Next Article in Journal
Comparative Analysis of Coupled Coordination Between Tourism Urbanization and Ecological Environment: Evidence from Tourism and Non-Tourism Coastal Cities in China
Previous Article in Journal
An Ontology-Aided Generative Computational Design Process for Ecological Building Envelopes: Development of a Conceptual Framework for the Early Design Stage
Previous Article in Special Issue
Soundscape Design in an Urban Natural Park
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 14
Issue 4
10.3390/land14040842
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Is the Sand Bubbler Crab (Scopimera globosa) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality?

by
Hai-Rui Huang
1,
Jian Liang
2,* and
Chae-Woo Ma
2,*
1
Department of Business Administration Management, Sejong University, Seoul 05006, Republic of Korea
2
Department of Biology, Soonchunhyang University, Asan 31538, Republic of Korea
*
Authors to whom correspondence should be addressed.
Land 2025, 14(4), 842; https://doi.org/10.3390/land14040842
Submission received: 11 March 2025 / Revised: 10 April 2025 / Accepted: 10 April 2025 / Published: 12 April 2025
(This article belongs to the Special Issue Conservation of Bio- and Geo-Diversity and Landscape Changes II)
Browse Figures
Versions Notes

Abstract

:
Urbanization in South Korea has significantly impacted the country’s sandy beach ecosystems. In our study, we investigated the population responses of sand bubbler crabs (Scopimera globosa) to beach urbanization and assessed the impact of adjacent terrestrial ecological quality. We employed the urbanization index to measure the urbanization levels of two sandy beaches and the remote-sensing ecological index (RSEI) to evaluate the ecological quality of adjacent terrestrial areas. The sampling of sand bubbler crab populations was conducted over five months. Our results show that urbanization significantly influences sand bubbler crab populations. While the ecological quality of adjacent terrestrial areas does not directly affect the crab populations, the land surface temperature (LST) of surrounding lands has a significant impact on sand bubbler crab biomass. These results suggest that sand bubbler crabs may serve as a useful indicator of anthropogenic disturbance on sandy beaches. This study provides critical ecological insights and offers a novel perspective for developing effective conservation strategies in South Korea’s sandy beach ecosystems.

1. Introduction

Sandy beaches, which constitute nearly 30% of the world’s ice-free coastline, are vital coastal habitats that deliver significant ecological functions and socio-economic benefits [1]. Ecologically, they provide diverse habitats for wildlife, acting as essential nesting and feeding grounds for a variety of species. Socio-economically, sandy beaches underpin human activities such as tourism and aquaculture, significantly enhancing local economies and livelihoods. Additionally, they serve as natural protective barriers against storm surges [2]. However, the intensification of human activities has markedly affected sandy beach environments, posing substantial challenges for their sustainable environmental management [3].
As the global population continues to grow, an increasing proportion of people now reside in coastal regions, leading to intensified urbanization [4]. This coastal urbanization has significantly impacted sandy beaches. Notable effects include the installation of breakwaters and ports, infrastructure construction on back shores and beaches, and heightened levels of pollution. These activities collectively contribute to the degradation of sandy beach ecosystems [5]. To evaluate the extent of sandy beach urbanization, González et al., 2014, developed a methodology that integrates direct field observations with data from local management authorities [6]. This approach has been extensively used to assess sandy beach urbanization across various South American coastal regions [7]. Extending this work, researchers have examined the responses of clams, ghost crabs, and Diptera to beach urbanization, investigating their viability as biological indicators to quantify and assess the degree of urbanization affecting sandy beach ecosystems [8,9,10,11].
Sand bubbler crabs (Scopimera globosa) are widely distributed across the Indo-Pacific region and are common on tropical and subtropical sandy beaches [12]. They derive nutrition from surface sediments, and one noteworthy characteristic is the radial clusters of sand pellets surrounding their burrows. During burrow maintenance and feeding activities, sand crabs facilitate sediment transport and mixing, thereby modifying sediment dynamics and the availability of food resources for microbial communities. These processes contribute to the release and recycling of previously sequestered nutrients [13]. Observations indicate that sand bubbler crabs are abundantly found on the sandy beaches of South Korea’s west coast. However, to date, no studies have explored the potential response of sand bubbler crab populations to beach urbanization.
The rapid economic development in South Korea has profoundly impacted the sandy beach environments, primarily due to land reclamation, urbanization, and agricultural activities [14]. These human activities have not only resulted in habitat degradation but have also led to a decline in biodiversity, presenting a significant challenge to the sustainability of sandy beach ecosystems [15]. Although the South Korean government has prioritized the conservation of intertidal ecosystems since the late 1990s, most policies have been largely restricted to chemical parameters or human activities within the intertidal zone, neglecting both the potential utility of biological indicators and the impact of anthropogenic activities in adjacent terrestrial areas [16,17]. This study aims to provide robust data for policymaking by relevant conservation agencies by assessing the correlations among the sand bubbler crab population, urbanization of sandy beaches, and ecological quality in neighboring terrestrial zones, thereby laying the groundwork for future research into sand bubbler crabs.

2. Materials and Methods

2.1. Study Area

In our study, we selected Gijipo and Kkotjji sandy beaches on Anmyeon Island, South Korea (Figure 1). Based on the sandy beach type’s influence on macrobenthic communities, our previous research confirmed that both beaches are classified as intermediate sandy beaches [2]. Gijipo sandy beach, located in the northern region of Anmyeon Island, features areas designated as restricted to protect wildlife habitats. In contrast, Kkotjji sandy beach, positioned in the central part of the island, serves as a major tourist destination in South Korea, drawing substantial crowds each summer.

2.2. Sampling Design and Sample Collection

To evaluate the impact of sandy beach urbanization on sand bubbler crab populations (abundance and biomass of individuals), we conducted surveys during the off-peak tourist seasons in June and October 2024, as well as during the peak tourist season from July to September 2024. The surveys were carried out during the spring tides of each month.
Observations indicated that tourist activity at both beaches was primarily concentrated around the access paths leading to the sandy areas. Sand bubbler crabs, which typically inhabit the upper intertidal zone, were used as indicators to identify the first sampling station. This station was located just below the access path, where the sand bubbler crabs initially appeared. From this starting point, additional sampling stations were successively located at 50 m intervals down shore, resulting in a total of five stations covering a 200 m stretch at each beach (Figure 1 and Figure S1).
At each sampling station, four samples were randomly collected using a quadrat with a side length of 0.25 m, resulting in a total sampling area of 0.25 m2 per station. The sampling depth was set at 30 cm. Samples were sieved on-site with a 0.5 mm mesh to extract sand bubbler crabs, which were then preserved in 70% ethyl alcohol (Avantor Inc., Radnor, PA, USA) for further analysis. During the collection, the temperature 15 cm beneath the beach surface was recorded using a waterproof thermometer (DT400, Summit Co., Ltd., Seoul, Republic of Korea). Additionally, 200 g of surface sediment was collected for grain size and organic matter content analysis.

2.3. Sample Processing

In the laboratory, sand bubbler crabs were rinsed with distilled water, and both the crabs and surface sediment samples were then dried at 70 °C for 24 h. The dried sand bubbler crabs were subsequently weighed on an analytical balance (CP-64, Sartorius AG., Göttingen, Deutschland) with an accuracy of 0.0001 g to determine their dry weight and counted to assess their abundance. We analyzed the grain size of the surface sediment using a series of sieves with mesh sizes ranging from 4 mm to 0.063 mm. The mean grain size was calculated using the GRADISTAT package [18]. Further, 30 g of dried surface sediment was heated at 550 °C for 4 h; the weight loss measured allowed us to calculate the ignition loss [19].

2.4. Urbanization Index

The urbanization index (UI), proposed by Schlender et al. (2023) [9], was used to measure the degree of urbanization on the beaches. This index integrates six indicators: proximity to urban centers, buildings on the sand, beach sanitation, solid waste in the sand, vehicles driving on the sand, and visitor frequency (Table 1). The visitor frequency indicator was determined by averaging visitor counts recorded at 10 a.m. and 2 p.m. during spring tide days. Two researchers (H-R, H and J, L) independently evaluated the six indicators, and a third researcher (C-W, M) resolved any discrepancies to achieve a consensus. To quantify the value of urbanization, a normalization technique was employed based on the method proposed by Gover, concerning Peres-Neto et al., 2006 [20]. The calculation follows the equation X′ = (X − Xmin)/(Xmax − Xmin), which transforms the raw scores of the six urbanization indicators into standardized values ranging from 0 to 1. In this formula, X denotes the recorded score for each variable, and Xmin and Xmax represent the lower and upper limits of the scoring scale, set at 0 and 5, respectively. Urbanization index values approaching 0 suggest lower degrees of human disturbance, whereas those near 1 imply a higher intensity of anthropogenic activity on the beach [11].

2.5. Remote-Sensing Ecological Index

The remote-sensing ecological index (RSEI), proposed by Xu (2013) [21], combines four individual remote-sensing indices: the normalized difference vegetation index (NDVI), normalized difference built-up and soil index (NDBSI), the wetness component of the tasseled cap transformation (wetness), and land surface temperature (LST) (Table S1). The RSEI has been extensively applied to evaluate the ecological quality of various landscapes [22]. The RSEI values range from 0 to 1, with values closer to 1 indicating higher ecological quality and lower levels of human disturbance. Conversely, values approaching 0 reflect poorer ecological conditions, often associated with increased urbanization, exposed soil surfaces, or reduced vegetation cover [21].
In our study, the RSEI calculation area was delineated as a 1 km2 square, with the midpoint of one side aligned with the first sampling station, to determine the RSEI of the land adjacent to the sandy beach (Figure S2). Previous research by our group has demonstrated that utilizing a 1 km2 RSEI sampling area effectively captures the influence of adjacent terrestrial ecological quality on intertidal macrobenthic communities [23]. The remote-sensing images, synchronized with the timing of the field sampling, were downloaded from the United States Geological Survey (www.usgs.gov) (accessed on 1 December 2024). The remote-sensing images were obtained from Landsat 8. Remote-sensing indices calculations were conducted using ENVI 5.1 software (NV5 Geospatial Software, Inc., Broomfield, CO, USA).

2.6. Data Analysis

To examine monthly variations in the abundance and biomass of sand bubbler crabs, as well as environmental factors between two sandy beaches, we initially applied the Shapiro–Wilk test to assess data normality. For datasets that deviated from normality, we conducted an analysis of differences using the Mann–Whitney U test. Before fitting the multivariate generalized linear models, the independence of response variables (crab abundance and biomass) across spatial locations and time points was assessed using the spatiotemporal Brett and Pinkse test [24], implemented in R 4.4.1 by loading the spdep and np packages. We explored the population dynamics of sand bubbler crabs and their associations with environmental variables, urbanization levels, adjacent land ecological quality (RSEI), and four remote-sensing indices, using multivariate generalized linear models. These evaluations were performed with the “mvabund” library and the “manyglm” function [25]. Stepwise regression within the multivariate generalized linear model framework was implemented to identify significant influencing factors. The corrected Akaike’s Information Criterion (AIC) [26] and model averaging methods were utilized to ascertain the relative importance (RI) of the significant factors. Models with an accumulated AIC weight of 0.95 were considered [27]. Factors with an RI around 0.9 were perceived as strong explanatory variables, those with an RI between 0.9 and 0.7 were deemed moderate, and those with an RI below 0.7 were classified as weak explanatory variables [11]. All statistical analyses were conducted using R 4.4.1 (https://cran.r-project.org/bin/windows/base/ accessed on 1 January 2025). According to Muff et al. (2022) [28], translating p-values into evidence language can significantly enhance clarity in scientific reporting (Table 2).

3. Results

3.1. Environmental Characteristics and Urbanization Index

Table 3 displays the environmental factor values for the two sandy beaches. In June, Gijipo sandy beach had the lowest average ground temperature at 21.4 °C, while Kkotjji sandy beach had the highest average ground temperature in August and September, reaching 31.4 °C. Regarding grain size, Gijipo sandy beach had the largest average size in July at 2.89∅, whereas Kkotjji sandy beach had the smallest average size during the same month at 2.33∅. For ignition loss, both Gijipo and Kkotjji sandy beaches had the lowest average value in August at 0.11%, but Kkotjji sandy beach exhibited the highest average value in July at 1.57% (Table 3). The Mann–Whitney U test revealed moderate evidence of a difference in ground temperature between the two beaches in August (p = 0.032). Moreover, strong evidence suggested significant differences in ground temperature between the beaches in August (p = 0.016), September (p = 0.016), and October (p = 0.008). For mean grain size and ignition loss, only weak evidence indicated differences between the beaches across all months (p > 0.05) (Table 3).
Every month, the urbanization value of Kkotjji sandy beach consistently exceeded that of Gijipo sandy beach. The monthly variation in urbanization values was influenced by three key indicators: solid waste in the sand, vehicles traffic on the sand, and frequency of visitors. Urbanization indicator values and urbanization index values for the two sandy beaches are presented in Table 4.

3.2. RSEI of Adjacent Land

Table S2 displays the monthly average values of the RSEI and four remote-sensing indices for the land adjacent to the beaches. The average RSEI value for the land adjacent to Gijipo sandy beaches was highest in October at 0.726 and lowest in August at 0.648. In contrast, the average RSEI value for the land adjacent to Kkotjji sandy beaches peaked in September at 0.595 and reached its lowest in July at 0.426. According to Figure 2, the ecological quality of the land adjacent to Kkotji sandy beach was generally inferior to that of Gijipo sandy beach.

3.3. Sand Bubbler Crab on Sandy Beaches

In October, Gijipo sandy beach recorded the lowest total abundance of sand bubbler crabs, with 35 individuals and an average density of 7 ind./0.25 m2 (Figure S3 and Figure 3); this was also the lowest recorded value. In contrast, at Kkotjji sandy beach, the total abundance was the highest, with 155 individuals and an average density of 31 ind./0.25 m2, which was the highest observed value (Figure S3 and Figure 3). Little or no evidence suggested monthly differences in the abundance of sand bubbler crabs between Gijipo and Kkotjji sandy beaches (p > 0.1).
In July, the average biomass of sand bubbler crabs at Gijipo sandy beach was the lowest, recorded at 0.0463 g, whereas in October, it reached its highest at 0.3408 g (Figure 4). There was very strong evidence of differences in the biomass of sand bubbler crabs between Gijipo and Kkotjji sandy beaches in both August and October (p < 0.001). Moderate evidence suggested differences in September (p = 0.021), while weak evidence indicated potential differences in July (p = 0.066). In June, there was little evidence to suggest any significant difference (p = 0.216).

3.4. Results of Multivariate Generalized Linear Models

In the spatiotemporal Brett and Pinkse test, the test statistic for crab abundance in the temporal domain was 0.006 (p = 0.885), and for crab biomass, it was 0 (p = 1). In the spatial domain, the test statistic for crab abundance was 0.0209 (p = 0.351), and for crab biomass, it was 0.0019 (p = 0.22). These results indicate little or no evidence of underlying spatiotemporal structure in the response variables.
Before conducting the multivariate generalized linear model (GLM) analysis, the urbanization index, NDBSI, and LST were identified as factors with moderate evidence of association with sand bubbler crab biomass (p < 0.05). Specifically, the urbanization index had an estimated effect of 0.39 (95% CI: 0.31–0.46), NDBSI had an estimated effect of 0.35 (95% CI: 0.28–0.41), and LST had an estimated effect of 0.34 (95% CI: 0.22–0.45). For crab abundance, moderate evidence also supported associations with the urbanization index, NDVI, and wetness (p < 0.05). The urbanization index had an estimated effect of 8.56 (95% CI: 6.80–10.30), NDVI had an estimated effect of −11.54 (95% CI: −17.17, −5.80), and wetness had an estimated effect of 25.19 (95% CI: 19.71–30.71).
The results of multivariate generalized linear models showed that the urbanization index (RI = 0.99) and LST (RI = 0.97) had high RI values, indicating that these factors significantly influenced the biomass of sand bubbler crabs (Table 5). Conversely, for the abundance of sand bubbler crabs, the urbanization index had a moderate RI value (RI = 0.83), suggesting a moderate impact on abundance (Table 6).

4. Discussion

4.1. Urbanization Index on Sandy Beaches

The urbanization index has been extensively employed to assess the degree of urbanization of beaches [2]. Based on this index, our study accurately evaluated the urbanization levels of two beaches. By analyzing data collected during different periods, we demonstrated the impact of peak and off-peak tourist seasons on the urbanization of sandy beaches. However, the index has limitations. In our previous study, we applied the original version of the urbanization index to evaluate the urbanization level of ten sandy beaches on Anmyeon Island. However, the six indicators included in the index relied heavily on subjective judgment, which compromised the objectivity of the assessment and hindered meaningful comparisons with similar studies [2]. To address this issue, we implemented a modified urbanization index by Schlender et al. (2023) [9], which employs specific, clearly defined assessment criteria. This revised index enables a more objective and accurate evaluation, overcoming the limitations of the original version. The urbanization index was originally developed based on human activity patterns typical of South American sandy beaches. However, fieldwork conducted on South Korean beaches revealed notable regional differences. In particular, the collection of invertebrates—especially edible shellfish—by tourists was found to be both frequent and widespread, yet this type of activity is not well accounted for in the original index. This suggests that relying solely on the index may overlook key local pressures. For instance, trampling by visitors can directly damage macrofaunal communities through crushing or sediment compaction [15,29]. Although recreational fisheries are often small in scale, they may still contribute to the decline of target species, disturb sediments, and reduce the ecological suitability of beach habitats for both target and non-target organisms [30]. In light of these findings, we suggest that assessments of beach urbanization should not only apply standardized indices but also consider the specific characteristics of local human activities.

4.2. Populations of Sand Bubbler Crabs on Sandy Beaches

Throughout the five-month survey period, little to no evidence indicated differences in the abundance of sand bubbler crabs between the two beaches (Figure 3). Nonetheless, significant variations in crab biomass were noted during August, September, and October (Figure 4). Sand bubbler crabs primarily consume organic matter within the sediment, and no significant differences in sediment organic matter content were detected between the two beaches. Previous research has demonstrated that human recreational activities can significantly affect sand bubbler crabs, prompting them to alter their foraging behaviors in response to such disturbances [31]. Therefore, we hypothesize that the observed variation in crab biomass is likely due to differences in tourist activity levels between the two beaches. Furthermore, sand bubbler crabs play an essential role in the functioning of sandy beach ecosystems, particularly in sediment reworking [13]. When their populations are negatively impacted, broader ecological consequences can be expected. This is especially significant on the upper intertidal beaches in South Korea, where sand bubbler crabs are the dominant species.

4.3. Impact of Urbanization and Ecological Quality on Sand Bubbler Crab Populations

The impact of urbanization on sandy beach ecosystems has traditionally held significant research interest. In our study, the results indicated that beach urbanization significantly influenced the population of sand bubbler crabs (Table 5 and Table 6). Previous research has shown that anthropogenic activities on land can indirectly affect macrobenthic communities in adjacent intertidal zones [17,23]. Although the ecological quality of the land adjacent to the beaches did not directly affect the crab populations, we observed a marked impact of the LST on their biomass (Table 5).
The LST is primarily influenced by the extent of built-up areas in the study region, as urban expansion often replaces natural land cover with impervious surfaces. This transformation reduces evapotranspiration and increases heat retention, leading to elevated LST values [32,33,34,35]. In coastal regions, larger built-up areas with better infrastructure tend to attract more tourists [36,37]. This pattern is also reflected in our findings: Kkotji sandy beach, which has relatively well-developed infrastructure, recorded a higher number of visitors (Table 4). This increased tourist activity directly impacts the biomass of sand bubbler crabs. As discussed in Section 4.2, human activities can alter the foraging behavior of sand bubbler crabs, subsequently influencing their biomass. Hence, the LST’s effect on sand bubbler crab biomass can be attributed to its indirect role in promoting human activities that modify the behavior of these crabs.
Urbanization has diverse and far-reaching impacts on the ecological systems of South Korean sandy beaches [38]. Observations indicate that the primary human-induced pressures on these beaches include recreational fisheries, trampling, and various beach activities, such as all-terrain vehicle riding and litter removal. Studies have demonstrated that these activities not only negatively affect macrobenthic communities but also lead to habitat alterations on sandy beaches [15,31,39]. Consequently, these disturbances are likely key factors influencing sand bubbler crab populations.

4.4. Recommendations for Future Research

In our study, we assessed the relationship between sand bubbler crab abundance, biomass, urbanization, and the ecological quality of adjacent terrestrial areas. Previous research has demonstrated that urbanization can influence ghost crab burrow size and depth, as well as morphological claw variation [40,41]. Future studies could explore the effects of urbanization on sand bubbler crab burrow characteristics and morphological claw variation. Additionally, sand bubbler crabs exploit various pellet designs on the beach to optimize food collection, efficiently utilize substrates, and minimize revisiting already foraged areas [42]. Future research should also investigate how urbanization impacts these pellet designs.
Our study suggests that sand bubbler crabs are effective indicators of beach urbanization. However, their small size presents challenges for collection and measurement. One study indicated that clams are also effective indicators of beach urbanization [11]. We observed that edible shellfish (i.e., Solen strictus, Serratina capsoides, and Ruditapes philippinarum) are frequently harvested by tourists, and future research could examine the relationship between edible shellfish populations and sandy beach urbanization.
Due to research constraints, this study was limited to a five-month investigation at two sandy beaches. To enhance the comprehensiveness of future findings, it is recommended that subsequent studies extend the survey to a full year and include a wider variety of beach types.

5. Conclusions

Our results support the use of sand bubbler crabs as a disturbance indicator of sandy beach urbanization. While the crab population did not show a clear response to the overall ecological quality of adjacent terrestrial areas, the land surface temperature (LST) of the surrounding land was significantly associated with crab biomass. These findings suggest that sand bubbler crabs may serve as effective indicators of urban-related pressures in coastal environments. Our study offers valuable insights that can inform beach conservation strategies in South Korea and guide future research focusing on species responses to anthropogenic impacts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14040842/s1, Table S1: The formulae for calculating the remote-sensing indicators I; Table S2: Average of values for remote-sensing indices off two sandy beaches; Figure S1: Five sampling stations on Gijipo sandy beach; Figure S2: Sampling area of RSEI on the land adjacent to Gijipo sandy beach; and Figure S3: Total abundance on sandy beaches.

Author Contributions

Conceptualization, H.-R.H.; Methodology, H.-R.H.; Software, H.-R.H.; Validation, H.-R.H.; Formal analysis, H.-R.H.; Investigation, H.-R.H. and J.L.; Resources, H.-R.H. and J.L.; Data curation, H.-R.H. and J.L.; Writing—original draft, H.-R.H., J.L. and C.-W.M.; Writing—review & editing, H.-R.H., J.L. and C.-W.M.; Visualization, C.-W.M.; Supervision, C.-W.M.; Project administration, C.-W.M.; Funding acquisition, C.-W.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Soonchunhyang University Research Fund.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Augusto, M.; Abude, R.R.S.; Cardoso, R.S.; Cabrini, T.M.B. Local Urbanization Impacts Sandy Beach Macrofauna Communities over Time. Front. Mar. Sci. 2023, 10, 1158413. [Google Scholar] [CrossRef]
  2. Liang, J.; Shu, M.-Y.; Huang, H.-R.; Ma, C.-W.; Kim, S.-K. Using Benthic Indices to Assess the Ecological Quality of Sandy Beaches and the Impact of Urbanisation on Sandy Beach Ecosystems. J. Mar. Sci. Eng. 2024, 12, 487. [Google Scholar] [CrossRef]
  3. Orlando, L.; Ortega, L.; Defeo, O. Urbanization Effects on Sandy Beach Macrofauna along an Estuarine Gradient. Ecol. Indic. 2020, 111, 106036. [Google Scholar] [CrossRef]
  4. Day, J.W.; Gunn, J.D.; Burger, J.R. Diminishing Opportunities for Sustainability of Coastal Cities in the Anthropocene: A Review. Front. Environ. Sci. 2021, 9, 663275. [Google Scholar] [CrossRef]
  5. Taibi, N.-E. Conflict Between Coastal Tourism Development and Sustainability: Case of Mostaganem, Western Algeria. Int. J. Sustain. Dev. 2016, 5, 13–24. [Google Scholar] [CrossRef]
  6. González, S.A.; Yáñez-Navea, K.; Muñoz, M. Effect of Coastal Urbanization on Sandy Beach Coleoptera Phaleria maculata (Kulzer, 1959) in Northern Chile. Mar. Pollut. Bull. 2014, 83, 265–274. [Google Scholar] [CrossRef]
  7. Checon, H.H.; Corte, G.N.; Shah Esmaeili, Y.; Muniz, P.; Turra, A. The Efficacy of Benthic Indices to Evaluate the Ecological Quality and Urbanization Effects on Sandy Beach Ecosystems. Sci. Total Environ. 2023, 856, 159190. [Google Scholar] [CrossRef]
  8. Costa, L.L.; Zalmon, I.R. Multiple Metrics of the Ghost Crab Ocypode quadrata (Fabricius, 1787) for Impact Assessments on Sandy Beaches. Estuar. Coast. Shelf Sci. 2019, 218, 237–245. [Google Scholar] [CrossRef]
  9. Schlender, K.; Corte, G.; Durdall, A.; Habtes, S.; Grimes, K.W. Urbanization Driving Ocypode quadrata Burrow Density, Depth, and Width across Caribbean Beaches. Ecol. Indic. 2023, 153, 110396. [Google Scholar] [CrossRef]
  10. Barbosa, T.M.; Carmo, R.F.R.; Silva, L.P.; Sales, R.G.; Vasconcelos, S.D. Diversity of Sarcosaprophagous Calyptratae (Diptera) on Sandy Beaches Exposed to Increasing Levels of Urbanization in Brazil. Environ. Entomol. 2017, 46, 460–469. [Google Scholar] [CrossRef]
  11. Laitano, M.V.; Chiaradia, N.M.; Nuñez, J.D. Clam Population Dynamics as an Indicator of Beach Urbanization Impacts. Ecol. Indic. 2019, 101, 926–932. [Google Scholar] [CrossRef]
  12. David, P.M. Crabs That Breathe Air with Their Legs-Scopimera and Dotilla. Nature 1986, 319, 493–495. [Google Scholar]
  13. Seo, J.; Koo, B.J. The Variation on Sediment Reworking with Influencing Factors by a Sand Bubbler Crab, Scopimera Globosa, in Intertidal Sediments of the Anmyeon Island, Korea. Sustainability 2021, 13, 5703. [Google Scholar] [CrossRef]
  14. Liang, J.; Ma, C.-W.; Kim, K.-B. Comparing the Environmental Impacts of Pollution from Two Types of Industrial Zones on the Coast. Front. Mar. Sci. 2024, 11, 1433536. [Google Scholar] [CrossRef]
  15. Schlacher, T.A.; Lucrezi, S.; Connolly, R.M.; Peterson, C.H.; Gilby, B.L.; Maslo, B.; Olds, A.D.; Walker, S.J.; Leon, J.X.; Huijbers, C.M.; et al. Human Threats to Sandy Beaches: A Meta-Analysis of Ghost Crabs Illustrates Global Anthropogenic Impacts. Estuar. Coast. Shelf Sci. 2016, 169, 56–73. [Google Scholar] [CrossRef]
  16. Koh, C.-H.; De Jonge, V.N. Stopping the Disastrous Embankments of Coastal Wetlands by Implementing Effective Management Principles: Yellow Sea and Korea Compared to the European Wadden Sea. Ocean Coast. Manag. 2014, 102, 604–621. [Google Scholar] [CrossRef]
  17. Liang, J.; Ma, C.-W. Impact of Anthropogenic Activities on the Biodiversity of Macrobenthos and Benthic Ecological Quality in the Mudflats of Hwangdo Island, South Korea: Field Surveys and Remote Sensing Assessments. Front. Mar. Sci. 2025, 12, 1533891. [Google Scholar] [CrossRef]
  18. Blott, S.J.; Pye, K. GRADISTAT: A Grain Size Distribution and Statistics Package for the Analysis of Unconsolidated Sediments. Earth Surf. Process. Landf. 2001, 26, 1237–1248. [Google Scholar] [CrossRef]
  19. Liang, J.; Ma, C.-W.; Kim, K.-B. Ecological Risk Assessment of Heavy Metals in Surface Sediments and Their Impact on Macrobenthos in Asan Bay, South Korea. Front. Mar. Sci. 2024, 11, 1450396. [Google Scholar] [CrossRef]
  20. Peres-Neto, P.R.; Legendre, P.; Dray, S.; Borcard, D. Variation Partitioning of Species Data Matrices: Estimation and Comparison of Fractions. Ecology 2006, 87, 2614–2625. [Google Scholar] [CrossRef]
  21. Xu, H. A Remote Sensing Urban Ecological Index and Its Application. Acta Ecol. Sin. 2013, 33, 7853–7862. [Google Scholar]
  22. Tang, H.; Fang, J.; Xie, R.; Ji, X.; Li, D.; Yuan, J. Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI). Sustainability 2022, 14, 12694. [Google Scholar] [CrossRef]
  23. Liang, J.; Huang, H.-R.; Shu, M.-Y.; Ma, C.-W. Assessing the Impact of Land-Based Anthropogenic Activities on the Macrobenthic Community in the Intertidal Zones of Anmyeon Island, South Korea. Land 2025, 14, 62. [Google Scholar] [CrossRef]
  24. López, F.A.; Matilla-García, M.; Mur, J.; Marín, M.R. Four Tests of Independence in Spatiotemporal Data. Pap. Reg. Sci. 2011, 90, 663–686. [Google Scholar] [CrossRef]
  25. Wang, Y.; Naumann, U.; Wright, S.T.; Warton, D.I. Mvabund– an R Package for Model-based Analysis of Multivariate Abundance Data. Methods Ecol. Evol. 2012, 3, 471–474. [Google Scholar] [CrossRef]
  26. Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. In Selected Papers of Hirotugu Akaike; Parzen, E., Tanabe, K., Kitagawa, G., Eds.; Springer Series in Statistics; Springer: New York, NY, USA, 1998; pp. 199–213. ISBN 978-1-4612-7248-9. [Google Scholar]
  27. Burnham, K.P.; Anderson, D.R. (Eds.) Model Selection and Multimodel Inference; Springer: New York, NY, USA, 2004; ISBN 978-0-387-95364-9. [Google Scholar]
  28. Muff, S.; Nilsen, E.B.; O’Hara, R.B.; Nater, C.R. Rewriting Results Sections in the Language of Evidence. Trends Ecol. Evol. 2022, 37, 203–210. [Google Scholar] [CrossRef]
  29. Reyes-Martínez, M.J.; Ruíz-Delgado, M.C.; Sánchez-Moyano, J.E.; García-García, F.J. Response of Intertidal Sandy-Beach Macrofauna to Human Trampling: An Urban vs. Natural Beach System Approach. Mar. Environ. Res. 2015, 103, 36–45. [Google Scholar] [CrossRef]
  30. Defeo, O.; McLachlan, A.; Schoeman, D.S.; Schlacher, T.A.; Dugan, J.; Jones, A.; Lastra, M.; Scapini, F. Threats to Sandy Beach Ecosystems: A Review. Estuar. Coast. Shelf Sci. 2009, 81, 1–12. [Google Scholar] [CrossRef]
  31. Chen, P.Z.; Su, T.L.; Lim, S.S.L. To Hide or to Feed: An Evaluation of Personality Traits in the Sand Bubbler Crab, Dotilla Wichmanni, When Responding to Environmental Interference. Behav. Process. 2019, 164, 123–132. [Google Scholar] [CrossRef]
  32. Zhang, J.; Zhou, Q.; Cao, M.; Liu, H. Spatiotemporal Change of Eco-Environmental Quality in the Oasis City and Its Correlation with Urbanization Based on RSEI: A Case Study of Urumqi, China. Sustainability 2022, 14, 9227. [Google Scholar] [CrossRef]
  33. Liu, Y.; Meng, Q.; Zhang, L.; Wu, C. NDBSI: A Normalized Difference Bare Soil Index for Remote Sensing to Improve Bare Soil Mapping Accuracy in Urban and Rural Areas. Catena 2022, 214, 106265. [Google Scholar] [CrossRef]
  34. Zhang, W.; Zhang, W.; Ji, J.; Chen, C. Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China. Sustainability 2024, 16, 3598. [Google Scholar] [CrossRef]
  35. Kong, W.; Chang, W.; Xie, M.; Li, Y.; Wan, T.; Nie, X.; Mo, D. Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province. Forests 2024, 15, 1541. [Google Scholar] [CrossRef]
  36. Gormsen, E. The Impact of Tourism on Coastal Areas. GeoJournal 1997, 42, 39–54. [Google Scholar] [CrossRef]
  37. Das, S.; Bhattacharya, S. Factors Affecting Beach Walkability-Tourists’ Perception Study at Selected Beaches of West Bengal, India. J. Outdoor Recreat. Tour. 2021, 35, 100423. [Google Scholar] [CrossRef]
  38. Choi, K.H.; Yoon, K.S.; Choi, J.H.; Shin, Y.K.; Lee, J.C.; Suh, M.H.; Munyikwa, K.; Oh, K.H. Anthropogenic Geomorphological Changes during the Last Century in the Kangneung Area along the East Coast of Korea. J. Coast. Res. 2007, 50, 1015–1022. [Google Scholar] [CrossRef]
  39. Machado, P.M.; Suciu, M.C.; Costa, L.L.; Tavares, D.C.; Zalmon, I.R. Tourism Impacts on Benthic Communities of Sandy Beaches. Mar. Ecol. 2017, 38, e12440. [Google Scholar] [CrossRef]
  40. Gül, M.R.; Griffen, B.D. Impacts of Human Disturbance on Ghost Crab Burrow Morphology and Distribution on Sandy Shores. PLoS ONE 2018, 13, e0209977. [Google Scholar] [CrossRef]
  41. Gül, M.R.; Griffen, B.D. Changes in Claw Morphology of a Bioindicator Species across Habitats That Differ in Human Disturbance. Hydrobiologia 2020, 847, 3025–3037. [Google Scholar] [CrossRef]
  42. De, C. Ethological Interpretation of Making the Pellet Designs by the Bubbler Crab Dotilla on the Modern Intertidal Beaches: A Study from the Bay of Bengal Coast, Eastern India. Palaeontol. Electron. 2024, 27, a11. [Google Scholar] [CrossRef]
Land 14 00842 g001
Figure 1. Sampling stations at Gijipo and Kkotji sandy beaches on Anmyeon Island, South Korea. Note: G1–G5 and K1–K5 indicate the sampling locations.
Figure 1. Sampling stations at Gijipo and Kkotji sandy beaches on Anmyeon Island, South Korea. Note: G1–G5 and K1–K5 indicate the sampling locations.
Land 14 00842 g001
Land 14 00842 g002
Figure 2. RSEI of adjacent land of Gijipo and Kkotjji sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; RSEI values range from 0 to 1, where values close to 1 (green) indicate high ecological quality, while values near 0 (red) reflect low ecological quality.
Figure 2. RSEI of adjacent land of Gijipo and Kkotjji sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; RSEI values range from 0 to 1, where values close to 1 (green) indicate high ecological quality, while values near 0 (red) reflect low ecological quality.
Land 14 00842 g002
Land 14 00842 g003
Figure 3. Boxplots for the abundance of sand bubbler crab on two sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; 6–10, month.
Figure 3. Boxplots for the abundance of sand bubbler crab on two sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; 6–10, month.
Land 14 00842 g003
Land 14 00842 g004
Figure 4. Boxplots for biomass of sand bubbler crab on two sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; 6–10, month; *, p < 0.05; ***, p < 0.001.
Figure 4. Boxplots for biomass of sand bubbler crab on two sandy beaches. Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; 6–10, month; *, p < 0.05; ***, p < 0.001.
Land 14 00842 g004
Table 1. The evaluation criteria for the six indicators in the urbanization index (adapted from Schlender et al., 2023) [9].
Table 1. The evaluation criteria for the six indicators in the urbanization index (adapted from Schlender et al., 2023) [9].
Proximity to Urban CentersBuildings on the SandCleaning of the BeachSolid Waste in the SandTraffic of Vehicles on the SandFrequency of Visitors
Low>4 km = 0
3–4 km = 1		No
buildings/structures = 0
One small building = 1		None = 0
Occasional trash pickup = 1		No trash observed across the entire beach = 0
Minimal (0–2 pieces of trash within 3 m2) = 1		No access/traces = 0
Faint traces of potential traffic
(1 old tire track observed) = 1		None = 0
Very few = 1
Medium2–3 km = 2
1–2 km = 3		2–3 small structures = 2
>3 small structures = 3		Occasional raking = 2
Regular raking = 3		Some waste (3–4 pieces of trash within 3 m2) = 2
Some waste
(5 pieces of trash within 3 m2) = 3		Weathered/scarce traces (1–2 faded tire tracks observed) = 2
Scarce/clear tracks
(1–2 clear fresh tire tracks observed) = 3		Several = 2
Many visitors; considered “popular” = 3
High0.5–1 km = 4
0–0.5 km = 5		2 buildings/structures = 4
>3 buildings = 5		Small machinery used = 4
Large machinery used = 5		Multiple types/pieces of waste (6–7 pieces of trash within 3 m2) = 4
(7 + pieces of trash within 3 m2) = 5		Frequent but limited traffic (3+ fresh tire tracks limited to one section of the beach) = 4
Frequent traffic across the entire beach
(3+ tire tracks) = 5		2–3 people/minute = 4
>3 people/minute = 5
Table 2. The evidence language corresponding to p-values.
Table 2. The evidence language corresponding to p-values.
p-ValueLanguage of Evidence
1–0.1Little or no evidence
0.1–0.05Weak evidence
0.05–0.01Moderate evidence
0.01–0.001Strong evidence
0.001–0.0001Very strong evidence
Table 3. Environmental variables (mean ± coefficient of variation) at Gijipo and Kkotji sandy beaches with significance tests of inter-beach differences.
Table 3. Environmental variables (mean ± coefficient of variation) at Gijipo and Kkotji sandy beaches with significance tests of inter-beach differences.
Sandy BeachBeach Temperaturep-ValueMean Grain Sizep-ValueIgnition Lossp-Value
G621–21.8 (21.4 ± 0.02)p > 0.052.45–3.05 (2.86 ± 0.09)p > 0.050.37–0.52 (0.42 ± 0.14)p > 0.05
K620.9–23.6 (22.6 ± 0.05)2.38–3.08 (2.85 ± 0.10)0.27–0.55 (0.43 ± 0.25)
G725.5–27.0 (26.3 ± 0.02)p > 0.052.72–3.20 (2.89 ± 0.06)p > 0.050.59–3.39 (1.54 ± 0.72)p > 0.05
K726.3–29.0 (27.8 ± 0.04) 1.05–2.88 (2.33 ± 0.32)0.89–2.56 (1.57 ± 0.42)
G828.7–30.8 (29.6 ± 0.03)p = 0.0322.54–3.02 (2.86 ± 0.07)p > 0.050.08–0.19 (0.11 ± 0.41)p > 0.05
K830.3–32.8 (31.4 ± 0.03)2.50–3.02 (2.81 ± 0.08)0.08–0.16 (0.11 ± 0.29)
G928.7–30.8 (29.6 ± 0.03)p = 0.0162.45–2.95 (2.80 ± 0.07)p > 0.050.49–0.73 (0.61 ± 0.16)p > 0.05
K930.3–32.8 (31.4 ± 0.03)1.51–2.92 (2.61 ± 0.24)0.40–0.81 (0.57 ± 0.28)
G1018.7–21.2 (20.1 ± 0.05)p = 0.0082.46–3.03 (2.78 ± 0.09)p > 0.050.22–0.52 (0.37 ± 0.33)p > 0.05
K1021.4–22.8 (21.9 ± 0.03)1.52–2.96 (2.66 ± 0.24)0.19–0.69 (0.44 ± 0.46)
Note: Beach temperature is expressed in degrees Celsius (°C); mean grain size is expressed in phi (ϕ) units. Ignition loss is reported as the percentage of dry sediment weight; p-values are based on Mann–Whitney U tests comparing Gijipo (G) and Kkotji (K) beaches in the same month (6–10 = June–October).
Table 4. Values of urbanization indicators and urbanization index value on two sandy beaches.
Table 4. Values of urbanization indicators and urbanization index value on two sandy beaches.
G6K6G7K7G8K8G9K9G10K10
Proximity to urban centers2323232323
Buildings on the sand0202020202
Cleaning of the beach0303030303
Solid waste in the sand3243333232
Vehicles traffic on the sand0113030301
Visitor frequency1535352515
Urbanization index value0.20.50.30.60.30.60.20.60.20.5
Note: G, Gijipo sandy beach; K, Kkotjji sandy beach; 6–10, month.
Table 5. Results of the best five models testing the effects of the urbanization index, NDBSI, and LST on sand bubbler crab biomass.
Table 5. Results of the best five models testing the effects of the urbanization index, NDBSI, and LST on sand bubbler crab biomass.
BiomassModelUrbanization IndexNDBSILSTAICiΔiWi
M1+-+527.19 00.96
M2+--534.08 6.890.03
M3-+-538.72 11.530
M4--+538.97 11.780
M5-++540.49 13.30
RI 0.990.030.97
Note: +, presence of variable factor; -, absence of variable factor; AICi, Akaike’s Information Criterion; Δi, difference between each model’s AICi value and the minimum AICi value; Wi, Akaike’s weight; RI, relative importance.
Table 6. Results of the best seven models testing the effects of the urbanization index, NDVI, and wetness on sand bubbler crab abundance.
Table 6. Results of the best seven models testing the effects of the urbanization index, NDVI, and wetness on sand bubbler crab abundance.
AbundanceModelUrbanization IndexNDVIWetnessAICiΔiWi
M1+--382.8300.27
M2++-383.230.400.22
M3+-+383.510.670.19
M4+++384.161.330.14
M5-+-385.352.520.08
M6--+385.582.740.07
M7-++387.264.430.03
RI 0.830.470.43
Note: +, presence of variable factor; -, absence of variable factor; AICi, Akaike’s Information Criterion; Δi, difference between each model’s AICi value and the minimum AICi value; Wi, Akaike’s weight; RI, relative importance.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, H.-R.; Liang, J.; Ma, C.-W. Is the Sand Bubbler Crab (Scopimera globosa) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality? Land 2025, 14, 842. https://doi.org/10.3390/land14040842

AMA Style

Huang H-R, Liang J, Ma C-W. Is the Sand Bubbler Crab (Scopimera globosa) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality? Land. 2025; 14(4):842. https://doi.org/10.3390/land14040842

Chicago/Turabian Style

Huang, Hai-Rui, Jian Liang, and Chae-Woo Ma. 2025. "Is the Sand Bubbler Crab (Scopimera globosa) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality?" Land 14, no. 4: 842. https://doi.org/10.3390/land14040842

APA Style

Huang, H.-R., Liang, J., & Ma, C.-W. (2025). Is the Sand Bubbler Crab (Scopimera globosa) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality? Land, 14(4), 842. https://doi.org/10.3390/land14040842

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop