Supplement: Jolly-Seber Model DescriptionAbundance at the start of the first trip (N1) is estimated in log-space () and transformed

prior to use in the model state calculations (Tables S1 and S2, eqn. 1). Recruitment for intervals

between each trip (Ri) is also estimated in log space (i) and transformed prior to use in the

model (eqn. 2). Survival over a 90 day interval (S90i) is estimated in logit space (i in eqn. 3).

The transformed value is then converted to survival over the interval between trips by converting

it to an instantaneous daily mortality rate adjusted for the days between trips, and then

reconverting that value back to a survival rate.

Capture probability on each trip and pass for fish that have not been previously captured

on a trip (pCapVi,j) is estimated in logit space (i,j) and transformed to a 0-1 scale prior to use in

the model (eqn. 4). Note this capture probability will be applied to unmarked fish that have not

been previously caught, as well as fish that were tagged on previous trips that have not been

previously caught on the current trip. We also estimate the capture probability for fish captured

on the trip that were released on previous passes on that trip (pCapRi,j) by estimating a within-

trip previous capture effect (, eqn. 5, Williams et al. 2002). Note that pCapRi,j = pCapVi,j when

= 0, pCapRi,j > pCapVi,j when >0 and pCapRi,j < pCapVi,j when <0. We assume that the

previous capture effect is consistent across trips and passes. Our use of pCapV and pCapR in the

model assumes that any behavioural response or individual heterogeneity effects on previous

capture only affects capture probabilities within the trip, but not on subsequent trips.

We use a negative binomial distribution to quantify the discrepancy between model

predictions and observations. This distribution allows for overdispersion in the extent of

differences between predictions and observations, but requires an estimate of the extent of

overdispersion, , which in our formulation is the variance-to-mean ratio (eqn. 6). Fixing at one

assumes no overdispersion in the data and is equivalent to assuming error is poisson-distributed.

This latter structure produces variance estimates very similar to those from a multinomial error

distribution.

The model predicts the fate of unmarked and marked components of the population

across passes within trips, and between trips (Fig. S2). As not all unmarked fish that were

captured were given a mark, the unmarked population at large consists of fish that have never

been previously captured (UVi,j) as well as fish that were previously captured on the trip but not

marked (UCi,j). We refer to these groups as unmarked ‘virgin’ fish and unmarked fish that were

previously captured (within the trip), respectively. On the first pass of the first trip, no fish have

been previously captured so the abundance of the unmarked virgin population is simply the

estimated abundance at the start of the 1st trip (N1, eqn. 7). The catch of unmarked virgin fish

(uvi,j) is the product the unmarked virgin population at large and the capture probability for

unmarked virgin fish (pCapVi,j, eqn. 8). Note that at the start of the 1st pass there can be no

unmarked fish that were previously captured on the trip and hence no catch of this group, so the

catch of unmarked fish is composed solely of unmarked virgin fish (eqn. 9).

On subsequent passes (2..J), the size of the unmarked virgin population at the start of the

pass is simply the size of the population on the previous pass less the catch of unmarked virgin

fish from the previous pass (eqn. 10). The abundance of the unmarked but previously captured

population at the start of pass 2+ is the sum of the value from the pass before (which will be 0 if

j=2 but >0 if j>2) as well as additions and losses to and from this population based on catches,

mortalities, movement out of the reach, and marking on the previous pass (eqn. 11). The

captured unmarked virgin population that survives capture but that was not given a PIT tag or fin

clip on the last pass, and that remains in the robust design section (as determined by the within-

trip residency proportion, pResi,i), is added to UC. The number of unmarked but previously

captured fish that survived capture and were tagged on the last pass is subtracted from UC. The

fraction of live unmarked fish given a PIT tag (q_pit) or fin clip (q_fin) is known and are based

on the product of the number of marks applied and reach- and trip-specific loss-rates from the

combined shed and mortality proportions determined from 24-hr. holding experiments. The

number of mortalities of unmarked fish is also known. The number of unmarked virgin and

previously captured mortalities is computed by assuming that mortalities in each group are

proportional to their abundance in the catch. The catch of unmarked virgin and previously

captured fish on the current pass depends on the capture probability for each group, pCapV and

pCapR, respectively (eqn. 12). Note that if modelling a previous capture effect, that effect is

applied to unmarked fish if they were previously captured on the trip. The total catch of

unmarked fish, which will be compared to the observed catch during model fitting, is the sum of

the two unmarked components (eqn. 12).

The recapture of fish that were given a fin clip on previous passes, on pass 2+, is the

product of the total number of fin clips released over all previous passes that remained in the

robust design section within the trip (pResi,i) less any mortality of recaptures on previous passes,

and the capture probability for fish previously caught on the trip (pCapR, eqn. 13). The

prediction of recaptures of PIT-tagged fish released on previous passes of the same trip follows

the same logic, but the available population to capture includes both marks applied on that trip

(m_pit) as well as fish originally PIT-tagged on previous trips that were recaptured on previous

passes of the current trip (eqn. 14). As for the fin clip recapture prediction, the proportion of fish

captured depends on the capture probability for fish previously captured on the trip. The PIT-

tagged population available for recapture is adjusted for any losses due to mortality due to

capture on previous passes as well as movement losses within the trip.

The marked population of PIT-tagged fish from previous release cohorts (i.e., releases on

previous trips) is reduced to account for fish recaptured on the current trip (eqn. 15). Thus we use

the standard ‘Yi,j’ approach in the mark-recapture literature (Williams et al. 2001) where a fish

from release trip ‘r’ that is caught on a later trip ‘i’ is removed from its original cohort ‘r’ (eqn.

15) and added to the release cohort ‘i’. The number of recaptures of fish PIT-tagged on previous

trips on the current trip and pass (eqn. 16) is simply the number of marks from that cohort at

large and the capture probability for fish not previously captured on the current trip (pCapV).

Thus, if modelling a previous capture effect, we assume that effect does not apply to fish that

were tagged on a previous trip that have not yet been captured on the current trip.

The abundance of unmarked fish at the start of the 1st pass on trips 2 and later depends on

abundance at the end of the last pass on the previous trip, and apparent survival and recruitment

between trips (eqn. 17). Note that the apparent survival is the product of the actual survival (S)

and the proportion of fish that remain in the robust design section between trips as defined by

pResi-1,i. When predicting residency for a release cohort ‘i’ after the 1st possible recovery trip (i.e,

on trip 3+ for release trip 1), the residency proportion is the incremental loss of fish due to

movement across trips. That is, if the proportion of marks remaining in the robust design sections

between trips 1 and 2, and trips 1 and 3 is 0.85 and 0.8, respectively, than pRes1,2 = 0.85 and

pRes1,3 = 1-(0.85-0.8) or 0.95. Because we assume that any behavioral responses to capture is

lost between trips, the number of unmarked but previously captured fish on the last pass (j=J) of

the last trip (i-1) is added to the unmarked virgin population at the beginning of the first pass of

the current trip. Any losses to the unmarked population due to mortality or PIT tagging on the

last pass of the previous trip are also accounted for. Any fish that were fin clipped on the

previous trip are treated as unmarked fish by the next trip (as these fish are not recorded as

recaptures owing to uncertainty in mark retention), so are also added to the virgin unmarked

population, and the number of mortalities of fin clipped fish on the previous trip are subtracted

(as for unmarked fish). The catch of the unmarked virgin population depends on the capture

probability for these fish (pCapV, eqn. 18). As for pass 1 of trip 1, there is no population of

previously captured unmarked fish on pass 1 of trip 2+, and hence no possible catch of this

component (eqn. 19). The abundance of the marked population released on previous trips is

reduced based on the estimated apparent survival rate between trip intervals, which is the product

of the actual survival and residency proportion between trips (eqn. 20). Capture of fish PIT-

tagged on previous trips depends on the abundance of that tagged cohort at the start of the pass

and the capture probability for fish not previously caught on the trip (eqn. 21). The total

population at the start of the trip is simply the sum of the unmarked virgin population at the start

of the trip and the sum of PIT-tagged fish at large from releases from previous trips (eqn. 22).

Computations thus far have predicted the size of the unmarked and marked populations at

the start of each trip and pass. However, to compute abundance on the next trip, the marked

populations must be updated to reflect their size at the end of the last pass of the current trip. The

prediction for the release cohort from the current trip is simply the sum of marks applied on that

trip across all passes as well as PIT-tag recaptures on that trip from previous release cohorts, less

any mortalities upon recapture (eqn. 23, r=i). The size of marked populations from previous

release cohorts (r<i) is simply their abundance at the start of the current trip less any recaptures

of these fish (eqn. 23, r<i). Here we see the transfer of recaptured fish from their original release

cohort (eqn. 23, bottom) to the cohort they were recaptured in (eqn. 23, top).

Parameters are estimated by assuming that observations of the catch of unmarked fish

(eqn. 24), recapture of PIT-tagged fish within- and across-trips (eqn. 25), and recapture of fin-

clipped fish within trips (eqn. 26) are negative binomially distributed. The mean of this

distribution is determined by the model predictions of these catches, which in turn depends on

parameter estimates. represents the variance-to-mean ratio, and is estimated. If the model fits

the data poorly, will increase to the level required to meet the assumptions of negative binomial

error. Thus, the extent of overdispersion is directly estimated and should accurately reflect the

amount of error associated with each model. For example, a simple model which does not allow

survival rate to vary among trip intervals or account for previous capture effects on capture

probability would not fit the data as well as a more complex model with interval-specific

survival and a previous capture effect. Thus, for the simpler model would be greater than for

the latter. In the traditional fitting approach, a post-fitting adjustment to likelihood and variance

estimates for each model is needed if there is an indication of overdispersion in the model fit.

With our approach, no adjustment to the likelihood is required since overdispersion is estimated

for each model during fitting. If this property is not desired, the likelihood model can be

converted to a non-overdispersed distribution (poisson) by fixing the value of at 1, which is a

close approximation to the multinomial error structure used in the majority of mark-recapture

model applications.

Parameters of the model were estimated by minimizing the negative value of the total log

likelihood (eqn. 27) using the nonlinear search procedure in the AD model-builder (ADMB)

software (Fournier et al. 2011). We ensured convergence had occurred based on the gradients of

change in parameter values relative to changes in the log likelihood, and the condition of the

Hessian matrix. Asymptotic estimates of the standard error of parameter estimates at their

maximum likelihood values were computed from the Hessian matrix.

We confirmed that the algebra in the model was correct by checking that the model

conserved fish under a variety of simulated conditions based on fixed parameter values. These

simulations checked that: abundance is constant across trips when pRes and S are set to 1;

abundance under this condition should remain constant regardless of capture probabilities

assuming no post-release mark loss and mortality or mortality due to capture; the sum of the

unmarked virgin and previously captured population within a trip should equal the total

abundance when capture probabilities equal 0; and the sum of unmarked and marked populations

should equal the total population size when capture probability is greater than zero. We

confirmed that our fitting procedures and likelihood did not result in any bias by comparing

estimated parameter values from simulated data against the values of the parameters used to

simulate the data.

ReferencesWilliams, B.K., Nichols, J.D., and M.J. Conroy. 2001. Analysis and management of animal

populations. Academic Press. 817 pp.

Table S1. Variables used in Jolly Seber model description (see Table S2).

Variable Description

Indices and data (observations)r Index for release tripi Index for tripj Index for passdi Number of days between trips ‘i’ and ‘i+1q_piti,j Proportion of unmarked catch that is PIT-tagged on trip ‘i’ and pass ‘j’q_fini,j Proportion of unmarked catch that is fin-clippedpResr,i Proportion of marks that remain in robust-design section from trip ‘r’ to ‘i’m_fini,j Number of fish fin-clipped, adjusted for post-release mortalitym_piti,j Number of fish PIT-tagged, adjusted for post-release mortality and sheddingui,j Number of unmarked fish capturedu_di,j Number of unmarked fish that diedr_fin_di,j Number of recaptures of fin-clipped fish that diedr_pit_dr,i,j Number of fish PIT-tagged on trip ‘r’ that were recaptured and diedr_fin_obsi,j Number of fin-clipped fish that were recapturedr_pit_obsr,i,j Number of fish PIT-tagged that were recaptured

Parameters Abundance as the start of trip 1 in log-spacei Recruitment between trips ‘i’ and i+1 in log-spacei 90-day survival rate between trips ‘i’ and i+1 in log-spacei,j Capture probability for fish not previously captured on trip I in logit-space Within-trip previous capture effect Variance-to-mean ratio (overdispersion term in negative binomial likelihood)

State Variables (predictions)N1 Abundance at the start of trip 1Ri Recruitment between trips ‘i’ and ‘i+1’S90i 90-day survival rate between trips ‘i’ and ‘i+1’pCapVi,j Capture probability for fish not previously captured on trip ‘i’pCapRi,j Capture probability for fish previously captured on trip ‘i’UVi,j Abundance of unmarked fish that were not previously captured on trip ‘i’UCi,j Abundance of unmarked fish that were previously captured on trip ‘i’uvi,j Catch of unmarked fish that were not previously captured on trip ‘i’uci,j Catch of unmarked fish that were previously captured on trip ‘i’Mr,i,j Number of PIT-tagged fish marked on trip ‘r’ alive by trip ‘i’ and pass ‘j’r_pitr,i,j Number of PIT-tagged fish that were recapturedr_fini,j Number of fin-clipped fish marked on trip ‘i’, recaptured on trip ‘i’ and pass ‘j’

Table S2. Jolly Seber model equations. ‘i’ and ‘j’ denote trip and pass number and ‘I’ and ‘J’

denote the maximum trip number and maximum pass. ‘r’ denotes the release trip. Subscripts 1:r

denote a sequence of integers (release trips) from trip 1 to trip ‘r’. Greek letters denote estimated

parameters. Italicized variables denote derived variables from transformed parameters. Bold

variables denote data variables that are not estimated. Variables that are capitalized denote states

that cannot be directly observed. ‘LL’ in eqn’s 24-27 denotes the log-likelihood, which is the log

of the probability returned from a negative binomial distribution (dlnegbin). See Table S1 for

definition of all model variables.

Parameter Transformations and Constraints

1) N1=eμ

2) Ri=eρ i

| i = 1:I-1

3)S 90i=

eφi

1+eφ i , Si=e

log (S 90i

90⋅di )

| i = 1:I-1

4)pCapV i , j=

eθi , j

1+eθi , j | i = 1:I, j = 1:J

5) pCapRi , j=pCapV i , j | i = 1:I, j = 1

pCapRi , j=pCapV i , j

1eβ

| i = 1:I, j > 1

6) τ≥1

Process Model Equations

Trip 1, pass 1 (i=1, j=1)

7) UVi,j = Ni

8) uvi,j = UVi,j · pCapVi,j

9) UCi,j = 0; uci,j= 0; ui,j= uvi,j + uci,j

Table S2. Con’t.

Trip 1+, Pass 2+ (i>=1,j>1)

10) UVi,j = UVi,j-1 – uvi,j-1

11) UCi,j = UCi,j-1 + (uvi,j-1 – u_di,j-1 · uvi,j-1/ui,j-1) · (1 – (q_piti,j-1 + q_fini,j-1)) · pResi,i – (uci,j-1 –

u_di,j-1 · uci,j-1/ui,j-1) · (q_piti,j-1 + q_fini,j-1)

12) uvi,j = UVi,j · pCapVi,j ; uci,j = UCi,j · pCapRi,j; ui,j = uvi,j + uci,j

13) r_fini,j = (m_fini,1:j-1 · pResi,i – r_fin_di,1:j-1) · pCapRi,j

14) r_piti,i,j = (m_piti,1:j-1 - r_pit_di,1:j-1) · pResi,i · pCapRi,j + r_pitr,i,1:j-1 | r = i-1

15) M1:r,i,j = M1:r,i,j-1 – r_pit_obs:r,i,j-1 | r = i-1

16) r_pit1:r,i,j = M1:r,i,j-1 · pCapVi,j | r = i-1

Trip 2+, Pass 1 (i>1, j=1)

17) UVi,j = (UVi-1,J + UCi-1,J - u_di-1,J – m_piti-1,J + m_fini-1,1:J – r_fin_di-1,1:J) · Si-1 · pResi-1,i

+ Ri-1

18) uvi,j = UVi,j · pCapVi,j

19) UCi,j = 0; uci,j = 0; ui,j = uvi,j + uci,j

20) M1:r,i,j = M1:r,i-1,J · Si-1 · pResi-1,i | r = i-1

21) r_pit1:r,i,j = M1:r,i,j · pCapVi,j | r = i-1

22) Ni = UVi,j + M1:r,i,j

Trip 2 and later, last pass (i>1,j=J)

23) Mr,i,j = m_piti,1:j · pResi,i + r_pit_obs1:r-1,i,1:j - r_pit_d1:i,i,1:j | r = i

M1:r,i,j = M1:r,i,j – r_pit_obs1:r,i,j | r < i

Table S2. Con’t.

Likelihood Equations

24) LLu = dlnegbin (u_obsi,j, ui,j, ) | i = 1:I, j = 1:J

25) LLpit = dlnegbin (r_pit_obsr,i,j, r_piti,i,j, ) | r = 1:I, i = 1:I, j = 1:J

26) LLfin = dlnegbin (r_fin_obsi,j, r_fini,j, ) | i = 1:I, j = 2:J

27) LL = LLu + LLpit + LLfin

Table S3. WinBUGS code for the bayesian across-reach movement model.

#Simulate error in survival rate by reach, and pCap for trip, by trip and reachfor(ir in 1:Nreaches){

for(irec in 2:Ntrips){lt_pCap[irec,ir]~dnorm(mu_pCap[irec,ir],tau_pCap[irec,ir])logit(pCap[irec,ir])<-lt_pCap[irec,ir]

}for(irel in 1:(Ntrips-1)){

lt_S[irel,ir]~dnorm(mu_S[irel,ir],tau_S[irel,ir])logit(S[irel,ir])<-lt_S[irel,ir]

}}

#Given estimates of Cauchy parameters, compute the probability that associated with min. and max. distance #to each reach. This probability is computed as the difference between the cumulative Cauchy at min. and #max. distance values

#Uninformative priors on Cauchy parameters defining movement distance distributionc_loc~dnorm(0,1.0E-3)c_scl~dunif(0.001,100)

#Compute the probability of moving distance between source reach to each reach (ir) given Cauchy #distribution. Compute this by integration using Ndists stepsfor(ir in 1:Nreaches){

#Compute cauchy probability at 0.1 km increments (km_dist) for the current reach irfor(j in 1:Ndists){

p_inc[ir,j]<-(1/3.141592654)*c_scl/(pow(km_dist[ir,j]-c_loc,2)+pow(c_scl,2))}#Sum up the probabilities across integration increments to get the probability mass from cauchyp[ir]<-sum(p_inc[ir,1:Ndists])

}

Table S3. Con’t.

for(ir in 1:Nreaches){pMove[ir]<-p[ir]/sum(p[]) #standardize sum of integration for each reach so sum across reaches =

#1 (needed for multinomial likelihood)

for (irel in 1:(Ntrips-1)){ #loop across release trips

#Assume marked fish move to reach between release trip and next tripM[Ind[irel,irel],ir]<-NewMarks[irel]*pMove[ir]

for(irec in (irel+1):Ntrips){ #loop across recovery tripsM[Ind[irel,irec],ir]<-M[Ind[irel,irec-1],ir]*S[irel,ir] #survive marks

R[Ind[irel,irec],ir]<-M[Ind[irel,irec],ir]*pCap[irec,ir]#predict recaptures}

#sum of recaps from release irel in reach ir across all recovery tripspRt[irel,ir]<-sum(R[Ind[irel,(irel+1)]:Ind[irel,Ntrips],ir])

}pR[ir]<-sum(pRt[1:(Ntrips-1),ir]) #sum of recaps across all release groups for reach ir

}

For (ir in 1:Nreaches){predR[ir]<-pR[ir]/sum(pR[]) #predicted ratio of recaps in 'to' reach relative to all recaps

#originally released in 'from' reachpercMove[ir]<-100*pMove[ir]

}

#likelihood of observing recaps by reach given total recaps from all reaches and predicted proportions #given survival and movementObsR[1:Nreaches]~dmulti(predR[1:Nreaches],SumObsR)

Table S3. Con’t.

##Estimate how many fish from FromReach are moving to 4A, 4B, or4A+4B on each trip to compare with #recruitment from JS model

#First compute the proportion of Cauchy mass of movement distance probabilities over the full range of #movement distance possibilities (sum of p2 is integral)for(i in 1:Nincs){p2[i]<-(1/3.141592654)*c_scl/(pow(ExpIncs[i]-c_loc,2)+pow(c_scl,2))}

#Second, compute the probability of moving from 1 km section to 4A and 4B. Ind4A and Ind4B are the movement #distance indices for 4A and 4B for each 1 km section#pLCR is the proportion of fish moving from the upstream 1 km section to 4A or 4B. Unlike pMove, pLCR's #integration constant is based on all possible movement distancesfor(i in 1:Nkmincs){

pLCR[i,1]<-sum(p2[Ind4A[i,1]:Ind4A[i,2]])/sum(p2[])pLCR[i,2]<-sum(p2[Ind4B[i,1]:Ind4B[i,2]])/sum(p2[])

}

#Third, for each trip, compute the predicted immigration from each 1km upstream increment, to 4A, 4B, and Both. This is based on prediction of proportion moving from pLCR and the abundance in that 1 km sectionfor(irel in 1:(Ntrips-1)){

for(i in 1:Nkmincs){N[irel,i]~dnorm(mu_N[irel,i],tau_N[irel]) # N in log space for 1 km upstream section with errorExportByTrip4A[irel,i]<-exp(N[irel,i])* pLCR[i,1] # Export to 4A ExportByTrip4B[irel,i]<-exp(N[irel,i])* pLCR[i,2] # Export to 4B

ExportByTrip4A4B[irel,i]<-ExportByTrip4A[irel,i]+ExportByTrip4B[irel,i] # Export to 4A+4B}

}

#Finally, get export from FromReach to 4A, 4B, and 4A+4B across all 1 km increments and trip intervalsTotExport[1]<-sum(ExportByTrip4A[1:Ntrips-1,1:Nkmincs])TotExport[2]<-sum(ExportByTrip4B[1:Ntrips-1,1:Nkmincs])TotExport[3]<-sum(ExportByTrip4A4B[1:Ntrips-1,1:Nkmincs])

Table S4. Summary of release and recapture data for rainbow trout that moved more than 20 km.

The column labelled “Reach” highlights whether a fish moved upstream from Marble Canyon or

the Little Colorado River inflow reach to Glen Canyon (MC-GC) or the opposite (GC-MC). The

column labelled “Size” highlights whether a fish was <=150 mm on both releases and recapture

and therefore moved at a small (‘S’) size, or whether it was >=250 mm on both release and

recaptured and therefore moved at a large (‘L’) size. The column labelled “Sep2014” highlights

if the fish was recaptured on the September 2014 trip.

Record KMNumber Release Recapture Release Recapture Release Recapture Moved Reach Size Sep2014

1 Sep2012 Jul2013 269 299 128 17 -110.7 MC-GC L2 Sep2012 Dec2013 241 282 90 25 -65.2 MC-GC3 Sep2012 Oct2013 360 365 56 6 -50.6 MC-GC L4 Jul2012 Dec2013 340 341 56 8 -48.4 MC-GC L5 Sep2012 Dec2013 178 199 55 7 -47.6 MC-GC6 Sep2012 Oct2013 204 275 55 10 -45.9 MC-GC7 Jan2013 Oct2013 205 238 55 12 -43.4 MC-GC8 Jan2013 Jul2014 230 283 55 16 -38.7 MC-GC9 Jan2014 Sep2014 273 285 56 18 -37.9 MC-GC L T10 Jan2014 Sep2014 289 296 55 18 -37.3 MC-GC L T11 Jul2012 Jul2013 250 271 128 91 -36.412 Jul2014 Sep2014 161 178 90 54 -36.3 T13 Sep2013 Sep2014 279 275 56 20 -36.2 MC-GC L T14 Jan2014 Sep2014 255 264 55 20 -35.5 MC-GC L T15 Sep2012 Sep2013 237 296 55 21 -34.3 MC-GC16 Jan2013 Oct2013 227 259 56 22 -34.2 MC-GC17 Apr2014 Sep2014 190 239 53 19 -34.2 MC-GC T18 Jan2013 Sep2014 223 265 89 55 -34.1 T19 Apr2014 Sep2014 195 210 90 56 -33.9 T20 Jan2013 Apr2013 278 281 89 55 -33.7 L21 Apr2012 Sep2013 158 271 89 55 -33.222 Sep2012 Jul2014 229 282 90 57 -32.723 Sep2012 Sep2013 254 274 89 57 -32.3 L24 Apr2014 Jul2014 310 311 122 90 -31.8 L25 Sep2012 Jan2014 273 311 123 92 -31.3 L26 Apr2013 Jan2014 236 249 87 56 -30.727 Nov2011 Jan2013 94 223 32 56 24.628 Nov2011 Jul2013 97 254 22 53 31.1 MC-GC29 Apr2014 Jul2014 260 267 55 87 31.9 L30 Sep2013 Jan2014 257 260 55 87 32.1 L31 Sep2012 Jan2013 209 216 90 122 32.332 Jul2012 Apr2014 219 288 90 122 32.533 Jul2013 Jul2014 163 232 89 123 33.334 Jan2013 Sep2014 251 276 89 122 33.4 L T35 Jul2013 Sep2013 314 302 89 123 33.5 L36 Apr2012 Sep2014 175 265 56 90 33.6 T37 Jul2013 Jan2014 265 263 55 89 33.7 L38 Apr2013 Jul2013 212 248 55 89 33.939 Jul2013 Sep2014 218 259 88 122 33.9 T

Trip Fork Length (mm) Kilometers from GCD Movement Type

Table S4. Con’t.

Record KMNumber Release Recapture Release Recapture Release Recapture Moved Reach Size Sep2014

40 Jan2013 Jan2014 207 244 90 124 3441 Apr2013 Sep2014 216 252 55 90 34.4 T42 Jul2013 Sep2014 265 268 55 89 34.4 L T43 Sep2012 Sep2014 174 247 55 89 34.6 T44 Apr2012 Sep2014 184 306 88 123 34.7 T45 Apr2014 Sep2014 235 253 55 90 34.8 T46 Sep2012 Sep2013 205 253 89 124 3547 Apr2012 Sep2014 168 264 53 89 35.5 T48 Sep2012 Sep2014 235 269 55 91 36.1 T49 Jul2014 Sep2014 260 256 87 124 36.5 L T50 Apr2013 Sep2014 139 256 55 92 36.9 T51 Nov2011 Apr2012 90 125 17 55 37.9 GC-MC S52 Apr2012 Apr2013 140 214 90 128 38.853 Jan2014 Sep2014 278 276 90 129 39 L T54 Apr2014 Jul2014 340 333 89 128 39 L55 Jul2012 Jan2013 289 284 88 128 39.3 L56 Jul2013 Sep2014 272 265 89 128 39.7 L T57 Apr2013 Sep2014 241 266 89 129 40.4 T58 Nov2011 Apr2014 90 273 13 54 40.7 GC-MC59 Jan2013 Apr2013 215 222 89 129 40.860 Nov2011 Sep2014 87 286 8 56 48.2 GC-MC T61 Nov2011 Sep2013 142 259 6 55 49 GC-MC62 Nov2011 Apr2012 93 107 7 56 49.4 GC-MC S63 Nov2011 Sep2012 120 240 5 56 50.8 GC-MC64 Nov2011 Sep2014 85 260 1 56 54.5 GC-MC T65 Apr2013 Apr2014 240 275 56 122 65.666 Jan2014 Sep2014 231 260 56 123 66.7 T67 Sep2012 Sep2013 204 272 55 122 66.868 Jul2013 Jan2014 273 268 55 122 67.1 L69 Jan2014 Sep2014 220 238 55 122 67.3 T70 Jul2012 Sep2012 292 297 56 123 67.6 L71 Jul2014 Sep2014 294 286 57 124 67.6 L T72 Sep2013 Sep2014 285 290 55 123 68.2 L T73 Jul2013 Sep2014 263 265 54 123 68.8 L T74 Jul2013 Jan2014 256 252 56 128 71.7 L75 Sep2013 Sep2014 255 258 56 128 72.1 L T76 Sep2012 Sep2014 223 295 55 127 72.3 T77 Jan2013 Sep2013 115 270 55 128 73.378 Jul2014 Sep2014 257 249 55 129 73.6 T79 Jul2013 Jul2014 242 258 56 130 7480 Jan2014 Sep2014 296 291 54 129 75 L T81 Jan2013 Jan2014 250 273 54 130 75.482 Oct2013 Sep2014 278 270 13 90 77.4 GC-MC L T83 Jul2012 Jul2014 162 254 19 127 108.9 GC-MC84 Jul2014 Sep2014 248 242 20 129 109.2 GC-MC T

Trip Fork Length (mm) Kilometers from GCD Movement Type

Supplementary Figure Captions

Figure S1. Time series of the minimum and maximum daily discharge (a) at Lees Ferry (rkm

25), mean daily water temperature at Lees Ferry and just above the confluence of the Little

Colorado River (rkm 122) and oxygen concentration at Lees Ferry (b), and mean daily turbidity

(c, in formazin nephelometric units) at Lees Ferry, just above the confluence of the Little

Colorado River, and at the nearest station downstream of the confluence (rkm 165). Data from

http://www.gcmrc.gov/discharge_qw_sediment/stations/GCDAMP accessed on January 28, 2015.

Figure S2. Diagram showing how capture probability (pCap) and residency (pRes) effect

predictions of state variables (black) and catches (gray) of marked and unmarked fish. See Table

S1 for definitions of model variables and Table S2 for the full set of model equations.

Figure S3. Comparison of predictions from the Jolly-Seber model (MtbSo) for the number of

recaptures (PIT-tagged and fin clipped trout) and unmarked fish to observations for all reaches.

Disc

harg

e(m

3s

1)

200

400

600

800

1000

1200

Apr11 Jul11 Oct11 Jan12 Apr12 Jul12 Oct12 Jan13 Apr13 Jul13 Oct13 Jan14 Apr14 Jul14 Oct14

Lees FerryMax. DailyMin. Daily

a )

Mea

nDa

ilyW

ater

Tem

pera

ture

(o C)

8

10

12

14

Apr11 Jul11 Oct11 Jan12 Apr12 Jul12 Oct12 Jan13 Apr13 Jul13 Oct13 Jan14 Apr14 Jul14 Oct14

6

7

8

9

10

11

O2(m

gl1

)

Temperature Upstream of LCRTemperature at Lees FerryOxygen at Lees Ferry

b )

Mea

n D

aily

Log

Tur

bidi

ty (F

NU

s)

-5

0

5

10

Apr11 Jul11 Oct11 Jan12 Apr12 Jul12 Oct12 Jan13 Apr13 Jul13 Oct13 Jan14 Apr14 Jul14 Oct14

Dow nstream of LCR Upstream of LCR Lees Ferry

Date

c )

Figure S1.

18

Figure S2.

19

0 10 20 30 40 50 60

020406080 Reach I

Across-TripWithin-Trip

200 400 600 800 1000 1200 1400

200400600800

100012001400

0 20 40 60 80 100

020406080

100Reach II

500 1000 1500

500

1000

1500

0 20 40 60 80

020406080

100Reach III

200 300 400 500 600 700 800 900

200300400500600700800900

0 20 40 60

020406080

Reach IVa

100 150 200 250

100150200250

0 10 20 30 40 50

01020304050 Reach IVb

50 100 150

0

50

100

150

Observed

Pred

icte

d Recaptures Unmarked

Figure S3.

20