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Knowledge - Cause - effect chain modelling of sand mining using Sandwich terns

Abstract: Using a cause-effect chain model to determine the effects of sand mining on Sandwich terns. Dredging leads to an increase of silt concentrations in the water column, which makes it more difficult for Sandwich terns to catch fish. This in turn can cause a decrease in breeding success.

Technology Readiness Level: 3 (experimental proof of concept)

Environment: Sandy shores
Keywords: Coastal development, Ecological studies, Impact assessment, Sand mining


In the Netherlands, the potential impacts of sand mining activities on populations of Sandwich terns form an important topic in Environmental Impact Assessments (EIAs). Sand mining causes an increase in water turbidity, which may affect populations of visually hunting birds such as terns. 


The quantification of ecological effects in EIAs is mostly done by deterministic modeling of cause-effect chains; a highly conservative approach with an unknown uncertainty margin. A probabilistic approach to the quantification of the possible ecological effects may be an alternative.


The objective of this case study is to explore how and to what extent a probabilistic approach can be applied to the quantitative modeling of the potential effects of sand mining on tern populations. As an example, the probabilistic methodology is worked out for the effects of a fictitious dredging project on a population of Sandwich Terns.

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Probabilistic Approach - Cause-effect chain 

This section describes a probabilistic approach to the quantitative modelling of the potential effects of sand mining on tern populations. The method described herein is mainly applicable in the planning and design phases of a dredging project. 

 

In order to model the impact of dredging activities on Sandwich Tern populations, a literature search was carried out to find out how dredging could affect these populations. This literature search led to the cause-effect chain, in which the relations that are expected to influence the population size are visualised.

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Modelling the cause-effect chain 

The cause-effect chain for the impact of dredging activities on tern populations contains several uncertain and variable factors. As these uncertainties may have a significant influence on the ultimate impact, a probabilistic model approach is taken. First, the relationships between the different elements in the cause-effect chain are made quantitative (see sections below). The resulting chain of equations was used in a Monte Carlo analysis, simulating the impact of dredging on the tern populations a large number of times (e.g. 1000 times), each time with a different selection of the stochastic inputs. This analysis results in a probability distribution of the change in population size due to the dredging activities. The following sections describe the relationships between the different elements in the cause-effect chain. 

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Case Study Results

This section describes the results of the demonstration case: the probabilistic methodology is worked out for the effects of a fictitious dredging project on a population of Sandwich Terns.
The impact of two different dredging scenarios is considered:

  1. Far field effects of large dredging project, which takes several years and;
  2. Far field effects of a (relatively) small, short duration dredging project.

 

Two different approaches to the fish capture rate are elaborated:

  1. the conservative approach, assuming such a capture rate at optimal conditions, that any decrease of the capture rate leads to a lower breeding success;
  2. the empirical approach, estimating the capture rate at optimal conditions from measurements. (If sufficient data are available, a probability density function of the capture rate at optimal conditions can be estimated. In this case, implicitly also the natural variation of prey availability can be taken into account).

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Lessons learned

To assess the impact of an activity on the environment, investigators often deal with limited knowledge about possible effects. Consequently assumptions have to be made, where the accumulation of worst case assumptions leads to unrealistically pessimistic estimates of the impact. A probabilistic approach will give more realistic estimates and also quantifies the uncertainty of this impact.

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References

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