| A |
|
Use Descriptive Statistics To Decribe Spawning Patterns In The WSR |
10/1/2012 |
9/30/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:19 PM |
|
Description: The total number of bull trout redds by stream and within each stream reach will be compiled for WSR and SC after the last survey. |
| B |
|
Graph Spawning Abundance Trends In WSR |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:19 PM |
|
Description: Graph redd count data collected from 1999 - 2012. |
| C |
|
Determine Changes In Spawning Locations In WSR |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:19 PM |
|
Description: Data will be pooled with survey data beginning in 1998 and analysis of variance will be performed to determine if bull trout utilize different stream reaches by year. Between-year variation in the spatial distribution of redds in each stream will be evaluated using a two-sample Kolmogorov-Smirnov test.
In order to determine the power to detect population trends, an analysis of variance, based on a model suggested by Urquhart et al. (1998), will be used to identify components of variance associated with year, observation site, measurement error, and any interactions. The results will be used to estimate the power to detect trends within the data and to determine the number of years required to detect a trend of a specified magnitude with a given power. The basic trend analysis will be a linear regression of the response on sample year. The response may be the actual observation or an appropriate linearizing transformation, e.g., the log of the observation. Explanatory co-variates will be incorporated into the linear model. The co-variates have the potential of reducing the magnitude of the year effect and substantially increasing the power to detect trends. |
| D |
|
Determine Changes In Spawn Timing In WSR |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:19 PM |
|
Description: Data will be pooled with survey data beginning in 1998 and analysis of variance will be performed to determine if bull trout peak spawning varies by year. Between-year variation in temporal distribution of redds will be evaluated using a two-sample Kolmogorov-Smirnov test.
In order to determine the power to detect population trends, an analysis of variance, based on a model suggested by Urquhart et al. (1998), will be used to identify components of variance associated with year, observation site, measurement error, and any interactions. The results will be used to estimate the power to detect trends within the data and to determine the number of years required to detect a trend of a specified magnitude with a given power. The basic trend analysis will be a linear regression of the response on sample year. The response may be the actual observation or an appropriate linearizing transformation, e.g., the log of the observation. Explanatory co-variates will be incorporated into the linear model. The co-variates have the potential of reducing the magnitude of the year effect and substantially increasing the power to detect trends. |
| E |
|
Use Descriptive Statistics To Describe Spawning Patterns In SC |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:19 PM |
|
Description: The total number of bull trout redds by stream and within each stream reach will be compiled for WSR and SC after the last survey. The two week period during which the maximum redd count occurred by stream and stream reach will be identified. |
| F |
|
Graph Spawning Abundance Trends In SC |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:21 PM |
|
Description: Graph redd count data collected from 1999 - 2012. |
| G |
|
Determine Changes In Spawning Locations In SC |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:21 PM |
|
Description: Data will be pooled with survey data beginning in 1998 and analysis of variance will be performed to determine if bull trout utilize different stream reaches by year. Between-year variation in the spatial distribution of redds in each stream will be evaluated using a two-sample Kolmogorov-Smirnov test.
In order to determine the power to detect population trends, an analysis of variance, based on a model suggested by Urquhart et al. (1998), will be used to identify components of variance associated with year, observation site, measurement error, and any interactions. The results will be used to estimate the power to detect trends within the data and to determine the number of years required to detect a trend of a specified magnitude with a given power. The basic trend analysis will be a linear regression of the response on sample year. The response may be the actual observation or an appropriate linearizing transformation, e.g., the log of the observation. Explanatory co-variates will be incorporated into the linear model. The co-variates have the potential of reducing the magnitude of the year effect and substantially increasing the power to detect trends. |
| H |
|
Determine Changes In Spawn Timing In SC |
10/1/2012 |
7/31/2013 |
Concluded |
Jen Graham |
6/15/2012 4:23:21 PM |
|
Description: Data will be pooled with survey data beginning in 1998 and analysis of variance will be performed to determine if bull trout peak spawning varies by year. Between-year variation in temporal distribution of redds will be evaluated using a two-sample Kolmogorov-Smirnov test.
In order to determine the power to detect population trends, an analysis of variance, based on a model suggested by Urquhart et al. (1998), will be used to identify components of variance associated with year, observation site, measurement error, and any interactions. The results will be used to estimate the power to detect trends within the data and to determine the number of years required to detect a trend of a specified magnitude with a given power. The basic trend analysis will be a linear regression of the response on sample year. The response may be the actual observation or an appropriate linearizing transformation, e.g., the log of the observation. Explanatory co-variates will be incorporated into the linear model. The co-variates have the potential of reducing the magnitude of the year effect and substantially increasing the power to detect trends. |
| I |
PUBPROTOCOL |
Review, Revise, & Publish Protocol, Study Design, & Methods In Monitoringmethods.org |
10/1/2012 |
9/30/2013 |
Concluded |
Jamie Cleveland (Inactive) |
7/31/2012 11:29:31 AM |
|
Description: The Protocol (including temporal and spatial design) and Methods for this work element are stored at monitoringmethods.org and need to be finalized (i.e., "Published" through monitoringmethods.org), preferably prior to data collection. Preparations for contract renewals must include reviewing any previously published Protocols/Methods to ensure that they are consistent with how work will be done in any subsequent contract. |
| J |
DELIV |
Summarize Redd Count Data |
|
9/30/2013 |
Concluded |
Jen Graham |
6/15/2012 4:24:03 PM |
|
Description: The total number of bull trout redds by stream and within each stream reach will be compiled for WSR and SC after the last survey. Data will be pooled with survey data beginning in 1998 and analysis of variance will be performed to determine if bull trout utilize different stream reaches by year and if peak spawning varies by year. Between-year variation in the spatial and temporal distribution of redds in each stream will be evaluated.
In order to determine the power to detect population trends, an analysis of variance, based on a model suggested by Urquhart et al. (1998), will be used to identify components of variance associated with year, observation site, measurement error, and any interactions. The results will be used to estimate the power to detect trends within the data and to determine the number of years required to detect a trend of a specified magnitude with a given power. The basic trend analysis will be a linear regression of the response on sample year. The response may be the actual observation or an appropriate linearizing transformation, e.g., the log of the observation. Explanatory co-variates will be incorporated into the linear model. The co-variates have the potential of reducing the magnitude of the year effect and substantially increasing the power to detect trends. |
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