From Amazon Catfish to Mekong Money Fish: Size‐based Assessment of Data‐Limited Commercial Inland Fisheries

Abstract

Inland fisheries are often complex, spatially dispersed, and seasonal. A lack of monitoring can result in unreliable or incomplete catch data, suggesting a role for assessment methods based on population size structure. This paper evaluates and compares empirical size-based indicators and the length-based spawning potential ratio model as candidate tools for assessing data-limited commercial fisheries in inland systems. Case study applications are presented for a contrasting set of important fisheries in the Amazon Basin (Brazil, Bolivia, Colombia, and Peru), the Tonlé Sap River (Cambodia), Paraná River (Argentina), and Bayano Reservoir (Panama). These case studies were selected to explore the effects on assessment of factors including lack of life history information, spatial separation of life history stages, modality in population size structure of floodplain river fish, and fishing gear selectivity. An international workshop was organized to bring together experts from the study systems and elsewhere to discuss the results, and to highlight potential issues and caveats. It was concluded that length-based models may work well in cases where size-selective gears are used to target a few larger species with reliable life history parameter estimates. Empirical surveillance indicators are more flexible for integrating quantitative data with local expert knowledge in common data-poor situations. In general, size-based assessment can provide guidance for the sustainable management of target species in diverse inland fisheries.

Data‐poor stock assessment of fish stocks co‐exploited by commercial and recreational fisheries: Applications to pike Esox lucius in the western Baltic Sea

“…This study demonstrates an approach for the data-limited stock assessment of mixed-use fisheries until better data become available and complements other recent studies that have shown how marine stock assessment methods can be used in small-scale inland and other small-scale fisheries in transitional waters, such as coastal lagoons (Fitzgerald et al, 2018;Shephard et al, 2021). The present work also demonstrates that recreational fisheries removals are important to be considered in stock assessments where recreational angling makes up a relevant share of total removals.…”

Community‐based monitoring, assessment and management of data‐limited inland fish stocks in North Rupununi, Guyana

“…However, this process is frequently impractical for data‐limited populations, so general estimators (Kenchington, 2014) or statistical approaches (Thorson et al, 2017) are often used. Values may also be taken from similar or nearby populations of the same fish species (Shephard, Valbo‐Jorgensen, et al, 2021). Indirect approaches to obtaining proxy LHP are risky because parameters such as L ∞ and M / k can vary greatly among populations and strongly influence LB‐SPR, thereby possibly inducing misleading estimates of ecological state.…”

“…Therefore, a difference between observed and expected length distributions expresses fishing pressure and fishing gear size selection. Examples of LBMs used in inland systems include the length‐based spawning potential ratio (LB‐SPR, Hordyk et al, 2015) model in the Brazilian Amazon (Shephard, Valbo‐Jorgensen, et al, 2021) and the length‐based Bayesian biomass (LBB) model in Lake Edward, East Africa (Musinguzi et al, 2021).…”

“…Appropriate data for length‐based assessments can be obtained by sampling commercial landings or from fisheries‐independent surveys (Shephard, Valbo‐Jorgensen, et al, 2021), without a need for overall removals from a target stock. However, the collection of representative data may not be practical for researchers across extensive, complex and poorly resourced inland fisheries, where local knowledge is paramount (Valbo‐Jørgensen & Poulsen, 2000).…”

“…Community‐based monitoring (CBM) provides an alternative solution in such small‐scale fisheries (Humber et al, 2017; Oviedo & Bursztyn, 2017), with an increasing role in supporting local food systems (Lowitt et al, 2020) and food security (Lam et al, 2019). Local user communities often already monitor patterns in fish abundance and catch as part of their fishing practices, and this insight can be captured as quantitative indicators (Thompson, 2018; Shephard, Valbo‐Jorgensen, et al, 2021).…”