Design method for cross-section stratified sampling of benthos in salt marsh wetlands where rivers flow into Haikou

Abstract

The invention relates to a design method for layered sampling of benthic animals in the salt marsh wetland where the river flows into the sea. On the basis of the measured values ​​of the total species abundance at each level of the cross section, the following steps are carried out: the first step: the cross section The second step: re-sampling at different intervals in the internal level of the cross-section; the third step: conduct Monte Carlo simulation for different sampling intervals; the fourth step: determine the optimal sampling interval at each level The fifth step: draw the species accumulation curve; the sixth step: determine the optimal design scheme of cross-sectional stratified sampling: the sampling spacing of the different degrees of deviation obtained in the fourth step, and the obtained in the sixth step The minimum sampling area was compared to determine the optimal sampling spacing and minimum sampling efficiency at different levels on the cross section.

Description

technical field [0001] The invention relates to a design method for cross-section layered sampling of benthic animals in salt marsh wetlands where rivers flow into Haikou, and belongs to the technical field of investigation and monitoring of benthic animals in salt marsh wetlands. Background technique [0002] The salt marsh wetland in the Yangtze Estuary is an important part of the estuary ecosystem. It exchanges nutrients frequently with land and marine ecosystems and has a complex and changeable environment. It has the ecological functions of supporting vegetation, providing animal habitats, and maintaining biodiversity. The macrobenthos community in the salt marsh wetland of the Yangtze River Estuary is the most direct indicator of habitat change in the intertidal zone of the estuary, and its species abundance and distribution can effectively reflect the health of the estuary ecosystem. Monitoring and mastering changes in macrobenthos communities can provide scientific b...

AI Technical Summary

Problems solved by technology

The sample size obtained under this method is small, and due to the low sampling efficiency, it leads to a large sampling bias

On the other hand, in the sampling process, due to the uncertainty of the spacing between random squares, for the collection of small-scale and highly aggregated macrobenthic populations, the obtained data tends to have a large number of zero-value data or cause real data Missing, the risk of uncertainty increases, and it cannot reflect the real situation

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