Traffic Light procedure for Decision making: its Rapid extention from fisheries to other sector of the Economy

John F. Caddy   Abstract- The Traffic Light approach to management based on empirical data is analogous to using traffic lights...

John F. Caddy

 

Abstract-The Traffic Light approach to management based on empirical data is analogous to using

traffic lights on highways to automate decisions by moving vehicles in the absence of a police 

officer, when three categories of information are presented to the driver: Green – Go; Orange – 

Prepare to stop; and Red – STOP! You face prosecution if you infringe the last rule protecting other 

road users. Part I of the paper is dedicated to a more detailed description of the first applications of 

the Traffic Light approach to the management of the fishery resources of the oceans, and how the 

method was integrated into the current scenario of research and management of fish resources in the 

1970’s and subsequently. Such a management rule has obvious applications outside of fisheries 

regulation, especially where the best judgment of society suggests that uncontrolled development, 

poor quality, or pollution of products or environments, uncertain data, or other processes tied to the 

rapid satisfaction of society’s needs, require urgent control. Such applications need to be supported 

by the members of the public concerned, often based on empirical data and pre-established decision 

rules. In fact, the premature numerical modeling of management processes using only one or two 

control variables can be misleading. The original paper presenting this method formally, was at the 

1998 meeting of the North Atlantic Fisheries Commission. This last paper cited preceded a host of 

applications outside the fisheries sector which are documented in brief anecdotal form in part II of 

the paper from 115 examples drawn largely from internet citations.

GJSFR: Global Journals Blog

I. INTRODUCTION

My original proposal to the Northwest Atlantic Fisheries Commission for a Traffic Light (TL) approach, was an aid to managing invertebrate stocks in the Northwest Atlantic using empirical data, given that the age composition data needed for the standard age-structured models, were unavailable for species such as lobsters, crabs and other invertebrates. The management methodology then used for finfish, essentially employed mathematical models of populations developed in the 20thC and their modern derivations. These depended for their appropriate implementation on managers having accurate estimates of several of the following time series of indicators, namely fishing effort, fish biomass, age compositions, and catch rates, all based on costly and efficient sampling. These models showed that landings generally increased as fishing effort and mortality rose to a peak referred to as the ‘Maximum Sustainable Yield’ (MSY). When resources are exploited more intensively than at MSY by larger fleets and improved technology, stocks tend to decline, potentially to extinction. Managing stocks to achieve MSY often depends on fragmentary or biased data. Even though MSY was enshrined in the Law of the Sea as the valid objective of fisheries management, it had proved to be excessive. Often overshoots in fishing effort beyond MSY occur, and are difficult to reverse.

More restrictive Limit Reference Points (LRPs) were introduced subsequently by the UN, corresponding to lower levels of fishing effort, such that when the mandatory LRP effort was exceeded (or if fish biomass fell below its LRP equivalent), a cut in fishing effort or landings was supposed to be implemented. Landing data, and a limit reference point derived from technical analysis of predominantly biological sampling data, then formed the critical reference points for a Traffic Light approach to management. The Reference Points marking the boundary between acceptable effort (green) and sub-optimally high fishing effort (yellow), and that marking the boundary between yellow and red (illegal overfishing) were established for each stock, and attempts made to maintain the fish stocks at a level where successful reproduction of the species was possible.

The basic advantage of the traffic light approach as envisaged for fisheries management, is that it recognized that overfishing could be detected by a wider number of variables than fishing effort, and that the approach could be easily configured for use by fisheries managers. The key variables used could be extended to include other issues that affect fish production, such as mean size of fish, age composition, % mature fish in the catch, environmental trends, or bio-economic variables such as unit values and profit margins.

The basic disadvantage of current fisheries models is that they assume that the very few variables used in modeling simple analytical or production models are the critical ones, and that all others, unknown or unmeasured, remain constant, or are of minor impact. At a time when we are concerned about climate change this approach has obvious disadvantages. A compromise approach would be to present all the time series variables which are possibly relevant,contemporaneously on the same plot for visual inspection, after converting them into 3 color categories, depending on whether their values were high, medium or low. Statistically testing to see which color variables vary together over time could provide new indicators of an overfished condition. These could then be incorporated into a decision framework for the fishery managers.

The number of applications detected over time from reports on the Web using the Traffic Light approach, including applications outside the fisheries sector, dramatically increased after my early fisheries management application in 1997, and showed a comparable increase in diversity of methodologies. Two main types of traffic light applications may be identified however:
1) Monitoring a resource or process to establish current trends allows a visual appreciation of changes in those variables believed to be mutually influential or correlated. Quantification of the indicators chosen can be expressed in one of three color values: the color depending on where the value for a given year falls within the observed (or theoretical) range for the observed data; i.e., at below 33% of the observed values, or if above 33%, whether or not below 66% of the maximum; thus dividing the time series into three color categories before plotting. Evidently this scheme may not result in the best choice of biologically important reference points, but clearly shows trends. Standard statistical approaches can be used for confirmation, or predicted model outputs for standard models can be used as color boundaries and incorporated into the joint plot.
2) Use a traffic light methodology in a more management- oriented approach by instituting a series of rules, so that stakeholders in an operation must organize their activities in such a way that orange and red color categories of information are minimized, with appropriate rules set for sharing access. Penalties for infringement are also established and respected.

In part II of the paper, brief summaries of 29 applications from outside the fisheries sector were presented. These include the use of the TL approach in:

• Sustainable development;
• Formulating Health and Safety critieria;
• Seafood quality;
• Special labeling of supermarket products;
• Managing resources for indigenous livelihoods;
• Sustainability and appropriate subsidies;
• Monitoring carbon dioxide production;
• Soil quality criteria;
• Protection of whales from offshore drilling;
• Grain research.

* Caddy, J. F. (1998). Deciding on precautionary management measures for a stock and appropriate limit reference points (LRPs) as a basis for a multi-LRP Harvest Law. NAFO SCR Doc., No 8, SN 2983,13p

Research articles:
https://globaljournals.org/GJHSS_Volume16/5-Did-the-Spiral-Engravings.pdf
https://globaljournals.org/GJSFR_Volume15/3-The-Traffic-Light-Procedure.pdf
https://globaljournals.org/GJSFR_Volume15/3-Night-Time-Pulses-of-Ground.pdf

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