Derrick T. de Kerckhove Henrique C. Giacomini Craig Paukert James T. Peterson 2024 <p><span>Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting fish sampling data collected using old methods so that they can be compared with data collected using new sampling methods. Similarly, fishery biologists often need a means to compare fish sampling data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator (e.g., occupancy estimation, capture-recapture estimation), the estimates can be validly compared even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to American Fisheries Society (AFS) standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the techniques that can be used to convert data, we describe the nature of fish sample data, their uses, and their limitations.</span></p> application/pdf 10.47886/9781934874769.ch16 en American Fisheries Society Converting non-standard data to standardized data chapter