 kg/day (24 lbs/day) on a diet of mackerel, herring, sprat, and squid. Multiplying the estimated daily energy requirement of 9.8 kg/day by 500 individual CSLs with an average residency of 32 days equals 156,800 kg (345,645 lbs). A 95% confidence interval is 83,200 to 262,400 kg (183,425 to 578,493 lbs). Translating these biomass estimates into numbers of salmon requires an estimate of the mean weight of salmon being consumed by sea lions. During 2006, a PIT-tag was recovered from a California sea lion scat at Bonneville Dam that we were able to trace back to an adult spring Chinook that was captured, tagged and released downstream from the Bonneville fish trap by University of Idaho researchers. The average weight of spring Chinook from that study was 6.60 kg (14.5 lbs) (SD=3.71, n=358) (personal communication, Chris Perry, University of Idaho). This translates to an estimated daily per capita predation rate of 1.48 salmon/day. Dividing the total salmon biomass requirements by 6.60 kg equals 23,758 salmon. A 95% confidence interval is 12,607 to 39,758 salmon. It should be noted that the result from the bioenergetic model likely underestimates energy requirements for sea lions at Bonneville Dam. This is due to an unrealistic assumptions about growth in body mass and potential underestimation of basal metabolic rates. Similarly, the assumption of a 100% salmonid diet is likely false too, though recent scat analyses suggest it is indeed upwards of 90-95%. Though certainly not the norm, anecdotal evidence that food consumption can be much higher than predicted comes from ACOE observations of an observed predation rate of 10 salmon (~66 kg, ~145 lbs) in one day and Kastelein et al.’s (2000) observations of seasonal maximums in food intake of up to 35 kg/day (77 lbs/day). 2007 observations The estimated take of salmon in 2007 by sea lions at Bonneville Dam was 3,859 fish. If we could divide this by the total number of sea-lion-days at the dam, then the result would be a daily per capita predation rate. For example, a take of 100 fish divided by 100 animal-days equals one fish per animal-day. Animals-days may be the product of 10 animals over 10 days (=100 animal-days), one animal over 100 days (=100 animal-days), or 100 animals over one day (=100 animal-days); while its not necessary to know the product components for this analysis, we will revisit this issue below in Question 3. So the question is “How many CSL-days were there in 2007”?. One approach is to sum up the daily counts of the minimum number of CSLs observed by the ACOE. This yields approximately 1,866 CSL-days (sum of n2i in Table 3A) and thus a daily per capita predation rate of 3,859 salmon/1,866 CSL-days=2.06 salmon/day. Multiplying the estimated daily predation rate of 2.06 salmon/day by 500 individual CSLs with an average residency of 32 days equals 32,960 salmon. A 95% confidence interval was not calculated for this exercise but is possible to compute using a bootstrap approach. One problem with using count data, however, is that count data almost always represent some unknown fraction of the true quantity being estimated (i.e., they are negatively biased). This is due to detection probabilities and/or sampling fractions being less than one. Conceptually we have the following situation (from Williams et al. 2002:241):

βα ˆˆˆ CN = ,

where N is the true quantity of interest (e.g., abundance), C is the count statistic (e.g., observed abundance), α is the sampled fraction of the total area over which the phenomena of interest is distributed, and β is the probability of detecting the item of interest (e.g., sea lion, predation event) given that it is present or occurred in the sampled area. While the ACOE does an exceptional job of identifying “marked” California sea lions (based on brands or natural characteristics such as scars, fungal patches, etc.), there are inevitably some animals that are not identified or even observed even though they are present in the tailrace. Therefore, as is noted by the ACOE, the annual tallies of identifiable sea lions reported at Bonneville Dam are minimum estimates of the true numbers of animals that occur at the dam each season. We attempted to account for imperfect detectability in the estimate of total CSL-days (1,866) by using the proportions of “marked” (branded and otherwise identifiable) and unmarked animals to construct daily closed population estimates of abundance based on simple two-sample bias-adjusted Lincoln-Peterson estimators (see Question 3, Table 3A). Doing so yields an estimate of 2636 CSL-days and thus a daily per capita predation rate of 3,859 salmon/2,636 CSL-days=1.46 salmon/day. Multiplying the estimated daily predation rate of 1.46 salmon/day by 500 individual CSLs with an average residency of 32 days equals 23,360 salmon. Again, a 95% confidence interval is possible to compute using a bootstrap approach but was not done for this exercise. Conclusions Per capita predation rate point estimates ranged from 0.76 to 2.06 salmon/day and associated point estimates of total salmon consumption from 12,160 to 32,960 salmon (Table 2.3). As noted above, considerable uncertainty exists in these estimates due to potential biases related to imperfect detectability and limited spatio-temporal sampling. Literature cited Kastelein, R. A., N. M. Schooneman, N. Vaughan, and P. R. Wiepkema. 2000. Food

consumption and growth of California sea lions (Zalophus californianus californianus). Zoo Biology 19:143-159.

Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of animal

populations. Academic Press. 817 pgs.

Table 2.3. Summary of daily per capita predation rates and salmon consumption estimates under four estimation methods.

Method Daily per capita predation

rate (salmon/day/CSL)Estimated salmon consumption

(N=500 CSLs, d=32 days)Regression analysis 0.76 12,160Bias-adjusted 2007 observations 1.46 23,360Bioenergetics analysis 1.48 23,758Unadjusted 2007 observations 2.06 32,960

a. b. c.

NoDays

NoS

alm

on

0 50 100 150 200

050

100

150

200

Quantiles of Standard Normal

Res

idua

ls-2 -1 0 1 2-6

0-4

0-2

00

2040

12

25

Fitted : (-1) + NoDays

Res

idua

ls

0 20 40 60 80 100 120 140

-60

-40

-20

020

40

12

25

Figure 2.3. Simple linear regression fit (a.) and regression diagnostics (b., c.) for observed salmonid consumption (y-axis: ‘NoSalmon’) on days observed (x-axis: ‘NoDays’) for 151 highly identifiable California sea lions at Bonneville Dam, 2002-2007. [Data source: R. Stansell, ACOE.]

Appendix 2.3. Draft California sea lion bioenergetics model. Bryan Wright, ODFW 10/13/06 (updated: 9/20/07) Introduction Bioenergetics is the study of energy flow through a system and can be used to model energy consumption by individuals, populations, or communities. Bioenergetic models can be simple or complex, but as with all models, it should be kept in mind that they are simplified abstractions of reality. They can be useful, however, as an aid to conceptualizing complex systems and to gain insight by comparing their predictions to observations. The objective of this analysis is to estimate the energy requirements of California sea lions based on a 100% salmon diet; it is based on a bioenergetic model for Steller sea lions developed by Winship et al. (2002) and Winship and Trites (2003). Methods Bioenergetic parameters The bioenergetic model used in this analysis was adapted from Winship et al. (2002) and Winship and Trites (2003). Daily salmon biomass requirements (BR) were estimated using:

diet

jufHIF

j ED

*prey*E*EE

(A*BM)P

BR jj⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ +

= (kg/d) (1)

The terms in parentheses in the numerator constitute the gross energy requirements (GER) for sea lions (measured in kJ/d). The denominator represents the energy density of prey (measured in kJ/kg). Parameter definitions and their values are the following: P—Production. Production is primarily growth in body mass. Winship et al. (2002) estimated age, sex, and season-specific energy requirements for Steller sea lion growth based on a multitude of inputs (see Eqs. 7-9 in Winship et al. 2002). This was necessary since their objective was to estimate population-wide energy requirements on an annual basis. In contrast, in the Columbia River we are only dealing with presumably adult male sea lions for a relatively short period (March-May). While it seems likely that they would be gaining mass in preparation for migration and (potentially) breeding, I have currently set P to zero pending future consideration of this topic. As a result, gross energy requirements will likely be underestimated.

0=P (2)

A—Activity metabolic multiplier. The energetic cost of activity is incorporated using a multiplier of basal metabolic rate. Winship et al. (2002) estimated A (in their case for each sex, sexual state and day of the year) according to:

landwater AwaterAwaterA *)1(* −+= (3)

where water is the proportion of time spent in the water, and Awater and Aland are multipliers of basal metabolic rate for water and land, respectively. I used Winship et al.’s (2002) parameter values for mature males during the non-breeding season:

water ~ triangle1(0.70, 0.55, 0.85) (3.1)

Aland ~ triangle(1.2, 1.0, 1.4) (3.2)

Awater ~ triangle(4.0, 2.5, 5.5) (3.3)

The distribution used to estimate the proportion of time spent in the water (3.1) may not be accurate for California sea lions in the Columbia River. For example, up until recently sea lions were not known to haul-out at Bonneville Dam. If water is an underestimate then the gross energy requirements for sea lions will be underestimated. BM—Basal metabolism. Basal metabolism is a function of body mass (M) and Winship et al. (2002) used the following predictive equation from Kleiber (1975)2:

BM (kJ/d) = 292.88*M 0.75 (4)3

1 Triangular sampling distributions are defined by a median, a lower limit, and an upper limit. Half of the values sampled were less than the median and half greater. Between the median and the limits, the probability of a value being sampled was directly proportional to its distance from the median. Triangular distributions were sampled using the R package ‘Triangle’. 2 “Kleiber’s Law”: “In the range between the lower and upper critical environmental temperatures, the fasting and resting animal has a minimal rate of heat production called the basal metabolic rate. This rate can be estimated rather closely from the body weight by the relation: BM = 70 (kcal/d) M0.75.” 3 Other authors use 293.76 instead of 292.88. In addition, Costa and Williams (1999) suggest that the basal metabolic rates for otariids are greater than predicted for terrestrial mammals. If so, then energy requirements will be underestimated. Energy conversions come from http://www.onlineconversion.com/energy.htm: 70 kcal[15°C] = 293.006 kJ/day; 70 kcal[I.T.] = 293.076 kJ/day; 70 kcal[thermochemical] = 292.88 kJ/day [used by Winship]; 70

Estimates for mass of California sea lions were based on the weights of 45 C-branded animals that had been observed at Bonneville Dam and had been weighed one or more times at ODFW’s trap in Astoria. The mean and standard deviation of these weights were used to define the following distribution:

M (kg) ~ normal(197, 42) [434, 93 lbs] (4.1)

Eu—Urinary digestive efficiency. In Winship et al. (2002), urinary (Eu) and fecal (Ef) digestive efficiency are specified as the product of their respective uniformly distributed random variables. In Winship and Trites (2003), the authors calculated Ef a different way (see Ef below). I therefore, used the Winship et al. (2002) parameterization for urinary efficiency (Eq. 5) and the Winship and Trites (2003) parameterization for fecal efficiency.

Eu~uniform(0.90, 0.93) (5)

Ef—Fecal digestive efficiency. Winship and Trites (2003) calculate fecal digestive efficiency and the heat increment of feeding (HIF) for maintenance (see EHIF below) differently than Winship et al. (2002). Namely, they estimate these processes as functions of the energetic density of the prey (see EDdiet below). This is done with the following equation:

))(exp(1 0EDEDk

XEj

f j −−+= (6)

X ~ normal(0.951, 0.0039) (6.1)

k ~ normal(1.86, 0.016) (6.2)

ED0 ~ normal(2.10, 0.089) (6.3)

and where EDj = energy density of prey category j (kJ/g wet mass) (see EDdiet below). The product of Eu and Ef replaces the values for Ef+u in Table 1 of Winship et al. (2002). EHIF—Efficiency of utilization of metabolizable energy. As with Ef, the efficiency of utilization of metabolizable energy (or one minus HIF) is calculated as a function of the energetic density of prey. This is done with the following equation: kcal / 24 hours[15°C] = 3.391 watthour; 70 kcal / 24 hours [I.T.] = 3.392 watthour; 70 kcal / 24 hours [thermochemical] = 3.389 watthour [3.4 Watts used by Olesiuk].

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ +−=

uf

jHIF EE

bEDaE

j

j **

1 (7)

a ~ normal(0.951, 0.0039) (7.1)

b ~ normal(1.86, 0.016) (7.2)

and where EDj = energy density of prey category j (kJ/g wet mass) (see EDdiet below). preyj—Proportion of total diet biomass comprised of prey species category j. Winship et al. (2002) used seven different prey categories in their model of Steller sea lion energy requirements: cephalopods, flatfish, forage fish, gadids, hexagrammids, salmon, and “other”. They portioned diet among these categories based on a split-sample frequency occurrence analysis of scat collected in Alaska in the 1990s. We have very little scat data for California sea lions in the Columbia River. Therefore, as a worst-case scenario, we are assuming sea lions are only consuming one prey (salmon) though they have been observed eating other prey types (shad, northern pikeminnow, lamprey, sturgeon). This assumption may bias salmon consumption estimates high in areas where there are multiple prey items available.

preyj = 1 (8)

EDdiet—Mean weighted (by preyj) energetic density of the diet. The formula used by Winship et al. (2002) was the following:

∑=j

jjdiet EDpreyED (9)

But since we are assuming only one prey category (salmon), EDdiet is simply EDj. The energetic density of salmon was assumed to follow the following distribution (from Winship et al. 2002):

EDj (kJ/g)~uniform(5.0, 9.0) (10)

Monte Carlo simulation The above formulas and parameters were programmed into an R function (available upon request) that computes the energy requirements for a user-specified number of animals and days. Uncertainty is assessed by calculating the 2.5 and 97.5 percentiles from 10,000 independent replications.

Results The average individual food requirement under this model was 9.8 kg/day. A 95% confidence interval was 5.2 to 16.4 kg/day. Discussion It is important to note that bioenergetic models produce estimates of food requirements, not food consumption. Nevertheless, the results from this model were consistent with data from captive California sea lions (Kastelein et al. 2000) that showed adult (age 10) males consumed approximately 4,000 kg/year or 10.9 kg/day on a diet of mackerel, herring, sprat, and squid However, when applied to pre-migratory California sea lions at Bonneville Dam this model likely underestimates energy requirements. This is due to an unrealistic assumption of no growth in body mass (P, Eq. 2) and potential underestimation of basal metabolism (BM, Eq. 4). With respect to production (Eq. 2), we know from capture-recapture data of animals before and after visiting Bonneville Dam that at least one had gained almost 500 pounds. The assumption of a 100% salmonid diet is likely false too, though recent scat analyses suggest it is indeed upwards of 90-95%. Future work on this model will include refinements to bioenergetic and diet inputs and a detailed comparison with ACOE estimates of consumption based on direct observation. Additional literature to review includes Jobling (1987) and Rosen and Trites (2000). Literature cited Costa, D. P. and T. M. Williams. 1999. Marine Mammal Energetics. Pages 176-217 in J. E.

Reynolds III and S. A. Rommel, editors. Biology of marine mammals. Smithsonian Institution Press, Washington, D.C., USA.

Jobling, M. 1987. Marine mammal faeces samples as indicators of prey importance--a source of

error in bioenergetics studies. Sarsia 72:255-260. Kastelein, R. A., N. M. Schooneman, N. Vaughan, and P. R. Wiepkema. 2000. Food

consumption and growth of California sea lions (Zalophus californianus californianus). Zoo Biology 19:143-159.

Kleiber, M. 1975. The fire of life: an introduction to animal energetics. Rosen, D. and A. W. Trites. 2000. Digestive efficiency and dry-matter digestibility in Steller

sea lions fed herring, pollock, squid, and salmon. Canadian Journal of Zoology 78(2):234–239.

Winship, A. J. and A. W. Trites. 2003. Prey consumption of Steller sea lions (Eumetopias

jubatus) off Alaska: how much prey do they require? Fisheries Bulletin 101:147-167.

Winship, A. J., A. W. Trites, and D. A. S. Rosen. 2002. A bioenergetic model for estimating the food requirements of Steller sea lions in Eumetopias jubatus in Alaska, USA. Marine Ecology Progress Series 229:291-312.

Question 2.4. What information is available on marine mammals in the Willamette? What can be said generally? Information on specific individuals? Are they also visiting Bonneville? Less than a dozen California sea lions have been seen annually at Willamette Falls, Oregon, for the past 12 years (1996-2007) (Table 2.4). A total of eight branded sea lions have been observed, four of which have been seen in multiple years (C122, C199, C257, and C313) and two of which have also been seen at Bonneville Dam (C235, C257). Table 2.4. Summary of California sea lion occurrence at Willamette Falls, Oregon, 1996-2007. Sea lion occurrence was monitored on a systematic basis from 1996-2002; thereafter observations have been on an anecdotal basis. [Data source: ODFW.]

Willamette brands (W=Willamette Falls, B=Bonneville Dam) Year

First obs.

Last obs.

Min. # CSLs (# brands) C117 C122 C172 C199 C235 C257 C313 C486

1996 4/2 5/4 7 1997 4/9 5/15 4 1998 3/25 5/15 4 1999 3/26 5/19 10 (2) W W 2000 2/7 6/1 5 (1) W 2001 3/22 5/18 5 (1) W 2002 4/4 5/15 8 (3) W W W, B 2003 2/19 6/4 5 (1) W B 2004 4/5? ? 2 (2) B B W W 2005 2/23 ? 2 (2) B W W 2006 3/30 4/12 4 (1) B W 2007 2/20 5/3 5 (2) W* W

* Partial brand-read (C_57) was assumed to be C257.

Question 3. Further information was requested on the marked animals identified each year. Information on marked CSLs is present below (Questions 3.1-3.3). First, however, we address a related issue of how to estimate the total number of animals (marked and unmarked) ever in the sample area (i.e., Bonneville Dam tailraces) between the first and last sampling occasions of the study (i.e., 1/1-5/31). Results from this analysis were used above in Question 2.3. As noted in Question 2.3, the ACOE does an exceptional job of identifying “marked” California sea lions (based on brands or natural characteristics such as scars, fungal patches, etc.) but there are inevitably some individuals that cannot be confidently identified from day to day or year to year. Therefore, as is noted by the ACOE, the annual tallies of identifiable sea lions (see Table 3.2) are minimum estimates. A variety of methods have been proposed for estimating the true number of animals using a site, variously referred to as “volume”, “superpopulation”, and “transient” population estimation. Most application have been applied to migratory bird populations using a particular stopover or staging site (Nichols and Kaiser 1999, Routledge et al. 1999, Frederiksen et al. 2001). The “superpopulation” approach (Williams et al. 2001:508) uses a reparametrized Jolly-Seber open population model to estimate the total number of animals available for capture at any time during the study. One potential problem of applying that approach to the Bonneville data is a violation of the assumption of no temporary emigration. We know from resight and satellite-tag data that sea lions frequently make several trips between Astoria and Bonneville during a season. Another approach is that used by Routledge et al. (1999) whereby the total population is estimated by taking the ratio of the estimated total number of days that animals stayed in the study area to the estimated average residence time. We used this basic idea but applied a different approach to estimation than presented in their paper. Methods The total number of CSLs ever in the Bonneville Dam tailraces between the 1/1/07 and 5/31/07 is denoted by N0 (the “superpopulation” size). It was estimated as:

⎟⎟⎠

⎞⎜⎜⎝

⎛=

prdN

ˆˆ

ˆˆ 0 ,

where: = estimated total number of CSL-days at the dam from 1/1/07-5/31/07 (based on the sum of daily mark-resight estimates of abundance; see Table 3A);

dr = estimated mean observed

residency time in days (based on observations of marked or otherwise identifiable CSLs that were marked or originally identified prior to 2007; see Table 3B); and = estimated mean daily probability of detection (based on observations of satellite-tagged CSLs known to be at Bonneville Dam during 2007; see Table 3C and Figure 3).

p

Uncertainty in was assessed using two methods: 0N Confidence interval estimation method 1.—Assuming independence of variables (Goodman 1960):

⎟⎠⎞

⎜⎝⎛−⎟

⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛=

rpd

rpd

rpdN

ˆˆ

rav)ˆr(avˆˆ

ravˆˆˆ

)ˆr(av)ˆr(av 22

0 ,

where

⎟⎠⎞

⎜⎝⎛−⎟

⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛=⎟

⎠⎞

⎜⎝⎛

rp

rp

rp

rp

ˆ1rav)ˆr(av

ˆ1ravˆ

ˆ1)ˆr(av

ˆˆ

rav 22

,

and

4ˆ)ˆr(av

ˆ1rav

rr

r≈⎟

⎠⎞

⎜⎝⎛ (from delta method);

)ˆr(av d , , and are shown in Tables 3A, 3B, and 3C, respectively. )ˆr(av r )ˆr(av p

A 95% confidence interval for volume was computed based on a log normal distribution (Johnson and Kotz 1970):

) ,ˆ( 00 cNcN ∗÷ where

⎟⎠⎞⎜

⎝⎛ += )]ˆ(1ln[96.1exp 02 Ncvc

and

20

002

ˆ)ˆr(av)ˆ(

NNNcv = .

Confidence interval estimation method 2.—A 95% confidence interval was calculated based on bootstrap methods implemented in an R function (available upon request). First, the observed residency (Table 3B) and detectability (Table 3C) datasets were sampled with replacement and new estimates r and p were computed. Bootstrap estimates of CSL-days (d) were computed by first sampling without replacement from daily simulated populations of size ( Table 3A) and outputting the resulting marked sample size (m

iN2i, Table 3A). These were then used to calculate a

new for that day. Summing all of the new daily population size estimates yielded a new

estimate for d. was then calculated based on the new bootstrap estimates of r, p, and d and then stored. This was repeated 10,000 times and the 2.5 and 97.5 percentiles from the bootstrap distribution of values were computed to yield confidence limits.

iN0N

Results The total number of animals (marked and unmarked) ever in the Bonneville Dam tailraces between 1/1/07 and 5/31/07 was estimated to be 83 animals. The upper 95% confidence limit based on asymptotic variance formulae was 299 animals; the upper 95% confidence limit based on the bootstrap approach was 111 animals. (The lower limits from each method were less than the known minimum number of animals and were thus uninformative.) Discussion The total number of marked (branded plus natural marks) CSLs that the ACOE identified during the 2007 season was 69 (Table 3.2). The maximum number of unmarked CSLs seen on any one day (i.e., max(n2i-n1i), Table 3A) was 17 (on 5/1/07). While some of the 17 may have subsequently been marked (and thus included in the list of 69) or had their marks overlooked, a “worst-case” scenario would simply be to add them to the marked population yielding an estimate of 86 total animals. This is almost identical to the point estimate of 83 animals. The upper 95% confidence limit based on the bootstrap procedure (the more realistic of the two upper bounds) suggests that the total number was no more than 111. Whatever the exact number, the narrow range (69-111) of estimates attest to the efforts devoted by the ACOE to keep track of all animals that entered their study area. There are many assumptions and caveats associated with an analysis such as this. Estimation of total CSL-days (d) requires the standard assumptions of closed-population mark-resight modeling (Williams et al. 2002:293): (1) geographic and demographic closure; (2) equal resight probability (within sampling occasions i and i+1); and (3) no misread or overlooked marks (i.e., “tag loss”). With respect to closure, we know that many animals travel back and forth between Astoria and Bonneville Dam several times during a season. We therefore chose a two-day sampling window to minimize the magnitude of the closure violation. Nevertheless, both types of migration surely occurred which leads to a positive bias in the Lincoln-Peterson estimator. With respect to equal resight probability, this assumption may be violated if some types of animals have higher probabilities of being observed and identified than others (e.g. branded vs. naturally-marked sea lions). Animals with a higher resight probability than average will have a greater chance of being included in both samples (n1, n2), inflating the resight probability and thus negatively biasing the population size estimate. Overlooking or only partially reading a brand or natural mark would negatively bias the resight probability (and inflate the abundance) only if it tended to occur on the second of the two sampling occasions, which does not seem like a likely scenario. Unbiased estimation of average residency time (r) assumes that the sample of 38 animals in Table 3B is representative of all animals that visited the dam in 2007 (estimated to be 83). The

sample was restricted to animals that were identified prior to 2007 to avoid a potential negative bias induced by the lag time between a truly new animal arriving and the establishment of its identity. If returning animals stay longer than the residency estimate will overestimated. Lastly, there are several issues associated with the use of satellite-tagged animals to estimate detectability p (Table 3C). One is that satellite-tags, while not necessarily increasing the probability of detection per se (i.e., was it seen or not), may increase the probability of identifying the animal (i.e., was it identified or not conditional on it being seen). This is simply due to the satellite-tag being one more “mark” to aid in identification (R. Stansell, personal communication). Unbiased estimation of detectability (p) assumes that the detection probabilities for the ten satellite-tagged animals in Table 3C are representative of the detection probabilities of the 38 animals in Table 3B for whom the bias-adjustment was made. While seven of the ten in Table 3C are also in Table 3B, it may be that detectectability of the other 31 without satellite-tags is lower. (Note that an alternative measure of detectability can be calculated from Table 3A as the mean of (m2i/n1(i-1)); this estimate was 0.712.) A second issue with the satellite-tag data is the spatial uncertainty associated with the location data. Figure 3 depicts the location estimates used to determine whether an animal was available to be seen at Bonneville Dam by ACOE staff. Only ARGOS location class (LC) 1-3 fixes were used, which have a reported error of between <150m (LC 3) and <1 km (LC 1). Even with this relatively small error, the animal may have been outside the tailrace areas used by the ACOE for observation and hence unavailable to them to detect. For example, the ACOE sees many animals only when they are hauled out (usually early morning and only checked 4-5 days a week). Some of these animals are not seen again until the next morning; either they are hunting elsewhere, are more "secretive" in their behavior, or maybe even foraging at night (R. Stansell, personal communication). It could also be that when large numbers of sea lions are present, only so many can be actively foraging in the tailrace observation area without incurring some conflict. Literature cited Frederiksen, M., A. D. Fox, J. Madsen, and K. Colhoun. 2001. Estimating the total number of

birds using a staging site. Journal of Wildlife Management 65:282-289. Goodman, L. A. 1960. On the exact variance of products. Journal of the American Statistical

Association 55708-713. Johnson, N. L. and S. Kotz. 1970. Distributions in statisics: continuous univariate distributions.

Wiley and Sons, New York, NY. Nichols, J. D. and A. Kaiser. 1999. Quantitative studies of bird movement: a methodological

review. Bird Study 46:S289-S298. Routledge, R. D., G. E. J. Smith, L. Sun, N. Dawe, E. Nygren, and J. S. Sedinger. 1999.

Estimating the size of a transient population. Biometrics 55:224-230. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and management of animal

populations. Academic Press. 817 pgs.

Table 3A. Total number of animal-days (d) at Bonneville Dam during 2007 based on sum of daily two-sample, bias-adjusted Lincoln-Peterson abundance estimates*. Marked animals used in analysis are from Table 3.3. [Data source: R. Stansell, ACOE.]

Date n1 n2 m2 N )ˆr(av N

1/1/07 0 0 0 01/2/07 0 0 0 0 0.001/3/07 0 0 0 0 0.001/4/07 0 0 0 0 0.001/5/07 0 0 0 0 0.001/6/07 0 0 0 0 0.001/7/07 0 0 0 0 0.001/8/07 1 1 0 1 0.001/9/07 1 1 1 1 0.00

1/10/07 1 1 1 1 0.001/11/07 1 1 1 1 0.001/12/07 1 3 1 3 0.001/13/07 0 0 0 1 0.001/14/07 0 0 0 0 0.001/15/07 0 0 0 0 0.001/16/07 0 0 0 0 0.001/17/07 0 0 0 0 0.001/18/07 0 0 0 0 0.001/19/07 0 0 0 0 0.001/20/07 0 0 0 0 0.001/21/07 0 0 0 0 0.001/22/07 1 3 0 3 0.001/23/07 1 1 1 1 0.001/24/07 1 1 1 1 0.001/25/07 1 1 1 1 0.001/26/07 2 3 1 3 0.001/27/07 0 0 0 2 0.001/28/07 0 0 0 0 0.001/29/07 2 3 0 3 0.001/30/07 1 1 1 2 0.001/31/07 0 0 0 1 0.002/1/07 0 0 0 0 0.002/2/07 0 0 0 0 0.002/3/07 0 0 0 0 0.002/4/07 0 1 0 1 0.00

Date n1 n2 m2 N )ˆr(av N

2/5/07 2 3 0 3 0.002/6/07 2 2 2 2 0.002/7/07 2 2 2 2 0.002/8/07 4 4 2 4 0.002/9/07 3 3 3 4 0.00

2/10/07 0 0 0 3 0.002/11/07 0 0 0 0 0.002/12/07 2 2 0 2 0.002/13/07 2 3 2 3 0.002/14/07 3 3 2 3 0.002/15/07 3 3 3 3 0.002/16/07 4 4 3 4 0.002/17/07 0 0 0 4 0.002/18/07 4 5 0 5 0.002/19/07 3 4 3 6 0.312/20/07 4 4 3 4 0.002/21/07 3 4 3 6 0.312/22/07 4 5 3 5 0.002/23/07 3 4 3 6 0.312/24/07 4 5 3 5 0.002/25/07 5 6 4 6 0.002/26/07 8 8 5 8 0.002/27/07 7 9 6 12 1.382/28/07 7 10 6 12 0.903/1/07 6 6 6 7 0.003/2/07 5 5 3 10 3.153/3/07 3 4 2 9 5.003/4/07 3 6 2 9 3.113/5/07 5 6 3 6 0.003/6/07 4 5 3 8 1.803/7/07 2 3 2 6 1.113/8/07 4 5 1 8 6.003/9/07 6 8 1 22 78.75

3/10/07 5 6 2 16 21.783/11/07 4 6 3 10 3.15

Date n1 n2 m2 N )ˆr(av N

3/12/07 6 8 3 11 2.813/13/07 5 7 2 18 31.113/14/07 7 9 3 14 9.003/15/07 8 9 6 11 0.613/16/07 7 8 5 13 2.893/17/07 6 8 4 14 5.763/18/07 5 7 3 13 8.403/19/07 10 14 5 14 0.003/20/07 8 9 6 15 3.373/21/07 8 10 6 14 2.023/22/07 6 8 5 13 2.893/23/07 8 11 6 11 0.003/24/07 5 7 5 11 1.713/25/07 7 9 4 11 2.003/26/07 8 12 7 12 0.003/27/07 6 9 4 17 12.003/28/07 14 16 6 16 0.003/29/07 12 14 9 22 5.113/30/07 11 13 9 18 1.993/31/07 11 13 10 15 0.354/1/07 12 17 10 19 1.044/2/07 9 14 8 21 5.784/3/07 14 19 8 22 2.724/4/07 20 27 11 34 10.774/5/07 14 18 11 33 13.434/6/07 17 21 11 27 5.294/7/07 10 19 7 44 75.004/8/07 12 20 7 28 15.644/9/07 11 19 8 28 14.12

4/10/07 17 21 8 29 12.714/11/07 21 25 13 33 7.644/12/07 20 27 15 38 10.194/13/07 24 29 18 33 1.924/14/07 19 24 15 39 11.634/15/07 17 22 13 32 8.454/16/07 31 36 17 36 0.004/17/07 27 32 22 45 7.494/18/07 28 33 24 38 1.58

Date n1 n2 m2 N )ˆr(av N

4/19/07 22 28 18 44 11.654/20/07 25 28 16 39 9.234/21/07 25 30 19 40 6.334/22/07 18 24 13 46 29.184/23/07 26 30 14 39 10.474/24/07 21 27 18 39 7.544/25/07 24 30 16 40 9.184/26/07 34 39 19 49 11.904/27/07 33 39 28 48 3.664/28/07 27 33 23 48 8.034/29/07 35 42 22 52 9.484/30/07 45 51 34 53 0.725/1/07 35 52 32 73 17.125/2/07 27 38 24 56 13.315/3/07 32 42 20 57 19.115/4/07 30 42 22 61 22.355/5/07 32 38 22 52 12.195/6/07 21 35 15 74 92.815/7/07 31 45 18 53 11.355/8/07 24 41 21 61 24.155/9/07 29 38 18 51 16.20

5/10/07 25 36 20 52 16.475/11/07 21 34 17 50 20.105/12/07 17 28 13 45 26.045/13/07 14 23 12 33 10.045/14/07 18 30 11 38 14.165/15/07 22 32 14 41 12.545/16/07 12 17 11 34 14.605/17/07 13 22 9 29 10.605/18/07 11 17 10 22 3.645/19/07 4 11 4 28 47.045/20/07 4 8 3 11 2.815/21/07 6 9 3 12 3.755/22/07 6 9 3 17 15.755/23/07 6 8 4 12 3.365/24/07 8 8 5 10 0.755/25/07 2 3 2 11 6.005/26/07 0 1 0 5 6.00

Date n1 n2 m2 N )ˆr(av N Date n1 n2 m2 N )ˆr(av N

5/27/07 0 0 0 0 5/30/07 0 0 0 00.00 0.005/28/07 0 0 0 0 0.00 5/31/07 0 0 0 0.005/29/07 0 0 0 0 0.00 Sum 1866 2636 984.13

* Formulas and notation used for mark-resight estimation: n1=number of marked (i.e., identifiable) CSLs seen on day i (e.g., 5/1/07). n2=number of CSLs seen on day i+1 (e.g., 5/2/07); sum of marked and unmarked animals. m2=number of marked CSLs seen on day i+1 that were also seen on day i.

( )( )( ) 1

111ˆ

2

21 −+

++=

mnnN [bias-adjusted Chapman estimator]

( )( )( )(( ) ( )

)21

211ˆrav2

22

22121

++−−++

=mm

mnmnnnN

Table 3B. Residency times (r) at Bonneville Dam during 2007 based on ACOE observations of 38 highly identifiable California sea lions marked or identified prior to 2007. [Data source: R. Stansell, ACOE.] # ID Days observed 1 B32 12 B68 13 B22 34 B214 35 B220 36 B31 47 C309 48 B127 79 C699 1010 B63 1111 4140 1512 C441 1613 B137 1614 B136 1815 B66 2116 C398 2317 B194 2318 C360 2319 C287 2420 B130 2621 C404 28

# ID Days observed 22 C668 2823 B108 2924 B9 2925 C554 3026 C643 3127 3341 3228 C640 3929 C417 4330 C265 4431 C192 4432 C390 4533 C440 5134 C507 5135 B208 6136 C635 6337 C443 6738 C319 70Mean 27.28SD 19.49SE 3.16

Table 3C. Detection probabilities (p) of ten California sea lions at Bonneville Dam during 2007. Detectability is defined as the number of days a satellite-tagged animal was observed by ACOE divided by the number of days the animal was known to be present at the dam based on satellite data. Satellite location fixes were filtered by ARGOS location class (≥1) and speed (≤ 3 m s-1); satellite-tags only transmit for four hours in the morning and four hours in the afternoon each day. [Data source: ODFW; R. Stansell, ACOE.] Days detected at Bonneville Dam Brand ACOE Satellite Detectability C309 2 2 1C669 1 2 0.500C653 3 3 1C319 6 6 1C443 10 10 1C645 10 17 0.588C644 17 18 0.944C643 16 26 0.615C507 31 31 1C265 36 39 0.923Mean 0.8571SD 0.2033SE 0.0412

0 1 2 mi

0 1 2 km

C309 C669

C653 C319

C443 C645

C643 C644

C507 C265

0 1 2km

Figure 3. Satellite location fixes (ARGOS location class ≥1 and speed ≤ 3 m s-1) for the ten California sea lions used to estimate detectability (Table 3C). [Data source: ODFW.]

Questions 3.1. Further information was requested on the marked animals identified each year: How many animals are marked per year Table 3.1. Summary of Columbia River CSL branding effort (1997-2007) and associated resights at Bonneville Dam (2002-2007). [Data source: ODFW; R. Stansell, ACOE.] Total # C-brands resighted at Bonneville Dam

Cohort Brands Annual Cum. ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 Total C-brands observed*

8/96-7/97 C1-C19 19 19 8/97-7/98 C20-C50 31 50 8/98-7/99 C51-C128 78 128 8/99-7/00 C129-C166 38 166 1 1 1 1 1 1 1475

8/00-7/01 C167-C236 70 236 2 4 4 4 1 1 6 1925, 1932, 2252, 226, 2352‡, 2364

8/01-7/02 C237-C294 58 294 3 7 9 6 4 2 10 2474, 248, 2512, 256, 2574†‡, 2584, 2593, 2656†, 2753, 2873

8/02-7/03 C295-C467 173 467 - 17 20 19 14 11 29

3013, 304, 3096†, 3113, 3196†, 3224, 3272†, 3343, 335, 347, 3602, 3613, 364, 3792, 3903†, 396, 3984, 4045, 405, 4174, 4263, 4404, 4414, 4422, 4434†, 4444, 445, 4493, 4552

8/03-7/04 C468-C509 42 509 - - 3 1 1 1 3 494, 497, 5074†

8/04-7/05 C510-C545 36 545 - - - 0 8/05-7/06 C546-C564 19 564 - - - - 1 1 1 5542

8/06-7/07 C565-C714 150 714 - - - - - 16 16 578, 579, 586, 622, 6353, 6402, 6436†, 644†, 645†, 652, 653†, 657, 6682, 669†, 6995, 700

Total C-brands 6 29 37 31 22 32 66** *Superscript numbers indicate multiple years observed (2=2 years, 3=3 years, etc.); strikethrough = dead; italics = seen more years than they have been branded (based on natural marks); underline = captured at Bonneville Dam; † = received satellite transmitter; and ‡ = seen at Willamette Falls. **Additional branded animals included 818, 3341, 3696, and 4140, as well as an identifiable partially-read C-brand (C_28) observed in 2004; revised totals=67 C-brands and 71 brands total.

Questions 3.2. Further information was requested on the marked animals identified each year: How many are new each year? Table 3.2. Summary of yearly minimum counts of California sea lions at Bonneville Dam, 2002-2007. Note that the ACOE database is still undergoing error-checking; exact numbers may change but no change is anticipated in overall patterns or conclusions. [Data source: R. Stansell, ACOE.]

2002

2003 2004 2005 2006 2007 ID category* All New

All New All New All New All New All New Total new

H 17 17

73 59 82 40 68 18 66 26 63 24 184 H1 15 15

61 48 74 34 59 13 61 22 57 19 151 H2 2 2

12 11 8 6 9 5 5 4 6 5 33% new

81% 49% 26% 39% 38%P 13 13

35 31 19 19 13 11 7 7 6 6 87 P1 4 4

8 5 4 4 2 1 0 0 1 1 15 P2 9 9

27 26 15 15 11 10 7 7 5 5 72% new

89% 100% 85% 100% 100% H+P 30 30

108 90 101 59 81 29 73 33 69 30 271 % new

83% 58% 36% 45% 43% * H = marked animals (branded and naturally marked) that can be identified unambiguously (H1) or with high confidence (H2) between years. P = naturally marked animals that can be identified unambiguously (P1) or with high confidence (P2) within years, but only potentially between years. Note that there are additional animals observed each year that bear no natural markings to aid within or between year tracking.

Question 3.3 Further information was requested on the marked animals identified each year: in terms of any decision to select individual animals to remove, the task force would like to see data on which marked animals were present during which years to determine from data sets which animals are the real ‘culprits’ (e.g. dossiers). Table 3.3 Minimum number of years, days, and salmonids consumed by 151 highly identifiable (ID category=“H1”, Table 3.2) California sea lions at Bonneville Dam, 2002-2007. Rows are sorted in descending order by years, days, salmonids. Branded animals (n=71) include those captured at Astoria or Bonneville (C-brands), Puget sound (818), and San Miguel Island (3341, 3696, and 4140). Non-branded but highly identifiable animals (n=80) are identified by natural identifying characteristics. Animals initially identified based on natural characteristics and subsequently branded are listed under the brand number (e.g., C699). [Data source: R. Stansell, ACOE]

Minimum observed Minimum days observed (salmonids consumed) # Brand* Other ID Years Days Salmonids 2007 2006 2005 2004 2003 2002 1 C319 6 189 190 70 (41) 61 (50) 16 (6) 15 (25) 16 (45) 11 (23) 2 C265 6 147 102 44 (43) 26 (23) 39 (10) 18 (10) 17 (11) 3 (5) 3 C309 6 142 157 4 (1) 68 (60) 19 (5) 21 (26) 19 (41) 11 (24) 4 C643 6 115 81 31 (13) 51 (47) 8 (2) 12 (7) 10 (10) 3 (2) 5 C404 5 148 76 28 (4) 58 (21) 28 (0) 14 (8) 20 (43) 0 (0) 6 C192 5 116 22 44 (6) 43 (12) 16 (4) 6 (0) 7 (0) 0 (0) 7 B130 5 106 110 26 (39) 28 (36) 17 (9) 20 (11) 15 (15) 0 (0) 8 B9 5 67 56 29 (35) 17 (6) 9 (0) 10 (7) 2 (8) 0 (0) 9 C699 5 66 91 10 (8) 0 (0) 3 (0) 32 (35) 9 (21) 12 (27)

10 B108 5 60 40 29 (19) 5 (0) 12 (3) 1 (0) 13 (18) 0 (0) 11 B63 5 59 28 11 (4) 17 (5) 16 (2) 4 (1) 11 (16) 0 (0) 12 C147 5 46 39 0 (0) 4 (0) 15 (8) 16 (11) 7 (13) 4 (7) 13 C443 4 159 122 67 (64) 47 (26) 15 (10) 30 (22) 0 (0) 0 (0) 14 C507 4 138 103 51 (64) 58 (35) 18 (3) 11 (1) 0 (0) 0 (0) 15 C322 4 126 133 0 (0) 73 (79) 16 (11) 27 (32) 10 (11) 0 (0) 16 C440 4 123 74 51 (34) 45 (32) 16 (1) 11 (7) 0 (0) 0 (0) 17 C417 4 83 56 43 (32) 20 (18) 11 (1) 9 (5) 0 (0) 0 (0) 18 C247 4 72 49 0 (0) 21 (15) 17 (2) 25 (16) 9 (16) 0 (0) 19 3341 4 62 55 32 (30) 23 (25) 4 (0) 3 (0) 0 (0) 0 (0) 20 C398 4 58 16 23 (1) 0 (0) 18 (4) 13 (6) 4 (5) 0 (0) 21 C258 4 54 54 0 (0) 0 (0) 16 (2) 17 (4) 20 (45) 1 (3) 22 C444 4 52 32 0 (0) 24 (10) 7 (3) 18 (16) 3 (3) 0 (0) 23 B66 4 36 8 21 (7) 9 (0) 2 (0) 4 (1) 0 (0) 0 (0) 24 B226 4 26 35 0 (0) 0 (0) 1 (0) 7 (4) 10 (12) 8 (19) 25 C441 4 23 0 16 (0) 3 (0) 1 (0) 0 (0) 3 (0) 0 (0) 26 C236 4 21 18 0 (0) 0 (0) 2 (0) 2 (2) 15 (13) 2 (3) 27 C257 4 20 13 0 (0) 6 (3) 0 (0) 2 (1) 4 (4) 8 (5) 28 B68 4 12 19 1 (0) 0 (0) 0 (0) 1 (0) 5 (3) 5 (16) 29 C390 3 95 64 45 (22) 41 (39) 9 (3) 0 (0) 0 (0) 0 (0) 30 C635 3 83 67 63 (61) 19 (6) 1 (0) 0 (0) 0 (0) 0 (0) 31 B214 3 80 62 3 (0) 64 (56) 13 (6) 0 (0) 0 (0) 0 (0) 32 B136 3 77 70 18 (10) 57 (60) 2 (0) 0 (0) 0 (0) 0 (0) 33 C287 3 51 55 24 (50) 21 (3) 6 (2) 0 (0) 0 (0) 0 (0)

Minimum observed Minimum days observed (salmonids consumed) # Brand* Other ID Years Days Salmonids 2007 2006 2005 2004 2003 2002 34 C301 3 51 24 0 (0) 0 (0) 15 (2) 22 (13) 14 (9) 0 (0) 35 C334 3 48 19 0 (0) 0 (0) 18 (4) 21 (2) 9 (13) 0 (0) 36 4140 3 46 9 15 (1) 0 (0) 20 (3) 11 (5) 0 (0) 0 (0) 37 B48 3 40 21 0 (0) 0 (0) 7 (0) 9 (5) 24 (16) 0 (0) 38 B101 3 36 22 0 (0) 0 (0) 5 (0) 24 (7) 7 (15) 0 (0) 39 B32 3 35 10 1 (0) 29 (10) 5 (0) 0 (0) 0 (0) 0 (0) 40 C275 3 33 27 0 (0) 0 (0) 12 (6) 17 (16) 4 (5) 0 (0) 41 B46 3 33 8 0 (0) 20 (5) 5 (0) 8 (3) 0 (0) 0 (0) 42 B145 3 30 68 0 (0) 0 (0) 0 (0) 3 (0) 19 (48) 8 (20) 43 B137 3 29 44 16 (32) 7 (11) 6 (1) 0 (0) 0 (0) 0 (0) 44 B183 3 28 14 0 (0) 0 (0) 8 (0) 18 (14) 2 (0) 0 (0) 45 B135 3 25 10 0 (0) 0 (0) 10 (1) 14 (7) 1 (2) 0 (0) 46 C311 3 23 0 0 (0) 0 (0) 15 (0) 6 (0) 2 (0) 0 (0) 47 B171 3 21 1 0 (0) 1 (0) 1 (0) 19 (1) 0 (0) 0 (0) 48 B2 3 20 12 0 (0) 8 (11) 3 (0) 9 (1) 0 (0) 0 (0) 49 B47 3 18 4 0 (0) 16 (2) 1 (0) 1 (2) 0 (0) 0 (0) 50 C259 3 16 2 0 (0) 0 (0) 8 (1) 6 (0) 2 (1) 0 (0) 51 C426 3 15 7 0 (0) 0 (0) 7 (0) 3 (1) 5 (6) 0 (0) 52 C449 3 9 8 0 (0) 1 (0) 0 (0) 7 (5) 1 (3) 0 (0) 53 818 3 6 0 0 (0) 1 (0) 0 (0) 1 (0) 4 (0) 0 (0) 54 B44 3 4 0 0 (0) 1 (0) 1 (0) 2 (0) 0 (0) 0 (0) 55 C361 3 3 0 0 (0) 0 (0) 1 (0) 1 (0) 1 (0) 0 (0) 56 B208 2 126 113 61 (61) 65 (52) 0 (0) 0 (0) 0 (0) 0 (0) 57 B194 2 90 38 23 (6) 67 (32) 0 (0) 0 (0) 0 (0) 0 (0) 58 C554 2 57 4 30 (2) 27 (2) 0 (0) 0 (0) 0 (0) 0 (0) 59 C379 2 53 61 0 (0) 38 (53) 15 (8) 0 (0) 0 (0) 0 (0) 60 C640 2 50 28 39 (26) 11 (2) 0 (0) 0 (0) 0 (0) 0 (0) 61 C668 2 41 23 28 (9) 13 (14) 0 (0) 0 (0) 0 (0) 0 (0) 62 C360 2 34 8 23 (8) 11 (0) 0 (0) 0 (0) 0 (0) 0 (0) 63 C455 2 26 9 0 (0) 9 (3) 17 (6) 0 (0) 0 (0) 0 (0) 64 C442 2 24 29 0 (0) 0 (0) 0 (0) 16 (14) 8 (15) 0 (0) 65 B220 2 23 3 3 (3) 20 (0) 0 (0) 0 (0) 0 (0) 0 (0) 66 C225 2 19 47 0 (0) 0 (0) 0 (0) 0 (0) 16 (43) 3 (4) 67 C327 2 18 3 NA NA NA 12 (0) 6 (3) 0 (0) 68 C193 2 12 7 0 (0) 0 (0) 1 (0) 11 (7) 0 (0) 0 (0) 69 C235 2 11 4 0 (0) 0 (0) 10 (2) 1 (2) 0 (0) 0 (0) 70 C251 2 10 8 0 (0) 0 (0) 0 (0) 2 (1) 8 (7) 0 (0) 71 B127 2 9 1 7 (1) 2 (0) 0 (0) 0 (0) 0 (0) 0 (0) 72 B31 2 9 1 4 (0) 0 (0) 0 (0) 0 (0) 5 (1) 0 (0) 73 B4 2 6 2 0 (0) 0 (0) 0 (0) 4 (1) 2 (1) 0 (0) 74 B22 2 5 0 3 (0) 2 (0) 0 (0) 0 (0) 0 (0) 0 (0) 75 B110 2 5 0 0 (0) 4 (0) 0 (0) 1 (0) 0 (0) 0 (0) 76 B205 2 5 0 0 (0) 0 (0) 3 (0) 2 (0) 0 (0) 0 (0) 77 B53 2 4 2 0 (0) 0 (0) 0 (0) 1 (0) 3 (2) 0 (0) 78 B198 1 45 77 0 (0) 45 (77) 0 (0) 0 (0) 0 (0) 0 (0) 79 C622 1 34 36 34 (36) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 80 C578 1 31 37 31 (37) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 81 C579 1 29 27 29 (27) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Minimum observed Minimum days observed (salmonids consumed) # Brand* Other ID Years Days Salmonids 2007 2006 2005 2004 2003 2002 82 C494 1 23 7 NA NA NA 23 (7) 0 (0) 0 (0) 83 B88 1 22 24 0 (0) 22 (24) 0 (0) 0 (0) 0 (0) 0 (0) 84 C644 1 21 21 21 (21) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 85 C586 1 21 10 21 (10) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 86 B81 1 20 26 20 (26) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 87 B124 1 20 10 0 (0) 0 (0) 0 (0) 20 (10) 0 (0) 0 (0) 88 B204 1 18 16 0 (0) 18 (16) 0 (0) 0 (0) 0 (0) 0 (0) 89 C645 1 18 1 18 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 90 B153 1 16 17 0 (0) 0 (0) 0 (0) 0 (0) 16 (17) 0 (0) 91 C248 1 16 3 0 (0) 0 (0) 0 (0) 16 (3) 0 (0) 0 (0) 92 B37 1 15 32 0 (0) 0 (0) 0 (0) 0 (0) 15 (32) 0 (0) 93 B216 1 13 17 0 (0) 13 (17) 0 (0) 0 (0) 0 (0) 0 (0) 94 C653 1 13 10 13 (10) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 95 C669 1 13 2 13 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 96 C652 1 10 10 10 (10) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 97 C347 1 10 0 10 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 98 B221 1 9 11 9 (11) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 99 B107 1 9 4 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 9 (4)

100 B40 1 8 2 8 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 101 C364 1 8 1 0 (0) 0 (0) 0 (0) 8 (1) 0 (0) 0 (0) 102 C657 1 8 0 8 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 103 C700 1 8 0 8 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 104 B20 1 7 6 0 (0) 0 (0) 0 (0) 0 (0) 7 (6) 0 (0) 105 B156 1 7 6 0 (0) 0 (0) 0 (0) 7 (6) 0 (0) 0 (0) 106 B185 1 7 0 0 (0) 0 (0) 0 (0) 0 (0) 7 (0) 0 (0) 107 B19 1 6 7 0 (0) 0 (0) 0 (0) 0 (0) 6 (7) 0 (0) 108 B132 1 6 6 0 (0) 6 (6) 0 (0) 0 (0) 0 (0) 0 (0) 109 B122 1 6 3 0 (0) 0 (0) 0 (0) 0 (0) 6 (3) 0 (0) 110 B97 1 5 6 0 (0) 5 (6) 0 (0) 0 (0) 0 (0) 0 (0) 111 B215 1 5 1 0 (0) 0 (0) 0 (0) 5 (1) 0 (0) 0 (0) 112 C256 1 5 0 NA NA NA 5 (0) 0 (0) 0 (0) 113 B98 1 4 3 0 (0) 0 (0) 0 (0) 0 (0) 4 (3) 0 (0) 114 B99 1 4 2 4 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 115 C396 1 3 5 0 (0) 0 (0) 0 (0) 0 (0) 3 (5) 0 (0) 116 B128 1 3 3 0 (0) 0 (0) 0 (0) 0 (0) 3 (3) 0 (0) 117 B49 1 3 2 0 (0) 3 (2) 0 (0) 0 (0) 0 (0) 0 (0) 118 C304 1 3 1 NA NA NA 3 (1) 0 (0) 0 (0) 119 B41 1 3 0 0 (0) 0 (0) 0 (0) 3 (0) 0 (0) 0 (0) 120 B58 1 3 0 0 (0) 0 (0) 0 (0) 3 (0) 0 (0) 0 (0) 121 B104 1 3 0 0 (0) 3 (0) 0 (0) 0 (0) 0 (0) 0 (0) 122 B42 1 2 1 0 (0) 0 (0) 0 (0) 0 (0) 2 (1) 0 (0) 123 C445 1 2 0 0 (0) 0 (0) 0 (0) 0 (0) 2 (0) 0 (0) 124 C497 1 2 0 0 (0) 0 (0) 0 (0) 2 (0) 0 (0) 0 (0) 125 B115 1 2 0 0 (0) 2 (0) 0 (0) 0 (0) 0 (0) 0 (0) 126 B193 1 2 0 0 (0) 2 (0) 0 (0) 0 (0) 0 (0) 0 (0) 127 B219 1 2 0 0 (0) 0 (0) 0 (0) 2 (0) 0 (0) 0 (0) 128 B70 1 1 4 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (4) 129 B17 1 1 2 0 (0) 0 (0) 0 (0) 0 (0) 1 (2) 0 (0)

Minimum observed Minimum days observed (salmonids consumed) # Brand* Other ID Years Days Salmonids 2007 2006 2005 2004 2003 2002

130 3696 1 1 2 0 (0) 0 (0) 0 (0) 0 (0) 1 (2) 0 (0) 131 B72 1 1 1 0 (0) 0 (0) 0 (0) 1 (1) 0 (0) 0 (0) 132 B148 1 1 1 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 133 C226 1 1 0 0 (0) 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 134 C335 1 1 0 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 135 C405 1 1 0 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 136 C_28 1 1 0 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 137 B69 1 1 0 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 138 B144 1 1 0 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 139 B223 1 1 0 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 140 B3 1 1 0 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 141 B8 1 1 0 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 142 B10 1 1 0 0 (0) 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 143 B16 1 1 0 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 144 B93 1 1 0 0 (0) 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 145 B125 1 1 0 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 146 B140 1 1 0 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 147 B141 1 1 0 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 148 B152 1 1 0 0 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 149 B165 1 1 0 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 150 B177 1 1 0 0 (0) 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) 151 B189 1 1 0 0 (0) 0 (0) 0 (0) 0 (0) 1 (0) 0 (0) *Strikethrough = dead; list includes partially-read C-brand (C_28) with natural mark.