Empirical evidence of plasticity in life-history characteristics across climatic and fish density gradients

Ward, Hillary G. M.; Post, John R.; Lester, Nigel P.; Askey, Paul J.; Godin, Theresa

doi:10.1139/cjfas-2016-0023

Cited by 28 publications

(55 citation statements)

References 39 publications

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“…; Leeseberg and Keeley ; Ward et al. ). All stream segment temperatures (for only 2008) were well below stressful conditions for RGCT (30‐d ultimate upper incipient lethal temperature of 21.7°C; Zeigler et al.…”

Section: Discussionmentioning

confidence: 99%

“…; Brunel and Dickey‐Collas ; Ward et al. ), we incorporated both effective density and the number of degree‐days (see equations and ) in influencing the growth rate ( k ) and asymptotic length ( L inf ) within a hierarchical Bayesian modeling framework. The VB curve had the following form:

L_{ij} \sim N o r m a l (μ_{L_{ij}}, σ_{L}),

where

{normalμ}_{L_{italicij}} = L_{{italicinf}_{j}} \times [1 - e^{{- k}_{j} \times (a g e_{ij} - t_{0_{j}})}]

such that

L_{{italicinf}_{j}} \sim N o r m a l (μ_{L_{{italicinf}_{j}}}, σ_{L_{\inf}}),

{log}_{e} (μ_{L_{{italicinf}_{j}}}) = {normalα}_{L_{italicinf}} + (β_{L_{DDay}} \times D D a y_{j}) + (β_{L_{Density}} \times D e n s i t y_{j}) + {normalη}_{L_{\inf_{s}}},

…”

Section: Methodsmentioning

confidence: 99%

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Effects of Temperature and Spatial Scale on Rio Grande Cutthroat Trout Growth and Abundance

Huntsman

Martin

Patten³

2018

Trans Am Fish Soc

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Diversity in habitat and life-history strategies promote a species' long-term persistence. However, life-history strategies are most commonly studied at broad spatial and temporal scales. We applied longevity growth models and closed N-mixture models to examine within- versus between stream variability in life-history characteristics of Rio Grande Cutthroat Trout in northern New Mexico streams. We developed a von Bertalanffy growth model and a closed N-mixture model in a hierarchical Bayesian framework to examine the importance of fine-scale variability in temperature and density-dependence on growth and abundance. The model indicated that accumulation of degree days likely positively influenced instantaneous growth rates and, to a lesser extent, negatively affected asymptotic body length. A nonlinear response of abundance to temperature was also observed, suggesting that Cutthroat Trout productivity along the temperature continuum was affected by physiological limitations ( optimal growth temperatures). Parameter variability was greatest at the segment level for asymptotic size and abundance, but greatest at the stream level for the rate at which asymptotic size is reached. In total, the results suggest that fine-scale habitat heterogeneity ( temperature) may play important roles in the continued persistence of Rio Grande Cutthroat Trout. Management actions should, therefore, consider the role of fine-scale processes for improving the likelihood of future population persistence.

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Section: Discussionmentioning

confidence: 99%

L_{ij} \sim N o r m a l (μ_{L_{ij}}, σ_{L}),

where

{normalμ}_{L_{italicij}} = L_{{italicinf}_{j}} \times [1 - e^{{- k}_{j} \times (a g e_{ij} - t_{0_{j}})}]

such that

L_{{italicinf}_{j}} \sim N o r m a l (μ_{L_{{italicinf}_{j}}}, σ_{L_{\inf}}),

{log}_{e} (μ_{L_{{italicinf}_{j}}}) = {normalα}_{L_{italicinf}} + (β_{L_{DDay}} \times D D a y_{j}) + (β_{L_{Density}} \times D e n s i t y_{j}) + {normalη}_{L_{\inf_{s}}},

…”

Section: Methodsmentioning

confidence: 99%

Effects of Temperature and Spatial Scale on Rio Grande Cutthroat Trout Growth and Abundance

Huntsman

Martin

Patten³

2018

Trans Am Fish Soc

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“…; Ward et al. ). We used a Bayesian approach to demonstrate the importance of understanding sources of error in estimated parameters when using GSI and escapement estimates.…”

Section: Discussionmentioning

confidence: 99%

Genetic Stock Identification Reveals That Angler Harvest Is Representative of Cryptic Stock Proportions in a High‐Profile Kokanee Fishery

Ward

Askey

Weir

et al. 2019

N American J Fish Manag

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Estimating fishery harvest and spawning escapement (spawning stock size) are critical components of fisheries management; however, they can be particularly challenging to measure in systems where visually indistinguishable, but reproductively isolated populations mix within a single fishing area. Genetic stock identification is a common tool used in such mixed‐stock fisheries to improve estimates of spawning escapement and productivity; however, there are few references for management applications, particularly for inland recreational fisheries. The kokanee Oncorhynchus nerka population in Wood Lake, British Columbia, is a highly productive and valuable mixed‐stock fishery that includes two reproductively distinct ecotypes: shore‐ and stream‐spawning. Enumeration of shore‐spawning kokanee is logistically challenging, as the spawning population is not confined to a defined area or depth like stream‐spawners. Here, we combined in‐lake sampling (angler harvest and age‐0 trawl samples) over a 9‐year period (2008–2016) with genetic stock identification and Bayesian statistics to develop a new method for enumerating shore‐spawning kokanee. Our results suggest that angler‐harvested kokanee are representative of the spawner age structure and stock proportions. Therefore, we used the angler harvest sample combined with known stream‐spawner escapement to reconstruct the shore‐spawner escapement time series. Shore‐spawner abundance varied between 2,040 spawners and 13,460 spawners across years, which is over four times that previously predicted using the peak estimate of visual survey counts. Our results demonstrate the recovery of both the shore‐ and stream‐spawning kokanee in Wood Lake following a well‐documented crash in 2011 and suggest that a larger harvestable surplus is available for this high‐value kokanee fishery.

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“…We chose the LM as our example model because it is rooted in life‐history theory and popular in the literature, particularly for trait‐based and eco‐evolutionary inference (Alós, Palmer, Balle, Grau, & Morales‐Nin, ; Nakayama et al., ; Uusi‐Heikkilä et al. ; Ward et al., ). However, our recommendations apply to many other biphasic models (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…The most inclusive method for fitting a biphasic model with maturity data is to estimate maturity using a maturity ogive that complements the growth model. This maturity model can be optimized on a joint likelihood (or posterior) alongside the biphasic model (see Matthias, Ahrens, Allen, Lombardi‐Carlson, & Fitzhugh, ; Quince et al., ; Ward, Post, Lester, Askey, & Godin, ). This method allows for improved estimates of variance around parameters because the errors and correlations among parameters are allowed to trade‐off naturally among model subcomponents.…”

Section: Techniquesmentioning

confidence: 99%

Growing the biphasic framework: Techniques and recommendations for fitting emerging growth models

et al. 2017

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Several new growth models have been proposed to account for the life‐history trade‐offs that occur when indeterminately growing species allocate energy between somatic growth and reproduction. These models can improve the understanding of lifetime growth and life history, but can be more difficult to fit than conventional growth models. Increased data demands, multiple growth phases and increased parameterization all serve as barriers to the adoption and proper use of these new models. We review and comment on confounding issues during model fitting for several of these models, and provide advice on surmounting such issues. We then simulation‐test an example model, the Lester biphasic growth model, using several common fitting approaches. We highlight the biases and precision of each approach and provide guiding documents using r and jags code. The Bayesian Markov chain Monte Carlo and likelihood profiling approaches generally provided the best fits. Simpler approaches can be unbiased and precise if sampled data are of relatively high quality (e.g. moderate sample sizes for juvenile and adult phases) and model assumptions are met. Bayesian hierarchical approaches can accommodate more complicated data scenarios (e.g. unbalanced design across multiple populations); we provide an example of such an approach by recovering growth trajectories and inferring growth‐associated trait variation and environmental effects across multiple populations. Conventional growth models provide limited inference on life history. Many biphasic growth models can provide direct inference on multiple life‐history traits, but can be difficult to fit. The recommended approaches herein provide a path forward for fitting biphasic growth models in a variety of scenarios, allowing for wider application and tests of life history and ecological theory.

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Empirical evidence of plasticity in life-history characteristics across climatic and fish density gradients

Cited by 28 publications

References 39 publications

Effects of Temperature and Spatial Scale on Rio Grande Cutthroat Trout Growth and Abundance

Effects of Temperature and Spatial Scale on Rio Grande Cutthroat Trout Growth and Abundance

Genetic Stock Identification Reveals That Angler Harvest Is Representative of Cryptic Stock Proportions in a High‐Profile Kokanee Fishery

Growing the biphasic framework: Techniques and recommendations for fitting emerging growth models

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