title: Experimental and environmental factors affect spurious detection of ecological thresholds.
abstract: Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (tau) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.
authors.affiliation: U.S. Geological Survey, Leetown Science Center, Aquatic Ecology Branch, 11649 Leetown Road, Kearneysville, West Virginia 25430, USA. jdaily@usgs.gov
authors.forename: Jonathan P
authors.initials: JP
authors.lastname: Daily
authors.forename: Nathaniel P
authors.initials: NP
authors.lastname: Hitt
authors.forename: David R
authors.initials: DR
authors.lastname: Smith
authors.forename: Craig D
authors.initials: CD
authors.lastname: Snyder
mesh_terms: D000818:Animals; D017753:Ecosystem; D007448:Invertebrates / Q000502:physiology*; D008954:Models, Biological; D011157:Population Dynamics; D012107:Research Design; D045483:Rivers; D013269:Stochastic Processes