Adult Model Covariates

There 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:

  1. Proportion of days where the temperature surpassed a threshold of 20 degrees Celsius (source)
  2. Growing degree days (GDD) with a base temperature of 0 degrees Celsius (source)
  3. 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
1999 deer creek 10.595238 0.0000000 10.60
1999 feather river 1.273471 0.0000000 1.27
1999 mill creek 2.688823 0.0000000 2.69
1999 yuba river 45.493129 0.0000000 45.49
2000 deer creek 26.173138 0.2142857 26.39
2000 feather river 6.537028 0.2142857 6.75
2000 mill creek 12.967262 0.2142857 13.18
2000 yuba river 53.182652 0.2142857 53.40
2001 clear creek 2.187934 2.0714286 4.26
2001 deer creek 37.718340 2.0714286 39.79

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 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
battle creek 2001 318.2253 567.4286
battle creek 2002 364.0824 515.2857
battle creek 2003 611.2473 1134.2857
battle creek 2004 474.1429 1093.8571

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 64
wet 54

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
1995 battle creek 1 2.4649268 NA
1996 battle creek 1 0.2895225 NA
1997 battle creek 1 -0.9432415 NA
1998 battle creek 1 1.7781908 NA
1999 battle creek 1 0.4807324 NA
2000 battle creek 1 0.2492953 NA
2001 battle creek 0 -0.8778505 NA
2002 battle creek 0 -0.9731942 NA
2003 battle creek 1 0.1586534 -0.6228421
2004 battle creek 0 0.0847294 -0.6228421

The following plot is of standardized covariates (covariates are colored by type) over time for all tributaries:

Save data object

Data object saved in SRJPEdata as p2s_model_covariates_standard.rds. To access documentation search ?SRJPEdata::p2s_model_covariates_standard.rds.