estimating adult sockeye salmon escapement into akalura ... · management of sustainable salmon...

Refuge Report 2017.1

Estimating Adult Sockeye Salmon Escapement into Akalura Lake using a remote video and time lapse system, Kodiak National Wildlife Refuge,

Alaska

2015 Progress Report

G. Kevin VanHatten

Kodiak National Wildlife Refuge Kodiak, Alaska

January 2017

U.S. Fish & Wildlife Service

US Fish and Wildlife Service – Kodiak National Wildlife Refuge ii

The mission of the National Wildlife Refuge System is to administer a national network of lands and waters for the conservation, management and where appropriate, restoration of the fish, wildlife, and plant resources and their habitats within the United States for the benefit of present and future generations of Americans.

Suggested Citation:

VanHatten, G. K. 2017. Estimating Adult Sockeye Salmon Escapement into Akalura Lake using a remote video and time lapse system, Kodiak National Wildlife Refuge, Alaska: 2014 progress report. Refuge Report 2017.1, U.S. Fish and Wildlife Service, Kodiak National Wildlife Refuge, Kodiak, Alaska.

Keywords:

Sockeye salmon, Oncorhynchus nerka, Kodiak Island, time-lapse cameras, Alitak District, Akalura River

Disclaimer: The use of trade names of commercial products in this report does not constitute endorsement or recommendation for use by the federal government.

US Fish and Wildlife Service – Kodiak National Wildlife Refuge iii

Table of Contents Page

ABSTRACT .................................................................................................................................................. 4

INTRODUCTION ........................................................................................................................................ 4

STUDY GOAL ............................................................................................................................................. 5

STUDY AREA ............................................................................................................................................. 5

METHODOLOGY ....................................................................................................................................... 5

Approach ................................................................................................................................................... 5

Time lapse camera system ........................................................................................................................ 6

Modelling salmon escapement (abundance) ............................................................................................. 7

RESULTS AND DISCUSSION ................................................................................................................... 8

CAMERA AND VIDEO SYSTEM .............................................................................................................. 8

Areas which need to be improved: ........................................................................................................ 9

Programming Video ............................................................................................................................ 10

SOCKEYE SALMON ESCAPEMENT ESTIMATE ................................................................................. 10

ACKNOWLEDGEMENTS ........................................................................................................................ 11

REFERENCES ........................................................................................................................................... 12

APPENDIX A ........................................................................................................................................... A-1

Refuge report 2014.6 U.S. Fish and Wildlife Service, Kodiak National Wildlife Refuge

US Fish and Wildlife Service – Kodiak National Wildlife Refuge 4

Estimating Adult Sockeye Salmon Escapement into Akalura Lake using a remote video and time lapse system, Kodiak National Wildlife Refuge, Alaska

2015 Progress Report

Gareth K. VanHatten

ABSTRACT

A time-lapse photography and video recording system were used to enumerate adult sockeye salmon

(Oncorhynchus nerka) entering Akalura Lake. This system was installed on June 12 and operated

through October 21, 2015. The time-lapse photography camera recorded 3 sequential pictures at the start

of each minute for the duration of the project, while the video camera was programmed to start recording

at 09:00 and stop at 17:00. Following a double sampling protocol, a model of the relationship between

time lapse and video counts was used to predict salmon passage across the season. Salmon passage

peaked on August 19, 2016 and by the end of the season 32,802 ± 7,336 (95% CI) sockeye was estimated

to have migrated into Akalura Lake.

INTRODUCTION

Management of salmon within the Kodiak Management Area (KMA) is the responsibility of Alaska

Department of Fish and Game (ADF&G) who monitors escapement on many systems within Kodiak

Refuge boundaries. Escapement goals for KMA salmon stocks are based on the Policy for the

Management of Sustainable Salmon Fisheries (SSFP; 5 AAC 39.222) and the Policy for Statewide

Salmon Escapement Goals (EGP; 5 AAC 39.223). These policies were adopted by the Alaska Board of

Fisheries in 2000 and 2001, respectively, to ensure the state’s salmon stocks would be conserved,

managed, and developed using the sustained yield principle. Escapement information provides the basis

for ensuring biological integrity, and is the foundation for in season management actions that regulate

commercial, sport, and subsistence harvest of salmon (Spalinger 2006). Historical studies have

documented salmon utilizing over 800 systems within the KMA (Johnson and Keyse 2013), but only

about 49 support yearly sockeye salmon spawning populations (Johnson and Blanche 2010). Of these 49,

escapement is monitored in only 10 systems. During the annual review of escapement goals in 2005 by

ADF&G, the Akalura, as well as Uganik and Little Rivers, escapement goals were eliminated. . The



elimination of these goals was based on reliable escapement estimates not being consistently collected

due to budget constraints (Nelson et al. 2005).

Sockeye salmon data/information has been collected on Akalura Lake intermittently starting in 1923

(Edmundson et al 1994), with the most recent study being conducted in 2002 (Sagalkin 2003). Historical

data (1992-2003) shows that at one time the Akalura sockeye salmon run was a bi-modal population with

a small early run occurring in June. But over time the early run has disappeared. This system has been

monitored from 1944 to 1958 and from 1987 to 2002, but due to lack of funding there was a 29-year

period where no escapement data was collected. To help fill this data gap, we will provide the results

from this study to ADF&G to aid in their management of sockeye salmon in the Alitak District. The lack

of recent escapement data is not only a problem for managers focused on sustainable yield but also the

energy and nutrients in spawning salmon provide critical inputs into riparian and terrestrial ecosystems

(Wipfli and Baxter, 2001; Willson and Halupka, 1995). Thus, it is also important for Kodiak National

Wildlife Refuge managers to have accurate escapement data to assess whether escapement is sufficient to

maintain historical ecosystem productivity and function as well as subsistence needs.

STUDY GOAL Quantify daily escapement of sockeye salmon into Akalura Lake using a time-lapse camera system.

STUDY AREA Akalura Lake has a surface area of 4.9 km2, a mean depth of 9.9 m, and a maximum depth of 22 m

(Schrof et al. 2000). There are four main creeks that drain into the lake: Mud Creek, Falls Creek, Eagle

Creek, and Crooked Creek (Edmundson et al. 1994). The outlet stream (Akalura Creek) is 1.9 km long

and drains into a small saltwater lagoon before entering Olga Bay. Akalura Creek is a small, clear water

creek and has a single 2.8 km tributary (Humpy Creek) (Fig 1).

METHODOLOGY

Approach Mangers utilize several different methods, each with their own strengths and weaknesses, to monitor

salmon escapement and passage. Examples of these methods include fixed or floating weirs, counting

towers, or sonar, and aerial surveys which are used on many large rivers in Alaska. Although these

methods collect the desired information, they are expensive and are relatively labor intensive. On small

streams (< 15m wide), remote video methods can collect comparable data and are less expensive and



labor intensive, however, the time required to review video footage can be prohibitive. To quantify the

sockeye salmon run in Akalura River, a double sampling time lapse and video camera system approach

was used (Deacy et al. 2016). This double sampling camera method was developed and tested in 2012,

and refined from 2013-2015 on 11 streams in southwest Kodiak, Alaska. The system has the benefits of a

remote video system (low maintenance, low impact on passing salmon, inexpensive, and accurate counts),

but labor costs for reviewing photo images is greatly reduced.

In an effort to utilize a remote camera system without time-consuming video enumeration, we utilized a

double sampling' scheme. Double sampling is used when the variable of interest is expensive to quantify,

but another variable is easily measured and has a predictable relationship with the variable of interest,

(Cochran 1977). For our study the variable of interest is the number of sockeye salmon that migrate

upstream each hour, which we can be accurately quantified with an above-water video camera. The more

easily measured and related measurement is the number of salmon detected in time-lapse images each

hour. Time-lapse photography is recorded for the entire study season, while the video recordings are only

measured on a subsample of units in order to model the relationship between the variables. The total time

required to review footage in this double sampling scheme is low relative to video-only approaches

because we only have to enumerate sockeye salmon in a subset of the hour long sample units. We

determined the sockeye salmon passage for the remaining hours by modelling the relationship between

the subsample of hourly video counts and time-lapse photography then used the model to predict sockeye

salmon migration across the entire run. In addition, the time-lapse photography counts provided a general

run timing picture over the course of the field season (Figure 2).

Time lapse camera system The camera/video system was installed before the start of the sockeye salmon run, June 12, 2015. Due to

the lack of information collected from this system over the past 10 years, it was imperative for the

camera/video system to be installed early enough before the start of the sockeye salmon run, which

according to historical data indicates this time to normally occur after late July to early August. In 2015,

the site was visited every 10 days, with the exception of one period lasting greater than 10 days

(Appendix A). The purposes of site visits were to ensure all equipment was working, clean the panels,

and change out SD memory cards. To record time lapse images of passing salmon, we used a ReconyxR

Hyperfire PC800 camera, programmed to take three photos in rapid succession (<1 s between frames)

each minute, 24 h/day. We followed the protocol of Deacy et al. 2016, for the use of time lapse and

infrared lights to capture salmon movement (Figure 3).



To make passing salmon easier to see, we secured 50.8 cm – 76.2 cm white High Density Polyethylene

(HDPE) contrast panels to the bottom of the stream below the cameras by attaching them to a heavy chain

(Figure 4 and 5, Alaska Department of Fish and Game Permit # FH-14-II-0076). For more detail, please

see Deacy et al. 2016.

At the end of the season, in mid-October, we separately counted the number of salmon moving up and

downstream past the contrast panels during each three-photo burst. We only counted a salmon as passing

if it moved at least 1/2 the length of the panels; we did not count stationary fish. Finally, we summed

upstream and downstream counts separately for each hour of the monitoring season.

Modelling salmon escapement (abundance)

We used a model-based double sampling approach to estimate salmon escapement following Deacy et al.

2016, where we used time lapse and video equipment to predict salmon passage for the entire season.

We selected hours that spanned the full range of hourly time-lapse salmon counts, including both periods

with high numbers and periods with low numbers of salmon passing over the panels in both directions.

Also, we selected hours where we were confident of nearly 100% detection, excluding hours where

conditions, such as bad glare or poor lighting prevented accurate quantification. In total, we quantified

salmon runs from 74 h of video in order to develop an accurate relationship between the numbers of

salmon observed in the video versus the number counted in our time-lapse photos.

Next, we modelled video counts as a function of time-lapse photo counts for the subsample. All statistical

analyses were conducted using the statistical program R 3.1.3 (R Development Core Team, 2015). We

compared four different models: first and second order linear regressions and first and second order

segmented or “split-point'' linear regressions (Muggeo, 2008) (Table 2). The segmented regression allows

the slope to differ across ranges of the predictor variable. This makes sense for salmon swimming in a

stream; salmon swimming upstream (positive values) might move slower, and thus have a greater chance

of being detected in a time-lapse burst . In contrast, salmon swimming downstream (negative values)

might move faster and have a lower likelihood of detection. To address this possibility, we including

segmented regression models with the split-point (slope inflection point) constrained to zero. To assess

relative model fit, we compared Akaike's Information Criterion values (AICc; Akaike, 1974). AICc is

Aikaike’s Information Criterion adjusted for small sample size (Akaike 1974). 95% confidence intervals

on escapement were calculated using bootstrap resampling methods. Accuracy is the percent difference



between the leave-one-out cross-validation predicted escapement and the actual escapement for the 70

hours where escapement was counted using video recording. Precision is the mean squared error (MSE).

To validate models and test for over-fitting, we performed leave one out cross validation (LOOCV;

Kohavi, 1995), and used the resulting predictions to calculate the precision (mean squared error, MSE)

and accuracy (the percent difference between the predicted and actual escapement of the 71 h for which

we watched video). Based on these metrics, we selected a top model for each stream. The top model for

each stream is in bold (Table 1).

Using the best model for each stream, we predicted the salmon passage for all of the hours of the

monitoring period. The sum of these predictions is the estimated escapement. Because we did not use

random sampling to select our modelling subsample, it is inappropriate to use the model variance to

calculate confidence intervals for total escapement. Instead, we bootstrapped our subsample (Canty &

Ripley, 2016) with replacement, refit our model using the top model structure, and re-predicted the total

escapement (Efron & Tibshirani, 1993). We repeated this 10,000 times and used the 2.5 and 97.5

percentile values as upper and lower 95% confidence intervals of total escapement.

RESULTS AND DISCUSSION

CAMERA AND VIDEO SYSTEM

Many methods have been used to enumerate sockeye salmon on different anadromous waters. Generally,

fisheries management within the KMA is based on salmon escapement estimates collected from weirs.

Neither ADF&G nor KNWR have the infrastructure or financial resources to run a weir on Akalura River.

Because it was inexpensive and time efficient, the time-lapse/video camera system proved to be a useful

alternative for counting sockeye salmon in this system. With bi-weekly visits to service the system and

checking to make sure it was operational, there was very little maintenance required for this study. These

benefits will be a useful tool for managers to quantify salmon runs in streams otherwise too difficult or

expensive to monitor by other means.

Although the time lapse camera system worked as planned, recording 3 photos per minute, 24 hours per

day, we encountered some problems during the study. Due to the large amounts of data being recorded

we needed to exchange SD memory cards every 10 days, otherwise they would fill up and we’d lose data.

There were two occasions when the camera system did not record approximately 67,000 pictures. The



first occasion was because of an SD card error and the second occasion was because we failed to service

the camera before the card reached memory capacity.

The video system worked remarkably well, recording 1,226 minutes of fish passage during the study

period. The video was programmed to start recording at 9:00 and stop at 17:00 each day. Out of a total

of 128 days of recording, there were 36 periodic days (291 hours) when recording did not occur (Figure

7). The cause of one period not being recorded was due to the DVR system being damaged by water. We

changed this system out for a new one and recording continued. The hard drive from the damaged system

was still intact and we were able to recover the recorded data. For the most part the battery system

provided ample power to both the camera and video systems for the entire field season although

occasionally there were times when lack of sunlight was insufficient to charge the batteries. Nevertheless,

we were still able to quantify escapement with the time-lapse camera system.

Areas which need to be improved:

Weather – Throughout the field season there were times when weather conditions hindered visibility

across the contrast panels. For example, on those days when cloud cover was absent, there was about 3-4

hours when glare from the sun was too great to see if any fish were moving across the panels. An option

that could overcome this problem is to move the camera setup from the west side of the river to the east

side. Another visibility problem was precipitation at night. Fog or high rain events obscured visibility.

Mixed Stock species – From mid-August to the end of the study period pink salmon (O. gorbuscha) were

present in the system. Fish differentiation of the two salmon species could be easily detected during the

day and for sex but became a problem during the night. Male pink salmon were easily identified but

female pink salmon were much harder to distinguish at night and when the footage and photos revert to

black and white. Using a video recording system that records color images would allow for better

identification of different species. In addition, it has been showed by Deacy et. al. that using underwater

LED lights at the top of the panels could provide better viewing during night. Resident species were

smaller than either pink or sockeye salmon and was easily identified.

Equipment specifications – The use of color in the video camera setup versus black and white would help

considerably in fish identification. The time-lapse photographs were in color but the video camera was

not.



Programming Video

A good escapement estimate is only possible if users accurately model the relationship between time

lapse and video counts (Deacy 2016). This relationship is critical given the large differences in

abundance estimates resulting from small differences in model structure or fit. Consequently, it is

important that we collect video data for an additional year to refine the model. With more data,

confidence intervals are very likely to tighten.

The video recorder was programmed to only record images from 0900 to 1700 each day. Through

analysis of the time lapse photographs, we noticed that there were days when salmon were moving before

and after the programmed video time frame (Table 3). If we re-programmed the video recordings to

include a broader time range, we would be able to achieve better confidence intervals.

Historically, the Alaska Department of Fish and Game (Department) has collected salmon escapement

information in the Alitak District to manage the fishery. It is the intent of the work conducted on Akalura

drainage to provide additional information for both state and federal fisheries managers. For example,

our estimates of escapement to this system could be used in management decisions. These methods and

procedures could also be used on other spawning streams that are not currently monitored.

SOCKEYE SALMON ESCAPEMENT ESTIMATE The 2015 Akalura River sockeye salmon escapement estimation project provided needed information to

help fishery managers in the Alitak District of the Kodiak Management Area. Out of the 4 models

relating time lapse counts to video counts, the top model was a non-segmented second order polynomial

(solid red line in Figure 6). This model produces an estimate of 32,802 plus or minus 7,336 (95% CI)

sockeye salmon. The top model had the lowest AIC (1488.596), second best precision (MSE = 1,477),

and second best accuracy (-9.2%)(Table 1). The segmented polynomial had slightly better precision and

accuracy (bottom right in fig. 6), but it is not appropriate to use a segmented model with so few fish

moving downstream. The negative accuracy metric for our top model suggests that we are probably

slightly under-estimating the number of salmon, so this estimate should be viewed as conservative.



ACKNOWLEDGEMENTS

The work conducted and analyses of the data in this report are the results of previous work conducted by

Mathew Sorum and refined by Will Deacy. We would like to thank their commitment and hard work to

their projects, which allowed us to adopt their methods and procedures to collect sockeye salmon

information from the Akalura drainage. We would also like to thank those individuals who helped in

construction and maintenance for the camera system. These include Carolyn Cheung, Shelby Fleming,

Andy Orlando, William Leacock, and Kurt Rees. We would also like to thank the numerous individuals

within the Kodiak National Wildlife Refuge office for their support and dedication to this project; these

include Cinda Childers, Lectia Monzon, Kent Sundseth (previous Deputy Refuge Manager), Tevis

Underwood and Ann Marie Larosa.



REFERENCES

Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 19:716-723. DOI 10.1109/TAC.1974.1100705.

Canty, A. and B. Ripley. 2016. Boot: Bootstrap R (S-Plust) functions. R package version 1.3-1.8.

Cochran, W.G. 1977. Sampling Techniques. 3rd edition. New York: John Wiley and Son.

Deacy, W. and W.B. Leacock. 2014. Kodiak brown bear-sockeye salmon foraging ecology in southwest Kodiak Island, Alaska. 2014 progress report. Refuge Report 2014.6, U.S. Fish and Wildlife Service, Kodiak National Wildlife Refuge, Kodiak, Alaska.

Deacy, W. 2012. The influence of salmon run abundance and timing on Kodiak bear ecology. Masters of Science Proposal. University of Montana. Missoula, Montana.

Deacy, W.W., W.B. Leacock, L.A. Eby and J.A. Sanford. 2016. A time-lapse photography method for monitoring salmon (Oncorhynchus spp.) passage and abundance in streams. PeerJ 4:e2120; DOI 10.7717/peerj.2120.

Edmundson, J.A., L.E. White, S.G. Honnold, and G.B. Kyle. 1994. Assessment of sockeye salmon production in Akalura Lake. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Information Report NO. 5J84-17.

Efron, B. and R.J. Tibshirani. 1993. Introduction to Bootstrap. 1st edition. Boca Rotan; Chapman and Hall.

Gregoire, T.G. 1998. Design-based and model-based inference in survey sampling: appreciating the difference. Canadian Journal of Forest Research. 28:1429-1447. DOI 10.1139/x98-166.

Hansen, M.H., W.G. Madow, and B.J. tepping. 1983. An evaluation of model-dependent and probability-sampling inferences in sample surveys. Journal of the American Statistical Association. 78:776-793. DOI 10.1080/01621459.1983.10477018.

Johnson, J. and M. Keyse. 2013. Kodiak Management Area commercial salmon fishery annual management report, 2013. Alaska Department of Fish and Game. Fishery Management Report No. 13-44. Anchorage.

Johnson, J. and P. Blanche. 2010. Catalog of waters important for spawning, rearing, or migration of anadromous fishes – Southwestern Region. Effective June 1, 2010. Alaska Department of Fish and Game. Special Publication No. 10-08. Anchorage.

Kohavi, R. 1985. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. 14:1137-1143.

Muggeo, V.M.R. 2008. Segmented: an R package to fit regression models with broken-line relationships. R News. 8(1):20-25. Available at http://cran.r-project.org/doc/Rnews.



Nelson, P.A., M.J. Witteveen, S.D. Honnold, I. Vining, and J.J. Hasbrouck. 2005. Review of salmon escapement goals in the Kodiak Management Area. Alaska Department of Fish and Game, Fishery Manuscript No. 05-05, Anchorage.

Sagalkin, N.H. 2003. Akalura Remote Video Research Project Results for 2002. Alaska Department of Fish and Game, Division of Commercial Fisheries. Memo. 2003.

Schrof, S.T., S.G. Honnold, C.J. Hicks, and J.A. Wadle. 2000. A summary of salmon enhancement, rehabilitation, evaluation, and monitoring efforts conducted in the Kodiak Management Area through 1998. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Information Report No. 4K00-57.

Spalinger, G. 2006. Kodiak Management Area Salmon Daily and Cumulative Escapement Counts for River Systems with Fish Weirs 1996-2005. Alaska Department of Fish and Game Commercial Fisheries, Fisheries Management Report No. 06-06, Anchorage.

Stephens, P.R., M.O. Kimberley, P.N Beets, T.S.H. Paul, N. Searles, A. Bell, C. Brack, and J. Broadley. 2012. Airborne scanning LiDAR in a double sampling forest carbon inventory. Remote sensing of Environment. 117:348-357. DOI 10.1016/j.rse.2011.10.009.

R Development Core Team. 2015. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available at http://www.r-project.org.

Wipfli, M. S., and C. V. Baxter. 2010. Linking ecosystems, food webs, and fish production: subsidies in salmonid watersheds. Fisheries 35:373–387.

Wilson, M.F. and K.C. Halupka. 1995. Anadromous fish as keystone species in vertebrate communities. Conservation Biology 9(3): 489-497.



Figure 1. Akalura Lake, Kodiak, Alaska.



Figure 2. Run timing of sockeye salmon from time-lapse photography, Akalura River, Alaska.

0

20

40

60

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22-J

un25

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e sa

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Figure 3. Sockeye salmon counting system showing time-lapse camera, infra-red light and video camera locations.



Figure 4. Time-Lapse photograph of fish moving upstream across HDPE contrast panels, Akalura River, Kodiak, Alaska.



Figure 5. Photographic capture of video recording, Akalura River, Alaska.



Figure 6. Relationship between hourly time-lapse and video counts of salmon passage for Akalura River, Alaska.



Figure 7. Comparison of time-lapse photographs and video counts subsample of sockeye salmon, Akalura River, Alaska.



Table 1. Model descriptions, escapement estimates and model validation metrics.

Model Name Model of Sockeye Passage k AICc

Escapement

Estimate

(±95% CI)

Accuracy Precision

Segmented first

order

pass=(x>0)*10.281+

(x<0)*1.556) 2 1519.070 23,947 -26.0% 1829

Segmented

polynomial

pass =(x>0)*15.92521+(x>0)2 *

-0.19454+(x<0)*1.27456+(x<0)2 *-0.19454 3 1488.860 32,268 -8.1% 1476

First order pass =x*11.8218 1 1517.658 26091

(± 6,995) -26.9% 1827

Polynomial pass =x* 17.08421+

(x2 * -0.19119) 2 1488.596

32,802

(± 7,336) -9.2% 1477



Table 2. Comparison of daily time lapse photographs and video count on August 25, 2015 for Akalura River, Kodiak, Alaska.

Time Photographs Video

UP DN passage UP DN passage 00:00 1 1 01:00 1 1 0 02:00 1 1 03:00 0 04:00 0 05:00 0 06:00 1 1 07:00 0 08:00 1 1 09:00 0 18 1 17 10:00 0 11:00 0 13:00 0 14:00 1 1 15:00 6 6 60 6 54 16:00 5 5 17:00 37 1 36 435 10 425 18:00 27 27 19:00 31 31 20:00 20 20 21:00 10 10 22:00 2 2 23:00 5 5 23:59 1 1 Total 150 2 148 513 17 496


US Fish and Wildlife Service – Kodiak National Wildlife Refuge A - 1

APPENDIX A

TIME-LAPSE PHOTOGRAPH AND VIDEO DATA



Table 2. Time-lapse photography and video data, Akalura River, Alaska.

VIDEO PHOTO

DATE DATA USEFUL

DATE VISTED

FISH PRESENT DATA COMMENTS FISH

PRESENT DATA COMMENTS

12-Jun-15 *

*

* Installed 13-Jun-15 *

*

*

14-Jun-15 *

*

* 15-Jun-15 *

*

*

16-Jun-15 *

*

* 17-Jun-15 *

*

*

18-Jun-15 *

*

* 19-Jun-15 *

*

*

20-Jun-15 *

*

* 21-Jun-15 *

*

*

22-Jun-15 *

*

* * 23-Jun-15 *

* * Analyze 1 hr * *

24-Jun-15 *

* * Analyze 1 hr * * 25-Jun-15 *

*

* *

26-Jun-15 *

*

* * 27-Jun-15 *

*

*

28-Jun-15 *

*

* * 29-Jun-15 *

*

* *

30-Jun-15 * *

*

* * 1-Jul-15 *

*

* *

2-Jul-15 *

*

* * 3-Jul-15 *

*

PARTIAL Bad SD Card

4-Jul-15 *

*

Bad SD Card 5-Jul-15 *

*

Bad SD Card

6-Jul-15 *

*

Bad SD Card 7-Jul-15 * *

*

* PARTIAL Bad SD Card

8-Jul-15 Unknown * * 9-Jul-15 * * * 10-Jul-15 * * * * 11-Jul-15 * * * * 12-Jul-15 * * * * 13-Jul-15 * * * 14-Jul-15 Unknown * * 15-Jul-15 Unknown * * 16-Jul-15 * * 17-Jul-15 * * * * * 18-Jul-15 * * * *



Table 2. Time-lapse photography and video data, Akalura River, Alaska. (Continued)

VIDEO PHOTO

DATE DATA USEFUL

DATE VISTED



19-Jul-15 * * * * 20-Jul-15 * * * * 21-Jul-15 * * * Analyze 3 hrs * * 22-Jul-15 * * * * 23-Jul-15 * * * * 24-Jul-15 * * * * 25-Jul-15 * * * * 26-Jul-15 * * * PARTIAL SD Card full 27-Jul-15 Unknown SD Card full 28-Jul-15 * * SD Card full 29-Jul-15 * * SD Card full 30-Jul-15 * * SD Card full 31-Jul-15 * * SD Card full 1-Aug-15 * * SD Card full 2-Aug-15 * * SD Card full 3-Aug-15 * * SD Card full 4-Aug-15 * * SD Card full 5-Aug-15 * * SD Card full 6-Aug-15 * * * SD Card full 7-Aug-15 * * * * PARTIAL SD Card full 8-Aug-15 * * * * 9-Aug-15 * * * *

10-Aug-15 * * * * 11-Aug-15 * * * * 12-Aug-15 * * * Analyze 2 hrs * * 13-Aug-15 * * * * 14-Aug-15 * * * Analyze 8 hrs * * 15-Aug-15 * * * Analyze 8 hrs * * 16-Aug-15 * * * * 17-Aug-15 * Unknown * * 18-Aug-15 * * * Analyze 2 hrs * * 19-Aug-15 * * * Analyze 8 hrs * * 20-Aug-15 * * * Analyze 5 hrs * * 21-Aug-15 * * * * 22-Aug-15 * * * Analyze 2 hrs * * 23-Aug-15 * * * *




VIDEO PHOTO

DATE DATA USEFUL

DATE VISTED



24-Aug-15 * * * Analyze 3 hrs * * 25-Aug-15 * * * Analyze 3 hrs * * 26-Aug-15 * * * * 27-Aug-15 * * * Analyze 4 hrs * * 28-Aug-15 * * * * * 29-Aug-15 Unknown * * 30-Aug-15 Unknown * * 31-Aug-15 Unknown * * 1-Sep-15 Unknown * * 2-Sep-15 * * * Analyze 1 hrs * * 3-Sep-15 * * * Analyze 4 hrs * * 4-Sep-15 * * * Analyze 7 hrs * * 5-Sep-15 * * * Analyze 3 hrs * * 6-Sep-15 Unknown * * 7-Sep-15 * * * * Analyze 1 hrs * * 8-Sep-15 * * * * 9-Sep-15 Unknown * *

10-Sep-15 Unknown * * 11-Sep-15 * * * * 12-Sep-15 * * * Analyze 1 hrs * * 13-Sep-15 * * * * 14-Sep-15 Unknown * * 15-Sep-15 Unknown * * 16-Sep-15 Unknown * * 17-Sep-15 * * * * * 18-Sep-15 * * * * 19-Sep-15 * * * * 20-Sep-15 * * * Analyze 5 hrs * * 21-Sep-15 * * * * 22-Sep-15 * * * * 23-Sep-15 * * * * 24-Sep-15 * * * FINISHED 25-Sep-15 * * * 26-Sep-15 Unknown * 27-Sep-15 * Unknown * 28-Sep-15 Unknown *




VIDEO PHOTO

DATE DATA USEFUL

DATE VISTED



29-Sep-15 * * * 30-Sep-15 * * * 1-Oct-15 * * * 2-Oct-15 Unknown * 3-Oct-15 Unknown * 4-Oct-15 * * * 5-Oct-15 * * * * 6-Oct-15 Unknown * 7-Oct-15 Unknown * 8-Oct-15 Unknown * 9-Oct-15 * * *

10-Oct-15 * * * 11-Oct-15 Unknown * 12-Oct-15 Unknown * 13-Oct-15 * * * 14-Oct-15 * Unknown * 15-Oct-15 Unknown * 16-Oct-15 Unknown * 17-Oct-15 Unknown * 18-Oct-15 * * * 19-Oct-15 Unknown * 20-Oct-15 Unknown * 21-Oct-15 * * * *

estimating adult sockeye salmon escapement into akalura ... · management of sustainable salmon...

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