Comparing assembly processes for multimetric indices of biotic integrity

Anthropogenic alterations to global ecosystems necessitate management action to conserve or restore biodiversity and ecosystem services. A major advancement in ecosystem management was the development of multimetric indices of biotic integrity (MMIBI) used to guide development of, and measure progress towards, restoration goals. Despite considerable refinement of MMIBI applications over the past three decades, a central challenge remains concerning the method of selecting ecological indicators for inclusion in MMIBI. We quantitatively compared MMIBI metric assembly processes across four sub-regions for fish assemblages in western Tennessee, USA to assess relative performance of three metric selection approaches. Metric selection methods we assessed included “filter gradient” using a multi-step approach to filter candidate metrics down to only the most reproducible and responsive, “indirect gradient” using a correlative unconstrained ordination approach, and “direct gradient” involving an automated constrained ordination approach. For each method, we calculated MMIBI using the selected metrics and compared their precision (i.e., stability across multiple samples), responsiveness (i.e., discrimination between most- and least-altered sites), and sensitivity (i.e., ability to detect landscape alterations). We found metric selection using the filter gradient approach produced MMIBI that were most responsive across all four sub-regions, while the indirect gradient approach produced the most sensitive and precise MMIBI for three of four sub-regions. The direct gradient metric selection approach produced the most sensitive MMIBI only for a single sub-region with a relatively short gradient in landscape alterations. These results reveal a tradeoff between filter and indirect gradient selection methods in which filter gradient metric selection provides high MMIBI responsiveness, but at the cost of increased number of steps and reduced precision and sensitivity. The “middle of the road”, indirect gradient metric selection approach produced precise and sensitive MMIBI, but at the cost of reduced responsiveness. These findings highlight the necessity to pair well-developed ecosystem management goals with MMIBI application, and provide a road map for the most appropriate assembly process for managers developing MMIBI. For example, identification of least- and most-altered sites might best be accomplished with MMIBI developed using the filter gradient approach, but assessing the factors contributing to alteration and precisely measuring progress towards restoration endpoints might best be accomplished with MMIBI developed using the indirect gradient approach. Restoration and management actions guided by MMIBI will become increasingly prevalent with increased future alteration to global ecosystems, and this work provides important insight into how technological and quantitative advances will improve application of ecological indicators.