00000nam 2200000zi 4500 9.930206 CaOODSP 20231128151358 m o d f cr mn||||||||| 231110t20232023onca ob f000 0 eng d 9780660687735 CaOODSP eng rda CaOODSP eng eng fre n-cn--- Fs97-4/3271E-PDF Lamothe, Karl A., author. Power to detect changes in occupancy and abundance of rare, imperfectly detected species using single-season hierarchical models / Karl A. Lamothe, Adam S. van der Lee, Scott M. Reid, and D. Andrew R. Drake. Burlington, ON : Fisheries and Oceans Canada, Ontario and Prairie Region, 2023. ©2023 1 online resource (v, 27 pages) : illustrations (some colour). text txt rdacontent computer c rdamedia online resource cr rdacarrier Canadian manuscript report of fisheries and aquatic sciences, 1488-5387 ; 3271 Includes bibliographical references (pages 24-27). "Monitoring for species listed under the Species at Risk Act often does not begin until a suspected decline in abundance or distribution has occurred, leading to challenges for documenting trends. Here, simulations were performed to evaluate the power and precision of single-season occupancy and N-mixture models to detect proportional reductions in occupancy probability and abundance for imperfectly detected species in low abundance between two time periods. The results suggest that many sites and surveys are needed to achieve sufficient statistical power (i.e., 0.80) for detecting change when occupancy probability, detection probability, and abundance are low. For example, quantifying a 30% reduction in occupancy probability for a species with high detection probability (0.7) and moderate occupancy probability (0.5), 200 sites surveyed three times (600 samples) were needed to achieve a power of 0.80. For the same species with a detection probability of 0.30, the number of samples required increased to 1400. Even greater effort was needed to detect significant changes in abundance. Occupancy models generated estimates with greater accuracy and precision than N-mixture models for a given level of effort. Overall, the results suggest the need to maximize detection probability for rare species, which will reduce the effort needed to quantify trends with sufficient statistical power"--Abstract, page iv. Includes abstracts in English and French. Fish populations Estimates Canada Mathematical models. Rare fishes Monitoring Canada. Freshwater fishes Monitoring Canada. Poissons Populations Estimation Canada Modèles mathématiques. Poissons rares Surveillance Canada. Poissons d'eau douce Surveillance Canada. Canada. Department of Fisheries and Oceans, issuing body. Canada. Department of Fisheries and Oceans. Ontario and Prairie Region, issuing body. Canadian manuscript report of fisheries and aquatic sciences ; 3271. (CaOODSP)9.505211 PDF 2.22 MB https://publications.gc.ca/collections/collection_2023/mpo-dfo/Fs97-4-3271-eng.pdf