First, make sure that there really are no data. Many fisheries can benefit from data collected for other purposes. If there is no local information on expected impacts of climate change, you can draw on reports that synthesize climate model outputs at the country or regional level (e.g., this report from the FAO in 2018) to carry out the climate change projections in Step 1 of FISHE. You can augment this information through interviews with local system experts (e.g., local fishers, processors, buyers, managers, scientists, long-term community members, etc.), and you can use the CARE analysis, and the accompanying Community Questionnaires, to guide these interviews, capture their outputs, and generate climate impact vulnerability scores, along with scores for the risks posed by all other threats facing the system.
To do ecosystem status assessments, it is sometimes possible to use visual survey data collected by scientists and students (Steps 3 and 4). These data can be used to calculate total fish density (number of fish of all species counted in all transects divided by the area surveyed in the transects) and fish densities for species of particular importance or concern (Step 3). For coral reefs in the Indian Ocean or Caribbean Sea, the total fish density can be compared to thresholds for coral reef health (Step 3) to estimate the status of the ecosystem and the impact of fishing (to see if fishing pressure should be reduced generally, for example by reducing the open season, imposing a size limit or catch limit, or restricting gear). Visual survey data can also be used to calculate the species-specific ratios of fish abundance on the fishing grounds compared to abundance in no-fishing zones. These species-specific fish density numbers can be used to assess stock status if you have a time series extending to the beginning of the fishery, or if you have a well-complied with marine reserve that has similar habitat as the fishing grounds, since they are proxies for the ratio of fished to unfished biomass, an important fishery status indicator (Steps 5 and 9). Another example of "hidden" fishery data are exports: countries usually keep fairly good records of exported goods, and if some of your species (e.g., lobster, conch, or shrimp) are mostly exported, the export numbers can be used as a proxy for catch (minus domestic consumption).
If there are truly no data at all for your fishery, then you can conduct a Productivity Susceptibility Analysis (Step 4) with your fishermen and local experts. It requires only fishermen's knowledge of the fish and their fishing techniques plus some life history parameters such as size at first maturity and maximum size. It's best to use local estimates of life history parameters, but if there are none you can use global estimates from www.fishbase.org. You can also omit some of these life history parameters from the analysis if you cannot find them anywhere. The PSA will result in a vulnerability score for each species analyzed: high scores indicate high vulnerability to overfishing. Moderate and low scores indicate moderate and low vulnerability. While it is not advisable to use PSA vulnerability scores for long-term fishery management, PSA vulnerability scores are a good way to get started with fishery management when no data are available. They are especially useful for prioritizing stocks for data collection and precautionary management (Step 6).
While it is possible to take precautionary actions without data, it will of course be necessary to collect data to carry out science-based adaptive management that will improve your fishery’s performance. The specific kinds of data you should collect will depend on your fishery’s goals and the indicators of those goals, because the data will be used to evaluate the indicators relative to goals. For example, if one of the fishery’s goals is to maintain sustainable levels of reproductive capacity, one indicator of that goal is egg production. If the target is 40% of the maximum potential egg production of the population, you would need to collect data on the length composition of the catch or population (relative abundance of fish of different sizes), size at maturity, and the number of eggs produced by each size class. Many data limited assessment methods require these kinds of data. Multiple years of data add precision and accuracy to assessments, but Just one year of length data can be sufficient to estimate length-based sustainability indicators, fishing mortality, spawning potential ratio, and in-season fluctuation of the catch that, when combined with targets and limits defined by managers/scientists/stakeholders, can be used to drive adaptive fisheries management (Steps 7 - 11). Many of these methods assume equilibrium conditions (i.e., factors that increase or decrease population abundance are in perfect balance) and constant recruitment. No fishery is ever in equilibrium or experiences constant recruitment because the factors that affect fish abundance and recruitment change over time. However, if changes in these factors (e.g., ocean productivity, predation rates, etc.) occur relatively slowly and assessments are made relatively frequently (e.g., annually), equilibrium methods can provide useful insights into stock status. Because climate change may accelerate changes in recruitment and other factors, we include a new method (Length-based Integrated Mixed Effects, or LIME) within FISHE (Step 9) developed by Merrill Rudd and James Thorson that can use only one year of length data to estimate spawning potential without equilibrium or constant recruitment assumptions. It can also use multiple years of length data, catch data, and abundance data to improve estimates.