Assessing the Health of Richibucto Estuary with the Latent Health Factor Index

TL;DR

This paper adapts the latent health factor index (LHFI) statistical model to assess estuarine ecosystem health, integrating qualitative ecological features and abiotic predictors to provide rigorous, quantitative health evaluations with uncertainty estimates.

Contribution

It extends the LHFI approach from freshwater to estuarine ecosystems, modeling key abiotic drivers influencing ecosystem health in Richibucto, Canada.

Findings

Identified key abiotic factors affecting estuarine health.

Provided quantitative health assessments with uncertainty measures.

Validated the LHFI model for estuarine ecosystems.

Abstract

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.