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All for One, and One P4 for All: Centralizing P4 for Nez Perce Tribe Fisheries

Samantha Smith, Clark B. Watry

Nez Perce Tribe, P.O. Box 365, Lapwai, ID 83540

The Nez Perce Tribe Department of Fisheries Resources Management – Research Division manages data resources for several research, monitoring, and evaluation (RM&E) projects located throughout the greater Snake Basin within their usual and accustomed hunting and fishing lands.  The area encompassed by these lands includes present day Washington, Oregon, and Idaho; states with which the Tribe currently co-manages fisheries and other resources.  The scope and scale of this area presents various challenges to programs monitoring fisheries for different species across all life stages, resulting in complex solutions to ensure the timely flow of information to staff and managers.  Although tribal fish managers assembled their data and results for annual reporting requirements in the past, each project operated in a siloed environment to meet contractual obligations.  Without a cohesive approach to manage all this data in an efficient manner, the fisheries program found it difficult to take a big picture approach to integrated fisheries management.  One example of this approach relates to how we centralized the P4 application for RST data collected by multiple projects across the landscape.  As part of this process, we went to great lengths to standardize individual project data, including protocols, in order to import all RST project data into a single standardized back-end database.  Meanwhile, users only had to make a one-time minor change to connect their individual P4 front-end to the centralized back-end database located on the network; all other P4 functionality was unchanged.  Furthermore, from the centralized P4 front-end, data could be managed/organized, edited/validated, and exported to PTAGIS as in the past without the need for any additional steps in the process.  We developed different data flows to make the P4 data accessible to our staff for integrated data analysis and reporting and for other integrated applications (e.g., R Studio, R Shiny, and eventually a web application).  These efforts streamlined the way our staff now interacts with their data and promotes cross-project data analysis to comprehensively observe differences, similarities, and temporal changes of juvenile fish metrics across the vast landscape of the Nez Perce Tribe.