A causal examination of the effects of confounding factors on multimetric indices

Schoolmaster, Donald; Grace, James; Schweiger, E.; Mitchell, Brian; Gentenspergen, Glenn

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystemcondition is becoming widespread. An increasingly recognized problem for the interpretability ofMMIs is controlling for the potentially confounding influences of environmental covariates. Most commonapproaches to handling covariates are based on simple notions of statistical control, leaving thecausal implications of covariates and their adjustment unstated. In this paper, we use graphical modelsto examine some of the potential impacts of environmental covariates on the observed signals betweenhuman disturbance and potential response metrics. Using simulations based on various causal networks,we show how environmental covariates can both obscure and exaggerate the effects of human disturbanceon individual metrics. We then examine from a causal interpretation standpoint the commonpractice of adjusting ecological metrics for environmental influences using only the set of sites deemedto be in reference condition. We present and examine the performance of an alternative approach tometric adjustment that uses the whole set of sites and models both environmental and human disturbanceeffects simultaneously. The findings from our analyses indicate that failing to model and adjustmetrics can result in a systematic bias towards those metrics in which environmental covariates functionto artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accuratelymeasure impacts of human disturbance. We also find that a “whole-set modeling approach” requiresfewer assumptions and is more efficient with the given information than the more commonly applied“reference-set” approach.

A causal examination of the effects of confounding factors on multimetric indices

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