Untangling climate and water chemistry to predict changes in freshwater macrophyte distributions.

ABSTRACT: Forecasting changes in the distributions of macrophytes is essential to understanding how aquatic ecosystems will respond to climate and environmental changes. Previous work in aquatic ecosystems has used climate data at large scales and chemistry data at small scales; the consequence of using these different data types has not been evaluated. This study combines a survey of macrophyte diversity and water chemistry measurements at a large regional scale to demonstrate the feasibility and necessity of including ecological measurements, in addition to climate data, in species distribution models of aquatic macrophytes. A survey of 740 water bodies stratified across 327,000 square kilometers was conducted to document Characeae (green macroalgae) species occurrence and water chemistry data. Chemistry variables and climate data were used separately and in concert to develop species distribution models for ten species across the study area. The impacts of future environmental changes on species distributions were modeled using a range of global climate models (GCMs), representative concentration pathways (RCPs), and pollution scenarios. Models developed with chemistry variables generally gave the most accurate predictions of species distributions when compared with those using climate variables. Calcium and conductivity had the highest total relative contribution to models across all species. Habitat changes were most pronounced in scenarios with increased road salt and deicer influences, with two species predicted to increase in range by >50% and four species predicted to decrease in range by >50%. Species of Characeae have distinct habitat ranges that closely follow spatial patterns of water chemistry. Species distribution models built with climate data alone were insufficient to predict changes in distributions in the study area. The development and implementation of standardized, large-scale water chemistry databases will aid predictions of habitat changes for aquatic ecosystems.

SUBMITTER: Sleith RS 

PROVIDER: S-EPMC5838067 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature