Robust planning for restoring diadromous fish species in New Zealand's lowland rivers and streams

Abstract

We used statistical models to predict the distributions of 15 native diadromous fish species across New Zealand's river and stream network, and demonstrate their potential use for guiding the restoration of freshwater ecosystems. Models were fitted to an extensive collection of field samples describing the distributions of individual fish species, coupled with a set of environmental predictors chosen primarily for their functional relevance to diadromous fish species. Models were fitted to observations of species occurrence using boosted regression trees (BRT), an advanced regression technique that combines excellent predictive performance with good description of relationships between species occurrence and individual environmental predictors. Environment‐based predictions of species distributions were then made for all river and stream segments. We explored the use of these predictions to guide the selection of sites where diadromous species are likely to occur, and to set restoration targets, once a restoration site has been selected. Descriptions of the environmental preferences of the different species, based on relationships revealed by the statistical models, provide important clues to the likely responses of species to restoration of favourable environmental conditions, e.g., by restoring riparian shade and/or when reinstating access to/from the sea.

Keywords:

• freshwater
• restoration
• species distribution model
• boosted regression trees