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Although sandy islands in arid environments are vital protection sites for endemic species, they face constant natural and anthropogenic hazards, such as storm surge impacts and the occasional presence of off-road vehicles. Monitoring the sedimentary dune-beach displacement and balance is essential because sediment transportation usually does not depend on external sources, such as rivers. The latest generation of geomatic applications may be relevant to understanding coastal vulnerability due to their ability to acquire and process spatial data at unprecedented scales. The objective of this study was to analyze the sedimentary dynamics of a distinctive dune corridor on Altamura Island in the Gulf of California, Mexico. We compared three ultra-high spatial resolution digital surface models (DSMs) with geomorphic change detection (DoD), covering the 1150 m coastal stretch. We used light detection and ranging (LiDAR) data and analyzed changes in the coastline with the semi-automatic CoastSat tool. Although the orthomosaics and the DSM collected in April, June, and October 2021 show different conditions in sediment distribution along the beach-dune interface, depending on the direction and intensity of the wind, the two DoDs showed a constant sediment distribution balance of ~13 M m3 between April and June and June and October. LiDAR data along the 40-km length of the sandy island indicate that the entire island could present a similar sedimentation pattern between the dune and beach interface. The CoastSat data indicate a constant accretion of 125 m in the beach-ocean interface between 2015 and 2022. This study demonstrates that the sediment balance between the dune and the beach on arid sandy islands is vital for conserving their shoreline and all associated coastal ecosystems.

The data obtained from field-based forest inventories, mainly basal area and stem density, are relevant for the analysis of aboveground biomass and forest fragmentation. Due to its persistently flooded ground, fieldwork in mangrove forests is time-consuming and complicated. Since mangroves are sensitive to the effects of climate change, selecting a reliable field method is of utmost importance. To this end, we analyzed 4 mangrove classes: Rhizophora mangle (RM), Laguncularia racemosa (LR), Avicennia germinans (AG), and AG shrub. We georeferenced and counted all mangrove stems within four 0.04 ha (20 × 20 m square). We analyzed data from 3 circular area plots and the plotless point-centered quarter method (PCQM) based on the original square plots. Depending on the mangrove class, PCQM overestimated basal area by up to 34% and stem density by 21%. The 3 circular plot surveys underestimated basal area from –1% to –29% and stem density from –3 to –25%. Based on the results, we suggest using a circular plot of 0.04 ha (r = 11.28 m) in less dense forests (RM and AG) and a circular plot of 0.015 ha (r = 6.9 m) with forest densities greater than 3,500 stems/ha (LR and AG shrub). The advantages of using the circular plot approach over PCQM are that mangrove inventories can be quantified quickly and do not require a minimum number of sampling points.

Mangrove forests are commonly mapped using spaceborne remote sensing data due to the challenges of field endeavors in such harsh environments. However, these methods usually require a substantial level of manual processing for each image. Hence, conservation practitioners prioritize using cloud computing platforms to obtain accurate canopy classifications of large extensions of mangrove forests. The objective of this study was to analyze the spatial distribution and rate of change (area gain and loss) of the red mangrove (Rhizophora mangle) and other dominant mangrove species, mainly Avicennia germinans and Laguncularia racemosa, between 2015 and 2020 throughout the northwestern coast of Mexico. Bimonthly data of the Combined Mangrove Recognition Index (CMRI) from all available Sentinel-2 data were processed with the Google Earth Engine cloud computing platform. The results indicated an extension of 42865 ha of red mangrove and 139602 ha of other dominant mangrove species in the Gulf of California and the Pacific northwestern coast of Mexico for 2020. The mangrove extension experienced a notable decline of 1817 ha from 2015 to 2020, largely attributed to the expansion of aquaculture ponds and the destructive effects of hurricanes. Considering the two mangrove classes, the overall classification accuracies were 90% and 92% for the 2015 and 2020 maps, respectively. The advantages of the method compared to supervised classifications and traditional vegetation indices are discussed, as are the disadvantages concerning the spatial resolution and the minimum detection area. The work is a national effort to assist in decision-making to prioritize resource allocations for blue carbon, rehabilitation, and climate change mitigation programs.

Tropical sandy beaches provide essential ecosystem services and support many local economies. In recent times, however, there has been a massive infrastructure expansion in popular tourist destinations worldwide. To investigate the shoreline variability at a popular tourist destination in Mexico, we used the novel semi-automatic CoastSat program (1980 to 2020) and the climate dataset ERA5 (wave energy and direction). We also measured the beach cross-shore distance and the foredune height with topographic surveys. The results indicate that the section of real estate seafront infrastructure in the study site presents a considerable shoreline erosion due to the fragmentation between the foredune ridge and the beach berm, based on the in situ transects. Moreover, foredune corridors with cross-shore distances of up to 70 to 90 m and dune heights of 8 m, can be seen in the short unobstructed passages between buildings. In the south section we found the coastline in a much more stable condition because this area has not had coastal infrastructures, as of yet. For the most part, the remote sensing analysis indicates constant erosion since 1990 in the real estate section (mainly seafront hotels) and an overall accretion pattern at the unobstructed beach-dune locations. This study demonstrates the catastrophic consequences of beach fragmentation due to unplanned real estate developments, by combining in situ surveys and a freely available big-data approach (CoastSat).

The effect of hydrological connectivity of the fish assemblages was assessed on a floodplain in the SE Gulf of California, Mexico using a before–after control-impact (BACI) design. Community structure attributes of species abundance, biomass, richness, diversity, and differences in the structure of fish assemblages were compared between two periods (January to June in 2011 and 2015) and two flood plains, one designated as the control zone which was divided by a road, and another one designated as the treatment zone which from January to June 2011 was divided by a road, and then sampled from January to June 2015, as the site was rehabilitated by removing the road in the year 2012. Fish were sampled at monthly intervals using a seine net at different stations in both floodplains. A total of 7024 organisms, comprising of 14 species belonging to 11 families, were analyzed. In the control zone during both periods and in the treatment zone before removal of the road Poecilia butleri was the most abundant species and two exotic species (Tilapia) accounted for more than 95% of the total biomass. After the removal of the road in the treatment zone Poeciliopsis latidens was the most abundant species and the relative biomass of both species of tilapia decreased to 85%, but was still the most important in terms of biomass. However, richness and diversity were higher and the structure of the fish assemblages was different in the treatment zone after the road was removed, due to the presence of species with estuarine and marine affinity in this zone and period. Our results indicate that hydrological connectivity proved to be a key factor influencing the fish composition and abundance in the floodplain system in the region. Hence, a better hydrological connectivity implied higher abundance and diversity, likely related to an increase in the habitat complexity. The priority in the management of the ecological functioning of the floodplain system should be set on minimizing the modifications of the natural flow and thus avoiding the changes on the fish biota.

Spectral discrimination of mangrove leaves is the first step in classifying remotely sensed imagery of mangrove forests. The objective of this study was to analyze spectroscopic data on leaves from the upper and lower parts of mangrove canopies to discriminate species and physiognomic types. Leaf samples from the upper and lower parts of the canopies of 3 mangrove species (Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle) in 2 physiognomic types (basin and fringe) were collected during 2 seasons (dry and rainy). Probability distribution and first-derivative plots were generated for every wavelength (450–1,000 nm) detected in all samples. With the plots, optimal wavelengths were selected and subsequently verified with a canonical discriminant analysis. Results indicated that all species in basin mangrove forests showed a unique distinction between the upper and lower leaves during the dry season. By contrast, species in fringe mangrove forests did not show this difference during both seasons. Optimal wavelengths for species discrimination were located between 540–560 nm and 700–720 nm, which correspond to the green and red-edge wavebands, respectively. Future studies using remote sensing data with the aforementioned wavebands can be conducted to discriminate physiognomic mangrove forest types and to increase accuracy in the classification of mangroves at the canopy level on the Pacific coast of Mexico.

Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD FieldSpec® 3 JR spectroradiometer (Analytical Spectral Devices, Inc., USA). R2 plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions.

The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species of mangrove (Avicennia germinans, Rhizophora mangle) representing a variety of conditions (healthy, poor condition, dwarf) of a degraded mangrove forest located in the Mexican Pacific. This is the first time leaf nitrogen content has been examined using close range hyperspectral remote sensing of a degraded mangrove forest. Simple comparisons between individual wavebands and N concentrations were examined, as well as two models employed to predict N concentrations based on multiple wavebands. For one model, an Artificial Neural Network (ANN) was developed based on known N absorption bands. For comparative purposes, a second model, based on the well-known Stepwise Multiple Linear Regression (SMLR) approach, was employed using the entire dataset. For both models, the input data included continuum removed reflectance, band depth at the centre of the absorption feature (BNC), and log (1/BNC). Weak to moderate correlations were found between N concentration and single band spectral responses. The results also indicate that ANNs were more predictive for N concentration than was SMLR, and had consistently higher r2 values. The highest r2 value (0.91) was observed in the prediction of black mangrove (A. germinans) leaf N concentration using the BNC transformation. It is thus suggested that artificial neural networks could be used in a complementary manner with other techniques to assess mangrove health, thereby improving environmental monitoring in coastal wetlands, which is of prime importance to local communities. In addition, it is recommended that the BNC transformation be used on the input for such N concentration prediction models.

Six silvofishery systems were constructed to estimate the potential use of mangrove seedlings for nutrient removal. Two systems did not contain seedlings (i.e., control treatment), while the remaining systems were divided into separate treatments using two mangrove species (Laguncularia racemosa and Rhizophora mangle). Each system consisted of two water tanks linked by two hoses. The first tank contained 150 poeciliid fishes, while the second contained a biological filter of gravel and sand on the bottom, as well as a hydroponic arrangement of 34 seedlings. Water exchange between both tanks was performed over a 24-h period, every ten days for seven months, and the concentration of nutrients (NH4+, NO2–, NO3–, and PO4–3) was measured every 8 h. Laguncularia racemosa showed a higher growth rate compared to R. mangle, but there were no differences regarding the growth of fishes among the six systems. Final dissolved inorganic nitrogen removal was 42% in the control treatment and 90% in both treatments using mangroves. Dissolved inorganic phosphorus removal was 45% in the control treatment, 44% in the L. racemosa treatment, and 35% in the R. mangle treatment. Our results indicate that both mangrove species are capable of removing a considerable amount of nitrogen, but phosphorus removal was unsatisfactory.

The effect of hydroperiod on the survival and growth of Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle seedlings under experimental conditions was investigated using a simulated tidal system. Seedlings of the three mangrove species were grown at four inundation levels. All levels were flooded every 12 h in order to create a simulated semi-diurnal tide cycle (i.e., two high tides and two low tides per day). Based on the hydroperiod for the study area, level 1 was flooded for 10 h, level 2 for 6 h, level 3 for 2 h, and level 4 for 6 min. After 570 days, the results showed that R. mangle presented the highest growth at level 1 and the lowest growth at level 4. The optimal growth of L. racemosa occurred at levels 2 and 3, but all the seedlings died at level 1, indicating that this species is highly sensitive to tidal regimes. Compared to L. racemosa and R. mangle, A. germinans had a moderate growth rate at all four levels. Mangrove growth differentiations were more prominent at levels 1 and 3, where the three species showed very different growth patterns. Studies of optimal growth using hydroperiod experiments are important in order to understand the physiological responses of mangrove forests to a possible sea level rise in the next decades, especially in subtropical environments where they are constantly exposed to stress.

The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400–1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships.

Los humedales costeros conjuntan numerosas comunidades que se establecen a lo largo de gradientes topográficos, donde variaciones en salinidad e inundación resultan en composiciones y dinámicas distintas. Un ejemplo es el perfil topográfico de los humedales de la Laguna La Mancha, Veracruz. La temporalidad y la intensidad de mareas y de flujos de agua dulce (escurrimientos o afloramiento del manto freático) determinan el hidroperíodo, definiendo las condiciones de salinidad y de oxidación/reducción del humedal, así como el tipo de humedal. Su restauración debe tomar en cuenta la microtopografía y su consecuencia inmediata, el hidroperíodo. Se presentan ejemplos de estudios topográficos que definen las especies de mangles que deben usarse para restaurar un humedal o para crear uno a partir de la conformación artificial de hábitats. Se muestra la importancia del hidroperíodo en el establecimiento de plantas nativas de humedales de agua dulce y cómo su modificación favorece la entrada y dominancia de especies invasoras

Los manglares, junto con los pastizales marinos y las marismas, se encuentran entre los ecosistemas costeros conocidos como ecosistemas de carbono azul. Estos ecosistemas son sumideros naturales de carbono, relevantes para la mitigación del calentamiento global debido a que son capaces de secuestrar importantes cantidades de CO2 para producción de biomasa, así como de almacenar materia orgánica en sus sedimentos durante muy largo plazo. Se presentan los resultados de la evaluación temporal de los flujos (g m-2 año-1) e inventarios (Mg ha-1) de carbono orga?nico (Corg) en núcleos sedimentarios recolectados en áreas de manglar asociados a la laguna costera Estero de Urías (Mazatlán, Sinaloa). La estrategia de trabajo incluyó la caracterización geoquímica de los sedimentos mediante el ana?lisis de composición elemental por espectrometría de fluorescencia de rayos-X para evaluar cambios en el origen de los sedimentos (terrígeno o marino); la determinación de la distribucio?n de taman?o de grano para evaluar cambios en la hidrodinámica de los sitios de estudio; y la cuantificación de las concentraciones de Corg mediante un analizador elemental. Por último, se presenta una estimación del costo económico de los impactos de las emisiones potenciales de CO2 como consecuencia de la oxidación del Corg, debido a las potenciales perturbaciones de los sitios de estudio. La información generada representa una componente más de los beneficios económicos de la preservación de los ecosistemas de manglar, relevante en los planes de manejo de humedales costeros, cuya conservación contribuye a la reducción de emisiones de gases de efecto invernadero, y podría ser aprovechada como parte de una estrategia de adaptación al cambio climático basada en ecosistemas, en zonas tropicales y subtropicales alrededor del mundo. doi: https://doi.org/10.22201/igeof.00167169p.2021.60.1.2011