Modelling response of single-species populations to microcontaminants as a function of species size with examples for waterfleas (Daphnia magna) and cormorants (Phalacrocorax carbo)

Hendriks, A. Jan; Enserink, E.L.

doi:10.1016/0304-3800(95)00111-5

Cited by 31 publications

(41 citation statements)

References 74 publications

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“…To promote population-level ecological risk management, the development of methods to estimate population-level effects of chemicals from individual-level laboratory toxicity test data are needed by risk assessors and managers. Previous studies of population-level ecological risk assessment have mainly employed intrinsic growth rate as an index of population-level effects (e.g., Forbes and Calow 2002;Forbes et al 2001a;Hendriks and Enserink 1996;Kuhn et al 2000;Lin et al 2005;Naito and Murata 2006;Raimondo et al 2006;Stark et al 2004). To estimate the effects of chemicals on intrinsic growth rates, mathematical models of population dynamics (e.g., logistic growth model, Leslie life-stage model) combined with knowledge of the relationship between a chemical's concentration and its effects on individual traits (e.g., individual survivability and fertility; hereafter, we consider toxic effects on these vital rates as individual-level effects) determined by laboratory tests have often been used (e.g., Lin et al 2005;Naito and Murata 2006;Raimondo et al 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Most previous studies on population-level effects of toxic chemicals have the common problem that densitydependence is not considered (e.g., Forbes and Calow 2002;Forbes et al 2001a;Hendriks and Enserink 1996;Kuhn et al 2000;Lin et al 2005;Naito and Murata 2006;Raimondo et al 2006;Stark et al 2004), even though most natural populations are subject to a density effect. For example, Lin et al (2005) estimated population-level effects of chemical toxicity based on results obtained by density-independent laboratory experiments, and therefore did not consider density-dependent effects at all.…”

Section: Introductionmentioning

confidence: 99%

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Population‐level ecological effect assessment: estimating the effect of toxic chemicals on density‐dependent populations

Hayashi

Kamo

Tanaka

2008

Ecological Research

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We examined the relationship between individual-level and population-level effects of toxic chemicals, employing the equilibrium population size as an index of population-level effects. We first analyzed twostage matrix models considering four life-history types and four density-dependent models, and then we analyzed ecotoxicological and life-history data of the fathead minnow (Pimephales promelas) and brook trout (Salvelinus fontinalis) as real examples. Our elasticity analysis showed that toxic impacts on density-dependent populations depended largely on the differences in density-dependence and in life histories of the organisms. In particular, the importance of adult survivability was considerably increased in iteroparous organisms with density-dependent juvenile survivability or fertility. Our results also suggested that population-level effects, as indicated by the percentage reduction in equilibrium population size, were often greater than the percentage reductions in vital rates of individuals. Our analysis indicates that assessing population-level risk and developing a risk-reduction strategy without considering density-dependence can be risky.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Population‐level ecological effect assessment: estimating the effect of toxic chemicals on density‐dependent populations

Hayashi

Kamo

Tanaka

2008

Ecological Research

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“…The use of speci"c variation in sensitivity within a test population can contribute to a sophisticated derivation of safety factors, which allow extrapolation of acute toxicity point estimates to chronic NOEC levels (e.g., EPA, 1984;OECD, 1991OECD, , 1992, instead of using an arbitrary factor of 10. Furthermore, concentration}e!ect curves and their slopes are of major importance in population modeling (Barnthouse et al, 1987(Barnthouse et al, , 1998(Barnthouse et al, , 1990Hendriks and Enserink, 1996;Schobben and Haenen, 1999). Finally, there is empirical and theoretical evidence that the mode of action may be related to the shape of concentration}e!ect curves.…”

Section: Introductionmentioning

confidence: 99%

The Variation in Slope of Concentration–Effect Relationships

Smit¹,

Hendriks²,

Schobben

et al. 2001

Ecotoxicology and Environmental Safety

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“…On l'évalue à partir des données classiques de toxicité (CE50, CL50) avec soit l'équation d'Euler (FORBES et CALOW, 1999 ;HENDRICKS et ENSERINK, 1996), soit les matrices de Leslie (KLOK et DE ROOS, 1996 ;MUNNS et al, 1997), cette dernière méthode n'étant que la représentation matricielle de la première.…”

Section: L'équation D'euler Et Les Matrices De Leslieunclassified

Apports de la modélisation des effets des toxiques sur l’individu et la population en écotoxicologie aquatique

Flammarion¹,

Péry²

2005

rseau

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SUMMARYTraditional analysis of toxicity tests provides toxicity parameters that are estimated with purely statistical methods. Consequently, thèse parameters do not hâve any intrinsic biological meaning and thèse methods provide no information about the mode of action of the tested chemicals. It is also difficult for thèse methods to change scale from the individual level to the population level, or to account for temporal and spatial heterogeneity. Modelling is an important tool in ecotoxicology and recently it appears to hâve gained more interest. Developments in modelling are currently expanding in two directions, modelling effects at the individual level and applying toxicity data obtained at the individual level to responses at the population level. The objective of the current study was to présent thèse two complementary modelling approaches together with the opportunities they offer.Modelling at the individual level provides parameters that are biologically relevant. Modelling also facilitâtes the formulation and the testing of hypothèses concerning toxicity processes (physiological mode of action and kinetics). Confounding factors such as time, varying exposure concentrations, or feeding can also be incorporated into models. In this paper, two kinds of models were examined: biochemistry-based models (Hill models) and energybased models (Dynamic Energy Budget models). In the Hill approach, effects are modelled as the interaction between chemicals and receptors in the organisms, which leads to a relationship between concentration and effects close to the logistic équation often used in toxicity test analysis. In the energybased approach, models are built on the dynamic energy budget theory, in which energy derived from food is used for maintenance, growth and reproduction. The effect of compounds is then described as a change in one of the parameters describing thèse physiological functions. Kinetics are taken into Rev. Sci. Eau, 17(4), 2004 P. Flammarion et A. Péry

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Modelling response of single-species populations to microcontaminants as a function of species size with examples for waterfleas (Daphnia magna) and cormorants (Phalacrocorax carbo)

Cited by 31 publications

References 74 publications

Population‐level ecological effect assessment: estimating the effect of toxic chemicals on density‐dependent populations

Population‐level ecological effect assessment: estimating the effect of toxic chemicals on density‐dependent populations

The Variation in Slope of Concentration–Effect Relationships

Apports de la modélisation des effets des toxiques sur l’individu et la population en écotoxicologie aquatique

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