Natural and anthropogenic factors and their interactions drive stream community integrity in a North American river basin at a large spatial scale

Abstract

Urbanization, agriculture, and other human activities can exert considerable influence on the health and integrity of stream ecosystems. These influences vary greatly over space, time, and scale. We investigated trends in stream biotic integrity over 19 years (1997–2016) in relation to natural and anthropogenic factors in their spatial context using data from a stream biomonitoring program in a region dominated by agricultural land use. Macroinvertebrate and fish diversity and abundance data were used to calculate four multimetric indices (MMIs) that described biotic integrity of streams from 1997 to 2016. Boosted regression trees (BRT), a machine learning technique, were used to model how stream integrity responded to catchment-level natural and anthropogenic drivers including land use, human population density, road density, runoff potential, and natural factors such as latitude and elevation. Neither natural nor anthropogenic factors were consistently more influential on the MMIs. Macroinvertebrate indices were most responsive to time, latitude, elevation, and road density. Fish indices were driven mostly by latitude and longitude, with agricultural land cover among the most influential anthropogenic factors. We concluded that 1) stream biotic integrity was mostly stable in the study region from 1997 to 2016, although macroinvertebrate MMIs had decreased approximately 10% since 2010; 2) stream biotic integrity was driven by a mix of factors including geography, human activity, and variability over yearly time intervals; 3) MMI responses to environmental drivers were nonlinear and often nonmonotonic; 4) MMI composition could influence causal inferences; and 5) although our findings were mostly consistent with the literature on drivers of stream integrity, some commonly seen patterns were not evident. Our findings highlight the utility of large-scale, publicly available spatial data for understanding drivers of stream biodiversity and illustrate some potential pitfalls of large scale, integrative analyses.