Carbon Dynamics of Mangrove Forests Submitted to Distinct Levels of Anthropogenic Stress

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Palacios Peñaranda, Enrique Peña-Salamanca, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6254743/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mangroves represent an important forestry resource in tropical and subtropical areas worldwide. Anthropogenic activities have significantly altered biodiversity in these strategic coastal ecosystems. In the Bay of Buenaventura in the Colombian Pacific, mangrove forests have been subjected to anthropogenic impacts. The present study estimated the aerial and underground (roots – sediment) carbon stock values of mangrove forests in the Bay of Buenaventura, based on the anthropogenic disturbance index, population density levels, and the distance from the city´s urban core. Four sampling stations were selected based on these criteria; the two stations closest to the city were Aguadulce and Islalba, and the farthest from the city were Piangüita and Punta Soldado. The magnitude of the carbon stock in aerial biomass was inversely related to the anthropogenic disturbance index, which was highest (8) close to the city of Buenaventura. Punta Soldado showed the highest carbon stock values, whereas, Aguadulce registered the lowest carbon stock. This station showed higher values of the anthropogenic disturbance index and was also the area with greatest amount of waste. These results highlight the importance of evaluating carbon stocks in these ecosystems as indicators of health status. Mangroves carbon storage anthropogenic disturbance index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights - The species spp., , and are dominant in the study area. - The compartment with the greatest carbon stocks was sediment. - Carbon stocks were affected by anthropogenic disturbance at the four sampling stations. - The mangroves closest to the most populated sites presented high levels of anthropogenic disturbance. - Greater plastic presence in the mangrove ecosystem was linked to lower carbon stocks. INTRODUCTION Mangrove ecosystems are found exclusively in the tropical and subtropical areas of the world. They comprise trees growing in coastal areas (Yáñez-Arancibia et al., 1998 ); they cover approximately 13,760,000 ha of coastal areas worldwide (Bunting et al., 2018 ) and store between 10 and 15% of organic carbon (24 Tg C y − 1 ) in coastal sediments (Alongi. D, 2014). It is estimated that mangroves contribute to carbon sequestration mainly through the production of roots (38%) and wood (31%). Soil also plays an important role in the carbon cycle of the earth, as the main reserve of the terrestrial ecosystem (Alongi D, 2014, Palacios & Cantera, 2017 ). Human impacts cause ecological changes in coastal ecosystems, with different intensities and spatial extensions. These changes affect functional and structural attributes and can lead to a reduction of aerial biomass and of structural forest complexity (Alongi D, 2014), leading to larger natural greenhouse effects and to climate change. The process of climate change comprises several components that influence social and economic aspects because it acts by creating or amplifying floods, tropical storms, rainfall, coastal erosion, and results in changes to the air and water temperatures (Wallace, 2000 ). Since 1960, close to 57% of mangrove cover has been lost due to transformations of the land for use in agricultural systems, housing construction, hotels, marine ports, canals, dams, roads, and more recently farms that use water and food to raise fish and shrimp (Mejía et al., 2014). Human activities, such as changes in soil use, alteration of biogeochemical cycles, destruction and fragmentation of habitats, loss of biodiversity, and alterations to climatic conditions, among others, are affecting severely the planet´s ecosystems (Rockström et al., 2009). In the Bay of Buenaventura in the Colombian Pacific, mangrove forests have been subjected to anthropogenic impacts such as deforestation, wood extraction, water pollution from industrial activities, illegal mining, and solid wastes, among others (Martín-López et al., 2009, Palacios & Cantera, 2017 ). Biomes are the basic units used to describe global patterns based on vegetation types. They have been identified and mapped based on soil processes, in association with regional climate variations. Ecosystems and biomes are characterized by the distinctive life forms adapted to their bioclimatic conditions. A new way of classifying the large life zones in the world, known as anthromes, incorporates the determinant function of human beings in ecosystem alteration, reflecting global patterns of human interactions with ecosystems (Ellis & Ramankutty, 2008 ). The hypothesis of the present study was that aerial and subterranean (roots and sediment) organic carbon stocks were significantly different among the study stations and were associated with the variability of the anthropogenic disturbance index (ADI) calculated for the Bay of Buenaventura. In consequence, the objectives of the study were to (1) estimate the values of aerial and subterranean carbon stocks in mangrove forests exposed to different levels of anthropogenic disturbance in the Bay of Buenaventura, and (2) to compare the carbon stocks with the spatial distribution of the ADI in the studied mangroves. MATERIALS AND METHODS Study area The study was carried out in the Bay of Buenaventura, Colombian Pacific (Fig. 1 ). Plant cover corresponded mainly to tropical wet forest bmh-T in the Holdridge life zone system (Gulh and Leiva, 1997). This coastal body is a flooded valley dominated by mud plains and sediment accumulations from the Tertiary and Quaternary periods, which allows the presence of typically riparian and fringe edge mangrove forests, among which the main species are Rhizophora spp., Laguncularia racemosa, Avicennia germinans, Pelliciera rhizophorae, Concocarpus erectus , and Mora oleifera (Cantera & Blanco, 2000). The tidal pattern in this area is multivariable with a range of between 3 and 4 m (Cantera & Blanco, 2000). Water temperature is 27.4ºC on average and salinity decreases during the rainy periods. Precipitation in this area is bimodal; the wettest month is November with 835 mm water column, and the driest months are January to March with 320 mm water column (Cantera & Blanco, 2000). The average precipitation in this area is above 6,000 mm year − 1 , which makes it one of the rainiest areas in the world (Cantera & Blanco, 2000). The coastal zone of the Valle del Cauca Department shows a differentiated matrix of use and occupation of the territory. The city of Buenaventura and scattered population in the bay constitute the densest population nucleus, with 415,770 projected residents for 2017, (DANE, 2017). Of this total population, the following densities were found for the locations along the bay: the township of Bazán, which includes Piangüita, Vista Hermosa, La Bocana, and Changai, has approximately 1,700 fixed residents and an indeterminate number of floating residents resulting from tourism (Palacios & Cantera, 2017 ). The township of Punta Soldado had 363 inhabitants grouped in 114 families in the year 2016 (Semana Sostenible, 2016 ). Field work The study area was categorized according to the anthrome classification system proposed by Ellis & Ramankutty ( 2008 ). Four mangrove forests, each located within distinct anthropogenic biomes in the bay, were selected for analysis. The anthromes identified in the study area include Dense Settlements (DSA), Residential Rainfed Mosaic (RrmA), Populated Forest (PfA), and Remote Forest (RfA) (Fig. 1 ). The global anthrome map was adjusted for the Bay of Buenaventura using the WGS 1984 geographic coordinate system. The classification spans mangrove forests near densely populated urban centers, such as the city of Buenaventura, through areas with moderate agricultural and livestock activity, to remote forests with minimal human disturbance (Gutiérrez-Yurrita, 2018). The linear distance from the nearest human settlement to the selected mangrove areas was determined using ArcGIS Desktop version 10.5 (ESRI, 2016). For the city of Buenaventura, the sampling point was located at the tourist pier. The selected sampling locations were as follows: Aguadulce (3°53'35" N − 77°05'47" W), located 1,823 m from the city of Buenaventura; Islalba (3°52'25" N − 77°05'47" W), located 1,767 m from the city; Piangüita (3°50'21" N − 77°11'48" W), located 452 m from Piangüita township; and Punta Soldado (3°46'06" N − 77°10'05" W), located 2,837 m from La Bocana. Aerial biomass The protocol proposed by Kauffman et al. ( 2013 ) was followed to measure, monitor, and report on the structure, biomass, and carbon stock of mangroves. At each sampling station, three replicate plots (154 m 2 ) were established to analyze the interior and exterior of the mangrove forest (Fig. 1 ). Plots were separated 25 to 50 m apart; the distance between the interior and exterior forest was approximately 25 m, depending on the length and width of the mangrove forest at each location. A modification to the sampling strategy (Kauffman et al. 2013 ) was carried out because the mangrove forest fringe is very narrow in the bay and did not allow us to adopt completely the distance and location of plots suggested in the protocol. Plots were located using a decameter and a Leika model Disto-d2 laser distance meter. The height of each individual tree was measured using a Haglöf model HCC digital clinometer. The vegetation at each sampling site was quantified in terms of aerial and subterranean biomass to assess the carbon stock component; the protocol considers that these clusters are sufficiently large, with available information comparable to other mangrove areas. These compartments are affected by the use or exploitation of the soil. Leaf litter is usually of little importance and was excluded from analysis in this study (Kauffman et al., 2013 ). The basic data recorded in this study included identification of the species, diameter of the main trunk (in cm), total tree height, and whether the tree was dead or alive. Diameter at breast height (DBH) of the trunk was measured 1.37 m from the ground (Kauffman et al., 2013 ). Given the particularities of mangroves, for trees with roots above 1.37 m in height or for trees with aerial roots (e.g., Rhizophora spp.), DBH was measured above the highest root. For individuals with roots reaching the tree crown, DBH was measured above aerial roots where a true trunk was found (Kauffman et al., 2013 ). All trees with over 50% of the main trunk within the plot diameter were measured (Kauffman et al., 2013 ). The aboveground biomass (AB) was calculated using allometric equations based on models developed by Chave et al. ( 2005 ), cited in Komiyama et al. ( 2008 ), to estimate the AB biomass in tropical mangroves [Eq. 1 ] and on the model proposed by Komiyama et al. ( 2005 ) to estimate belowground biomass (BB) [Eq. 2]. We decided to use the models developed by Chave et al. ( 2005 ) due to the robustness of their estimations and the replicability of methods in a large number of studies. $$\:Equation\:\left[1\right]\:\:\:BA=0.168*{\rho\:}^{2.471}\:\:\:\left({r}^{2}=0.99,\:n=84,\:\:DHBmax=50\:cm\right)$$ $$\:Equation\:\left[2\right]\:\:\:BB=0.199*{\rho\:}^{0.899}*{DAP}^{2.22}\:\:\:\left({r}^{2}=0.95,\:n=26,\:DHBmax=45\:cm\right)$$ To find the value of AB and BB the specific density of the wood of each mangrove species was necessary (ρ = g/cm 3 ). In this case, the values used for the genus Rhizophora spp. (0,88 g/cm 3 ) and for Avicennia germinans (0.67 g/cm 3 ) were obtained from the Global Wood Density Database (Chave et al., 2009 ); the value used for Mora oleifera (0,74 g/cm 3 ) was obtained from Cordero (1971), cited by Gross et al. ( 2014 ); and the value used for Pelliciera rhizophorae (0,75 g/cm 3 ) was obtained from Southwell and Bultman ( 1971 ), cited by Gross et al. ( 2014 ). The AB and BB were converted to carbon mass (Mg/ha) using carbon conversion proportions of 41.5% for AB (Bouillon et al., 2008 ) and of 39% for BB (Kauffman et al., 2013 ). These biomass values were converted to equivalent CO 2 by multiplying by a factor of 3.67 (Kauffman et al., 2013 ). The total carbon stock in the ecosystem was defined as the total sum of organic carbon in the vegetation (above and below ground). The aerial biomass was estimated from general allometric equations for mangroves in tropical areas (Komiyana et al., 2008). These equations took into account the values of DBH recorded at a different height for each tree species inside the plots and the density data for each species reported in the Global Wood Density Database (Chave et al., 2009 ). Organic carbon in sediment Sediment samples were collected at each plot using a 1 m high open-end soil probe with 1,963.5 cm 3 volume. The soilsediment core was divided into three sections with depth intervals of 0–33 cm, 34–67 cm, and 67–100 cm. Samples were stored in tagged plastic bags and taken to the laboratory for later analysis. Samples were dried at 105ºC. Anthropogenic disturbance index The anthropogenic disturbance index (ADI) [Eq. 3] (Blanco-Libreros and Estrada-Urrea, 2015 ) (Ortega Giraldo, 2019) was used to evaluate human impacts at each location, according to the following categoric variables: 1) Trampling (T) – presence of human and livestock footprints, 2) Logging (L) – evidence of selective logging of mangroves, such as stumps and downed wood, 3) Wastes (W) – presence of wastes on the forest floor and roots, and 4) Structures (S) – evidence of human modifications to the hydrology and topography of mangroves. Each variable was assigned a score of 0 to 3, where 0 is the absence of disturbance, 1 is little evidence of disturbance, 2 is evidence of disturbance, and 3 is maximum evidence of disturbance. The scores of each variable were added as follows: Equation [3]: ADI = T + L + W + S Experimental Design We used a nested or hierarchical design in two stages. As a result of the experiment we measured DBH, Height, Mg C and IPAS index as factors we considered the sector at 2 levels (1: coast and 2: interior of the forest) nested within the localities at 3 levels (1: Punta Soldado, 2: Agua Dulce and 3: Islaba), we have 3 sampling points within each locality. The model proposed for the hierarchical design is presented in Eq. 4, where y ijk is the response variable (DBH, height, Mg C and IPAS index), µ is the overall mean, τ i is the effect of the locality (1: Punta soldado, 2: Agua dulce and 3: Islaba), β j is the effect of the sector (1: coast and 2: forest interior). Equation [4] \(\:{\text{y}}_{\text{i}\text{j}\text{k}}={\mu\:}+{{\tau\:}}_{\text{i}}+{{\beta\:}}_{\text{j}\left(\text{i}\right)}+{\text{ϵ}}_{\left(\text{i}\text{j}\right)\text{k}}\) This model must satisfy the error assumptions, i.e. εij ∼N (0, σ 2 ), with σ 2 constant and Cov (εijk, εi'j'k') = 0 ∀i≠i'; j≠j'; k≠k'. The hypotheses associated with the hierarchical design state that H 0 : (τ) i =0 and H 0 :(β) j(i) = 0. The method used to compare the means of the treatments is called ANALYSIS OF VARIANCE (ANOVA), which is used to test the stated hypotheses. The traditional analysis of variance approach to analyse a two level hierarchical experiment. The corresponding calculations were carried out using Minitab 19 software. RESULTS Anthromes The area was categorized in anthromes, following the proposal by Ellis & Ramankutty ( 2008 ). Four mangrove forests located in different anthropogenic biomes in the bay were selected. The anthromes found in the study area were Dense settlements (DSA), Residential rainfed mosaic (RrmA), Populated forest (PfA), and Remote forest (RfA) (Fig. 2 ). The worldwide anthrome map was adjusted to the Bay of Buenaventura using the global projection WGS 1984 geographic coordinate system. Anthrome classification comprised from mangroves close to densely populated urban cores, such as the city of Buenaventura, to mangroves close to areas with sparsely populated forest, passing through a range of agricultural and livestock raising scenarios and natural areas with differing degrees of construction for diverse activities (Gutiérrez-Yurrita, 2018). Each sampling site was named according to the closest human settlement or sampled area. The linear distance from the closest human settlement to the selected mangrove area was calculated with the geographic information processing software ArcGIS Desktop ver. 10.5 (ESRI, 2016). In the case of the city of Buenaventura, the selected location was the tourist pier. The selected sampling locations were: Aguadulce (3°53´35" N − 77°05´47" W) located 1,823 m from the city of Buenaventura; Islalba (3°52´25" N − 77°05´47"W) located 1,767 m from the city of Buenaventura; Piangüita (3°50´21" N − 77°11´48" W) located 452 from the Piangüita township; and Punta Soldado (3°46´06" N − 77°10´05" W) located 2,837 m from la Bocana. Anthropogenic biomes corresponding to each sampled location were identified as follows. Aguadulce and Islalba were categorized as residential rainfed mosaic anthrome (RrmA) in a peri-urban context. Impacts were lower in rural contexts; Punta soldado belongs to the forest reserve Río Anchicayá National Park and was categorized as populated forest anthrome (Pfa); Piangüita was categorized as remote forest anthrome (RfA). Aerial biomass Mangrove forests in the Bay of Buenaventura were represented by four dominant species: Rhizophora spp., Pelliciera rhizophorae, Mora oleifera , and Avicennia germinans , in order of abundance. The contribution of carbon stock on the surface of live trees was approximately 138.6 Mg C ha − 1 at Aguadulce, 199.2 Mg C ha − 1 at Islalba, 151.6 Mg C ha − 1 at Piangüita, and 235.7 Mg C ha − 1 at Punta Soldado (Fig. 3 ). Carbon accumulation in aerial biomass was lowest at Aguadulce. Punta Soldado had high average carbon accumulation values. Subterranean biomass The greatest average subterranean biomass stocks (roots – sediment) were found at Piangüita (6,641 Mg C ha − 1 ); this value was statistically similar to that observed at Punta Soldado (6,105 Mg C ha − 1 ) (Fig. 3 ). Analysis of variance (ANOVA) model variables DBH, Height and Mg C. The models proposed for the three variables (DBH, Height and Mgc) do not comply with normality or homogeneity of variance, so we applied the Box-Cox transformation, a technique that suggests possible transformations of the variables in order to have a normal behaviour that guarantees unbiased parameter estimation and minimum variance. Using the Box-Cox transformation, for each variable we obtained: DBH: a 95% confidence interval was found for λ between (-0.06 to 0.16) Height: A 95% confidence interval for λ was found between (0.02 to 0.25) Mg C: A 95% confidence interval was found for λ between and (-0.02 to 0.07) None of the intervals includes 1, indicating that transformation is appropriate for these variables. The estimated value of the optimal λ for the variable DBH is 0.06, for height it is 0.13 and for Mg C it is 0.02, so for each variable the following transformation is applied: DAP 0.06 ; and for Mg C 0.02 . In the case of the height variable, it was not possible to obtain a value of λ that met the assumptions, so this variable is analyzed using non-parametric statistics.These transformations effectively achieved the desired normality and homogeneity of variances for DAT and Mg C in the errors of the ANOVA models for these variables at significance levels greater than 15%. In the analysis of variance of the DAT and Mg C variables, it was found that there were differences between the nested sectors within each locality at a significance level of less than 0.001 (p-value), both for the DAT variable and for Mg C. There were also differences between localities at a significance level of 0.000 (Table 1 ). It was also found that the sum of the squares of the treatments is not greater than the sum of the squares of the errors, which is to be expected because it is very difficult to control the experiment. Table 1 ANOVA of the variables DAT y Mg C DAT Mg C SOURCE GL Sum of squares Mean squares F Value P Value Sum of squares Mean squares F Value P Value Location 3 0,160 0,053 27,58 0.000 0,088 0,029 29,1 0.000 Sector (locality) 4 0,036 0,009 4.77 0,001 0.021 0.005 5,17 0,000 Error 686 1,323 0,002 0,695 0,001 Total 693 1,512 0,799 Given the differences between the sectors (coastal and interior forest) nested within the localities (Punta Soldado, Agua Dulce and Islalba), we performed a Fisher's LSD post-annova test (Table 2 ) and found that there are three main groups (A, B, C) that classify the sectors nested within the localities according to the DAT averages. The localities of Punta Soldado and Aguadulce tend to have higher DAT, both on the coast and inside the forest (group A), while the localities of Islalba and Piangüita have the lowest values, both on the coast and inside the forest (group C). With regard to Mg C, there are two main groups (A, B) that coincide with the sectors nested within the locality with the DAT variable, in addition to this group Costa (Islalba). Table 2. Grouping of information using Fisher's LSD method with 95% confidence. DAT Mg C SECTOR (LOCALIDAD) N Average Aggrupation N Average Aggrupation Coast ( Punta Soldado) 46 10.07 A 46 1.76 A Inland Forest (Punta Soldado) 85 8.72 A B 83 1.23 A Inland Forest (Aguadulce) 43 8.65 A B 44 1.61 A Coast (Aguadulce) 26 8.64 A B 26 1.29 A B Coast (Islalba) 110 8.13 B 110 1.22 A Inland Forest (Islalba) 112 6.05 C 112 0.58 B Inland Forest (Piangüita) 148 5.67 C 148 0.65 B Coast (Piangüita) 123 5.01 C 123 0.45 B Non-parametric analysis for the height variable The Kruskal-Wallis non-parametric technique is used to determine whether there are significant differences between the medians, so the null hypothesis is that the population means are all equal or different. The results are shown in Table 3 , and it was found that with a significance level of (0.000), both the null hypothesis without adjustment for ties and with adjustment for ties (a tie occurs when the same value occurs in more than one sample), the null hypothesis is rejected, i.e. not all population medians are equal. The estimates of the sample medians for the treatments are shown in Table 3 . The Z-value is used to compare the mean rank of each group with the mean rank of all observations, i.e. (the furthest apart are the most different): Islalba-Interior Forest treatment, has the highest Z value, /-5.87/, indicating that it is the furthest from the overall average rank range, followed by Punta Soldado - Coast /4.90/ and Punta Soldado - Interior Forest /4.43/. The negative Z-value indicates that the mean rank of the Islalba interior forest treatment is lower than the overall mean rank, while the positive Z-values of the Punta Soldado-coast /4.90/ and Punta Soldado - interior forest /4.43/ treatments indicate that the mean rank of these treatments is higher than the overall mean rank. Table 3 Kruskal – Wallis test LOCALITY-SECTOR N Average Classification of averages Z Value Aguadulce - Inland Forest 44 9,0 408,1 2,15 Aguadulce - Coast 26 9,5 404,9 1,55 Islalba - Inland Forest 110 5,0 243,2 -5,87 Islalba - Coast 110 8,0 389,1 2,50 Piangüita - Inland Forest 147 6,5 311,7 -2,32 Piangüita - Coast 123 6,0 289,7 -3,43 Punta Soldado - Inland Forest 84 8,5 435,8 4,43 Punta Soldado- Coast 46 9,0 484,5 4,90 General 690 345,5 Null hypothesis H₀: All medians are equal. Alternate hypothesis H₁: At least one median is different Method GL H Value P Value Not adjusted for ties 7 93.64 0,000 Tight for ties 7 94.09 0,000 Analysis of the IPAS INDEX With 25% of the indices below 2.5, 50% below 6.5 and 75% below 7, there is greater variability towards the lower values of the IPAS index. (Fig. 4 ). Table 4 shows the ANOVA of a hierarchical experiment to evaluate the IPAS index. The model satisfies the error assumptions of both normality and homogeneity of variances at significance levels greater than 15%. The ANOVA shows that, at a significance level of 0.015 (p-value), there are differences between sectors (coastal and interior forest) nested within localities (Punta soldado, Agua dulce and Islalba). There are also differences between localities (p-value 0.000). It was also found that the sum of the squares of the treatments is greater than the sum of the squares of the errors, indicating a good control of the experiment (Table 4 ), the variation is explained by the two factors considered (R 2 = 0.906). Table 4 Analysis of Variance IPAS index Source GL Sum of squares Mean squares F Value P Value Locality 3 119,79 39,93 45,63 0,000 Sector(locality) 4 15,17 3.79 4.33 0,015 Error 16 14,00 0,875 Total 23 148,96 R 2 90,6% Given the differences between the sectors (coastal and interior forest) nested within the localities (Punta Soldier, Agua Dulce and Islaba), we performed a Fisher's LSD post-anova test (Table 5 , Fig. 5 ), which allows us to compare pairs of means and identify groups with statistically significant differences at a 95% confidence level. The highest indices and statistically equal effect on the IPAS index are found in group A. Note that the lowest values of the IPAS index are found at the Soldier Point station, both in the forest and on the coast. Table 5 Clustering information using Fisher's LSD method and 95% confidence SECTOR (LOCALITY) N Average Aggrupation Coast (Aguadulce) 3 8,33 A Inland Forest (Islalba) 3 7,00 A B Coast (Islalba) 3 6,67 B Coast (Pianguita) 3 6,67 B Inland Forest (Aguadulce) 3 5,67 B C Inland Forest (Pianguita) 3 5,00 C Inland Forest (Punta Soldado) 3 1,67 D Coast (Punta Soldado) 3 1,33 D Anthropogenic disturbance index (ADI) The magnitude of the carbon stock in aerial biomass was inversely related to the anthropogenic disturbance index, which was highest (ADI = 8) close to the city of Buenaventura (Fig. 6 ). The mangrove with the greatest quantity of stored carbon and furthest from the city, Punta Soldado, presented the lowest disturbance value (ADI = 2) of all sampled sites. The ADI value oscillates between 0 and 12. Values close to 0 indicate a low level of disturbance, whereas mangroves with ADI close to 12 are considered more disturbed. This index took into account data on the composition and structure of the forests. The comparison of locations in terms of ADI did not show human structures in any of the sampled sites. Solid wastes and human footprints did not show a pattern related to the distance from populated areas. Aguadulce and Islalba had the greatest concentration of solid wastes (Table 6 ) and Aguadulce and Piangüita had the greatest evidence of footprints. The logging categoric variable of the disturbance index reflects the permanent logging pressure on mangroves in Islalba and Piangüita. Some of the probable stressors observed for mangroves in the Bay of Buenaventura included tourism in the mangroves of Piangüita and in the port, and residential and commercial activities in the mangroves of Aguadulce and Islalba (Fig. 6 ). Some natural and anthropogenic effects were observed on the mangroves of the bay (Fig. 6 ). Table 6 Anthropogenic disturbance index (ADI) and categorical variables of disturbance by mangrove zone (interior: towards the forest, exterior: towards the coastline) for four sampled mangrove forests in the Bay of Buenaventura, Colombian Pacific. Location Mangrove Trampling Logging Wastes Structures ADI Aguadulce (RrmA) Interior 2 1 3 0 6 Exterior 3 2 3 0 8 Islalba (RrmA) Interior 1 3 3 0 7 Exterior 1 3 3 0 7 Pianguita (RfA) Interior 3 1 1 0 5 Exterior 3 3 1 0 7 Punta Soldado (PfA) Interior 0 1 1 0 2 Exterior 0 1 1 0 1 The anthropogenic pressures on the mangroves of the study area were quantified by calculating the ADI for each location. Index values ranged between 2 and 8. The mangrove forest at Punta Soldado (ADI = 2) showed the lowest signs of disturbance, whereas the mangrove forest at Aguadulce (ADI = 8) showed the greatest signs of impact (Fig. 7 ). The locations where impacts from wastes or garbage were more evident were Aguadulce and Islalba, with scores of 3 for this variable, whereas logging was the most important category for Islalba and Piangüita (Fig. 7 ). Footprints were more evident at Aguadulce and Piangüita, which are the closest, by water for the first and by land for the second, to populated areas. ADI scores were highest at Aguadulce and Islalba, which were categorized as peri-urban under the residential rainfed mosaic anthrome (Fig. 7 ). These scores were lower in rural contexts, such as the Punta Soldado mangrove, categorized as populated forest anthrome (forest with human populations and agriculture). After comparing the total carbon stock (Mg C/ha) and the ADI for each of the sampled mangroves, the greatest value of disturbance (ADI = 8) corresponded to the mangrove with the lowest total carbon stock, Aguadulce (Fig. 6 ). On the contrary, the mangrove with the greatest quantity of carbon stock, Punta Soldado, presented the lowest value of disturbance of all sampled sites. This suggests, therefore, a type of descriptive negative relationship between human pressure and the amount of carbon stocked in the mangroves of the Bay of Buenaventura (Fig. 6 ). On the other hand, the locations of Islalba and Piangüita had the same ADI value (7), but the value of stocked carbon was greater at Islalba, which suggests that differences could lie in other variables that make up the ADI or in other physiographic characteristics (Fig. 6 ). Human structures such as fences, drainage canals, and small cement structures were not observed in the studied mangroves, whereas waste showed a pattern related to the distance from the city of Buenaventura (Fig. 7 ). The pattern was more evident when we compared the coastline and the mangrove interior. The greatest ADI value (8) was observed in the mangrove closest to the city of Buenaventura, Aguadulce, which was also closest to the coastline. The lowest ADI value was found in the coastal area of Punta Soldado (ADI = 1) (Table 6 ). The comparison of the areas of development of the mangrove trees (interior: forest; exterior: coastline) showed that Punta Soldado was the only sampled location where ADI values were higher in the forest interior than in the exterior (Table 6 ). DISCUSSION The spatial distribution of mangroves in the study area forms distinct patches in the landscape that are delimited by the availability of subterranean and superficial water flow along coastal waters influenced by semidiurnal tides (Sandoval & Gómez-Valdés, 1997). The mangrove patches are significantly influenced by human activities, hence their classification as peri-urban wetlands (Lee et al, 2014 ). The greatest carbon stocks in the studied mangrove forests were found in sediment. These stocks were significantly greater at Piangüita and at Punta Soldado. These results are similar to values observed in other areas of tropical mangrove, where the greatest carbon stocks were found in subterranean biomass (for example at Quebrada Valencia (130.3 Mg C/ha; Palacios 2017 ) and at La Paz, Mexico (175 Mg C/ha; Ochoa-Gómez et al. 2019 ). This pattern of carbon stocks in the study area could be explained by the combination of nutrient inputs from alluvial material and the action of the tides, which allows assimilation by mangrove plants (Saintilan et al., 2013; Tue et al., 2012). Mangroves are linked to frequent water inputs from rain or runoff, and this is connected in general with high plant biomass and great productivity, mainly due to conditions of carbon sequestration in tropical coasts (Krauss et al, 2009 ). There was an inverse relationship between carbon accumulated in aerial biomass and index values, with higher carbon stocks at lower index values. This was highly related, in descriptive terms, to the closeness and density of populated areas. This relationship can be explained by the fact that carbon stocks are highly vulnerable to impacts from anthropogenic disturbances such as deforestation, pollution, and inadequate use of soil in mangrove areas (Komiyama et al., 1988 ; Poungparn, 2003), as well as rising sea levels and the availability in terms of quantity and temporality of freshwater (Palacios, 2017 ). The relationship between the size of the reservoir and the state of conservation of the forest corresponds to what was reported by Blanco et al. (2015) for different mangroves in the Gulf of Urabá and the municipality of Turbo, in the same area where Blanco-Libreros and Estrada-Urrea ( 2015 ) compared the anthropogenic disturbance index with the fragmentation dependent on anthromes. The more relevant pressures on mangrove carbon stocks are associated with changes that occur with regional socioeconomic processes (shrimp farming, palm oil, coconut agriculture, or tourism, among others) or with punctual needs of the population, such as wood for construction or fuel (Palacios & Cantera, 2017 ). Maintaining the vegetation structure plays a key role in the sequestration and storage of carbon in the forest ecosystems of the Colombian Pacific. Results reported in this study highlight the importance of formulating policies and mangrove conservation and management plans that recognize human communities as an important element, but not the only element, in the use and management of mangroves. Declarations CRediT authorship contribution statement Martha L. Palacios Peñaranda : Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Enrique Peña Salamanca : Writing – review & editing, Supervision, Resources, Project administration, Methodology, Conceptualization. Marisol Gordillo Suarez : Writing – review & editing, Visualization, Resources, Methodology, Funding acquisition, Formal analysis, Conceptualization. Daniela Enríquez : Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Data curation. Juan Felipe Ortega : Writing – review & editing, Visualization, Validation, Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ACKNOWLEDGMENTS This study was supported by the Universidad Autónoma de Occidente-Colombia, Directorate for Research and Technological Development Code: 18INTER-304. It was undertaken under the framework of the project Assessing the role of benthic and microbial links on carbon dynamics of mangrove forests submitted to distinct levels of anthropogenic stress , with code Univalle CI 71113, Internal Call 105 2017, Office of the Vice-Rector of Research. References Alongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual review of marine science , 6 , 195-219. https://doi.org/10.1146/annurev-marine-010213-135020 Alongi, D. M. (2008). Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1-13.https://doi.org/10.1016/j.ecss.2007.08.024 Blanco-Libreros, J., & Estrada-Urrea, E. (2015). Mangroves on the edge: Anthrome-dependent fragmentation influences ecological condition (Turbo, Colombia, Southern Caribbean). Diversity 7(3), 206-228. https://doi.org/10.3390/d7030206 Blanco Libreros, J.F., Ortiz Acevedo, L.F. & Urrego, L.E. (2015). Reservorios de biomasa aérea y de carbono en los manglares del golfo de Urabá (Caribe colombiano). Actualidades Biológicas 37 (103): 131-141.https://doi.org/10.17533/udea.acbi.328993 Bouillon, S., Borges, A.V., Castañeda‐Moya, E., Diele, K., Dittmar, T., Duke, N.C., Twilley, R. (2008) Mangrove production and carbon sinks: a revision of global budget estimates. Global Biogeochemical Cycles 22:GB2013. 12p.https://doi.org/10.1029/2007GB003052 Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L. M., Hilarides, L., Thomas, N., & Finlayson, C. (2018). The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sensing , 10 (10), 1669. https://doi.org/10.3390/rs10101669 Cantera, J. R., & Blanco, J. F. (2001). The estuary ecosystem of Buenaventura bay, Colombia. In Coastal marine ecosystems of Latin America (pp. 265-280). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04482-7_19 Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D. & Lescure, J. P. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia , 145 (1), 87-99. https://doi.org/10.1007/s00442-005-0100-x Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. Ecology letters , 12 (4), 351-366. https://doi.org/10.1111/j.1461-0248.2009.01285.x Cordero-Llach, L. 1971. Report on a wood testing programme carried out for undp/sf project 234, Inventory and forest demostrations. Panama. Physical and mechanical properties of 113 species. IICA. Instituto interamericano de ciencias agricolas. Turrialba - Costa Rica. DANE – Departamento Administrativo Nacional de Estadística (2017). Proyecciones nacionales y departamentales de población 2005-2020. Bogotá D.C. 300 p. Ellis, E. C., & Ramankutty, N. (2008). Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment , 6 (8), 439-447.https://doi.org/10.1890/070062 ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. Gross, J., Flores, E. & Schwendenmann, L. (2014). Stand Structure and Aboveground Biomass of a Pelliciera rhizophorae Mangrove Forest, Gulf of Monitjo Ramsar Site, Pacific Coast, Panama. Wetlands (2014) 34:55–65. https://doi.org/10.1007/s13157-013-0482-1 Gulh, A. y Leyva, P. (1997). Zonificación Ecológica de Colombia usando las Zonas de Vida de Holdridge. C.D. IDEAM, Bogotá, Colombia. Gutiérrez-Yurritta, P.J. (2018) Antromas : ¿El futuro de la ecología del paisaje? Serendipia. Recuperado de http://www.revistaserendipia.com. Kauffman, J. B., Donato, D. C., & Adame, M. F. (2013). Protocolo para la medición, monitoreo y reporte de la estructura, biomasa y reservas de carbono de los manglares (Vol. 117). CIFOR. Komiyama A, Moriya H, Prawiroatmodjo S, Toma T, Ogino K (1988) Primary productivity of mangrove forest. In: Ogino K, Chihara M (eds) Biological system of mangroves. A report of east Indonesian mangrove expedition 1986. Ehime University, Ehime, pp 97–117 Komiyama, A., Poungparn, S. & Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. J. Trop. Ecol. 21, 471–477. http://doi.org/10.1017/SO266467405002476 Komiyama, A., Ong, J. & Poungparn, S. (2008). Allometry, biomass, and productivity of mangrove forests: A review. Aquatic Botany, 89. 128–137. https://doi.org/10.1016/j.aquabot.2007.12.006 Krauss, K. W., T. Doyle, Doyle, T., C. Swarzenski, From, A., R. Day y Conner, W. 2009. Water Water Level Observations in Mangrove Swamps During Two Hurricanes in Florida Wetlands 29(1):142-149. 2009. https://doi.org/10.1672/07-232.1 Lee, S. Y., Primavera, J. H., Dahdouh‐Guebas, F., McKee, K., Bosire, J. O., Cannicci, S., ... & Mendelssohn, I. (2014). Ecological role and services of tropical mangrove ecosystems: a reassessment. Global Ecology and Biogeography , 23 (7), 726-743.https://doi.org/10.1111/geb.12155 Mejia, L., Molina, M., Sanjuan, A., Grijalba, M., & NIño, L. (2014). Bosque de manglar, un ecosistemas que debemos cuidar. Universidad Jorge Tadeo Lozano, Instituto Colombiano de Desarrollo Rural, Cartagena de Indias. Ochoa-Gómez, J. G., Lluch-Cota, S. E., Rivera-Monroy, V. H., Lluch-Cota, D. B., Troyo-Diéguez, E., Oechel, W., & Serviere-Zaragoza, E. (2019). Mangrove wetland productivity and carbon stocks in an arid zone of the Gulf of California (La Paz Bay, Mexico). Forest Ecology and Management, 442, 135-147.https://doi.org/10.1016/j.foreco.2019.03.059 Ortega-Giraldo, J.F. (2019). Evaluación del almacenamiento de carbono y la estructura vegetal de manglares ubicados en diferentes biomas antrópicos de la bahía de buenaventura, pacífico colombiano. (Tesis de maestría inédita). Programa de Maestría en Desarrollo Sustentable, Universidad del Valle, Cali, Colombia. Palacios, M. L., & Cantera, J. R. (2017). Mangrove timber use as an ecosystem service in the Colombian Pacific. Hydrobiologia , 803 (1), 345-358. https://doi.org/10.1007/s10750-017-3309-x Palacios, M. L. (2017). Evaluación del almacenamiento de Carbono como servicio ecosistémico en bosques de manglar de la Costa Pacífica Colombiana (Tesis doctoral inédita). Programa de Doctorado en Ciencias del Mar, Universidad del Valle, Cali, Colombia. Poungparn, S., Komiyama, A., Patanaponpaipoon, P., Jintana, V., Sangtiean, T., Tanapermpool, P., Piriyayota, S., Maknual, C., Kato, S., 2003. Site-independent allometric relationships for estimating above-ground weights of mangroves. Tropics 12, 147–158.https://doi.org/10.3759/tropics.12.147 Saintilan, N., N. Wilson, Rogers, K., A. Rrajkaran, Krauss K. W. 2014. Mangrove expansion and salt marsh decline at mangrove poleward limits. Global Change Biol 20: 147-157. https://doi.org/10.1111/gcb.12341 Sandoval, F. J., & Valdés, J. G. (1997). Tides and tidal currents in Ensenada de la Paz lagoon, Baja California Sur, Mexico. Geofísica Internacional , 36 (1), 0.https://doi.org/10.22201/igeof. 00167169p.1997.36.1.619 Semana Sostenible (2016). Energía para el progreso. Recuperado de https://sostenibilidad.semana.com/medio-ambiente/multimedia/paneles-solares-en-buenaventura-energia-para-el-progreso/37136. Southwell, C. R., & Bultman, J. D. (1971). Marine borer resistance of untreated woods over long periods of immersion in tropical waters. Biotropica , 81-107. https://doi.org/10.2307/2989709 Superservicios - Superintendencia de Servicios Públicos Domiciliarios (2016). Producción de residuos sólidos y sistemas de tratamiento de agua residuales en los municipios costeros, Bogotá, Colombia. Yáñez-Arancibia, A., Twilley, R. R., & Domínguez, A. L. L. (1998). Los ecosistemas de manglar frente al cambio climático global. Madera y Bosques , 4 (2), 3-19.https://myb.ojs.inecol.mx /index.php/myb/article/view/1356 Tue, N.T., L.V. Dung, M.T. Nhuan, K. Omori. 2014. Carbon storage of a tropical mangrove forest in Mui Ca Mau National Park. Vietnam Catena 121:119–126. https://doi.org/10.1016/j.catena.2014.05.008 Wallace, D. (2000). Capture and storage of CO2, what needs to be done? IEA Greenhouse Gases R & D Programmer. Paris, France. 10 p. Supplementary Files graphicalsummary.pptx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6254743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435958077,"identity":"64b70da8-778f-462b-aafc-b55a7fe28c06","order_by":0,"name":"Marisol Gordillo Suarez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYLACHgMLGQb2BjCbsYFILRI8DDwHSNLCANQikUCkFt3+xcc+vCmQ4OGXfGP44QODjeyGA8wPH+DTYnbjWfLMOUCHSc7OMZacwZBmvOEAm7EBfi1njJlBfjG4nWMgzcNwOHHDAR42CeK03Dxj/JuH4T8RWs73QLXc4DED2nKAGFvYkhnBfulJK7OcYZBsPPMwIb+cP3yY4c0fGzl+9sObb3yosJPtO96MP8Rg0QEFIOOZ8aoHAv4DhFSMglEwCkbBiAcAQRNDIF+EqDUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1602-5547","institution":"Universidad Autónoma de Occidente: Universidad Autonoma de Occidente","correspondingAuthor":true,"prefix":"","firstName":"Marisol","middleName":"Gordillo","lastName":"Suarez","suffix":""},{"id":435958078,"identity":"02a8fb5e-76bf-4460-9e20-1164efe38e9c","order_by":1,"name":"Martha L. 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Photographic record of some effects on mangroves (C) and anthropogenic pressures (A, B, D) observed in the mangrove forests of the Bay of Buenaventura, Colombian Pacific.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6254743/v1/eda7eda04b918fd87a4454e1.jpg"},{"id":81010191,"identity":"bc663626-649b-4184-ace5-8449d2af1e38","added_by":"auto","created_at":"2025-04-21 08:15:22","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":64231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7.\u003c/strong\u003e Anthropogenic disturbance index and categorical variables of disturbance for four mangrove forest sites in the Bay of Buenaventura, Colombian Pacific.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6254743/v1/9e630f5bbdd6e1ab5a0f6cbb.jpg"},{"id":87178065,"identity":"8c197dbf-1bde-4663-8793-34011dd64d34","added_by":"auto","created_at":"2025-07-21 09:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1526862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6254743/v1/e0d391b7-2740-41a9-9be8-0e6738f0efee.pdf"},{"id":81015053,"identity":"32500f83-cb9c-4fe7-b842-f61e8aec0789","added_by":"auto","created_at":"2025-04-21 08:47:23","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":753829,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalsummary.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6254743/v1/4963be807f479f4b941fd15e.pptx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCarbon Dynamics of Mangrove Forests Submitted to Distinct Levels of Anthropogenic Stress\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e- The species spp., , and are dominant in the study area.\u003c/p\u003e\u003cp\u003e- The compartment with the greatest carbon stocks was sediment.\u003c/p\u003e\u003cp\u003e- Carbon stocks were affected by anthropogenic disturbance at the four sampling stations.\u003c/p\u003e\u003cp\u003e- The mangroves closest to the most populated sites presented high levels of anthropogenic disturbance.\u003c/p\u003e\u003cp\u003e- Greater plastic presence in the mangrove ecosystem was linked to lower carbon stocks.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eMangrove ecosystems are found exclusively in the tropical and subtropical areas of the world. They comprise trees growing in coastal areas (Y\u0026aacute;\u0026ntilde;ez-Arancibia et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); they cover approximately 13,760,000 ha of coastal areas worldwide (Bunting et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and store between 10 and 15% of organic carbon (24 Tg C y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in coastal sediments (Alongi. D, 2014). It is estimated that mangroves contribute to carbon sequestration mainly through the production of roots (38%) and wood (31%). Soil also plays an important role in the carbon cycle of the earth, as the main reserve of the terrestrial ecosystem (Alongi D, 2014, Palacios \u0026amp; Cantera, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman impacts cause ecological changes in coastal ecosystems, with different intensities and spatial extensions. These changes affect functional and structural attributes and can lead to a reduction of aerial biomass and of structural forest complexity (Alongi D, 2014), leading to larger natural greenhouse effects and to climate change. The process of climate change comprises several components that influence social and economic aspects because it acts by creating or amplifying floods, tropical storms, rainfall, coastal erosion, and results in changes to the air and water temperatures (Wallace, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Since 1960, close to 57% of mangrove cover has been lost due to transformations of the land for use in agricultural systems, housing construction, hotels, marine ports, canals, dams, roads, and more recently farms that use water and food to raise fish and shrimp (Mej\u0026iacute;a et al., 2014).\u003c/p\u003e \u003cp\u003eHuman activities, such as changes in soil use, alteration of biogeochemical cycles, destruction and fragmentation of habitats, loss of biodiversity, and alterations to climatic conditions, among others, are affecting severely the planet\u0026acute;s ecosystems (Rockstr\u0026ouml;m et al., 2009). In the Bay of Buenaventura in the Colombian Pacific, mangrove forests have been subjected to anthropogenic impacts such as deforestation, wood extraction, water pollution from industrial activities, illegal mining, and solid wastes, among others (Mart\u0026iacute;n-L\u0026oacute;pez et al., 2009, Palacios \u0026amp; Cantera, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiomes are the basic units used to describe global patterns based on vegetation types. They have been identified and mapped based on soil processes, in association with regional climate variations. Ecosystems and biomes are characterized by the distinctive life forms adapted to their bioclimatic conditions. A new way of classifying the large life zones in the world, known as anthromes, incorporates the determinant function of human beings in ecosystem alteration, reflecting global patterns of human interactions with ecosystems (Ellis \u0026amp; Ramankutty, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe hypothesis of the present study was that aerial and subterranean (roots and sediment) organic carbon stocks were significantly different among the study stations and were associated with the variability of the anthropogenic disturbance index (ADI) calculated for the Bay of Buenaventura. In consequence, the objectives of the study were to (1) estimate the values of aerial and subterranean carbon stocks in mangrove forests exposed to different levels of anthropogenic disturbance in the Bay of Buenaventura, and (2) to compare the carbon stocks with the spatial distribution of the ADI in the studied mangroves.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was carried out in the Bay of Buenaventura, Colombian Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plant cover corresponded mainly to tropical wet forest \u003cem\u003ebmh-T\u003c/em\u003e in the Holdridge life zone system (Gulh and Leiva, 1997). This coastal body is a flooded valley dominated by mud plains and sediment accumulations from the Tertiary and Quaternary periods, which allows the presence of typically riparian and fringe edge mangrove forests, among which the main species are \u003cem\u003eRhizophora\u003c/em\u003e spp., \u003cem\u003eLaguncularia racemosa, Avicennia germinans, Pelliciera rhizophorae, Concocarpus erectus\u003c/em\u003e, and \u003cem\u003eMora oleifera\u003c/em\u003e (Cantera \u0026amp; Blanco, 2000). The tidal pattern in this area is multivariable with a range of between 3 and 4 m (Cantera \u0026amp; Blanco, 2000). Water temperature is 27.4\u0026ordm;C on average and salinity decreases during the rainy periods. Precipitation in this area is bimodal; the wettest month is November with 835 mm water column, and the driest months are January to March with 320 mm water column (Cantera \u0026amp; Blanco, 2000). The average precipitation in this area is above 6,000 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which makes it one of the rainiest areas in the world (Cantera \u0026amp; Blanco, 2000).\u003c/p\u003e \u003cp\u003eThe coastal zone of the Valle del Cauca Department shows a differentiated matrix of use and occupation of the territory. The city of Buenaventura and scattered population in the bay constitute the densest population nucleus, with 415,770 projected residents for 2017, (DANE, 2017). Of this total population, the following densities were found for the locations along the bay: the township of Baz\u0026aacute;n, which includes Piang\u0026uuml;ita, Vista Hermosa, La Bocana, and Changai, has approximately 1,700 fixed residents and an indeterminate number of floating residents resulting from tourism (Palacios \u0026amp; Cantera, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The township of Punta Soldado had 363 inhabitants grouped in 114 families in the year 2016 (Semana Sostenible, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField work\u003c/h3\u003e\n\u003cp\u003eThe study area was categorized according to the anthrome classification system proposed by Ellis \u0026amp; Ramankutty (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Four mangrove forests, each located within distinct anthropogenic biomes in the bay, were selected for analysis. The anthromes identified in the study area include Dense Settlements (DSA), Residential Rainfed Mosaic (RrmA), Populated Forest (PfA), and Remote Forest (RfA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The global anthrome map was adjusted for the Bay of Buenaventura using the WGS 1984 geographic coordinate system.\u003c/p\u003e \u003cp\u003eThe classification spans mangrove forests near densely populated urban centers, such as the city of Buenaventura, through areas with moderate agricultural and livestock activity, to remote forests with minimal human disturbance (Guti\u0026eacute;rrez-Yurrita, 2018). The linear distance from the nearest human settlement to the selected mangrove areas was determined using ArcGIS Desktop version 10.5 (ESRI, 2016). For the city of Buenaventura, the sampling point was located at the tourist pier. The selected sampling locations were as follows: Aguadulce (3\u0026deg;53'35\" N \u0026minus;\u0026thinsp;77\u0026deg;05'47\" W), located 1,823 m from the city of Buenaventura; Islalba (3\u0026deg;52'25\" N \u0026minus;\u0026thinsp;77\u0026deg;05'47\" W), located 1,767 m from the city; Piang\u0026uuml;ita (3\u0026deg;50'21\" N \u0026minus;\u0026thinsp;77\u0026deg;11'48\" W), located 452 m from Piang\u0026uuml;ita township; and Punta Soldado (3\u0026deg;46'06\" N \u0026minus;\u0026thinsp;77\u0026deg;10'05\" W), located 2,837 m from La Bocana.\u003c/p\u003e\n\u003ch3\u003eAerial biomass\u003c/h3\u003e\n\u003cp\u003eThe protocol proposed by Kauffman et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was followed to measure, monitor, and report on the structure, biomass, and carbon stock of mangroves. At each sampling station, three replicate plots (154 m\u003csup\u003e2\u003c/sup\u003e) were established to analyze the interior and exterior of the mangrove forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plots were separated 25 to 50 m apart; the distance between the interior and exterior forest was approximately 25 m, depending on the length and width of the mangrove forest at each location.\u003c/p\u003e \u003cp\u003eA modification to the sampling strategy (Kauffman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was carried out because the mangrove forest fringe is very narrow in the bay and did not allow us to adopt completely the distance and location of plots suggested in the protocol. Plots were located using a decameter and a Leika model Disto-d2 laser distance meter. The height of each individual tree was measured using a Hagl\u0026ouml;f model HCC digital clinometer.\u003c/p\u003e \u003cp\u003eThe vegetation at each sampling site was quantified in terms of aerial and subterranean biomass to assess the carbon stock component; the protocol considers that these clusters are sufficiently large, with available information comparable to other mangrove areas. These compartments are affected by the use or exploitation of the soil. Leaf litter is usually of little importance and was excluded from analysis in this study (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe basic data recorded in this study included identification of the species, diameter of the main trunk (in cm), total tree height, and whether the tree was dead or alive. Diameter at breast height (DBH) of the trunk was measured 1.37 m from the ground (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given the particularities of mangroves, for trees with roots above 1.37 m in height or for trees with aerial roots (e.g., \u003cem\u003eRhizophora\u003c/em\u003e spp.), DBH was measured above the highest root. For individuals with roots reaching the tree crown, DBH was measured above aerial roots where a true trunk was found (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). All trees with over 50% of the main trunk within the plot diameter were measured (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aboveground biomass (AB) was calculated using allometric equations based on models developed by Chave et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), cited in Komiyama et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), to estimate the AB biomass in tropical mangroves [Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003e] and on the model proposed by Komiyama et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) to estimate belowground biomass (BB) [Eq.\u0026nbsp;2]. We decided to use the models developed by Chave et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) due to the robustness of their estimations and the replicability of methods in a large number of studies.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Equation\\:\\left[1\\right]\\:\\:\\:BA=0.168*{\\rho\\:}^{2.471}\\:\\:\\:\\left({r}^{2}=0.99,\\:n=84,\\:\\:DHBmax=50\\:cm\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Equation\\:\\left[2\\right]\\:\\:\\:BB=0.199*{\\rho\\:}^{0.899}*{DAP}^{2.22}\\:\\:\\:\\left({r}^{2}=0.95,\\:n=26,\\:DHBmax=45\\:cm\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo find the value of AB and BB the specific density of the wood of each mangrove species was necessary (ρ\u0026thinsp;=\u0026thinsp;g/cm\u003csup\u003e3\u003c/sup\u003e). In this case, the values used for the genus \u003cem\u003eRhizophora\u003c/em\u003e spp. (0,88 g/cm\u003csup\u003e3\u003c/sup\u003e) and for \u003cem\u003eAvicennia germinans\u003c/em\u003e (0.67 g/cm\u003csup\u003e3\u003c/sup\u003e) were obtained from the Global Wood Density Database (Chave et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); the value used for \u003cem\u003eMora oleifera\u003c/em\u003e (0,74 g/cm\u003csup\u003e3\u003c/sup\u003e) was obtained from Cordero (1971), cited by Gross et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); and the value used for \u003cem\u003ePelliciera rhizophorae\u003c/em\u003e (0,75 g/cm\u003csup\u003e3\u003c/sup\u003e) was obtained from Southwell and Bultman (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), cited by Gross et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe AB and BB were converted to carbon mass (Mg/ha) using carbon conversion proportions of 41.5% for AB (Bouillon et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and of 39% for BB (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These biomass values were converted to equivalent CO\u003csub\u003e2\u003c/sub\u003e by multiplying by a factor of 3.67 (Kauffman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The total carbon stock in the ecosystem was defined as the total sum of organic carbon in the vegetation (above and below ground).\u003c/p\u003e \u003cp\u003eThe aerial biomass was estimated from general allometric equations for mangroves in tropical areas (Komiyana et al., 2008). These equations took into account the values of DBH recorded at a different height for each tree species inside the plots and the density data for each species reported in the Global Wood Density Database (Chave et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eOrganic carbon in sediment\u003c/h3\u003e\n\u003cp\u003eSediment samples were collected at each plot using a 1 m high open-end soil probe with 1,963.5 cm\u003csup\u003e3\u003c/sup\u003e volume. The soilsediment core was divided into three sections with depth intervals of 0\u0026ndash;33 cm, 34\u0026ndash;67 cm, and 67\u0026ndash;100 cm. Samples were stored in tagged plastic bags and taken to the laboratory for later analysis. Samples were dried at 105\u0026ordm;C.\u003c/p\u003e\n\u003ch3\u003eAnthropogenic disturbance index\u003c/h3\u003e\n\u003cp\u003eThe anthropogenic disturbance index (ADI) [Eq.\u0026nbsp;3] (Blanco-Libreros and Estrada-Urrea, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Ortega Giraldo, 2019) was used to evaluate human impacts at each location, according to the following categoric variables: 1) Trampling (T) \u0026ndash; presence of human and livestock footprints, 2) Logging (L) \u0026ndash; evidence of selective logging of mangroves, such as stumps and downed wood, 3) Wastes (W) \u0026ndash; presence of wastes on the forest floor and roots, and 4) Structures (S) \u0026ndash; evidence of human modifications to the hydrology and topography of mangroves. Each variable was assigned a score of 0 to 3, where 0 is the absence of disturbance, 1 is little evidence of disturbance, 2 is evidence of disturbance, and 3 is maximum evidence of disturbance. The scores of each variable were added as follows:\u003c/p\u003e \u003cp\u003eEquation [3]: ADI\u0026thinsp;=\u0026thinsp;T\u0026thinsp;+\u0026thinsp;L\u0026thinsp;+\u0026thinsp;W\u0026thinsp;+\u0026thinsp;S\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Design\u003c/h2\u003e \u003cp\u003eWe used a nested or hierarchical design in two stages. As a result of the experiment we measured DBH, Height, Mg C and IPAS index as factors we considered the sector at 2 levels (1: coast and 2: interior of the forest) nested within the localities at 3 levels (1: Punta Soldado, 2: Agua Dulce and 3: Islaba), we have 3 sampling points within each locality.\u003c/p\u003e \u003cp\u003eThe model proposed for the hierarchical design is presented in Eq.\u0026nbsp;4, where y\u003csub\u003eijk\u003c/sub\u003e is the response variable (DBH, height, Mg C and IPAS index), \u0026micro; is the overall mean, τ\u003csub\u003ei\u003c/sub\u003e is the effect of the locality (1: Punta soldado, 2: Agua dulce and 3: Islaba), β\u003csub\u003ej\u003c/sub\u003e is the effect of the sector (1: coast and 2: forest interior).\u003c/p\u003e \u003cp\u003eEquation [4] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{y}}_{\\text{i}\\text{j}\\text{k}}={\\mu\\:}+{{\\tau\\:}}_{\\text{i}}+{{\\beta\\:}}_{\\text{j}\\left(\\text{i}\\right)}+{\\text{ϵ}}_{\\left(\\text{i}\\text{j}\\right)\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThis model must satisfy the error assumptions, i.e. εij \u0026sim;N (0, σ\u003csup\u003e2\u003c/sup\u003e), with σ\u003csup\u003e2\u003c/sup\u003e constant and Cov (εijk, εi'j'k')\u0026thinsp;=\u0026thinsp;0 \u0026forall;i\u0026ne;i'; j\u0026ne;j'; k\u0026ne;k'. The hypotheses associated with the hierarchical design state that H\u003csub\u003e0\u003c/sub\u003e: (τ)\u003csub\u003ei\u003c/sub\u003e =0 and H\u003csub\u003e0\u003c/sub\u003e:(β)\u003csub\u003ej(i)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.\u003c/p\u003e \u003cp\u003eThe method used to compare the means of the treatments is called ANALYSIS OF VARIANCE (ANOVA), which is used to test the stated hypotheses. The traditional analysis of variance approach to analyse a two level hierarchical experiment. The corresponding calculations were carried out using Minitab 19 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnthromes\u003c/h2\u003e \u003cp\u003eThe area was categorized in anthromes, following the proposal by Ellis \u0026amp; Ramankutty (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Four mangrove forests located in different anthropogenic biomes in the bay were selected. The anthromes found in the study area were Dense settlements (DSA), Residential rainfed mosaic (RrmA), Populated forest (PfA), and Remote forest (RfA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The worldwide anthrome map was adjusted to the Bay of Buenaventura using the global projection WGS 1984 geographic coordinate system.\u003c/p\u003e \u003cp\u003eAnthrome classification comprised from mangroves close to densely populated urban cores, such as the city of Buenaventura, to mangroves close to areas with sparsely populated forest, passing through a range of agricultural and livestock raising scenarios and natural areas with differing degrees of construction for diverse activities (Guti\u0026eacute;rrez-Yurrita, 2018). Each sampling site was named according to the closest human settlement or sampled area. The linear distance from the closest human settlement to the selected mangrove area was calculated with the geographic information processing software ArcGIS Desktop ver. 10.5 (ESRI, 2016). In the case of the city of Buenaventura, the selected location was the tourist pier. The selected sampling locations were: Aguadulce (3\u0026deg;53\u0026acute;35\" N \u0026minus;\u0026thinsp;77\u0026deg;05\u0026acute;47\" W) located 1,823 m from the city of Buenaventura; Islalba (3\u0026deg;52\u0026acute;25\" N \u0026minus;\u0026thinsp;77\u0026deg;05\u0026acute;47\"W) located 1,767 m from the city of Buenaventura; Piang\u0026uuml;ita (3\u0026deg;50\u0026acute;21\" N \u0026minus;\u0026thinsp;77\u0026deg;11\u0026acute;48\" W) located 452 from the Piang\u0026uuml;ita township; and Punta Soldado (3\u0026deg;46\u0026acute;06\" N \u0026minus;\u0026thinsp;77\u0026deg;10\u0026acute;05\" W) located 2,837 m from la Bocana.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnthropogenic biomes corresponding to each sampled location were identified as follows. Aguadulce and Islalba were categorized as residential rainfed mosaic anthrome (RrmA) in a peri-urban context. Impacts were lower in rural contexts; Punta soldado belongs to the forest reserve R\u0026iacute;o Anchicay\u0026aacute; National Park and was categorized as populated forest anthrome (Pfa); Piang\u0026uuml;ita was categorized as remote forest anthrome (RfA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAerial biomass\u003c/h2\u003e \u003cp\u003eMangrove forests in the Bay of Buenaventura were represented by four dominant species: \u003cem\u003eRhizophora\u003c/em\u003e spp., \u003cem\u003ePelliciera rhizophorae, Mora oleifera\u003c/em\u003e, and \u003cem\u003eAvicennia germinans\u003c/em\u003e, in order of abundance. The contribution of carbon stock on the surface of live trees was approximately 138.6 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at Aguadulce, 199.2 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at Islalba, 151.6 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at Piang\u0026uuml;ita, and 235.7 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at Punta Soldado (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Carbon accumulation in aerial biomass was lowest at Aguadulce. Punta Soldado had high average carbon accumulation values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubterranean biomass\u003c/h2\u003e \u003cp\u003eThe greatest average subterranean biomass stocks (roots \u0026ndash; sediment) were found at Piang\u0026uuml;ita (6,641 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); this value was statistically similar to that observed at Punta Soldado (6,105 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of variance (ANOVA) model variables DBH, Height and Mg C.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe models proposed for the three variables (DBH, Height and Mgc) do not comply with normality or homogeneity of variance, so we applied the Box-Cox transformation, a technique that suggests possible transformations of the variables in order to have a normal behaviour that guarantees unbiased parameter estimation and minimum variance. Using the Box-Cox transformation, for each variable we obtained:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDBH: a 95% confidence interval was found for λ between (-0.06 to 0.16)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHeight: A 95% confidence interval for λ was found between (0.02 to 0.25)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMg C: A 95% confidence interval was found for λ between and (-0.02 to 0.07)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eNone of the intervals includes 1, indicating that transformation is appropriate for these variables. The estimated value of the optimal λ for the variable DBH is 0.06, for height it is 0.13 and for Mg C it is 0.02, so for each variable the following transformation is applied: DAP\u003csup\u003e0.06\u003c/sup\u003e; and for Mg C\u003csup\u003e0.02\u003c/sup\u003e. In the case of the height variable, it was not possible to obtain a value of λ that met the assumptions, so this variable is analyzed using non-parametric statistics.These transformations effectively achieved the desired normality and homogeneity of variances for DAT and Mg C in the errors of the ANOVA models for these variables at significance levels greater than 15%.\u003c/p\u003e \u003cp\u003eIn the analysis of variance of the DAT and Mg C variables, it was found that there were differences between the nested sectors within each locality at a significance level of less than 0.001 (p-value), both for the DAT variable and for Mg C. There were also differences between localities at a significance level of 0.000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It was also found that the sum of the squares of the treatments is not greater than the sum of the squares of the errors, which is to be expected because it is very difficult to control the experiment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA of the variables DAT y Mg C\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eDAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eMg C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOURCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e29,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (locality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGiven the differences between the sectors (coastal and interior forest) nested within the localities (Punta Soldado, Agua Dulce and Islalba), we performed a Fisher's LSD post-annova test (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and found that there are three main groups (A, B, C) that classify the sectors nested within the localities according to the DAT averages. The localities of Punta Soldado and Aguadulce tend to have higher DAT, both on the coast and inside the forest (group A), while the localities of Islalba and Piang\u0026uuml;ita have the lowest values, both on the coast and inside the forest (group C).\u003c/p\u003e \u003cp\u003eWith regard to Mg C, there are two main groups (A, B) that coincide with the sectors nested within the locality with the DAT variable, in addition to this group Costa (Islalba).\u003c/p\u003e \n\u003cp style='margin:0in;text-align:justify;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:6.0pt;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eTable 2.\u003c/span\u003e\u003c/strong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp; Grouping of information using Fisher\u0026apos;s LSD method with 95% confidence.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border: none;width:418.2pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.8pt;border: 1pt solid windowtext;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 27.8pt;border: 1pt solid windowtext;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 113.65pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid black;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eDAT\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2pt;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 26.95pt;border: 1pt solid windowtext;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 113.4pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid black;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eMg C\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:134.4pt;border:solid windowtext 1.0pt;padding:0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eSECTOR (LOCALIDAD)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: 0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eN\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.35pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eAverage\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:64.3pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid black 1.0pt;padding:0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eAggrupation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2pt;padding: 0in;height: 29.25pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:26.95pt;border:solid windowtext 1.0pt;border-top: none;padding:0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eN\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:42.5pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: 0in 0in 0in 0in;height:29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eAverage\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:70.9pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid black 1.0pt;padding:0in 0in 0in 0in;height: 29.25pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eAggrupation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:134.4pt;border:solid windowtext 1.0pt;border-top: none;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eCoast ( Punta Soldado)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: .75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.35pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e10.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:21.1pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;background: #F2F2F2;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:justify;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:justify;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2pt;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 26.95pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 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242, 242);padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eB\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:134.4pt;border:solid windowtext 1.0pt;border-top: none;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eCoast (Islalba)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: .75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp 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bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 26.95pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e110\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e1.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.45pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(242, 242, 242);padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.45pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:134.4pt;border:solid windowtext 1.0pt;border-top: none;padding:.75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eInland Forest (Islalba)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: .75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e112\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.35pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:.75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp 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bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eB\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:134.4pt;border:solid windowtext 1.0pt;border-top: none;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eInland Forest (Piang\u0026uuml;ita)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: .75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e148\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.35pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:.75pt 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e5.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:21.1pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: 0in 0in 0in 0in;height:15.75pt;\"\u003e\n \u003cp 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15.75pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 26.95pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e148\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 15.75pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eCoast (Piang\u0026uuml;ita)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:27.8pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: .75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e123\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.35pt;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:.75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e5.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:21.1pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: 0in 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:21.1pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding: 0in 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:22.1pt;border-top:none;border-left:none;border-bottom: solid windowtext 1.0pt;border-right:solid windowtext 1.0pt;padding:.75pt 0in 0in 0in;height:16.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eC\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2pt;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 26.95pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e123\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e0.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.45pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 35.45pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(242, 242, 242);padding: 0in;height: 16.5pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eB\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;text-align:justify;line-height:normal;font-size:16px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNon-parametric analysis for the height variable\u003c/h2\u003e \u003cp\u003eThe Kruskal-Wallis non-parametric technique is used to determine whether there are significant differences between the medians, so the null hypothesis is that the population means are all equal or different. The results are shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and it was found that with a significance level of (0.000), both the null hypothesis without adjustment for ties and with adjustment for ties (a tie occurs when the same value occurs in more than one sample), the null hypothesis is rejected, i.e. not all population medians are equal.\u003c/p\u003e \u003cp\u003eThe estimates of the sample medians for the treatments are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Z-value is used to compare the mean rank of each group with the mean rank of all observations, i.e. (the furthest apart are the most different):\u003c/p\u003e \u003cp\u003eIslalba-Interior Forest treatment, has the highest Z value, /-5.87/, indicating that it is the furthest from the overall average rank range, followed by Punta Soldado - Coast /4.90/ and Punta Soldado - Interior Forest /4.43/.\u003c/p\u003e \u003cp\u003eThe negative Z-value indicates that the mean rank of the Islalba interior forest treatment is lower than the overall mean rank, while the positive Z-values of the Punta Soldado-coast /4.90/ and Punta Soldado - interior forest /4.43/ treatments indicate that the mean rank of these treatments is higher than the overall mean rank.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKruskal \u0026ndash; Wallis test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLOCALITY-SECTOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eClassification of averages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eZ Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAguadulce - Inland Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e408,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAguadulce - Coast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e404,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIslalba - Inland Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e243,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-5,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIslalba - Coast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e389,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePiang\u0026uuml;ita - Inland Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e311,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-2,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePiang\u0026uuml;ita - Coast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e289,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-3,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePunta Soldado - Inland Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e435,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePunta Soldado- Coast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e484,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e345,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNull hypothesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eH₀: All medians are equal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlternate hypothesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eH₁: At least one median is different\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eH Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot adjusted for ties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e93.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTight for ties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e94.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the IPAS INDEX\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith 25% of the indices below 2.5, 50% below 6.5 and 75% below 7, there is greater variability towards the lower values of the IPAS index. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the ANOVA of a hierarchical experiment to evaluate the IPAS index. The model satisfies the error assumptions of both normality and homogeneity of variances at significance levels greater than 15%. The ANOVA shows that, at a significance level of 0.015 (p-value), there are differences between sectors (coastal and interior forest) nested within localities (Punta soldado, Agua dulce and Islalba). There are also differences between localities (p-value 0.000).\u003c/p\u003e \u003cp\u003eIt was also found that the sum of the squares of the treatments is greater than the sum of the squares of the errors, indicating a good control of the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the variation is explained by the two factors considered (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.906).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Variance IPAS index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector(locality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eR\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e 90,6%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGiven the differences between the sectors (coastal and interior forest) nested within the localities (Punta Soldier, Agua Dulce and Islaba), we performed a Fisher's LSD post-anova test (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which allows us to compare pairs of means and identify groups with statistically significant differences at a 95% confidence level. The highest indices and statistically equal effect on the IPAS index are found in group A. Note that the lowest values of the IPAS index are found at the Soldier Point station, both in the forest and on the coast.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClustering information using Fisher's LSD method and 95% confidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSECTOR (LOCALITY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAggrupation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast (Aguadulce)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInland Forest (Islalba)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast (Islalba)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast (Pianguita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInland Forest (Aguadulce)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInland Forest (Pianguita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInland Forest (Punta Soldado)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast (Punta Soldado)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnthropogenic disturbance index (ADI)\u003c/h2\u003e \u003cp\u003eThe magnitude of the carbon stock in aerial biomass was inversely related to the anthropogenic disturbance index, which was highest (ADI\u0026thinsp;=\u0026thinsp;8) close to the city of Buenaventura (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The mangrove with the greatest quantity of stored carbon and furthest from the city, Punta Soldado, presented the lowest disturbance value (ADI\u0026thinsp;=\u0026thinsp;2) of all sampled sites.\u003c/p\u003e \u003cp\u003eThe ADI value oscillates between 0 and 12. Values close to 0 indicate a low level of disturbance, whereas mangroves with ADI close to 12 are considered more disturbed. This index took into account data on the composition and structure of the forests.\u003c/p\u003e \u003cp\u003eThe comparison of locations in terms of ADI did not show human structures in any of the sampled sites. Solid wastes and human footprints did not show a pattern related to the distance from populated areas. Aguadulce and Islalba had the greatest concentration of solid wastes (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and Aguadulce and Piang\u0026uuml;ita had the greatest evidence of footprints.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe logging categoric variable of the disturbance index reflects the permanent logging pressure on mangroves in Islalba and Piang\u0026uuml;ita. Some of the probable stressors observed for mangroves in the Bay of Buenaventura included tourism in the mangroves of Piang\u0026uuml;ita and in the port, and residential and commercial activities in the mangroves of Aguadulce and Islalba (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Some natural and anthropogenic effects were observed on the mangroves of the bay (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropogenic disturbance index (ADI) and categorical variables of disturbance by mangrove zone (interior: towards the forest, exterior: towards the coastline) for four sampled mangrove forests in the Bay of Buenaventura, Colombian Pacific.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMangrove\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrampling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLogging\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWastes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStructures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAguadulce (RrmA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIslalba (RrmA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePianguita (RfA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePunta Soldado (PfA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe anthropogenic pressures on the mangroves of the study area were quantified by calculating the ADI for each location. Index values ranged between 2 and 8. The mangrove forest at Punta Soldado (ADI\u0026thinsp;=\u0026thinsp;2) showed the lowest signs of disturbance, whereas the mangrove forest at Aguadulce (ADI\u0026thinsp;=\u0026thinsp;8) showed the greatest signs of impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The locations where impacts from wastes or garbage were more evident were Aguadulce and Islalba, with scores of 3 for this variable, whereas logging was the most important category for Islalba and Piang\u0026uuml;ita (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Footprints were more evident at Aguadulce and Piang\u0026uuml;ita, which are the closest, by water for the first and by land for the second, to populated areas.\u003c/p\u003e \u003cp\u003eADI scores were highest at Aguadulce and Islalba, which were categorized as peri-urban under the residential rainfed mosaic anthrome (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These scores were lower in rural contexts, such as the Punta Soldado mangrove, categorized as populated forest anthrome (forest with human populations and agriculture).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter comparing the total carbon stock (Mg C/ha) and the ADI for each of the sampled mangroves, the greatest value of disturbance (ADI\u0026thinsp;=\u0026thinsp;8) corresponded to the mangrove with the lowest total carbon stock, Aguadulce (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). On the contrary, the mangrove with the greatest quantity of carbon stock, Punta Soldado, presented the lowest value of disturbance of all sampled sites. This suggests, therefore, a type of descriptive negative relationship between human pressure and the amount of carbon stocked in the mangroves of the Bay of Buenaventura (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). On the other hand, the locations of Islalba and Piang\u0026uuml;ita had the same ADI value (7), but the value of stocked carbon was greater at Islalba, which suggests that differences could lie in other variables that make up the ADI or in other physiographic characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman structures such as fences, drainage canals, and small cement structures were not observed in the studied mangroves, whereas waste showed a pattern related to the distance from the city of Buenaventura (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The pattern was more evident when we compared the coastline and the mangrove interior. The greatest ADI value (8) was observed in the mangrove closest to the city of Buenaventura, Aguadulce, which was also closest to the coastline. The lowest ADI value was found in the coastal area of Punta Soldado (ADI\u0026thinsp;=\u0026thinsp;1) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe comparison of the areas of development of the mangrove trees (interior: forest; exterior: coastline) showed that Punta Soldado was the only sampled location where ADI values were higher in the forest interior than in the exterior (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe spatial distribution of mangroves in the study area forms distinct patches in the landscape that are delimited by the availability of subterranean and superficial water flow along coastal waters influenced by semidiurnal tides (Sandoval \u0026amp; G\u0026oacute;mez-Vald\u0026eacute;s, 1997). The mangrove patches are significantly influenced by human activities, hence their classification as peri-urban wetlands (Lee et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe greatest carbon stocks in the studied mangrove forests were found in sediment. These stocks were significantly greater at Piang\u0026uuml;ita and at Punta Soldado. These results are similar to values observed in other areas of tropical mangrove, where the greatest carbon stocks were found in subterranean biomass (for example at Quebrada Valencia (130.3 Mg C/ha; Palacios \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and at La Paz, Mexico (175 Mg C/ha; Ochoa-G\u0026oacute;mez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This pattern of carbon stocks in the study area could be explained by the combination of nutrient inputs from alluvial material and the action of the tides, which allows assimilation by mangrove plants (Saintilan et al., 2013; Tue et al., 2012). Mangroves are linked to frequent water inputs from rain or runoff, and this is connected in general with high plant biomass and great productivity, mainly due to conditions of carbon sequestration in tropical coasts (Krauss et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere was an inverse relationship between carbon accumulated in aerial biomass and index values, with higher carbon stocks at lower index values. This was highly related, in descriptive terms, to the closeness and density of populated areas. This relationship can be explained by the fact that carbon stocks are highly vulnerable to impacts from anthropogenic disturbances such as deforestation, pollution, and inadequate use of soil in mangrove areas (Komiyama et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Poungparn, 2003), as well as rising sea levels and the availability in terms of quantity and temporality of freshwater (Palacios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The relationship between the size of the reservoir and the state of conservation of the forest corresponds to what was reported by Blanco et al. (2015) for different mangroves in the Gulf of Urab\u0026aacute; and the municipality of Turbo, in the same area where Blanco-Libreros and Estrada-Urrea (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) compared the anthropogenic disturbance index with the fragmentation dependent on anthromes.\u003c/p\u003e \u003cp\u003eThe more relevant pressures on mangrove carbon stocks are associated with changes that occur with regional socioeconomic processes (shrimp farming, palm oil, coconut agriculture, or tourism, among others) or with punctual needs of the population, such as wood for construction or fuel (Palacios \u0026amp; Cantera, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Maintaining the vegetation structure plays a key role in the sequestration and storage of carbon in the forest ecosystems of the Colombian Pacific. Results reported in this study highlight the importance of formulating policies and mangrove conservation and management plans that recognize human communities as an important element, but not the only element, in the use and management of mangroves.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eMartha L. Palacios Peñaranda\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.\u0026nbsp;\u003cstrong\u003eEnrique Peña Salamanca\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Supervision, Resources, Project administration, Methodology, Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarisol Gordillo Suarez\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Visualization, Resources, Methodology, Funding acquisition, Formal analysis, Conceptualization.\u0026nbsp;\u003cstrong\u003eDaniela Enríquez\u003c/strong\u003e:\u0026nbsp;Writing – review \u0026amp; editing, Visualization, Validation, Investigation, Formal analysis, Data curation.\u0026nbsp;\u003cstrong\u003eJuan Felipe Ortega\u003c/strong\u003e: \u0026nbsp;Writing – review \u0026amp; editing, Visualization, Validation, Investigation.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Universidad Autónoma de Occidente-Colombia, Directorate for Research and Technological Development Code: 18INTER-304. It was undertaken under the framework of the project \u003cem\u003eAssessing the role of benthic and microbial links on carbon dynamics of mangrove forests submitted to distinct levels of anthropogenic stress\u003c/em\u003e, with code Univalle CI 71113, Internal Call 105 2017, Office of the Vice-Rector of Research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlongi, D. M. (2014). Carbon cycling and storage in mangrove forests. \u003cem\u003eAnnual review of marine science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 195-219. https://doi.org/10.1146/annurev-marine-010213-135020\u003c/li\u003e\n\u003cli\u003eAlongi, D. M. (2008). Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1-13.https://doi.org/10.1016/j.ecss.2007.08.024\u003c/li\u003e\n\u003cli\u003eBlanco-Libreros, J., \u0026amp; Estrada-Urrea, E. (2015). 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(2018) \u003cem\u003eAntromas\u003c/em\u003e: \u0026iquest;El futuro de la ecolog\u0026iacute;a del paisaje? Serendipia. Recuperado de http://www.revistaserendipia.com.\u003c/li\u003e\n\u003cli\u003eKauffman, J. B., Donato, D. C., \u0026amp; Adame, M. F. (2013). \u003cem\u003eProtocolo para la medici\u0026oacute;n, monitoreo y reporte de la estructura, biomasa y reservas de carbono de los manglares\u003c/em\u003e (Vol. 117). CIFOR.\u003c/li\u003e\n\u003cli\u003eKomiyama A, Moriya H, Prawiroatmodjo S, Toma T, Ogino K (1988) Primary productivity of mangrove forest. In: Ogino K, Chihara M (eds) Biological system of mangroves. A report of east Indonesian mangrove expedition 1986. Ehime University, Ehime, pp 97\u0026ndash;117\u003c/li\u003e\n\u003cli\u003eKomiyama, A., Poungparn, S. \u0026amp; Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. J. Trop. 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Paris, France. 10 p.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangroves, carbon storage, anthropogenic disturbance index","lastPublishedDoi":"10.21203/rs.3.rs-6254743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6254743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangroves represent an important forestry resource in tropical and subtropical areas worldwide. Anthropogenic activities have significantly altered biodiversity in these strategic coastal ecosystems. In the Bay of Buenaventura in the Colombian Pacific, mangrove forests have been subjected to anthropogenic impacts. The present study estimated the aerial and underground (roots \u0026ndash; sediment) carbon stock values of mangrove forests in the Bay of Buenaventura, based on the anthropogenic disturbance index, population density levels, and the distance from the city\u0026acute;s urban core. Four sampling stations were selected based on these criteria; the two stations closest to the city were Aguadulce and Islalba, and the farthest from the city were Piang\u0026uuml;ita and Punta Soldado. The magnitude of the carbon stock in aerial biomass was inversely related to the anthropogenic disturbance index, which was highest (8) close to the city of Buenaventura. Punta Soldado showed the highest carbon stock values, whereas, Aguadulce registered the lowest carbon stock. This station showed higher values of the anthropogenic disturbance index and was also the area with greatest amount of waste. These results highlight the importance of evaluating carbon stocks in these ecosystems as indicators of health status.\u003c/p\u003e","manuscriptTitle":"Carbon Dynamics of Mangrove Forests Submitted to Distinct Levels of Anthropogenic Stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:15:17","doi":"10.21203/rs.3.rs-6254743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be8c21eb-5948-48c6-a241-2feea04b3063","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T09:01:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:15:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6254743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6254743","identity":"rs-6254743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}