DYNAMIC DATA-BASED INTERACTION MODEL

1 REX BALEÑA University of the in the , , , Philippines 5023

ABSTRACT

A potentially useful environmental model is introduced in this study in support of robust resource management. The concept builds on the principles of dynamic databasing and ocean sciences, and is thus suited to complement weak areas identified of CRM-type models, particularly, the fundamental problem of data handling and quality control. An application is outlined for Malalag Bay, del Sur, Philippines where resource conflict has been instigated by the crowding of competing passive fish gears. The problem offers the golden opportunity to demonstrate the sound analysis and resolution possible from the systematically implemented integrated methodology. Model implementation is found simple but scientific, efficient, and cost-effective. Outright, the findings describe unequivocally the bay as conditionally suitable for mariculture, such that concrete policy recommendations are made available.

This study delivers significant outputs along with progressive ideas. A Recipe of Procedures and the Pens and Cages software back the model’s commitment to make its technology useable by a wide range of beneficiaries. Among other seminal ideas, oceanography is rationalized as the proper background science of CRM. A new simplified oceanographic definition is thus offered of resource management.

INTRODUCTION

The term Coastal Resource Management (CRM) has been popularized and understood loosely as some wise use of coastal resources to promote sustainable development. The management practice is thought to be a cycle of four stages: Information gathering, institutional development, planning, and implementation (Walters et al. 1998). Since the 70’s, similar constructs have come in rather quick succession and only in slightly different palatable prefixes such as Coastal Management, Integrated Coastal Management, Integrated Coastal Zone Management, Participatory Coastal Resource Management, and Community-Based Coastal Resource Management (e.g. DENR/DA-BFAR/DILG 2001). They are among those identified with the most active and heavily funded research activities in Asia with further claims of success in developing countries like the Philippines (e.g. White et al. 1998). Unfortunately, as opposed to the impression created by abundant publications, both the claims and the CRM methodology itself may be debatable.

This study addresses the fundamental databasing problem and the ensuing inadequate control of data quality. While it seems that the CRM goal is a wholesome integration, it does

1 Mail: PO Box 249, , PHILIPPINES 5000 tel: 63.33.3158378 email: [email protected] 2 appear from its monotonous applications2 that robust oceanographic considerations are not met and a rigorous treatment is not clearly evident of the quality and, hence, utility of data (environmental, social, economic, political, etc.). To illustrate, in the information gathering stage of the CRM cycle, any laxity in quality control during the collection and processing of data can spawn errors or uncertainties, the presence and unpredictable propagation of which in the cycle can be disastrous. Coupled with the lack of a concrete standard against which to gauge CRM performance, as activities cycle through time, management of uncertainties (even if feasible) already reduces to a viscous trial-and-error exercise, which can be very costly and, hence, also unsustainable (cf. White et al. 1998). Certainly, a methodology that is far from being cost- effective can hardly be called successful.

Adopting the oceanography stand, this study introduces an uncomplicated databasing technology and demonstrates its capability to generate concrete, quantifiable results. Named as the Dynamic Data-Based Interaction Model (DDIM), it ensures, first of all, understanding of three significant lessons: The dynamic nature of both the environment and the problem, interactive data utilization in management, and systematic and integrated handling of information. Second, because the methodology is data-based, the model is intrinsically “participatory.” However, it is data “participation” that is emphasized to promote the viewpoint that reliable scientific information must be the start-off point and heart of any sound management process. Presently, this is demonstrated only in terms of environmental data. Third, the model is a pioneering attempt at quantifying the carrying capacity of the water environment, one overly used yet tenuous quantity in countless coastal studies (Baleña 1998a). Finally, the model is a pragmatic decision-making tool equipped with a layman’s guide for water quality analysis and a non-technical software to compute for the suitability of any area for mariculture (Baleña 1998b). Quite significantly also, these products are obtained within reasonable methodological bounds and availability of resources- a further inspiration to curb the wasteful malpractices of costly projects, the proliferation of which have had unquestioned but questionable status in the country.

MATERIALS AND METHODS

DDIM: The Concept

DDIM is an evolving approach to environmental modeling or management. It rests on three fundamental principles:

i) Concrete databasing is prerequisite to management. ii) Environmental management is a dynamic interactive process. iii) A truly effective methodology must be both simple and cost-effective.

First, it is nonsensical (and truly wasteful) to indulge in management devoid of facts or dealing with half-baked, unverified, or distorted facts (a malpractice akin to ). To begin with, the task already is complex such that additional problems with the data will preclude concrete analysis. For instance, hand waving policies are products of abstraction and unlikely to

2 Usually an indication of the declining utility or quality of a concept. 3 be amenable to further systematic processing and use. Concreteness implies necessarily representativeness because it is difficult to piece out an integral picture from defective, inaccurate, or inadequate data. Certainly, data gathered must reflect also the adequacy of measurement besides being in quantifiable form possible.

Second, the dynamic character of the environment is a constant complication to human attempts at understanding and foretelling its natural processes. Thus, a persevering manager cannot remain invariant. He must interact continuously by updating his database and ensuing strategies. In doing this, he maximizes the utility of concurrent data while improving progressively the ability of his growing database to project trends or recurring patterns. Ultimately, through this responsive interaction, the shaky task of prediction may become well founded. Other than this recourse, a manager must refrain from managing at all.

Last, especially because appreciable time and financial resources are involved, a model must optimize its requirements. Thus, one big aim of DDIM is to simplify its methodology with foresight of an effective application down to the level of non-technical users. Simplification results in cost-reduction that, in turn, allows for repeat applications. Iteration has the potential to improve model accuracy and predictive skill.

Procedures

The essential DDIM procedures are schematized in Figure 1. The major end points are labeled as planning, useable products, and archiving. The crucial tasks introduced presently are dynamic databasing and documentation.

Planning is the initialization and must involve the crucial participation of scientists in defining the problem, feasible solutions, approaches, and anticipated complications or innovations. Subsequently, the plan goes through the series of execution steps labelled collectively as dynamic databasing. Data collection is the search for data, and may involve primary field, laboratory and theoretical work, and secondary data. Data processing refers to the conversion of raw data into convenient forms for analysis, while taking quality control into prime consideration. Processing may utilize modern automated techniques, programming and visualization. Integrated analysis is mainly the job of a specialist and involves, among others, the arduous tasks of collating analyses, consistency checks, validations, etc.- all for the purpose of threading through the processed materials a clear unified account of what is revealed of the study problem and the overall problem-solving analysis. Modeling is the building of a model that best describes the input data, optimized procedures, and the integrated analysis. Techno-packaging pertains to the utilization of the model concept to create suitable products for identified beneficiaries. For instance, multilevel presentations are demonstration tools suitable to a wide range of beneficiaries and, simultaneously, used as means for them to review the study presented. When found satisfactory, technologies end up as useable products. Otherwise, the model does allow for iterations at any convenient stage. Iteration is never a guarantee but does provide flexibility to the model, help ensure consistent procedures and improve the quality of products. Finally, all procedures, iterated or otherwise, undergo documentation and deposit their data, write-ups, and products for permanent archiving.

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Figure1. Schematic of DDIM and its procedures. Iteration between steps is allowed, although not shown explicitly by arrows. Planning is considered as the usual initialization step. Scientific databasing to produce quality data is at the heart of the model. Documentation is emphasized as essential to perform at every step.

To verify on its basic principles, DDIM does ensure concrete databasing because its processes and results are archived systematically and made available for examination at any desired step. Dynamic interaction is accomplished by active intervention and the provision of iteration, likewise, at any desired step. Simplicity and cost-effectiveness are evident with the well-defined steps, data recycling (during iteration), and flexible intervention, which altogether preclude costly revisions.

Problem Application

Malalag Bay, is one learning area of the USAID-funded Coastal Resource Management Project (CRMP) that is suited for a test application. The bay is situated inside 5 , which faces Celebes Sea south of the Philippine archipelago (Figure 2). Confined arbitrarily by longitudes 125°22'35" - 125°27'10"E and latitudes 06°34'25" - 06°39'05"N, the bay area is roughly 20.5 km2 up to its entrance. Five municipalities (, Hagonoy, , Malalag, and Santa Maria) and 10 coastal barangays (smallest political units) surround the area along some 19.6 km of coastline characterized predominantly by secondary patches of , seagrasses, algal beds and coral reefs. A relatively comprehensive description of the physical features of the area, natural resources, socio-economic and political factors, management issues and opportunities can be found in Valle et al. (2000).

Malalag Bay has become controversial because of the unregulated congestion of passive floating devices that support the intensive culture of milkfish (local name, bangus) (Figure 2, bottom). Consequently, because of an intensifying resource conflict and the apprehension that the carrying capacity of the bay has been surpassed to a point of destruction, an assessment has become imperative of the impacts of mariculture. Findings can be used to guide technicians, LGUs, and coastal resource managers with their plan for “sustainable” activities while preventing damage to fragile habitats. Already, mariculture has occupied some 7.3% of bay area, in addition to some 15% of unproductive shallow (< 5 m) areas.

This opportune application of DDIM helps validate, among others, the efficacy of its databasing in terms of ease of implementation and concreteness of results.

RESULTS AND DISCUSSION

DDIM proved a very capable tool to use in resolving the mariculture conflict in Malalag Bay. This section outlines the successful application of procedures and discusses the effect of updated observations, the limitations and recommendations, and a special postlude on the importance of the science of oceanography as backbone of CRM studies.

Procedural Application

Planning

Plans, equipment, and protocols were readied weeks prior to sampling in close coordination with the CRMP team based at City, CRMP field workers, contact persons at State University, a dozen research interns, and LGU personnel at Davao del Sur. It was decided that determining the reference condition of Malalag Bay would require a joint analysis of field observations and model simulation: The field component would provide the baseline environmental data for modeling and a pragmatic sampling protocol. The modeling component would quantify the water dynamics and provide the optimized procedures to compute suitability, the quantity used to proxy carrying capacity. Overall, the study was made unsophisticated as to cut on cost.

All activities aimed at answering these basic questions:

i) Is the water quality of the bay deteriorated? 6

Figure 2. The Study Area: Malalag Bay. (See text for geographic description.) Dash lines delineate the three characteristic zones: 1) mariculture 2) freshwater tributary, and 3) the remaining area free from influences. Zoning was based objectively on 21 stations (Baleña 1998b). Mariculture gears, scaled to their actual sizes, their densities and locations are marked by squares for cages (732), letter “H” for pens (21) and caret for corrals (13). Altogether with their interspaces, they occupy some 1.5 km2 or 7.3% of the bay’s area. The large dark rectangles mark the approximate location of the sanctuary, which occupies an area between about 1.4-1.7 km2. 7 ii) Is the productivity potential low? iii) Does the existing mariculture pollute the bay? iv) Is the water circulation able to disperse the pollutants?

Dynamic Databasing

Considered as the meat of DDIM is the task of producing a quality dataset, strictly and rigorously in a scientific manner. The entire success of the modeling depends crucially on this database. In this spirit, this section devotes a lengthy documentation of the databasing implemented in this study.

a. Data collection

The fieldwork on 25-28 May 1998 occupied 21 stations, which provided sufficient areal coverage of about 1 station per km2. These were used to further reduce cost by identifying only three characteristic zones, with and without culture, and a major freshwater tributary (recall Figure 2). Sampling required a pump boat and a total of 12 personnel, split into two groups of six, each working in rotation every six hours. Weather remained fair. Winds were light to moderate, and there was no precipitation. Waves were relatively small and favorable within the bay area. Notably, the freshwater tributaries were basically inactive, and this simplified significantly the analyses. The overall field condition reflected the El Niño, which was blamed for the prolonged dryness in the area.

Straightforward surface measurements of the following were made directly with portable instruments and simple procedures (Baleña 1998b): Currents (CUR), winds, waves, dissolved oxygen (DO), water depth (BATHY), color (COLOR) and temperature (T), salinity (S), and transparency (TRANS). Sub-surface T and S were taken along the bay’s axis to verify depth variability and homogeneity of the water column (an essential assumption of simulation). Some samples were reserved for the laboratory determination of turbidity (TURB), pH, and dissolved (DS) and suspended solids (SUSPS). Additionally, settling particulates (SETTS) and net primary productivity (NPP) were measured but only at the representative zones. Particle traps were tethered at 4-16 m at an averaged exposure time of 63.2h. Productivity was measured via DO, through light and dark bottle setups, at an averaged exposure period of 5.9 h.

Finally, the dominant influence of circulation on the distribution of other variables was made the index of the ability of the water medium to cleanse itself of pollutant load. To quantify this index, a simple dispersion-advection simulation (Borthwick and Joynes 1989) was done utilizing observed currents (Miller 1990). Called as dispersion (DISP), it had the special advantage over currents particularly in quantifying the dispersive ability of any localized portion of the bay without the necessity of knowing the water inflows (or outflows) and the bay's complex configuration. Simulation with data assimilation required much less time to run and evaluate, and it eliminated excessive guesswork. Homogeneity of the water column was ensured fairly because the maximum depths were found to be in the vicinity of only 50-60 m, and wind effects could extend further to 150-200 m.

8 b. Data processing

Laboratory work was done at the Ocean-Weather Laboratory station located at the University of the Philippines in the Visayas, Miagao, Iloilo. Procedures were referenced to APHA/AWWA/WPCF (1985) and Tchobanoglous and Burton (1991).

TURB samples (25 ml) were read by a portable turbidimeter. The same samples were used to obtain DS and pH. Larger samples of 350 ml were used to process SUSPS, the filtration and drying of which was tedious. Upon retrieval, SETTS from sediment traps were filtered out and analyzed following similar procedure for SUSPS. NPP was approximated using on-site DO at set-up and retrieval. Productivity formulations were converted to standard units of mgC/m3d. Another DO derivative, the 5-day biological oxygen demand (BOD5), was calculated following Hammer (1977) but using a modified constant of k=0.033 (in units of [T-1]). The calculation assumed the usual generalization that 68% of the ultimate (carbonaceous) BOD is exerted in 5 days.

Processed observations were scrutinized mechanically and manually to isolate gross errors from significant variations. Error estimation for field variables was referenced to an earlier work by Baleña (1995). The entirety of field notes and observations were transcribed to a spreadsheet. A short database routine was written to sort and decimate observations according to tidal phase.

c. Integrated analysis: A summary

Implemented in this study was an unsophisticated methodology involving a fieldwork to collect mainly surface observations and computer modeling to assess the water dynamics of Malalag Bay. The main objective was to assess the water quality condition, productivity potential, and effects of mariculture, all taken against the ability of the water circulation to disperse suspected pollutants. The following integrated account of the analyses will show how DDIM accomplished this objective. The interrelationship of the variables involved is depicted in Figure 3.

The procedure for distinguishing healthy from unhealthy waters used a set of prescribed criteria, most of which are listed in Table 1. The following observations were compared directly to their standard values (DENR-EMB 1990): DS, T, S, pH, DO, BOD, SUSPS, and TURB. On this ground, the verdict was: DS, T, and S were within limits and invariant, and pH and DO content were tolerable. However, BOD was intolerably low, possibly due to abundant consumers and unoxidized organics. In fact, SUSPS load was high and could not be from rivers because of the prevailing dry El Niño conditions. Lastly, TURB was as turbid as heavily silted estuary (Baleña 1993) and corroborated by TRANS. Thus, overall, the water quality was deteriorated.

Regarding production potential, NPP was found very low from DO measurements. This supported BOD and with visual corroboration from COLOR. Evidently, in view of mariculture, it was clarified that the bay was incapable of producing extra food to support further fish growth.

Figure 3. A schematic representation of the interrelationships of variables in Malalag Bay. The diagram is best studied with the listing in Table 1.

Certainly, mariculture introduced pollutants to the bay, mainly in the form of waste feeds (cf. Coasta-Pierce and Roem 1990 and Beveridge 1996). When the amount of feeds raining down from pens and cages (fallout) was used as indicator of the intensity of activities (Figure 4), the result was even dismal (Baleña 1998b): SETTS was found excessive, almost the state of domestic wastewater. The fallout measured at 11.7 m was 1.6% of input and exceeded limits by about 2.5 times. Some 53.4% of total solids input to the water column (SUSPS and DS) was found to be due to feeds. Therefore, simply stated, mariculture was a major polluter of the bay.

On the dynamics, DISP from computer simulation (the dispersion index) proved inadequate to disperse pollutants out of the bay. While tidal flushing could remove wasted feeds (one feeding) in about a week, no dispersal mechanism could catch up with the wastes accumulated from the continuous feeding mode.

Finally, the decision to declare a given portion of Malalag Bay as suitable or unsuitable for mariculture was based simply on a direct parameter comparison with all the standards listed in Table 1. Some limits were derived upon consultations with aqua-culturists and from earlier studies of Wyrtki (1961) and Baleña (1993, 1995 and 1998b). BATHY was given outright consideration because of the unarguable navigational requirement. A 5 m-draught for fishing, recreational, and commercial vessels was used to delineate navigable passages. But the actual space allocated varied with locality, for instance, in certain places, tidal ranges exceeding 5 m were used in lieu of the draught.

As shown in Table 1 the standards not only had prescribed/derived limits but relative importance as well (Baleña 1998b). DISP was given the most weight by virtue of its being the only dynamical parameter. SETTS was ranked second because of its significance as the definitive indicator of feeds wasted by mariculture. The rest of the variables were reasoned out similarly, relative to their importance in the analyses. In a plain linear combination, the weights of the variables were used to determine the simple ratings for the suitability of an area for mariculture; LOW for values between 0-33%, MEDIUM for 34-67%, and HIGH for 68-100%. The last consideration of suitability (unlisted) was the prohibition at designated navigational lanes, critical channels, and sanctuaries.

The valuable Suitability Map is shown in Figure 5. Evidently, on the average, Malalag Bay is conditionally suitable for mariculture.The map sums up concisely the basis for deciding on where and how precisely mariculture must take place in the bay. No hand waving argument needed- the map is rigorously science-based and concretely quantitative.

In sum, the results point to the fact that Malalag Bay is dynamically incapable of flushing wastes generated by the on-going mariculture. Circulation and mixing appear appreciable but their influence could be confined to within the bay. Most water quality variables respond accordingly to this predicament. While some photosynthetic activities may produce ample oxygen, many organisms (surface and subsurface) respire, too, consuming this oxygen. Or, simply that the bay carries an excessive amount of unoxidized organic matter. Indeed, suspended and settleable solids are excessive, as corroborated by turbidity and transparency, and there are indications that these are being dispersed mainly within the bay. Using waste fallout, a concrete measure of the bay's degradation is found to be about 2.5 times pass the limit. The overall environmental 11 condition of the bay is thus quantified and could be interpreted in terms of equivalent proscriptions on the feeding rate, culture area, or stocking density. The details of derivations and findings are found in Baleña (1998b).

Figure 4. The Feeds Budget. Constructed from available values in the literature, it shows the fate of input feeds as they go through the process of ingestion resulting to growth or excretion and the final fallout stage which incorporates also the feeds wasted during feeding.Values show arbitrarily the concentrations of the feeds and the CNP components. Simultaneously, the diagram illustrates the original Feeds Extinction Depth (FED) concept (Baleña 1998b). The fallout at 11.7 m is roughly 1.6% of the input. A practical depth limit at 39.5 m corresponds to a feeds concentration of 1 ppm. This depth varies dynamically with feeds components and their detailed allocations. 12

Table 1. The variables used in the study. Their use as parameters for water quality determination requires corresponding limits and relative importance.

VAR MIN MAX UNIT RelWt REMARKS 2 SETTS -9999 860 mg/m h 75 SUSPS -9999 80 mg/l 50 DS -9999 0.9756 ppt 20 important with rivers TURB -9999 3.5 NTU 40

DO 5 9999 mg/l 50

BOD mg/l (5d) 65 5 10 T 28 31 °C 50 S 10 35 ppt 50 32 ppt- min. in open sea pH 6 8.5 50 (no units) NPP 828.3 9999 mgC/m3d 65 DISP -9999 2.7 % 100 per settling time

.25mm/s 39.5m

3.5 cycles n=2604

LEGEND:

SETTS -settleable solids -9999 >> no specific data SUSPS -suspended solids 9999 >> no specific data DS -dissolved solids

TURB -turbidity DO -dissolved oxygen BOD -biological oxygen demand T -water temperature S -salinity NPP -net primary productivity DISP -dispersion index

d. Modeling

Modeling approaches to aquaculture vary widely and can be conceivably vague as complex (Cuenco 1989). Treatment of this subject was avoided. Instead, the pragmatic model in this study was conceived only to best describe the present data, procedures, and the integrated analysis. (Note, however, that the feeds budget of Figure 4 is one of this study’s contributions to aquaculture dynamics.)

In reality, the model can be thought of simply as a computer program for automating the data handling tasks. Users must find it convenient that all tedious tasks run in a single batch job, pre-supposing only that the observations are coded in their required formats. The sequence of steps is as follows. First, processed observations are coded. Second, interpolations are done in a uniform grid. Intermediate routines compute for components of vector variables like CUR, 13 interpolate between tide phases, and calculate BOD and NPP from oxygen measurements. Third, advective-dispersive simulation is performed to determine DISP. There is flexibility to modify sources and amounts of dispersants (or stocking density) as initial condition of the simulation. Finally, information is collated to produce a set of indices describing jointly the suitability of the environment for mariculture. The Suitability Map is the final product.

Altogether, these ideas were modeled conveniently as the Recipe of Procedures which, overall, outlined the logical steps to obtain suitability: 1) Preparation of the bathymetry setup, digitization, and sampling plan, 2) field sampling, 3) processing and coding, 4) model (automated) computation of suitability, and 5) iteration if data updates are made available. This recipe was one major product of the study but there is not enough space presently to warrant its inclusion (Baleña 1998b).

e. Techonology packaging plus review

Consistent with the study objectives, the methodology was bundled in a manner for non- technical personnel to comprehend and redo DDIM with ease and efficiency- just by following the recipe. Accordingly, Pens and Cages software was built to automate all the computations. The software came with an easy-to-use manual (Baleña 1998b). Therefore, in sum, to implement DDIM, the user reads the recipe (the overall guideline), performs the fieldwork, codes the observations, and runs the software. The end result is a set of maps of suitability with respect to each variable used, separately from the joint variable Suitability Map (recall Figure 5).

The initial study review was held on 26-27 June 1998 at CRMP-Cebu. The formal lecture was delivered on 06-07 April 1999 and attended by representatives from various agencies of the government and organizations. The efficacy of the methods and automated capability of the software was demonstrated by processing the entire Malalag Bay database, including the advection-dispersion simulation, in a matter of a few minutes using only an old 486-DX2 personal computer. Written reviews from external consultants were addressed fully as well. Several key features of DDIM were stressed during the lecture: The cost-effective methodology, systematic protocols, and the science generated. Consuming appreciable time were the clarifications on the model’s iterative concept, sampling, additional variables, cautions on limited data, and the dangers of prediction, which was explained with ample examples. For the direct benefit of all stakeholders, a workshop was held on 18-21 May 1999 at to discuss the findings in the presence of government officials. Accordingly, the Malalag Bay resource conflict was resolved with full cooperation from the participants. The Suitability Map had the much- awaited information, and it accomplished successfully its purpose.

Archiving

Next only to databasing, systematic and rigorous documentation is required of DDIM. Thus, records of all sort, from original field notes to the complete Recipe of Procedures, manual or printouts, were archived systematically either as raw or processed information in both hard copy and computerized forms. They were classified according to type, format, and date of recording. All the data, documents and illustrations were so stored as to make them available for any future use. 14

Figure 5. The Suitability Map. As proxy to carrying capacity, suitability is even concrete and convenient to use. Shown is the overall value of suitability (computed from individual “suitabilities” of the variables in Table 1) at every location in the bay. The ratings are: LOW (0-33%) or unsuitable, MEDIUM (34-67%) or conditionally suitable (requires regulation), and HIGH (68-100%) or suitable. On the average, Malalag Bay is conditionally suitable for mariculture. A large portion of the bay is shown to be unsuitable and must not be utilized for fish culture.

An Opportunity for Iteration

A year after the sampling of May 1998, a re-count by field technicians found a reduction in the number of fishing structures by about a factor of 10. As expected, there was commensurate 15 decrease in waste generation. However, model computations revealed an increase in suitability of only about 12.2%. With reference to the Suitability Map, the increase appeared, on the average, to have insignificant effect on uplifting the condition of the bay (MEDIUM) although, in detail, upgraded closer to HIGH suitability the condition of some portions of the bay. The non- trivial lesson learned from this case is that a positive response from operators on decreasing their number of gears may not result necessarily to an overwhelming (desired) positive response from the environment. For one, a number of variables (other than solids) have to be taken into account. Another reason is the unknown lag in the environment's response, which may be understood only if there were simultaneous updates of other variables. These variables may be mutually time-dependent.

Limitations and Recommendations

Mainly, potential users are forewarned that current results are appropriate only to conditions prevailing during the sampling. Continued observations are necessary to improve progressively on desired projections (the DDIM essence). Nevertheless, predictions must be viewed with extreme caution. Similar caution applies to the variables used, their relative importance, and assumed linear combination. The ensuing recommendation is to ease down culture intensity by 2.5 times, either as proportional reduction in area of culture, stocking density or feeds, and to schedule/relocate activities dynamically with the seasons. The detailed account of the study limitations and recommendations are found in Baleña (1998b).

Oceanography as Backbone of CRM

Especially to a poor and maritime country like the Philippines, nothing impoverishes science growth more than the seemingly perennial and monopolyzed funding of trial-and-error research approaches gaining only debatable, reiterative or, at the most, insignificant contributions to quality information. Already, the lack of objective evaluations of and debate venues for these undertakings, plus possible dabbling by politics, had spawned widespread mistrust of a gray science. Perhaps, then, the case with CRM is no exemption (cf. Salamanca 2003). But as to when and how CRM activities would slow down and allow full and unequivocal review is a hard question. Certainly, when dealing with any aquatic environment, still the appropriate science to adopt is oceanography, also known as ocean science(s), marine science(s) or even aquatic science(s). Its central role in aquatic studies cannot just be downplayed. But why has not oceanography been put to good use is another puzzle (Baleña 2003).

An inspection of CRM studies reveals weak (if not absent) guidance by oceanographic principles. As a result, crucial integration of multidisciplinary facts is hardly guaranteed, apart from non-trivial mismatches in spatio-temporal analyses. It is usual for such studies to rely instead on fishery science that, in reality, is but a small fraction of biological oceanography. In fact, there lingers the misnomer (maybe manipulated) that the study of the oceans is fisheries and that (worse) everything that matters in the ocean is the fish! For one, this promotion of fisheries over oceanography may have brought to critical state the Philippine archipelagic environment. Indeed, the absence of proper guidance in CRM and fisheries studies manifests in familiar ways, such as the hasty use of catchy yet shaky concepts the likes of over-exploitation, sustainability and, consequently, protectionism and conservation (Baleña 1998a). This is not without 16 commensurate expense to the government as programs and policies may be designed inadvertently from these “scientific” ideas (cf. Salamanca 2003). Worst, any parochial indoctrination in institutions deprives young learners of true education of the water environment. Remarkably, this may leave them one monumental perversion of their educational legacy.

Therefore, a bold view must be put forward to rectify certain misconstructions or malpractices. For a start, mentioning of just two basic arguments is in order. First, it is unarguable that humans are terrestrial beings making primary use of land-based products as food. This is an admission that the water environment is but an alternate source of food. Thus, it is enigmatic to promote, for example, the fish (among many significant uses of the oceans) as the key to sustain or even improve the quality of human life. (Afterall, in spite of claims, decades of fisheries projects have not really uplifted coastal conditions or the livelihood of fishermen.) In fact, of the total oceanic volume, hardly 1% is organic matter, and the dominant portion of this already miniscule amount is even unpalatable: 95% POM, 5% DOM, 0.1% plankters, and 0.01% zoo-herbivores. The fish is utterly only 0.0001% of the remainder (Gross 1990), or hardly about one millionth of the aqueous volume.3 This illustrates clearly that the fish, even if driven close to shore (the shelf-sea mindset), to begin with, already is an extremely scarce entity. Certainly, the enigma becomes most serious when promoting fish for food security. Second, it is deliberate misunderstanding that oceanography, the umbrella science of the oceans, specializes only on the physical environment, and its analyses are devoid of socio-economic considerations. This is both an error of perception as one lack of academic perspective. Odum (1971) is much misunderstood also here in his advocacy of ecology because, to date, certain scientists still believe that oceanography is but a sub-discipline of either fisheries or ecology.

It is conceivable that the globalized obsession to promote so-called sustainability (and other buzzwords) may have pressured CRM studies to embrace hasty management rather than the careful, factual scientific investigation, thereby neglecting in the process the quality of information. Hence, even if so-called integration is claimed, costly iterations (trial-and-error) become inevitable of CRM cycles, when money and attention must be spent instead on the crucial probing of data utility. Even management has to be factual and, therefore, resource studies may opt to consider managing this fundamental problem first. Meantime, DDIM highlights the urgency of exploring for viable methodological alternatives. Or, perhaps, why not promote instead the management of primary resources on land, if manage at all?4

CRM Made Simple

Unfortunately, misleading viewpoints on the nature of water environments have pervasive roots in the improper introduction of the commodity-oriented sciences- in spite of the availability of oceanography as the only true multidisciplinary and integral science of the oceans.

3 In layman's parlance, this is savoring hardly one tiny drop of chocolate mixed in a liter of milk! 4 Big research money may be spent instead on hospitals, schools, medicines, books, etc.- and (Yes!) food. Maybe a modest stopgap measure against research spending is simply to buy the much-needed food of the coastal population. Thereafter, the oceans can rest and recharge for a while. Indeed, the paradox of environment advocacy nowadays is that everybody goes over-acting (the non-technical equivalent of proactive) to protect and conserve almost everything- except human beings!

17 The fallacy is an anachronism of the advanced systems thinking that has come of age (e.g. Cavana et al. 1999) and, in fact, of the relatively recent conceptual facelifts on research by oceanographic agencies (e.g. NMFS 2001). Evidently, therefore, oceanography must have the desired fundamentals of CRM (cf. White et al.1998). Consequently, it is timely and proper also to redefine CRM in the context of ocean sciences- operationally, meaningfully and simply as “coastal ocean management.” This definition is meaningfully concise and explicit of the very straightforward application of good management to oceanography of only the coastal ocean domain. There is no conflict with the popular definition by White and Lopez (1991) except that, presently, there is clear restraint on extending management connotation to ill-defined concepts as sustainability and overexploitation Baleña (1998a). Particularly, sustainability in the current context is more of a desired objective than an intrinsic construct of management.

Present ideas may not be acceptable to certain entities that remain comfortable with and wish to maintain the debatable thrust on Philippine marine science. But with them this very valid question by a taxpayer remains unanswered: What sort of concrete progress can CRM claim through decades of luxurious funding? (Salamanca 2003). Already, the same has been asked of Philippine Fisheries Science (Baleña 2003). My simplistic theory is that both CRM and fisheries sciences have long outlived their conceptual utility and, hence, nothing more to offer.

ACKNOWLEDGEMENT

This study found incredible assistance from Dr. Catherine Courtney and her CRMP office and field personnel. Profs. Imelda Vego-Jamero and Malou Ang-Lopez offered uncompromising help to Alvin Garingalao, Mark Cifra, Mary Jul Vicente, and Aida Gange, my indispensable research assistants. This research was funded fully by the USAID. All views and opinions in this documentation are solely the author’s.

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