{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/193413"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/193413","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Comparing estimates of fishing effort and lake choice derived from aerial creel surveys and smartphone application data in Ontario, Canada","abstract":"Anglers make decisions that have consequences for the fish stocks, ecosystems, and socio-economics with which they interact. Smartphone angling applications (apps), are a potentially less expensive and more comprehensive data source than conventional methods, but their utility has not been evaluated. In this study, I compared results from app and aerial creel survey data from Ontario, Canada. A standard major axis regression found low agreement between effort estimates (n=111, R2=0.20, p=8.2458e-07) and app-based effort was poorly explained by lake characteristics in a random forest analysis (7.66% vs. 29.52% for creels). Explained variation improved when I included more lakes, but province-wide effort prediction did not agree with those based on creel data. I attribute these inconsistent results to low app data volumes and inherent differences between collection and analyses. Until more app data are generated, I recommend using app data to supplement conventional surveys and gain novel insights into angler behavior.","abstract_html":"Anglers make decisions that have consequences for the fish stocks, ecosystems, and socio-economics with which they interact. Smartphone angling applications (apps), are a potentially less expensive and more comprehensive data source than conventional methods, but their utility has not been evaluated. In this study, I compared results from app and aerial creel survey data from Ontario, Canada. A standard major axis regression found low agreement between effort estimates (n=111, R2=0.20, p=8.2458e-07) and app-based effort was poorly explained by lake characteristics in a random forest analysis (7.66% vs. 29.52% for creels). Explained variation improved when I included more lakes, but province-wide effort prediction did not agree with those based on creel data. I attribute these inconsistent results to low app data volumes and inherent differences between collection and analyses. Until more app data are generated, I recommend using app data to supplement conventional surveys and gain novel insights into angler behavior.","abstract_has_math":false,"creators":["Martin, Timothy"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-09","date_published":"2017-09","updated_at":"2026-07-24T05:20:01Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11299/193413","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Martin, Timothy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-02-13T17:40:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-13T17:40:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-09"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11299/193413"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota M.S. thesis. September 2017. Major: Water Resources Science. Advisor: Paul Venturelli. 1 computer file (PDF); v, 53 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["Anglers make decisions that have consequences for the fish stocks, ecosystems, and socio-economics with which they interact. Smartphone angling applications (apps), are a potentially less expensive and more comprehensive data source than conventional methods, but their utility has not been evaluated. In this study, I compared results from app and aerial creel survey data from Ontario, Canada. A standard major axis regression found low agreement between effort estimates (n=111, R2=0.20, p=8.2458e-07) and app-based effort was poorly explained by lake characteristics in a random forest analysis (7.66% vs. 29.52% for creels). Explained variation improved when I included more lakes, but province-wide effort prediction did not agree with those based on creel data. I attribute these inconsistent results to low app data volumes and inherent differences between collection and analyses. Until more app data are generated, I recommend using app data to supplement conventional surveys and gain novel insights into angler behavior."]},{"key":"dc:title","label":"Title","values":["Comparing estimates of fishing effort and lake choice derived from aerial creel surveys and smartphone application data in Ontario, Canada"]}]}],"canonical_facts":{"dc:creator":["Martin, Timothy"],"dc:date.accessioned":["2018-02-13T17:40:22Z"],"dc:date.available":["2018-02-13T17:40:22Z"],"dc:date.issued":["2017-09"],"dc:description":["University of Minnesota M.S. thesis. September 2017. Major: Water Resources Science. Advisor: Paul Venturelli. 1 computer file (PDF); v, 53 pages."],"dc:description.abstract":["Anglers make decisions that have consequences for the fish stocks, ecosystems, and socio-economics with which they interact. Smartphone angling applications (apps), are a potentially less expensive and more comprehensive data source than conventional methods, but their utility has not been evaluated. In this study, I compared results from app and aerial creel survey data from Ontario, Canada. A standard major axis regression found low agreement between effort estimates (n=111, R2=0.20, p=8.2458e-07) and app-based effort was poorly explained by lake characteristics in a random forest analysis (7.66% vs. 29.52% for creels). Explained variation improved when I included more lakes, but province-wide effort prediction did not agree with those based on creel data. I attribute these inconsistent results to low app data volumes and inherent differences between collection and analyses. Until more app data are generated, I recommend using app data to supplement conventional surveys and gain novel insights into angler behavior."],"dc:identifier.uri":["http://hdl.handle.net/11299/193413"],"dc:language.iso":["en"],"dc:title":["Comparing estimates of fishing effort and lake choice derived from aerial creel surveys and smartphone application data in Ontario, Canada"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:20:01Z"}