The max bin regression method to identify maximum bioindicator responses to ecological drivers

A need in environmental science is to determine the response range of a bioindicator to environmental drivers. A common problem with regression based approaches is the indicator response may be obfuscated by many zero or low-value observations when the organisms are not present because of life history characteristics. A method is described for determining the maximum response of an ecological variable to environmental drivers using a median approach. A regression is performed on the relationship histogram after taking the maximum ecological response value from a set of evenly spaced bins. This technique is an alternative to quantile regression at the 95% level and is analyzed on general examples and specific applications in coastal ecology using benthic macroinfauna as bioindicators. This new technique is useful for ecosystem management purposes where the maximum response of a biological indicator to an environmental driver is desired and is analogous to determining the ‘area under the curve’ of a regression

The max bin regression method to identify maximum bioindicator responses to ecological drivers

A need in environmental science is to determine the response range of a bioindicator to environmental drivers. A common problem with regression based approaches is the indicator response may be obfuscated by many zero or low-value observations when the organisms are not present because of life history characteristics. A method is described for determining the maximum response of an ecological variable to environmental drivers using a median approach. A regression is performed on the relationship histogram after taking the maximum ecological response value from a set of evenly spaced bins. This technique is an alternative to quantile regression at the 95% level and is analyzed on general examples and specific applications in coastal ecology using benthic macroinfauna as bioindicators. This new technique is useful for ecosystem management purposes where the maximum response of a biological indicator to an environmental driver is desired and is analogous to determining the ‘area under the curve’ of a regression