Predicting biological impairment from habitat assessments

Abstract

The goal of biological monitoring programs is to determine impairment classification and identify local stressors. Biological monitoring performs well at detecting impairment but when used alone falls short of determining the cause of the impairment. Following detection a more thorough survey is often conducted using extensive biological, chemical, and physical analysis coupled with exhaustive statistical treatments. These methods can be prohibitive for small programs that are limited by time and budget. The objective of this study was to develop a simple and useful model to predict the probability of biological impairment based on routinely collected habitat assessments. Biological communities were assessed with the Index of Biotic Integrity (IBI), and habitat was assessed with the Qualitative Habitat Evaluation Index. Two models were constructed from a validation dataset. The first predicted a binary outcome of impaired (IBI < 35) or non-impaired (IBI ≥ 35) and the second predicted a categorical gradient of impairment. Categories include very poor, poor, fair, good, and excellent. The models were then validated with an independently collected dataset. Both models successfully predicted biological integrity of the validation dataset with an accuracy of 0.84 (binary) and 0.75 (categorical). Based on the binary outcome model, 22 sites were observed to be impaired while the model predicted them to not be impaired. The categorical model misclassified 47 samples while only seven of those were misclassified by two or more categories. The impairment source was subsequently identified by known stressors. The models developed here can be easily applied to other datasets from the Eastern Corn Belt Plain to aid in stressor identification by predicting the probability of observing an impaired fish community based on habitat. Predicted probabilities from the models can also be used to support conclusions that have already been determined.

Fish community responses to antecedent hydrological conditions based on long-term data in Mediterranean river basins (Iberian Peninsula)

“…This statistical tool was introduced in ecology by Cade et al (1999) and can be used to test the role of environmental factors as limiting factors. Moreover, its application allows to predict not only the most probable values of the studied biological metric, but also the maximum or minimum values that could be expected in environmental conditions comparable to the ones used for the model fitting (Cade and Noon, 2003;Doll, 2011;Fornaroli et al, 2016).…”

Optimal flow for brown trout: Habitat – prey optimization

“…This statistical tool was introduced in ecology by Cade et al (1999) and can be used to test the role of environmental factors as constraints. Moreover, its application allows the predictions not only of the more probable values of the studied biological metric but also of the maximum or minimum values that could be expected in environmental conditions comparable to the ones used for the model fitting (Cade and Noon, 2003;Doll, 2011;Fornaroli et al, 2015).…”

Development of a Multimetric Index for Fish Assemblages in a Cold Tailwater in Tennessee

“…In instances where an appropriate reference site cannot be identified, multivariate statistics can be used to identify links between resident biotic assemblages and abiotic conditions (Manolakos et al 2007;Collier 2009). Multivariate statistics have been applied as an exploratory tool for comparing trends in environmental disturbances to macroinvertebrate assemblages (Gerritsen 1995;Fore et al 1996;Reynoldson et al 1997) and fish assemblages (Angradi et al 2009;Flinders et al 2009;Doll 2011). Recently, multivariate procedures have also been used to directly identify sets of suitable metrics for multimetric indices (Hallett et al 2012;Miranda et al 2012).…”