Annual spawning migrations in modelling brown trout population dynamics inside an arborescent river network

Charles, Sandrine; Parra, Rafael; Mallet, J.-P.; Persat, H.; Auger, Pierre

doi:10.1016/s0304-3800(00)00277-5

Cited by 40 publications

(27 citation statements)

References 34 publications

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“…This structure is commonly used for modelling animal populations. For example, recent examples include studies on birds [15][16][17][18], mammals [19][20][21][22], insects [23,24] and fish [25]. The model has a Fig.…”

Section: The Baseline Modelmentioning

confidence: 99%

Equivalence relationships between stage-structured population models

Yearsley¹,

Fletcher

2002

Mathematical Biosciences

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Matrix population models are widely applied in conservation ecology to help predict future population trends and guide conservation effort. Researchers must decide upon an appropriate level of model complexity, yet there is little theoretical work to guide such decisions. In this paper we present an analysis of a stage-structured model, and prove that the model's structure can be simplified and parameterised in such a way that the long-term growth rate, the stable-stage distribution and the generation time are all invariant to the simplification. We further show that for certain structures of model the simplified models require less effort in data collection. We also discuss features of the models which are not invariant to the simplification and the implications of our results for the selection of an appropriate model. We illustrate the ideas using a population model for short-tailed shearwaters (Puffinus tenuirostris). In this example, model simplification can increase parameter elasticity, indicating that an intermediate level of complexity is likely to be preferred.

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Section: The Baseline Modelmentioning

confidence: 99%

Equivalence relationships between stage-structured population models

Yearsley¹,

Fletcher

2002

Mathematical Biosciences

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“…Implicitly, it is assumed that two‐dimensional metapopulation concepts and results (sensu Hanski 1999) apply to the case of riverine systems. Yet the concern for the role of dendritic networks on population dynamics is growing: Schick and Lindley (2007) developed an original approach using the mathematical graph theory to investigate connectivity between salmon populations, Charles et al (2000) and Chaumot et al (2006) modelled explicitly a dendritic river network for demography. By simulating population dynamics in symmetric and constantly bifurcating dendritic networks, Fagan (2002) clearly demonstrated that dendritic networks may produce demographic patterns very different from those expected from classical one‐dimensional stepping‐stone models, depending on dispersal mechanism.…”

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confidence: 99%

Linking dendritic network structures to population demogenetics: The downside of connectivity

Labonne¹,

Ravigné²,

Parisi³

et al. 2008

Oikos

113

100

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Spatial structures strongly influence ecological processes. Connectivity is known to positively influence metapopulation demography and genetics by increasing the rescue effect and thus favoring individual and gene flow between populations. This result has not been tested in the special case of dendritic networks, which encompass stream and cave ecosystem for instance. We propose a first approach using an individual based model to explore the population demography and genetics in various dendritic networks. To do so, we first generate a large number of different networks, and we analyze the relationship between their hydrographical characteristics and connectivity. We show that connectivity mean and variance of connectivity are strongly correlated in dendritic networks. Connectivity segregates two types of networks: Hortonian and non-Hortonian networks. We then simulate the population dynamics for a simple life cycle in each of the generated networks, and we analyze persistence time as well as populations structure at quasi-stationary state. Our main results show that connectivity in dendritic networks can promote local extinction and genetic isolation by distance at low dispersal and diminish the size of the metapopulation at high dispersal. We discuss these unexpected findings in the light of connectivity spatial distribution in dendritic networks in the case of our model.

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“…Nonetheless, at least in their basic form, they do not attempt to describe many relevant processes and factors that would influence the effects of stressors on populations, including, for example, inter-species interactions Vøllestad 2001a, 2000b;Holmen et al 2003), migratory processes (Charles et al 1998a(Charles et al , 2000Lund et al 2003), density-dependence (Charles et al 1998b;Jenkins et al 1999;Vøllestad et al 2002), environmental stochasticity (Carroll 2002), unmodeled stressor-response relationships (Marschall and Crowder 1996;Olsen and Vøllestad 2005), and parameter correlations (e.g., negative correlation between adult fecundity and juvenile survival rate) (Crisp 1993).…”

Section: Discussionmentioning

confidence: 99%

“…Site-specific data could, for example, be used to quantify the following: the influence of dispersal on apparent survival estimates (that is suspected to be an important factor in the nonsite-specific range estimate used for S 0 in this analysis); and the influence of size and age on fecundity. The model does not account for potentially important metapopulation processes, in particular: movements of individuals between locations (on fast or slow time scales relative to demographic processes) (Charles et al 1998a(Charles et al , 1998b(Charles et al , 2000; and spatial variation in vital rates (e.g., due to food supply, population density, water temperature differences).…”

Section: Risk Analysismentioning

confidence: 99%

Assessing population‐level effects of zinc exposure to brown trout (Salmo trutta) in the Arkansas River at Leadville, Colorado

Toll

Garber

DeForest

et al. 2012

Integr Envir Assess & Manag

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We assessed population-level risk to upper Arkansas River brown trout (Salmo trutta L.) due to juvenile exposure to Zn. During spring, individuals in the sensitive young-of-the-year life stage are exposed to elevated Zn concentrations from acid mine drainage. We built and used a simple life-history population model for the risk assessment, with survival and fecundity parameter values drawn from published data on brown trout populations located in the United States and Europe. From experimental data, we derived a toxicity model to predict mortality in brown trout fry after chronic exposure to Zn. We tested sensitivity of risk estimates to uncertainties in the life-history parameters. We reached 5 conclusions. First, population projections are highly uncertain. A wide range of estimates for brown trout population growth is consistent with the scientific literature. The low end of this range corresponds to an unsustainable population, a physically unrealistic condition due to combining minimum parameter values from several studies. The upper end of the range corresponds to an annual population growth rate of 281%. Second, excess mortality from Zn exposure is relatively more predictable. Using our exposure-response model for excess mortality to brown trout fry due to Zn exposure in the upper Arkansas River at the mouth of California Gulch in the years 2000 to 2005, we derived a mean estimate of 6.1% excess mortality (90% confidence interval ¼ 1.6%-14.1%). Third, population projections are sensitive to all the parameters that contribute to the onset of reproduction. The weight of evidence suggests that young-of-the-year survival is most important; it is inconclusive about the ranking of other parameters. Fourth, population-level risk from Zn exposure is sensitive to young-of-the-year survival. If young-of-the-year survival exceeds 20% to 25%, then the marginal effect of excess juvenile mortality on population growth is low. The potential effect increases if youngof-the-year survival is less than 20%. Fifth, the effect of Zn on population growth is predictable despite high uncertainty in population projections. The estimate was insensitive to model uncertainties. This work could be useful to ecological risk assessors and managers interested in using population-level endpoints in other risk assessments. Integr Environ Assess Manag 2013;9:50-62. ß 2012 SETAC

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Annual spawning migrations in modelling brown trout population dynamics inside an arborescent river network

Cited by 40 publications

References 34 publications

Equivalence relationships between stage-structured population models

Equivalence relationships between stage-structured population models

Linking dendritic network structures to population demogenetics: The downside of connectivity

Assessing population‐level effects of zinc exposure to brown trout (Salmo trutta) in the Arkansas River at Leadville, Colorado

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