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Article

The Geometry of Southern China’s Mangroves: Small and Elongated

by
Lin Zhang
1,2,†,
Yijuan Deng
1,2,†,
Wenqing Wang
1,2 and
Mao Wang
1,2,*
1
Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment & Ecology, Xiamen University, Xiamen 361005, China
2
Zhangjiang Estuary Mangrove Wetland Ecosystem Station, National Observation and Research Station for the Taiwan Strait Marine Ecosystem, Xiamen University, Zhangzhou 363105, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2025, 16(2), 212; https://doi.org/10.3390/f16020212
Submission received: 20 December 2024 / Revised: 20 January 2025 / Accepted: 21 January 2025 / Published: 23 January 2025
(This article belongs to the Special Issue Mangrove Forest Ecosystems: Present Status, Challenges, and Future Directions)
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Abstract

:
Mangrove wetlands are naturally divided into habitat patches by tidal creeks, with patch edges highly vulnerable to human activities and biological invasions, making them critical areas for mangrove degradation. Understanding the geometrical characteristics of these patches is essential for mangrove management in the Anthropocene, yet their exploration remains limited. Using a high-resolution (2 m) mangrove distribution dataset from 2018, we analyzed the patch structure of mangroves in southern China. This study revealed predominantly small and elongated patches, with an average area of 0.044 km2 and a median of 0.011 km2 across 5857 patches. About 65% of patches had a major-axis length over twice their minor-axis length. The patch number and area peaked between 19° N and 22° N. The patch number and area peaked between 19° N and 22° N. In the 0.1° × 0.1° latitudinal-longitudinal grid, the maximum mangrove area was 9.03 km2, consisting of 192 patches. Additionally, the patch composition and geometric characteristics differed significantly among the existing reserves. These findings highlight the need to prioritize the patch geometry in management strategies, especially in regions with numerous small patches prone to degradation and invasion. Additionally, this study underscores a critical research gap: the ecological impacts of mangrove fragmentation on biodiversity and ecosystem services remain poorly understood. Future research should focus on how the patch structure and landscape configuration influence ecological processes in mangrove wetlands.

1. Introduction

In the Anthropocene, the impact of habitat fragmentation on biodiversity and ecosystem functions has been extensively studied [1,2,3]. Although there is debate over the extrapolation of patterns and mechanisms from patch to landscape scales [4,5,6], the influence of the patch geometry on various ecological processes at the patch scale is widely accepted [1,7,8]. For example, the invasibility of interior versus edge areas of habitat patches differs [9,10], the trade-offs between forest biodiversity and ecosystem services vary between edges and interiors [11], and forest patch shapes influence soil retention ecosystem services [12]. These phenomena, typically summarized as edge effects, are observed by comparing differences between the interiors and edges of habitat patches [13,14]. Road construction, deforestation, and agricultural use are considered the primary human activities driving global habitat loss and fragmentation over the past several decades [15,16,17]. However, there are also naturally fragmented ecosystems that are shaped by natural forces. For example, coastal marshes and mangrove wetland ecosystems are segmented by networks of tidal channels [18,19]. This fragmentation creates unique landscape features while also influencing various ecological processes and functions in coastal wetlands [20,21,22].
Globally, mangroves are primarily distributed between the latitudes of 30 degrees north and south [23,24] and provide numbers of key services and benefits to people [25,26]. For example, they support biodiversity in coastal ecosystems [27], provide fisheries and timber resources [28], and offer protection against typhoons [29]. These functions make mangroves one of the most valuable ecosystems in terms of global ecosystem services. However, over the past few decades, mangroves worldwide have experienced significant degradation, primarily characterized by a substantial decline in area [30,31,32,33]. This global decline has garnered widespread attention and prompted numerous mangrove restoration efforts [34,35,36]. Recently, an ecosystem vulnerability assessment by the IUCN indicated that due to rising sea levels, half of the world’s mangroves are at risk of collapse by 2050 [37]. Against the backdrop of global decline, China’s mangrove forests have been growing at an annual rate of 1.8% since the start of the 21st century, thanks to extensive conservation efforts and reforestation projects that have led to significant vegetation restoration [38,39]. Despite the increase in area, China’s mangrove forests are still in a state of degradation, particularly under the pressures of rising sea levels and terrestrial human activities [38,40].
The landscape characteristic of mangrove forests along China’s coast has undergone rapid changes due to the combined effects of the sea-level rise, human destruction, biological invasions, and ecological restoration [39,41]. The dynamics of coastal mangroves have received widespread attention, with research focusing on the temporal trends in area [39], the spatiotemporal patterns of disturbance [42], and land use and land cover changes in mangroves and adjacent areas along with their ecological effects [43,44]. These studies heavily rely on the rapidly evolving remote sensing technologies and the extensive datasets available [45,46,47,48]. Beyond the changing climate and human activities, the rapid spread of invasive species is also a significant threat to China’s coastal mangrove forests [38,49]. The main invasive mangrove species in China are Laguncularia racemosa and Sonneratia apetala. Current evidence indicates that these two species are now widely distributed across most of the mangrove regions in China [50,51]. Another invasive species affecting mangrove wetlands is the salt marsh plant Spartina alterniflora, commonly known as smooth cordgrass [49]. Recent studies indicate that the colonization and invasion of coastal wetland plants primarily occurs at the edges of native vegetation patches [52,53]. In mangrove wetlands, the composition of animal and plant species as well as the physicochemical properties of sediments at the edges of mangroves differ significantly from those in the interior [27,54]. The edges of mangrove forests are particularly vulnerable to human activities, such as boat traffic, which can cause the degradation of the mangroves along these peripheral areas [55]. The edges of mangrove forests are often the initial points where degradation begins [56,57]. Therefore, assessing the geometric characteristics of mangrove patches is important for the conservation and management of mangrove ecosystems, especially for those along China’s south coast facing multiple stress factors.
Therefore, this study focuses on the mangrove patches along China’s coastal areas, utilizing high spatial resolution distribution datasets to examine the geometric properties of mangrove patches. Specifically, this research aims to explore the geometric characteristics and spatial patterns of mangrove patches in China to facilitate multiscale conservation and management planning for mangrove ecosystems.

2. Materials and Methods

2.1. Study Region and Dataset Summary

This study was conducted in the mangrove forests along the southern coast of China (Figure 1a), extending from Sanya in Hainan (18.2° N) to Wenzhou in Zhejiang Province (28.4° N). A significant concentration of mangroves is primarily found south of 22° N latitude (Figure 1b). Although the mangroves in Zhejiang are artificially planted, they have a long history of introduction and have entered a natural reproduction phase, thus they were also included in this analysis. Thanks to the rapid development of remote sensing technology, the mapping of mangrove distribution has evolved quickly from 30 m to 10 m resolution [58,59]. Recently, Jia et al., 2023 [23] released a global mangrove distribution dataset at a 10 m resolution based on Sentinel-2 satellite imagery. Considering the potential impacts of resolution on the calculation of geometric properties, we selected a dataset integrating data from the Gaofen-1 and Ziyuan-3 satellite, which offers a 2 m resolution map of mangrove distribution in China for 2018 [46]. This dataset is currently one of the highest-resolution datasets available for mangrove vegetation cover in the China region. Although recent 10 m resolution remote sensing datasets are available for China’s mangrove [23], they do not provide comparable capabilities in depicting geometric features as the 2 m resolution datasets. Therefore, our study did not consider using additional data from the 10 m resolution datasets. The dataset used for mangrove distribution in China for 2018 (MC2018) has a high level of accuracy, with the overall accuracy being 99.3%, the kappa coefficient at 0.985, the mapping accuracy at 99.3%, and the user accuracy at 99.0% [46]. Compared to other low-resolution mangrove datasets, the MC2018 dataset features smooth patch edges and complete structures for small patches, with fewer missing errors [46]. Therefore, we selected this dataset for further geometric characteristic analysis. The MC2018 dataset can be publicly downloaded from the Science Data Bank and used in accordance with the CC BY 4.0 license [60].

2.2. Data Preprocessing and Geometric Metrics Calculation

We first applied the “st_is_valid” function within the “sf” package, to validate each polygon in the MC2018 dataset. Of the original 5859 polygons, two had redundant duplicated points. These were rectified using the “st_make_valid” function and the corrected polygons were replaced in the dataset. We then calculated the area of each polygon using the “st_area” function, finding that two polygons were smaller than 1 m2, which led to their exclusion from the dataset. The remaining 5857 polygons were then subject to geometric property analysis. Following the methodologies of Schauman et al., 2023 [61], we used ten metrics related to shape and size (Table 1). Each patch’s area (#1) was calculated on an Alberta Equal Area projection. Using the World Mollweide projection, we calculated Maximum depth (MaxD, #2), Perimeter (PER, #3), Perimeter-to-area ratio (PAR, #4), and Depth compactness (DC, #5). The World Mercator projection was employed to minimize shape distortions and to calculate the Polsby–Popper (PPR, #6), Convex Hull Ratio (CHR, #7), Reock (#8), Bounding box ratio (BBR, #9), and Elongation (#10). For the MaxD calculation, the polygon was first transformed into one of the patch edges using the “as.lines” function. We then rasterized the polygon to a 1 m resolution using the “rasterize” function. Next, we calculated the distance to the closest edge for cells at a 1 m spatial resolution. Finally, we determined the MaxD for each polygon as the maximum distance to the closest edge. The calculation of geometric metrics was primarily carried out using the “terra” and “sf” packages [62,63]. These metrics have been widely used in earlier studies to characterize the geometric properties of geographical objects [61]. Area, MaxD, and PER metrics describe patch size from different perspectives, while the remaining parameters are primarily related to patch shape. These shape-related metrics mainly characterize the differences between an individual object (in this study, each mangrove patch) and standard geometric shapes, such as circles, rectangles, and convex polygons. Therefore, these shape-related metrics primarily reflect the degree of shape irregularity. Size-related metrics are primarily used in conservation planning, while shape-related metrics influence the connectivity of patches with their surrounding environment. These shape characteristics at the patch scale affect ecological processes, such as biological invasion [64,65].

2.3. Local Mangrove Aggregation

Mangrove conservation and management are typically conducted at the local scale rather than at the patch scale. Therefore, we aggregated all patches spatially and calculated the mangrove area for each local region. Patch aggregation involved computing a minimum distance matrix between patch pairs and conducting a connected component analysis at various distance thresholds. We selected an appropriate distance threshold based on how the number of patches changed with increasing distance thresholds, identifying each connected area as a local mangrove region. The minimum distance between patch pairs was determined by calculating the shortest distance between points on one polygon to the edge points of another polygon. The local mangrove aggregation was implemented using the “igraph” package [66].

2.4. Statistical Analysis

To represent the heterogeneity of patch shapes in a multidimensional space, we employed a non-metric multidimensional scaling ordination model (NMDS). Prior to the NMDS analysis, the data were log-transformed and then range-standardized to ensure uniform scale across all geometric metrics. The NMDS was performed using the “metaMDS” function from the “vegan” package in R [67]. To evaluate how well the NMDS represents the distance among the patches, we calculated the stress value of the ordination [68]. A stress value below 0.20 is generally considered to indicate a good fit, while values lower than 0.10 suggest an excellent fit, providing robust support for the spatial interpretations derived from the NMDS analysis [68,69]. We used the “envfit” function from the “vegan” package to fit the ten geometric metrics to the result of NMDS ordination. We calculated Pearson correlation coefficients between any two metrics, setting the significance level for correlations at 0.05. We used the “nparcomp” package to perform non-parametric tests for comparing differences in geometric metrics among five selected reserves [68,69]. All data analyses, numerical computations, and the creation of maps and statistical graphs were performed using the R version 4.3.3 [70].

3. Results

3.1. Summary of Coastal China’s Mangrove Patches

Overall, the MC2018 dataset includes 5857 valid mangrove patches. The distribution of patch areas is highly concentrated below 0.1 km2, with an average area of 0.044 km2 and a median of 0.011 km2. After log transformation, the area distribution of the patches approaches a symmetric bell-shaped curve (see Figure 2a). Among the 105 local mangrove areas aggregated using a 9 km threshold, the area distribution is also highly concentrated, with an average area of 2.45 km2 and a median of 0.13 km2, with one region having the largest area of 60.7 km2 (see Figure 2b,c). The largest local aggregation (Figure 2c) is currently designated as two national nature reserves and a provincial nature reserve (www.china-mangrove.org (accessed on 24 September 2024)). In addition to the patch area, four out of the nine other geometric metrics (DC, MaxD, PAR, and Perimeter) also exhibit highly concentrated distributions, with medians greater than the means (Figure 3). The other five shape-related metrics (BBR, CHR, Elongation, PPR, and Reock) each display varying degrees of skewness (Figure 3).
The cross-reserve analysis revealed significant differences in the geometric characteristics among the existing reserves (Figure 4). Size-related metrics show significant variability within the reserves. For example, in all five reserves, the standard deviation of the patch area exceeds the mean (Figure 4c). Shape-related metrics, however, exhibited less variability (Figure 4c). The northernmost mangrove national nature reserve, Zhangjiang Estuary Mangrove National Nature Reserve (ZJE), is the smallest, with only 37 patches and a total area of 0.9 km2 (Figure 4b). Several geometric metrics of ZJE also showed significant differences compared to the other four sites (Figure 4c). The two largest reserves also differ significantly in their patch composition. The largest reserve, Dongzhaigang Mangrove National Nature Reserve (DZG), has fewer patches with larger areas, while the second-largest reserve, Beilun Estuary National Nature Reserve (BLE), consists of numerous small patches (Figure 4b,c). The mangroves in Shenzhen Bay (SZB) had the fewest patches among the five reserves. Its average patch area was close to that of DZG and larger than the average patch area in the other three reserves (Figure 4b,c).

3.2. Correlations Between Geometric Metrics

Geometric metrics often show significant correlations. Among all 45 pairs of geometric metrics, only 3 pairs exhibited non-significant correlations (Figure 5). These metrics can be broadly classified into two categories based on the correlation analysis. The first category includes size-related metrics, such as the area, perimeter, and MaxD, which exhibit strong positive correlations with each other (Figure 5). The second category comprises shape-related metrics, including CHR, PPR, DC, Elongation, and Reock, which also show strong positive correlations among themselves (Figure 5). In contrast, the correlations between these two categories are generally negative and relatively weak (Figure 5). The observed negative correlation between the size and shape metrics suggests that smaller patches tend to exhibit irregular geometries, often appearing elongated or disproportionate despite their limited area. Although Pearson correlation coefficients were used, the relationships between geometric metrics are generally non-linear. For example, the relationship between the perimeter and area follows an exponential function with a power less than one (Figure 6a). The actual perimeter of mangrove patches is greater than that of a hypothetical circle with the same area, and this discrepancy (the distance between the blue and red lines in Figure 6a) increases with the increasing area. We also found a wide range of variations in the reciprocal of elongation, with a maximum value of 67.4, indicating an extremely elongated patch where the length of the minimum bounding rectangle is 67.4 times its width, with 164 patches having a long axis more than ten times their short axis (Figure 6b). Additionally, as the area increases, the elongation approaches 1 (Figure 6b).

3.3. Geographic Patterns of Mangrove Area and Patch Number

We used a 0.1° × 0.1° grid (totaling 341 grids) to statistically analyze the distribution of mangroves in China in terms of the area and number of patches. The results indicate that both the number of patches and the area are highest at around latitudes 19–22° N. Within a single grid, the maximum area recorded was 9.03 km2 (Figure 7a), with the highest number of patches being 192 (Figure 7b). In most 0.5° × 0.5° grid cells, the area of mangrove vegetation patches is less than 10 km2 (Figure 7c). The spatial distribution patterns of the patch area and patch number are largely consistent (Figure 7c,d). Large mangrove patches are primarily located in southern Guangdong, northern Hainan, and southern Guangxi (Figure 7c). Elongated patches (i.e., those with higher 1/elongation values) are also mainly distributed in the Guangdong Province (Figure 7e). In the two-dimensional NMDS plot, the variations in the shape of mangrove patches were well represented, with a stress value of 0.11. The two axes of the NMDS mainly reflected the size and shape information of the patches: the first axis primarily related to the area and perimeter, while the second axis reflected multiple geometric metrics (Figure 8a,b). A large number of patches are clustered near the origin of the two-dimensional NMDS map (Figure 8a), indicating that many patches share highly similar characteristics. This observation is consistent with the aggregated distribution of our geometric parameter data (Figure 3). Shape- and size-related variables collectively determined the two axes in the NMDS two-dimensional space. Along the NMDS2 axis, the area, perimeter, and elongation increased with the increasing NMDS1 values, while PAR decreased (Figure 8b). Along the NMDS1 axis, Reock and MaxD decreased with the increasing NMDS1 values. PPR, CHR, BBR, and DC had negative projections on both NMDS axes (Figure 8b).

4. Discussion

Our findings indicate that mangroves, as naturally fragmented landscapes, predominantly exhibit small and elongated patch characteristics along China’s southern coast. These characteristics should be considered in future conservation and management efforts for mangrove forests. The average mangrove patch size in China (0.044 km2) is only one-tenth of the global average mangrove patch size (0.43 km2) reported by Jia et al., 2023 [23], and one-fifth of the average patch size of high-latitude mangroves in New Zealand’s southwestern Auckland (0.26 km2, located at 36–37° S) [71]. This highlights that China’s mangroves are predominantly composed of small patches. In summary, we have revealed that coastal mangroves in China consist of irregular small patches, a system generally considered more susceptible to external environmental disturbances [49,55]. For instance, elongated shapes with higher perimeter-to-area ratios are more exposed to external influences for the same patch area. Other shape-related metrics support similar conclusions. Given the potential invasion of S. alterniflora, S. apetala, and L. racemosa into China’s mangrove ecosystems, the small patches and extensive edge areas of natural mangroves provide numerous potential niches for alien species to establish and invade [49,53]. For example, in the largest local cluster identified using a 9 km distance threshold (Figure 2c), there are over 1336 patches, with an average patch size of only 0.05 km2. This region is also experiencing an invasion by the alien species S. alterniflora [72,73], posing significant challenges to the conservation and management of mangroves. Additionally, the significant differences in the patch composition and geometric indices among reserves (Figure 4) indicate the need for differentiated management strategies. Therefore, we recommend that mangrove forests predominantly composed of small patches, such as reserves within the largest local aggregation cluster (Figure 2c), should receive enhanced monitoring for biological invasions and degradation.
Mangrove ecosystems differ significantly from terrestrial ecosystems due to the high connectivity between mangrove patches, facilitated by a tide that links materials and organisms across the landscape [74]. This connectivity results in substantial differences from fragmented terrestrial forests, particularly in the challenge of quantifying the isolation of individual patches. Therefore, this study focuses more on the geometric properties of patches rather than landscape-level characteristics. Our research highlights the extensive edge areas present within mangrove patches, but a more detailed categorization was not conducted. In mangrove ecosystems, edges can be broadly categorized into two types: seaward and landward, each facing different stress factors. Seaward edges are primarily threatened by sea-level rise and erosion [75], leading to degradation, while landward edges are more affected by human activities, such as aquaculture ponds [31,76]. In this study, we used the highest resolution national-scale mangrove distribution dataset available, a 2 m spatial resolution dataset [46]. However, a 2 m resolution may still lack the capability to identify those narrow tidal channels, potentially leading to underestimations of the patch perimeter and possibly the number of patches. Conducting a detailed analysis of the edge types with a very-high-resolution (e.g., UAV remote sensing data) dataset at a national scale might be constrained by data limitations. However, future studies could systematically assess the effects of different edge types within local scales, such as within protected areas, to better understand their ecological impacts on mangrove patches.
In this study, we report on the landscape characteristics of a naturally fragmented mangrove wetland system. However, this study does not analyze the drivers behind the mangrove landscape pattern, which are complex for mangroves. On one hand, diverse human activities alter the mangrove landscape, such as deforestation to create aquaculture ponds or afforestation on mudflats to restore the mangrove cover [31,38,42]. On the other hand, tidal creeks consistently fragment mangrove wetland ecosystems, and these creeks interact with vegetation cover [77,78]. Coastal geomorphology is an important factor influencing hydrodynamics and tidal channel networks in wetlands. A feasible approach for future research could be to explore whether the shapes of mangrove patches differ across different geomorphological types. Our results also suggest that elongated strips of mangroves are usually found in areas with significant human activity, thus future studies need to delve deeper into quantifying human impacts.
In fragmented patch-based ecosystems, the mechanisms underlying biodiversity maintenance have long been a focus of ecological research [2,4]. Although mangroves are naturally fragmented landscapes, the mechanisms by which mangrove habitat patches maintain biodiversity are still under-researched, such as the widely studied relationship between patch size and species diversity in terrestrial ecosystems [2,5,6]. In our study, based on previous research on biological invasions, human destruction, and spatial patterns of mangrove degradation [53,55,57], we summarize that edge areas are fragile and sensitive. This view was widely accepted in terrestrial landscapes [9], but still requires further investigation in mangroves. Future work needs to deeply explore the ecological effects of landscape patterns and patch geometry. This is particularly important for those macrobenthos that are heavily dependent on mangrove habitats [74,79]. Here, our study focuses on the mangrove wetland ecosystems along the Chinese coast. However, the past fragmentation of mangroves is not unique to China; it is a challenge faced by mangroves worldwide [80,81]. Fragmentation is often accompanied by a reduction in area, but compared to changes in area, fragmentation has received far less attention. Future research should focus on mangrove fragmentation at the landscape scale while further exploring its driving forces. It is also essential to investigate the ecological impacts of mangrove fragmentation, assess the connectivity of fragmented patches, and identify potential restoration and conservation strategies.

5. Conclusions

Our study reveals that mangrove patches along the southeastern coast of China are predominantly small and elongated. This geometric characteristic presents significant challenges for mangrove conservation in China. The prevalence of small patches results in limited core areas, while the extensive perimeters increase vulnerability to biological invasions and human activities. Future mangrove management approaches should prioritize targeted monitoring of exotic species colonization and edge degradation in mangroves with numerous small patches. We recommend that future research evaluate the impacts of patch geometry on ecosystem vulnerability at both patch and landscape scales. Additionally, greater attention should be directed toward evaluating the ecosystem services provided by these small mangrove patches and their role in biodiversity maintenance. In summary, this study reveals the geometric characteristics of mangrove patches along coastal China and highlights the management challenges faced by fragmented wetlands. Furthermore, mangrove fragmentation is a global issue. We urge further research on this topic, focusing on its driving factors, impacts on ecosystem processes and services, and vulnerability under global change.

Author Contributions

Conceptualization, L.Z. and Y.D.; Data curation, L.Z. and Y.D.; Formal analysis, L.Z. and Y.D.; Funding acquisition, W.W. and M.W.; Methodology, L.Z. and Y.D.; Project administration, W.W. and M.W.; Software, L.Z. and Y.D.; Supervision, Y.D., W.W. and M.W.; Validation, L.Z. and Y.D.; Writing—original draft, L.Z. and Y.D.; Writing—review and editing, L.Z., Y.D., W.W. and M.W. All authors will be updated at each stage of manuscript processing, including submission, revision, and revision reminder, via emails from our system or the assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 42176169).

Data Availability Statement

This study did not generate any new data. All data used in this article are publicly available, and the data sources have been cited within the text.

Acknowledgments

The researchers express their sincere gratitude to those who created and publicly shared the data used in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Spatial distribution of mangroves along the southern coast of China in 2018. (a) A map illustrates the spatial distribution of mangroves. (b) Graph depicting the variation in mangrove area and the number of patches, summarized across 0.1° latitudinal intervals.
Figure 1. Spatial distribution of mangroves along the southern coast of China in 2018. (a) A map illustrates the spatial distribution of mangroves. (b) Graph depicting the variation in mangrove area and the number of patches, summarized across 0.1° latitudinal intervals.
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Figure 2. Distribution patterns of mangrove area at patch scale and local aggregation scale. (a) Relationship between patch area and rank order of patch area; the inset in the upper right displays the original distribution of patch areas, while the subplot in the lower left shows the distribution after log-transformation. Notes on the number of patches (N), mean, and median are included. (b) Relationship between the number of connected groups and the distance threshold, with a red mark indicating the distance threshold of 9 km chosen for this study, which resulted in 105 connected groups; the histogram in the upper right displays the distribution of areas for these 105 connected groups. (c) Map illustrating the largest connected group among the 105 identified groups, annotated with patch number and total area. A map with pentagram marks its geographical location.
Figure 2. Distribution patterns of mangrove area at patch scale and local aggregation scale. (a) Relationship between patch area and rank order of patch area; the inset in the upper right displays the original distribution of patch areas, while the subplot in the lower left shows the distribution after log-transformation. Notes on the number of patches (N), mean, and median are included. (b) Relationship between the number of connected groups and the distance threshold, with a red mark indicating the distance threshold of 9 km chosen for this study, which resulted in 105 connected groups; the histogram in the upper right displays the distribution of areas for these 105 connected groups. (c) Map illustrating the largest connected group among the 105 identified groups, annotated with patch number and total area. A map with pentagram marks its geographical location.
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Figure 3. Frequency distribution histograms of the nine geometric metrics used in this study. Each panel displays the histogram for a specific metric with the mean and median values indicated inside. All metrics were calculated across the 5857 patches included in the analysis.
Figure 3. Frequency distribution histograms of the nine geometric metrics used in this study. Each panel displays the histogram for a specific metric with the mean and median values indicated inside. All metrics were calculated across the 5857 patches included in the analysis.
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Figure 4. Comparison of geometric metrics across five selected national nature reserves or internationally important wetlands. (a) Locations of the five selected sites. (b) The relationship between the total mangrove area and number of patches in each reserve. (c) Differences in 10 metrics among the five sites. Different letters denote significant differences at the 0.05 level, based on a non-parametric test. The error bars represent one standard deviation. ZJE: Zhangjiang Estuary Mangrove National Nature Reserve; SZB: Shenzhen Bay Mangrove Wetland; SK: Shankou Mangrove National Nature Reserve; BLE: Beilun Estuary National Nature Reserve; DZG: Dongzhaigang Bay Mangrove National Nature Reserve.
Figure 4. Comparison of geometric metrics across five selected national nature reserves or internationally important wetlands. (a) Locations of the five selected sites. (b) The relationship between the total mangrove area and number of patches in each reserve. (c) Differences in 10 metrics among the five sites. Different letters denote significant differences at the 0.05 level, based on a non-parametric test. The error bars represent one standard deviation. ZJE: Zhangjiang Estuary Mangrove National Nature Reserve; SZB: Shenzhen Bay Mangrove Wetland; SK: Shankou Mangrove National Nature Reserve; BLE: Beilun Estuary National Nature Reserve; DZG: Dongzhaigang Bay Mangrove National Nature Reserve.
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Figure 5. Correlations between all ten geometric metrics. Colors and numbers represent the corresponding Pearson correlation coefficients. An “×” indicates that the correlation is not significantly different from zero at the p < 0.05 significance level. For definitions of each shape metric, refer to Table 1.
Figure 5. Correlations between all ten geometric metrics. Colors and numbers represent the corresponding Pearson correlation coefficients. An “×” indicates that the correlation is not significantly different from zero at the p < 0.05 significance level. For definitions of each shape metric, refer to Table 1.
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Figure 6. Relationship between patch area and geometric metrics. (a) Perimeter versus patch area. The blue solid line added via the “geom_smooth” function represents the smoothed trend, while the red solid line illustrates the theoretical relationship between perimeter and area for circles. (b) The inverse of elongation (1/elongation) versus patch area. The top right inset in panel (b) features a satellite image from Google Earth of the patch with the smallest elongation value, highlighting its elongated shape.
Figure 6. Relationship between patch area and geometric metrics. (a) Perimeter versus patch area. The blue solid line added via the “geom_smooth” function represents the smoothed trend, while the red solid line illustrates the theoretical relationship between perimeter and area for circles. (b) The inverse of elongation (1/elongation) versus patch area. The top right inset in panel (b) features a satellite image from Google Earth of the patch with the smallest elongation value, highlighting its elongated shape.
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Figure 7. Geographic patterns of mangrove geometry characteristics. (a) Variation in mangrove area with latitude. (b) Variation in mangrove patch number with latitude. (c) Map of mangrove area distribution. (d) Map of mangrove patch number distribution. (e) Distribution map of average 1/Elongation. Insets in the top-right corners of Panels (a,b) displaying satellite images from Google Earth showing the mangrove landscapes with the largest area and the highest number of patches, respectively. Panels (a,b) are summarized using a 0.1° by 0.1° latitude–longitude grid, while Panels (c–e) use a 0.5° by 0.5° latitude–longitude grid for mapping purposes.
Figure 7. Geographic patterns of mangrove geometry characteristics. (a) Variation in mangrove area with latitude. (b) Variation in mangrove patch number with latitude. (c) Map of mangrove area distribution. (d) Map of mangrove patch number distribution. (e) Distribution map of average 1/Elongation. Insets in the top-right corners of Panels (a,b) displaying satellite images from Google Earth showing the mangrove landscapes with the largest area and the highest number of patches, respectively. Panels (a,b) are summarized using a 0.1° by 0.1° latitude–longitude grid, while Panels (c–e) use a 0.5° by 0.5° latitude–longitude grid for mapping purposes.
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Figure 8. Non-metric Multidimensional Scaling (NMDS) plot illustrating the variation of all mangrove patches. (a) A 2D hexagonal heatmap of bin counts, used to show the true distribution where many data points overlap. (b) Envfit analysis results overlaid on the NMDS ordination plot. Arrows represent geometric metrics, with their direction indicating the gradient of influence and their length reflecting the strength of correlation with the ordination axes.
Figure 8. Non-metric Multidimensional Scaling (NMDS) plot illustrating the variation of all mangrove patches. (a) A 2D hexagonal heatmap of bin counts, used to show the true distribution where many data points overlap. (b) Envfit analysis results overlaid on the NMDS ordination plot. Arrows represent geometric metrics, with their direction indicating the gradient of influence and their length reflecting the strength of correlation with the ordination axes.
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Table 1. Metrics used to characterize the geometry of mangrove patches.
Table 1. Metrics used to characterize the geometry of mangrove patches.
IDMetricsDefinitionUnit
#1AreaThe area of patch polygon.m2
#2Maximum depth
(MaxD)		The maximum value of the distance from any point within the polygon to the nearest edge.m
#3Perimeter (PER)The perimeter of each patch.m
#4Perimeter-to-area ratio
(PAR)		Ratio of the perimeter to the area of the patch.m−1
#5Depth compactness
(DC)		Ratio of the maximum depth to the radius of a circle equal in area to the patch.unitless
#6Polsby–Popper
(PPR)		Ratio of the patch area to the area of equal perimeter circle.unitless
#7Convex Hull Ratio
(CHR)		Ratio of the patch area to the area of the minimum convex polygon that encloses the patch polygon.unitless
#8ReockRatio of the patch area to the area of the minimum bounding circle that encloses the patch polygon.unitless
#9Bounding box ratio
(BBR)		Ratio of the patch area to the area of the minimum bounding box that encloses the patch polygon.unitless
#10ElongationRatio of the minor axis to the major axis of the patch polygon.unitless
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Zhang, L.; Deng, Y.; Wang, W.; Wang, M. The Geometry of Southern China’s Mangroves: Small and Elongated. Forests 2025, 16, 212. https://doi.org/10.3390/f16020212

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Zhang L, Deng Y, Wang W, Wang M. The Geometry of Southern China’s Mangroves: Small and Elongated. Forests. 2025; 16(2):212. https://doi.org/10.3390/f16020212

Chicago/Turabian Style

Zhang, Lin, Yijuan Deng, Wenqing Wang, and Mao Wang. 2025. "The Geometry of Southern China’s Mangroves: Small and Elongated" Forests 16, no. 2: 212. https://doi.org/10.3390/f16020212

APA Style

Zhang, L., Deng, Y., Wang, W., & Wang, M. (2025). The Geometry of Southern China’s Mangroves: Small and Elongated. Forests, 16(2), 212. https://doi.org/10.3390/f16020212

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