The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.

The review of existing technologies and knowledge assets highlighted some of the challenges faced by probability-based surveys due to changing communication practices and sampling limitations. Although smartphone apps are acknowledged for their potential to engage users and collect recreational fishing data, they have limitations such as participant non-response and possible unknown biases that may affect reported catch rates and data quality. It was acknowledged that recruiting participants without a license frame is challenging, requires substantial communication investment, and that private companies may be able to advance app technology for broader user appeal. While app-based data collection is likely to complement probability-based methods, successful implementation requires validation, bias control, user-friendly design, transparency, and measures to ensure adequate recruitment and retention. Key to this is addressing concerns around privacy, security, and representativeness to encourage app adoption, which has the potential to promote the collection of near real-time data to inform fisheries assessment and management.