Trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales), Mexico: An approach using open-access geospatial data

Trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales), Mexico: An approach using open-access geospatial data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales), Mexico: An approach using open-access geospatial data César A. Berlanga-Robles This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3783054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Wetlands → Version 1 posted 5 You are reading this latest preprint version Abstract Mangroves face multiple threats, including land cover and land use changes, overexploitation, and contamination, resulting in local, regional, and global impacts. Understanding these changes is essential for conserving these important coastal ecosystems. Remote sensing provides detailed and long-term data and offers an invaluable advantage in such analyses. This study focuses on the Teacapan-Agua Brava Lagoon System in Mexico, integrating a GIS with open-access geospatial data, multiple Landsat 5 satellite images, MODIS vegetation index data (MOD13Q1 v. 6.1), and thematic maps of mangrove cover from various sources to analyze change trends in mangrove canopy and cover. Using the Mangrove Vegetation Index (MVI), mangroves were effectively distinguished from other cover classes (overall accuracy = 92%, Kappa coefficient = 0.93), resulting in an estimated mangrove cover of 67,334 ha in 2005. The Enhanced Vegetation Index (EVI) time series from 2005 to 2022 revealed a generally positive trend in mangrove canopy (p < 0.0001). The principal component analysis (PCA) and hierarchical clustering identified four distinct clusters with varying EVI profiles. Of the total mangrove area, 3% was vulnerable (negative trend), 29% exhibited no significant trend, and 58% was resilient (positive trend). Data from CONABIO and Clark Labs resulted in different deforestation and reforestation trends (average annual deforestation rate of -0.87% and average annual reforestation rate of 0.49%, respectively). These findings underscore the complex and diverse trends in mangrove cover and canopy, emphasizing the need for continued research, standardized mapping, and consistent remote sensing approaches to conserve and manage mangroves and their valuable ecosystem services. Mangroves Remote sensing Open access data Vegetation index Time series Coverage change detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Mangrove wetlands provide a wide range of ecosystem services to millions of inhabitants in tropical and subtropical coastal regions worldwide. These services include providing fisheries resources, wood, fuel, and cultural goods and protecting against storms, floods, erosion, and pollution. Moreover, mangrove wetlands provide breeding and refuge habitats for numerous aquatic species (Friess et al., 2019 ; Getzner and Islam, 2020 ; Lee et al., 2014 ). In addition to these notable local and regional benefits, as blue carbon ecosystems, mangroves sequester approximately 32 million tons of carbon annually, making these ecosystems crucial to mitigating climate change (Howard et al., 2017 ). However, mangroves face multiple anthropogenic threats that arise from changes in land cover and land use, such as their conversion into aquaculture farms, agricultural fields, or urban settlements, as well as overexploitation and pollution. These threats have resulted in a notable loss of mangrove cover. Indeed, during the 1980s, approximately 35% of mangrove surface area was lost worldwide (FAO 2023 ; Friess et al., 2019 ). This trend has continued, with mangrove loss totaling 62% in the first fifteen years of the present century (Goldberg et al., 2020). Currently, mangrove threats due to human activities are compounded by long-term stressors, including relative sea level rise and sea level fluctuations related to climate oscillations (Friess et al., 2019 ; Lovelock et al., 2015 ). Given the numerous goods and services provided by mangroves, their high rates of deforestation and degradation, and current and future threats, interest in implementing management actions to protect and conserve these wetlands has grown. However, proper management requires up-to-date information on their extent, distribution, and change trends, which can be obtained by analyzing satellite imagery (Giri, 2016 ; 2020; Nepita-Villanueva et al., 2019 ). Earth observation satellites are excellent tools for monitoring mangroves, as they repeatedly collect data in different spatiotemporal resolutions, covering various sections of the electromagnetic spectrum over large land surface areas. From these data, qualitative and quantitative indicators of vegetation characteristics (e.g., thematic maps and vegetation indices) can be derived to evaluate the extent, composition, distribution, productivity, and temporal changes in mangrove cover over the last five decades (Giri, 2016 ; Kuenzer, 2011; Younes et al., 2017 ). Hundreds of Earth observation satellites provide data that may be used to synthesize and systematize information on the distribution, health, productivity, and dynamics of vegetation (Chétet and Denux, 2011 ). Indeed, numerous studies have demonstrated their utility to monitor mangroves at local, regional, national, and even global scales (Berlanga-Robles et al., 2018 ; Berlanga-Robles & Ruiz-Luna, 2020 ; Chen et al., 2017 ; Giri et al. 2011; Jia et al. 2017; Velázquez-Salazar et al., 2021 ; Zhu et al. 2017). However, most of these studies have analyzed a limited number of images when describing changes over long periods (e.g., decades), overlooking the potential offered by the vast archive of open-access satellite images to detect either cyclical seasonal changes or gradual interannual changes. Thus, satellite images are crucial to assessing the potential bias and errors of long-term change estimates, identifying different disturbance factors, and evaluating the effects of these factors on mangrove health and phenology (Berlanga-Robles & Ruiz-Luna, 2020 ; Rogan et al., 2011 ; Younes et al., 2017 ). In addition to numerous satellite images, open geospatial data on the extent and distribution of mangroves are also available online. For example, data can be found in the Global Mangrove Watch portal ( https://www.globalmangrovewatch.org/ ) or, in the case of Mexico, in the National Biodiversity Information System (SNIB) portal of the National Commission for the Knowledge and Use of Biodiversity (CONABIO; http://www.conabio.gob.mx/informacion/gis/ ). These data can be used to complement information from satellite images when classifying, calibrating (training), and validating (testing) classification models and even estimating change indicators directly (Berlanga-Robles et al., 2018 ; Berlanga-Robles & Ruiz-Luna, 2020 ; Quintero-Morales et al., 2021 ; Osorio-Olvera et al., 2023 ). Given the global and regional importance of mangroves and the potential of satellite data to monitor these valuable ecosystems, conservation actions and attention must be focused on specific ecosystems that exhibit particular dynamics and face specific threats. Thus, this study examined open-access geospatial data, including satellite imagery and thematic maps, to evaluate change trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales). This ecosystem, the most extensive mangrove system on the Mexican Pacific coast, has been subject to natural and anthropogenic disturbances over the past five decades. These disturbances have changed the composition, structure, and condition of the ecosystem, resulting in one of the highest deforestation rates in northwestern Mexico (Berlanga, 2006; Berlanga-Robles & Ruiz-Luna, 2007). Fortunately, signs of resilience and recovery in the Teacapan -Agua Brava Lagoon System have been identified during the first decades of the current century (Berlanga-Robles et al., 2018 ; Nepita-Villanueva, 2019). 2 METHODS 2.1 Study Area The Teacapan -Agua Brava Lagoon System (Marismas Nacionales) is located in northwestern Mexico (21° 44' and 22° 52' N, 105° 15' and 106° 04' W; Fig. 1 ). The climate of the region is warm sub-humid (Aw), exhibiting a north-to-south gradient of arid-to-humid conditions, an average annual temperature of 22°C, and annual precipitation ranging from 500 to 2500 mm, primarily occurring in summer (De la Lanza & Hernández, 2017 ). The Teacapan -Agua Brava Lagoon System (163,000 ha) is composed of 29 tidal sub-basins within 8 tidal basins and exhibits a mosaic of estuaries, coastal lagoons, marshes, and mangroves (Blanco et al., 2011 ). The mangrove vegetation in the Teacapan -Agua Brava Lagoon System, which covered ~ 75,000 ha in 2000 (Berlanga-Robles and Ruiz-Luna, 2007), consists of patches dominated by Avicennia germinans (black mangrove) in the north, Laguncularia racemosa (white mangrove) and Rhizophora mangle (red mangrove) in the south, and Conocarpus erectus (buttonwood mangrove) throughout the system (De la Lanza et al. 1996; De la Lanza & Hernández, 2017 ). 2.2 Database and Preprocessing This study utilized a Geographic Information System (GIS) comprised of seven layers of information at various spatiotemporal scales in vector and raster formats (Table 1 ). All layers were projected to the EPSG:32613 reference system (UTM zone 13N / WGS 84). Additionally, the polygons of the eight tidal basins in layer 1 were merged and reclassified with a value of 1, editing the study area polygon (Fig. 1 ) and masking the other layers. The 2-band Enhanced Vegetation Index (EVI) composites contained in the Hierarchical Data Format (HDF) files corresponding to layer 5 were exported to TerrSet 19.07 (Eastman, 2020) to generate a monthly time series of the EVI from January 2005 to December 2022. This series was edited using the mean as an aggregation function and adjusting the storage values, which initially ranged from − 2000 to 10000 to match the original index values (-0.2, 1). This resulted in a series of 216 images. The start date of the series was selected based on the creation date (2005) of the first map of mangrove distribution in Mexico, produced by CONABIO (layer 4). A total of 187 points were extracted from the 2005 mangrove map (layer 4) using simple random sampling without replacement in TerrSet to generate calibration and validation points (layer 7; Eastman, 2020). An independent analyst with knowledge of the study area verified these points by evaluating Google Earth Pro 7.3 images from 2004 and 2005. A similar process was followed with the 2000 thematic map of coastal wetlands in the Teacapan -Agua Brava Lagoon System (layer 3) produced by Berlanga-Robles and Ruiz-Luna (2007) to generate calibration and validation points for the cover classes of lagoons (including estuaries; 190 points), saltmarshes (96 points), and additional land cover classes (terrestrial vegetation, agricultural land, settlements, and aquaculture farms; 84 points). 2.3 Mangrove Canopy Changes Vegetation indices derived from satellite imagery are significantly related to canopy and phenological changes in vegetation cover, allowing seasonal and long-term change trends to be summarized, systematized, and described by parameters estimated from their time series (Vázquez et al., 2013; Berlanga-Robles et al., 2018 ; Nepita-Villanueva et al., 2019 ). In the present study, changes in mangrove canopy were evaluated by examining the EVI time series using an approach similar to those implemented by Berlanga-Robles et al. ( 2018 ) and Nepita-Villanueva et al. ( 2019 ). These authors focused on analyzing deciduous tropical forest and mangrove cover, respectively, at the beginning of the time series utilizing Principal Component Analysis (PCA) to decompose the time series of vegetation indices. The current study focused on analyzing mangrove cover in 2005. A base map was used to distinguish mangrove cover from those of other cover types using the Mangrove Vegetation Index (MVI). The MVI was defined by Baloloy et al. ( 2020 ) as: \(\text{M}\text{V}\text{I}=\frac{NIR-G}{SWIR1-G}\) , Eq. (1) where NIR , SWIR1, and G correspond to reflectance values in the near-infrared, shortwave infrared 1, and green wavelengths, respectively. In Eq. (1), the numerator emphasizes greenness, while the denominator relates to moisture. Both are essential attributes of mangrove vegetation. The MVI can differentiate mangroves from other vegetation types without additional data (e.g., tidal amplitude) or indices like the Modified Normalized Difference Water Index (Baloloy et al., 2020 ). The MVI was determined with bands B2 (green), B4 (near-infrared), and B5 (shortwave infrared 1) of the Landsat TM image from 2005 (layer 2). The MVI threshold used to distinguish the mangrove class from the three other classes (i.e., lagoons, marshes, and additional) was established using 50 points per class (Baloloy et al., 2020 ). These points were randomly selected (without replacement) from the collection of points in layer 7. Once the MVI threshold was determined, it was used to transform the MVI map into a binary map displaying only mangrove (1) and other (0) cover. The accuracy of this map was evaluated using an error matrix based on 442 test points, also randomly selected from layer 7, of which 187 were mangrove points, and 255 were other cover points. The binary map was edited to remove mangrove patches of less than 6.25 ha. This area corresponds to the pixel size of MOD13Q1 v. 6.01 products. The maps were edited to limit the analysis of the EVI time series (2005 to 2022) to the mangrove cover in 2005 in patches with areas equal to or greater than the spatial resolution of the images comprising that series. The producer and user accuracies for each class, along with the overall accuracy and the Kappa coefficient estimator, were determined from the error matrix. The overall accuracy represents the proportion of correctly classified points, regardless of their specific class, relative to the total number of evaluated points. The Kappa coefficient measures the agreement between two classifications, adjusting for the agreement that could occur by chance. A Kappa value of 1 indicates perfect agreement, while a value of 0 indicates the agreement is no better than chance. Producer accuracy is associated with omission error (Type I) detected in the classification of a class, while user accuracy is related to commission error (Type II; Congalton & Green, 1999 ). The monthly EVI time series (2005 to 2022) was decomposed using a PCA with orientation (mode) T. The PCA orientation denotes how the images are organized for analysis. In mode T, each image acts as a variable (sample) over time, enabling spatiotemporal patterns of change to be identified (Machado-Machado et al., 2011 ; Neeti & Eastman, 2014). The PCA was performed in parallel in TerrSet (Eastman, 2022) and R (v. 4.2.2, R Core Team, 2022 ) in the RStudio platform (v. 2023.06, RStudio Team, 2023 ). With TerrSet, images of the first three principal components were generated and combined into a false-color composition using red, green, and blue filters to identify areas with different long-term change trends. The analysis was complemented with PCA-based hierarchical clustering in R (Kassambara, 2017 ). For the analysis in R, the image stack from the time series was loaded into the RStudio environment (Hijmans, 2023) to generate a data frame with 218 variables. The data frame included geographic coordinates and monthly EVI values from January 2005 to December 2022, totaling 1,975,734 observations (pixels). The number of observations was reduced to 10,246 by eliminating invalid values (pixels masked with the base map). After excluding the geographic coordinates, a Pearson correlation matrix was generated and the sampling adequacy of the PCA was verified through the Kaiser-Meyer-Olkin (KMO) test. A scree plot was used to determine the number of components (Cureton & D’Agostino, 1983 ; Sánchez, 2019 ). The PCA was conducted using the PCA() function from the ‘ FactorMineR ’ R package (Le et al., 2008 ). In order to identify groups with similar scores within the set of previously extracted principal components, PCA-based hierarchical clustering was conducted using the HCPC() function from the same R package (Kassambara, 2017 ). These clusters reflected areas with comparable temporal patterns in EVI. Euclidean distance between components and the Ward clustering method were employed for hierarchical clustering, with a minimum of 4 and a maximum of 8 clusters (Le et al., 2008 ; Kassambara, 2017 ). The grouping hypothesis (H 1 ) was evaluated by testing the hypothesis of group absence (H 0 ) using a one-way analysis of variance (ANOVA) with observations (replicates) of the average EVI over the entire period. Five replicates per cluster were selected, corresponding to the most emblematic observations of each cluster. These were given as outputs of the HCPC() function ( k = 4, n i = 5, n = 20). A data frame with columns of the original variables was an HCPC() function output, which included an additional column that assigned a cluster to each observation. By reassigning geographic coordinates to each observation, a map showing the distribution of each cluster was generated, from which the area of each cluster in each tidal basin of the system was calculated. Monthly EVI profiles of each cluster were determined using the same data frame and the mean as the aggregation measure. The seasonal component of these profiles was obtained with the decompose() function of the ‘ R Stats ’ R package using a moving average fit (Cowpertwait & Metcalfe, 2009 ). The trend profiles were evaluated using the Mann-Kendall test (Neeti & Eastman, 2011 ; Nepita-Villanueva et al., 2019 ), implemented with the mk.test() function of the ‘ trend ’ R package (Pohlert, 2023 ). 2.4 Mangrove Cover Change Trends Independent analyses of change trends in mangrove cover were conducted using the maps produced by CONABIO (layer 4) and the maps generated by Clark Labs (layer 6). The average annual rate of change in mangrove cover for the entire system was determined based on the maps from the extreme years of each dataset: 2005 to 2020 for CONABIO data and 1999 to 2020 for Clark Labs data. This rate was calculated using the equation proposed by Palacio-Prieto et al. ( 2004 ): \(dn={\left[\frac{{S}_{2}}{{S}_{1}}\right]}^{\frac{1}{n}}-1\) , Eq. (2) where dn is the average annual rate of change, S 1 and S 2 are the (mangrove) areas at date 1 and 2, respectively, and n is the number of years between the two dates. Following a strategy similar to Osorio-Olvera et al. ( 2023 ), changes in mangrove cover were analyzed using the 29 tidal sub-basins of the Teacapan -Agua Brava Lagoon System as sampling units. For each sub-basin, mangrove area was estimated based on each of the four CONABIO maps from different years. Then, the change rate was determined by fitting a linear model of the area over time, using the unique Theil-Sen median slope (Mangiafico, 2016; Wilcox, 2021). This analysis was conducted using the mblm() function of the R package of the same name (Komsta, 2022). The procedure was replicated for the Clark Labs maps. 2.5 Large Language Models (LLMs) Chat GPT 4 assisted in verifying and correcting some codes to implement loops in R (e.g., to estimate Theil-Sen median slopes for the 29 tidal sub-basins) and in translating the original manuscript, written entirely by the author, from Spanish to English. The resulting English manuscript was thoroughly reviewed and edited by a human. 3 RESULTS 3.1 Mangrove canopy change trends The MVI values for the 50 mangrove calibration points ranged from 0.91 to 15.7 (median = 3.4, mean = 4.4, SD = 3.5). On the other hand, the MVI values of the 150 points from the other three classes were much lower, ranging from 0 to 2.3 (median = 0.52, mean = 0.8, SD = 0.53). An MVI threshold of 1.8 was established to distinguish mangrove cover from other cover classes (see section 2.3). This threshold corresponded to the 82nd percentile of the additional cover class points and the 15th percentile of the mangrove cover class points. The threshold was above the maximum values of the lagoon and marsh cover classes (Fig. 2 ). The MVI threshold was used to distinguish mangrove cover from the other cover classes (mangrove (1) = MVI ≥ 1.8, other (0) = MVI < 1.8). A binary map (Fig. 2 ) was produced with an overall accuracy of 92% and a Kappa coefficient estimator of 0.93, indicating strong agreement between the reference data and classification data (Congalton & Green; 1999 ). The producer and user accuracy values for the mangrove class were 87% and 95%, respectively. From the binary map, mangrove cover was estimated to be 67,334 ha, reflecting a reduction of 3,569 ha compared to the CONABIO map from 2005, which estimated mangrove cover to be 70,903 ha. By removing patches smaller than 6.25 ha and resampling the binary map to the spatial resolution of the EVI images (250 m), mangrove cover was further reduced to 64,038 ha. Therefore, the monthly EVI time series analysis included 95% of the initially identified mangrove cover. The monthly EVI profile from January 2005 to December 2022 for the entire area ranged from 0.19 to 0.36, with the minimum and maximum values occurring in December 2007 and October 2017, respectively. The EVI values tended to increase from 2005 to 2016 and then decreased towards the end of the series, although the overall trend for the entire period was positive (τ = 0.22, p < 0.0001). The seasonal component of the series showed peaks in October and valleys in June (Fig. 3 ). The overall KMO measure for the monthly EVI observations was 1, indicating the appropriateness of conducting the PCA. The first two components were selected based on the scree plot, which explained nearly 85% of the total variation. All variables were strongly correlated with each other (average Pearson correlation of 0.81) and with the first component (Fig. 4 ). Hierarchical clustering of the first two components yielded four clusters, with EVI means of 0.91, 0.23, 0.34, and 0.44 for clusters 1, 2, 3, and 4, respectively (Fig. 4 ). All means were significantly different (F = 14622, p < 0.0001, η 2 = 0.999, µi - µj ≥ 0.107, p = 0). The monthly EVI profiles and seasonal components of clusters 3 and 4 exhibited behavior similar to the estimates for total mangrove cover. In other words, they showed a positive monotonic trend (τ ≥ 0.24, p < 0.0001) and seasonal fluctuations with peaks in October and valleys in June. In contrast, the profile of cluster 1 showed a negative monotonic trend (τ ≥ -0.13, p = 0.0043), and its seasonal component exhibited smaller oscillations compared to the other cases. Peaks were observed in February, and valleys occurred between July and September. The monotonic trend of cluster 2 was less evident (τ ≥ 0.28, p = 0.09), and its seasonal component showed peaks between November and January, with valleys also in June (Fig. 5 ). By combining the images of the first three components and applying a filter that displays the first in red, the second in green, and the third in blue, a false-color image (Fig. 6 ) was generated to identify areas with different change trends. Green tones in the image represented areas with negative change trends, specifically associated with cluster 1. For example, in the area marked 1, the Mann-Kendall statistic (τ) was − 0.17 (p = 0.0004). Areas that showed no trend were represented in light blue and were associated with cluster 2. In the area marked 2, the τ statistic was 0.02 (p = 0.7163). On the other hand, violet tones represented areas with positive trends, which were associated with clusters 3 and 4. In the area marked 3, the τ statistic was 0.41 (p < 0.0001). Of the 64,038 ha of mangroves considered in the EVI time series analysis, approximately 60% (38,739 ha) were in the Agua-Brava tidal basin. Of this subset, less than 10% were classified in cluster 1, characterized by a negative change trend, while 72% were included in clusters 3 and 4, which exhibited positive change trends. In contrast, in the Los Corchos basin, mangroves covered only 6 ha, equivalent to the area of one pixel, and were classified into cluster 1. Of the total mangrove area analyzed, 13% was classified into cluster 1, which was primarily distributed in the Agua Brava and Teacapan -Agua Grande basins, 29% belonged to cluster 2, 32% to cluster 3, and 26% to cluster 4 (Fig. 6 , Table 2 ). 3.2 Mangrove cover changes Contrasting change trends in mangrove cover were identified from the maps provided by CONABIO (layer 4) and Clark Labs (layer 6). The CONABIO map indicated mangrove deforestation at an average annual rate of -0.87% between 2005 and 2020. In contrast, the Clark Labs map indicated mangrove reforestation at an average annual rate of 0.49% between 1999 and 2020. Furthermore, the Theil-Sen slope estimated with CONABIO data for the entire lagoon system was negative, while the slope estimated with Clark Labs data was positive. Both slopes were statistically significant at α = 0.1 (Table 3 ). Thus, the Clark Labs map from 2014 was compared with the CONABIO map from 2015 and the maps from both sources from 2020, following a procedure similar to the one used to validate the 2005 binary mangrove cover map. In the first comparison, the overall accuracy (coincidence) was 76%. In both maps, of the nearly 163,000 ha analyzed, 56% was classified as non-mangrove cover, and 33% was classified as mangrove cover. In the Clark Labs and CONABIO maps, ~ 3% and 9% of the study area were classified as only mangrove cover, respectively. In the comparison of the 2020 maps, the overall accuracy was 89%. The areas classified as non-mangrove and mangrove cover in both maps were similar to those of the previous comparison, with 6% and 5% being classified as only mangrove cover in the Clark Labs and CONABIO maps, respectively (Supplementary Material 1). In four tidal sub-basins, the analysis of change trends in mangrove cover using CONABIO data revealed no coverage in at least one year. Significant (α = 0.05) Theil-Sen slopes (five negative and two positive) were identified in seven sub-basins. In 15 sub-basins, the estimated slopes were significant (α = 0.1; 11 negative and 4 positive). In eight sub-basins, the slopes were not significantly different from zero (Table 3 ). The largest slope was negative and corresponded to the Teacapan-Agua Grande tidal sub-basin, although it was not significant (p = 0.313). However, it is important to note that this result should be interpreted with caution, as the power of the test at a significance level of α = 0.1 was γ = 0.19. The next largest slope was also negative but statistically significant (p = 0.0313) and corresponded to the El Colorado-La Palicienta tidal sub-basin (Table 3 ). In five tidal sub-basins, the analysis of change trends in mangrove cover using Clark Labs data indicated no mangrove cover in at least one year. Statistically significant slopes (α = 0.1) were identified in five cases, all of which were positive. The largest positive slope (p = 0.098) pertained to the Agua Brava tidal sub-basin. It is important to note that in the previous analysis using CONABIO data, a significant negative slope (p = 0.031) was estimated for the same sub-basin (Table 3 ). 4 DISCUSSION Mangroves must be inventoried and monitored to quantify their ecosystem services, analyze threats, and design conservation strategies (Friess & Webb, 2014 ). Due to its extensive coverage and continuous monitoring capability, remote sensing has emerged as an irreplaceable tool in this field, surpassing the limitations of traditional field-based assessment methods (Maurya et al., 2021 ; Lee et al., 2014 ). However, most mangrove studies using remote sensing data rely on a limited number of images. Indeed, inventories are often conducted using satellite images from a single date or short time windows, assuming the resulting mangrove cover represents the entire year (Maurya et al., 2021 ; Younes et al., 2017 ). Bitemporal analyses, which use two images to describe changes in spectral or thematic characteristics of a given environment, have also predominated, reflecting an underutilization of the potential offered by the extensive repositories of satellite images available that may be used to identify stochastic and cyclical changes in mangrove cover (Hansen & Loveland, 2012 ). In contrast, multi-temporal analyses capitalize on using multiple images from different satellites, allowing for a deeper understanding of long-term changes and cyclical phenomena in mangrove ecosystems (Kennedy et al., 2014 ). This study adopted a more detailed approach than most mangrove studies using remote sensing data, leveraging the richness of available satellite data. For the classification of the 2005 base map, a composition derived from 24 Landsat 5 images was used, covering various stages of the phenological cycle of the mangroves in the Teacapan -Agua Brava Lagoon System, which was described by Berlanga-Robles & Ruiz-Luna ( 2020 ). Subsequently, a multi-temporal analysis was conducted using an EVI time series from 828 MODIS images between 2005 and 2022. This data-intensive approach provided a more comprehensive view of mangrove dynamics, starkly contrasting traditional remote sensing methodologies that have relied on a limited number of images. The MVI provided essential information to distinguish between mangrove and other cover classes. An MVI threshold of 1.8 effectively differentiated mangroves from other cover classes, with the resulting binary map being highly accurate (overall accuracy = 92%, Kappa coefficient = 0.93). However, it is important to note that the MVI threshold of 1.8 and the average MVI estimated for the mangroves in the Teacapan -Agua Brava Lagoon System of 4.4 were below the values reported by Baloloy et al. ( 2020 ). Their study of 11 mangrove sites in the Philippines and one site in Japan resulted in thresholds between 4.5 and 4.6 and average MVI values between 7.7 and 8.6 using Sentinel-2 and Landsat OLI images. Differences in MVI values between the two studies can be attributed to a combination of factors, including differences in sensors, environmental conditions, processing methodologies, and the composition, structure, and phenology of vegetation cover in the respective study areas. These factors and the temporal differences in the images make it challenging to compare MVI values between the two studies directly. In the study by Baloloy et al. ( 2020 ), each site was sampled at a particular time, even though the images collectively covered all seasons of the year. In contrast, this study employed a composition synthesizing vegetative mangrove phenology over a year. Despite these differences, the MVI resulted in accurate mangrove classification in both cases. Baloloy et al. ( 2020 ) also obtained overall accuracies of 92%. These results underscore the utility and robustness of the MVI in identifying and classifying mangroves despite differences in absolute values between studies. The temporal characterization of mangrove canopy through the monthly EVI time series from 2005 to 2022 revealed a significant overall positive trend. This trend agrees with the findings of Nepita-Villanueva et al. ( 2019 ) and Berlanga-Robles et al. ( 2018 ) for 2002 to 2016. These authors suggested that the positive trend was indicative of the resilience of the mangroves in the Teacapan -Agua Brava Lagoon System and reflective of their recovery and new equilibrium following the strong disturbances of the last decades of the 20th century (Berlanga-Robles & Ruiz-Luna, 2007). However, despite the overall trend, a decline in EVI values was also recorded in the last six years of the series. This decline agrees with the results of Vizcaya-Martínez et al. ( 2022 ), who studied the same mangroves from 2018 to 2021. These authors reported that the impact of Hurricane Willa in 2018 resulted in 79% defoliation. Moreover, after Hurricane Willa, in October 2022, Hurricane Roslyn made landfall to the south of the system as a category 3 hurricane, with wind gusts between 100 and 105 kt (NOAA, 2023). Although the impacts of this hurricane on the mangrove canopy have not been assessed, it is possible that it contributed to the significant decrease in EVI values observed in the last three months of the series. The decomposition of the EVI time series allowed four mangrove classes to be identified. Two of these, representing nearly 60% of the analyzed mangrove area, exhibited profiles and seasonal components similar to those identified for all mangroves in the system. However, 13% of the analyzed mangroves were grouped into a class with a negative monotonic trend and a lagged and lower-amplitude seasonal component than those of the remaining mangroves. The mangroves exhibiting a negative monotonic trend were primarily located in the Teacapan -Agua Grande tidal basin in the north of the lagoon system. In this area, mangroves are dominated by Avicennia germinans shrubs with an average density of 2872 trees/ha (Monzalvo, 2006 ). This density is notably lower than the average reported for A. germinans -dominated mangroves in other lagoon systems in northwestern Mexico (6741 trees/ha; Monzalvo, 2006 ) but similar to the density observed in patches of this species under management in parts of the Agua Brava tidal basin to the south of the system (Valdez, 2002 ). Additionally, these mangroves have been characterized as vulnerable or disturbed due to long-term negative trends (Nepita-Villanueva et al., 2019 ) and altered seasonality, with their season of maturity shifting from autumn to winter (Berlanga-Robles et al., 2020). Despite the opening of the Cuautla canal in the early 1970s to connect the Agua-Brava lagoon with the Pacific Ocean, which notably altered the hydrodynamics of the system and the composition, structure, and condition of the mangroves in the area (Berlanga-Robles & Ruiz-Luna, 2007), a new canal project was approved in the mid-1990s. This project involved building a canal (18 km long and 80 m wide) to connect the Agua Grande lagoon with the Las Cañas lagoon in the Teacapan -Agua Grande tidal basin (Supplementary Material 2). This intervention and the construction of other canals and roads further disturbed the hydrodynamics and water quality of the tidal basin (Berlanga-Robles & Ruiz-Luna, 2007). Consequently, these disturbances may have intensified the negative monotonic trend observed in mangrove cover in this tidal basin, exacerbating mangrove vulnerability and resulting in a different change trend from those of other mangroves. The notable differences between the land cover change estimates based on data from CONABIO and Clark Labs highlight the consequences of divergent interpretations of mangrove dynamics. While the analysis with CONABIO data identified a trend suggesting deforestation, the analysis with Clark Labs data suggested reforestation. When examining the potential causes of this discrepancy, it is crucial to consider the techniques and tools employed by both entities, as factors ranging from the choice of satellite images to the processing algorithms can significantly influence the results. Moreover, the spatial resolution, which may vary among sources, can result in identifying different trends or omitting smaller mangrove segments. The mangrove maps created by CONABIO were based on SPOT 5 and Sentinel-2 satellite images and exhibited overall accuracies ranging from 75.6–90.5% (Palacio-Prieto et al., 2004 ; Velázquez-Salazar, 2021). In contrast, Clark Labs employed Landsat 5 and 8 images, adopting a consistent methodology for various regions worldwide, including Mexico. Although specific data on the accuracy of Mexican maps are not available, in their study conducted in Vietnam, Clark Labs reported an overall accuracy of 87%. In the case of mangroves, user and producer accuracies were 94% and 92%, respectively (Clark Labs, 2023). However, it is important to note that the accuracy indicators provided for both data sources are at the national level. In specific contexts, such as the Teacapan -Agua Brava Lagoon System, the accuracy of mangrove classification may have varied and diverged from nationally reported values. These variations in local accuracy can be influenced by factors such as the spatial resolution of images, landscape complexity, and specific characteristics of mangroves in the area. The primary differences between the two datasets may lie in the first two maps of each series, which were not directly compared because they were temporally mismatched. However, good agreement was obtained (Kappa > 0.61, Landis and Koch, 1977 ) when comparing CONABIO maps from 2015 and 2020 with Clark Labs maps from 2014 and 2000, respectively. On the other hand, the mangrove area for 1999, calculated from Clark Labs data, was approximately 20,000 ha less than the 75,000 ha in 2000 reported for the system by Berlanga-Robles & Ruiz-Luna (2007). This difference suggests that the mangrove area based on these data for that year may have been underestimated, leading to a misinterpretation of mangrove reforestation, especially considering that the possibly underestimated area corresponded to the first year of the series. These discrepancies illustrate the inherent challenges in monitoring and interpreting complex systems like mangroves and how different methodologies or data sources can lead to divergent conclusions (Ruiz-Luna et al., 2003). For example, the specific deforestation rate estimated from CONABIO data (-0.87) exceeded the rate reported by Berlanga-Robles and Ruiz-Luna (2007) from 1973 to 2000 in the same system. The rate reported by Berlanga-Robles and Ruiz-Luna (2007) was previously the highest in northwestern Mexico and contrasts with the positive change rates observed in other Sinaloa mangroves between 2002 and 2016 (Berlanga-Robles & Ruiz-Luna, 2019). These differences mark a critical turning point in discussing how these important ecosystems should be conserved. Therefore, adopting a uniform methodology based on clear definitions for mangrove mapping is essential. This uniformity will enhance the accuracy of estimates and ensure a solid foundation for conservation and management decisions. Indeed, consistency and precision in mapping are crucial for addressing the conservation challenges faced by these ecosystems (Acosta-Velazquez et al., 2023 ). Based on the sub-basin-level change rates in mangrove coverage identified with CONABIO maps, the most notable negative change was detected in a sub-basin of the Teacapan -Agua Grande tidal basin. However, this change was not statistically significant (p > 0.1). This lack of significance could be due to the area estimated for 2005, which is notably different from the others and might have been overestimated. Despite precedents that would explain the general negative trend, no factor was identified to explain the mangrove recovery of approximately 1000 ha in that sub-basin between 2010 and 2015. Despite the lack of significance of the observed negative change rate in the Teacapan -Agua Grande tidal basin, this trend was consistent with the results of the EVI time series, which, unlike the land cover analysis conducted with CONABIO data, focused on assessing trends in mangrove canopy condition and vigor. In the land cover analysis, vulnerable mangroves (class 1) were primarily concentrated in the Teacapan -Agua Grande basin. Additionally, the second-highest negative change rate, which was statistically significant (p < 0.05), was identified in the El Colorado-La Palicienta tidal sub-basin in the analysis with CONABIO data. In this sub-basin, 38% of mangroves were vulnerable. Although the negative change trends observed in the Teacapan -Agua Grande and El Colorado-La Palicienta tidal basins were consistent in both analyses, the overall results for the mangroves of the lagoon system revealed different information. While the mean annual change rate in mangrove coverage estimated from CONABIO data from 2005 to 2020 (-0.87) indicated deforestation, the overall trend of the EVI time series was positive, indicating most mangroves were resilient or resistant. 5 CONCLUSIONS The comprehensive analysis of data and satellite imagery in this study has notably advanced our understanding of mangrove dynamics in the Teacapan -Agua Brava Lagoon System. Using multiple Landsat 5 and MODIS images has allowed us to overcome the limitations of previous studies that relied on limited data ranges, providing a more detailed and complete view of the change trends in mangrove canopy and cover. This approach strengthens the foundation for effective conservation strategies and underscores the importance of multi-temporal remote sensing in managing vital ecosystems. The resilience of the mangroves in the Teacapan -Agua Brava Lagoon System is evident by the overall positive trends in growth and canopy cover. However, the susceptibility of these ecosystems to extreme events is evident in the decline of the EVI in recent years, highlighting the notable influence of intense meteorological phenomena. Furthermore, the variability in the density of specific tree species and changes in seasonality highlight the complexity of mangrove responses to anthropogenic and natural disturbances. This comprehensive understanding underscores the need for adaptive management strategies that consider both the long-term dynamics of mangroves and their responses to imminent catastrophic events. This study highlights the importance of a critical approach when using different databases to analyze mangrove dynamics. Each dataset, with its inherent strengths and limitations, requires careful interpretation and a methodical application of analytical techniques to reveal change trends in mangrove cover accurately. This approach ensures that data assessments faithfully reflect complex interactions and ongoing ecological processes, providing a solid basis to inform conservation and management actions for these vital ecosystems. This study underscores the critical importance of monitoring mangroves through remote sensing technologies and data analysis to comprehensively understand, conserve, and effectively manage these vital ecosystems. The results provide a robust foundation for crafting adaptive conservation strategies that consider both the resilience of mangroves and their susceptibility to extreme events. Furthermore, it emphasizes the need to approach the interpretation of different data sources carefully and critically, ensuring the dynamics of these ecosystems are understood precisely. Ultimately, this study reinforces the ongoing importance of conducting research of this nature to preserve biodiversity and safeguard the valuable ecosystem services provided by mangroves that are essential to local communities. Declarations Acknowledgements I thank Miguel Ángel Sánchez Rodríguez for independently verifying the calibration and validation points used in this study. The MODQ131 v. 6.1 products were obtained online from the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) and the USGS/Earth Resources Observation and Science (EROS) Center (Sioux Falls, South Dakota, DAAC). The mangrove distribution maps utilized in this study were provided by the National Commission for the Knowledge and Use of Biodiversity (CONABIO) and Clark Labs/Gordon and Betty Moore Foundation/Oceans and Seafood Markets Initiative. This research received financial support from the National Council of Science and Technology (CONACYT) under the SEP-CONACYT Basic Science Project 157533 “Modeling the relationships between the spatial patterns of the mangrove forest and the distribution and abundance of penaeid shrimp in the Teacapan-Agua Brava lagoon system, Mexico.” Large Language Models (LLMs) Chat GPT-4 assisted in verifying and correcting some codes to implement loops in R (e.g., to estimate Theil-Sen median slopes for the 29 tidal sub-basins) and in translating the original manuscript, written entirely by the author, from Spanish to English. The resulting English manuscript was thoroughly reviewed and edited by a human (in Methods section: lines 249-253). Funding This research received financial support from the National Council of Science and Technology (CONACYT) under the SEP-CONACYT Basic Science Project 157533 “Modeling the relationships between the spatial patterns of the mangrove forest and the distribution and abundance of penaeid shrimp in the Teacapan-Agua Brava lagoon system, Mexico.” Competing Interests The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Author Contributions As the sole author of this study, César A. Berlanga-Robles, I was responsible for its conception and design, as well as for the preparation of materials, data collection, and analysis. I also wrote the initial draft of the manuscript and conducted subsequent revisions. For the final English language editing of the manuscript, I enlisted the services of a professional editor. 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Scale 1:250,000 NA 2) Landsat TM images b Raster Compositions of Landsat 5 optical bands from 2005: B1 blue (0.45-0.52 µm), B2 green (0.52-0.60 µm), B3 red (0.63-0.69 µm), B4 near-infrared (0.77-0.90 µm), B5 shortwave infrared 1 (1.55-1.75 µm), and B7 shortwave infrared 2 (2.08-2.35 µm). Processed in Google Earth Engine (Gorelick et al., 2017) using cropping and median functions. Based on 24 Landsat 5 images (path/row 31/44, 31/45) from the LANDSAT/LT05/C02/T1_L2 collection with <20% cloud cover. Resolution: 30 m. 2005-05-01 2005-11-09 3) Coastal wetlands of Teacapan -Agua Brava Lagoon System c Raster Thematic map of the distribution of four natural wetlands, one artificial wetland, and a class grouping different terrestrial covers. Resolution: 30 m 2000 4) Distribution of mangroves in Mexico d Vector Maps of the distribution of mangroves in Mexico produced by the classification of SPOT-5, Landsat ETM+, and Sentinel-2 images. Scale 1:50,000 2005, 2010, 2015, 2020 5) MOD13Q1 v. 6.1products e Raster 828 HDF files from version 6.1 of the 16-day Vegetation Index products of the MODIS Terra satellite. Resolution: 250 m 2005-01-01 2005-12-19 6) Aquaculture ponds and mangroves in Mexico f Raster Thematic maps of the distribution of aquaculture ponds, mangroves, other coastal wetlands, and terrestrial cover along the Mexican coast produced by Landsat TM , ETM+ and OLI Image Classification. Resolution: 15 m 1999, 2014, 2018, 2020 7) Calibration and validation points c,d,g Vector 557 points randomly extracted from the thematic map of layer 3 and the 2005 map of layer 4, updated and verified for 2005 by the independent photointerpretation of images available in Google Earth Pro v. 7.3 from 2004 and 2005. 2005 Sources: a Blanco et al. (2011); b Earth Engine Data Catalog: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_L2; c Berlanga-Robles and Ruiz-Luna (2007); d National Commission for the Knowledge and Use of Biodiversity (CONABIO): http://www.conabio.gob.mx/informacion/gis/; e Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/data_access/data_pool, f Clark Labs: https://clarklabs.org/aquaculture/landcover-data/, g Google LLC (2022). Table 2. Area of mangrove clusters identified by hierarchical clustering based on the principal component analysis (PCA) of the monthly time series of the Enhanced Vegetation Index (EVI) from 2005 to 2022. Cluster 1 2 3 4 Tidal Basin (TB) ha % 1 ha % 1 ha % 1 ha % 1 Total TB % Río Santiago ha 75 0.9 94 0.5 283 1.4 401 2.4 853 1.3 % 2 8.8 11.0 33.2 47.0 Los Corchos ha 6 0.1 0 0.0 0 0.0 0 0.0 6 0.0 % 2 100.0 0.0 0.0 0.0 El Sesteo ha 13 0.1 44 0.2 82 0.4 81 0.5 219 0.3 % 2 5.7 20.0 37.2 37.1 Mexcaltitán Camichín ha 282 3.4 2543 13.7 3843 18.9 2577 15.4 9245 14.5 % 2 3.0 27.5 41.6 27.9 El Colorado-La Palicienta ha 282 3.4 257 1.4 163 0.8 100 0.6 738 1.2 % 2 38.2 34.8 22.1 13.6 Agua Brava ha 3430 40.8 8000 43.0 13861 68.3 13448 80.4 38739 60.6 % 2 8.9 20.7 35.8 34.7 Teacapan -Agua Grande ha 4006 47.7 7518 40.4 1987 9.8 125 0.7 13636 21.3 % 2 29.4 55.1 14.6 0.9 Laguna Grande-Chametla ha 307 3.7 169 0.9 63 0.3 0 0.0 539 0.8 % 2 57.0 31.4 11.6 0.0 Cluster total ha 8400 18625 20282 16732 63975 % % 2 13 29 32 26 Notes: % 1 with respect to the cluster total, % 2 with respect to the total tidal basin (TB), and % with respect to the overall total (63,975 ha). Table 3. Changes in mangrove cover in the tidal sub-basins of the Teacapan -Agua Brava Lagoon System, Mexico. CONABIO Clark Labs Area (ha) Area (ha) TSB 2005 2010 2015 2020 Pdt p 1999 2014 2018 2020 Pdt p 07 Rio Santiago tidal basin 07.01 503 503 505 489 -0.5 0.281 536 466 514 514 -0.5 0.787 07.02 0 0 2 2 0.2 0.098 0 0 3 3 0.2 1.000 08 Los Corchos tidal basin 08.01 0 0 1 0 2.7 0.031 0 0 0 0 0 NA 09 El Sesteo tidal basin 09.01 210 229 237 251 0.0 1.000 156 158 256 256 5 0.059 10 Mexcaltitan-Camichin tidal basin 10.01 6266 6145 6223 6304 9.1 0.688 5909 5698 5991 5991 4.1 0.281 10.02 4382 3785 3782 3766 -22.1 0.031 2677 2198 3030 3030 17.7 0.281 11 El Colorado-La Palicienta tidal basin 11.01 775 774 779 779 0.3 0.106 630 624 667 667 1.9 0.106 11.02 2142 1845 1386 1095 -72.4 0.031 4 4 6 6 0.1 12 Agua Brava tidal basin 12.01 12062 11850 11788 11808 -14.7 0.063 11444 11432 11572 11571 6.4 0.156 12.02 8166 7505 7475 7319 -43.8 0.031 2916 2805 5283 5283 118.6 0.098 12.03 2547 2289 2331 2572 5.0 0.844 2178 2178 2228 2228 2.5 0.100 12.04 0 0 0 0 0.0 NA 0 0 0 0 0.0 NA 12.05 0 0 1 1 0.1 0.098 0 0 0 0 0.0 NA 12.06 2505 1814 1930 1953 -16.1 0.438 1570 1570 1601 1601 1.6 0.100 12.07 4337 4180 4175 4346 -0.2 1.000 4198 4196 4220 4220 1.1 0.106 12.08 4402 4392 4384 4334 -3.3 0.031 4115 4115 4115 4115 0.0 NA 12.09 4873 4746 4757 4735 -6.8 0.094 4533 4518 4676 4676 7.2 0.106 12.10 2311 2261 2264 2326 0.8 0.688 1968 1946 2151 2151 9.2 0.106 12.11 900 905 928 1015 6.1 0.031 932 932 936 936 0.2 0.100 13 Teacapán-Agua Brava tidal basin 13.01 521 492 512 383 -7.5 0.094 467 464 558 547 4.3 0.313 13.02 1253 1261 1295 1124 -3.5 0.438 1261 1261 1410 1409 7.4 0.106 13.03 28 28 28 35 0.2 0.181 8 8 12 12 0.2 0.100 13.04 80 67 81 66 -0.5 0.462 68 68 68 68 0.0 NA 13.05 1741 1686 1686 1514 -13.1 0.059 1668 1596 1740 1740 3.6 0.281 13.06 2804 2771 2871 1400 -50.1 0.313 1903 2705 2722 2722 21.6 0.059 13.07 2905 2752 2821 2222 -38.1 0.094 2160 2886 3075 3075 45.4 0.059 13.08 4994 4534 5618 2255 -137.3 0.313 3951 6012 6236 6236 82.4 0.100 14 Laguna Grande-Chametla tidal basin 14.01 45 42 42 18 -1.2 0.059 1 1 2 2 0.1 0.100 14.02 28 15 14 8 -1.3 0.031 1 0 1 1 0.0 0.423 14.03 121 92 98 113 0.3 1.000 48 9 68 68 1.0 0.281 TAB 70903 66963 68014 62235 -525 0.063 55306 57849 63143 63131 393 0.063 Notes: TSB: tidal sub-basin, Pdt: Sen's Slope, p:bilateral p-value, TAB: Teacapan -Agua Brava Lagoon System." Supplementary Files CambiosTAB0703supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Wetlands → Version 1 posted Reviewers agreed at journal 17 Apr, 2024 Reviewers invited by journal 23 Jan, 2024 Editor invited by journal 30 Dec, 2023 Editor assigned by journal 21 Dec, 2023 First submitted to journal 20 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3783054","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268944551,"identity":"0a27438d-741a-4080-876f-56bac861d96e","order_by":0,"name":"César A. Berlanga-Robles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYNCCCgYeBoYEkrScIVkLYxuIJFYLf3v7w8+V8+xk+NsTWDfzEKNF4swZY8mz25J5JM48YLs5gxgtBhI5DJKN2w7wMNxIYLvxgSgt8s8f/2ycc4BHHqQlgThbGMwkGxsO8BgQbYvEmRwzy4ZjyTyGZx62EecX/vbjj2821NjZyx1PPnabqBBDAowNJGoYBaNgFIyCUYATAABz1TKaa2M0QAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3860-3523","institution":"Centro de Investigacion en Alimentacion y Desarrollo AC","correspondingAuthor":true,"prefix":"","firstName":"César","middleName":"A.","lastName":"Berlanga-Robles","suffix":""}],"badges":[],"createdAt":"2023-12-20 17:30:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3783054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3783054/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13157-024-01877-6","type":"published","date":"2024-12-16T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50182419,"identity":"c96604ec-16c1-465c-9731-e3a1853ec17f","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1543626,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area. Teacapan -Agua Brava Lagoon System (Marimas Nacionales), Mexico.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/57b5a836b998bd74799ae69d.jpg"},{"id":50182422,"identity":"ead3e3b9-02db-426c-9bbc-f7f39df52bd7","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3070686,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of the base map of mangrove cover in the Teacapan-Agua Brava Lagoon System in 2005. A) Box plot of the Mangrove Vegetation Index (MVI) for four ground covers. B) Map of the MVI calculated with Landsat TM band 2 (green), 4 (near-infrared), and 5 (shortwave infrared 1). C) Binary base map produced by the MVI classification: mangrove (1) = MVI ≥ 1.8, other (0) = MVI \u0026lt; 1.8.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/9d6a70e0e3e193a1c7f21b8e.jpg"},{"id":50182421,"identity":"a37ca670-b2de-4d99-b9de-9097a5c4a005","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2267804,"visible":true,"origin":"","legend":"\u003cp\u003eProfile (A) and seasonal component (B) of the monthly time series (2005–2022) of the Enhanced Vegetation Index (EVI) of the mangroves of the Teacapan-Agua Brava Lagoon System.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/a07e571418e77d1df5e60eb0.jpg"},{"id":50182832,"identity":"205f1fd9-ee51-4944-851e-f3b8db4ba5d4","added_by":"auto","created_at":"2024-01-25 19:02:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2025071,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of the monthly time series of the Enhanced Vegetation Index (EVI) from 2005 to 2022. A) Scree plot. B) Biplot of the variables. C) Biplot of the observations with the clusters identified using hierarchical clustering based on the PCA. D) Box plot of the global averages of the EVI for the four identified clusters.\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/33104961bedc1bf2f4729f40.jpeg"},{"id":50182423,"identity":"506334c4-7f9d-4a7f-b230-4aaa0a72841a","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":272696,"visible":true,"origin":"","legend":"\u003cp\u003eProfiles (A) and seasonal components (B) of the monthly time series of the Enhanced Vegetation Index (EVI) of the clusters identified by hierarchical clustering based on the principal component analysis (PCA).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/72bc7a2d244dc456e3913350.jpg"},{"id":50182424,"identity":"48edd092-8f9a-4750-a215-500a521c2a22","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1018858,"visible":true,"origin":"","legend":"\u003cp\u003eA) False color composition of the first three principal components of the principal component analysis (PCA). Arrows indicate areas with different change trends: 1) negative trend (τ = -0.17, p = 0.0004), 2) no trend (τ = 0.02, p = 0.7163), 3) positive trend (τ = 0.41, p \u0026lt; 0.0001). B) Thematic map of mangrove clusters identified by hierarchical clustering based on the PCA.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/3fe136f118d113718b613310.jpg"},{"id":72202741,"identity":"c5166495-2d42-44dd-b119-b58de8e6879e","added_by":"auto","created_at":"2024-12-23 16:15:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5950598,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/dbd01066-5f4b-43a7-bc90-13598ba8576f.pdf"},{"id":50182425,"identity":"3c34d365-3a10-456f-837b-c52f030eb6c1","added_by":"auto","created_at":"2024-01-25 18:54:28","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":1389443,"visible":true,"origin":"","legend":"","description":"","filename":"CambiosTAB0703supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-3783054/v1/d45e1df2d73ca4dafe96ed2a.docx"}],"financialInterests":"","formattedTitle":"Trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales), Mexico: An approach using open-access geospatial data","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eMangrove wetlands provide a wide range of ecosystem services to millions of inhabitants in tropical and subtropical coastal regions worldwide. These services include providing fisheries resources, wood, fuel, and cultural goods and protecting against storms, floods, erosion, and pollution. Moreover, mangrove wetlands provide breeding and refuge habitats for numerous aquatic species (Friess et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Getzner and Islam, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition to these notable local and regional benefits, as blue carbon ecosystems, mangroves sequester approximately 32\u0026nbsp;million tons of carbon annually, making these ecosystems crucial to mitigating climate change (Howard et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, mangroves face multiple anthropogenic threats that arise from changes in land cover and land use, such as their conversion into aquaculture farms, agricultural fields, or urban settlements, as well as overexploitation and pollution. These threats have resulted in a notable loss of mangrove cover. Indeed, during the 1980s, approximately 35% of mangrove surface area was lost worldwide (FAO \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Friess et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This trend has continued, with mangrove loss totaling 62% in the first fifteen years of the present century (Goldberg et al., 2020). Currently, mangrove threats due to human activities are compounded by long-term stressors, including relative sea level rise and sea level fluctuations related to climate oscillations (Friess et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lovelock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the numerous goods and services provided by mangroves, their high rates of deforestation and degradation, and current and future threats, interest in implementing management actions to protect and conserve these wetlands has grown. However, proper management requires up-to-date information on their extent, distribution, and change trends, which can be obtained by analyzing satellite imagery (Giri, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; 2020; Nepita-Villanueva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Earth observation satellites are excellent tools for monitoring mangroves, as they repeatedly collect data in different spatiotemporal resolutions, covering various sections of the electromagnetic spectrum over large land surface areas. From these data, qualitative and quantitative indicators of vegetation characteristics (e.g., thematic maps and vegetation indices) can be derived to evaluate the extent, composition, distribution, productivity, and temporal changes in mangrove cover over the last five decades (Giri, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kuenzer, 2011; Younes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHundreds of Earth observation satellites provide data that may be used to synthesize and systematize information on the distribution, health, productivity, and dynamics of vegetation (Ch\u0026eacute;tet and Denux, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Indeed, numerous studies have demonstrated their utility to monitor mangroves at local, regional, national, and even global scales (Berlanga-Robles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Berlanga-Robles \u0026amp; Ruiz-Luna, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Giri et al. 2011; Jia et al. 2017; Vel\u0026aacute;zquez-Salazar et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al. 2017). However, most of these studies have analyzed a limited number of images when describing changes over long periods (e.g., decades), overlooking the potential offered by the vast archive of open-access satellite images to detect either cyclical seasonal changes or gradual interannual changes. Thus, satellite images are crucial to assessing the potential bias and errors of long-term change estimates, identifying different disturbance factors, and evaluating the effects of these factors on mangrove health and phenology (Berlanga-Robles \u0026amp; Ruiz-Luna, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rogan et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Younes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to numerous satellite images, open geospatial data on the extent and distribution of mangroves are also available online. For example, data can be found in the Global Mangrove Watch portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalmangrovewatch.org/\u003c/span\u003e\u003cspan address=\"https://www.globalmangrovewatch.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or, in the case of Mexico, in the National Biodiversity Information System (SNIB) portal of the National Commission for the Knowledge and Use of Biodiversity (CONABIO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.conabio.gob.mx/informacion/gis/\u003c/span\u003e\u003cspan address=\"http://www.conabio.gob.mx/informacion/gis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These data can be used to complement information from satellite images when classifying, calibrating (training), and validating (testing) classification models and even estimating change indicators directly (Berlanga-Robles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Berlanga-Robles \u0026amp; Ruiz-Luna, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Quintero-Morales et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osorio-Olvera et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the global and regional importance of mangroves and the potential of satellite data to monitor these valuable ecosystems, conservation actions and attention must be focused on specific ecosystems that exhibit particular dynamics and face specific threats. Thus, this study examined open-access geospatial data, including satellite imagery and thematic maps, to evaluate change trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales). This ecosystem, the most extensive mangrove system on the Mexican Pacific coast, has been subject to natural and anthropogenic disturbances over the past five decades. These disturbances have changed the composition, structure, and condition of the ecosystem, resulting in one of the highest deforestation rates in northwestern Mexico (Berlanga, 2006; Berlanga-Robles \u0026amp; Ruiz-Luna, 2007). Fortunately, signs of resilience and recovery in the Teacapan -Agua Brava Lagoon System have been identified during the first decades of the current century (Berlanga-Robles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nepita-Villanueva, 2019).\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe Teacapan -Agua Brava Lagoon System (Marismas Nacionales) is located in northwestern Mexico (21\u0026deg; 44' and 22\u0026deg; 52' N, 105\u0026deg; 15' and 106\u0026deg; 04' W; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The climate of the region is warm sub-humid (Aw), exhibiting a north-to-south gradient of arid-to-humid conditions, an average annual temperature of 22\u0026deg;C, and annual precipitation ranging from 500 to 2500 mm, primarily occurring in summer (De la Lanza \u0026amp; Hern\u0026aacute;ndez, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Teacapan -Agua Brava Lagoon System (163,000 ha) is composed of 29 tidal sub-basins within 8 tidal basins and exhibits a mosaic of estuaries, coastal lagoons, marshes, and mangroves (Blanco et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The mangrove vegetation in the Teacapan -Agua Brava Lagoon System, which covered\u0026thinsp;~\u0026thinsp;75,000 ha in 2000 (Berlanga-Robles and Ruiz-Luna, 2007), consists of patches dominated by \u003cem\u003eAvicennia germinans\u003c/em\u003e (black mangrove) in the north, \u003cem\u003eLaguncularia racemosa\u003c/em\u003e (white mangrove) and \u003cem\u003eRhizophora mangle\u003c/em\u003e (red mangrove) in the south, and \u003cem\u003eConocarpus erectus\u003c/em\u003e (buttonwood mangrove) throughout the system (De la Lanza et al. 1996; De la Lanza \u0026amp; Hern\u0026aacute;ndez, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Database and Preprocessing\u003c/h2\u003e \u003cp\u003eThis study utilized a Geographic Information System (GIS) comprised of seven layers of information at various spatiotemporal scales in vector and raster formats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All layers were projected to the EPSG:32613 reference system (UTM zone 13N / WGS 84). Additionally, the polygons of the eight tidal basins in layer 1 were merged and reclassified with a value of 1, editing the study area polygon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and masking the other layers. The 2-band Enhanced Vegetation Index (EVI) composites contained in the Hierarchical Data Format (HDF) files corresponding to layer 5 were exported to TerrSet 19.07 (Eastman, 2020) to generate a monthly time series of the EVI from January 2005 to December 2022. This series was edited using the mean as an aggregation function and adjusting the storage values, which initially ranged from \u0026minus;\u0026thinsp;2000 to 10000 to match the original index values (-0.2, 1). This resulted in a series of 216 images. The start date of the series was selected based on the creation date (2005) of the first map of mangrove distribution in Mexico, produced by CONABIO (layer 4).\u003c/p\u003e \u003cp\u003eA total of 187 points were extracted from the 2005 mangrove map (layer 4) using simple random sampling without replacement in TerrSet to generate calibration and validation points (layer 7; Eastman, 2020). An independent analyst with knowledge of the study area verified these points by evaluating Google Earth Pro 7.3 images from 2004 and 2005. A similar process was followed with the 2000 thematic map of coastal wetlands in the Teacapan -Agua Brava Lagoon System (layer 3) produced by Berlanga-Robles and Ruiz-Luna (2007) to generate calibration and validation points for the cover classes of lagoons (including estuaries; 190 points), saltmarshes (96 points), and additional land cover classes (terrestrial vegetation, agricultural land, settlements, and aquaculture farms; 84 points).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mangrove Canopy Changes\u003c/h2\u003e \u003cp\u003eVegetation indices derived from satellite imagery are significantly related to canopy and phenological changes in vegetation cover, allowing seasonal and long-term change trends to be summarized, systematized, and described by parameters estimated from their time series (V\u0026aacute;zquez et al., 2013; Berlanga-Robles et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nepita-Villanueva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the present study, changes in mangrove canopy were evaluated by examining the EVI time series using an approach similar to those implemented by Berlanga-Robles et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Nepita-Villanueva et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These authors focused on analyzing deciduous tropical forest and mangrove cover, respectively, at the beginning of the time series utilizing Principal Component Analysis (PCA) to decompose the time series of vegetation indices.\u003c/p\u003e \u003cp\u003eThe current study focused on analyzing mangrove cover in 2005. A base map was used to distinguish mangrove cover from those of other cover types using the Mangrove Vegetation Index (MVI). The MVI was defined by Baloloy et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{M}\\text{V}\\text{I}=\\frac{NIR-G}{SWIR1-G}\\)\u003c/span\u003e \u003c/span\u003e, Eq.\u0026nbsp;(1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eNIR\u003c/em\u003e, SWIR1, and \u003cem\u003eG\u003c/em\u003e correspond to reflectance values in the near-infrared, shortwave infrared 1, and green wavelengths, respectively. In Eq.\u0026nbsp;(1), the numerator emphasizes greenness, while the denominator relates to moisture. Both are essential attributes of mangrove vegetation. The MVI can differentiate mangroves from other vegetation types without additional data (e.g., tidal amplitude) or indices like the Modified Normalized Difference Water Index (Baloloy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MVI was determined with bands B2 (green), B4 (near-infrared), and B5 (shortwave infrared 1) of the Landsat TM image from 2005 (layer 2).\u003c/p\u003e \u003cp\u003eThe MVI threshold used to distinguish the mangrove class from the three other classes (i.e., lagoons, marshes, and additional) was established using 50 points per class (Baloloy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These points were randomly selected (without replacement) from the collection of points in layer 7. Once the MVI threshold was determined, it was used to transform the MVI map into a binary map displaying only mangrove (1) and other (0) cover. The accuracy of this map was evaluated using an error matrix based on 442 test points, also randomly selected from layer 7, of which 187 were mangrove points, and 255 were other cover points. The binary map was edited to remove mangrove patches of less than 6.25 ha. This area corresponds to the pixel size of MOD13Q1 v. 6.01 products. The maps were edited to limit the analysis of the EVI time series (2005 to 2022) to the mangrove cover in 2005 in patches with areas equal to or greater than the spatial resolution of the images comprising that series.\u003c/p\u003e \u003cp\u003eThe producer and user accuracies for each class, along with the overall accuracy and the Kappa coefficient estimator, were determined from the error matrix. The overall accuracy represents the proportion of correctly classified points, regardless of their specific class, relative to the total number of evaluated points. The Kappa coefficient measures the agreement between two classifications, adjusting for the agreement that could occur by chance. A Kappa value of 1 indicates perfect agreement, while a value of 0 indicates the agreement is no better than chance. Producer accuracy is associated with omission error (Type I) detected in the classification of a class, while user accuracy is related to commission error (Type II; Congalton \u0026amp; Green, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe monthly EVI time series (2005 to 2022) was decomposed using a PCA with orientation (mode) T. The PCA orientation denotes how the images are organized for analysis. In mode T, each image acts as a variable (sample) over time, enabling spatiotemporal patterns of change to be identified (Machado-Machado et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Neeti \u0026amp; Eastman, 2014). The PCA was performed in parallel in TerrSet (Eastman, 2022) and R (v. 4.2.2, R Core Team, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the RStudio platform (v. 2023.06, RStudio Team, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With TerrSet, images of the first three principal components were generated and combined into a false-color composition using red, green, and blue filters to identify areas with different long-term change trends. The analysis was complemented with PCA-based hierarchical clustering in R (Kassambara, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the analysis in R, the image stack from the time series was loaded into the RStudio environment (Hijmans, 2023) to generate a data frame with 218 variables. The data frame included geographic coordinates and monthly EVI values from January 2005 to December 2022, totaling 1,975,734 observations (pixels). The number of observations was reduced to 10,246 by eliminating invalid values (pixels masked with the base map). After excluding the geographic coordinates, a Pearson correlation matrix was generated and the sampling adequacy of the PCA was verified through the Kaiser-Meyer-Olkin (KMO) test. A scree plot was used to determine the number of components (Cureton \u0026amp; D\u0026rsquo;Agostino, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; S\u0026aacute;nchez, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PCA was conducted using the PCA() function from the \u0026lsquo;\u003cem\u003eFactorMineR\u003c/em\u003e\u0026rsquo; R package (Le et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In order to identify groups with similar scores within the set of previously extracted principal components, PCA-based hierarchical clustering was conducted using the HCPC() function from the same R package (Kassambara, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These clusters reflected areas with comparable temporal patterns in EVI. Euclidean distance between components and the Ward clustering method were employed for hierarchical clustering, with a minimum of 4 and a maximum of 8 clusters (Le et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kassambara, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The grouping hypothesis (H\u003csub\u003e1\u003c/sub\u003e) was evaluated by testing the hypothesis of group absence (H\u003csub\u003e0\u003c/sub\u003e) using a one-way analysis of variance (ANOVA) with observations (replicates) of the average EVI over the entire period. Five replicates per cluster were selected, corresponding to the most emblematic observations of each cluster. These were given as outputs of the HCPC() function (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, \u003cem\u003en\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e = 5, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20).\u003c/p\u003e \u003cp\u003eA data frame with columns of the original variables was an HCPC() function output, which included an additional column that assigned a cluster to each observation. By reassigning geographic coordinates to each observation, a map showing the distribution of each cluster was generated, from which the area of each cluster in each tidal basin of the system was calculated. Monthly EVI profiles of each cluster were determined using the same data frame and the mean as the aggregation measure. The seasonal component of these profiles was obtained with the decompose() function of the \u0026lsquo;\u003cem\u003eR Stats\u003c/em\u003e\u0026rsquo; R package using a moving average fit (Cowpertwait \u0026amp; Metcalfe, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The trend profiles were evaluated using the Mann-Kendall test (Neeti \u0026amp; Eastman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nepita-Villanueva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), implemented with the mk.test() function of the \u0026lsquo;\u003cem\u003etrend\u003c/em\u003e\u0026rsquo; R package (Pohlert, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mangrove Cover Change Trends\u003c/h2\u003e \u003cp\u003eIndependent analyses of change trends in mangrove cover were conducted using the maps produced by CONABIO (layer 4) and the maps generated by Clark Labs (layer 6). The average annual rate of change in mangrove cover for the entire system was determined based on the maps from the extreme years of each dataset: 2005 to 2020 for CONABIO data and 1999 to 2020 for Clark Labs data. This rate was calculated using the equation proposed by Palacio-Prieto et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(dn={\\left[\\frac{{S}_{2}}{{S}_{1}}\\right]}^{\\frac{1}{n}}-1\\)\u003c/span\u003e \u003c/span\u003e, Eq.\u0026nbsp;(2)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003edn\u003c/em\u003e is the average annual rate of change, \u003cem\u003eS\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e and \u003cem\u003eS\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e are the (mangrove) areas at date 1 and 2, respectively, and \u003cem\u003en\u003c/em\u003e is the number of years between the two dates.\u003c/p\u003e \u003cp\u003eFollowing a strategy similar to Osorio-Olvera et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), changes in mangrove cover were analyzed using the 29 tidal sub-basins of the Teacapan -Agua Brava Lagoon System as sampling units. For each sub-basin, mangrove area was estimated based on each of the four CONABIO maps from different years. Then, the change rate was determined by fitting a linear model of the area over time, using the unique Theil-Sen median slope (Mangiafico, 2016; Wilcox, 2021). This analysis was conducted using the mblm() function of the R package of the same name (Komsta, 2022). The procedure was replicated for the Clark Labs maps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Large Language Models (LLMs)\u003c/h2\u003e \u003cp\u003eChat GPT 4 assisted in verifying and correcting some codes to implement loops in R (e.g., to estimate Theil-Sen median slopes for the 29 tidal sub-basins) and in translating the original manuscript, written entirely by the author, from Spanish to English. The resulting English manuscript was thoroughly reviewed and edited by a human.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Mangrove canopy change trends\u003c/h2\u003e \u003cp\u003eThe MVI values for the 50 mangrove calibration points ranged from 0.91 to 15.7 (median\u0026thinsp;=\u0026thinsp;3.4, mean\u0026thinsp;=\u0026thinsp;4.4, SD\u0026thinsp;=\u0026thinsp;3.5). On the other hand, the MVI values of the 150 points from the other three classes were much lower, ranging from 0 to 2.3 (median\u0026thinsp;=\u0026thinsp;0.52, mean\u0026thinsp;=\u0026thinsp;0.8, SD\u0026thinsp;=\u0026thinsp;0.53). An MVI threshold of 1.8 was established to distinguish mangrove cover from other cover classes (see section 2.3). This threshold corresponded to the 82nd percentile of the additional cover class points and the 15th percentile of the mangrove cover class points. The threshold was above the maximum values of the lagoon and marsh cover classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The MVI threshold was used to distinguish mangrove cover from the other cover classes (mangrove (1)\u0026thinsp;=\u0026thinsp;MVI\u0026thinsp;\u0026ge;\u0026thinsp;1.8, other (0)\u0026thinsp;=\u0026thinsp;MVI\u0026thinsp;\u0026lt;\u0026thinsp;1.8). A binary map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was produced with an overall accuracy of 92% and a Kappa coefficient estimator of 0.93, indicating strong agreement between the reference data and classification data (Congalton \u0026amp; Green; \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The producer and user accuracy values for the mangrove class were 87% and 95%, respectively. From the binary map, mangrove cover was estimated to be 67,334 ha, reflecting a reduction of 3,569 ha compared to the CONABIO map from 2005, which estimated mangrove cover to be 70,903 ha.\u003c/p\u003e \u003cp\u003eBy removing patches smaller than 6.25 ha and resampling the binary map to the spatial resolution of the EVI images (250 m), mangrove cover was further reduced to 64,038 ha. Therefore, the monthly EVI time series analysis included 95% of the initially identified mangrove cover. The monthly EVI profile from January 2005 to December 2022 for the entire area ranged from 0.19 to 0.36, with the minimum and maximum values occurring in December 2007 and October 2017, respectively. The EVI values tended to increase from 2005 to 2016 and then decreased towards the end of the series, although the overall trend for the entire period was positive (τ\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The seasonal component of the series showed peaks in October and valleys in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall KMO measure for the monthly EVI observations was 1, indicating the appropriateness of conducting the PCA. The first two components were selected based on the scree plot, which explained nearly 85% of the total variation. All variables were strongly correlated with each other (average Pearson correlation of 0.81) and with the first component (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hierarchical clustering of the first two components yielded four clusters, with EVI means of 0.91, 0.23, 0.34, and 0.44 for clusters 1, 2, 3, and 4, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All means were significantly different (F\u0026thinsp;=\u0026thinsp;14622, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.999, \u0026micro;i - \u0026micro;j\u0026thinsp;\u0026ge;\u0026thinsp;0.107, p\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eThe monthly EVI profiles and seasonal components of clusters 3 and 4 exhibited behavior similar to the estimates for total mangrove cover. In other words, they showed a positive monotonic trend (τ\u0026thinsp;\u0026ge;\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and seasonal fluctuations with peaks in October and valleys in June. In contrast, the profile of cluster 1 showed a negative monotonic trend (τ \u0026ge; -0.13, p\u0026thinsp;=\u0026thinsp;0.0043), and its seasonal component exhibited smaller oscillations compared to the other cases. Peaks were observed in February, and valleys occurred between July and September. The monotonic trend of cluster 2 was less evident (τ\u0026thinsp;\u0026ge;\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.09), and its seasonal component showed peaks between November and January, with valleys also in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy combining the images of the first three components and applying a filter that displays the first in red, the second in green, and the third in blue, a false-color image (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e) was generated to identify areas with different change trends. Green tones in the image represented areas with negative change trends, specifically associated with cluster 1. For example, in the area marked 1, the Mann-Kendall statistic (τ) was \u0026minus;\u0026thinsp;0.17 (p\u0026thinsp;=\u0026thinsp;0.0004). Areas that showed no trend were represented in light blue and were associated with cluster 2. In the area marked 2, the τ statistic was 0.02 (p\u0026thinsp;=\u0026thinsp;0.7163). On the other hand, violet tones represented areas with positive trends, which were associated with clusters 3 and 4. In the area marked 3, the τ statistic was 0.41 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eOf the 64,038 ha of mangroves considered in the EVI time series analysis, approximately 60% (38,739 ha) were in the Agua-Brava tidal basin. Of this subset, less than 10% were classified in cluster 1, characterized by a negative change trend, while 72% were included in clusters 3 and 4, which exhibited positive change trends. In contrast, in the Los Corchos basin, mangroves covered only 6 ha, equivalent to the area of one pixel, and were classified into cluster 1. Of the total mangrove area analyzed, 13% was classified into cluster 1, which was primarily distributed in the Agua Brava and Teacapan -Agua Grande basins, 29% belonged to cluster 2, 32% to cluster 3, and 26% to cluster 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mangrove cover changes\u003c/h2\u003e \u003cp\u003eContrasting change trends in mangrove cover were identified from the maps provided by CONABIO (layer 4) and Clark Labs (layer 6). The CONABIO map indicated mangrove deforestation at an average annual rate of -0.87% between 2005 and 2020. In contrast, the Clark Labs map indicated mangrove reforestation at an average annual rate of 0.49% between 1999 and 2020. Furthermore, the Theil-Sen slope estimated with CONABIO data for the entire lagoon system was negative, while the slope estimated with Clark Labs data was positive. Both slopes were statistically significant at α\u0026thinsp;=\u0026thinsp;0.1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, the Clark Labs map from 2014 was compared with the CONABIO map from 2015 and the maps from both sources from 2020, following a procedure similar to the one used to validate the 2005 binary mangrove cover map. In the first comparison, the overall accuracy (coincidence) was 76%. In both maps, of the nearly 163,000 ha analyzed, 56% was classified as non-mangrove cover, and 33% was classified as mangrove cover. In the Clark Labs and CONABIO maps, ~ 3% and 9% of the study area were classified as only mangrove cover, respectively. In the comparison of the 2020 maps, the overall accuracy was 89%. The areas classified as non-mangrove and mangrove cover in both maps were similar to those of the previous comparison, with 6% and 5% being classified as only mangrove cover in the Clark Labs and CONABIO maps, respectively (Supplementary Material 1).\u003c/p\u003e \u003cp\u003eIn four tidal sub-basins, the analysis of change trends in mangrove cover using CONABIO data revealed no coverage in at least one year. Significant (α\u0026thinsp;=\u0026thinsp;0.05) Theil-Sen slopes (five negative and two positive) were identified in seven sub-basins. In 15 sub-basins, the estimated slopes were significant (α\u0026thinsp;=\u0026thinsp;0.1; 11 negative and 4 positive). In eight sub-basins, the slopes were not significantly different from zero (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The largest slope was negative and corresponded to the Teacapan-Agua Grande tidal sub-basin, although it was not significant (p\u0026thinsp;=\u0026thinsp;0.313). However, it is important to note that this result should be interpreted with caution, as the power of the test at a significance level of α\u0026thinsp;=\u0026thinsp;0.1 was γ\u0026thinsp;=\u0026thinsp;0.19. The next largest slope was also negative but statistically significant (p\u0026thinsp;=\u0026thinsp;0.0313) and corresponded to the El Colorado-La Palicienta tidal sub-basin (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn five tidal sub-basins, the analysis of change trends in mangrove cover using Clark Labs data indicated no mangrove cover in at least one year. Statistically significant slopes (α\u0026thinsp;=\u0026thinsp;0.1) were identified in five cases, all of which were positive. The largest positive slope (p\u0026thinsp;=\u0026thinsp;0.098) pertained to the Agua Brava tidal sub-basin. It is important to note that in the previous analysis using CONABIO data, a significant negative slope (p\u0026thinsp;=\u0026thinsp;0.031) was estimated for the same sub-basin (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eMangroves must be inventoried and monitored to quantify their ecosystem services, analyze threats, and design conservation strategies (Friess \u0026amp; Webb, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to its extensive coverage and continuous monitoring capability, remote sensing has emerged as an irreplaceable tool in this field, surpassing the limitations of traditional field-based assessment methods (Maurya et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, most mangrove studies using remote sensing data rely on a limited number of images. Indeed, inventories are often conducted using satellite images from a single date or short time windows, assuming the resulting mangrove cover represents the entire year (Maurya et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Younes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bitemporal analyses, which use two images to describe changes in spectral or thematic characteristics of a given environment, have also predominated, reflecting an underutilization of the potential offered by the extensive repositories of satellite images available that may be used to identify stochastic and cyclical changes in mangrove cover (Hansen \u0026amp; Loveland, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, multi-temporal analyses capitalize on using multiple images from different satellites, allowing for a deeper understanding of long-term changes and cyclical phenomena in mangrove ecosystems (Kennedy et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This study adopted a more detailed approach than most mangrove studies using remote sensing data, leveraging the richness of available satellite data. For the classification of the 2005 base map, a composition derived from 24 Landsat 5 images was used, covering various stages of the phenological cycle of the mangroves in the Teacapan -Agua Brava Lagoon System, which was described by Berlanga-Robles \u0026amp; Ruiz-Luna (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Subsequently, a multi-temporal analysis was conducted using an EVI time series from 828 MODIS images between 2005 and 2022. This data-intensive approach provided a more comprehensive view of mangrove dynamics, starkly contrasting traditional remote sensing methodologies that have relied on a limited number of images.\u003c/p\u003e \u003cp\u003eThe MVI provided essential information to distinguish between mangrove and other cover classes. An MVI threshold of 1.8 effectively differentiated mangroves from other cover classes, with the resulting binary map being highly accurate (overall accuracy\u0026thinsp;=\u0026thinsp;92%, Kappa coefficient\u0026thinsp;=\u0026thinsp;0.93). However, it is important to note that the MVI threshold of 1.8 and the average MVI estimated for the mangroves in the Teacapan -Agua Brava Lagoon System of 4.4 were below the values reported by Baloloy et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their study of 11 mangrove sites in the Philippines and one site in Japan resulted in thresholds between 4.5 and 4.6 and average MVI values between 7.7 and 8.6 using Sentinel-2 and Landsat OLI images.\u003c/p\u003e \u003cp\u003eDifferences in MVI values between the two studies can be attributed to a combination of factors, including differences in sensors, environmental conditions, processing methodologies, and the composition, structure, and phenology of vegetation cover in the respective study areas. These factors and the temporal differences in the images make it challenging to compare MVI values between the two studies directly. In the study by Baloloy et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), each site was sampled at a particular time, even though the images collectively covered all seasons of the year. In contrast, this study employed a composition synthesizing vegetative mangrove phenology over a year. Despite these differences, the MVI resulted in accurate mangrove classification in both cases. Baloloy et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also obtained overall accuracies of 92%. These results underscore the utility and robustness of the MVI in identifying and classifying mangroves despite differences in absolute values between studies.\u003c/p\u003e \u003cp\u003eThe temporal characterization of mangrove canopy through the monthly EVI time series from 2005 to 2022 revealed a significant overall positive trend. This trend agrees with the findings of Nepita-Villanueva et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Berlanga-Robles et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for 2002 to 2016. These authors suggested that the positive trend was indicative of the resilience of the mangroves in the Teacapan -Agua Brava Lagoon System and reflective of their recovery and new equilibrium following the strong disturbances of the last decades of the 20th century (Berlanga-Robles \u0026amp; Ruiz-Luna, 2007). However, despite the overall trend, a decline in EVI values was also recorded in the last six years of the series. This decline agrees with the results of Vizcaya-Mart\u0026iacute;nez et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who studied the same mangroves from 2018 to 2021. These authors reported that the impact of Hurricane Willa in 2018 resulted in 79% defoliation. Moreover, after Hurricane Willa, in October 2022, Hurricane Roslyn made landfall to the south of the system as a category 3 hurricane, with wind gusts between 100 and 105 kt (NOAA, 2023). Although the impacts of this hurricane on the mangrove canopy have not been assessed, it is possible that it contributed to the significant decrease in EVI values observed in the last three months of the series.\u003c/p\u003e \u003cp\u003eThe decomposition of the EVI time series allowed four mangrove classes to be identified. Two of these, representing nearly 60% of the analyzed mangrove area, exhibited profiles and seasonal components similar to those identified for all mangroves in the system. However, 13% of the analyzed mangroves were grouped into a class with a negative monotonic trend and a lagged and lower-amplitude seasonal component than those of the remaining mangroves. The mangroves exhibiting a negative monotonic trend were primarily located in the Teacapan -Agua Grande tidal basin in the north of the lagoon system. In this area, mangroves are dominated by \u003cem\u003eAvicennia germinans\u003c/em\u003e shrubs with an average density of 2872 trees/ha (Monzalvo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This density is notably lower than the average reported for \u003cem\u003eA. germinans\u003c/em\u003e-dominated mangroves in other lagoon systems in northwestern Mexico (6741 trees/ha; Monzalvo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) but similar to the density observed in patches of this species under management in parts of the Agua Brava tidal basin to the south of the system (Valdez, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Additionally, these mangroves have been characterized as vulnerable or disturbed due to long-term negative trends (Nepita-Villanueva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and altered seasonality, with their season of maturity shifting from autumn to winter (Berlanga-Robles et al., 2020).\u003c/p\u003e \u003cp\u003eDespite the opening of the Cuautla canal in the early 1970s to connect the Agua-Brava lagoon with the Pacific Ocean, which notably altered the hydrodynamics of the system and the composition, structure, and condition of the mangroves in the area (Berlanga-Robles \u0026amp; Ruiz-Luna, 2007), a new canal project was approved in the mid-1990s. This project involved building a canal (18 km long and 80 m wide) to connect the Agua Grande lagoon with the Las Ca\u0026ntilde;as lagoon in the Teacapan -Agua Grande tidal basin (Supplementary Material 2). This intervention and the construction of other canals and roads further disturbed the hydrodynamics and water quality of the tidal basin (Berlanga-Robles \u0026amp; Ruiz-Luna, 2007). Consequently, these disturbances may have intensified the negative monotonic trend observed in mangrove cover in this tidal basin, exacerbating mangrove vulnerability and resulting in a different change trend from those of other mangroves.\u003c/p\u003e \u003cp\u003eThe notable differences between the land cover change estimates based on data from CONABIO and Clark Labs highlight the consequences of divergent interpretations of mangrove dynamics. While the analysis with CONABIO data identified a trend suggesting deforestation, the analysis with Clark Labs data suggested reforestation. When examining the potential causes of this discrepancy, it is crucial to consider the techniques and tools employed by both entities, as factors ranging from the choice of satellite images to the processing algorithms can significantly influence the results. Moreover, the spatial resolution, which may vary among sources, can result in identifying different trends or omitting smaller mangrove segments.\u003c/p\u003e \u003cp\u003eThe mangrove maps created by CONABIO were based on SPOT 5 and Sentinel-2 satellite images and exhibited overall accuracies ranging from 75.6\u0026ndash;90.5% (Palacio-Prieto et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vel\u0026aacute;zquez-Salazar, 2021). In contrast, Clark Labs employed Landsat 5 and 8 images, adopting a consistent methodology for various regions worldwide, including Mexico. Although specific data on the accuracy of Mexican maps are not available, in their study conducted in Vietnam, Clark Labs reported an overall accuracy of 87%. In the case of mangroves, user and producer accuracies were 94% and 92%, respectively (Clark Labs, 2023). However, it is important to note that the accuracy indicators provided for both data sources are at the national level. In specific contexts, such as the Teacapan -Agua Brava Lagoon System, the accuracy of mangrove classification may have varied and diverged from nationally reported values. These variations in local accuracy can be influenced by factors such as the spatial resolution of images, landscape complexity, and specific characteristics of mangroves in the area.\u003c/p\u003e \u003cp\u003eThe primary differences between the two datasets may lie in the first two maps of each series, which were not directly compared because they were temporally mismatched. However, good agreement was obtained (Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.61, Landis and Koch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) when comparing CONABIO maps from 2015 and 2020 with Clark Labs maps from 2014 and 2000, respectively. On the other hand, the mangrove area for 1999, calculated from Clark Labs data, was approximately 20,000 ha less than the 75,000 ha in 2000 reported for the system by Berlanga-Robles \u0026amp; Ruiz-Luna (2007). This difference suggests that the mangrove area based on these data for that year may have been underestimated, leading to a misinterpretation of mangrove reforestation, especially considering that the possibly underestimated area corresponded to the first year of the series.\u003c/p\u003e \u003cp\u003eThese discrepancies illustrate the inherent challenges in monitoring and interpreting complex systems like mangroves and how different methodologies or data sources can lead to divergent conclusions (Ruiz-Luna et al., 2003). For example, the specific deforestation rate estimated from CONABIO data (-0.87) exceeded the rate reported by Berlanga-Robles and Ruiz-Luna (2007) from 1973 to 2000 in the same system. The rate reported by Berlanga-Robles and Ruiz-Luna (2007) was previously the highest in northwestern Mexico and contrasts with the positive change rates observed in other Sinaloa mangroves between 2002 and 2016 (Berlanga-Robles \u0026amp; Ruiz-Luna, 2019). These differences mark a critical turning point in discussing how these important ecosystems should be conserved. Therefore, adopting a uniform methodology based on clear definitions for mangrove mapping is essential. This uniformity will enhance the accuracy of estimates and ensure a solid foundation for conservation and management decisions. Indeed, consistency and precision in mapping are crucial for addressing the conservation challenges faced by these ecosystems (Acosta-Velazquez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the sub-basin-level change rates in mangrove coverage identified with CONABIO maps, the most notable negative change was detected in a sub-basin of the Teacapan -Agua Grande tidal basin. However, this change was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1). This lack of significance could be due to the area estimated for 2005, which is notably different from the others and might have been overestimated. Despite precedents that would explain the general negative trend, no factor was identified to explain the mangrove recovery of approximately 1000 ha in that sub-basin between 2010 and 2015.\u003c/p\u003e \u003cp\u003eDespite the lack of significance of the observed negative change rate in the Teacapan -Agua Grande tidal basin, this trend was consistent with the results of the EVI time series, which, unlike the land cover analysis conducted with CONABIO data, focused on assessing trends in mangrove canopy condition and vigor. In the land cover analysis, vulnerable mangroves (class 1) were primarily concentrated in the Teacapan -Agua Grande basin. Additionally, the second-highest negative change rate, which was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), was identified in the El Colorado-La Palicienta tidal sub-basin in the analysis with CONABIO data. In this sub-basin, 38% of mangroves were vulnerable. Although the negative change trends observed in the Teacapan -Agua Grande and El Colorado-La Palicienta tidal basins were consistent in both analyses, the overall results for the mangroves of the lagoon system revealed different information. While the mean annual change rate in mangrove coverage estimated from CONABIO data from 2005 to 2020 (-0.87) indicated deforestation, the overall trend of the EVI time series was positive, indicating most mangroves were resilient or resistant.\u003c/p\u003e"},{"header":"5 CONCLUSIONS","content":"\u003cp\u003eThe comprehensive analysis of data and satellite imagery in this study has notably advanced our understanding of mangrove dynamics in the Teacapan -Agua Brava Lagoon System. Using multiple Landsat 5 and MODIS images has allowed us to overcome the limitations of previous studies that relied on limited data ranges, providing a more detailed and complete view of the change trends in mangrove canopy and cover. This approach strengthens the foundation for effective conservation strategies and underscores the importance of multi-temporal remote sensing in managing vital ecosystems.\u003c/p\u003e \u003cp\u003eThe resilience of the mangroves in the Teacapan -Agua Brava Lagoon System is evident by the overall positive trends in growth and canopy cover. However, the susceptibility of these ecosystems to extreme events is evident in the decline of the EVI in recent years, highlighting the notable influence of intense meteorological phenomena. Furthermore, the variability in the density of specific tree species and changes in seasonality highlight the complexity of mangrove responses to anthropogenic and natural disturbances. This comprehensive understanding underscores the need for adaptive management strategies that consider both the long-term dynamics of mangroves and their responses to imminent catastrophic events.\u003c/p\u003e \u003cp\u003eThis study highlights the importance of a critical approach when using different databases to analyze mangrove dynamics. Each dataset, with its inherent strengths and limitations, requires careful interpretation and a methodical application of analytical techniques to reveal change trends in mangrove cover accurately. This approach ensures that data assessments faithfully reflect complex interactions and ongoing ecological processes, providing a solid basis to inform conservation and management actions for these vital ecosystems.\u003c/p\u003e \u003cp\u003eThis study underscores the critical importance of monitoring mangroves through remote sensing technologies and data analysis to comprehensively understand, conserve, and effectively manage these vital ecosystems. The results provide a robust foundation for crafting adaptive conservation strategies that consider both the resilience of mangroves and their susceptibility to extreme events. Furthermore, it emphasizes the need to approach the interpretation of different data sources carefully and critically, ensuring the dynamics of these ecosystems are understood precisely. Ultimately, this study reinforces the ongoing importance of conducting research of this nature to preserve biodiversity and safeguard the valuable ecosystem services provided by mangroves that are essential to local communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thank Miguel \u0026Aacute;ngel S\u0026aacute;nchez Rodr\u0026iacute;guez for independently verifying the calibration and validation points used in this study. The MODQ131 v. 6.1 products were obtained online from the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) and the USGS/Earth Resources Observation and Science (EROS) Center (Sioux Falls, South Dakota, DAAC). The mangrove distribution maps utilized in this study were provided by the National Commission for the Knowledge and Use of Biodiversity (CONABIO) and Clark Labs/Gordon and Betty Moore Foundation/Oceans and Seafood Markets Initiative. This research received financial support from the National Council of Science and Technology (CONACYT) under the SEP-CONACYT Basic Science Project 157533 \u0026ldquo;Modeling the relationships between the spatial patterns of the mangrove forest and the distribution and abundance of penaeid shrimp in the Teacapan-Agua Brava lagoon system, Mexico.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLarge Language Models (LLMs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChat GPT-4 assisted in verifying and correcting some codes to implement loops in R (e.g., to estimate Theil-Sen median slopes for the 29 tidal sub-basins) and in translating the original manuscript, written entirely by the author, from Spanish to English. The resulting English manuscript was thoroughly reviewed and edited by a human (in Methods section: lines 249-253).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received financial support from the National Council of Science and Technology (CONACYT) under the SEP-CONACYT Basic Science Project 157533 \u0026ldquo;Modeling the relationships between the spatial patterns of the mangrove forest and the distribution and abundance of penaeid shrimp in the Teacapan-Agua Brava lagoon system, Mexico.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the sole author of this study, C\u0026eacute;sar A. Berlanga-Robles, I was responsible for its conception and design, as well as for the preparation of materials, data collection, and analysis. I also wrote the initial draft of the manuscript and conducted subsequent revisions. For the final English language editing of the manuscript, I enlisted the services of a professional editor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data related to this study are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta-Velazquez J, Ochoa-G\u0026oacute;mez J, V\u0026aacute;zquez-Lule A, Guevara M (2023) Changes in mangrove coverage classification criteria could impact the conservation of mangroves in Mexico. 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Int J Appl Earth Obs Geoinf 63:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.jag.2017.07.004\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.jag.2017.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Information layers used to analyze mangrove canopy and cover changes in the Teacapan -Agua Brava Lagoon System, Mexico.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003eLayer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eFormat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003eTemporality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e1) Marismas Nacionales tidal subsystems\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eA set of vector data delineating basins, sub-basins, and tidal systems of the study area. Scale 1:250,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e2) Landsat TM images\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eCompositions of Landsat 5 optical bands from 2005: B1 blue (0.45-0.52 \u0026micro;m), B2 green (0.52-0.60 \u0026micro;m), B3 red (0.63-0.69 \u0026micro;m), B4 near-infrared (0.77-0.90 \u0026micro;m), B5 shortwave infrared 1 (1.55-1.75 \u0026micro;m), and B7 shortwave infrared 2 (2.08-2.35 \u0026micro;m). Processed in Google Earth Engine (Gorelick et al., 2017) using cropping and median functions. Based on 24 Landsat 5 images (path/row 31/44, 31/45) from the LANDSAT/LT05/C02/T1_L2 collection with \u0026lt;20% cloud cover. Resolution: 30 m.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e2005-05-01\u003c/p\u003e\n \u003cp\u003e2005-11-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e3) Coastal wetlands of Teacapan -Agua Brava Lagoon System\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eThematic map of the distribution of four natural wetlands, one artificial wetland, and a class grouping different terrestrial covers. Resolution: 30 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e4) Distribution of mangroves in Mexico\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eMaps of the distribution of mangroves in Mexico produced by the classification of SPOT-5, Landsat ETM+, and Sentinel-2 images. Scale 1:50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e2005, 2010, 2015, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e5) MOD13Q1 v. 6.1products \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003e828 HDF files from version 6.1 of the 16-day Vegetation Index products of the MODIS Terra satellite. Resolution: 250 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e2005-01-01\u003c/p\u003e\n \u003cp\u003e2005-12-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e6) Aquaculture ponds and mangroves in Mexico\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003eThematic maps of the distribution of aquaculture ponds, mangroves, other coastal wetlands, and terrestrial cover along the Mexican coast produced by Landsat\u003csup\u003eTM\u003c/sup\u003e, ETM+ and OLI Image Classification. Resolution: 15 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e1999, 2014, 2018, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.147707979626485%\" valign=\"top\"\u003e\n \u003cp\u003e7) Calibration and validation points\u003csup\u003ec,d,g\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.714770797962649%\" valign=\"top\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.66893039049236%\" valign=\"top\"\u003e\n \u003cp\u003e557 points randomly extracted from the thematic map of layer 3 and the 2005 map of layer 4, updated and verified for 2005 by the independent photointerpretation of images available in Google Earth Pro v. 7.3 from 2004 and 2005.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.468590831918505%\" valign=\"top\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSources: \u003csup\u003ea\u003c/sup\u003eBlanco et al. (2011); \u003csup\u003eb\u003c/sup\u003eEarth Engine Data Catalog: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_L2; \u003csup\u003ec\u003c/sup\u003eBerlanga-Robles and Ruiz-Luna (2007); \u003csup\u003ed\u003c/sup\u003e National Commission for the Knowledge and Use of Biodiversity (CONABIO): http://www.conabio.gob.mx/informacion/gis/; \u003csup\u003ee\u003c/sup\u003eLand Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/data_access/data_pool, \u003csup\u003ef\u003c/sup\u003eClark Labs: https://clarklabs.org/aquaculture/landcover-data/, \u003csup\u003eg\u003c/sup\u003eGoogle LLC (2022).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Area of mangrove clusters identified by hierarchical clustering based on the principal component analysis (PCA) of the monthly time series of the Enhanced Vegetation Index (EVI) from 2005 to 2022.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.39871382636656%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.109324758842444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.057877813504824%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.089743589743589%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544871794871796%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544871794871796%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544871794871796%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544871794871796%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" valign=\"bottom\"\u003e\n \u003cp\u003eTidal Basin (TB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eR\u0026iacute;o Santiago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eLos Corchos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eEl Sesteo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eMexcaltit\u0026aacute;n Camich\u0026iacute;n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e2543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e3843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e2577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e9245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eEl Colorado-La Palicienta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eAgua Brava\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e3430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e38739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eTeacapan -Agua Grande\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e4006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e7518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e1987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" rowspan=\"2\"\u003e\n \u003cp\u003eLaguna Grande-Chametla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.335907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.266409266409266%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" valign=\"bottom\"\u003e\n \u003cp\u003eCluster total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003eha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e8400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e18625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e20282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e16732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e63975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.451612903225808%\" valign=\"bottom\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.129032258064516%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.741935483870968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003eNotes: %\u003csup\u003e1\u003c/sup\u003e with respect to the cluster total, %\u003csup\u003e2\u003c/sup\u003e with respect to the total tidal basin (TB), and % with respect to the overall total (63,975 ha).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Changes in mangrove cover in the tidal sub-basins of the Teacapan -Agua Brava Lagoon System, Mexico.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.223113964686998%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.3884430176565%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eCONABIO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.3884430176565%\" colspan=\"6\" valign=\"bottom\"\u003e\n \u003cp\u003eClark Labs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.868167202572348%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.868167202572348%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"bottom\"\u003e\n \u003cp\u003eTSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003ePdt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003ePdt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e07 Rio Santiago tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e07.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e07.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e08 Los Corchos tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e08.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e09 El Sesteo tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e09.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e10 Mexcaltitan-Camichin tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e11 El Colorado-La Palicienta tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e12 Agua Brava tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e12062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e11571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e8166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e7505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e7475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e7319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e118.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e13 Teacap\u0026aacute;n-Agua Brava tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e4534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e5618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-137.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003e14 Laguna Grande-Chametla tidal basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e14.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003e14.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234726688102894%\" valign=\"top\"\u003e\n \u003cp\u003eTAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e70903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e66963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e68014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e62235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.877813504823151%\" valign=\"bottom\"\u003e\n \u003cp\u003e-525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e55306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e57849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e63143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e63131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.717041800643087%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\" valign=\"top\"\u003e\n \u003cp\u003eNotes: TSB: tidal sub-basin, Pdt: Sen\u0026apos;s Slope, p:bilateral p-value, TAB: Teacapan -Agua Brava Lagoon System.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mangroves, Remote sensing, Open access data, Vegetation index, Time series, Coverage change detection","lastPublishedDoi":"10.21203/rs.3.rs-3783054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3783054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangroves face multiple threats, including land cover and land use changes, overexploitation, and contamination, resulting in local, regional, and global impacts. Understanding these changes is essential for conserving these important coastal ecosystems. Remote sensing provides detailed and long-term data and offers an invaluable advantage in such analyses. This study focuses on the Teacapan-Agua Brava Lagoon System in Mexico, integrating a GIS with open-access geospatial data, multiple Landsat 5 satellite images, MODIS vegetation index data (MOD13Q1 v. 6.1), and thematic maps of mangrove cover from various sources to analyze change trends in mangrove canopy and cover. Using the Mangrove Vegetation Index (MVI), mangroves were effectively distinguished from other cover classes (overall accuracy\u0026thinsp;=\u0026thinsp;92%, Kappa coefficient\u0026thinsp;=\u0026thinsp;0.93), resulting in an estimated mangrove cover of 67,334 ha in 2005. The Enhanced Vegetation Index (EVI) time series from 2005 to 2022 revealed a generally positive trend in mangrove canopy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The principal component analysis (PCA) and hierarchical clustering identified four distinct clusters with varying EVI profiles. Of the total mangrove area, 3% was vulnerable (negative trend), 29% exhibited no significant trend, and 58% was resilient (positive trend). Data from CONABIO and Clark Labs resulted in different deforestation and reforestation trends (average annual deforestation rate of -0.87% and average annual reforestation rate of 0.49%, respectively). These findings underscore the complex and diverse trends in mangrove cover and canopy, emphasizing the need for continued research, standardized mapping, and consistent remote sensing approaches to conserve and manage mangroves and their valuable ecosystem services.\u003c/p\u003e","manuscriptTitle":"Trends in mangrove canopy and cover in the Teacapan -Agua Brava Lagoon System (Marismas Nacionales), Mexico: An approach using open-access geospatial data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 18:54:23","doi":"10.21203/rs.3.rs-3783054/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-04-17T13:23:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-23T10:33:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2023-12-30T15:30:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-21T16:12:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2023-12-20T12:57:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f95ce49b-ff09-4c79-987b-d5c87b923883","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:09:22+00:00","versionOfRecord":{"articleIdentity":"rs-3783054","link":"https://doi.org/10.1007/s13157-024-01877-6","journal":{"identity":"wetlands","isVorOnly":false,"title":"Wetlands"},"publishedOn":"2024-12-16 15:58:36","publishedOnDateReadable":"December 16th, 2024"},"versionCreatedAt":"2024-01-25 18:54:23","video":"","vorDoi":"10.1007/s13157-024-01877-6","vorDoiUrl":"https://doi.org/10.1007/s13157-024-01877-6","workflowStages":[]},"version":"v1","identity":"rs-3783054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3783054","identity":"rs-3783054","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}