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Review

Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020)

by
Pablo Antúnez
División de Estudios de Postgrado-Instituto de Estudios Ambientales, Universidad de la Sierra Juárez, Avenida Universidad S/N, Oaxaca 68725, Oaxaca, Mexico
Coasts 2024, 4(4), 726-739; https://doi.org/10.3390/coasts4040038
Submission received: 18 October 2024 / Revised: 22 November 2024 / Accepted: 25 November 2024 / Published: 28 November 2024
Browse Figures
Figure 1
<p>Map of the five regions in Mexico with mangrove forests, adapted from CONABIO’s 2020 map.</p> "> Figure 2
<p>(<b>A</b>) Net change in mangrove area in Mexico from 1996 to 2020, based on Global Mangrove Watch data, and (<b>B</b>) Trend of marginal mean mangrove area from 1970 to 2020, derived from a generalized linear mixed model based on historical records compiled by CONABIO [<a href="#B10-coasts-04-00038" class="html-bibr">10</a>].</p> "> Figure 3
<p>Marginal means calculated from a linear mixed-effects model of disturbed mangrove surface area from 1970 to 2020, using years as a fixed effect and states as a random effect.</p> "> Figure 4
<p>Boxplots showing anthropogenic activities driving mangrove area loss in Mexico (2005–2015). (<b>A</b>) Activities contributing to mangrove loss from 2005 to 2015. (<b>B</b>) Comparison of the most impactful activities in 2005, 2010 and 2015. Act. 1 is airports and runways; Act. 2 is aquaculture farms and artificial ponds; Act. 3 is hydraulic infrastructure; Act. 4 is Settlements; Act. 5 is transport; Act. 6 is Building zones; Act. 7 is industrial zones; Act. 8 is port zones; Act. 9 is touristic zones; and Act. 10 is Reclassification zones.</p> "> Figure 5
<p>Comparison of mangrove area between the initial year and the years 2005, 2010, 2012, 2015 and 2020.</p> ">
Versions Notes

Abstract

:
This paper presents a meta-analysis of mangrove area in Mexico, using linear mixed models to assess trends from 1970 to 2020. The objective is to highlight the changes in the extent of these vital ecosystems over the past five decades. The analysis reveals a concerning decline of approximately 163.33 hectares per year from 1970 to 2005. Although a rebound was observed starting in 2016—likely due to effective conservation efforts—these ecosystems continue to decline overall. The states that have shown a consistent decline in mangrove area include Campeche, Sinaloa, Nayarit, Chiapas, Veracruz, Oaxaca, Guerrero, Colima, and Jalisco. Threats to mangroves vary significantly by region. In the North Pacific, the expansion of aquaculture farms has contributed to over 60% of mangrove loss. In contrast, the Yucatán Peninsula faces challenges from urban development, oil exploitation, and road expansion. Additionally, tourism activities have severely impacted the states of Colima, Jalisco, Guerrero, and Quintana Roo. In the Gulf of Mexico, the primary threats include aquaculture, transportation routes, and hydraulic infrastructure. Based on these findings, seven action strategies for the ecological restoration of mangroves are proposed. These strategies, drawn from successful case studies and existing literature, include: comprehensive restoration initiatives, expansion of research and data sources, updates to current regulations, regulation of anthropogenic activities, inter-institutional coordination, education and awareness-raising efforts, and continuous monitoring and evaluation.

1. Introduction

Despite the significant increase in studies focused on the protection and conservation of mangroves worldwide [1], research that analyzes changes in mangrove surface area over time is scarce and has been concentrated in a few regions of the world [2]. This has been partly due to the fact that mangrove ecosystems are dynamic and subject to constant interference [3,4]. In addition, in remote sensing studies (one of the analysis tools used to quantify surface areas), mangroves can be confused with other aquatic and underwater communities [5]. As a result of these constraints, some regions have recorded increases in mangrove area over the years, while others have recorded significant losses. Increases are mainly attributed to restoration projects [6], such as in Australia [7] and Indonesia [8]. In contrast, decreases are mainly attributed to anthropogenic activities [9] and, to a lesser extent, to natural events such as hurricanes or storms [10].
Mexico plays an important role in the global mangrove ecosystem, ranking fourth among countries with the highest mangrove ecosystem coverage, behind only Indonesia (19.5%), Brazil (8%) and Australia (7.1%) [11]. Yet, it faces a significant challenge, as it is one of the five countries with the largest net change in the extent of mangrove habitats [12]. Indeed, Mexico has the highest degree of mangrove deforestation in the Americas [13]. Between 1996 and 2020, the country experienced a net loss of 447.88 km2 of mangrove habitat [12]. The annual rate of loss has fluctuated over time, and various studies indicate a worrying trend [14,15]. In addition, the lack of adequate recognition of the ecological, economic, and social importance of mangroves has contributed to the increase in anthropogenic activities in these areas.
Global Mangrove Watch (GMW), an organization dedicated exclusively to monitoring mangrove area worldwide, reported a total area of 147,358.99 km2 in 2020, up from 152,604.23 km2 in 1996, a decrease of 5245.24 km2. A slight increase has been reported in recent years (Figure 1), but Leal and Spalding [16] attribute this increase more to improvements in map accuracy than to actual growth in mangrove areas. Bunting et al. [2] estimated a 3.4% loss over the 24-year period from 1996 to 2020 worldwide. In any event, it is estimated that mangrove loss occurs 3 to 5 times faster than global forest loss [17,18].
The monitoring of mangrove area decline in Mexico is scarce, scattered, and discontinuous. Some authors suggest loss rates for some regions, another more for some years or specific periods. For example, Cissell et al. [19] calculated that, in the two decades prior to 2018, the total mangrove area had decreased by more than 20%. Valderrama et al. [20] estimated a 10% loss from 1970 to 2005 for the country and a 2.4% loss from 2010 to 2015. Hirales-Cota et al. [13] determined an annual deforestation rate of 0.85% per hectare in Quintana Roo. Due to the lack of a historical record of the net loss and gain of the area of these forests, there is, therefore, a need for an analysis of mangrove area trends over the last few decades.
This review provides a critical analysis of changes in Mexico’s mangrove areas between 1970 and 2020. Through a meta-analysis using mixed-effects models (LMM) and generalized linear mixed models (GLMM), the global trend in mangrove area reported in the existing scientific literature was quantified, particularly in studies by Valderrama et al. [20], CONABIO [10], Velázquez-Salazar et al. [21,22], whose data were obtained from satellite images, aerial photographs, and direct field observations. In addition, the disturbance rate was analyzed, and the main anthropogenic activities that have altered mangrove areas were synthesized. Finally, the review concludes with the proposal of ecological restoration strategies for mangrove ecosystems.

2. Overview of Mangroves in Mexico

Mangrove forests in Mexico are found on both the Atlantic and Pacific coasts, covering approximately 60% of the country’s coastline [1]. Seventeen states are home to this forest type, being Campeche, Quintana Roo, Yucatán, Sinaloa, Nayarit, and Chiapas the ones with the largest areas. In contrast, Baja California, Michoacán, Jalisco, and Tamaulipas have the smallest areas [10,23]. By convention, the states are grouped into five regions: Central Pacific, North Pacific, South Pacific, Yucatan Peninsula, and Gulf of Mexico (Figure 1). Each of these regions plays a crucial role in protecting coastal areas from erosion and providing vital ecosystem services. On the Pacific coasts, mangroves are often found in discontinuous strips or patches. In contrast, the Gulf of Mexico and the Caribbean feature more compact and continuous mangrove forests.
In Mexico, a total of six mangrove species have been verified. The most abundant species are red mangrove (Rhizophora mangle L.), white mangrove (Laguncularia racemosa L.), black mangrove (Avicennia germinans L. Stearn), and button mangrove (Conocarpus erectus L. and Conocarpus erectus var. sericeus L.) [10]. Two less abundant species, Avicennia bicolor (Standl) and Rhizophora harrisonii (Leechm), form small populations in strips or patches, primarily in the states of Chiapas and Oaxaca [3,5,24]. Notably, Rhizophora samoensis (Hochr.) Salvoza is also presumed to be present on the Mexican Pacific coast [25], which, if confirmed, would bring the total number of species to seven. Furthermore, Avicennia bicolor is listed as vulnerable on the IUCN Red List [26].
The first studies on mangroves date back to 1958 and were later complemented with the mapping of these ecosystems in subsequent years, specifically in 1971, 1978, 1990, and 1994 [5]. However, despite these initial and subsequent efforts, the country lacked continuous national data on mangrove cover before 2005 [22]. This has led to the existing literature presenting an incomplete and fragmented database prior to 2005.
Initial records of mangrove cover vary significantly among states. For example, the first record in the state of Nayarit dates from 1970, while in Sinaloa coverage was not documented until 1985. In addition, certain states or regions do not report areas for specific years, which contributes to the discontinuity of information. The most complete record comes from the National Biodiversity Commission (CONABIO) [10], which provides data on mangrove areas for at least one year in each of the 17 states with this ecosystem, covering the period from 1970 to 1985. This dataset is further supplemented by the work of Valderrama et al. [20], which includes mangrove area data for five regions of Mexico, specifically for the year 1981.
Starting in 2005, mangrove coverage and adjacent areas were updated every five years [9]. This is due to a nationwide monitoring strategy called the Mexican Mangrove Monitoring System, implemented by the National Biodiversity Commission, with information available for approximately 20 years (2005–2020). In addition, Rodríguez-Zúñiga [27] reports the areas for Mexico for 2009 and 2012. Valderrama et al. [20] also report areas from 2005. All updates found in the literature were based on remote sensing techniques and geographic information systems, mainly using satellite images [28,29], verified, in most cases, with field information [21].

3. Analysis of Mangrove Cover Change

Using data from the available literature, specifically from CONABIO [10], Valderrama et al. [20], Velázquez-Salazar [21], and Rodríguez-Zúñiga [27], summarized in Table 1, the rate of change in mangrove area between 1970 and 2020 was evaluated. Due to variations in data quality, the analysis was divided into two periods: (1) 1970 to 2005, which was characterized by significant data gaps in several regions and years, and (2) 2005 to 2020, which was characterized by consistent and systematic data across all states and regions, thanks to the implementation of the Mexican Mangrove Monitoring System.
To analyze the data, a linear mixed-effects model (LMM) and its variant, the generalized linear mixed model (GLMM), were employed. Mixed-effects models are particularly effective for handling missing or discontinuous data [30,31] such as that found in the first period (1970–2005). GLMMs are especially useful when the residuals do not follow a normal distribution or exhibit heteroscedasticity [32,33,34]. These models account for both fixed and random effects, providing a nuanced understanding of how state-level and regional variations influence overall trends in mangrove area changes. In these models, years were treated as fixed effects, while states and regions were considered random effects. Both models were implemented using the lme4 package in R [35].

3.1. Average Trend of Mangrove Area from 1970 to 2005

First, both states and regions were found to be significant grouping variables that explain part of the variation in area change over the years (p < 0.05) (Table 2). Although the specific trend may vary slightly between states and regions, the mixed-effects model suggested that the national average decrease in area between 1970 and 2005 was approximately 163.33 hectares per year (p < 0.01). On the other hand, the generalized linear mixed model indicated that, on average, the area decreases by about 0.67% per year (Table S1).
Valderrama et al. [20] reported that, during this period, cover loss was mainly concentrated in 13 states of the Mexican Republic, especially in Veracruz, Nayarit, Michoacán, and Chiapas. In addition, Tovilla [15] estimated a loss of 79,236 hectares during the period from 1980 to 1990. Changes in mangrove cover between 1970 and 2005 are mainly attributed to agricultural and livestock activities, as well as anthropogenic infrastructure [20].
Comparing these findings with studies conducted in other parts of the world reveals a similar trend of mangrove decline driven by human activities, particularly before the year 2000. Goldberg et al. [10] estimated that 62% of global mangrove losses were due to land-use change, primarily resulting from the expansion of aquaculture and agriculture. Yet, regional variations are evident. For example, in the Brazilian Amazonian coasts, anthropogenic impacts were less pronounced from 1975 to 2015 [36]. In contrast, in Southeast Asia—regions such as Indonesia, Myanmar, Malaysia, the Philippines, Thailand, and Vietnam—82% of mangrove losses between 2000 and 2016 were linked to human activities [10]. Similarly, in Africa, Naidoo [37] documented a reduction of 152.2 km2 during the period from 2016 to 2020, primarily caused by urban development, aquaculture, and agriculture, with significant impacts in countries such as Ghana, Cameroon, Sierra Leone, and Gabon.

3.2. Average Trend of Mangrove Area from 2005 to 2020

The estimated coefficients for the 2005–2020 period in the fixed effects model were −138 × 104 for the intercept and 710.00 for “years.” The coefficient for “years” was significant at an alpha level of 0.05, while the intercept was marginally significant at a more relaxed alpha level of 0.1 (Table 3). The p-value (0.0432) is less than 0.05, indicating statistical significance and suggesting a positive trend in mangrove area in Mexico from 2005 to 2020. This contrasts with the 1970–2005 period, where the coefficient was negative, indicating a clear shift from mangrove loss to recovery (Table 3). Specifically, for each additional year after 2005, the dependent variable increases by approximately 710 units, holding other variables constant. The Generalized Linear Mixed Model (GLMM) suggests that, on average, the mangrove area increases by about 1.03% per year (Table S2 and Table 3).
These findings align with recent global trends indicating a slowdown in mangrove loss in some countries [12]. In Mexico, this deceleration became noticeable from 2016 onwards (Figure 2A). Similarly, Naidoo [37] observed a reduction in mangrove loss in Africa after 2000. This shift suggests that international conservation efforts, such as habitat restoration programs and stricter land-use regulations, may be yielding positive results in preserving mangroves.

4. Restoration Efforts and Regulatory Measures Amid Ongoing Mangrove Loss

The increasing trend observed in some years, especially since 2016 (Figure 2A), could be attributed to the implementation of numerous restoration efforts in Mexico, with reforestation being the most conventional action [38]. One of the first restoration programs was carried out on the Pacific coasts in 1976, specifically on the northern coast of Nayarit (North Pacific region) [39]. This initiative was implemented after almost four decades of significant changes in the hydrological patterns of estuarine and lagoon systems in the area [39]. The restoration work was developed in a collaborative framework between the Mexican National Forestry Commission, and the UK Government’s Department for Environment, Food, and Rural Affairs [40].
Over time, restoration practices improved, especially in terms of revegetation with mangrove propagules or seedlings [38]. These actions were extended to various regions of Mexico. Between 2013 and 2016, SEMARNAT [39] reported the restoration of just over 5000 hectares of wetlands in several states of the country, including Campeche, Nayarit, Oaxaca, Sinaloa, Tabasco, Veracruz, Colima, Guerrero, Quintana Roo and Sonora. In the North Pacific, reforestation was carried out in Sinaloa [41]. By 2018, in the Peninsula region, approximately 56 hectares of degraded mangrove had been restored [42].
In addition, two strategies were implemented at the national level. The first strategy focused on regulations. On the one hand, the most dominant mangrove species in the country—Rhizophora mangle L., Laguncularia racemosa (L.) Gaertn, Avicennia germinans (L.) Stearn, and Conocarpus erectus L.—were included in the Mexican Official Standard NOM-059-SEMARNAT-2010, cataloging them as threatened species [21]. On the other hand, Article 60 TER of the General Wildlife Law prohibited the removal, filling, transplanting, pruning, or any activity that affects the integrity of the hydrological flow of the mangrove [43].
The second strategy consisted of the implementation of the Mexican Mangrove Monitoring System, mentioned above. Although no updates have been published for the post-2020 period as of this writing, this program has generated valuable information on the spatial distribution of mangroves and has enabled the tracking of their dynamics (see the interactive map here: https://www.biodiversidad.gob.mx/CMS-scripts/mapa_interactivo/manglares_leaflet.html# (accessed on 22 November 2024)). One of the most outstanding achievements of this program is the monitoring of coastal erosion and shoreline displacement [1].
Nevertheless, despite these restoration efforts, the overall trend in mangrove area has been downward, according to records from Global Mangrove Watch [12]. Between 1996 and 2020, Indonesia experienced the greatest loss of mangroves, totaling −1739.04 km2, followed by Australia (−483.91 km2), Mexico (−447.88 km2), and Myanmar (−385.81 km2).
In Mexico’s case, data from 1996 to 2016 revealed an average loss of 414.86 km2 over that twenty-year period. Although this trend began to reverse in 2016—likely due to conservation initiatives—these slight gains do not offset the significant losses that occurred prior to that year, nor the losses experienced between 1970 and 2005. Overall, from 1996 to 2020, the net loss of mangroves amounted to 447.88 km2 (Figure 2A), underscoring the magnitude of the challenge posed by mangrove deforestation
This challenge has intensified considerably in recent decades, with mangrove deforestation now considered more severe than that of tropical rainforests [1]. From the late 1990s to 2015, an estimated 25% of global mangroves were lost, equivalent to approximately 5 million hectares. Figure 2B illustrates the trend of the marginal mean mangrove area from 1970 to 2020, based on a generalized linear mixed model. The gray band in the figure, indicating the confidence interval (1 − α = 95%), suggests greater uncertainty between 1985 and 2005 due to missing data. Nonetheless, a general decreasing pattern is observed throughout the entire study period (1970–2020) (Figure S1).

5. Anthropogenic Activity: The Biggest Challenge to Mangrove

Despite the implementation of plans and programs, as well as the recognition of mangroves’ importance in public environmental policies, their degradation persists in Mexico and globally [10]. Anthropogenic activities, such as agriculture, livestock, and infrastructure development, are primarily responsible for this deterioration [20,21,38].
When a mangrove forest experiences partial damage or deterioration, either by natural events such as hurricanes, storms, or cyclones or by human activities, it is considered a disturbed forest [44]. A disturbed mangrove is characterized by the presence of damaged, dead, fallen, or delaminated trees. It is crucial to address this phase in a timely manner, as it can be a precursor to total mangrove loss. In cases of anthropogenic disturbance, regardless of whether it involves agricultural activities, livestock, or urban development, it is more probable that the affected area will expand or remain in this state in the long term.
When analyzing the disturbed area of mangroves from 1970 and 2020, reported by CONABIO [44] and Velázquez-Salazar et al. [22], an increasing trend was observed. Between 1970 and 2005, the average disturbed area increased by 453.76 hectares (p = 0.068). According to the mixed model coefficients, from 2005 to 2010, the average annual increase was approximately 69.61 hectares, while from 2010 to 2015 it was approximately 41.29 hectares per year. From 2015 to 2020, there was a decrease of 101.80 hectares per year. The marginal mean graph (Figure 3) shows that, from 1970 to 1990, the rate of change was relatively low; however, between 1990 and 2000, growth began to accelerate, peaking in 2015, followed by a deceleration phase. Despite this deceleration, the current trend is still higher than the increases recorded before 2005 (Figure 3). In addition, the random effect of states proved significant, while the random effect of regions was not, suggesting that the variation in the disturbance surface is better explained by the state factor than by the region factor (Table 4).
The Kruskal-Wallis non-parametric test was used to analyze the loss of mangrove areas due to anthropogenic activities, with the aim of determining whether there were significant differences in the magnitude of impact across different activities. The results revealed that the magnitude of impact varied significantly across activities (p = 0.00077) (Figure 3). Furthermore, it was noted that many of the activities reported in the latter decades of the 20th century remain relevant today. For instance, according to De Alba and Reyes [45], aquaculture and the extraction of mangrove wood for housing construction, furniture manufacturing, or musical instruments were major activities during that period. These activities continue to have a substantial impact on several coasts of Mexico, while new activities have emerged, as documented by Velázquez-Salazar et al. [22].
Between 2005 and 2020, the most significant anthropogenic activities included human settlements, aquaculture farms, artificial ponds, transportation, and tourism (Figure 4A,B). These developments in aquaculture highlight a broader trend of anthropogenic pressures on coastal ecosystems. In the North Pacific, aquaculture farms and artificial ponds have proliferated, especially in Sinaloa, where they have been established since 1987 for shrimp production. Subsequently, other aquaculture activities spread to the coasts of Sonora and Nayarit [38]. Between 2005 and 2020, aquaculture farms in the Pacific accounted for more than 60% of the affected mangrove areas [44].
In the Yucatan Peninsula, the construction of housing infrastructure has had a notable impact on mangroves [44]. In Tabasco, oil exploitation has caused severe environmental impact, while in Yucatán, highways crossing wetlands have contributed to degradation [46]. Tourism activity has severely affected the states of Colima, Jalisco, Guerrero, and Quintana Roo [47]. The latter, together with the increase in human settlements, has had a significant impact on mangroves [48]. In addition, it is important to consider the effects of tourism activity on the coasts of Oaxaca, especially from 2024, with the opening of the new highway from the state capital to the coast.
The loss of mangrove area has fluctuated over time and across different states (Figure 5). For example, Baja California had the lowest mangrove loss between 1981 and 2010, while Jalisco experienced the greatest loss during the same period [27]. In general, the states that have shown a continuous decline, excluding total area considerations, include Campeche, Sinaloa, Nayarit, Chiapas, Veracruz, Oaxaca, Guerrero, Colima, and Jalisco. Tabasco was the only state to show a slight increase in mangrove area from its initial registration in 1972 to 2020. As for Quintana Roo, although it showed a decline until 2015, by 2020 it experienced the largest rebound in mangrove area of all the states (Figure 5).

6. Conclusions and Suggestions

Mangrove forests are crucial ecosystems that provide multiple services, including coastal protection, erosion control, and water sanitation. They are vital for carbon capture and storage, sequestering over 1000 tons of carbon per hectare due to the low decomposition rates of organic matter in anaerobic conditions [42]. This process prevents carbon from being released into the atmosphere, allowing it to remain in the soil for millennia. This highlights the significance of edaphic carbon in mangroves compared to other ecosystems that rely on aerial biomass. Additionally, many mangroves serve as habitats for threatened plant species [49].
Despite their importance, mangrove deforestation accounts for about 10% of global carbon emissions annually [21]. Different regions face unique challenges related to this loss, as some agents of change are persistent, and others have shown variations in time and space [1]. In this context, it is essential to implement action strategies that not only prevent further losses but also accelerate the restoration of these ecosystems [50]. To this end, these strategies should be based on the interactions between geomorphology, hydrology, and the structural and functional characteristics of the mangrove ecosystem [38]. The following are seven proposals, many of which have already been suggested in the existing literature [10,38,42,51,52]:
1
Comprehensive restoration strategies: Develop and implement ecological restoration strategies based on a comprehensive understanding of mangrove ecosystems. These strategies should include actions such as channel desilting and attention to phytosanitary aspects, especially the detection of pests or damage that are often ignored during reforestation. In addition, it is essential to encourage social participation through projects that generate economic, ecological, and social benefits.
2
Expand research and data sources: It is essential to collect and systematize information using various research methods and data sources. This will allow the proper identification of the values and utilities that different social actors assign to mangrove products and services. It is also important to increase studies with different approaches in order to make more informed decisions in the context of climate change. For example, research is needed on the impacts of oil exploitation and siltation on mangroves [46], as well as studies on the quantification of carbon pools in these ecosystems [42].
3
Update current regulations: Include mangrove species omitted from NOM-059-SEMARNAT-2010 to ensure more complete protection. CONABIO identified the lack of inclusion of Rhizophora harrisonii (Leechm) and Avicennia bicolor (Standl) in this standard. In addition, it is crucial to strengthen the regulatory framework on land-use changes in mangroves and to enforce Article 60 of the General Wildlife Law, which protects the integrity of hydrological flow in these ecosystems [53].
4
Regulate anthropogenic activities: In Mexico, many activities related to land use (on the coasts) lack regulation and planning. For example, the accelerated increase of aquaculture on the Pacific coasts [52] and the expansion of tourist areas, such as the construction of hotels and housing areas in Cancun (Peninsula Region), have generated significant negative impacts on mangroves [54]. This urbanization also introduces additional disturbances, such as the construction of roads and streets, which perpetuate the elimination of mangrove forests. Consequently, it is imperative to regulate anthropogenic activities to stop deforestation and the conversion of these areas into port, tourism, and aquaculture facilities. To achieve this, planning and responsible land use in coastal areas are necessary.
5
Inter-institutional coordination: Institutions should act in a coordinated manner, creating synergies between the government, academia, and non-governmental organizations to guide actions and thus make appropriate decisions for the management of coastal areas.
6
Education and awareness-raising: Increase public education and awareness efforts on the importance of mangroves and the consequences of their degradation. One alternative could be awareness tourism in these ecosystems.
7
Monitoring and evaluation: It is recommended to implement a continuous monitoring and evaluation system with regular intervals to track the progress of mangrove ecosystems and identify potential risks. This approach enables the adjustment of actions in response to emerging issues, ensuring the effectiveness of conservation measures.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/coasts4040038/s1. Table S1: Generalized Linear Mixed Model Output fitted by maximum likelihood (laplace approximation) for data 1970–2005. Table S2: Generalized Linear Mixed Model Output fitted by maximum likelihood (laplace approximation) for data 2005–2020. Figure S1: Estimated coefficients of the generalized linear mixed model and their confidence intervals.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable for studies not involving humans or animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Thanks to the anonymous reviewers for their valuable comments and suggestions.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. Map of the five regions in Mexico with mangrove forests, adapted from CONABIO’s 2020 map.
Figure 1. Map of the five regions in Mexico with mangrove forests, adapted from CONABIO’s 2020 map.
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Figure 2. (A) Net change in mangrove area in Mexico from 1996 to 2020, based on Global Mangrove Watch data, and (B) Trend of marginal mean mangrove area from 1970 to 2020, derived from a generalized linear mixed model based on historical records compiled by CONABIO [10].
Figure 2. (A) Net change in mangrove area in Mexico from 1996 to 2020, based on Global Mangrove Watch data, and (B) Trend of marginal mean mangrove area from 1970 to 2020, derived from a generalized linear mixed model based on historical records compiled by CONABIO [10].
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Figure 3. Marginal means calculated from a linear mixed-effects model of disturbed mangrove surface area from 1970 to 2020, using years as a fixed effect and states as a random effect.
Figure 3. Marginal means calculated from a linear mixed-effects model of disturbed mangrove surface area from 1970 to 2020, using years as a fixed effect and states as a random effect.
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Figure 4. Boxplots showing anthropogenic activities driving mangrove area loss in Mexico (2005–2015). (A) Activities contributing to mangrove loss from 2005 to 2015. (B) Comparison of the most impactful activities in 2005, 2010 and 2015. Act. 1 is airports and runways; Act. 2 is aquaculture farms and artificial ponds; Act. 3 is hydraulic infrastructure; Act. 4 is Settlements; Act. 5 is transport; Act. 6 is Building zones; Act. 7 is industrial zones; Act. 8 is port zones; Act. 9 is touristic zones; and Act. 10 is Reclassification zones.
Figure 4. Boxplots showing anthropogenic activities driving mangrove area loss in Mexico (2005–2015). (A) Activities contributing to mangrove loss from 2005 to 2015. (B) Comparison of the most impactful activities in 2005, 2010 and 2015. Act. 1 is airports and runways; Act. 2 is aquaculture farms and artificial ponds; Act. 3 is hydraulic infrastructure; Act. 4 is Settlements; Act. 5 is transport; Act. 6 is Building zones; Act. 7 is industrial zones; Act. 8 is port zones; Act. 9 is touristic zones; and Act. 10 is Reclassification zones.
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Figure 5. Comparison of mangrove area between the initial year and the years 2005, 2010, 2012, 2015 and 2020.
Figure 5. Comparison of mangrove area between the initial year and the years 2005, 2010, 2012, 2015 and 2020.
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Table 1. Descriptive statistics of mangrove area records from 1970 to 2020.
Table 1. Descriptive statistics of mangrove area records from 1970 to 2020.
RegionsStatesYear of Initial RecordYear of Last RecordAverageMaximumMinimumStandard DeviationSkewness Coefficient
Pacífico NorteBaja California1982202036.142.028.04.3−1.0
Baja California Sur1978202026,367.726,724.025,511.0484.7−1.3
Sonora1973202011,405.912,334.010,682.0606.00.7
Sinaloa1985202079,155.082,171.076,300.02343.80.1
Nayarit1970202070,049.678,024.066,849.04549.01.2
Pacífico CentroJalisco197120202904.98098.02010.02233.52.6
Colima197120203524.46589.03074.01259.42.6
Michoacán197420201567.31788.01419.0131.42.0
Pacífico SurGuerrero197920208123.116,348.06693.03288.82.5
Oaxaca1979202017,735.028,501.017,297.03817.72.5
Chiapas1972202041,381.453,901.041,540.03895.60.7
Golfo de MéxicoTamaulipas197620203033.53664.02831.0269.50.7
Veracruz1976202034,866.844,820.036,237.03058.71.0
Tabasco1972202039,747.449,225.041,999.02131.50.9
Península de YucatánCampeche19812020200,742.3216,969.0194,190.07423.72.2
Yucatán1981202094,692.199,640.091,348.03636.10.5
Quintana Roo19792020147,293.9247,017.0128,048.044,101.62.6
Table 2. Analysis of random and fixed effects in a linear mixed model for data 1970–2005: Variability by states and regions: Variance components and fixed effects estimates. LogLik: This is the log-likelihood of the model. It represents how well the model explains the data; AIC is Akaike information criterion. LRT: Likelihood Ratio Test. DF: Degrees of freedom. p-value associated with the Chi-squared test for the LRT. This analysis examines random and fixed effects in a linear mixed model using data from 1970 to 2005, presenting variance components and estimates of fixed effects. The log-likelihood (LogLik) indicates how well the model explains the data, while the Akaike Information Criterion (AIC) and the Likelihood Ratio Test (LRT) are employed to assess model quality. Additionally, the degrees of freedom (DF) and the p-value associated with the Chi-square test for the LRT are reported.
Table 2. Analysis of random and fixed effects in a linear mixed model for data 1970–2005: Variability by states and regions: Variance components and fixed effects estimates. LogLik: This is the log-likelihood of the model. It represents how well the model explains the data; AIC is Akaike information criterion. LRT: Likelihood Ratio Test. DF: Degrees of freedom. p-value associated with the Chi-squared test for the LRT. This analysis examines random and fixed effects in a linear mixed model using data from 1970 to 2005, presenting variance components and estimates of fixed effects. The log-likelihood (LogLik) indicates how well the model explains the data, while the Akaike Information Criterion (AIC) and the Likelihood Ratio Test (LRT) are employed to assess model quality. Additionally, the degrees of freedom (DF) and the p-value associated with the Chi-square test for the LRT are reported.
Linear Mixed Model Output
ComponentEstimateStd. ErrorDFT-Valuep-Value
Intercept374 × 103700 × 10234.865.350.00000567
Years−163.3233.0030.99−4.960.0000244
Random effects
GroupVarianceStd. Dev.
States116 × 107340 × 102
Region269 × 107519 × 102
Residual873 × 107296 × 102
Model fit statistics
ModellogLikAICLRTDFPr (>Chisq)
Full model−500.571011.1
Reduced model−562.721133.4124.2891<0.0001
Single term deletion−504.021016.87.62710.00575
Percentage rate of change0.67%
Table 3. Analysis of random and fixed effects in a linear mixed model for data 1970–2005: Variability by states and regions: variance components and fixed effects estimates. LogLik: This is the log-likelihood of the model. It represents how well the model explains the data; AIC is Akaike information criterion. LRT: Likelihood ratio test. DF: Degrees of freedom. p-value associated with the Chi-squared test for the LRT.
Table 3. Analysis of random and fixed effects in a linear mixed model for data 1970–2005: Variability by states and regions: variance components and fixed effects estimates. LogLik: This is the log-likelihood of the model. It represents how well the model explains the data; AIC is Akaike information criterion. LRT: Likelihood ratio test. DF: Degrees of freedom. p-value associated with the Chi-squared test for the LRT.
Linear Mixed Model Output
ComponentEstimateStd. ErrorDFT-Valuep-Value
Intercept−138 × 104695 × 10367.20−1.990.0507
Years710.00345.0067.002.060.0432
Random effects
GroupVarianceStd. Dev.
States970 × 106312 × 102
Region294 × 107542 × 102
Residual159 × 106126 × 102
Model fit statistics
ModellogLikAICLRTDFPr(>Chisq)
Full model−939.691889.4
Reduced model−987.181982.494.9871<0.0001
Single term deletion−944.331896.79.27710.002321
Percentage rate of change1.03%
Table 4. Summary of the linear mixed-effects model assessing the impact of years on disturbed mangrove area, including fixed-effects estimates, random-effects variance, and model fit statistics.
Table 4. Summary of the linear mixed-effects model assessing the impact of years on disturbed mangrove area, including fixed-effects estimates, random-effects variance, and model fit statistics.
Linear Mixed Model Output
EstimateDFT-Valuep-Value
Intercept70.1030.900.246<0.0001
yearsyear_2005454.0064.001.854<0.0001
yearsyear_2010802.0064.003.276<0.0001
yearsyear_20151010.0064.004.12<0.0001
yearsyear_2020499.29642.040.045458
Random effects
GroupVarianceStd. Dev.
States869 × 103932.00
Region0.01510.123
Residual509 × 103713.00
Model fit statistics
ModellogLikAICLRTPr(>Chisq)
Full model−664.251344.5
Reduced model−664.251342.501
Single term deletion−683.791381.639.077<0.0001
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Antúnez, P. Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020). Coasts 2024, 4, 726-739. https://doi.org/10.3390/coasts4040038

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Antúnez P. Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020). Coasts. 2024; 4(4):726-739. https://doi.org/10.3390/coasts4040038

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Antúnez, Pablo. 2024. "Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020)" Coasts 4, no. 4: 726-739. https://doi.org/10.3390/coasts4040038

APA Style

Antúnez, P. (2024). Mangrove Area Trends in Mexico Due to Anthropogenic Activities: A Synthesis of Five Decades (1970–2020). Coasts, 4(4), 726-739. https://doi.org/10.3390/coasts4040038

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