BIOMAG: a model for biomagnification in terrestrial food chains. The case of cadmium in the Kempen, The Netherlands

Gorree, M.; Tamis, W.L.M.; Traas, Theo P.; Elbers, M.A.

doi:10.1016/0048-9697(95)04557-h

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…Some persistent toxicants such as cadmium accumulate with age leading to an increase of BAFs with age. Some BAFs could be derived from situations in reasons, it was suggested to treat BAFs as stochastic variables (Gorree et al, 1995). BAFs for the same (group of) species Percentile are thought to derive from a log-logistic distribution.…”

Section: Fig 3 Comparison Of Log Ld 50 'S For Predators (Gray Squarmentioning

confidence: 99%

A Probabilistic Model for Deriving Soil Quality Criteria Based on Secondary Poisoning of Top Predators

Traas

Luttik

Jongbloed

1996

Ecotoxicology and Environmental Safety

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Section: Fig 3 Comparison Of Log Ld 50 'S For Predators (Gray Squarmentioning

confidence: 99%

A Probabilistic Model for Deriving Soil Quality Criteria Based on Secondary Poisoning of Top Predators

Traas

Luttik

Jongbloed

1996

Ecotoxicology and Environmental Safety

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“…Likewise, estimates of mercury and polychlorinated biphenyl residues in great blue heron (Ardea herodias) eggs made with a stochastic food web model were in reasonable agreement with concentrations measured in a limited number of egg samples [7]. Gorree et al [13] successfully estimated cadmium levels in kidneys of roe deer (Capreolus capreolus) using a stochastic food chain model based on BAFs, but concentrations calculated for barn owls (Tyto alba) were excessive.…”

Section: Introductionmentioning

confidence: 91%

Testing of metal bioaccumulation models with measured body burdens in mice

Torres

Johnson

2001

Enviro Toxic and Chemistry

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Estimates of chemical accumulation in prey organisms can contribute considerable uncertainty to predictive ecological risk assessments. Comparing body burdens calculated in food web models with measured tissue concentrations provides essential information about the expected accuracy of risk indices. Estimates of arsenic, cadmium, copper, lead, and nickel body burdens in house mice (Mus musculus) inhabiting a seasonal wetland were generated with two small mammal bioaccumulation models. Published soil-to-small mammal bioaccumulation regression models produced accurate estimates of arsenic and lead body burdens but failed to adequately predict copper and nickel levels in mice. Incorporating conservative prediction intervals in the regression models shows potential for successful applications in screening-level risk assessments. A simple mechanistic cumulative ingestion bioaccumulation model overpredicted lead levels in mice generally by less than one order of magnitude but greatly overpredicted concentrations of arsenic, copper, and nickel. Better estimates of absorption and elimination of ingested metals and knowledge of specific arthropod taxa in house mouse diets are likely to improve the accuracy of the cumulative ingestion model. Applying Monte Carlo simulations to the soil-small mammal regression models generated probabilistic estimates of body burdens that were consistent with deterministic results. However, deterministic minimum and maximum predictions of the ingestion model were excessively conservative (widely spaced) relative to lower and upper probabilistic percentiles. Metal levels predicted in individual mice on the basis of mouse-specific parameter values and exposures were not significantly more accurate than bioaccumulation predictions for the sitewide population.

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“…The food‐web approach, which takes into account feeding relationships between species in ecosystems, is thought to provide an opportunity to address effects of toxic substances at the ecosystem level (Moriarty & Walker, 1987; Van den Berg, Tamis & Van Straalen, 1998; Kooistra et al ., 2001b). Commonly used food web models include CATS (Traas et al ., 1995), BIOMAG (Gorree et al ., 1995), and the BKH model (Noppert et al ., 1993; Kooistra et al ., 2001b). Using simple food webs and Monte Carlo sampling, De Ruiter, Neutel & Moore (1995) and Klepper et al .…”

Section: Tools To Assess Ecological Risksmentioning

confidence: 99%

Riverine landscape dynamics and ecological risk assessment

Leuven

Poudevigne

2002

Freshwater Biology

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1. The aim of ecological risk assessments is to evaluate the likelihood that ecosystems are adversely affected by human‐induced disturbance that brings the ecosystem into a new dynamic equilibrium with a simpler structure and lower potential energy. The risk probability depends on the threshold capacity of the system (resistance) and on the capacity of the system to return to a state of equilibrium (resilience). 2. There are two complementary approaches to assessing ecological risks of riverine landscape dynamics. The reductionist approach aims at identifying risk to the ecosystem on the basis of accumulated data on simple stressor–effect relationships. The holistic approach aims at taking the whole ecosystem performance into account, which implies meso‐scale analysis. 3. Landscape patterns and their dynamics represent the physical framework of processes determining the ecosystem's equilibrium. Assessing risks of landscape dynamics to riverine ecosystems implies addressing complex interactions of system components (e.g. population dynamics and biogeochemical cycles) occurring at multiple scales of space and time. 4. One of the most important steps in ecological risk assessment is to establish clear assessment endpoints (e.g. vital ecosystem and landscape attributes). Their formulation must recognise that riverine ecosystems are dynamic, structurally complex and composed of both deterministic and stochastic components. 5. Remote sensing (geo)statistics and geographical information systems are primary tools for quantifying spatial and temporal components of riverine ecosystem and landscape attributes. 6. The difficulty to experiment at the riverine landscape level means that ecological risk management is heavily dependent on models. Current models are targeted towards simulating ecological risk at levels ranging from single species to habitats, food webs and meta‐populations to ecosystems and entire riverine landscapes, with some including socio‐economic considerations.

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BIOMAG: a model for biomagnification in terrestrial food chains. The case of cadmium in the Kempen, The Netherlands

Cited by 21 publications

References 13 publications

A Probabilistic Model for Deriving Soil Quality Criteria Based on Secondary Poisoning of Top Predators

A Probabilistic Model for Deriving Soil Quality Criteria Based on Secondary Poisoning of Top Predators

Testing of metal bioaccumulation models with measured body burdens in mice

Riverine landscape dynamics and ecological risk assessment

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