Integrated Modeling Techniques for Understanding the Distribution and Impact of Human Activities on the Bryde’s Whale (Balaenoptera edeni) in the Sichang Islands, Thailand

Abstract

The degradation of marine and coastal resources, caused mainly by human activities, underscores the urgent need for conservation. In waters around the Sichang Islands, the Bryde’s whale is listed as an endangered marine species. The extensive human activities in this area have raised serious concerns about the possible negative influence on this species. To conserve the species effectively and efficiently, we need to understand where it distributes and how human activities influence its distribution. For this purpose, we use spatial modeling techniques alongside diverse databases encompassing various spatial and ecological factors to analyze the distribution of, and human activities’ impact on, the Bryde’s whale (Balaenoptera edeni) in the Sichang Islands, Thailand. We also use the MaxEnt model to predict habitat suitability and the result reveals key factors influencing whale occurrence across seasons. During the dry season, TSS (32.8%), chlorophyll-a (20.1%), and DO (15.9%) levels play significant roles, while DO (29.9%), NH₃ (29.4%), and distance to shore (13.3%) are crucial in the wet season. Furthermore, the Species Risk Assessment (SRA) model indicates the mooring area (14.95%) is the high-risk area for the Bryde’s Whale, particularly during the wet season. In contrast, moderate risks are observed during the dry season, notably in fishing zones (99.68%) and mooring areas (99.28%). The study also highlights that the factors mentioned above influence prey availability and habitat suitability for the Bryde’s whale and identifies potential threats posed by human activities, such as fishing and other maritime operations, that are likely to decrease water quality and prey abundance. These results are useful information for identifying sensitive areas and measures for risk mitigation, thus supporting the development of MSP or MPA plans.

1. Introduction

The Bryde’s whale (Balaenoptera edeni), part of the Balaenopteridae family, which distributes in tropical and warm waters with a year-round temperature of 16.3 °C or higher in the tropical regions around the world, plays an important part in marine ecosystems [1]. Baleen whales are filter feeders, ingesting large amounts of krill, copepods, and small fish, which helps to regulate their numbers. Their feeding habits also help with nutrient cycling in the ocean, as their waste products return important nutrients to the water column, helping phytoplankton and other marine organisms [2].

Typically, 12–15 m in length, they have a streamlined body, dark grey to bluish-grey coloration, and slender, pointed dorsal fins. They reach reproductive maturity at the age of six and give birth to only one calf every two years. While exact population numbers are challenging to determine, ongoing research uses methods like photo identification and mark and recapture to estimate the size and trends of the Bryde’s whale population in Thailand [3]. In Thailand, they are also found in groups along the upper Gulf of Thailand coastline; the total area around it is about 9565 km² covering seven provinces, including Chonburi, Chachoengsao, Samut Prakan, Bangkok, Samut Songkhram, Samut Sakhon, and Phetchaburi provinces [4]. The Department of Marine and Coastal Resources has surveyed Bryde’s whale populations in the Gulf of Thailand since 2008 using photo identification (Photo-ID), capturing key physical features to track individual whales through boat-based surveys [4]. A population survey of Bryde’s whales in the Gulf of Thailand (2010–2021) using photo identification estimated 136–178 individuals, with an average of 156 ± 10.66 (95% confidence) [5]. Their presence depends on food availability in the seasonal food chain, primarily feeding on small surface-dwelling fish found in groups. Bryde’s whales employ diverse feeding strategies. These include cooperative lateral lunge feeding, trap feeding, tread-water feeding, pirouette feeding, right-side lunge feeding, surface skim feeding, high-speed seafloor chases, and even bubble-net feeding [6]. In Thailand, they are known for their spectacular lunge-feeding behavior, where they surface with their mouths wide open to engulf schools of fish [7].

According to a report from DMCR [8], Chonburi province has found at least nine endangered marine species, including the Bryde’s Whale along the coastline and around the Sichang Islands. The area around Sichang Islands is a major international center for international maritime transportation in Chonburi Province, which is a partial result of the nation’s plans to become a major seaport and industrial hub. This initiative, however, raises sensitive issues about economic growth and environmental protection. The significant increase in maritime and coastal activities raises concerns about the possible consequences for the substantial natural resources in this region, including coastal habitats, coral reefs, and endangered marine species [9,10]. A particular concern is the marine endangered species residing in these areas. The current update from The IUCN Red List of Threatened SpeciesTM highlights a barrage of threats affecting marine species, including illegal and unsustainable fishing, pollution, climate change, and disease [11].

Bryde’s whale significantly balances marine ecosystems as apex predators, regulating prey populations, and maintaining ecosystem health [12]. Its presence and distribution serve as indicators of ecosystem productivity and integrity. The survival of Bryde’s whale populations is threatened by several anthropogenic factors, primarily entanglement in fishing gear, vessel strikes, and habitat degradation due to human activities. These threats are exacerbated by the species’ unique behaviors and habitat preferences, which make them particularly vulnerable. According to some studies in the Gulf of Mexico, Bryde’s whales face threats from vessel strikes, entanglement, and oil pollution, endangering their survival. The worsening impacts of oil spills further threaten marine species, highlighting their vulnerability to environmental disasters and the urgent need for conservation efforts [13,14]. Recent studies in South Africa indicate that Bryde’s whales are at risk of entanglement due to their novel feeding behavior, which involves high-speed chases near the seafloor, making them susceptible to bottom-mounted fishing gear [15,16]. The presence of commercial shipping lanes and fishing activities in their habitats increases the likelihood of fatal encounters. The Bryde’s whales in the Gulf of Mexico and New Zealand are facing significant risks from vessel strikes, particularly as they often spend time near the surface, especially at night [13,17]. Additionally, the studies by Zhang [18] indicated a nearshore baleen whale population in mainland China, thus facing threats from intense human activities. The study also highlights a negative correlation between the number of ship lights and Bryde’s whale sightings, suggesting that fisheries may pose a threat to these whales. Similarly, human activities in the Sriracha–Sichang Islands have raised severe worries about the possible negative influence on this endangered marine species in these areas. Balancing economic growth and ecological preservation is important for protecting ecosystems and preserving their unique biodiversity [19]. Their well-being is not just to indicate healthy marine ecology but also to influence the region’s economy, culture, education, and research. The decline of marine endangered species presents significant ecological, economic, and societal challenges [20]. The environmental impact of this issue includes biodiversity loss and ecosystem instability, while the economic impacts threaten industries like fishing and tourism, impacting livelihoods and coastal communities [21]. Comprehensive conservation strategies are required to uphold marine biodiversity and promote sustainable resource usage [22]. Mitigating human activities’ impact on marine animals is a crucial goal [23]. Specific conservation strategies, such as local habitat restoration, marine spatial planning, and comprehensive policy, are needed to protect vulnerable coastal ecosystems [24]. A multidisciplinary approach involving scientific research, policy development, stakeholder engagement, and community participation is necessary to protect the Bryde’s whale and their ecosystems [25].

This study analyzes Bryde’s whale habitat preferences in the Sichang Islands to deliver science-based data, informing maritime management and balancing economic development with the preservation of endangered marine species. MaxEnt version 3.4.1 and InVEST modeling software version 3.10.2 are used to analyze the relationship between environmental variables and Bryde’s whale distribution on the Sichang Islands. The potential impact of human activities on this whale is also explored. The paper ends with conclusions and limitations of this research.

2. Materials and Methods

2.1. Study Sites

This study focuses on the Bryde’s whale in the Sichang Islands area. Data surveys and social reports from the DMCR, Thailand, indicate that sightings of marine endangered species in the target area occur multiple times yearly. Furthermore, the areas where these whales are spotted overlap with various human activities, such as boat mooring areas, cargo transportation zones, and fishing areas (Figure 1).

2.2. Data Collection

2.2.1. Physical and Biological Data

We collected data on the physical and biological environment, as well as animal sightings, from surveys and social reports through DMCR (

Table 1. Sighting table of Bryde’s whale from 2019–2023.

No	Date	Season	Lat	Group Size	Data Source	Behavior
1	22 May 2019	Dry	13.20111, 100.7827	2	Social Report	Feeding
2	26 May 2019	Wet	13.19218, 100.7985	2	Social Report	Feeding
3	1 June 2019	Wet	13.20566, 100.7944	1	Survey	Feeding
4	20 July 2019	Wet	13.16322, 100.7944	2	Social Report	Feeding
5	2 September 2019	Wet	13.17006, 100.7717	2	Survey	Feeding
6	4 September 2019	Wet	13.18666, 100.817	3	Survey	Feeding
7	5 September 2019	Wet	13.1288, 100.7919	2	Survey	Feeding
8	9 September 2019	Wet	13.19679, 100.8104	3	Social Report	Feeding
9	10 March 2020	Dry	13.16632, 100.8337	2	Social Report	Feeding
10	9 April 2020	Dry	13.1659, 100.8325	1	Social Report	Swimming
11	27 May 2020	Wet	13.14556, 100.7769	3	Social Report	Feeding
12	20 August 2020	Wet	13.16624, 100.8331	3	Social Report	Feeding
13	4 September 2020	Wet	13.1659, 100.8325	1	Social Report	Swimming
14	3 October 2020	Wet	13.16632, 100.8337	2	Social Report	Feeding
15	22 January 2021	Dry	13.14909, 100.7813	2	Social Report	Swimming
16	4 February 2021	Dry	13.1234, 100.8267	1	Social Report	Swimming
17	28 April 2021	Dry	13.19172, 100.7982	4	Social Report	Feeding
18	29 June 2021	Wet	13.1641, 100.8193	1	Social Report	Swimming
19	30 April 2022	Dry	13.18739, 100.7942	2	Social Report	Feeding
20	21 March 2023	Dry	13.19058, 100.8001	1	Social Report	Feeding

Note: The seasonal division is based on the annual weather forecasts of the Thai Meteorological Department.

). The DMCR collects whale data through Line Transect Surveys and public sighting reports covering the upper Gulf of Thailand coastline. Individual Bryde’s whales were identified and their population was estimated using photo identification (Photo-ID) and mark and recapture methods, a process that began in 2008 and continues to the present day [4,5]. These methods are used to improve the accuracy of population monitoring to avoid duplicate counts, thereby providing a more precise estimate of the Bryde’s whale population. For this research, we selected data reported from 2019 to 2023 around Sichang Islands for analysis, categorizing the analysis by the seasons in which these marine endangered species were sighted, as shown in Figure 2. These seasons are divided into dry and wet seasons, as reported by observers, with sightings occurring in 1–4 individuals per sighting. The study utilized water quality data from DMCR stations between 2019 and 2023 to analyze environmental factors as shown in

Table 2. Water quality for Bryde’s whale distribution analysis.

Water Quality (Dry Season)	2019	2020	2021	2022	2023
Total depth (m)	2.17 ± 2.95	6.06 ± 2.14	6.36 ± 2.74	5.73 ± 2.57	6.77 ± 1.57
Temp (°C)	30.02 ± 1	30.08 ± 0.75	30.10 ± 2.06	28.35 ± 1.12	29.02 ± 0.82
pH	7.92 ± 0.15	7.99 ± 0.06	7.96 ± 0.09	7.98 ± 0.06	7.99 ± 0.04
Salinity	31.75 ± 1.08	32.51 ± 0.83	32.15 ± 0.76	32.02 ± 0.12	31.82 ± 0.52
Dissolved oxygen (mg/L)	6.66 ± 0.66	6.32 ± 0.45	6.32 ± 0.59	6.74 ± 0.58	7.93 ± 0.04
Total suspended solids (mg/L)	50.36 ± 32.39	15.6 ± 2.76	21.44 ± 8.75	12.24 ± 3.60	6.22 ± 0.51
Nitrate (ug-at/L)	2.57 ± 2.79	0.36 ± 0.37	0.36 ± 0.41	0.23 ± 0.30	14.20 ± 4.08
Ammonia (ug-at/L)	0.75 ± 1.89	0.38 ± 1.11	0.59 ± 069	0.31 ± 0.25	0.40 ± 0.27
Water Quality (Wet Season)	2019	2020	2021	2022	2023
Total depth (m)	2.12 ± 2.84	5.71 ± 1.90	6.27 ± 2.64	5.59 ± 1.05	5.48 ± 2.05
Temp (°C)	30.11 ± 0.91	29.84 ± 0.61	29.92 ± 0.49	30.37 ± 0.25	30.58 ± 0.57
pH	7.90 ± 0.30	7.95 ± 0.04	7.94 ± 0.12	7.98 ± 0.07	8.07 ± 0.06
Salinity	30.14 ± 3.91	26.99 ± 2.18	32.16 ± 1.29	30.87 ± 1.30	30.85 ± 1.46
Dissolved oxygen (mg/L)	6.97 ± 2.23	6.83 ± 0.48	5.25 ± 0.48	6.40 ± 0.64	6.24 ± 0.44
Total suspended solids (mg/L)	10.87 ± 3.21	9.77 ± 2.77	18.65 ± 8.38	13.71 ± 2.83	16.42 ± 3.85
Nitrate (ug-at/L)	1.31 ± 1.66	1.73 ± 0.73	0.47 ± 0.67	0.70 ± 0.56	0.53 ± 0.85
Ammonia (ug-at/L)	0.07 ± 0.27	0.25 ± 0.05	0.04 ± 0.17	0.09 ± 0.03	1.14 ± 0.65

. Data sourced from the Thai Navy were employed to create depth and slope maps within the study area. We generated maps indicating distance from land and river by utilizing the distance command within the spatial analyst extension of GIS applications. Physical and biological data will be shown in

Table 3. Ecological parameters for Bryde’s whale distribution analysis.

Environmental Parameter	Resolution	Duration	Sources
Bathymetry (depth and slope) (m)	0.007 × 0.007 m	2019–2023	Royal Thai Navy
Distance to the river mouth (m) Distance to land (m)	0.007 × 0.007 m	2019–2023	ArcGIS, QGIS
Sighting Dissolved oxygen (DO) (mg/L) Sea surface temperature (SST) (°C) Nitrate (NO3) (ug-at/L) Ammonia (NH3) (ug-at/L) Total suspended solids (TSS) (mg/L) pH Salinity (ppt)	0.007 × 0.007 m	2019–2023	DMCR
Chlorophyll a (ug/L)	0.007 × 0.007 m	2019–2023	NASA (https://oceancolor.gsfc.nasa.gov, accessed on 10 March 2023)

.

Whale presence is shaped by oceanographic conditions, prey availability, and climate change. Studies on environmental variables affecting cetacean behavior highlight key habitat selection factors, including bathymetry, sea surface temperature (SST), distance to the coast [26], hydrodynamics, chlorophyll-a, salinity, tides, and wind speed [27,28,29]. Large-scale climate phenomena like El Niño-Southern Oscillation (ENSO) and La Nina further influence oceanographic conditions and whale distribution. As climate change continues to alter these variables, understanding their effects is crucial for predicting future shifts and developing effective conservation strategies, including dynamic protected areas [30]. Higher chlorophyll levels indicate abundant zooplankton, a primary food source for baleen whales, influencing their distribution [31,32]. Several studies show the SST, salinity, chlorophyll-a concentrations, and distance to shore are significant factors that influence Bryde’s whale [29,33,34,35]. For this study, we used 12 environmental factors for MaxEnt model analysis. These factors represent the standard water quality in the study area. To improve clarity in understanding the study area and the relationships between model outcomes and environmental data, we applied Principal Component Analysis (PCA) to summarize and condense two different season datasets into a more concise and interpretable form. The PCA biplot analysis summarizes the relationship between environmental variables and the distribution of data points (various years) in the reduced dimensions from the PCA results. It was found that the principal components can explain the variance of the data in the dry season up to 86.82% of the time and in the wet season 78.71% of the time, as shown in Figure 3 and Figure 4, respectively. These values indicate that these principal components can be used to summarize the data effectively.

2.2.2. Human Activities

Based on the analysis of collected data on land use and activities within the study area, five primary activities in the marine and coastal zones were identified (

Table 4. A table detailing human activities for the SRA model.

Type	Resolution	Duration	Source
Mooring zone Transportation	30 × 30 m	2019–2023	Port Authority, Marine Department, satellite imagery (Google Earth)
Fishing Aquaculture	30 × 30 m	2019–2023	Department of Fisheries
Coastal activity	30 × 30 m	2019–2023	Land Development Department, Tourism Authority of Thailand, Department of Marine and Coastal Resources

). The information pertaining to these activities was compiled from publicly available data disclosed by relevant national agencies (

Table 5. Statistics of human activities in the study area between 2014 and 2023.

Human Activity	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Number of ship calls (voyages)	11,975	12,478	12,607	13,461	13,310	12,391	11,092	11,041	11,696	11,692
Number of tourists (Persons)	137,724	193,340	228,888	243,525	240,431	256,546	186,459	80,574	486,345	555,635
Quantity of marine aquatic animals (Ton)	26,505	27,273	28,860	72,133	97,730	72,663	70,300	75,022	62,834	79,597
Yield of shellfish aquaculture (Ton)	20,688	20,531	13,294	11,212	2002	765	69	2086	2048	1254

Note: The total number of tourists in 2014–2020 is from the Performance Statistics of Laem Chabang Port of Port Authority of Thailand (https://lcp.port.co.th/cs/internet/lcp/index.html) (accessed on 10 March 2023) and the data from 2021–2022 is from Passenger/Tourist Vessel Inspection Report of Marine Department Thailand (https://md.go.th/category/stat-info/ผู้โดยสารทางน้ำ/page/4/?106) (accessed on 22 March 2023). The data for fishing and coastal aquaculture are referenced from the fisheries statistics of the Department of Fisheries (https://www4.fisheries.go.th/local/index.php/main2/search_process/1408) (accessed on 25 March 2023).

). This study conducted a comprehensive risk assessment to evaluate the potential impacts of these activities on Bryde’s whales. The assessment incorporated seasonal variations to account for temporal fluctuations in activity patterns. The analysis of vessel traffic, including the number of boats utilizing mooring areas and waterways, was based on data presented in

Table 6. Number of ship calls (voyages) in different seasons.

Season		2015	2016	2017	2018	2019	2020	2021	2022	2023
Dry	Nov	1003	1021	1145	1171	1100	984	857	897	979
	Dec	1033	1085	1127	1117	914	1021	936	965	993
	Jan	1000	1009	1144	1105	1070	986	863	965	936
	Feb	1001	1013	1065	1040	1075	971	872	971	923
	Mar	1114	1083	1132	1122	1154	987	925	993	1008
	Apr	1058	981	1071	1063	1068	908	934	897	964
Wet	May	1077	1064	1133	1140	1033	946	971	977	1000
	Jun	1026	1066	1170	1089	983	882	942	996	949
	Jul	1052	1096	1127	1108	951	904	971	1036	1022
	Aug	1030	1086	1124	1143	924	709	971	1040	957
	Sep	1012	1061	1104	1146	978	777	901	968	952
	Oct	1072	1042	1119	1066	1141	1017	898	991	1009
Summary		12,478	12,607	13,461	13,310	12,391	11,092	11,041	11,696	11,692

Note: The number of ship calls from the Performance Statistics of Laem Chabang Port of Port Authority of Thailand (https://lcp.port.co.th/cs/internet/lcp/index.html) (accessed on 10 March 2023).

. Similarly, information on tourism and coastal tourism activities was derived from records of tourist arrivals in Sichang Islands (

Table 7. Number of tourists in different seasons.

Season		2015	2016	2017	2018	2019	2020	2021	2022	2023
Dry	Nov	13,641	19,234	14,339	21,601	31,446	14,422	-	-	2290
	Dec	29,017	46,349	34,444	33,912	54,122	45,524	-	-	4534
	Jan	21,543	20,680	57,447	39,849	49,572	72,497	-	-	4729
	Feb	34,350	40,152	35,667	39,974	38,845	41,582	-	-	16,595
	Mar	38,130	43,739	49,523	37,789	30,364	6608	-	-	18,425
	Apr	12,358	25,599	14,696	5509	14,373	-	-	-	8045
Wet	May	-	6550	-	11,160	6357	-	-	-	13,161
	Jun	-	-	14,084	15,657	-	-	-	-	-
	Jul	11,376	4073	2719	7202	7188	-	-	-	-
	Aug	7633	5338	-	7877	-	-	-	-	-
	Sep	3701	3938	-	15,919	7198	-	-	-	-
	Oct	21,591	13,236	20,606	3982	17,081	5826	-	-	-
Summary		193,340	228,888	243,525	240,431	256,546	186,459	0	0	67,779

Note: The number of tourists in this table is referenced from the Performance Statistics of Laem Chabang Port of the Port Authority of Thailand (https://lcp.port.co.th/cs/internet/lcp/index.html) (accessed on 10 March 2023). There were no reports for the years 2021–2022.

). Furthermore, data related to fishing and coastal aquaculture were obtained from the annual fisheries statistics reported by the Department of Fisheries (Table 5).

2.3. Methods

During the initial phase of our research, we employed the MaxEnt model to examine the relationship between different environmental variables and the occurrence of Bryde’s whale. This analytical tool enabled us to pinpoint the factors influencing the presence of this whale. Subsequently, we applied the SRA model to assess the potential threats posed by human activities to Bryde’s whale in Sichang Island. The data analysis process is shown in Figure 5.

2.3.1. MaxEnt Model

The maximum entropy (MaxEnt) model is a statistical technique that estimates the probability distribution of variables based on constraints [36]. This model is commonly used in various fields, including image processing, bioinformatics, and econometrics. The display settings of the MaxEnt model, including the data used for analysis, utilized sightings of Bryde’s whale along with environmental variables to estimate the probability distribution of Bryde’s whale varying with different environmental conditions. The MaxEnt model is utilized when biological survey data are sparse or have limited coverage. It does not necessitate the use of absence data; rather, it leverages background environmental data from the study area. MaxEnt estimates a target probability distribution by identifying the distribution with maximum entropy—essentially the one that is most spread out or nearly uniform while incorporating constraints based on the available but incomplete information [37].

The analysis used MaxEnt software version 3.4.3 with five replications to reduce data simulation errors [38]. The analysis results, presented in a logistic format, show the probability of presence ranging from 0.00 (low) to 1.00 (high). Parameter settings were comparable to those of Engler et al. [39] and Phillips et al. [36], with the random test percentage set at 25%. Other values are set in the program’s default settings. The unknown probability distribution, denoted as π, is defined over a finite set x. This set x will later be interpreted as the set of pixels in the study area. Each element of x is referred to as a point. The distribution π assigns a non-negative probability value π(x) to each point x. The probabilities assigned to each point sum up to 1. The approximation of the distribution π is also a probability distribution and can denote it as $\widehat{\pi }$, with computational equations available as shown below.

$$H\left(\widehat{\pi }\right)=-{\sum }_{x\in X}\widehat{\pi }\left(x\right) ln \widehat{\pi }\left(x\right)$$

(1)

where ln is the natural logarithm, the entropy is non-negative. It is, at most, the natural log of the number of elements in x. Therefore, the maximum entropy principle can be interpreted as saying that no unfounded constraints should be placed on $\widehat{\pi }$, or alternatively. The data analysis process is shown in Figure 6.

2.3.2. The InVEST Species Risk Assessment (SRA) Model

The SRA toolkit is based on InVEST Software version 3.13.0. This model allows users to assess the cumulative risk posed to habitats or species by human activities and to explore consequences for the delivery of ecosystem services and biodiversity. Scoring criteria are adjusted based on the overlap between areas of rare marine animal sightings and human activities. These criteria and scoring adjustments align with this study’s objectives by being grounded in the model’s data analysis framework and specifications. Criteria scoring and weighting are informed by expert input and literature reviews, as well as relevant data quality assessment. These factors collectively assess the relationships between stressors and species to ensure coherence in overall patterns [40]. Scores for various analysis criteria will adhere to model usage guidelines and draw upon references from previous studies. The tool has been adapted to assess the risk of marine mammal entanglement [41]. For the exposure and consequence tables with ratings score to use for preparing to input data (Supplementary Materials), see

Table 8. Definitions for the exposure and consequence criteria of the SRA model.

HABITAT RESILIENCE ATTRIBUTES	Rating Instruction
	3	2	1	0
Age of maturity	>4 years	2–4 years	<2 years	No score
Reproductive strategy	Long calving interval, high parental investment	Medium calving interval/high parental investment	Short calving interval/short to medium parental investment	No score
Population connectivity	Negligible movement/exchange between the focal regional population and other populations	Occasional movement/exchange between the focal regional population and other populations	Regular movement/exchange between the focal regional population and other populations	No score
Local species status	Endangered	Threatened or of concern	Low concern
STRESSOR OVERLAP PROPERTIES
Mortality	Lethal	Sublethal	Negligible	No score
Potential threat	>5 problem	3–4 problem	1–2 problem	Unknown
Intensity	High	Medium	Low	No score
Likelihood of interaction	Calculated based on the suitability model
Temporal overlap	Timing of overlap all year (12 mo)	Most of the year (4–11 mo)	Occasional (1–3 mo)	No score
Frequency of disturbance	Annually or less often	Several times per year	Weekly or, more often,	No score
Current status of management	No strategy identified	Management strategy identified	Management strategy identified and implemented	No score

. The SRA model allows for any number of criteria to be used when evaluating the risk to habitat areas. As a default, the model provides a set of typical considerations for evaluating the risk of stressors to habitats. Except for spatial overlap at a grid cell scale, these criteria are rated on a scale of 1–3, with 0 = no score. In all cases, higher numbers represent greater exposure or consequence and result in higher risk scores. Using a score of 0 will always indicate that the given criteria should be excluded from exposure and consequence equations. The result of the consequence is the overlap between the distribution of species and the extent of human activity across space and time. The data analysis process is shown in Figure 7.

2.3.3. Uncertainty Standards

The limited data on Bryde’s whale sighting in the study area might affect the output from the MaxEnt model. The output accuracy depends on high-quality input data. Poor occurrence records, low-resolution environmental variables, and sampling biases can lead to misleading habitat predictions. Overfitting, underfitting, and misrepresentation of whale distribution often result from data limitations [42]. To improve reliability, systematic surveys, high-resolution environmental layers, and independent validation are essential. Addressing these issues will help to enhance the quality of the output.

Analyzing and assessing human and ecological systems involves integrating a large amount of data. Prioritizing data quality is crucial for making reliable decisions and establishing a standard for data uncertainty [43]. A ranking system helps understand the sources of uncertainty in assessments and data analysis. Effective methods for enhancing data quality include creating a reference table from existing data, adjusting formats from previous studies [41], and managing data from model user guides [40]. We can rank data quality by specifying levels of data reliability using the different levels to identify the quality of data. The best data reliability is based on data collected in the study region (or nearby). The best data are obtained from records collected in the study site, typically through official surveys or publicly disclosed information by government agencies. Acceptable indicates adequate data reliability and is information obtained from literature reviews, relevant research efforts, expert interviews, and feedback from officials and local communities. Limited denotes the limited data of reliability, which is information that has not been systematically recorded or documented in the literature. This type of data is not sufficient to substantiate scores on each criterion, analyze data, or obtain reliable information. It can only be obtained from informal interviews with fishermen that have not been formally recorded. The table below outlines the criteria for evaluating the reliability of existing information (

Table 9. A table detailing uncertainty standard for classifying data analysis criteria.

Uncertainty standards	Best	Acceptable	Limited
	Data collected in study region/official data	Data collected based on literature review, interviews, or expert opinion	Limited reliability data derived from informal interviews with fishermen, or no empirical literature exists to justify scoring

). By using high-quality data for analysis and decision making, we can improve research outcomes and monitor results more efficiently in the future [44].

3. Results

Through data collection, we can generate a map showing the variations between dry and wet seasons in the study area by analyzing information obtained from various sectors. The spatial distribution of Bryde’s whale and human activities is shown in Figure 8. The map shows the distribution of Bryde’s Whale, based on the report from the social report and the record by DMCR. A total of 20 sightings of 40 Bryde’s whale were recorded in this area, with sighting ranges typically between one and four individuals per case. Human activity can be accessed online or through official documents from government agencies. For the data on the number of cargo ships, we estimated the quantity by comparing satellite images with the vessel, cargo, and container passed through the Laem Chabang Port annual report from the Marine Department. These data can help to identify the extent of sea use and specify the complexity of human activities in the study area. Bryde’s whales have also been observed in the area during both seasons. They are commonly found around the Sichang Islands as well as in areas designated for fishing grounds, cargo ship mooring, and waterways.

3.1. MaxEnt Model Output

The MaxEnt model was employed to predict the potential distribution of Bryde’s whale habitat on Sichang Island, providing insights and data into the factors influencing their spatial occurrence patterns. Through the analysis, key environmental variables driving whale habitat suitability were identified. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) for the dry (January–half of May) and wet (mid-May–October) seasons is 0.87 ± 0.04 and 0.96 ± 0.01, respectively. The AUC values within this range are considered informative and of high precision [45], indicating the influence of environmental variables on the distribution of Bryde’s Whale. The results are based on the percent contribution of the environmental variable. The percent contribution analysis indicates an important correlation between occurrence spots and environmental parameters, implying that environmental factors considerably impact Bryde’s whale dispersion [36]. The percent contribution of these factors influences the presence of Bryde’s Whale. The study found that the percentage of environmental factors affecting Bryde’s whale presence varied between seasons. The most important percent contribution for dry seasons is the total suspended solids (TSS) at 32.8% with averages of 25.1 mg/L, chlorophyll a at 20.1% with averages of 6.9–7.2 ug/L, and dissolved oxygen (DO) at 15.9% with averages of 6.12 mg/L. In particular, the most important percent contribution for wet seasons is the dissolved oxygen (DO) at 15.9% with averages of 6.19 mg/L, ammonia (NH₃) at 29.4% with averages of 3 ug-at/L, and distance to land at 13.3% with averages of 300 m. In addition, additional variables are also associated with the presence of Bryde’s Whale. The analysis found that depth did not affect the presence of Bryde’s whale during both seasons, while sea surface temperature (SST) did not affect the wet season, as shown in

Table 10. Table of the percent contributions of environmental variables.

Type		Resolution	Duration
Sea surface temperature (SST)	(°C)	30.23 (5)	30.20–30.24 (0)
Dissolved oxygen (DO)	(mg/L)	*** 6.12 (15.9)	* 6.19 (29.9)
Nitrate (NO3)	(ug-at/L)	2.8 (2.6)	11.5 (0.1)
Ammonia (NH3)	(ug-at/L)	5.5 (6)	** 3 (29.4)
Total suspended solids (TSS)	(mg/L)	* 25.1 (32.8)	18.4 (1)
Chlorophyll a	(ug/L)	** 6.9–7.2 (20.1)	5.8 (1.8)
Salinity	(ppt)	30.86 (9)	30.69 (2.5)
pH		8.07 (0.8)	8.09 (3.9)
Depth	(m)	5.5 (0)	5.3 (0)
Slope	(m)	15–65 (3.9)	36–65 (13)
Distance to the river mouth	(m)	3300 (1.9)	2200 (5.1)

Note: The MaxEnt model has been graded by * as the top, ** as the second, and *** as the third for the most important percent contribution.

. The MaxEnt model’s estimate table shows the relative contributions of environmental variables, with the top three factors influencing percent contribution highlighted by *, **, and ***.

Additionally, the MaxEnt model provides response curves illustrating the relationships between environmental factors. These curves depict how predicted suitability varies with each selected variable and reveal dependencies induced by correlations between variables. This visualization aids interpretation, especially in strong correlations between variables, as shown in Figure 9.

The MaxEnt model uses environmental factors and confirmed occurrences to estimate a species’ suitability for its specific habitat, combining predictions with independent observations. The model generates probability maps and finds significant environmental factors that affect the distribution of species. The resulting distribution maps were divided into the dry season values ranging from 0.00 to 0.60 and the wet season values ranging from 0.00 to 0.85. The analysis found that during the wet season, the logistic probability value is relatively high at 0.85 (with 1.00 is the maximum). The model identifies important parameters influencing species presence by analyzing the link between environmental variables and known species locations. In order to forecast species presence throughout the study area, it creates probability maps. Their presence is more likely to be supported in areas with environmental conditions comparable to those found in whale habitats. The MaxEnt map’s highest likelihood regions should probably line up with real whale observations. The model also identifies the key environmental factors influencing whale dispersion. According to the investigation, the appropriate sites for Bryde’s whale dispersal during the dry season are located around the Sichang Islands except in the southwest part, as shown in Figure 10a. Similarly, suitable locations for Bryde’s whale dispersal during the wet season are found in the northeast and southwest parts of the Sichang Islands, as shown in Figure 10b. The visualization displays the performance of a MaxEnt model. Warmer colors (orange to red) show the range from 0.5 to 0.85 in wet season indicate high suitability predicted for distribution conditions, this indicates a potential whale sighting within these designated areas. Even though the dry season shows a warm color, the range value from 0.41 to 0.6 indicates a low probability, but still a possibility, of finding whales. Cool colors, specifically blue to yellow within the 0 to <0.5 range, highlight areas where the predicted suitability is low, translating to a diminished chance of encountering whales [36]. This model helps identify areas with higher confidence and areas needing refinement or additional data, providing a spatial context for evaluating performance across different geographical locations.

3.2. SRA Model Output

The SRA model provided valuable insights into the potential impacts of human activities on Bryde’s whale in the Sichang Islands. The analysis revealed a complex interaction between various factors, including coastal and maritime activities. From the risk analysis of human activities on Bryde’s whale during the dry season, it is possible to classify the level of risk for each activity into two levels. Activities with moderate risk include fishing activities at 99.68% and cargo ship mooring areas at 96.28%. Other activities are classified as low risk, including maritime traffic areas at 99.30%, coastal activities areas at 99.86%, and coastal aquaculture areas at 99.99%, as shown in Figure 11.

The risk analysis results of human activities on Bryde’s whale during the wet season can be classified into three risk levels for each activity. Activities with high risk include cargo ship mooring areas at 14.95%, moderate risk activities include fishing activities at 99.96%, and cargo ship mooring areas at 49.02%. Other activities are classified as low risk, including waterways at 99.53%, coastal activities at 96.39%, and coastal aquaculture at 99.90%, as shown in Figure 12.

Map output shows the cumulative risk from all stressors on Bryde’s whale. The risk map shows the geographical regions where Bryde’s whale and human activities overlap during both seasons. The map is divided into three levels of risk, with the red areas indicating high risk, yellow indicating moderate risk, and green indicating low risk as shown in Figure 13. This map assesses risk by focusing on the species’ occurrence and how it changes depending on the distribution of stressors on the species. The risk map presentation is based on predefined criteria, which involve analyzing the risk values from pollution sources and threats that can affect the health and food sources of Bryde’s Whale. Based on the map output, it is evident that certain areas pose a high risk due to the overlapping human activities that take place during both seasons. These areas are located in the cargo ship anchorage, fishing grounds, and shipping lanes from Sichang Island to Siracha. During the dry season, high-risk areas are found to the north of Sichang Island, east of Kham Yai Island, and south of Sichang Island, as shown in Figure 13a,b, showing the result of the wet season in three particularly vulnerable areas. The first area is situated in the northeast and western part of Sichang Island. The second area is the coastal activities around Sichang Island. The third area is around Kham Yai, Kham Noi, and Prong Island, as shown in Figure 13b.

3.3. Uncertainly

The data used for analysis in each criterion have a low to moderate level of uncertainty. Most of the data utilized are officially recorded and publicly available from government agencies in the country. Partly due to habitat quality and habitat risk assessment, we used data on marine habitats from the DMCR. The animal sighting data were derived from social sightings records with verified photographs. We used the environmental variable data such as DO and depth gathered by the DMCR for the data of SST and chlorophyll, a concentration we used from NASA ocean color. Relevant agencies have delivered data on coastal area utilization and coastal activities. The certainty of these utilization data ranges from best to acceptable. Acceptable data include fishing areas where the available data cannot specify the position and number of fishing gear. We only have information about fishing boundaries. For the data supporting the HRA/SRA criteria, scores in each category have a moderate level of certainty for risk assessment. These scores were compiled from relevant reports and literature.

During the study period analyzed in this research, from 2019 to 2023, which encompasses the COVID-19 pandemic, human activities related to transportation, tourism, and marine resource management were significantly impacted. The report indicates that lockdowns and travel restrictions led to a temporary decline in maritime activities, fishing operations, and tourism-related activities [46]. According to the data shown in Table 5, it indicates that there was a decline of about 11.52% in the total number of boats in the research area during 2015. Ship calls increased until 2019, after which they decreased in 2020–2021, reflecting disruptions in international trade and transportation. However, a gradual recovery was observed in 2022 and 2023, aligning with trends in tourism, where visitor numbers significantly declined from mid-2020 to early 2022. Although tourism returned to near-normal levels in 2023, with a strong recovery in 2022 and 2023, it is projected to increase significantly in the coming years. The production from aquaculture and capture fisheries both decreased concerning fishery activities. Production in the aquaculture industry began to decline in 2016, at its lowest point in 2020 and then increased in 2021–2022. Capture fisheries, on the other hand, reported declining landings in 2019–2020 before seeing an increase in 2023 as shown in Figure 14. Preliminary analysis of these data suggests that these changes may have influenced human pressure levels in the study area, potentially leading to an underestimation of human activity during “normal years” and affecting the outcomes of risk assessment models.

4. Discussion

Based on data from the Department of Marine and Coastal Resources (DMCR), Bryde’s whales are spotted less frequently around the Sichang Islands compared to other areas, possibly due to differences in food availability, habitat conditions, or human activities that may affect their presence. The limited sightings of Bryde’s whales around Sichang Islands can be explained by Tongsukdee et al.’s study on Bryde’s whale sightings in the Gulf of Thailand (2010–2021) [5], who found that Bryde’s whales in the Gulf of Thailand are primarily semi-resident with distinct population groups. Mother whales remain in the upper Gulf to nurse their calves, while weaned juveniles migrate elsewhere for feeding or breeding, as shown in Figure 15. Moreover, the previous study in these areas indicates that every year, there is a period from December to March when Bryde’s whales are rarely sighted. After this period, they return to feed in the upper Gulf of Thailand. This pattern suggests that this group of Bryde’s whales exhibits semi-resident behavior and may migrate elsewhere for breeding [47], since Sichang Islands are not a known nursing ground and migrating juveniles are likely dispersed across wider areas for breeding. This explains why Bryde’s whales can be observed in this area only during certain periods.

The MaxEnt model output shows that during the dry season, three major environmental factors are identified to be correlated with Bryde’s whale occurrences: TSS (32.8%), chlorophyll a (20.1%), and DO (15.9%). The study found a negative correlation between TSS and chlorophyll-a, indicating that higher TSS levels result in lower levels of chlorophyll-a [48]. Higher TSS concentrations in water impact phytoplankton photosynthesis, affecting the dissolved oxygen rate. In the wet season, three factors are DO (29.9%), NH₃ (29.4%), and distance to land (13.3%). During the wet season, runoff from the land introduces nutrients into the sea, initiating the breakdown of organic nitrogen and ammonia generation [49]. Municipal wastewater with higher ammonia levels mixes with runoff containing pollutants and pathogens [8], affecting coastal seawater quality due to its higher levels than natural water. Nitrates and ammonium are vital nitrogen sources for phytoplankton, influencing primary productivity [50]. Eutrophication in nutrient-rich seawater can trigger extensive algal blooms, leading to oxygen depletion [51]. This is in the same direction as the results of the SRA model. It was found that cargo ship mooring areas are in the high–medium range. In this area, a dry bulk unloaded between ships causes small dust to fall into the sea, affecting water quality and sediment quality around the loading area. This can affect life in the marine ecosystem around Sichang Island. Reports show below benchmark dissolved oxygen content in the eastern part of Sichang Island and accumulation of organic matter in sediment [8]. The results of the SRA model reveal the potential impacts of human activities on Bryde’s whale in the Sichang Islands, primarily involving maritime and coastal activities. During the dry season, fishing activities and cargo ship mooring areas were identified as moderate risk factors. In contrast, in the wet season, cargo mooring areas posed a high risk, while fishing activities were at moderate risk. Other activities, such as waterways, coastal activities, and coastal aquaculture areas, are classified as low risk for both seasons. Overall, the mooring zone and fishing areas were classified as the primary risk factors in both seasons. Therefore, the distance from the coastline is also the key factor related to the presence of Bryde’s whale during the wet season. Overall, these primary factors are related to the growth of plankton, which are essential food sources for small fish and krill and are vital prey for Bryde’s whale [52]. While these animals may consume plankton, their waste excretion promotes increased uptake rates of phytoplankton [53]. Marine organisms consume and release waste, contributing to elevated ammonia levels. If these levels exceed safe limits, they may cause toxicity to water and marine animals [54]. The MaxEnt model reveals that dissolved oxygen is the primary factor influencing Bryde’s whale presence in both seasons, indicating that water quality is crucial for aquatic survival. The decline in dissolved oxygen concentration indicates climate change’s impact on marine environments, causing behavioral and distributional shifts in fish and zooplankton in coastal marine ecosystems. The spatiotemporal variation in dissolved oxygen levels can affect predator–prey interactions [55]. Yu’s [56] studies highlight the crucial role of environmental factors, such as SST, DO, salinity, and chlorophyll-a concentration, in shaping the distribution and abundance of small pelagic fish, which are prey for Bryde’s whale. Similar studies in southern African waters also indicate that these factors are essential in shaping the seasonal habitat preferences of different whale species, such as Bryde’s whale, humpback whale, southern right whale, and sperm whale, leading to highly accurate predicted distributions [33]. Whale shark studies in the Mexican Caribbean indicate that habitat suitability and environmental factors, such as primary productivity and sea surface temperature (SST) when the SST is higher than 28 °C, significantly influence whale shark aggregation [37]. However, studies from various countries have indicated that SST has a crucial role in whale distribution due to significant seasonal temperature fluctuations. In contrast, Na-U-Dom et al. [49] investigation of water quality in Thailand’s upper Gulf revealed that temperature distributions were similar over both seasons. Consequently, the model estimated that SST is not the primary factor influencing Bryde’s whale sightings in the Sichang Islands area.

According to the Marine Department’s statistical data, the number of international cargo vessels and transport vessels entering the anchorage area around Sichang Island is expected to increase, resulting in environmental concerns. A study by Raluekmul and Jarayabhand (2019) [57] revealed marine pollution issues in the Sichang anchorage area due to waste generated by cargo vessels and barges. The report highlighted issues with current waste management systems, including lack of garbage data recording, inefficiency due to low service frequency, discontinuity of the current model, and inadequacy of waste reception facilities. These issues lead to ineffective litter elimination and environmental littering, posing a significant threat to global cetaceans. Studies by Fossi et al. (2020) [58] indicate that entanglement and ingestion are the two significant impacts of marine litter on cetaceans. This type of pollution strongly correlates with some cetaceans due to their feeding habits. Further, oil spills and wastewater discharge from mooring areas directly impact cetaceans and indirectly affect their food sources. Additionally, interactions between marine mammals and fisheries, including bycatch and depredation, are widespread in both commercial and small-scale fisheries (SSF) [59]. SSF also contributes to the interaction between fishing activities and marine mammals, resulting in potential co-occurrence hotspots. This overlap between SSF and marine mammals can lead to entanglement and gear damage [60]. Although there have been no reports of Bryde’s whale bycatch or stranding caused by fishing gear in the study area, various records and research activities have shown that fishing activities significantly impact marine mammals’ mortality due to entanglement [16,61]. This activity is mainly because these animals’ fishing grounds and feeding areas often overlap, particularly in the case of Bryde’s whale, which is frequently encountered in fishing zones. All these activities can affect the availability of food resources and the habitat of Bryde’s whale in Sichang Islands [62]. The logistic predicted distribution map from MaxEnt indicates that during the wet season, the suitability value of Bryde’s whale habitat is relatively high, with a logistic value of 0.85, suggesting that the habitat is suitable. In contrast, during the dry season, the suitability value is relatively low, reaching a maximum value of only 0.60 out of a logistic value with the highest ranking of 1.00. Likewise, the previous study by the Marine and Coastal Resources Research Center (upper Gulf of Thailand) [3] was conducted on the Bryde’s whale in the upper Gulf of Thailand from 2010 to 2019. The study indicates a decline in the Bryde’s whale numbers in the upper GoT from January to March. They found they may migrate for food, reproduction, and offspring, affecting their sighting due to food source distribution. This information could be why we can observe Bryde’s whale in the area of Sichang Islands during the dry season. Usually, Bryde’s whale is a marine endangered species that is frequently observed along the coastlines of the upper Gulf of Thailand. In the Sichang Islands, Bryde’s whale has encountered relatively low numbers during different seasons because the area is not a habitat of feeding grounds, with the highest sightings recorded along Samut Sakhon’s coastline [47].

The Coastal Resources Conservation Division from DMCR [63] has recently started working on marine spatial planning and a spatial management plan for the Sichang Islands area. The primary goal of this report is to ensure that resource utilization is in line with the area’s needs while preserving marine and coastal resources. The management plan categorizes the utilization zones into four areas, prioritizing resource conservation and resolving conflicts related to area usage. The report suggests various measures to mitigate the impact on coral reefs. It identifies areas for environmental conservation but does not comprehensively cover resolving conflicts between human activities and areas overlapping with critical marine endangered species feeding grounds in the region. Several strategies for reducing the effects on coral reefs and identifying places for environmental protection are presented in this report. These strategies do not effectively resolve conflicts that arise between human activity and areas that interact with the feeding grounds of endangered marine species in this region. The findings provide insights into the human-induced impacts on Bryde’s whales, emphasizing the importance of protecting endangered marine species. The SRA output maps illustrate the cumulative risks to Bryde’s whales from all stressors, including human activities that overlap with their habitats and feeding grounds. Identifying exposure locations, especially in shipping routes, fishing grounds, and mooring zones, the visual helps facilitate more accurate risk assessment. The analysis revealed that during the dry season, high-risk areas were concentrated on the north, east, and south of Sichang Island. In contrast, during the wet season, vulnerable areas were the northeast and western parts of Sichang Island, coastal activities around Sichang Island, and areas around neighboring islands such as Kham Yai, Kham Noi, and Prong Island. These findings can aid in decision making related to maritime safety and help prioritize resources for mitigation efforts. Furthermore, MaxEnt’s output can help to understand the seasonal variations in risk distribution, and stakeholders can implement targeted strategies to enhance safety measures in the identified vulnerable areas. Therefore, this research emphasizes the importance of Bryde’s whale and other vulnerable marine species in determining directions for addressing the issues and mitigating the impacts and risks that could affect top predators in this food chain. Also, addressing these issues will benefit other marine species.

To preserve their habitat and control potentially dangerous human activities, these model results might be used as a guide for thinking about conservation actions for Bryde’s whales in this region [64]. This includes restricting harmful activities under relevant legal frameworks, particularly the Marine and Coastal Resources Management Promotion Act, B.E. 2558 (2015): Section 20 mandates the conservation and restoration of marine and coastal resources, including rare marine species such as Bryde’s whales, Section 23 establishes protective measures for areas critical to marine and coastal resource conservation, which may include Bryde’s whale habitats, Section 25 requires the development of marine and coastal resource management plans that consider the conservation and protection of rare marine species, and Section 27 stipulates penalties for offenses against marine and coastal resources, including hunting or harming Bryde’s whales. In addition, other relevant laws include the following: the Royal Ordinance on Fisheries, B.E. 2558 (2015) (and its amendments): Section 66 prohibits the use of trawl nets and push nets in coastal waters (within 3 nautical miles from shore or designated areas) unless granted special permission, to protect juvenile aquatic species and coastal ecosystems, Section 67 regulates fishing activities that impact fishery resources, such as trawl or push nets with specifications that may harm aquatic resources, and Section 69 prohibits fishing practices that may harm the environment, including restrictions on destructive fishing gear, which could impact Bryde’s whales by affecting their food sources, habitats, and preventing accidental entanglement; the National Environmental Quality Promotion and Conservation Act, B.E. 2535 (1992): this law serves as Thailand’s primary legislation for pollution control, addressing water pollution that may impact Bryde’s whales, such as industrial wastewater discharge or marine littering; the Factory Act, B.E. 2535 (1992): this law regulates factory operations, which may be sources of water pollution affecting Bryde’s whales, requiring industries to implement standard pollution control systems; the Navigation in Thai Waters Act, B.E. 2556 (2013): this act governs maritime navigation in Thai waters, addressing potential sources of marine pollution such as oil spills and ship waste that could impact Bryde’s whales; and the National Parks Act, B.E. 2562 (2019): if Bryde’s whale habitats fall within national park boundaries, tourism activities in those areas must comply with national park regulations, which may include restrictions on visitor numbers or activities that could impact the species. Incorporating legal frameworks into marine spatial planning, the results of these models can be used as a guide for the implementation of conservation measures for Bryde’s whales in this area. This approach aims to comprehensively address various issues, prevent impacts on habitats and feeding grounds, regulate activities that cause disturbances, and restrict potentially harmful activities Bryde’s whales and their habitat.

Although model outputs can identify sensitive areas, guiding resource allocation for risk mitigation, maritime safety, and the protection of endangered marine species, the limitation of the model is the important thing that should be recognized. One of MaxEnt’s model limitations is reliance on randomly assigned pseudo-absences that can impact species distribution predictions. Model performance metrics like AUC and COR are influenced by pseudo-absence selection, making AUC uncertain for evaluation [65]. Sampling bias, especially with opportunistic data like whale sightings, can lead to inaccuracies. While integrating multiple data sources helps, biases persist if the area is not well surveyed. Addressing sample bias and pseudo-absences is crucial for improving model accuracy. The InVEST model is a valuable tool for assessing ecosystem services, though its effectiveness is limited by data availability and ecosystem-specific suitability. To enhance its applicability, it should complement it with high-quality local data and consider expert validation to improve the accuracy of ecosystem service assessments. The limitation of both models is the accuracy of data input. This study recommends utilizing high-quality, area-specific data which is crucial for accuracy, reducing biases, and reliably capturing marine resource dynamics.

5. Conclusions

This study showed that spatial modeling could be applied to various databases covering multiple spatial and ecological factors. The results clarify the correlation between environmental variables and human activities on Bryde’s whale. The study area’s map shows variations in Bryde’s whale sightings between dry and wet seasons. Statistical analysis revealed no significant difference in Bryde’s whale sightings between seasons (p > 0.05). The study reveals Bryde’s whale distribution and habitat characteristics, with the MaxEnt model predicting the potential habitat suitability of Bryde’s whale based on key environmental variables in the dry and wet seasons. The AUC of the receiver operating characteristic plot for the dry and wet seasons is 0.87 ± 0.04 and 0.96 ± 0.01, respectively. The MaxEnt model analysis identifies key environmental factors influencing Bryde’s whale occurrences in the Sichang Islands across different seasons. During the dry season, TSS (32.8%), chlorophyll a (20.1%), and DO (15.9%) levels play significant roles, with TSS negatively correlated with chlorophyll-a levels. Conversely, in the wet season, DO (29.9%), NH₃ (29.4%), and distance to land (13.3%) emerge as primary factors influenced by nutrient runoff and municipal wastewater. These factors, crucial for plankton growth, ultimately affect Bryde’s whale prey availability and habitat suitability. The logistic predicted distribution map shows that Bryde’s whale habitat’s suitability value is high during the wet season at 0.85, suggesting its suitability, while its suitability value is low during the dry season at 0.60. Moreover, the SRA model highlights potential threats to Bryde’s whale posed by human activities, particularly maritime and coastal operations. Fishing activities (99.68%) and mooring (99.28%) areas pose moderate risks during the dry season, escalating to high risk (14.95%) in cargo mooring areas during the wet season. However, these activities impact the quality of seawater, which is crucial for food supply and the abundance of prey species, thereby affecting their distribution. In addition, marine pollution issues, including ineffective waste management and littering, alongside interactions between marine mammals and fisheries, further compound risks. Despite no reported bycatch in the study area, the overlap between fishing zones and Bryde’s whale habitats suggests potential impacts on food resource availability and habitat integrity.

Despite these models aiding in risk mitigation and marine protection, they have limitations. Maxent’s reliance on pseudo-absences affects predictions, and sampling bias can distort results. InVEST’s effectiveness depends on data quality and ecosystem suitability. High-quality, area-specific data and expert validation are crucial for accuracy.