Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates

Abstract

We used the presence and absence data of sentinel invertebrates (stonefly, order Plecoptera) collected by citizen scientists over 17 years to approximate trends in stream health in urban Detroit, Michigan, USA. Citizen science data are commonly collected based on availability of limited funds. Thus, survey locations lack consistent data collection, and missing values are common. While citizen science data sets can be large, on a regional and local scale, they are often undervalued but present an opportunity for managers to inform their decisions if the missing data can be addressed. To overcome this hurdle, here, missing values were modeled with a combination of spatial (inverse distance weighting and spatial stream network), temporal (Bayesian state space), and machine learning (ensemble random forest) models combining atmospheric, hydrologic, and biologic data. Using the estimated missing values, we determined negative population trends in stoneflies driven by stream temperature via a dynamic occupancy model. Urban streams present a challenge to resource managers because data are collected at disparate locations and frequencies and inconsistently recorded. However, we show how a combination of methods with publicly available and citizen science data from across disciplines can inform managers and support land-use decisions.