An integrated population model for estimating the relative effects of natural and anthropogenic factors on a threatened population of Pacific trout

Abstract

1 1. Assessing the degree to which at-risk species are regulated by density dependent versus 2 density independent factors is often complicated by incomplete or biased information. If not 3 addressed in an appropriate manner, errors in the data can affect estimates of population 4 demographics, which may obfuscate the anticipated response of the population to a specific 5 action. 6 2. We developed a Bayesian integrated population model that accounts explicitly for interannual 7 variability in the number of reproducing adults and their age structure, harvest, and 8 environmental conditions. We apply the model to 41 years of data for a population of threatened 9 steelhead trout Oncorhynchus mykiss using freshwater flows, ocean indices, and releases of 10 hatchery-born conspecifics as covariates. 11 3. We found compelling evidence that the population is under strong density dependence, despite 12 being well below its historical population size. In the freshwater portion of the lifecycle, we 13 found a negative relationship between productivity (offspring per parent) and peak winter flows, 14 and a positive relationship with summer flows. We also found a negative relationship between 15 productivity and releases of hatchery conspecifics. In the marine portion of the lifecycle, we 16 found a positive correlation between productivity and the North Pacific Gyre Oscillation. 17 Furthermore, harvest rates on wild fish have been sufficiently low to ensure very little risk of 18 overfishing.19 4. Synthesis and applications. The evidence for density dependent population regulation, 20combined with the substantial loss of juvenile rearing habitat in this river basin, suggests that 21 habitat restoration could benefit this population of at-risk steelhead. Our results also imply that 22 hatchery programs for steelhead need to be considered carefully with respect to habitat 23 3 availability and recovery goals for wild steelhead. If releases of hatchery steelhead have indeed 24 limited the production potential of wild steelhead, there are likely significant tradeoffs between 25 providing harvest opportunities via hatchery steelhead production, and achieving wild steelhead 26 recovery goals. 27 28 Managing at-risk species requires an understanding of the degree to which population 29 dynamics are self-regulated versus driven by external factors. However, the data used to identify 30 potentially important density-dependent and population-environment relationships are rarely, if 31 ever, fully comprehensive or error free. Rather, imperfect detection, misidentification, and non-32 exhaustive sampling all lead to a somewhat distorted view of the true state of nature. For 33 example, when not addressed in an appropriate manner, errors in population censuses may cause 34 underestimates of recruitment (Sanz-Aguilar et al. 2016) or overestimates of the strength of 35 density dependence (Knape & de Valpine 2012). Similarly, imprecision in the estimated age 36 composition of the population also biases the estimated strength of density depe...

Differential impacts of freshwater and marine covariates on wild and hatchery Chinook salmon marine survival

“…Additionally, maturation schedules, the fraction of a salmon maturing and returning to spawn at different ages, are also size-dependent-larger and faster growing fish tend to mature earlier [2]. Recent spawnerrecruit analysis suggests that climate conditions affect both the maturation schedule and the survival of some stocks of salmon [54]; however, timing and size were not a part of these models. Future iterations of our model could examine the effects of size and maturation simultaneously, with the goal of understanding how management actions in freshwater environment affect size, maturation, and ultimately, survival.…”

Efficient use of harvest data: a size‐class‐structured integrated population model for exploited populations

“…More recently, IPMs have been applied to harvested populations in terrestrial ecosystems (Gauthier et al 2007, Conn et al 2009, Fieberg et al 2010, Péron et al 2012, Lee et al 2015, Staton et al 2017, Arnold et al 2018). Importantly, IPMs built on exploited populations often integrate age‐at‐harvest data and capture–mark–recapture–recovery (CMRR) data into age‐structured population models (Methot Jr and Wetzel 2013, Arnold et al 2018, Scheuerell et al 2019). However, it is noteworthy that the applicability of this type of IPMs for exploited populations is limited because age‐at‐harvest data is often not available.…”

Efficient use of harvest data: An integrated population model for exploited animal populations

“…Besbeas et al 2002 for a study on northern lapwing Vanellus venellus and grey heron Ardea cinerea ) and more recently, they have been applied to exploited populations in terrestrial ecosystems (Gauthier et al 2007; Arnold et al 2018). Strikingly, IPM built on exploited populations usually integrate population surveys (of alive individuals), catch-at-age data and capture-mark-recapture-recovery (CMRR) data into age-structured population models (Methot Jr & Wetzel 2013; Arnold et al 2018; Scheuerell et al 2019). This state-of-the-art limited up-to-now the applicability of IPM for two reasons: i) the age of harvested individuals is often not available because its determination is challenging and generally involves expensive and time-demanding analyses.…”