Adult Model Covariates
prep_environmental_covariates.RmdThere are multiple covariates used in SR JPE modeling that were developed through separate but related processes. There are currently two documents describing covariate selction and processing for different models: (1) Stock Recruit Covariates and (2) Adult Model Covariates (this document).
The Passage to Spawner (P2S) model relates spawner counts (from redd or holding surveys) to upstream passage counts obtained by video systems. This model is restricted to streams with reliable redd or holding surveys and reliable upstream passage counts. It is also restricted to years where the redd/holding and upstream passage datasets overlap. This vignette describes the process of pulling and processing environmental covariates for use in the P2S model.
Selecting Adult Environmental Covariates
Potential environmental covariates hypothesized to influence prespawn mortality were proposed in meetings with the SR JPE Modeling Advisory Team (MAT). Five initial categories were identified:
- temperature
- flow
- water year type
- passage timing
- total passage
There are many ways to summarize each of these categories and initial
analyses helped identify collinearity and performance of each potential
method by regressing prespawn mortality (calculated as
upstream_count / spawner_count) against the environmental
variable. When we were using redd counts as spawner_count,
our model assumed a 50/50 sex ratio and modified that equation to be
upstream_count / (spawner_count * 0.5). Generally, one redd
per female is a reasonable assumption although our model left the
possibility open for more than one redd per female (source).
Note that this covariate analysis and preparation does not include the
Sacramento River mainstem as spring run do not spawn on the mainstem
Sacramento.
Preparing Covariates
Temperature
Several approaches were considered for summarizing temperature:
- Proportion of days where the temperature surpassed a threshold of 20 degrees Celsius (source)
- Growing degree days (GDD) with a base temperature of 0 degrees
Celsius (source)
- Degree Day 20 (DD20), where cumulative degree days are calculated against a threshold of 20 degrees Celsius (source)
Following initial analyses and discussions, we focused on approach 3 because it showed the most consistent relationship with prespawn mortality across streams and accounts for cumulative stress. We calculated the metric for migration months (March - May) in the Sacramento River and holding months (May - August) in each tributary. The resulting dataset is as follows (note that DD less than 0 are set to 0):
| year | stream | gdd_trib | gdd_sac | gdd_total |
|---|---|---|---|---|
| 1962 | butte creek | 0.0000000 | 0 | 0.00 |
| 1963 | butte creek | 0.0000000 | 0 | 0.00 |
| 1964 | butte creek | 0.7857143 | 0 | 0.79 |
| 1965 | butte creek | 0.0000000 | 0 | 0.00 |
| 1966 | butte creek | 4.2928571 | 0 | 4.29 |
| 1967 | butte creek | 3.1214286 | 0 | 3.12 |
| 1968 | butte creek | 0.0000000 | 0 | 0.00 |
| 1969 | butte creek | 1.9285714 | 0 | 1.93 |
| 1970 | butte creek | 0.0000000 | 0 | 0.00 |
| 1971 | butte creek | 5.5357143 | 0 | 5.54 |
The following plot is of the growing degree days above the 20 degree threshold over time for all tributaries:
Flow
Maximum flow more effectively captures the high flow events that support migration speed and passage to upstream holding areas. Additionally, upon inspection of the data source across multiple years average maximum flow over the migratory and holding months (March-May and May-August, respectively) was more representative of the fluctuations in flow over the entire year. The resulting dataset is as follows:
| stream | year | mean_flow | max_flow |
|---|---|---|---|
| battle creek | 1991 | 338.5220 | 602.0000 |
| battle creek | 1992 | 253.9231 | 388.8571 |
| battle creek | 1993 | 677.7308 | 1307.7143 |
| battle creek | 1994 | 303.8516 | 501.0000 |
| battle creek | 1995 | 1013.6758 | 2395.5714 |
| battle creek | 1996 | 624.3736 | 1205.8571 |
| battle creek | 1997 | 389.7031 | 531.6667 |
| battle creek | 1998 | 1089.8352 | 2020.0000 |
| battle creek | 1999 | 610.8681 | 1310.4286 |
| battle creek | 2000 | 517.2527 | 1183.8571 |
The following plot is of max flow (cfs) over time for all tributaries:
Water Year Type
To account for the influence of large scale interannual climate variability, we investigated water year type as a covariate as described by the California Department of Water Resources and available in the waterYearType package in R.
We used the waterYearType package to pull water year
assignments as a categorical covariate. Some streams had very few data
points (i.e. for Mill Creek, only seven years were considered
dependable), so we simplified all potential categories of water year
type into either dry (Dry, Below Normal,
Critical) or wet (Wet,
Above Normal). The table below summarizes the number of dry
and wet years that were included:
| Water Year Type | Count |
|---|---|
| dry | 60 |
| wet | 52 |
Combine and Save Covariate Data
Both continuous environmental variables (flow and temperature) were
standardized and centered within streams before performing any analyses
so that the scale of the data did not affect results. Water year type
was coded as a binary variable as 1 for wet (wet, above
normal) and 0 for dry (below normal, dry, critical). The
resulting dataset is as follows (note that flow data has the longest
time series available):
| year | stream | wy_type | max_flow_std | gdd_std |
|---|---|---|---|---|
| 1991 | battle creek | 0 | -0.7672214 | NA |
| 1992 | battle creek | 0 | -1.1672905 | NA |
| 1993 | battle creek | 1 | 0.5574045 | NA |
| 1994 | battle creek | 0 | -0.9567984 | NA |
| 1995 | battle creek | 1 | 2.5993126 | NA |
| 1996 | battle creek | 1 | 0.3662186 | NA |
| 1997 | battle creek | 1 | -0.8992370 | NA |
| 1998 | battle creek | 1 | 1.8943649 | NA |
| 1999 | battle creek | 1 | 0.5624992 | NA |
| 2000 | battle creek | 1 | 0.3249246 | NA |
The following plot is of standardized covariates (covariates are colored by type) over time for all tributaries:
