Size- and stage-dependence in cause-specific mortality of migratory brown trout

Vindenes, Yngvild; Aass, Per; Cole, Diana J.; Langangen, Øystein; Moe, S. Jannicke; Rustadbakken, Atle; Turek, Daniel; Vøllestad, Leif Asbjørn; Ergon, Torbjørn

doi:10.1101/544742

Cited by 5 publications

(7 citation statements)

References 66 publications

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“…We hypothesized that increases in hunter numbers would increase the hunting mortality hazard rate and increases in teal abundance would reduce hunting mortality hazard rate as each individual would be less susceptible to harvest . Thus, we modelled hunting mortality hazard rate (h ,t , Ergon et al, 2018, Nater et al, 2020 as a function of z-standardized z x i = x i − x sd(x) breeding pair abundance, z(n), the z-standardized number of hunters, z(H), and random temporal variation ( ), where mortality hazard rates are the instantaneous intensity of mortality events integrated over the exposure interval (Ergon et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…We modelled changes in teal abundance using an integrated population model (Figure 2; Schaub & Kéry, 2022), where the population in the next year (

n_{t + 1}

; Table 1) was a function of the population in the previous year (

n_{t}

), natural mortality during the previous year (

η_{t}

), hunting mortality (

κ_{t + 1}

) between the breeding seasons, and the mean number of female recruits produced per capita during the previous breeding season (

ξ_{t}

n_{t + 1} goodbreak= n_{t} ()(, 1 goodbreak- κ_{t + 1} goodbreak- η_{t}) goodbreak+ n_{t} ξ_{t} .

We hypothesized that increases in hunter numbers would increase the hunting mortality hazard rate and increases in teal abundance would reduce hunting mortality hazard rate as each individual would be less susceptible to harvest (Riecke, Sedinger, et al, 2022). Thus, we modelled hunting mortality hazard rate (

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

\log ()(, h_{κ, t}) goodbreak= α_{1} goodbreak+ α_{2} goodbreak\times z ()(, n_{t}) goodbreak+ α_{3} ...

…”

Section: Methodsmentioning

confidence: 99%

“…Teal hunting primarily occurs in September and October (Devink et al, 2013), while natural mortality of adult female ducks primarily occurs during the breeding season (Arnold et al, 2012; Dufour & Clark, 2002; Hoekman et al, 2002; Riecke, Sedinger, et al, 2022). Thus, we modelled natural mortality probability as conditional on having survived previous hunting mortality,

\begin{matrix} lefttrue \\ κ_{t} = 1 - e^{- h_{κ, t}}, \\ η_{t} = (1 - κ_{t}) (1 - e^{- h_{η, t}}) . \end{matrix}

To model the band‐recovery data, we built modified band‐recovery models following Brownie and Pollock (1985), Kéry and Schaub (2012), Ergon et al (2018), Nater et al (2020) and Riecke, Sedinger, et al (2022), where recoveries of previously marked adult females occurred as a function of annual survival (

s_{t}

) and band‐recovery (

f_{t}

) probabilities. We estimated annual band‐recovery probability as a function of hunting mortality probability (

κ

), band‐reporting probability (

ρ

), and the probability that birds killed by hunting are not retrieved, that is, crippling loss probability (

c

).…”

Section: Methodsmentioning

confidence: 99%

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Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Riecke

Lohman

Sedinger

et al. 2022

Journal of Animal Ecology

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Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters. Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long‐term data (1973–2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue‐winged teal Spatula discors, an abundant North American dabbling duck with a relatively fast‐paced life history strategy. Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly (<0.02), and natural mortality probability increased substantially (>0.1) at greater population densities. We demonstrate strong density‐dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory mortality due to multicollinearity between abundance, natural mortality and hunting mortality. Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally.

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Section: Discussionmentioning

confidence: 99%

“…We modelled changes in teal abundance using an integrated population model (Figure 2; Schaub & Kéry, 2022), where the population in the next year (

n_{t + 1}

; Table 1) was a function of the population in the previous year (

n_{t}

), natural mortality during the previous year (

η_{t}

), hunting mortality (

κ_{t + 1}

) between the breeding seasons, and the mean number of female recruits produced per capita during the previous breeding season (

ξ_{t}

n_{t + 1} goodbreak= n_{t} ()(, 1 goodbreak- κ_{t + 1} goodbreak- η_{t}) goodbreak+ n_{t} ξ_{t} .

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

\log ()(, h_{κ, t}) goodbreak= α_{1} goodbreak+ α_{2} goodbreak\times z ()(, n_{t}) goodbreak+ α_{3} ...

…”

Section: Methodsmentioning

confidence: 99%

\begin{matrix} lefttrue \\ κ_{t} = 1 - e^{- h_{κ, t}}, \\ η_{t} = (1 - κ_{t}) (1 - e^{- h_{η, t}}) . \end{matrix}

s_{t}

) and band‐recovery (

f_{t}

) probabilities. We estimated annual band‐recovery probability as a function of hunting mortality probability (

κ

), band‐reporting probability (

ρ

), and the probability that birds killed by hunting are not retrieved, that is, crippling loss probability (

c

).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Riecke

Lohman

Sedinger

et al. 2022

Journal of Animal Ecology

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“…These can introduce some bias in the population size estimations. In order to solve this, studies of tagged individuals can constitute a highly valuable source of demographic data (Nater et al, 2020), and could be a good complement in long-term monitoring studies.…”

Section: Discussionmentioning

confidence: 99%

Upstream migration of anadromous and potamodromous brown trout: patterns and triggers in a 25-year overview

et al. 2021

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River fragmentation and alterations in flow and thermal regimes are the main stressors affecting migrating fish, which could be aggravated by climate change and increasing water demand. To assess these impacts and define mitigation measures, it is vital to understand fish movement patterns and the environmental variables affecting them. This study presents a long-term (1995–2019) analysis of upstream migration patterns of anadromous and potamodromous brown trout in the lower River Bidasoa (Spain). For this, captures in a monitoring station were analyzed using Survival Analysis and Random Forest techniques. Results showed that most upstream movements of potamodromous trout occurred in October–December, whereas in June–July for anadromous trout, although with differences regarding sex and size. Both, fish numbers and dates varied over time and were related to the environmental conditions, with different influence on each ecotype. The information provided from comparative studies can be used as a basis to develop adaptive management strategies to ensure freshwater species conservation. Moreover, studies in the southern distribution range can be crucial under climate warming scenarios, where species are expected to shift coldwards.

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“…Furthermore, both traits are influenced by genomic (Ali et al, 2020;Hecht, Campbell, Holecek, & Narum, 2013;Kelson, Miller, Thompson, O'rourke, & Carlson, 2019;Pearse et al, 2019;Prince et al, 2017), environmental (Kanno et al, 2015Olsson, Greenberg, Bergman, & Wysujack, 2006;Thompson & Beauchamp, 2016;, and GxE factors (Baerum, Haugen, Kiffney, Moland Olsen, & Vøllestad, 2013;Nater et al, 2018;Yates, Debes, Fraser, & Hutchings, 2015). Growth is affected by a suite of environmental conditions (Kovach, Muhlfeld, Dunham, Letcher, & Kershner, 2016b), but the effects of temperature on growth have been documented in WCT and RBT (Bear, McMahon, & Zale, 2007).…”

Section: Accepted Articlementioning

confidence: 99%

Hybridization alters growth and migratory life‐history expression of native trout

Strait

Eby

Kovach

et al. 2020

Evolutionary Applications

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Human-mediated hybridization threatens many native species, but the effects of introgressive hybridization on life history expression are rarely quantified, especially in vertebrates. We quantified the effects of non-native rainbow trout admixture on important life history traits including growth and partial migration behavior in three populations of westslope cutthroat trout over five years. Rainbow trout admixture was associated with increased summer growth rates in all populations, and decreased spring growth rates in two populations with cooler spring temperatures. These results indicate that non-native admixture may increase growth under warmer conditions, but cutthroat trout have higher growth rates during cooler periods. Non-native admixture consistently increased expression of migratory behavior, suggesting that there is a genomic basis for life history differences between these species. Our results show that effects of interspecific hybridization on fitness traits Accepted Article This article is protected by copyright. All rights reserved can be the product of genotype-by-environment interactions even when there are minor differences in environmental optima between hybridizing species. These results also indicate that while environmentallymediated traits like growth may play an role in population-level consequences of admixture, strong genetic influences on migratory life history differences between these species likely explains the continued spread of non-native hybridization at the landscape-level, despite selection against hybrids at the population-level.

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Size- and stage-dependence in cause-specific mortality of migratory brown trout

Cited by 5 publications

References 66 publications

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Upstream migration of anadromous and potamodromous brown trout: patterns and triggers in a 25-year overview

Hybridization alters growth and migratory life‐history expression of native trout

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