Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach

Abstract

Trout species often segregate along elevational gradients, yet the mechanisms driving this pattern are not fully understood. On the Logan River, Utah, USA, exotic brown trout (Salmo trutta) dominate at low elevations but are near-absent from high elevations with native Bonneville cutthroat trout (Onchorhynchus clarkii utah). We used a spatially-explicit Bayesian modeling approach to evaluate how abiotic conditions (describing mechanisms related to temperature and physical habitat) as well as propagule pressure explained the distribution of brown trout in this system. Many covariates strongly explained redd abundance based on model performance and coefficient strength, including average annual temperature, average summer temperature, gravel availability, distance from a concentrated stocking area, and anchor ice-impeded distance from a concentrated stocking area. In contrast, covariates that exhibited low performance in models and/or a weak relationship to redd abundance included reach-average water depth, stocking intensity to the reach, average winter temperature, and number of days with anchor ice. Even if climate change creates more suitable summer temperature conditions for brown trout at high elevations, our findings suggest their success may be limited by other conditions. The potential role of anchor ice in limiting movement upstream is compelling considering evidence suggesting anchor ice prevalence on the Logan River has decreased significantly over the last several decades, likely in response to climatic changes. Further experimental and field research is needed to explore the role of anchor ice, spawning gravel availability, and locations of historical stocking in structuring brown trout distributions on the Logan River and elsewhere.

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Acknowledgements

The Utah Division of Wildlife Resources, the U. S. Geological Survey Utah Cooperative Fish and Wildlife Research Unit (in-kind), the U.S. Forest Service, Utah State University Ecology Center, Utah State University School of Graduate Studies, and a George L. Disborough Trout Unlimited Research grant provided funding and/or materials towards this study. We would like to thank Gary Thiede for providing logistical support for this project as well as numerous field technicians and volunteers who helped in data collection, especially L. Goss, P. Mason, E. Castro, M. Weston, J. Randall, and C. Saunders. Brett Roper, Chris Luecke and Jack Schmidt reviewed previous versions of this manuscript. We also thank the Utah Division of Wildlife Resources, especially Matt McKell, for providing stocking records. In addition, we are grateful to our anonymous reviewers for providing suggestions to improve the manuscript. We performed this research under the auspices of Utah State University IACUC Protocol 2022. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.

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Correspondence to Christy S. Meredith.

Appendix

Appendix

Model Statement:

$$\begin{aligned} & y_{i} \sim \left\{ {\begin{array}{*{20}l} {\text NegBinom\;(\mu_{i} ,\varphi ),} \hfill & {\text{with}\,\text{probability}\;p} \hfill \\ {0,} \hfill & {{\text with\,probability}\;1 - p} \hfill \\ \end{array} } \right. \\ & \quad {\text{i}} = 1, \ldots ,{\text{n}} \\ \end{aligned}$$
(1)
$$\log\;(\mu_{i} ) = \beta_{0 } + \beta_{1} x_{1} + \ldots + \beta_{q} x_{qi } + \varepsilon_{i}$$
(2)
$$\varepsilon{{ \sim {\text N\left( {\bf 0,\bf \Sigma } \right)} }}$$
(3)
$$\Sigma = {\sigma}^{ 2}\user2{Q}^{ - 1}$$
(4)
$$\beta_{0} ,\beta_{j} \sim {\text{N}}\;\left( {0,1000} \right),\;{\text{where}}\;{\text{ j}} = 1, \ldots ,{\text{q}}$$
(5)
$$(\sigma^{2} )^{ - 1} \sim {\text{Gamma}\;}(1,1/20000)$$
(6)
$$\varphi \sim {\text N}\;\left( {0,100} \right),\quad \text{logit}\; \left( p \right) \sim {\text N}\;\left( { - 1,0.2} \right)$$
(7)

where Eq. 1 describes the negative binomial likelihood, with µ equal to the negative binomial mean, \(\varphi\) represents the overdispersion parameter, and p is the zero inflation probability; Eq. 2 represents the process model, with regression coefficients \(\beta\) and error arising from a multivariate normal distribution; Eq. 3 describes the spatial CAR model, where Σ is the precision parameter and matrix Q describes the spatial structure; and Eqs. 46 represent the prior distributions for the remaining model parameters. We used relatively vague priors for the regression coefficients and relied on standard default priors for the remaining parameters.

The LS (logarithmic score), as described in Schrödle et al. (2010), can be estimated as follows:

$$LS = -mean(\log (CPO)),$$

where CPO is based on the conditional predictive ordinate at each observation.

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Meredith, C.S., Budy, P., Hooten, M.B. et al. Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach. Biol Invasions 19, 503–519 (2017). https://doi.org/10.1007/s10530-016-1322-z

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Keywords

  • Brown trout
  • Invasion
  • Anchor ice
  • Temperature
  • Spawning gravel
  • Propagule pressure