Googling ‘Potential Fishing Zones’ will hit you on several links and most of them (well all) may refer to Indian fisheries. To my knowledge, no other country currently use remotely sensed data to provide fishing locations (on a daily basis) where the fishermen may target to improve their profitability (thus it reduces the time and effort on searching fish shoals). These locations are provided by the INCOIS (Indian National Centre for Ocean Information Services) who use satellite data (for chlorophyll concentrations and sea surface temperature) to map areas of fish aggregations (see the NEWS). The method inherently assume that fish aggregations occur at areas with high phytoplankton density (the probability is high for small pelagic fishes since they directly feed on phytoplanktons, thus skipping many levels of the trophic pyramid; see here). Since 1999, many authors have validated this assumption proving higher Catch Per Unit Effort (CPUE) at PFZs adviced by the INCOIS (see here, here and here).
Pros and Cons
The PFZ advisory mechanism in India is one of the finest example where other countries could potentially follow because it has a direct impact on the socio-economic status of the fishermen. I highlight this because many criticisms have been raised recently on pumping tax payers money to research areas where no benefits (in short term) are visible to the public. Having said that, there are increasing evidences on declining fish populations all over the world (due to overfishing and possibly the ‘climate change’). In that context, providing advice on PFZs is neither good for the fish population or the fishermen community (unless a high abundance of the fish population exist).
State of Indian fisheries
Based on global definitions, most fish stocks along the Indian coast can be considered as ‘data-limited’ or ‘data poor’ because the information available is limited to a time series of fish landings (note that the landings are not the actual amount of fish harvested from the population because a good proportion is discarded back to sea). Hence a fully quantitative based analysis or stock assessment is not possible to determine whether the fish stock is being underfished, sustainably fished, overfished or collapsed. A few fish stocks have length-frequency data but the analysis is limited to methods described in the FAO technical paper (i.e., Introduction to tropical fish stock assessment by Per Sparre and Venema 1998). However, the reports on fish stock assessments appear every 5-10 year (on an average) which means that the risk to the stock is not monitored in a per annum basis (even though the fish landings are monitored annually). The country also lack expertise (more than the data availability itself) to develop or run the more complex and efficient analytical models (e.g. Integrated size-structured models) that are currently being used for length based fish stock assessments elsewhere. In a nutshell, there is no information on the state of fisheries (underfished, overfished or collapsed) even for the well studied fish stocks along the Indian coast.
PFZs – are they too risky for Indian fisheries?
It is risky to advice the hot fishing spots when the abundance of fish populations are not available. More importantly, the shoal forming pelagic species such as Indian Oil Sardine (Sardinella longiceps) and Indian Mackerel (Rastrelliger kanagurta) are potentially more vulnerable to PFZ implementation as they feed directly on the planktons. These species are short lived and have a recruitment driven fisheries but the Spawning Stock Biomass (the biomass of mature fish in the population) estimates are not available (at least to the public) due to the lack of annual fish stock assessments. Even if they are available, the only regulatory mechanism to control overfishing is the monsoon trawl ban (trawl fishing is closed between June-August for a fixed period of 45 days during the breeding season) but the period of ‘closed season’ do not change with the abundance level of the fish populations.
Whether PFZs are working?
There is no information on whether PFZs are actually giving any benefits to the fishermen (or declining the fish stocks). All research so far has been spent on improving the identification of PFZs (remote sensing) and validation of fisheries data (using CPUE) through research vessel surveys. There are no reports on whether the PFZ advisories are currently being used by the fishermen to improve their profit (or do they even rely on PFZ fishing spots!). This is a potential area of research for a Masters or PhD thesis.
P.S: Why not ‘Potential No-take Zones (PNZs)’ instead?
Well, the answer is not an absolute NO. You do require a few observations but, a long time series is not necessary. My recent paper talks about the potential application of Self-Starting CUSUM in fisheries research. The paper investigates, “Whether a fishery can be managed if no historical data are available?” The fish cartoon depicted above is a symbolic representation to highlight the key messages.
1. Number of samples: A minimum of three observations could help detecting a trend.
2. Reference point: Reference point (value indicating ‘OK’) is not required to detect a trend.
3. Large fish indicator: Large fishes in the population indicate the health of the fish stock.
What is a CUSUM?
The CUSUM refers to Cumulative Sum control chart in ‘Statistical Process Control (SPC)’ theory. They are used for monitoring purposes and help detecting whether the observations in a time series are deviating consistently from a desired level of performance. The graphical version of SPC is known as a control chart and the simplest version is the ‘Shewhart’. In Shewhart chart, the data is monitored by checking whether the observations cross an upper or lower limit (red lines). The example demonstrated below shows the pH observations in an aquarium where the desired level of performance (or reference point) is pH=7 in blue line (neither acidic nor alkaline).
The CUSUM control chart is a modified version of Shewhart, where it computes the deviation of each observation from the reference point and compute their cumulative sum until the last observation. Hence, CUSUM has the advantage of detecting small and gradual shift in the time series (or trend) as early as it occurs (see the graph below).
What is a Self-Starting CUSUM?
The SS-CUSUM works on the same principle but, a reference point is not used in the computational process i.e., you do not have to say that the desired level of performance should be pH=7 (and hence you do not need that information to detect a trend). In SS-CUSUM, a ‘running mean’ is used instead of the reference point. The running mean is estimated from the data itself and updated when new observations are added to the time series. In long term, the running mean will approach closer to the reference point and as a result, the SS-CUSUM will appear similar to a normal basic CUSUM (see the graphs below).
Obviously, the running mean will drive you bananas if the initial observations are not close to the reference point. However, SS-CUSUM is an excellent method if the user is sure about the distributional properties of data i.e., the initial observations are not outliers.
What is the story of my paper?
In the context of a data poor situation, my paper investigated ‘Whether SS-CUSUM can be used for monitoring indicators such as mean length, mean weight etc. so that a change in state of the fish stock can be detected at the earliest possible?’. The performance of SS-CUSUM is displayed below in the form of ‘Receiver Operator Characteristic’ (ROC) curves. The closer the apex of the ROC curve towards the upper left corner, the better is the performance of SS-CUSUM in detecting the change in state of the stock.