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Severe degradation of habitat from historic mining and significant flow diversions for several decades on the Trinity River led to the near-collapse of the salmonid fisheries, which instigated recovery efforts that culminated in a 2000 Record of Decision to create the Trinity River Restoration Program (TRRP) and adopt the rehabilitation objectives and strategies outlined in the science-based Trinity River Flow Evaluation Final Report (TRFE). One specific riparian vegetation objective was to encourage riparian germination and establishment (especially of black cottonwood and tree willows) onto bar crests and floodplain surfaces in Normal and wetter water years. The TRFE presented qualitative seed dispersal periods for black cottonwood that served as a guide to annual flow release planning; however, after a decade of ROD flows, black cottonwood recruitment is not occurring to the extent expected, and the defined seed dispersal window is being re-evaluated. The goal of this report is to quantify the seed dispersal period that can be consistently applied during annual flow scheduling so that the timing of intentional flow benches (e.g., 169.9 cms) coincides with cottonwood seed release.

 

Seed dispersal occurred between April 19 and July 3 during five years of monitoring. Sometimes seed dispersal occurred earlier within this broad window (e.g., in 2004, 2015, 2016), and sometimes later (2017, 2018). Therefore, further analyses were conducted to refine the broad seed dispersal window into a target seed dispersal period for annual flow release planning. Survey weeks 10 and 11 had the highest median seed dispersal, and seed dispersal for all trees always occurred during these two weeks. Therefore, the calendar dates corresponding to survey weeks 10 and 11 (May 15 to May 28) are recommended as the black cottonwood target seed dispersal period for annual flow release planning. The target seed dispersal period corresponded well to actual ROD releases during the years when seed dispersal was monitored.

 

We examined two potential causal factors influencing the timing of seed dispersal: photoperiod and air temperature. These factors were chosen largely due to the availability of data. We used Julian date as a proxy for photoperiod and accumulated degree-hours as a proxy for air temperature. Seed dispersal did not occur before 2,000 accumulated degree-hours during 2015–2018 and was nearly always completed by 3,000 accumulated degree-hours.

 

We developed a predictive generalized linear model (GLM) for a binomial distribution to develop the best statistical relationships between seed dispersal (response variable) and selected predictor variables (Julian date, accumulated degree-hours) to predict the timing of the highest probability of seed dispersal occurring in any year. There is a 72% probability of seed dispersal on Julian date 139 (May 19), regardless of the accumulated degree-hour value on that date. May 19 is within the target seed dispersal period, which according to the model, will capture the highest probability of seed dispersal in hotter years and increasing (though not the highest) probabilities of seed dispersal in cooler years.

 

Seed dispersal monitoring should be continued to meet sample size requirements and improve management flexibility in response to climate change. The model developed here was used in WY 2019 annual flow planning and can continue to be used in future flow planning by targeting May 19 as the beginning of seed dispersal and scheduling flow ramp-downs to coincide with this date. A continuation of seed dispersal monitoring can facilitate any necessary changes to flow scheduling based on field observations, while also providing additional data to improve the model. A truly predictive model that reflects environmental variables that influence seed dispersal timing should be developed to allow annual flow release planning to incorporate annual conditions earlier in the planning process (i.e., warmer years might use a modified target seed dispersal period). Site-specific environmental conditions have not been monitored and therefore data are not available. Coupling future seed dispersal observations with local environmental monitoring (via HOBO sensors) could improve the predictive model and contribute to climate change monitoring and scientific knowledge.