
doi: 10.1111/faf.12181
AbstractCatches are commonly misreported in many fisheries worldwide, resulting in inaccurate data that hinder our ability to assess population status and manage fisheries sustainably. Under‐reported catch is generally perceived to lead to overfishing, and hence, catch reconstructions are increasingly used to account for sectors that may be unreliably reported, including illegal harvest, recreational and subsistence fisheries, and discards. However, improved monitoring and/or catch reconstructions only aid in the first step of a fisheries management plan: collecting data to make inferences on stock status. Misreported catch impacts estimates of population parameters, which in turn influences management decisions, but the pattern and degree of these impacts are not necessarily intuitive. We conducted a simulation study to test the effect of different patterns of catch misreporting on estimated fishery status and recommended catches. If, for example, 50% of all fishery catches are consistently unreported, estimates of population size and sustainable yield will be 50% lower, but estimates of current exploitation rate and fishery status will be unbiased. As a result, constant under‐ or over‐reporting of catches results in recommended catches that are sustainable. However, when there are trends in catch reporting over time, the estimates of important parameters are inaccurate, generally leading to underutilization when reporting rates improve, and overfishing when reporting rates degrade. Thus, while quantifying total catch is necessary for understanding the impact of fisheries on businesses, communities and ecosystems, detecting trends in reporting rates is more important for estimating fishery status and setting sustainable catches into the future.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 64 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
