Abstract

The Index of Biotic Integrity (IBI) is a multi-metric index designed to measure the changes in ecological and environmental conditions as affected by human disturbances. Hence, IBI is used in practice to detect divergence from biological integrity attributable to human actions. The incorporation of biological attributes is often done at both the individual and higher level assemblages. The objective of this paper is to demonstrate the construction and statistical evaluation of a multi-metric Avian Index of Biotic Integrity (A-IBI). Canonical correlation analyses are utilized to select pertinent avian metrics as impacted by vegetation and hydrology variables. The resulting avian metrics are then ranked, according to a pre-specified scale of human disturbance, and the A-IBI scores are subsequently computed. The multivariate models as well as the final A-IBI scores are statistically validated, both spatially and temporally, using independent data sets. The techniques are demonstrated using five years of avian survey conducted on the terrestrial environments of the Kootenai River watershed in Northern Idaho and Northwestern Montana.

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Apr 29th, 9:00 AM

MULTIVARIATE STATISTICAL ANALYSIS OF AVIAN INDEX OF BIOTIC INTEGRITY

The Index of Biotic Integrity (IBI) is a multi-metric index designed to measure the changes in ecological and environmental conditions as affected by human disturbances. Hence, IBI is used in practice to detect divergence from biological integrity attributable to human actions. The incorporation of biological attributes is often done at both the individual and higher level assemblages. The objective of this paper is to demonstrate the construction and statistical evaluation of a multi-metric Avian Index of Biotic Integrity (A-IBI). Canonical correlation analyses are utilized to select pertinent avian metrics as impacted by vegetation and hydrology variables. The resulting avian metrics are then ranked, according to a pre-specified scale of human disturbance, and the A-IBI scores are subsequently computed. The multivariate models as well as the final A-IBI scores are statistically validated, both spatially and temporally, using independent data sets. The techniques are demonstrated using five years of avian survey conducted on the terrestrial environments of the Kootenai River watershed in Northern Idaho and Northwestern Montana.