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Article

Geoforms and Biogeography Defining Mangrove Primary Productivity: A Meta-Analysis for the American Pacific

by
Carolina Velázquez-Pérez
1,*,
Emilio I. Romero-Berny
2,*,
Clara Luz Miceli-Méndez
3,
Patricia Moreno-Casasola
4 and
Sergio López
5
1
Programa de Doctorado en Ciencias en Biodiversidad y Conservación de Ecosistemas Tropicales, Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas, Tuxtla Gutiérrez 29039, Chiapas, Mexico
2
Laboratorio Interdisciplinario de Ecología Costera, Centro de Investigaciones Costeras, Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas, Tonalá 30500, Chiapas, Mexico
3
Laboratorio de Cultivo de Tejidos Vegetales, Centro de Investigaciones en Biodiversidad Tropical, Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas, Tuxtla Gutiérrez 29039, Chiapas, Mexico
4
Red de Ecología Funcional, Instituto de Ecología A. C., Xalapa 91073, Veracruz, Mexico
5
Laboratorio de Ecología Evolutiva, Centro de Investigaciones en Biodiversidad Tropical, Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas, Tuxtla Gutiérrez 29039, Chiapas, Mexico
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(7), 1215; https://doi.org/10.3390/f15071215
Submission received: 9 June 2024 / Revised: 4 July 2024 / Accepted: 9 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue Effect of Mangrove Ecosystems on Coastal Ecology and Climate Change)
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Abstract

:
We present a meta-analysis of mangrove litterfall across 58 sites in the American Pacific, exploring its variability among geoforms, ecoregions, and provinces. This study contributes to filling the information gap on litter-based primary productivity in American mangroves at the ecoregional level and directly examines the effects of geomorphological and biogeographic factors on mangrove productivity. The objective was to evaluate how geoform, ecoregion, and province factors, along with eight environmental variables, influence litterfall-based primary productivity. Each site was categorized according to its landform through the analysis of satellite images obtained from various sensors on the Google Earth Pro v. 7.3.6 platform. Additionally, it was categorized according to its ecoregion and province by analyzing the occurrence of the sites on biogeographic unit coverage in ArcMap 10.4.1. We then analyzed the effect of each factor and the efficiency of categorization using multivariate methods. Our results showed significant differences in litterfall among the geoforms, with estuaries exhibiting higher litterfall production (11.90 Mg ha−1 year−1) compared to lagoons (7.49 ± 4.13 Mg ha−1 year−1). Differences were also observed among provinces, with the highest average in the Tropical Eastern Pacific (11.19 ± 3.63 Mg ha−1 year−1) and the lowest in the Warm Temperate Northeast Pacific (7.34 ± 4.28 Mg ha−1 year−1). Allocation success analyses indicated that sites classified by dominant species and province were more predictable (>60.34%) for litterfall production. Additionally, the maximum temperature and the precipitation of the wettest month and the driest month explained 34.13% of the variability in mangrove litter-based primary productivity. We conclude that mangrove litterfall production is influenced by coastal geomorphic characteristics and biogeography, which are, in turn, affected by latitude-induced climate variation.

1. Introduction

Mangroves are communities of trees and shrubs that are renowned for their high primary productivity and their substantial contribution of biomass to carbon storage along tropical and subtropical coasts [1]. When incorporated into the soil, biomass—which includes leaves, branches, stems, roots, and wood—contributes to the net primary productivity of the ecosystem and the carbon stored within the soil [2]. Although the net primary productivity of mangroves at the community level includes basal plant growth and root production, the litterfall rate per unit area over time is considered a key indicator. This is due to the constant renewal of leaves and the cycles of flower and fruit production, which are influenced by hydrological and seasonal dynamics [3,4,5]. Litterfall estimation alone accounts for about 30% of the net primary productivity of mangroves and internal carbon flux [1]. Litterfall production allows for the evaluation of annual reproductive patterns and the amount of organic matter potentially available for incorporation into detritus and export to the ocean [6]. Additionally, litterfall production enables the functional analysis of mangroves, serving as an essential element in calculating the energy and nutrient fluxes that represent a small portion of the carbon (C) stored by vegetation [7].
Also, environmental gradients defined by nutrients, salinity, and hydroperiods influence primary productivity patterns in mangroves [8]. For example, riverine mangroves may exhibit optimal structural development and high litterfall values because of high nutrient availability and lower levels of interstitial salinity, which are controlled by river discharge; conversely, a contrasting effect occurs in mangroves of arid areas, where lower litterfall would be influenced by seasonal droughts and high salinity [9,10,11]. Coastal geomorphology interacts with climatic factors (temperature, precipitation, evaporation) and eight geophysical factors (river inputs, tidal range, and wave energy) to regulate gradients [12,13,14].
Biogeographically, mangroves have been described as having a longitudinal gradient of taxonomic richness between two major biogeographic areas: the Western Indo-Pacific and Eastern Pacific-Atlantic [15]. Efforts have been made to identify functional patterns characteristic of specific regions and to consider the relevance of mangrove ecological attributes. These attributes include vegetation structure, litterfall production, aboveground and belowground biomass, and carbon stocks [2]. These factors may be essential to evaluate the ecosystem services provided by mangroves, for example, for fish production [16]. It is assumed that similarities in precipitation and temperature conditions, as well as in hydrological ranges, will determine mangrove attributes in nearby regions and in turn, will highlight differences between more distant regions [17]. However, the regional factors that define ecological patterns in mangroves can be differentially influenced by biogeographic provinces or their latitudinal distribution limits due to the contrasting temperature and precipitation characteristics these provinces can present based on their latitudinal location [1,18]. Recent studies have provided evidence to understand the role of local controls within specific regions, such as the geomorphological setting (e.g., lagoon, estuarine, deltaic, and open coast) in which mangroves develop. Geophysical factors like waves, tides, and river input affect the development and productivity of mangroves in various ways depending on their proximity to the sea [19,20]. Similarly, less structural variation in mangroves has been found within biogeographic units and in different geomorphic forms [21], reaffirming the natural importance of local conditions in shaping patterns at a macroecological scale. Regarding primary productivity based on mangrove litterfall, some studies have sought to identify patterns based on latitudinal changes and the influence of geophysical variables [1,14,22]. However, they have yet to explore the effect of geomorphological and biogeographical factors directly.
In the tropical and subtropical coasts of more than 120 countries around the world, there are 147,358.99 km2 of mangroves [23,24]. Although their structure and biodiversity vary between regions, their potential for mitigating climate change and their importance as sources of ecosystem services for multiple users are globally recognized. Recent research in the world’s main mangrove hotspots, such as the Sundarbans, northern Queensland, and the southeastern coast of Africa, has revealed that blue carbon storage rates in biomass are high, even in impacted areas or those with recent changes in their distribution dynamics [25,26,27,28]. This should have significant implications for the development of conservation policies, even in understudied areas, considering that many sites continue to experience high rates of mangrove loss [29].
In the Neotropical Pacific basin, about 26.6% of the world’s mangroves are found, extending along approximately 6000 km of coastline [23,24]. Due to its complex geological history, the American Pacific is characterized as a collision coast with steep coastlines, tectonic activity, and a narrow continental shelf. This region exhibits a variety of hydrodynamic conditions, sediment flows, and organic and geochemical characteristics of the substrate [30], which create different environmental conditions and resources that influence the variability of mangroves. In the American Pacific, various types of mangroves are distributed within a latitudinal range of 30° N to 5° S. These mangroves exhibit different structural developments and are found in two provinces and seven ecoregions of the marine-coastal biogeographic classification by Spalding et al. [31]. Although information on litter production is limited in some areas, data are available for all ecoregions, allowing for the analysis of the influence of geomorphological and biogeographic factors through meta-analysis.
For this study, the following research questions were posed: (1) What is the variability of mangrove leaf litter among different geoforms, ecoregions, and provinces in the American Pacific? (2) What is the level of predictability of mangrove litterfall based on geoform, ecoregion, province, and dominant species? (3) Additionally, what set of environmental variables determine mangrove litterfall in the American Pacific? The main objective of this study was to evaluate how these factors and eight environmental variables determine litterfall using a dataset of mangrove litterfall productivity from 58 sites. We also aimed to assess the spatial effects exerted by geomorphic forms and biogeographic units represented by provinces and ecoregions in the American Pacific. Our hypotheses suggest that mangrove litterfall in the American Pacific increases closer to the Equator and decreases at higher latitudes, influenced by climatic variations associated with latitude. Additionally, leaf litter accumulation is greater in landforms dominated by rivers due to nutrient-rich input and salinity regulation, while it diminishes in landforms distant from river mouths and sea connections due to reduced flooding frequency. Environmental factors, such as flooding and extreme conditions, exert a stronger influence on mangrove litter deposition compared to large-scale monthly fluctuations.

2. Materials and Methods

A review of the available information on litterfall in mangroves of the American Pacific was conducted for a meta-analysis. Information retrieval was conducted in both English and Spanish using combinations of keywords such as mangroves, productivity, primary productivity, net primary productivity, litterfall, litter production, litter, biomass, aerial carbon, carbon flux, Eastern Pacific, and Neotropical Pacific. The searches were performed in databases and search engines, including Web of Science, Scopus, Dialnet, DOAJ, Redalyc, Latindex, SciELO, PubMed, and Google Scholar. We also consider the studies and reports found in repositories of universities, research centers, and organizations. The keywords were also combined with the names of countries in Central and South America, covering the entire distribution area of mangroves in the Eastern Pacific, from northwest Mexico to northern Peru. References found online but without access to the document were requested directly from the authors. The reviewed studies spanned the range from 1981 to 2023. For locations with more than one study, only the most recent one was included in the meta-analysis (Table S1). We present the total number of studies identified, excluded, and selected based on the aforementioned criteria following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [32] (Figure 1).
The compilation of studies considered the following types of publications: (I) Peer-reviewed articles (indexed articles, peer-reviewed academic bulletins), (II) Chapters in peer-reviewed books, (III) Theses and Dissertations (graduate and undergraduate), (IV) Conference proceedings and (V) Technical reports. For type III, we decided to include bachelor’s theses, considering that, in many cases, they may represent the only available sources of information for understudied areas. Additionally, meta-analyses benefit from the inclusion of a larger number of studies, which increases the statistical power and generalizability of the results by providing additional data that can be crucial for detecting trends or effects that might otherwise go unnoticed. We ensured that all studies included in the meta-analysis reported litterfall data obtained using litter traps following the standard methodology derived or adapted from the original procedure for mangroves proposed by Bunt [33]. When we found two or more studies for the same site, we prioritized including the peer-reviewed academic publication and, secondly, the most recent one. A total of 45 studies were reviewed, of which 39 were selected: 11 in English and 28 in Spanish, all meeting the aforementioned criteria (Table S1).
Interstitial salinity was recorded for each site, taking the average of all reported measurements. In the case of sites that did not present the salinity value, salinity was compiled from other sources, such as studies related to hydrology, water quality, saline intrusion, forest structure, and natural regeneration, as well as from Ramsar site sheets or protected areas, reported for the same site. Twenty-six percent of salinity data were collected through these criteria (see Tables S1 and S2). Climatic data, such as the mean annual temperature and annual precipitation not recorded in litterfall studies, the maximum temperature of the warmest month, minimum temperature of the coldest month, precipitation of the wettest month, precipitation of the driest month, and solar radiation, were obtained from the WorldClim platform [34].
The sites reviewed were classified into three categories of geoforms based on the sedimentary and geomorphological environments in which they were found: lagoon, estuary, and open coast (see [20]) (Figure 2; Table S1). Geoforms are understood as coastal spaces, units that are well formed by coastal processes such as erosion, sediment deposition, tectonic activity, waves, and water flow [18,19]. A brief definition of each geoform based on the considered geomorphological features, as well as the relationship with two previous typologies for categorizing mangrove systems, is presented in Table 1. The geoform assignment for each site was performed by analyzing satellite images at different scales and recorded by various sensors on the Google Earth Pro platform v. 7.3.6.
Additionally, each site was categorized according to its belonging to the biogeographic units of province and ecoregion, from the global classification for coastal and shelf areas by Spalding et al. [31], assuming the existence of a close link between mangroves and the marine-coastal biota that depends on these ecosystems as breeding, protection, and feeding sites, upon which the development of this classification is based. The geographical coordinates of the sites were overlaid on the coverage of Marine Ecoregions of the World [31] in ArcMap v. 10.4.1 [35]. The sites were also categorized according to the dominant mangrove species (Table S1) reported in basal area, importance value, or density, considering three taxonomic units: Avicennia germinans L., Laguncularia racemosa (L.) C.F. Gaertn, and Rhizophora spp., the latter including the species Rhizophora mangle L., Rhizophora racemosa G. Mey, and Rhizophora harrisonii Leechm.
For this analysis, biogeographic provinces were considered as large areas defined by the presence of distinct biotas that exhibit some level of coherence over evolutionary time frames, as well as a certain level of taxonomic endemism, likely encompassing the broader life history of many taxa, including species with higher dispersal capacity. Ecoregions are the smallest-scale biogeographic units [31] and are defined as areas of relatively homogeneous species composition, clearly distinct from adjacent systems. These groups are characterized by their strong ecological cohesiveness and encompass a significant range of life history processes for most species or populations with limited dispersal capacity. They are sizeable enough to support and facilitate the maintenance of essential ecological and life history processes. These groups have been identified as critical ecological units that play an important role in maintaining the balance of natural systems [31].
Litterfall data extracted from publications were standardized to Mg ha−1 year−1. For the analysis of litterfall data among provinces, ecoregions, geoforms, and dominant species, a multivariate permutation analysis of variance (Permanova), with 9999 permutations and type I sum of squares, was performed on Bray–Curtis dissimilarity matrices to determine differences between factors and their interactions, at a significance level of α = 0.05. Pairwise Tests were only applied to factors with significant results. Leave-One-Out Cross-Validation (LOOCV) tests were applied in Canonical Analysis of Principal Coordinates (CAP) to assess the percentage efficiency in assigning province, ecorregion, geoform, and dominant species categories at each site, which provides a measure of how different the groups are for each factor in multivariate space [36].
Finally, to determine a group of environmental variables that best explain the variation in mangrove primary productivity across the American Pacific, a distance-based linear model (DistLM) with a stepwise procedure and adjusted R2 criterion was used. Previously, for a set of eight variables, values of high collinearity (r ≤ 0.8) were determined among them [37], and the exclusion of those that could produce biases in the analysis, subsequently exploring the data ordination and correlation with the variables in a two-dimensional plot of principal component analysis (PCA) on a Euclidean distance matrix. Statistical analyses were conducted using the Primer 6 + Permanova v. 1.0.1 package [38].

3. Results

3.1. Distribution of Sites with Mangrove Litterfall by Category

Of the 58 sites analyzed in this study, 48.3% were located in the Warm Temperate Northeast Pacific Province (WTNP) and 51.7% in the Tropical Eastern Pacific Province (TEP). Likewise, the sites were distributed across seven ecoregions: Magdalena transition (MT), Cortezian (Cor), Mexican Tropical Pacific (MTP), Chiapas-Nicaragua (CN), Nicoya (Nic), Panama Bight (PB), and Guayaquil (Guay). The ecoregions MT and Nic, which only presented one litterfall value each, were analyzed together with the Cortezian and CN ecoregions, respectively. Regarding the geoforms of the WTNP province, 82% of the sites were classified as lagoons, 11% as open coasts, and 7% as estuaries, while in the TEP, 53% of the sites were classified as estuaries, 37% as lagoons, and 10% as open coasts (Figure 3). The dominant species varied in their distribution among geoforms: A. germinans was dominant in 59% of lagoons, Rhizophora spp. dominated 72% of estuaries, and these two taxa each dominated 50% of open coasts.

3.2. Effect of Factors on Mangrove Litterfall

According to the Permanova results, significant differences were detected in mangrove productivity among the province, geoform (p = 0.01), and dominant species (p < 0.01) factors; the ecoregion factor did not present significant differences (Table 2). Among the two biogeographic provinces analyzed, the average productivity varied from 11.19 ± 3.63 Mg ha−1 year−1 in TEP to 7.34 ± 4.28 Mg ha−1 year−1 in WTNP (Figure 4a). In the case of geoforms, the highest and lowest average values were recorded in estuaries (11.90 ± 3.6 Mg ha−1 year−1) and lagoons (7.49 ± 4.13 Mg ha−1 year−1), respectively (Figure 4b). Finally, regarding the dominant species, the highest productivity value was recorded in Rhizophora spp. sites (11.72 ± 3.58 Mg ha−1 year−1) and the lowest in A. germinans sites (6.5 ± 4 Mg ha−1 year−1) (Figure 4c).
According to the results of CAP for each factor, the highest canonical correlations were found in the dominant species (δ2 = 0.34, p = 0.0002) and ecoregion (δ2 = 0.27, p = 0.002), followed by geoform (δ2 = 0.25, p = 0.0006), and province (δ2 = 0.21, p = 0.0003). The gradient of the ordinations for the sites in each factor on the first axis of the CAP are shown in Figure 5a–d. In contrast, in LOOCV tests, the highest overall classification success rates were observed in provinces (74.14%) and dominant species (60.34%). Regarding province, the highest individual classification success was for TEP (86.67%), indicating that a given litterfall value would be more predictable in this province than in WTNP (60.71%). In the individual validation of the dominant species factor, litterfall at sites dominated by A. germinans and Rhizophora spp. was predicted to be 64%. Regarding validation tests for ecoregion and geoform, only 34.48% and 48.27% were achieved in global classification success, respectively. In the individual validation for ecoregion, CN-Nic, and MT-Cor achieved the highest percentage assignments of 54.54% and 46.42%, respectively. Regarding the geoform factor, the lagoon achieved a higher percentage of assignment success of 58.82% (Table 3).

3.3. Influence of Environmental Variables on Mangrove Litterfall

A set of seven uncorrelated environmental variables was used to evaluate their influence on the litterfall of mangroves in the American Pacific (Tables S1 and S2); the minimum temperature was suppressed because it was correlated with precipitation and rainfall of the wettest month. The PCA ordination of environmental variables of mangrove sites in the American Pacific explained 72.5% of the variance for the first two axes, with the first axis contributing 50.1%. The variables most correlated with the first axis of ordination were precipitation (−0.50), maximum temperature (0.44), and precipitation of the wettest month (−0.40), and for the second axis, temperature (−0.54), and radiation (−0.50). In the two-dimensional PCA diagram, the formation of a discrete group of sites close to the TEP was observed, as seen in the categorization by the province factor (Figure 6).
The distance-based linear model (DistLM) most explanatory for mangrove litterfall identified four environmental variables as significant (p < 0.05) in the individual analysis (salinity, precipitation, maximum temperature, wettest month precipitation) (Table 4); however, in the selected model, three environmental variables, maximum temperature, wettest month precipitation, and driest month precipitation, collectively showed a cumulative correlation of R2 = 0.3413. This analysis indicates that the variables selected by the model exert the most significant influence on mangrove primary productivity in the American Pacific, collectively explaining 34.13% of the total variance of the data.

4. Discussion

The results obtained from this meta-analysis provide evidence to establish patterns of primary productivity based on mangrove litterfall across biogeographic units and geomorphic features, as well as the dominant species found at sites across the American Pacific. Globally, studies conducted at local scales have shown that a set of factors, including the availability of critical resources such as sunlight and nutrients, regulatory factors such as salinity and pH, and hydroperiod and geomorphological configuration, directly impact the net primary productivity of mangroves [14,39]. On the other hand, at regional and global scales, climatic factors associated with latitude are relevant, affecting the structural and litterfall patterns in mangroves [21,24].
In a global comparative overview, it has been shown that litter fall rates in Neotropical Pacific mangroves vary between 4 and 12 tons per hectare per year in regions such as Mexico, Costa Rica, and Colombia. In contrast, Indo-Pacific mangroves generally show higher litter fall rates, with typical ranges of 8 to 16 tons per hectare per year in Southeast Asia (Indonesia, Thailand) and northern Australia. In both regions, litter fall rates are influenced by variables such as salinity, tides, and seasonality [40]. However, species dominance is a highly explanatory factor in understanding primary productivity based on mangrove litter fall. For example, it has been observed that R. mangle and A. germinans, two of the most common species in the Neotropical Pacific, have litter production rates that vary in response to changes in salinity and nutrient availability [14]. On the other hand, the greater diversity of mangrove species in the Indo-Pacific, such as Sonneratia alba Sm., Bruguiera gymnorhiza (L.) Lam., and Ceriops tagal (Perr.) C.B. Rob., contributes to greater variability and often higher rates of litter production [40].
A scarcely evaluated factor is the effect that anthropogenic impact can have on mangrove litter fall productivity [7]. It is necessary to consider that mangroves in the Neotropics, Southeast Asia, and southeastern coast of Africa may face common threats such as deforestation, pollution, and climate change, but each region also presents specific threats that reflect their socioeconomic contexts and land use patterns. For example, the conversion of mangroves for aquaculture is particularly severe in Southeast Asia, while logging for fuel and agriculture is more prominent in Africa. These differences underscore the need for conservation approaches tailored to local circumstances, as well as global efforts to mitigate the common threats faced by these valuable ecosystems [41].

4.1. Effect of Geomorphic and Biogeographic Factors on Mangrove Litter-Based Primary Productivity

The litterfall for the TEP was 11.19 ± 3.63 Mg ha−1 year−1, while for the WTNP, it was 7.34 ± 4.28 Mg ha−1 year−1, which was significantly different (p < 0.001). This difference may be due to the latitudinal effect that occurs in the provinces [1,42]. The TEP is located at latitudes 5° S to 20° N on the tropical belt, a humid region with high precipitation rates, in this case with a range of 105 to 3500 mm annually and 262 principal rivers (Table S2) [43,44,45,46,47,48]. These data indicate that the TEP has a greater fluvial contribution, which results in a high contribution of nutrients and the regulation of salinity, conditions that favor greater production of litterfall in mangroves. On the contrary, the WTNP, where the mangroves are distributed, has a latitudinal location of 20°–30° N. The northern subtropical strip has an annual rainfall range of 99 to 1299 mm and 31 main rivers. It is a drier region with less range of precipitation, lower incidence of rivers, and low river input, which limits the supply of nutrients and the regulation of salinity, resulting in reduced litterfall production in mangrove ecosystems [14,49,50,51]. This also explains the greater predictability or consistency in litterfall values, depending on the province where they are produced, as indicated by the CAP analysis (Table 3).
The latitudinal effect on the variability of litterfall in mangroves globally was also found by [1], reported significantly higher litter fall rates in the 0–10° regions (10.4 ± 4.6 Mg ha−1 year−1) compared to other latitudes (10–20°, 20–30° N) and significantly lower in the latitudes >30° (4.7 ± 2.1 Mg ha−1 year−1). The results of this study confirm the relevance of the latitudinal location of mangroves [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94].
The differences in the contribution of litterfall by geomorphological characteristics (lagoon = 7.49 ± 4.13 and estuary = 11.90 ± 3.55 Mg ha−1 year−1) were significant (p = 0.05), with the open coast presenting a contribution of litterfall of 12.05 ± 2.70 Mg ha−1 year−1, similar to the estuary. Ribeiro et al. [14] reported similar results: higher average rates of litterfall in landforms dominated by rivers, particularly in deltas (11.5 Mg ha−1 year−1) and a lower rate for lagoons (9 Mg ha−1 year−1).
The results of this study are attributed to the frequent flooding between the estuary and the open coast, although it was found that the average salinity was different (35.4, 18.9, 29.4‰, lagoon, estuary, and open coast, respectively). Deltas and estuaries can form large expanses of individual mangroves, where the accumulation of fluvially transported terrigenous sediments allows opportunistic colonization of mangroves, which, when developed, contributes to increased input of litter into the mangrove forest. Open coast mangroves prevail in areas with limited inputs of freshwater and terrigenous sediments. However, there is a high frequency of flooding, which allows the exchange of nutrients and soil aeration, which favors forest development and, therefore, the litterfall [20,95]. The lagoon presented the lowest average litterfall, possibly because it is an environment with a low frequency of flooding due to its distance from the mouth of the river. Lagoons are largely restricted to coasts with high-energy waves, which are conditions that limit the potential establishment of mangroves. This combination of factors helps explain the minimum global coverage of lagoon mangroves and, thus, the lower production of litterfall [19].
The dominant species of the genus Rhizophora and A. germinans showed significant differences (p < 0.05) in litterfall with a production of 11.72 ± 3.6 and 6.62 ± 4 Mg ha−1 year−1 respectively, while L. racemosa presented an intermediate litterfall of 10.91 ± 2.5 Mg ha−1 year−1. Rhizophora has been reported globally as the dominant genus in fringe mangroves due to the high nutrient input and lower salinity of fringe mangroves. Species within the genus Rhizophora generally exhibit a higher average litterfall in mangrove ecosystems. A. germinans, on the other hand, is typically found under harsh conditions of high salinity, low nutrient content, and arid zones, which explains its lower litterfall input attributed to these ecological conditions.
Latitude also influences litterfall input by species, [96] found a significant negative correlation (R = −0.50, p < 0.05) considering latitude. This negative relationship explains why higher litterfall production is observed at lower latitudes (tropical region) and decreases linearly with increasing latitudes (subtropical region). This prediction was confirmed in the present study. The genus Rhizophora dominated the TEP province (American tropical region). It showed the highest litterfall input compared to the WTNP province (American subtropical northern region), dominated by A. germinans. This pattern makes litterfall values more predictable according to the dominant species prevailing in different biogeographic units (Table 3).

4.2. Influence of Environmental Variables on Mangrove Litter-Based Primary Productivity

Our study found that salinity, precipitation, maximum temperature, and precipitation of the wettest month as the variables that independently explained most of the variation in mangrove litterfall production in the American Pacific. Regarding the interaction of variables, the best explanatory model for primary productivity included maximum temperature, precipitation of the wettest month, and precipitation of the driest month as the environmental variables determining the variation in mangrove litterfall in the American Pacific. This confirms that mangrove macroecology influences regional litterfall; in other words, ecological conditions predicting primary productivity at larger spatial scales also affect regional mangrove primary productivity in the American Pacific [14].
The litterfall input of mangroves in the American Pacific was 9.2 Mg ha−1 year−1, which converted to carbon (C), with a conversion factor of 0.45, yielding a value of 4.1 Mg C ha−1 year−1 [97], slightly lower than the average reported for the Neotropics, which was 5 Mg C ha−1 year−1 [14], and similar to the global average of 4.03 Mg C ha−1 year−1 [1].
Mangroves produce organic C well above ecosystem respiration. They are considered essential sites for soil carbon storage (−10%) and C export (−40%) to adjacent coastal waters, indicating their significant contribution to coastal C biogeochemistry [1,5,98,99]. Carbon fixed through litterfall is a critical process for the C balance between mangroves and adjacent coastal waters, considering that 40% of litterfall is exported to estuaries and oceans [5,100]. Therefore, estimating carbon fluxes generated from litterfall input is vital to understanding the carbon flow rate that offsets CO2 emission rates and complements C budgets in coastal wetlands, essential information for designing management programs focused on conserving coastal resources, including mangrove conservation [52,101]. They also facilitate access to international carbon programs aimed at reducing CO2 in the atmosphere and climate change mitigation.

5. Conclusions

Based on the available information on mangrove litterfall in the American Pacific, evidence was provided regarding the biogeographic spatial effect on litterfall patterns and the latitudinal effect, which is influenced by ecological conditions resulting from climate variation determined by latitude. Additionally, the importance of the geomorphological factor on mangrove ecological processes at different spatial scales was confirmed, with detectable litterfall variations among geomorphological features, depending on species dominance, with the genus Rhizophora being the most widely distributed in riverine mangroves and the most productive in the American Pacific. Regarding the evaluated environmental variables, extreme variables such as maximum temperature, precipitation of the wettest month, and precipitation of the driest month significantly influenced Pacific American mangrove primary productivity. This confirms that extreme environmental variables play a more significant role in primary productivity at the macroscale than average environmental variables, as found in other studies. However, it is necessary to evaluate other environmental variables (e.g., REDOX potential, nutrients, and volume of river water contribution) in meta-analyses that, in interaction with others, may more completely explain primary productivity variability in mangroves. Finally, it is advisable to develop a more precise quantitative classification of geomorphological features in the American Pacific to generate a more robust variation factor for distinguishing net primary productivity patterns.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15071215/s1, Table S1: Data matrix with selected studies in mangrove sites and regions for the American Pacific; Table S2: Environmental data matrix of mangrove sites and regions in the American Pacific.

Author Contributions

Conceptualization, methodology, formal analysis, resources, writing—original draft preparation, visualization, and project administration, C.V.-P. and E.I.R.-B.; validation, E.I.R.-B., C.L.M.-M., P.M.-C. and S.L.; investigation, data curation, and funding acquisition, C.V.-P.; writing—review and editing, C.V.-P., E.I.R.-B., C.L.M.-M., P.M.-C. and S.L.; supervision, E.I.R.-B., C.L.M.-M., P.M.-C. and S.L. All authors have read and agreed to the published version of the manuscript.

Funding

C.V-P. is grateful for the scholarship (No. 769229) provided by the Mexican National Council of Humanities, Sciences, and Technologies (CONAHCYT) to pursue doctoral studies in Biodiversity and Conservation of Tropical Ecosystems at the University of Sciences and Arts of Chiapas (UNICACH). E.I.R-B. wish to thank the project 063-05-2023-UNICACH Team for their support.

Data Availability Statement

Data are contained within the article or Supplementary Materials.

Acknowledgments

All authors thank the anonymous reviewers for their comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Flowchart showing the number of studies reviewed, excluded, and included in the meta-analysis.
Figure 1. Flowchart showing the number of studies reviewed, excluded, and included in the meta-analysis.
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Figure 2. Geographical distribution of sites with mangrove litterfall data categorized by geoforms in provinces and ecoregions of the American Pacific, according to the classification of Spalding et al. [31]; Site names, litterfall values, and quotes are provided in Table S1.
Figure 2. Geographical distribution of sites with mangrove litterfall data categorized by geoforms in provinces and ecoregions of the American Pacific, according to the classification of Spalding et al. [31]; Site names, litterfall values, and quotes are provided in Table S1.
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Figure 3. Distribution of sites with mangrove litterfall data categorized by geoforms in two Provinces of the American Pacific, according to the classification of Spalding et al. [31]; Number of sites classified by each geoform in the Warm Temperate Northeast Pacific Province (a) and in the Tropical Eastern Pacific Province (b).
Figure 3. Distribution of sites with mangrove litterfall data categorized by geoforms in two Provinces of the American Pacific, according to the classification of Spalding et al. [31]; Number of sites classified by each geoform in the Warm Temperate Northeast Pacific Province (a) and in the Tropical Eastern Pacific Province (b).
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Figure 4. Distribution of mangrove litterfall for the American Pacific in (a) provinces, (b) geoforms, and (c) dominant species. WTPN, Warm Temperate Northeast Pacific Province; TEP, Tropical East Pacific; Provinces are according to the classification of Spalding et al. [31].
Figure 4. Distribution of mangrove litterfall for the American Pacific in (a) provinces, (b) geoforms, and (c) dominant species. WTPN, Warm Temperate Northeast Pacific Province; TEP, Tropical East Pacific; Provinces are according to the classification of Spalding et al. [31].
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Figure 5. Canonical Analysis of Principal Coordinates (CAP) ordination, showing the position of mangrove litterfall sites in the American Pacific categorized by (a) provinces, (b) ecoregions, (c) geoforms, and (d) dominant species. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific; MT-Cor, Magdalena Transition-Cortezian; MTP, Mexican Tropical Pacific; CN-Nic, Chiapas-Nicaragua-Nicoya; PB, Panama Bight; Guay, Guayaquil; R. spp., Rhizophora spp.; A. g., Avicennia germinans; L. r., Laguncularia racemosa.
Figure 5. Canonical Analysis of Principal Coordinates (CAP) ordination, showing the position of mangrove litterfall sites in the American Pacific categorized by (a) provinces, (b) ecoregions, (c) geoforms, and (d) dominant species. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific; MT-Cor, Magdalena Transition-Cortezian; MTP, Mexican Tropical Pacific; CN-Nic, Chiapas-Nicaragua-Nicoya; PB, Panama Bight; Guay, Guayaquil; R. spp., Rhizophora spp.; A. g., Avicennia germinans; L. r., Laguncularia racemosa.
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Figure 6. Biplot of Principal Component Analysis (PCA) for environmental data of mangrove litterfall sites in the American Pacific. The points on the plane indicate observations categorized by province. The vectors indicate the Pearson correlations between the selected variables and axes. The length and direction of the vectors represent the strength of the relationships relative to a unit circle. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific. Tables S1 and S2 provide the environmental variables for each site.
Figure 6. Biplot of Principal Component Analysis (PCA) for environmental data of mangrove litterfall sites in the American Pacific. The points on the plane indicate observations categorized by province. The vectors indicate the Pearson correlations between the selected variables and axes. The length and direction of the vectors represent the strength of the relationships relative to a unit circle. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific. Tables S1 and S2 provide the environmental variables for each site.
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Table 1. Characteristics of the geoforms considered for categorizing sites with mangrove data and their relationship with the typologies used by Woodroffe et al. [19] and Worthington et al. [20].
Table 1. Characteristics of the geoforms considered for categorizing sites with mangrove data and their relationship with the typologies used by Woodroffe et al. [19] and Worthington et al. [20].
Geoforms for This ClassificationDefinition and Geomorphic SettingWoodroffe et al. [19]Whortington et al. [20]
LagoonSheltered coastal waters (coastal lagoons and tidal creeks) that are usually shallow and intermittently separated from oceanic inputs by wave-dominated barriers.Lagoon; Tidal estuaryLagoonal; Estuarine
EstuaryPermanently or temporarily open linear systems, deltaic fans, and channels with high fluvial input, where mixing processes are dominated by fluvial and tidal inputs, producing horizontal salinity gradients.DeltaDeltaic
Open coastHigh-energy systems dominated by waves and protected by coastal protrusions, such as bays, coves, and sub-merged rocky beds.-Open coast
Table 2. Results of Permanova are based on Bray–Curtis dissimilarities in mangrove litterfall data in response to province, ecoregion, geoform, dominant species, and their interactions as factors in the American Pacific. d.f.: degrees of freedom, SS: sum of squares; MS: mean square, F: pseudo-F statistical value, p: p values by permutation. Residuals are permuted under a sequential sum of squares model (Type I); maximum number of permutations: 9999.
Table 2. Results of Permanova are based on Bray–Curtis dissimilarities in mangrove litterfall data in response to province, ecoregion, geoform, dominant species, and their interactions as factors in the American Pacific. d.f.: degrees of freedom, SS: sum of squares; MS: mean square, F: pseudo-F statistical value, p: p values by permutation. Residuals are permuted under a sequential sum of squares model (Type I); maximum number of permutations: 9999.
Factord.f.SSMSFp
Province1261.82261.8218.530.0003 *
Ecoregion484.9921.241.50.221
Geoform2131.965.954.670.015 *
Dominant species2185.8692.936.580.003 *
Province × Geoform278.1139.062.760.076
Province × Dominant species24.562.280.160.847
Ecoregion × Geoform46.591.650.120.972
Ecoregion × Dominant species219.39.650.680.504
Residuals38
* Denotes significant p values (<0.05).
Table 3. Results of Canonical Analysis of Principal Coordinates (CAP) and Category Assignment Success tests for mangrove litterfall data in the American Pacific. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific; MT-Cor, Magdalena Transition-Cortezian; MTP, Mexican Tropical Pacific; CN-Nic, Chiapas-Nicaragua-Nicoya; PB, Panama Bight; Guay, Guayaquil. n: number of observations; δ2: square canonical correlation of the first axis m of the principal coordinate. Maximum number of permutations: 9999.
Table 3. Results of Canonical Analysis of Principal Coordinates (CAP) and Category Assignment Success tests for mangrove litterfall data in the American Pacific. WTNP, Warm Temperate Northeast Pacific; TEP, Tropical East Pacific; MT-Cor, Magdalena Transition-Cortezian; MTP, Mexican Tropical Pacific; CN-Nic, Chiapas-Nicaragua-Nicoya; PB, Panama Bight; Guay, Guayaquil. n: number of observations; δ2: square canonical correlation of the first axis m of the principal coordinate. Maximum number of permutations: 9999.
Factorδ2pnAllocation Success %
Correct for Individual ReclassificationCorrect for Global Reclassification
Province0.210.0003 * 74.14
WTNP 2860.71
TEP 3086.67
Ecoregion0.270.002 * 34.48
MT-Cor 2846.42
MTP 60
CN-Nic 1154.54
PB 911.11
Guay 40
Geoform0.250.0006 * 48.27
Lagoon 3458.82
Estuary 1833.33
Open coast 633.33
Dominant species0.340.0002 * 60.34
A. germinans 2664
L. racemosa 737.5
Rhizophora spp. 2564
* Denotes significant p values (<0.05).
Table 4. Marginal test and best group of variables selected by a distance-based linear model, using the stepwise procedure and the adjusted R2 selection criterion from Bray–Curtis dissimilarities on mangrove litterfall data in the American Pacific. Max Temp: Maximum Temperature; P wettest month: Precipitation wettest month; P driest month: Precipitation driest month; SS: sum of squares; F: pseudo-F statistical value, p: p values by permutation; RSS: residual sum of squares. The values of the environmental variables for each site are provided in Tables S1 and S2.
Table 4. Marginal test and best group of variables selected by a distance-based linear model, using the stepwise procedure and the adjusted R2 selection criterion from Bray–Curtis dissimilarities on mangrove litterfall data in the American Pacific. Max Temp: Maximum Temperature; P wettest month: Precipitation wettest month; P driest month: Precipitation driest month; SS: sum of squares; F: pseudo-F statistical value, p: p values by permutation; RSS: residual sum of squares. The values of the environmental variables for each site are provided in Tables S1 and S2.
Marginal TestSelected Model
VariablesSSFp% Variation ExplainedSelectionOverall Best Solution
Salinity186.569.2980.0034 *14.2+Max TempAdj R2 = 0.3047
Temperature83.3053.8020.06096.36+ P wettest monthR2 = 0.3413
Precipitation283.1915.440.0007 *21.62+ P driest monthRSS = 862.97
Max Temp160.147.7980.0074 *12.22
P wettest month396.3924.2940.0001 *30.26
P driest month32.7111.4340.23822.5
Radiation36.0731.5850.21112.75
* Denotes significant p values (<0.05) for the marginal test.
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Velázquez-Pérez, C.; Romero-Berny, E.I.; Miceli-Méndez, C.L.; Moreno-Casasola, P.; López, S. Geoforms and Biogeography Defining Mangrove Primary Productivity: A Meta-Analysis for the American Pacific. Forests 2024, 15, 1215. https://doi.org/10.3390/f15071215

AMA Style

Velázquez-Pérez C, Romero-Berny EI, Miceli-Méndez CL, Moreno-Casasola P, López S. Geoforms and Biogeography Defining Mangrove Primary Productivity: A Meta-Analysis for the American Pacific. Forests. 2024; 15(7):1215. https://doi.org/10.3390/f15071215

Chicago/Turabian Style

Velázquez-Pérez, Carolina, Emilio I. Romero-Berny, Clara Luz Miceli-Méndez, Patricia Moreno-Casasola, and Sergio López. 2024. "Geoforms and Biogeography Defining Mangrove Primary Productivity: A Meta-Analysis for the American Pacific" Forests 15, no. 7: 1215. https://doi.org/10.3390/f15071215

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