Environmental monitoring data are collected successively in time. Therefore, it is self evident to think about analyzing the data sequentially. We propose the use of a sequential probability ratio test (SPRT), developed to test against a simple hypothesis, to test against a minimal relevant trend q₁ (i.e., a composite hypothesis). Three refinements are introduced: the boundaries A_n and B_n are made funnel shaped, q₁ is replaced by q if the norm of q exceeds the norm of q₁ and observation is stopped as soon as a predescribed accuracy is attained. Simulation studies show that these refinements generalize the SPRT for testing against a composite hypothesis and improve the performance in terms of power and expected sample size of the test. The robustness of the adjusted SPRT against spatial and serial correlation is studied. We demonstrate that the test is robust against serial correlation between –.5 and +.5 and spatial correlation between –.5 and +.2. The use of the SPRT is illustrated with filed data on three Tern species—the Sandwich Tern, the Arctic Tern, and the Common Tern.

Key Words
Environmental monitoring; Minimal relevant trend; Sequential analysis; Sequential Probability Ratio Test.

Maria Schipper is Assistant Professor, Center for Biostatistics, University of Utrecht, P.O. Box 80058, 3508 TB Utrecht, The Netherlands (E-mail: m.schipper@biol.ruu.nl). Evert Meelis is Associate Professor, Institute of Evolutionary and Ecological Sciences, Section Theoretical Biology, Van der Klaauw Laboratory, University of Leiden, P.O. Box 9516, 2300 RA Leiden, The Netherlands (E-mail: meelis@rulsfb.leidenuniv.nl).