Sustainability status of bay management: case study in Jor Bay, East Lombok Regency, West Nusa Tenggara Province

Jor Bay in East Lombok - West Nusatenggara is a small bay system characterized by a common pool resource, in which found a contestation of utilization among resources users. Even though the bay has been managed by a local institution, namely Lembaga Pemangku Awiq-awiq Teluk Jor (LPATJ), the role and perform of institutions in managing the bay is still very low. To ensure the sustainability of the bay, the need to converge the different resource users’ interests by balancing utilization to accommodate economical, ecological and social dimensions. This study aimed to assess the sustainability status of bay management and design future sustainable bay management strategies. To measure the sustainability status, we used a sustainability index intended to develop to bay ecosystem form. The current sustainability status of Jor Bay management showed a moderate level. The institutional dimension provides the greatest sustainability value, while the lowest degree shows in the ecological dimension. The governability of Jor Bay management shows low institutional interaction, limited scale (local), minimal initiative and low willingness to cooperate among elements. For this reason, the ICM (integrated coastal management) based management mechanism needs to be strengthened to ensure the functioning of the Jor Bay management system.


Introduction
Bay is a coastal complex system that plays important economic and ecological roles in regional socio-ecological systems, and their habitats link land to the ocean [1,2]. The complexity of coastal areas is increasingly problematic, degradation is increasing, and the characteristics of environmental problems are now generally caused by anthropogenic factors rather than natural phenomena [3] because all anthropogenic impacts lead to the sea. System variability in coastal areas, complex process interactions, and increasing pressures require interdisciplinary studies on a deeper understanding of processes and management scenario experiments carried out with model applications [4].
The current sustainability science approach is not about global biophysical systems or socioeconomic-political systems only but also uses a location-based model that allows the study of human and environmental interactions [5]. The most appropriate analytical unit for sustainability  3 institutions, government officials from the hamlet, village, sub-district, district and provincial levels.
2.2 Data analysis 2.2.1. Sustainability analysis of bay management. The analysis was carried out using a Multi-Dimensional Scaling (MDS) approach using RAPFISH (Rapid Assessment Techniques for Fisheries), which can be modified dimensions and attributes according to the evaluated aspects [13][14][15]. The indicators for the sustainability of Jor Bay management are determined based on criteria that can represent the complexity of Jor Bay management. This study formulates five dimensions as the basic elements of sustainability: 1) ecology dimension, 2) economy dimension, 3) social dimension, 4) knowledge system dimension, and 5) institutional dimension. Each dimension contains indicators, each of which determines the status of sustainability which explain in table 1table 5. The arrangement of attributes and criteria on the five dimensions was compiled referring to several previous studies [16][17][18][19][20] and then adjusted to the characteristics and conditions of the resources studied. The Best-Worst Scaling (BWS) model was used. BWS was conducted to collect data on priority sustainability indicators for each dimension [21]. Attributes and scoring criteria for each dimension are presented in tables 1 to table 4. The width and quality of the mangrove ecosystem 0-3 3 0 The greater (3), the more (2); getting a little smaller, partially protected (1); keep decreasing (0) 4 The width and quality of the coral reef ecosystem 0-3 3 0 The greater (3), the more (2); getting a little smaller, partially protected (1); keep decreasing (0) 5 The width and quality of seagrass ecosystem 0-3 3 0 The greater (3), the more (2); getting a little smaller, partially protected (1); keep decreasing (0) 6 Fish landing distance to the fishing ground 0-3 3 0 The farther away (0); far outside the region (1); out of the bay (2); in the bay (3)  7 Presence of conservation areas (protected zones) 0-3 3 0 None (0); newly initiated (1); exists but not yet effective (2); running well (3) (3); medium (2); weak (1); Not working (0) Use of technology in resource utilization 0;1;2;3 3 0 Greatly increased (3); increase (2); constant (1); decrease (0)  (1), exists and has been determined (2)  2 Zoning (bay spatial plan), including the protection zone 0;1;2;3 3 0 Not yet establish (0), initiation of formulation (1), exists and has not been determined (2); exists and determined (3)  3 Multi-stakeholder comanagement strategies and plans 0;1;2;3 3 0 Not yet establish (0), initiation of formulation (1), exists and has not been determined (2); exists and determined (3)  4 Availability of binding rules 0;1;2 2 0 Not yet available (0), exists but has not been determined (1), exists but has not been effectively implemented (2) exists and has been effectively implemented (3)  5 Existence of management institution (legal) 0;1;2;3 3 0 None (0); exists but has not been fully recognized (1); there is enough community recognition (2); highly recognized (3)  6 Effective performance of managers in carrying out roles and functions 0;1;2;3 3 0 Ineffective (0); less effective (1); effective (2) (2); very effective (3) An Ordination assessment was carried out to determine a point that reflected the relative position of management activities in Teluk Jor with respect to two main reference points: the good point and the bad point. In the MDS analysis, the ordination technique is carried out by determining the distance based on the Euclidian Distance, which in n-dimensional space can be written as follows [17]: Configuration/ordination of an object or point in MDS analysis is approximated by regressing Euclidian distance ( ) from point i to point j with origin ( ) as the following equation [17] : ALSCAL algorithm is used to regress in Rapfish, using SPSS software. The ALSCAL method optimizes the squared distance (square distance = ) to square (origin = ), in three dimension (i, j, k) expressed in a formula called S-Stress as follows: Where the squared distance is the Euclidean distance breached or expressed as follows: A low-stress value indicates a good fit, while a high S value indicates otherwise. The analysis results of a good RAPFISH method will show a stress value that is smaller than 0.25 (S < 0.25).
Determination of sustainability status with RAPFISH refers to an index with an interval between 0-100, which refers to four levels as in table 6. The next stages of RAPFISH analysis are Monte Carlo analysis and Laverage analysis. The Monte Carlo analysis was repeated 25 times using the scatter plot method to determine the stability of the RAPFISH ordinance results. Laverage analysis is carried out to find out what attributes are sensitive from all dimensions of sustainability. The most sensitive attribute will contribute to sustainability change in Root Mean Square (RMS) on the x-axis (sustainability scale). The greater value of the change in RMS, the greater the role of the attribute and the more sensitive it is in the value of sustainability.
Management prospective analysis. The prospective analysis is a follow-up analysis in the RAPFISH method based on the importance value that influences and depends on sensitive attributes. Management prospective analysis was carried out based on the PPA (Participatory Prospective Analysis) method [23]. This analysis is to synthesize information in comparative policy formulation. The stages are as follows: 1) Identify and define important attributes in bay management.
2) Assess the direct or indirect influence between attributes. If there is no effect between the attributes being compared, then the value is zero (0); if the effect is small, then it is given a value of one (1); if the effect is moderate, then it is given a score of two (2); and if the effect is very large or strong, then it is given a score of three (3). 3) Putting attribute positions in four (4) quadrants (figure 2). Quadrant I is a determining factor (driving variables), contains attributes that have a strong influence, but the dependence is not strong. Factors in this quadrant are important factors or drivers included Figure 2. Grouping of resource management attributes in a prospective analysis diagram [24].

Driving Variables
Input  11 in the most powerful factors in the system under study. Quadrant II is a connecting factor (leverage variables), which shows the strong influence and strong dependence between factors. Quadrant III is a dependent factor (output variables) representing small influence but strong dependence. Quadrant IV is an independent factor (marginal variable) with a small influence and low dependence so that this factor is independent in the system [25]. 4) Develop management policies based on prospective analysis. The main priority policy is based on the attributes located in quadrant I (driving variable). The policy and strategies have a strong level of influence and a low level of dependence. Supporting policies are set on attributes located in quadrants II and III, while attributes located in quadrant IV can be ignored.    The comparison of the difference between the MDS values and the results of the Monte Carlo analysis on each sustainability dimension shows a varied value. However, the value is low (<1) (table 8). The small difference in values indicates that the error in scoring each attribute is relatively small, the variance of scoring for each attribute is relatively small, the analysis process carried out repeatedly is stable, and data entry errors can be avoided.

Ecological sustainability. The ecological dimension index value is 47.38 in Paremas
Village (less sustainable) and 50.99 in Jerowaru Village (quite sustainable). In Jerorawu Village, the ecological sustainability status is better than in Paremas Village. The results of the Rapfish analysis can be accepted with an R 2 value of 95.17% and a stress level of 14.21%. The results are considered very well because they meet the statistical criteria (goodness of fit). The stress value <25% and the R 2 value close to 1 (100%) indicates the MDS (multidimensional scaling) model produced has good accuracy [16,17]. If the R 2 (r-squared) value is greater than 80%, the MDS model results are categorized as good [23]. The ordinance value of the ecological dimensions is  Only three of the fourteen attributes/indicators on the ecological dimension, which are quite sensitive and are considered to affect this dimension which is water residence time (2.27), freshwater input (1.34) and the quality of mangrove ecosystems (1.42). Based on this fact, the sustainability of Jor Bay management on the ecological dimension is strongly influenced by the     The sustainability status on the economic dimension is influenced by attributes that have high leverage values (Figure 8). Three of the fifteen attributes are sensitive indicators and affect this dimension, which is the ability to fulfil basic needs (1.86), access to financing/investment (1.62), and marketing distribution reach (1.58). The factors that most influence economic sustainability in management as sensitive attributes are : (1) the ability of the bay to fulfil basic needs, (3) access to financing or investment and (3) marketing distribution reach. Three indicators are currently contributing the most in boosting the economy of Jor Bay.

Social sustainability
The results of the RAPFISH analysis on the social dimension show that the social sustainability status in Jerowaru Village has an index score of 55.48 (quite sustainable), while in Paremas    16 Village it is 50.39 (entirely sustainable). Although both are quite sustainable, Jerowaru Village scores better than Paremas Village. The results can be accepted with an R 2 value of 94.67% with a stress value of 14.64%. These results show very good results because they meet the statistical criteria (goodness of fit). The stress value <25% and the R 2 value close to 1 (100%) indicate the MDS model produced has good accuracy. The results of the ordination of social dimensions are presented in figure 9.
The social dimension is influenced by attributes with a fairly high leverage value in determining the achievement of sustainability ( Figure 10). However, only two of the 15 attributes in social dimensions are the most sensitive and affect this dimension : (1) the independent program initiative from the community (1.55) and (2) the number of users of the bay for non-fishing activities (1.47). The social dimension is influenced by the two most sensitive attributes, it is also found that other attributes, although their influence is small, can be encouraged to make a greater    (Figure 11). The results can be accepted with an R 2 value of 94.61% with a stress value of 14.22%. The results are considered very well because they meet the statistical criteria (goodness of fit). A stress value of <25% and an R 2 value close to 1 (100%) indicates that the MDS model produced in the analysis has good accuracy.
Attributes influence sustainability status on the knowledge system dimension as knowledge indicators with a high leverage value ( figure 12). There are three attributes of the nine attributes that are sensitive and affect this dimension: the attributes of innovation and community initiatives (4.02), community attitudes (3.69), and capacity building programs (3.58).
a. The power of innovation and community initiative b. The attitude of the community around Jor Bay, which is generally pro-conservation and shows support for the sustainability of the bay. This atribute is also related to an   , where the stress value is <25%, and the R 2 value is close to 1 (100%), indicating the MDS model has good accuracy. The results of the ordination of the dimensions of economic sustainability are presented in figure 13. Attributes with a high leverage value influence the status of management sustainability on the institutional dimension. There are three of nine attributes as indicators as the most sensitive indicator: regulatory attributes as binding management rules (4.69), bay management organizations/institutions (2.72), and sustainable financing schemes (2.60). The results of the analysis of the leverage of each attribute on the institutional dimension are presented in figure 14. Three attributes control this difference in achievement as the most sensitive indicator and the most influencing this dimension. These three indicators play an important role so that they also determine the current institutional condition of Teluk Jor management. Based on the results of the leverage analysis on each dimension, the three most sensitive and influential attributes on each dimension need to be cross-tested against the attributes of other dimensions. For this reason, sensitive attributes in each dimension of sustainability need to be analyzed further. Attributes that are sensitive to each of these dimensions are presented in table 9.