TRAINING FOR LOCAL UNIVERSITY LECTURERS IN ON EAFM AND FISH STOCK ASSESSMENT

TERNATE, FEBRUARY 3-7, 2020

Prepared by: Purwanto, PhD, USAID SEA Senior Fisheries Advisort and Ses Rini Mardiani, USAID SEA Sustainable Fishery Specialist

DISCLAIMER This report is made possible by the generous support of the American People through the United States Agency for the International Development (USAID) with the close collaboration of the Government of (GoI). The contents of this report are the sole responsibility of Tetra Tech and do not necessary reflect the view of USAID or the United States Government (Delete this blank page after creating pdf. It’s here to make facing pages and left/right page numbers sequence correctly in word. Be careful to not delete this section break either, until after you have generated a final pdf. It will throw off the left/right page layout.) TABLE OF CONTENTS INTRODUCTION 3 OBJECTIVES OF ACTIVITY 3 IMPLEMENTATION 4 SUMMARY OF PARTICIPANTS 5 KEY OUTPUTS, OUTCOMES AND ACHIEVEMENT 6 A. OPENING SESSION 6 B. TRAINING SESSION 6 C. ROLE OF UNIVERSITY IN FISHERIES MANAGEMENT 11 D. ASSESSMENT RESULT OF THE IMPLEMENTATION OF THE TRAINING 12 E. DISTRIBUTION THE “STATE OF THE SEA: INDONESIA” 17 RECOMMENDATION 17 FOLLOW-UP 18 REFERENCE 18 ANNEX 20 ANNEX 1. LETTER FROM KHAIRUN UNIVERSITY REQUESTING TO SUPPORT ON CAPACITY BUILDING TROUGH TRAINING ON STOCK ASSESSMENT. 20 ANNEX 2. LETTER FROM KHAIRUN UNIVERSITY REGARDING THE NAMES OF LECTURER ASSIGNED TO PARTICIPATE IN TRAINING ON STOCK ASSESSMENT. 21 ANNEX 3. ONE OF LETTER FROM KHAIRUN UNIVERSITY INVITING LOCAL UNIVERSITY IN NORTH MALUKU TO PARTICIPATE IN TRAINING ON STOCK ASSESSMENT. 22 ANNEX 4. DOCUMENTATION 23

Figure 1. Modelling Flow with ASPIC Suite ...... 9 Figure 2. Participant's Profiles ...... 13 Figure 3. The Changing Before and After the Training for Each Participant ...... 13 Figure 4. The Proportion of Participants Before and After Training ...... 14 Figure 5. The Proportion of Participants Based on Their Academic Background in Responding the Assessment Questions ...... 14 Figure 6. (A) Participant's Evaluation for Overall Training, (B) Participant's Evaluation for Training Benefit ...... 15 Figure 7. (A) Evaluation for Material and Process; (B) Evaluation for Trainers ...... 15 Figure 8. (A) Evaluation for Approach and Tools; (B) Evaluation for the Venue ...... 16

Table 1. The agenda of Training-Workshop for Local University Lecturers in North Maluku on EAFM and Fish Stock Assessment ...... 4 Table 2. The Participants of the Training ...... 5 Table 3. The Result of Group 1 Discussion ...... 11 Table 4. The Result of Group 2 Discussion ...... 11

1 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV Table 5. Participant's Opinion Related the Training Benefit and Things to be Improved ...... 16 Table 6. List of Agencies for Distribution of "State of the Sea: Indonesia" Book ...... 17

USAID.GOV PREPARATION OF REEF FISHERIES MANAGEMENT PLAN AND SMALL PELAGIC FISHERY | 2 INTRODUCTION

USAID Indonesia Sustainable Ecosystem Advanced (SEA) Project provides support to strengthen the Indonesian fishery management including stock assessment and development of harvest strategy for targeted species, namely red-snapper, grouper, and small-pelagic fish species. There are two considerations in producing good result of stock assessment: good data collection and better assessment methods. Robust data collection methodology is critical to represent population, spatial and temporal variations for stock assessment, and a suitable modeling is important to projecting fish stock assessment result.

To date, at the third year of project implementation, in collaboration with the national- and provincial- level Fisheries Management units and MMAF Research units, USAID SEA Project and Partners have made significant progress on data collection, undertaken by NGOs as USAID SEA Project Partners, and sock assessment and harvest strategy development, undertaken by MMAF/BRPL. During the analysis, which was part of capacity building of MMAF technical staffs, USAID SEA involved the national researchers of the MMAF Research Institute for Marine Fisheries (Balai Riset Perikanan Laut - BRPL).

Wider capacity building on catch monitoring and stock assessments to local scientist affiliated to the local universities are required, considering their roles in fisheries management and development at provincial level. The Faculty missions in the provincial development cover (1) implementation of tertiary education, (2) contribution of ideas in the formulation of sustainable economic development strategies, and (3) contribution of thoughts in formulating fisheries management policies, to ensure that fish stocks produce optimal support for regional development. Unfortunately, in the implementation of the missions, the Faculty members were constrained by the limited availability of the required data for policy and strategy formulation, and lack of knowledge and expertise on the concepts and analysis for the formulation of the policies and strategies in very limited data situations. Concern regarding those issues have been raised and the effort to address those issues by conducting a training for local university lecturers has been requested by the Dean of the Khairun University Faculty of Fisheries and Marine Sciences, as written in his letter sent to the USAID SEA Project. The material obtained from the training will also be used for curriculum development in the Department of Fisheries.

Responding to the Dean request, USAID SEA Project in partnership with Khairun University have conducted the training on the concept of fish resource assessment and fisheries management using the ecosystem approach, as well as analytical training for the assessment of fish and fishery resources and the formulation of fisheries management strategies with an ecosystem approach.

OBJECTIVES OF ACTIVITY The objectives of training for local university Lecturer in North Maluku on EAFM and Fish Stock Assessment: 1) To introduce fisheries management adopting ecosystem approach covering dynamic of fish stock and fishery, set operational objectives, and determine indicators, referent points and management measures; 2) To introduce and exercise stock assessment by using biomass dynamic model using non- equilibrium methods;

3 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV 3) To introduce and exercise stock assessment by using length-based model covering life history parameters and spawning potential ratio.

IMPLEMENTATION Training-workshop for Local University Lecturers in North Maluku on EAFM and Fish Stock Assessment held in with the agenda presented in Table 1. Day, Date : Monday-Friday, 3-7 February 2020 Time : 08.00 – 16.00 WIB Place : Aula Babullah, Floor 4, Rectorate Building, Khairun University, Ternate Ternate – North Maluku

Table 1. The agenda of Training-Workshop for Local University Lecturers in North Maluku on EAFM and Fish Stock Assessment Day Session Topic 1 Opening Introduction to Fisheries Management and Fish Stock Assessment . Concept/Theory Introduction to dynamic of fish stock and fishery Introduction to fishery management Introduction to ecosystem approach to fisheries (EAF) Introduction to fisheries management in Indonesia Introduction to fish stock assessment Softwares Brief and installation of OpenOffice-Calc, Notepad++, ASPIC and R 2 Stock Assessment by using Biomass Dynamic Model . Concept/Theory Introduction to surplus production model/biomass dynamic model Introduction to equilibrium and non-equilibrium methods Introduction to fishery control measures 3 . Practice Estimation of parameters by using Microsoft excel Estimation of parameters by using ASPIC program 4 Stock Assessment by using Length-based Model . Concept/Theory Life history parameters spawning potential ratio Introduction to technical measures 5 . Practice Estimation of life history parameters Estimation of spawning potential ratio Roles of the Universities in North Maluku Province in fish stock assessment and fisheries management . Group Discussion Role of the University in the monitoring of fishery performance targeting local fish stocks Role of the University in the assessment of fishery performance targeting local fish stocks, and fisheries management . Plenary discussion Roles of the Universities in fish stock assessment and fisheries management Closing

Concept/Theory on fisheries management and stock assessment, and introduction to the software used in the stock assessment were delivered by Purwanto. Meanwhile, practices on estimation of parameters and stock status was facilitated by Ses Rini Mardiani.

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 4 SUMMARY OF PARTICIPANTS

The training workshop was attended by lecturers from representatives from six local universities in North Maluku (Khairun University, University, Nuku University, University, AKN Halmahera Tengah, STP Labuha), Representative from DKP, postgraduate students, and NGOs. The list of participants is presented in Table 2.

Table 2. The Participants of the Training February 2020 No Name Institution Gender 3 4 5 6 7 1. Dr. M Djanib Ahmad, S.Pi., M.Sc Khairun University, Ternate M V V V V V 2. Nursanti Abdullah, S.Pi., M.Si Khairun University, Ternate F V V V V V 3. Fatma Muchdar, S.Pi., M.Si Khairun University, Ternate F V V V V V 4. Adi Noman Susanto, S.Pi., M.Si Khairun University, Ternate M V V V V V 5. Dr. Gamal M. Samadan, S.Pi., M.Si Khairun University, Ternate M V V V V V 6. Dr. Rugaya Serosero, Sp., M.Si Khairun University, Ternate F V V V V - 7. Dr. Ir. Martini Djamhur, M.Si Khairun University, Ternate F V V - V V 8. Salim Abubakar, SP., M.Si Khairun University, Ternate M V V V V V 9. Supyan, S.Pi., M.Si Khairun University, Ternate M V V V V V 10. Dr. Sri Endah Widiyanti, S.Pi., M.P Khairun University, Ternate F V V V V V Rustam E. Paembonan, S.Kel., V V V V V 11. Khairun University, Ternate M M.Si 12. Firdaut Ismail, S.Pi., M.Si Khairun University, Ternate F V V V V V 13. Rovina Andriani, S.St.Pi., M.P Khairun University, Ternate F V V V V V 14. Umar Tangke, S.Pi., M.Si Muhammadiyah Univ, Ternate M V V V V V 15. Syahnul.S.Titaheluw, S.Kel., M.Si Muhammadiyah Univ, Ternate M V V V V V 16. Ir. Armain Naim, SH., M.Si Muhammadiyah Univ, Ternate M V V V V V 17. Irma Ekawati Bayan, S.Pi., M.Si Muhammadiyah Univ, Ternate F V - V V V 18. Aisyah Bafagih, S.Pi., M.Si Muhammadiyah Univ, Ternate F V V V V V 19. Nuraini A. Damsiki, S.Pi.,M.Si Nuku University, Tidore F V V V V V 20. Ir. Darius Arkwright, ST., M.T Halmahera Univ, Tobelo M V V V V V AKN Halmahera Tengah, V V V V V 21. Munawir.M.Nur, S.Pi M Weda 22. Muslim Hi. Salim, S.Pi., M.Si STP Labuha, Bacan M V V V V V 23. Masri Rajak, S.Pi DKP Prov Maluku Utara M V V V V V 24. Rusli Syam, S.Pi Postgrad. Student/STP Labuha M V V V V V 25. Mardia Ambodalle, Sp Postgrad. Student F V V V V V Postgrad. Student/Poltek 26. Julkarnain Ahmad, S.Pi, M.Si M V V V V V Halmahera, Labuha 27. Karapesina, S.Pi WCS M - V V - - 28. Chaerul Ahadi, S.Pi WWF M - - V V V 28 Persons, consist of TOTAL PARTICIPANTS 11 F and 17 M F=Female; M=Male; Postgrad.=Postgraduate

5 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV KEY OUTPUTS, OUTCOMES AND ACHIEVEMENT

A. OPENING SESSION The training was organized and held in Khairun University, Ternate, under support of USAID SEA Project. The training was participated by 28 participants representing six universities that held fisheries program related to fish stock assessment (Table 2). The activity was a-five-day training which combined theory and concept, as well as practical skill in the analysis. The training was officially opened by vice chancellor of Khairun University (Wakil Rektor II), Prof. Dr. Abdul Wahab Hasyim SE, M.Si., while welcoming remarks was delivered by Dean of Fisheries and Marine Science, Dr. M Janib Achmad, S.Pi. M.Sc. In their remarks, they expected that this training could increase the skill and knowledge for researchers and lecturers. They also expected that the new methodology in fish stock assessment introduced through this training would be used in the formulation of scientific-based advice to management authority in North Maluku. The remarks from USAID SEA Project was conveyed by Purwanto, PhD (Senior Fisheries Advisor of USAID SEA Project). He informed that assessment expected to provide information, required by fisheries management institution, on the nature, dynamic and exploitation status of fish stock and fishery was constrained by poor or limited data availability. It was a common case in Indonesia, including Province of North Maluku. He informed further that a methodology of stock assessment for fisheries in data poor/limited situation was required and would be introduced to lecturers on the universities in North Maluku Province during the training.

After opening ceremony, the training was begun with introduction by each participant. During the participant introduction, they explained their backgrounds, and their course responsibility. Even though, the targeted participants should have background on fisheries particularly on fisheries biology, fish stock assessment, fish population dynamics, oceanography and marine sciences, however, it was found that 30% of participants were from aquaculture and fishing business. Then pretest was conducted to assess the background of each participants. The test covered general concept and theory on stock assessment, stock assessment using biomass dynamic model and length-based model.

B. TRAINING SESSION

1. Introduction to Fisheries Management

The materials delivered for the first day covered concept and theory on dynamic of fish stock and fishery, fishery management, ecosystem approach to fisheries (EAF), and fisheries management practice in Indonesia. Participants were explained simple dynamic of fish stock, and quantity of fish that can be harvest at various levels of fishing effort. The need to manage fishery resources was also explained to the participants, including consequences in production and economic profit from fishing at different levels of fishing effort. In fisheries management, the objectives covered not only human welfare but also ecological healthiness. Fisheries management strategy should be developed to balance those two aspects by using EAF. Then participants were informed current practice of Indonesia in managing fishery resources as well as relevant laws and regulations. Current issues on fisheries management in Indonesia and in North Maluku were also explained. One issue was limited availability of data that constrained the formulation of fisheries management strategy.

2. Introduction to Fish Stock Assessment

The importance of stock assessment in managing fishery resources was explained to participants. Further, concept and theory of stock assessment were introduced. Introduction was started with the definition and generic flowchart of stock assessment. Then, classification models used in the stock

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 6 assessment, and evolution of fisheries modelling since 1900. The differences between the two main groups of fish stock assessment models, i.e. holistic models and analytical models, were explained. It was described to the participants the robustness and practicality of BDM compared to the analytical model to be implemented in Indonesian context.

“Analytical” models include a number of intermediate processes (both biological and fishing-related) to represent the process by which catches result from fishing. The analytical models require a large number of parameter inputs, some of which will only be known with low accuracy. It is quite difficult to age many fishes, particularly tropical ones, and age-structured analysis is often not practical in these fisheries.

Meanwhile, biomass dynamic models are the simplest stock assessment models that are commonly used, which relates inputs (fishing) to outputs (catches). Biomass dynamic models are more direct “in-out” approaches, usually using only catch, effort and/or abundance data either from the fishery or from surveys. The work of Ludwig and Walters (1985, 1989) showed that biomass dynamic models may provide better estimates of management parameters than age-structured approaches, even when important parameters such as growth and vulnerability are known.1,2

The participants were also informed multispecies nature of fish stock in Indonesian waters, issues regarding assessment of multispecies stock, and methodology used. In many tropical fisheries, the catch consists of many species, and the catch data are difficult if not impossible to collect by species. Ralston and Polovina (1982) explored various methods of aggregation for a multi species tropical handline fishery in Hawaii. They found that the results from production model analysis implemented on a species-by- species basis were highly erratic.3 On the contrary, when the data were aggregated into major species groups, consistent trends in catch rate and yield became apparent. This has often been found in other mixed-species tropical fisheries. Moreover, from the management point of view, management regulations are also difficult to make species specific. In these circumstances, treating the entire catch as a biomass dynamics pool may be more appropriate than trying to look at single species dynamics.

At last, data limited method on stock assessment was briefly informed to address issue on data limited fishery. After introducing brief theory and concept on fisheries management and stock assessment, software that would be used during training were distributed to participants. The participants installed the software in their computers after receiving information on the procedure to install. The programs or software used were OpenOffice-Calc, Notepad++, ASPIC and R.

3. Stock Assessment by using Biomass Dynamic Model

Referring to Hilborn & Walters (1992), the participants were explained that these models are called biomass dynamics models because they deal primarily with the total biomass from the fish stock, rather than characteristics of age and size. Biomass dynamic models are commonly called production models,

1 Ludwig, D., and C. J. Walters. 1985. Are age-structured models appropriate for catch-effort data? Canadian Journal of Fisheries and Aquatic Sciences 42:1066–72. 2 Ludwig, D., and C. J. Walters. 1989. A robust method for parameter estimation from catch and effort data. Canadian Journal of Fisheries and Aquatic Sciences 46:137–44. 3 Ralston, S. & J.J. Polovina. 1982. A multispecies analysis of the commercial deep-sea handline fishery in Hawaii. Fishery Bulletin 80(3): 435-448.

7 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV or even surplus production models.4 Meanwhile, the surplus production, as the name implies, relates to the production from a stock beyond that required to replace losses due to natural mortality. Surplus production, in this case, is the sum of new recruitment and the growth of individuals already in the population minus those dying naturally (Haddon, 2011).5

The participants were introduced to the mathematical concepts of surplus production, developed from the Russell equation. Equilibrium assumption in the biomass dynamic model, and methodology to estimate parameters of the production models. The participants were also explained based on equilibrium biomass dynamic models, the trend of short-term and long-term production. It was described to the participants the robustness and practicality of BDM compared to the analytical model to be implemented in Indonesian context, and the reason to used non-equilibrium BDM rather than equilibrium BDM.

The participants were explained the equilibrium methods are so risky to use, since fish stocks are rarely at equilibrium. Equilibrium methods rely on the assumption that for each level of fishing effort there is an equilibrium sustainable yield. If the fishing regime is changed, the stock is assumed to move immediately to a different stable biomass with its associated surplus production. This is wrong, as it ignores the difference in standing crop between the two different biomass levels and the time it takes the system to respond to changed conditions. The basic problem is that equilibrium methods usually overestimate surplus production and optimum fishing effort. Wherever possible, equilibrium methods should be avoided in fisheries assessments. Surplus production models no longer need the assumption of equilibrium to enable them to be fitted to fisheries data. The non-equilibrium approach to fitting the models means they are better able to represent the dynamics of fished populations.

In this session, the participants were introduced to stock assessment methodology to estimate parameters of BDM. The parameters of equilibrium BDM was estimated by using a spreadsheet software, while the parameters of non-equilibrium BDM was estimated by using ASPIC software version 7.05 (Praeger, 1994; 2016).6,7

The case study of small pelagic fishery in Sea was distributed, and it was shown how to estimate the parameters using Microsoft excel through Data Analysis Tools. The minor issue in this session was how to show the tools, and to change the formatting of participants’ computer which some using Indonesian format and some other using International format. In this case was the use of period (.) and comma (,) in numerical characters reversed. Then, the participants carried-out exercises in the analysis to estimate the equilibrium BDM parameters. The result from the analysis which was the coefficient on logistic model was discussed and interpreted.

The following day, the participants were introduced using non-equilibrium method and analyzed using ASPIC program. Using the same data that used the day before, participants were exercised and compared the results. In this session, the participants were enthusiastic as they were new with this

4 Hilborn, R., and C. J. Walters. 1992. Quantitative fisheries stock assessment: Choice, dynamics, and uncertainty. London: Chapman & Hall. 5 Haddon, M. 2011. Modelling and Quantitative Methods in Fisheries. 2nd edn. Chapman & Hall/CRC. Boca Raton. 449p. 6 Prager, M. H. 1994. A suite of extensions to a nonequilibrium surplus–production model. Fishery Bulletin 92: 374–389. 7 Prager, M. H. 2016. User’s Guide for ASPIC Suite, version 7: A Stock–Production Model Incorporating Covariates and auxiliary programs. Prager Consulting Portland, Oregon, USA. www.mhprager.com

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 8 program. The first step was fitting which comparing the best result provided between Fox and Logistic model through their R square and goodness of data. The second step was bootstrapping, and the third step was projection (Figure 1).

Figure 1. Modelling Flow with ASPIC Suite

The constraint for this session was limitation of time and the ability of participants using the computers varies. Target of this session were the participants understand and able to operate the software so that they could explore using their own data. For those participants who could run the program, it became trigger for other participants to keep trying and they voluntarily assisted others. However, some participants were having problem with their laptop as some of them failed to change the format of numerical into standard international format.

4. Stock Assessment by using Length-based Model

In the fourth day, the material was planned to move forward to stock assessment by using length-based model, i.e. analytical model. However, before coming to new material, some of participants still discussed some of result and technical issues that finally solved in the evening. Length-based model to assess fish stock was relatively new for the participants.

The participants were explained that a basic feature of analytical models as developed by, among others, Baranov (1914), Thompson and Bell (1934) and Beverton and Holt (1957), is that they require the age

9 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV composition of catches to be known.8,9,10 The basic ideas behind the analytical models are (1) If there are "too few old fish" the stock is overfished and the fishing pressure on the stock should be reduced, and (2) If there are "very many old fish" the stock is underfished and more fish should be caught in order to maximize the yield. The analytical models are "age-structured models" working with concepts such as mortality rates and individual body growth rates. Age of tropical fish species was difficult to be determined. Moreover, fisheries were in data limited situation, which constrains the application of full analytical models. The only data that can easily be collected were fish length composition. Therefore, the participants were introduced to the length-based model for data limited fisheries. The stock assessment was carried out by using fish length frequency data with spawning potential ratio (SPR) used as a benchmark of the status of fish stock. The SPR measures the reproductive potential of a fished stock relative to that of an unfished stock, as indicated by the proportion of the unfished reproductive potential of a stock left by any given fishing pressure (Goodyear, 1990; 1993).11,12

Input for the SPR estimation were length frequency data and parameters of fish growth and mortality. Fish growth is represented by using the equation of the von Bertalanffy Growth Function (VBGF) as formulated by Beverton & Holt (1957).13 Growth parameters of the VBGF, i.e. asymptotic length ( ) and the growth coefficient or the rate at which is approached ( ), were derived from length 𝑖𝑖𝑖𝑖𝑖𝑖 frequency data by using ELEFAN methods (Pauly & David, 1980; Brey et al., 1988).14,15 Meanwhile, 𝐿𝐿the 𝑖𝑖𝑖𝑖𝑖𝑖 estimation of SPR was carried out by using the R𝐿𝐿 package LBSPR developed𝐾𝐾 by Hordyk (2019; 2020).16,17 R program itself is powerful program which mostly used by scientist nowadays as it is an open source. However, it needs internet to install the program and the packages and frequently updated.

After the theory of stock assessment using length-based model, it started by showing the operation of R studio using a case study of Decapterus macarellus in Halmahera Island of North Maluku. The data provided was raw material, so that the participants could process the data from the scratch. The length of fish measured was transformed into length frequency and saved into csv file. Using the script in R program, the data was analyzed by ELEFAN in which life history parameter estimated, such as the values of L infinitive, growth coefficient, length at first capture, length of maturity and natural mortality. The challenges using R program in this training were internet connection and technical specification of

8 Baranov, T. I. 1918. On the question of the biological basis of fisheries. Nauchn Issledov. Ikhtiologicheskii Inst. Izv. 1:81–128. (Cited in Beverton and Holt, 1957.) 9 Beverton, R. J. H., and S. J. Holt. 1957. On the dynamics of exploited fish populations. U.K. Ministry of Agriculture and Fisheries, Fisheries Investigations (Series 2) 19:1–533. 10 Thompson, W. F. and F. H. Bell, 1934. Biological statistics of the Pacific halibut fishery. 2. Effect of changes in intensity upon total yield and yield per unit of gear. Rep. Int. Fish. (Pacific Halibut) Comm. (8). 49p. 11 Goodyear, C.P. 1990. Spawning stock biomass per recruit: the biological basis for a fisheries management tool. ICCAT Col. Vol. Sci. Pap., Vol. XXXII (2): 487-497. 12 Goodyear, C.P. 1993. Spawning stock biomass per recruit in fisheries management: foundation and current use. p. 67-81. In S.J. Smith, J.J. Hunt and D. Rivard [ed.] Risk evaluation and biological reference points for fisheries management. Can. Spec. Publ. Fish. Aquat. Sci. 120. 13 Beverton, R.J.H., & S.J. Holt, 1957, 1993. On the Dynamics of Exploited Fish Populations. Chapman & Hall Fish and Fisheries Series 11. 533p. http://dx.doi.org/10.1007/978-94-011-2106-4. 14 Pauly, D., & N. David, 1980. A BASIC program for the objective extraction of growth parameters from length-frequency data. International Council for the Exploration of the Sea, CM 1980/D:7. Demersal Fish Committee. 14 p 15 Brey, T., M. Soriano and D. Pauly. 1988. Electronic length frequency analysis: a revised and expanded user's guide to ELEFAN 0, 1 and 2 (2nd edition). Berichte des Institut fur Meereskunde an der Universitat Kiel No. 177, 31 p. 16 Hordyk. A. 2019. LBSPR: An R package for simulation and estimation using life-history ratios and length composition data. https://cran.r- project.org/web/packages/LBSPR/vignettes/LBSPR.html 17 Hordyk. A. 2020. Length Based Spawning Potential Ratio R Package. https://github.com/AdrianHordyk/LBSPR

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 10 computer used by participants. In final day, the participants continued the exercise and discussed the interpretation of the result from length-based model.

C. ROLE OF UNIVERSITY IN FISHERIES MANAGEMENT

After the discussion related the stock assessment, then, it was discussed the role of university in fisheries management. This was a part of real action that could be provided to support local government in the area where the universities located. The training was then closed officially by Dean of Fisheries and Marine Science, Khairun University. The discussion on role of university in fisheries management begun with small group discussion. The participants was divided into two groups which led by Dr. Sri Endah Widiyanti, S.Pi., M.P (group 1) and Supyan, S.Pi., M.Si (group 2). Each group discussed the how the academics activity could be integrated in supporting fisheries management in North Maluku. Each of groups presented their result of discussion into panel discussion. This panel discussion was chaired by Dean of Fisheries and Marine Science. The result of discussion was shown in Table 3 resulted from Group 1 and Table 4 was from Group 2. Group 1 discussed more in technical issues related to fishery monitoring strategy and identify possible constraint. Meanwhile, Group 2 discussed deeply on the role of the university on stock assessment and providing input on fisheries management.

Table 3. The Result of Group 1 Discussion Items Description Landing catch Predominant species in North Maluku Small pelagic fish (Decapterus macarelus) Fishing gears Purse seine Vessel with ± 10 meter of length Type of data recording Species identification Length and weight of the species Gonad maturity Location of recording of 3 parts of North Maluku area was selected to present the population fishing landing . North: & Tobelo . Center: Ternate, Tikep & Moti . South: Bacan Time of recording of fishing Monthly (fishing cycle) landing Data source . Marine and Fisheries Agency: data recorded in the fishing port . Universities: data recorded outside fishing port Type of sampling mechanism . Practicum . Field Work Practices . Thesis Research . Standard guidelines . Students involved in data retrieval can participate in capture operations & take measurements (Length, Weight & Gonad maturity level) Constraints . To get data monthly . Time series research that involved the students.

Table 4. The Result of Group 2 Discussion Items Description Roles of University To address the issue on data validation and the needs of competent surveyors and enumerators: . Improve the capacity and awareness of society to participate in providing the data . Involve the students into the research . Enhance the participation of community in sustainable fisheries

To address the issue on lack of information for Maximum Sustainable Yield (MSY), optimum effort and optimum length for key species: . Conduct research and validation data of fish stock

11 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV Items Description To ensure the success on fisheries management: . Monitor and evaluate fisheries activity not only in the beneficiaries of fishing, but also in policy makers. . Assist on development of strategic policy for legislative and executive, and also to control implementation of those policies. . Dissemination information of assessment that can be applicated by community through public education, therefore group of community could be adaptive in utilization fish resources. Annual program Year 1: establishment team for survey and assessment, identify location, data recording, data analyzing, policy recommendation (gears, size limitation, closing areas and season) Year 2: Draft of regulation on fisheries management and monitoring Year 3: Implementation and action plan on monitoring Year 4: Monitoring and evaluation Urgency the . Based on information from management authority (DKP), there is a need of involvement of several policies to be supported by assessments. university in stock . There is no valid data on fisheries potency for each targetted regions. assessment and . In fish processing, there is no assessment related to the contraints that fisheries management inhibiting the continuity fish processing business. . Some of environmental parameter are not available, such as land suitability for marine cultivation, therefore it needs the assessment from university. Stages of the Targeted fish resource: Key species that dominated in the area (TTC; Snapper involvement of and grouper) university on Location: North Maluku (fishing ground and fish landing) fisheries management Assessment coverage: Fish landing data that consist of length frequency, total catch, total effort and type of fishing gears Methods: Direct survey which based on EAFM Data Source: . Primary data (length frequency data, types of gear, time of fishing, fish stock status) . Secondary data (previous assessment conducted by university or DKP related the catch Tools: ASPIC 7, R studio Output of the assessment: MSY and F opt Life history parameter (mortality, recruitment, catch composition)

The result of discussion on the university roles on fisheries management became inputs in monitoring strategy that developed by DKP and USAID SEA Project on Fisheries Management Plan for Snapper and Grouper in North Maluku.

D. ASSESSMENT RESULT OF THE IMPLEMENTATION OF THE TRAINING 1. Participants Profiles The participants of the training were from four types of background. They were fisheries biology, marine science, fishing business and aquaculture (Figure 2). The majority of participants was from fisheries biology, who was targeted in this training, dominated about 39%. Meanwhile, Aquaculture which was the second big group in this training, unfortunately was the least related to fish stock assessment.

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 12 Fisheries biology, 39%

Fishing business, 9%

Marine Science, 22%

Aquaculture, 30%

Figure 2. Participant's Profiles

2. Pre-test and Post-test Result The pre-test and post-test questions were provided to the participants to assess their improvement during the training. The assessment consisted of 7 (seven) questions covered knowledge on general stock assessment (2 questions), catch-based assessment (2 questions), and length-based assessment (3 questions). Overall, the majority of participants showed the significant improvement as shown in Figure 3. Before the training, the majority participants could only answer one right question (48%) and about another 43% could not answer it correctly at all. However, after 5-days intensive training, 96% participants have significant understanding about the material presented and the exercises.

7 6 5 4 3 2 1

Number of right answers 0 Aquaculture Aquaculture Aquaculture Aquaculture Aquaculture Aquaculture Marine Science Marine Science Marine Science Marine Science Marine Science Fishing Business Fishing Business Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Biology Fisheries Participants

Before Training After Training Figure 3. The Changing Before and After the Training for Each Participant

The improvement among the participants varied, however, after the training, at least more than half of participants could answer four correct questions (Figure 3).

13 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV 50% 45% 40% 35% 30% 25% 20% 15% 10% Proportion of participants 5% 0% 0 1 2 3 4 5 6 7 Number of right answers

Before training After training

Figure 4. The Proportion of Participants Before and After Training

Based on the background of the participants, the participants could be grouped into three groups. Their academic background affected the ability of participants to understand the materials. It could be seen that the proportion of participants who could answer the most was participants from fisheries biologist group with 6 (six) right question out of 7 (seven) questions. In addition, it was 66 percent of participants could answer at least 5 questions. Meanwhile, the percentage of participants from marine scientist groups dominated with 60% who could answer four questions. The least group was from aquaculture with only 29% percent who could answer four right question. The detail distribution of participants from each representative group was presented in Figure 5.

Aquaculturist Fisheries Biologist Marine scientist

70% 70% 70% 60% 60% 60% 50% 50% 50% 40% 40% 40% 30% 30% 30% 20% 20% 20% Proportion of participants Proportion of participants 10% Proportion of participants 10% 10% 0% 0% 0% 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 Number of right answers Number of right answers Number of right answers

Before training After training Before training After training Before training After training

Figure 5. The Proportion of Participants Based on Their Academic Background in Responding the Assessment Questions

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 14 3. Training Evaluation Training evaluation was conducted to assess the implementation of the activity based on participant’s perspective. Overall, the participants were satisfied with the overall training and feel the benefit from training in their works (Figure 6).

(A) Evaluation of Positiveness for Overall (B) Evaluation of Positiveness for Training Training Benefit 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 0% Overall, I am Overall I am Overall, I am I do need this This training is I believe that this satisfied with this satisfied with this satisfied with the training. relevant with the training has training. trainer / resource training needs to perform provided good person. environment / my duties. simulations for my atmosphere. tasks.

Figure 6. (A) Participant's Evaluation for Overall Training, (B) Participant's Evaluation for Training Benefit

The participants completely agreed and have positiveness for the material and process, except for the time (duration) of the training (Figure 7A). The participants agreed that this training material met the objectives of the training, the design of material and the activity of training triggers participants to involve on the training, the training provides opportunity to practice and strengthen understanding, and the material delivered has met the level of the purpose and capacity of participants. The participants also showed the positiveness for the trainers which covered the knowledge, respond to questions, presentations, communication, and conducive environment (Figure 7B).

(A) Evaluation of Positiveness for Material (B) Evaluation of Positiveness for Trainers and Process 100% 100%

80% 80%

60% 60%

40% 40%

20% 20% 0% The materials of Time (duration) The design of The training The materials 0% The trainers The trainer has The trainers The trainers The trainers The trainers the training of trainings has material and the provides delivered has are well knowledge on have well provide are able to create meet the been sufficient activity of opportunities to met the level of prepared training's respond to the presentations communicate conducive objective of the training triggers practice and the purpose and content participants' and facilitate in well environment to training participants to strengthen capacity of questions and an interesting participatory involve on the understanding. participants. needs way and interactive training learning.

Figure 7. (A) Evaluation for Material and Process; (B) Evaluation for Trainers

The evaluation of approach and tools using in the training, and also the venue of the training showed the positiveness from the participants by more than 90% (Figure 8). The venue was chosen in the university area which provided access easily for the participants.

15 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV (A) Evaluation of Positiveness for Approch and (B) Evaluation of Positiveness for the Tools of Training Venue 100% 100%

80% 80%

60% 60%

40% 40%

20% 20%

0% 0% The questions help The training's tools The technological There are variations The training facility The accommodations The location of to understand the help to learn the devices used work of training methods suitable for learning. are good and able to training can be training materials. material better (eg: well (LCD that are useful for meet the participants reached easily. worksheets, hand- Projector, Internet, learning (eg, with special needs. outs, exercises, etc.). presentation, visual presentations) presentations, interactive discussions, etc.). Figure 8. (A) Evaluation for Approach and Tools; (B) Evaluation for the Venue

In the self-evaluation from, the participants also expressed some of their comments in regards of specific parts of the training, which summarized in the Table 5. The comment and inputs varied; however, all the participants agreed the benefit from the training and expect for follow-up training to improve their skill and knowledge on stock assessment.

Table 5. Participant's Opinion Related the Training Benefit and Things to be Improved Descriptions Participants’ comments The most . Data analysis on stock assessment benefit part of . Model and method used on stock assessment (Fox and Schaefer, equilibrium and the training non-equilibrium) . Understanding and use the software (ASPIC, R studio) . Estimate life history parameter New knowledge . Concept, Model on fish stock assessment, including equilibrium and non-equilibrium as take away . Analysis using ASPIC, R Program lessons by . Different analysis between Schaefer and Fox participants . Description on Kobe plot . Indicator on fisheries management . Length frequency of fish and parameter used in stock assessment. . Estimate on SPR . Interpretation of data analysis . Policy direction and role of university New topics . Data processing from the field recommended . Extra practical course start from data input recorded through observation to by participants interpretation result of analysis . Sampling technique and data missing . Relationship between stock assessment and oceanography parameter in one FMA . Analysis model for policy maker after being run by application . Analysis multispecies . Spatial analysis . Aquaculture and fish processing Things that . The program that need to simpler for medium laptop specification need to be . Alternative software improved . Time allocation need to be added

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 16 E. DISTRIBUTION THE “STATE OF THE SEA: INDONESIA” The “State of the Sea: Indonesia” is a three-volume publication that provides an overview of the current status of marine and coastal management in Indonesia, and in-depth insights into the management actions underway at the national, regional and local level. This publication was jointly produced by the Ministry of Marine Affairs and Fisheries (MMAF) of the Republic of Indonesia, and the USAID SEA Project. In this training, the sets of publication distributed to representative of universities and agencies for their library collection, in order it could be benefitted by most of the people, not just individual (Table 6).

Table 6. List of Agencies for Distribution of "State of the Sea: Indonesia" Book Number of No Agency copies 1. Rectorate, Khairun University, Ternate 1 set 2. Faculty of Fisheries and Marine Science, Khairun University, Ternate 1 set 3. Muhammadiyah Univ, Ternate 1 set 4. Nuku University, Tidore 1 set 5. Halmahera University, Tobelo 1 set 6. Akademi Komunitas Negeri (AKN) Halmahera Tengah, Weda 1 set 7. Sekolah Tinggi Pertanian (STP) Labuha, Bacan 1 set 8. DKP Prov Maluku Utara 1 set 9. Stasiun Karantina Ikan, Pengendalian Mutu dan Keamanan Hasil Perikanan, Ternate 1 set 10. Politeknik Halmahera, Labuha 1 set

RECOMMENDATION

1. The role of local university in fisheries management is important in order to support management authority to achieve sustainable fisheries in their respective area. The roles of the university cover from monitoring and assessment to understand the status of fisheries as well as providing scientific advice in other issues related to fisheries management.

2. The training on fish stock assessment for university lecturers is needed periodically as part of capacity building to strengthen fisheries management. Through the training, the scientist could be exposed to the development of methodology, so that the approach used in the assessment could use the update based on the best available data.

3. The limitation of data and budget should not become the reason for not conducting the monitoring or assessment toward the condition of fish stock and fisheries, because it will become basis for management authority to generate the management actions to ensure the sustainability for future generation.

4. The synergy between local universities and management authority should be internalized in both institutions with clear planning followed by providing resources such as budget allocation and personnel.

17 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV FOLLOW-UP

1. The result of discussion on role of local universities in North Maluku will be integrated into Fisheries Management Plan of Snapper and Groupers which developed by DKP North Maluku in collaboration with USAID SEA Project.

2. The collaboration between local universities and DKP needs to be discussed further to support the implementation of fisheries management plan.

REFERENCE

Baranov, T. I. 1918. On the question of the biological basis of fisheries. Nauchn Issledov. Ikhtiologicheskii Inst. Izv. 1:81–128. (Cited in Beverton and Holt, 1957.) Beverton, R. J. H., and S. J. Holt. 1957. On the dynamics of exploited fish populations. U.K. Ministry of Agriculture and Fisheries, Fisheries Investigations (Series 2) 19:1–533.

Beverton, R.J.H., & S.J. Holt, 1957, 1993. On the Dynamics of Exploited Fish Populations. Chapman & Hall Fish and Fisheries Series 11. 533p. http://dx.doi.org/10.1007/978-94-011-2106-4.

Brey, T., M. Soriano and D. Pauly. 1988. Electronic length frequency analysis: a revised and expanded user's guide to ELEFAN 0, 1 and 2 (2nd edition). Berichte des Institut fur Meereskunde an der Universitat Kiel No. 177, 31 p.

Goodyear, C.P. 1990. Spawning stock biomass per recruit: the biological basis for a fisheries management tool. ICCAT Col. Vol. Sci. Pap., Vol. XXXII (2): 487-497.

Goodyear, C.P. 1993. Spawning stock biomass per recruit in fisheries management: foundation and current use. p. 67-81. In S.J. Smith, J.J. Hunt and D. Rivard [ed.] Risk evaluation and biological reference points for fisheries management. Can. Spec. Publ. Fish. Aquat. Sci. 120.

Haddon, M. 2011. Modelling and Quantitative Methods in Fisheries. 2nd edn. Chapman & Hall/CRC. Boca Raton. 449p.

Hilborn, R., and C. J. Walters. 1992. Quantitative fisheries stock assessment: Choice, dynamics, and uncertainty. London: Chapman & Hall.

Hordyk. A. 2019. LBSPR: An R package for simulation and estimation using life-history ratios and length composition data. https://cran.r-project.org/web/packages/LBSPR/vignettes/LBSPR.html

Hordyk. A. 2020. Length Based Spawning Potential Ratio R Package. https://github.com/AdrianHordyk/LBSPR

Ludwig, D., and C. J. Walters. 1985. Are age-structured models appropriate for catch-effort data? Canadian Journal of Fisheries and Aquatic Sciences 42:1066–72.

Ludwig, D., and C. J. Walters. 1989. A robust method for parameter estimation from catch and effort data. Canadian Journal of Fisheries and Aquatic Sciences 46:137–44.

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 18 Pauly, D., & N. David, 1980. A BASIC program for the objective extraction of growth parameters from length-frequency data. International Council for the Exploration of the Sea, CM 1980/D:7. Demersal Fish Committee. 14 p

Prager, M. H. 1994. A suite of extensions to a nonequilibrium surplus–production model. Fishery Bulletin 92: 374–389.

Prager, M. H. 2016. User’s Guide for ASPIC Suite, version 7: A Stock–Production Model Incorporating Covariates and auxiliary programs. Prager Consulting Portland, Oregon, USA. www.mhprager.com

Ralston, S. & J.J. Polovina. 1982. A multispecies analysis of the commercial deep-sea handline fishery in Hawaii. Fishery Bulletin 80(3): 435-448.

Thompson, W. F. and F. H. Bell, 1934. Biological statistics of the Pacific halibut fishery. 2. Effect of changes in intensity upon total yield and yield per unit of gear. Rep. Int. Fish. (Pacific Halibut) Comm. (8). 49p.

19 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV ANNEX

ANNEX 1. LETTER FROM KHAIRUN UNIVERSITY REQUESTING TO SUPPORT ON CAPACITY BUILDING TROUGH TRAINING ON STOCK ASSESSMENT.

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 20 ANNEX 2. LETTER FROM KHAIRUN UNIVERSITY REGARDING THE NAMES OF LECTURER ASSIGNED TO PARTICIPATE IN TRAINING ON STOCK ASSESSMENT.

21 | TRAINING FOR LOCAL UNIVERSITY LECTURERS USAID.GOV ANNEX 3. ONE OF LETTER FROM KHAIRUN UNIVERSITY INVITING LOCAL UNIVERSITY IN NORTH MALUKU TO PARTICIPATE IN TRAINING ON STOCK ASSESSMENT.

USAID.GOV TRAINING FOR LOCAL UNIVERSITY LECTURERS | 22 ANNEX 4. DOCUMENTATION

Figure 1. The activity was officially opened by Vice Chancellor of Khairun University (Wakil Rektor II), Prof. Dr. Abdul Wahab Hasyim SE, M.Si

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Figure 2. Participants representing from several universities and institutions

Figure 3. Purwanto, Ph.D, Senior Fisheries Advisor, provided the materials to participants

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Figure 4. The lecturers participated actively during the training.

Figure 5. The participants were also assisted directly during the exercises

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Figure 6. The activity was colored by the games facilitated by Ms. Rosita Tariola, North Maluku Provincial Coordinator.

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Figure 7. In session of group discussion on role of universities, Group 1 was led by Dr. Sri Endah Widiyanti, S.Pi., M.P

Figure 8. In session of group discussion on role of universities, Group 2 was led by Mr. Supyan, S.Pi., M.Si.

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Figure 9. Mr. Purwanto, Ph.D as representative of USAID SEA Project handled The “State of the Sea: Indonesia” to Dean of Fisheries and Marine Science, Dr. M Janib Achmad, S.Pi. M.Sc.

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Figure 10. Each of representative from universities and institutions was handled The “State of the Sea: Indonesia”.

Figure 11. The activity was closed officially by Dean of Fisheries and Marine Science, Dr. M Janib Achmad, S.Pi. M.Sc.

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