Interactions of Mangrove and Seagrass Productivity and Biomass In Two Watersheds in Toledo District, Belize

§Introduction

The primary goal of this study is establishment of a baseline for standing stock and rate of primary productivity. This study also aims to examine relationships between seagrass productivity, watershed hydrology, and mangrove productivity, thus providing a preliminary examination of the hypothesis that nutrient from mangroves is carried by rivers to sea benefiting sea grasses. This hypothesis is examined through the assumption that higher productivity would result with closer proximity to river influence. This would result from a higher net efflux of nutrients closer to river mouth and more spreading and thus lower nutrient concentration with increasing distance.

I begin by examining literature on connections between mangroves and sea grasses. I then examine how these systems are also connected to coral reefs and secondary productivity. To conclude the literature review I discuss the biology and economic value of sea grasses and mangroves. Following the literature review I present my analysis of mangrove and seagrass data from the work I was involved in with the Belize Center for Environmental Studies (BCES) in the Port of Honduras.

§Interconnection of Mangroves and Sea grasses

Mangroves and sea grasses often exist as fundamentally linked co-dependent eco-systems whose interaction maximizes each other’s bio-diversity and productivity. Mangroves primarily contribute to estuarine and inshore productivity through litter-fall (Day et al. 1987). This litter-fall is directly fed upon and used by fish and other animals within the system (Cintron and Shaeffer-Novelli 1983). Liter-fall also undergoes degradation and forms carbon rich organic compounds, which are exported to near by areas to be used for seagrass primary productivity (Guanghui 1991). Ultimately both systems interact to increase secondary productivity, resulting in significant influence on fisheries (Yanez-Arancibia et al. 1993).

Alongi (1992) states that mangroves significantly out-well dissolved organic nitrogen, and particulate nitrogen. Robertson et al. (1991) found that mangroves in the Fly River estuary, Papua New Guinea, export 31% of their primary production to outside waters (~678 t carbon/day). Robertson et al. (1991) used the light transmission method which assumes that light transmission, through an established mangrove canopy, can be explained by the relationship log (I0 /I)=K[chl]. By this equation, I0 is the incident photosynthetically active radiation and K is a constant derived from calibration with leaves of total chlorophyll content in a given area. Many I0/I measurements are used to estimate the average chlorophyll content. This is then converted to productivity by using an assimilation coefficient. Robertson et al. (1991) believes that this method generally underestimates primary productivity because it doesn’t take into account seedlings or mangroves in the understory. Furthermore, this method was compared to the gas-exchange method and was found to have significantly lower results. As a result, though significant in percentage and absolute mass, the estimate of 31% export of productivity out-welled is conservative and may in fact indicate even higher export of available nutrient available for connected systems.

Moran et al. (1991 cited in Robertson et al. 1992) found that 10 percent of dissolved organic matter 1 km away from the nearest mangrove sites was of mangrove origin. This indicates that a high efflux of mangroves is likely to arrive at nearby seagrass beds to potentially influence primary and secondary productivity.

Guanghui et al. (1991) quantitatively depicted one aspect of this link between mangroves and sea grasses by measuring carbon isotope ratios. Guanghui et al. found that the 13C value for seagrass leaves in sites near mangroves were significantly lower than those far away from mangrove forests. They also found that 13C values were significantly correlated with those of shell carbonates, indicating that the input of carbon dioxide from mineralization of mangrove detritus resulted in the difference in ratio of carbon isotopes. The significance of this is that the value of a shell’s carbon isotope is determined by the inorganic carbon in surrounding sea water. If shell carbonates and sea grasses have a lower ratio of 13C, it indicates that there is a significant source of lighter carbon based respiratory CO2 entering the seagrass bed. Instead of the heavier 13C, the sea grasses are using the lighter carbon during photosynthesis, resulting in a lower ratio of 13C in tissue (and presumably a higher ratio in the surrounding water column). The possibility of this effect is amplified by Guanghui et al.’s finding that the difference in light intensity was not responsible for the isotopic variation because sites with lower light intensity near mangroves had significantly lower 13C values than deep water sites away from mangroves; the opposite of that expected without any influence of a lighter carbon isotope. Paffen and Roelofs (1991) have shown that carbon dioxide is of vital importance towards productivity in Sphagnum cuspidatum, thus, by extrapolation, the high amount of a lighter carbon based carbon isotope in the environment may be resulting in enhanced productivity in the site near mangroves. This research indicates that there is an important and quantifiable link between mangroves and sea grasses.

§Linkages with Secondary Productivity

Evidence also exists for linkages between mangroves, sea grasses, and secondary productivity. Yanez-Arancibia et al. (1993) found that more than 80 different fish species use both mangrove and seagrass habitats in Terminos Lagoon, Mexico, indicating a very diverse assemblage of important links of primary and secondary productivity. Of these, eleven species are common in both areas, ten species solely used seagrass habitats, and seven species were dominant only in areas of mangrove and seagrass interaction. Furthermore, biomass and diversity of fish assemblages in these habitats were synchronized with primary productivity. Of the dependent species, three types were found: 1) those whose larvae and eggs are transported and distributed through the lagoon by currents following spawning, 2) species that use the lagoons differing habitats as a nursery area, and 3) species which complete their entire life cycle within the mangrove and seagrass systems.

Yanez-Arancibia et al. (1993) found evidence that mangroves are used more often as a nursery and sea grasses as a foraging ground for mature fish. The fish in sea grasses were usually adult, significantly larger, and fewer in number, while in the mangrove seagrass interaction zone, they were usually juvenile, smaller, but more abundant. The reason for the use of mangrove and seagrass by fish and other organisms includes (Yanez-Arancibia et al. 1993: 7): “...protection, calm waters, abundant food resources, and a diversity of habitats.”

The link between primary production and secondary production is further clarified by the correlation between life history patterns of migratory fish and patterns of primary production in mangroves. Fish usually use habitats during high primary production. Yanez-Arancibia et al. (1993) showed that these habitats were primarily used during the June to October, the season of highest rain. At this time the adult fish arrive and spawn, so that the larvae will hatch to benefit from the abundant riverine input, organic matter, and phytoplankton productivity. Possibly due to low riverine input, concurrently with the highest primary productivity in tidal creeks and shallow waters associated with mangroves, the dry season has the lowest productivity in mangroves. Thus mangrove output is implicated in increasing productivity in adjacent systems during the dry season.

Despite this strong evidence for relationships of secondary productivity to mangrove and seagrass primary productivity, the causative factors are still not completely understood. For instance, though habitat complexity is often implicated, there is still no definitive experimental evidence indicating why peneid prawns and their associated predators are found more often in mangroves than adjacent habitats. Furthermore, despite the strong evidence that penaieds use mangroves as a nursery ground, no study has unequivocally shown an associated decrease in penaeid prawn catch with mangrove habitat (Robertson and Blaber (1992). Robertson and Blaber (1992) do point to studies that found that increased habitat complexity is related to increased fish density. One of these studies (Pinto 1986, in Robertson and Blaber 1992) found that Sri Lanka fishermen improve their catches in lagoons by creating thickets of mangrove sticks on the lagoon floor. Following some weeks, net catches were found to be significantly greater in these areas of increased habitat complexity. Habitat complexity and migratory breeding patterns are good hypotheses with strong support but more experimental evidence is important for understanding the connections between these systems (Roberston and Blaber 1992).

§Links to Coral Systems

Coral reefs also play an important role in nutrient cycling of mangrove and seagrass systems because they provide habitat for herbivorous fish and invertebrates that feed on the plants within these habitats. In intense grazing, the coral fish community can indirectly effect the nutrient cycles of seagrass primary productivity (Mcroy 1983).

This link with reefs seems less directly important than that between mangroves and sea grasses; sea grasses often grow directly in mangrove areas, apparently gaining shelter and nutrient replenishment, both eco-systems acting co-dependently. Coral reefs do provide habitat for many fish species that are associated with mangroves and sea grasses as well but this crossover is only a small percentage; less than 1% of fish species were common in all areas in a study in New Caledonia (Robertson and Blaber 1992). Perhaps the prime interconnection of corals is less direct, acting more as an ocean barrier, while sea grasses and mangroves act on the terrestrial side to divert and scatter sediment and low salinity water associated with river systems, limiting the potentially deadly impact on the reef systems (Ogden and Gladfelter 1983).

Ultimately, the interactions of all three of these systems, coral, mangrove, and seagrass communities are fundamentally linked in terms of primary and secondary productivity yet continual study of these interactions is of vital importance.

§Mangrove Biology

Mangroves are economically important plants existing in much of the world, excepting very cold temperate, sub-arctic, and arctic climates. The exact extent of mangroves is in question; Day et al. (1987) used McGill (1959), a section of a book on coastal classification maps, to conclude that mangroves exist on approximately 75% of the coastline between 25o N and 25o S, while Clough (1992) doesn’t site his source but states that the range is between 35o N and 35o S. Though this might indicate that mangroves have significantly declined, from the article it isn’t possible to determine if Clough is using a more modern source, a different source of the same time period, or simply relying on memory. Mangroves distribution is limited by temperature and rainfall; temperature restricts latitude and areas of high rainfall show higher species diversity. The size of an estuary also affects diversity but probably primarily due to increased specialized habitats (Duke 1992).

By definition, a mangrove is (Duke 1992, pp64), “....a tree, shrub, palm, or ground fern, generally exceeding one half meter in height, and which normally grows above mean sea level in the intertidal zone of marine coastal environments or estuarine margins.” This broad definition includes many different unrelated taxa. Mangroves are adapted in specific ways, depending on the taxa, to grow in hypersaline environments with anaerobic sediment. These adaptations include breathing structures on roots and the trunk.

Biologically, mangroves are highly productive eco-systems that out-well nutrients and organic matter to near shore estuarine food webs, while geologically they are sediment sinks. This results in a complex relationship with net flux varying with environmental factors. Environmental factors, including salinity and substrate type can determine the zonation of nutrients within the water column and the net export from a given system at a specific time. In general, river dominated mangroves with high fresh-water input will result in high nutrient efflux and out-welling while tide dominated mangroves will have a greater bi-directional flux of water and thus greater net import. This interaction is due to a greater mixing of bottom and top waters and because there is less net force going towards the sea (Woodroffe 1992). Day et al. (1987) found that net productivity, net biomass, and the ratio of productivity to biomass were positively related to fresh water input. This positive indicates a positive interaction of mangroves with decreased salinity and presumably increased input of terrestrially based nutrient.

§Value of Mangroves

Mangroves are important sources of tannin, fuel, construction timber, charcoal, prawns, and fish (Lugo and Snedaker 1974; Saenger et al. 1983; Thayer et al. 1987). Many humans have traditionally been dependent on mangroves for such products, and prior to modern expansion, most uses have caused limited resource impact.

Though even today there exist some examples, such as the Matang forest in Malaysia where a continual high yield of mangrove derived timber and charcoal might be sustainable, many areas currently suffer from unsustainable, short-sighted environmental mining (Bunt 1992). In fact, until recently, much perception of mangroves has been negative; they have been considered mosquito swamps to be destroyed for coastal development with disregard for any potential negative environmental consequences (Lugo and Snedaker 1974). As a result of such thinking, mangroves have been diminished in areas resulting in cases like the Muwanda village in Zanzibar that fell into the ocean due to mangrove related coastal erosion (Ngoile and Shunula 1992), or the massive defoliation of mangroves in South Vietnam where high siltation and turbidity have decreased the spawning habitats for fishes, phytoplankton, and zooplankton.

Mangroves are now realized as directly important in trapping and holding sediment, stabilizing soils, reducing coastal erosion, providing storm protection (Carlton 1974), and increasing fisheries yield. Snedaker (1978) found that areas with mangroves had significantly larger and more diverse shrimp yields. Similarly, significantly larger fish catches were found by Thayer et al. (1987) and Heald (1969; in Lugo and Snedaker 1974) near mangroves.

Along with these direct uses, mangroves are primarily important due to their importance as independent, biologically diverse eco-systems, and eco-systems which interdependently link terrestrial systems to near shore communities, including sea grasses and coral reefs. Mangroves impact on secondary productivity and ultimately affect human health.

The conservation of mangroves is important for fisheries because shrimp., mullet, and other detritovores feed upon the end products of leaf litter fall. Mangroves act as an interdependent link because of the high organic productivity resulting from litter fall. Leaf litter, high in carbohydrates and low in protein, falls to the forest floor and decomposes into organic matter. The carbon to nitrogen ratio of decomposing material decreased during decomposition resulting in a higher nutritional value. This enrichment is partially the result of meiofauna grazing on nutrient rich leaf decomposers. Microbial enrichment occurs as the organic matter breaks down into small segments. Finally, detritovores use the detritus to strip the microbial coating. This results in nutrient than can readily be exported to adjacent waters or used within, allowing mangroves to be important nurseries for young fish, food exporters for mature animals, and nutrient suppliers for adjacent primary production, as in sea grasses (Cintron and Shaeffer-Novelli 1983). Mangrove leaf litter is also likely to be directly exported to sea grasses because their leaves are slow to degrade due to high amounts of structural lignin and cellulose. This resistance implicates whole mangrove leaves as likely candidates to be exported to sea grasses during efflux of fresh water from riverine sources (Mcroy 1983, Alongi et al. 1992).

Though mangroves have many direct and indirect uses, many current usage patterns are unsustainable. These include: clear felling of virgin forest for paper pulp, large scale conversion of mangrove areas for mariculture, and reclamation for agricultural, industrial, urban, and other development (Bunt 1992).

§Biology and Value of Sea grasses

Sea grasses are very productive eco-systems common in temperate and tropical estuarine environments which are the richest nursery and breeding grounds for marine organisms. Sea grasses also act to stabilize sediment, preventing excess accumulation on nearby coral reefs, and distill wave action, reducing impact on mangrove systems (Zieman 1983).

Many of the organisms dependent on sea grasses are of commercial or sports fishing importance. Sea grasses also provide a substratum for epiphytic and other colonization, supporting biologic diversity in created habitat niches (Zieman 1986). They also act as a direct food source for marine herbivores (Jagtap 1991). The productivity can be so high that the number and biomass of organisms reliant on the system often exceed the plant population. Seagrass leaves grow from 5 to over 10 mm a day (Zieman 1986).

The primary reason that sea grasses systems are among the most productive in the world is their tightly woven trophic support for higher level consumers. The sea grasses combine with associated algae, infauna, and epifaunal secondary productivity to produce widely diverse and rapidly growing communities (Fredette et al. 1990, Zieman 1983). In fact, Fredette et al. (1990) found that, due to low values of standing stock as compared to productivity, 92% of production might be consumed by predators in two seagrass beds in Lower Chesapeake Bay. This high rate of productive consumption indicates a very tightly woven eco-system where producers can rapidly grow due to constant fertilization by nutrient in the consumers’ wastes.

Seagrass density and productivity are affected by many environmental factors including depth, level of sedimentation, and salinity (Jaqtap 1991; Duarte 1991). Jagtap (1991) shows that density of sea grasses increases with water clarity and salinity. Some species, including Cymodocea spp., S. Isoetifolium, and Halophila stipulacea require diminished light intensity and lower temperatures. Mcroy (1983) states that sea grasses acquire most of their nutrient from underlying sediment but that sea grasses, and other macrophytic algaes, sometimes rely on nutrient in overlying water masses. Mcroy (1983) believes that much of the nutrient in sediment is mangrove derived and that the level of nutrient in sediment determines the succession stage, with a climax community consisting of nearly uniform stands of Thalassia testinudum. The productivity of these climax communities of Thalassia testinudum is negatively affected by low salinity and high temperature (Zieman 1975).

Secondary productivity and primary productivity are both closely interlinked in seagrass communities. Secondary productivity influences primary productivity primarily through herbivory. In turn, the greatest numbers of organisms have been found in beds with dense vegetation, implicating high growth in increasing abundance and possibly productivity of seagrass dependent organisms (Yanez-Arancibia et al. 1988, Zieman 1983).

§Methods

Mangrove and Seagrass analysis were performed to establish baseline data for characterization of community structure in the proposed marine protected area. All sites were selected to be representative of the total surrounding area to minimize problems of extrapolation and generalization.

§Statistics

All statistics were performed using Minitab for Windows 10. For seagrass data I used balanced analysis of variance (ANOVA) and least significant difference (LSD) tests to determine variance in means. To determine distance effects for seagrass data I used regression. For mangrove analysis I used nested ANOVAs.

§Mangrove

§Materials

Sighting compasses

Flagging tape

Tree tags

Aluminum nails

Hammer

Cloth measuring tape (cm increments)

2 X 20 m lines (1 m increments)

Stakes for plot corner markers

1 m2 quadrat framer

Stakes to mark corners of subplots

String to mark subplots

Scale to measure wet weights of saplings

DGPS at corners of plots

§Site Selection

Sites were selected as near as possible to the mouths of Deep and Monkey River to strengthen conclusions drawn from comparative analysis. Sites were selected to: 1) establish a baseline description of the community including productivity and standing stock biomass, 2) determine seasonal variation in primary productivity, and 3) determine the relationship between watershed hydrology, vegetation, adjacent seagrass communities, and secondary productivity.

Methodology is closely adapted from Sullivan et al. (1994) and CARICOMP (1989) by Will Heyman. Due to differences in densities and heights of mangroves, sites were chosen to accurately depict the general habitat. At the mouth of each river, three replicate 10m2 plots were chosen. All sites were as adjacent and parallel to the shoreline as possible..

§Site Mapping and Measurement

Once an ideal location was determined, the site was mapped and plots were designated, and tall trees were measured with cloth measuring tape (cm increments) and labeled with tree tags. First, a tree for each corner tree was marked with a tree tag and flagging tape. The plot was then mapped with a sighting compass allowing determination of perpendicular sides and four measured lines for gauging distance. The plot was then divided into 10 by 10 one m2 segments. Following plot delineation, DGPS points were taken at each plot corner and trees with diameter greater than 2.5 cm were mapped.

All trees were identified to species, measured, and marked at breast height (just above highest prop root). Tags were then hammered in for future measurement consistency. When more than one trunk was arising from prop roots, each trunk was measured as separate trees. Falling drop roots were ignored when measuring circumference.

Each tree larger than 2.5 cm was measured for circumference and diameter at breast height (CBH), mass, basal area, and height. Height was measured in three parameters: H1 is the height above sediment and closest to the highest prop root, H2 is the height from prop roots to main area of branching, and H3 is the height of the canopy from the main area of branching. Height was measured using a clinometer and telescoping rod. Basal area was calculated as m2 ha-1 . A ring was painted around the area measured on each tree for comparison of growth following future measurements.

Following plot mapping and tree measurement, 10 leaf litter traps were erected and five subplots emplaced, measured, and weighed. Within each plot, five randomly placed 1 m2 sub-plots were measured and all saplings less than 2.5 cm dbh (diameter at breast height) and rooted seedlings were tagged, identified, mapped, and measured.

See Figures 53 - 58 for maps of mangrove sites and sub-plots. Sub-plots are labeled S1 - S5. Each dot marks a tree and the number next to it indicates its designation; for example, M1R1 indicates that the site is M1 (Monkey River 1) and the three is the first Rhizophera mangla established (thus labeled R1).

§Calculation of Total Biomass

Total biomass was calculated by summing individual tree measurements. Mean biomass was calculated by determining mean of total biomass and expressing as kg X M-2. Biomass was determined by the following formulae from Golley et al. (1962):

BIOMASS (g) = dbh (cm) X 3,390

§Seagrass

Six sites near Deep River and one near Monkey River were selected to examine possible relationships between distance from river, community composition, and productivity (Figure 22). Methods were adapted by Will Heyman from CARICOMP (Caribbean Coastal Marine Productivity) (1991) and the Nature Conservancy - South Florida and Caribbean National Parks Data Center (Sullivan et al., 1994).

Analysis was based on Thalasia testudinum because they contribute more biomass and aerial productivity to total seagrass bed production than any other species, and are the dominant climax species in most Caribbean seagrass communities (Zieman, 1983). Furthermore, though Thalasia productivity can be a function of seasonal changes in salinity and temperature (Ogdfen and Gladfelter, 1983; 1986), production is primarily dependent on available nutrients taken through roots (McRoy, 1983). Thalasia are thus ideal due to their abundance and its potential strength in showing the impact of river-borne nutrients.

§Standing Stock Biomass

Materials

dive gear

PVC corer (15 - 20 cm diameter)

4 mesh dive bags

4 buckets with lids

sieve box (2mm screen)

aluminum foil

drying oven

balance (0.01g - 250 g capacity)

thermometers

toothbrush

hydrochloric acid

acetic acid

deep trays for sorting

wash basins

Summary

Standing stock biomass measurements are directly adapted from CARICOMP (1991) by Will Heyman. Standing crop (aboveground, photosynthetic tissue) and total biomass, including mass of rhizomes and roots of benthic algaes and sea grasses were measured. Following collection, biomass was cleaned, dried, and weighed. Data were then entered into Seagrass Biomass data forms and converted to g/dry weight/m2.

Sample Collection

A corer made of a 6” diameter PVC pipe was forced into sediment and resulting core was placed into a prelabled 2mm mesh dive bag. After the each bag was filled, it was shaken underwater to remove any excess attached sediment. Four cores were taken at each site. Samples were held in shade or submerged for less than one day prior to analysis

Sample Sorting

Following collection, core samples were sorted into: 1) green leaves 2) non-green leaves and short shoots, 3) live rhizomes, 4) live roots, and 5) dead below ground material. Samples were first coarsely sorted through a 2mm screen box, allowing retention of plant material. A screen and pan containing 10 cm of fresh water were used for fine sorting. Sorting was performed using the following criteria: Live rhizomes and roots float and are white or very light gray and crisp when squeezed or broken while dead roots and rhizomes sink and are dark and flaccid. Species other than Thalassia were separated into green and non-green tissue. Thalasia leaves were separated from short shoots at the node. Remaining sediment and epiphytes were scrubbed off with a toothbrush. Samples were bathed in 10% hydrochloric acid until CO2 bubbling stopped (10 minutes or less). Bath was changed when CO2 effectiveness diminished. Biomass was then rinsed in fresh water to remove salt and acid.

Sample Drying and Weighing

Each fraction was dried at 60-90o to constant weight on a foil weighing pan. Samples were cooled to 45o and weighed; pan weight was subtracted to determine biomass. To insure all samples were dry, heaviest fractions were weighed until they showed a constant weight for 12 hours.

When weighing calcareous macroalgae, all sediments were removed, and samples were decalcified in acetic acid for several days. Fleshy macroalgae was rinsed in fresh water, dried, and weighed using the above procedure.

§Productivity

Materials

Scuba Gear

6 quadrats/site (6” x 12” galvanized, epoxy painted, fence wire with middle cut out and corner tabs left to anchor in sediment. All marked with flagging tape.)

hypodermic needles

aluminum foil, 6 ziplock bags per site

drying oven, cooler with ice

thermometers

data sheets

Summary

Productivity measurements were used to determine growth rate, turnover rate (growth per unit of plant), and aerial productivity (growth per unit of sea floor) in the seven seagrass beds surveyed. Due to the difficulty of measuring below-ground material, whole total plant growth was not determined. This study focuses on the amount of new leaf biomass or standing crop of Thalasia testudinum produced.

Quadrat Placement and Field Measurement of Growth

Six quadrats were evenly spaced and firmly pushed into the sea bottom of each site. Each leaf in every quadrat was marked with a hypodermic needle 2mm above the green white interface or at the sediment surface if buried. All leaves of short shoots were marked simultaneously. Following 8-12 days, all short shoots from sediments were placed in marked ziplock bags. All leaves were clipped at the needle marks; leaves marked at sediment surface were harvested at sediment surface.

Laboratory Sample Separation and Biomass Determination

In the laboratory, the number of shoots, blades, and maximum length (cm) of shoots was determined for each quadrat. The mean number of shoots, blades, blades per shoot, and maximum blade length (cm) was calculated for each quadrat. Leaves were separated into 1) new growth: the leaf from the needle mark to base where it was harvested, 2) old growth: the area of leaves above the needle mark, and 3) new leaves: any new leaves with no needle marks. Following separation, all groups were decalcified in weak hydrochloric acid, washed in fresh water, dried on an aluminum pan, and weighed. Total biomass of each fraction was determined by weighing each pan alone, and subtracting the weight from the total measurement.

Calculation of Turnover and Productivity

Turnover rate, the percent of plant growth per unit day was calculated by:

TURNOVER RATE (%/day) = Daily Production/Standing Crop

Areal productivity, new material produced per unit area per day, was calculated by summing new material and multiplying by quadrat size (50) to get m2:

DAILY PRODUCTION =

Weight of New Leaves + Weight of New Growth X 50

§Results

§Summary of Mangrove Data

There was a significant difference between mangrove sites by rivers for CBH (p = .0301), DBH (p = .0301), mass (p = .034), basal area (p = .0161), and total height (p = .0474). There were no significant differences between sites within rivers for any factors.

§Summary of Seagrass Data

A significant negative trend of biomass of old growth (Figure 59, R-Squared = .063, p = .005), number of shoots (Figure 60, R-Squared = .159, p=.009), and number of blades (Figure 61, R-Squared = .140, p = .015) versus distance from river mouth was found. The relatively high “R-Squared” values indicate that many variables along with distance have an impact.

The table below summarizes significant differences between sites shown by balanced ANOVA and LSD tests for seagrass cores and quadrats. This table begins with the category that evinced a significant result. The second column depicts the site(s) that were significantly different from others. The third column indicates whether the significantly different mean is greater or less than other sites. The fourth column depicts which site(s) were different (sometimes I depict several degrees of differences in means with multiple “>“ or “<“ signs). The fifth, sixth, and seventh columns provide the “p”, “F”, and df (degrees of freedom) values. There were no significant differences between cores or quadrats within sites.

Table 3: Results of ANOVA and LSD for Seagrass Data

§Discussion

§Figure System

See Figure 1 for site map. Figures 23 - 52 contain graphs of data. Figures 53-58 contain plot maps of mangrove sites. Tables 4-11 are summaries of seagrass and mangrove data.

§Mangrove Quadrats

Deep River had fewer mangroves but the average size of trees was larger (Table 4). Though Monkey River had smaller trees, it had over twice as many as deep river and thus a greater total biomass (Figure 23). That the trees were larger in Deep river is initially evident by the nearly two-fold increase in average mass at Deep River as compared to Monkey River (Figure 24). This is further shown by the similar pattern depicted for average CBH and DBH, and basal area depicted in Figure s 25 - 28.

Nearly all the mangrove measurements display the pattern depicted in the graph of average mass (Figure 31); site four with the highest mass, followed by sites two, three, six, five, and one. Similar patterns were evident in examination of the graphs for CBH, DBH, and basal area (Figures 25 - 30). This pattern indicates that, though average values for Deep River show that the trees were bigger, the results may be skewed due to the large trees in site four. This Deep River site was always much greater than the rest; the two sites with the next biggest trees were in Monkey River.

The markedly larger average size of mangroves in Deep River is exemplified by the similar total basal area and much larger average total height compared to mangroves in Monkey River (Figures 32 and 33). That the total basal area is so close, despite the much greater number of trees shows that the trees in Deep River must, on average, be much bigger.

Though the average total height of mangroves was greater in Deep River compared to Monkey River, the pattern of dispersion was slightly different; four was still largest but was followed by three, two, five, one, and six (Figure 34). Maximum heights for both rivers were nearly the same at approximately 15 M (Figure 35). The hypothesis of larger trees in Deep River is supported by a nearly one meter increase in minimum height (Figure 36).

§Seagrass Quadrats

Complex factors, potentially including differing growth cycles, current, and tide changes made the analysis of patterns difficult for growth factors between the seven Seagrass sites. Furthermore, different growth factors were not strongly associated with each other within sites. Some potential patterns are investigated which might gain greater support with future measurements at the same sites. See Table 5 to reference numbers found in discussion of graphs and Figure 22 for site map. All Figures are ordered left to right by estimated distance from Deep River excepting site one (the furthest right) which is near Monkey River.

§Shoots and Blades

Mean number of blades and mean number of shoots did evince a similar pattern by site. Examination of Figures 37 and 38 shows that site 7 has the highest mean number of shoots and blades, followed by four and three. Sites five, two, and one are approximately the same and site six is the lowest. This indicates that proximity to river mouth is a factor for number of shoots or blades but other factors rapidly gain importance away from the river.

Site seven is the closest to the Deep River mouth and is expectedly much higher than others. The next highest mean shoots and blades in site four might be explained by it’s potential influence by both Monkey and Deep River (see Figure 1). Assuming a South-East current, this site might attain significant nutrient enrichment from both rivers and potentially mangroves in the surrounding Cayes.

Site three follows and is logically explainable by it’s very near proximity to Deep River and the Ycacos Lagoon; an area very high in mangrove growth and secondary productivity. This site is slightly further away from Deep River than the lower productivity site five but is in a direct line with no land-shelter that might divert river flow. Furthermore, the Ycacos Lagoon is likely to add considerable nutrient input to this site.

That site one has the next highest mean shoots and blades is also expected due to it’s near proximity to Monkey River. Possibly this site isn’t quite as high in growth as some of preceding sites because Deep River has a higher gross area of vegetation which is hypothesized as an influence on productivity (Deegan et al. and Yanez-Arancibia et al. 1986) and because the site is actually slightly to the North of Monkey River. Again, a hypothetical South Eastern current would limit the potential productive influence of monkey River. Furthermore, Deep River’s watershed is structured as a shallower flat plain that might rapidly run off nutrients, as opposed to Monkey River which might retain more due to it’s bowl-like nature (Heyman, personal communication).

As expected, site five, then two are the next lowest in mean number of shoots and blades. These sites are relatively close to Deep River’s mouth but do not gain the benefit of the Ycacos lagoon in site three. Furthermore, site five is sheltered from the input of Deep River by a relatively large hump of land. Site two is expectedly lower than five because it is further away from the mouth of Deep River.

Site six has the lowest numbers of shoots and blades. This is predictable because the site is the furthest from any influence of Deep River, Monkey River, and the Ycacos Lagoon.

The mean blades per shoot did not show much variance between sites (Figure 39). Interestingly, two of the lowest sites for mean number of blades and shoots, two and six, were the two highest for this ratio. The argument for an inverse relationship is weakened by the relatively high value in site seven which was the highest in the mean number of blades and shoots analysis.

§Biomass Measurements in Quadrats

In line with the discussion of shoots and blades, site seven was consistently high for most measurements (Figures 40-45). As stated before, this might indicate the influence of mangrove derived nutrient from deep river. Interestingly, though sight seven was generally high, it had the lowest mean for new growth biomass. Synchronously, sight seven the highest biomass of new leaves, new material, and old growth (Figures 40-41).

This, along with the higher number of shoots and blades (Figures 37 and 38) indicates that the Seagrass in this site were allocating more energy towards growth and development of new leaves, blades and shoots, and less towards growth of each individual component. This hypothesis is strengthened by the high biomass of old growth, which shows that the whole plant growth has been very high in the past. Furthermore old growth and new growth graphs (Figure 41 and 42) depict an almost inverse relationship in that sites seven, five, and six, with high old growth biomass, all have relatively low new growth biomass. A similar inverse pattern is found with sites, three and four which have two of the lowest measurements for old growth biomass but among the highest for new growth biomass. Possibly these plants utilize different phases in which they grow new parts during one cycle and increase in gross biomass more during cycles when the leaves, shoots, and blades are in place (during growth of the stem area).

Site five, the second closest to the mouth of Deep River had the highest total biomass, second highest biomass of old growth, and high biomass of new material and new leaves(Figures 44, 40 - 42). Site five is the second closest site to the Monkey River mouth. This finding is interesting in that the mean # of shoots and blades in this site was relatively low. This might indicate that the river and mangrove derived nutrients have a significant impact on the area but the sea grasses were devoting more energy towards absolute growth and less on developmental growth, as in new leaf and blade formation.

Site two consistently had the lowest amount of biomass. This site is not directly near any potential nutrient sources. Though relatively close to Deep River and Punta Ycacos, unlike site four and six, it isn’t particularly close to any cayes that might contain mangroves, which might increase nutrient enrichment. It’s relatively remoteness might also indicate that it would be slightly deeper. Seagrass require light for photosynthesis and thus greater depth will tend to have a negative effect on growth. One further potential reason might be that sedimentation from both the Ycacos and Deep river fall in this area, thus reducing visibility and potential nutrient benefits due to lack of photosynthetic ability.

Sites three and four were generally average in biomass measurements. This average pattern was consistent for all measurements but new growth and new leaves. In these respective measurements, site three had the highest biomass of new growth and site four had nearly the highest biomass of new leaves. These high measurements might, as stated for other measurements, be partially due to their location. Sight three is near the Punta Ycacos Lagoon and Deep River and sight four is in a location near mangroves and, though far off, directly in line with Deep River and potentially, depending on current patterns, also influenced by Monkey River. The reason for the abundance of new leaves in site four, as opposed to site six which had high increased new growth might be an indicator that the plants in this site are in a stage of developmental growth. This possibility and the reason for such growth patterns need to be investigated further and with future measurements.

Site six and one, contrary to expectations from shoots and blades measurements had very high biomass measurements. Site six had the second highest total biomass and third highest biomass of old growth. Site six, in-line with other sites that had a low biomass of shoots and blades, had a relatively low biomass of new leaves (Figure 40), but a high total, old growth, and new material biomass. This further strengthens the hypothesis that new leaves and new shoot and blades often grow synchronously, while net increase in biomass acts at different times. The high biomass in site one might be due to its relative proximity to Monkey River.

§Aerial Productivity and Turnover

Examination of productivity data provides further support for the hypothetical patterns of influence discussed previously. Interestingly, both turnover and productivity graphs (Figures 45 and 46) depict a trend of decreasing productivity with distance away from river-mouth; particularly evident following site three for productivity (Figure 45) and site two for turnover. It is interesting to note that this general pattern is nearly identical to that for mean Seagrass biomass of new growth (Figure 43). Possibly new growth and productivity are functionally and predictably related.

Site seven and five, the first and second closest to Deep River had relatively average productivity and site two, the next closest had the lowest productivity. Site one had nearly the same productivity and turnover as site seven. That these did not have the highest productivity might indicate that the higher productivity sites three and four benefited from two riverine sources; site three from the Ycacos Lagoon, and site 4 from Monkey River.

The interesting pattern with site three with the highest and second highest productivity might indicate, as previously hypothesized, that site three is significantly affected by both Deep River and the Ycacos Lagoon. That site four evinced the second highest productivity is in line with the hypothesis stated previously that this site benefits both from Deep River nutrients, Monkey River, via a hypothetical South East current, and nearby mangrove on the Cayes.

Site six is the farthest away from riverine influence and is thus expected to have the low productivity and turnover demonstrated in Figures 45 and 46. This site’s lower productivity, despite its proximity to Deep River, may be explained by the nearby Cayes and mangrove derived nutrients.

Site two surprisingly had the highest turnover rate (Figure 46). This site was nearly universally low in all other measurements. This perplexing result is further interesting because it is the only striking anomaly between the turnover graph and the graph for mean aerial productivity and mean biomass of new growth (Figure 45 and 43). In fact, it follows the same pattern of decreasing riverine influence resulting in decreasing productivity excepting that in this graph it starts one site earlier.

§Seagrass Cores

Direct proximity to river influence seems to be strongly related with core biomass. This is shown by the much higher relative biomass of either seven or one in all core measurements (Figures 47 - 52). Following immediate proximity, river influence seems to be less important but might explain the consistent lowest level of site six, the furthest from any freshwater sources.

Site seven, closest to Deep River, had the highest total and live biomass (Figure 47 and 48). Site seven also had the highest mean total aerial biomass and mean live aerial biomass (Figure 50 and 51). Site one, closest to Monkey River, had the second highest total and total live biomass. This indicates that direct proximity to river influence exerts a strong influence on these sites.

Site one had the highest dead biomass and dead aerial biomass (Figure 49 and 52). All other sites were nearly equal for these measurements. This might indicate that site one takes longer to decompose or circulate its biomass, possibly due to variance in sediment type or amount of biomass exported; the river is close by, thus depositing large amounts in the area but might not cause the sediment to be pushed out farther to sea due to the slightly off-river path position of the site.

§Summary and Conclusions

Analysis of seagrass measurements provides initial support for the hypothesis that distance from river mouth and potential mangrove productivity negatively influences Seagrass productivity. Though the consistently high ratings for growth, productivity, and biomass in sites immediately near mangrove sources qualitatively indicates influence, it is also obvious that the system is complex and any direct relationship is rapidly lost with increasing distance from river influence.

I hypothesize a SE current as responsible for certain patterns unexplainable by linear analysis; other possibilities are certainly possible and it is likely that many factors are influencing the nutrient flow and productivity patterns for every site. The basic hypothesis did hold up very well; all of the sites that had the consistently highest measurements were very close to large areas of mangroves and freshwater sources capable of out-welling available nutrients (sites 1, 3, and 7). Similarly, the site most detached from obvious mangrove influence (site 6) almost universally had the lowest measurements. Though the patterns demonstrate complexity and conclusions are tentative, mangrove derived nutrient carried by riverine fresh-water does appear to have a positive effect on Seagrass productivity in this area.

Though initially it seems that a clear-cut distinction between Deep River and Monkey River is found in that mangroves in Deep River are larger and those in Monkey River are more numerous and smaller, further analysis showed that the results may be skewed. The nearly two-fold mean size of trees in Deep River is deceptive because it is apparently based almost entirely on one site with much larger trees than any of the other sites. The other two sites with largest mean trees were actually in Monkey River while the two remaining Deep River sites had a small number of average sized trees. Productivity data from leaf litter fall might help unlock the puzzle of mangrove differences in the Monkey and Deep River watersheds but unfortunately that data was not available to me at the time of writing.

The initially stated hypothetical linkage that rivers push mangrove productivity out to sea grasses, increase their productivity, and, in turn, increase secondary productivity is obviously an oversimplification. The current study doesn’t take physical factors such as freshwater hydrology or tidal and current patterns into consideration. These might have a profound influence on productivity, the direction of nutrient flows, and the time allowed for an eco-system to utilize nutrients passing through. For example nutrients may be effectively pushed passed the directly proximate Seagrass sites due to high water pressure, thus complicating patterns and possibly even increasing influence on slightly more distant sites. One hypothesis forwarded throughout the analysis is that there is a strong SE current influencing the direction of nutrient flow; determining current patterns would allow the testing of such hypothesis.

This analysis also fails to account for chemical factors such as salinity, dissolved organic matter, carbon isotopes, nitrogen, and phosphorous. Knowing such factors would allow a better understanding of productivity and biomass patterns. For example, using carbon isotopes in a manner similar to Guanghui (1991) would help determine whether mangroves in fact contribute significantly to Seagrass productivity, if purely physical factors such as hydrological output are responsible, or if the apparent patterns are simply constructed in hope of strengthening a hypothesis.

Many of the results in the study seem to show patterns but not strongly enough to account for a simple analysis as in the hypothesis of distance of Seagrass site from river mouth being solely responsible for fluctuations in primary productivity. This preliminary analysis examines mangrove growth factors such as biomass and size measurements but does not use primary productivity data such as litter-fall, illumination, or biogas. There is only one site near Monkey River and six Seagrass sites near Deep River; this does not allow for adequate analysis to determine the potential influence of mangrove primary productivity on Seagrass productivity in these two watersheds.

Fortunately, the Belize Center for Environmental studies is currently involved in many studies that will examine such factors as discussed. These studies, include nutrient, hydrological, and biologic analyses, which more effectively and testably incorporate an even distribution of both watersheds. These studies are occurring concurrently with socio-economic analysis, including surveys of local population. Furthermore, the data in this study are simply the first baseline measurements; future measurements, incorporating other factors, including mangrove productivity from leaf litter fall will greatly focus the currently speculative picture on the ecological systems of the Port of Honduras. This paper primarily functions to examine initial results in order to set course for the direction of future studies and the establishment of initial management strategy. A preliminary baseline and context for future work is now emplaced.

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§Figures and tables in this chapter

The captions as listed in the manuscript. The plates themselves are in the figure appendix.

• Figure 22: Mangrove and Seagrass Site Map
• Figure 23: Sum of Mass by River
• Figure 24: Average Mass by River
• Figure 25: Average Circumference at Breast Height (CBH) by River
• Figure 26: Average Circumference at Breast Height(CBH) by Site
• Figure 27: Average Diameter at Breast Height (DBH) by River
• Figure 28: Average Diameter at Breast Height (DBH) by site
• Figure 29: Average Basal Area by River
• Figure 30: Average Basal Area by Site
• Figure 31: Average Mass by Site
• Figure 32: Sum of Basal Area by River
• Figure 33: Average Total Height by River
• Figure 34: Average Total Height by Site
• Figure 35: Maximum Total Height by River
• Figure 36: Minimum Total Height by River
• Figure 37: Mean Number of Shoots by Site
• Figure 38: Mean Number of Blades by Site
• Figure 39: Mean Number of Blades/Shoot by Site
• Figure 40: Mean Biomass New Leaves by SiteFigure
• Figure 41: Mean Biomass New Material by Site
• Figure 42: Mean Biomass Old Growth by Site
• Figure 43: Mean Biomass New Growth by Site
• Figure 44: Mean Total Biomass by Site
• Figure 45: Mean Areal Productivity by Site
• Figure 46: Mean Turnover by Site
• Figure 47: Total Biomass by Site
• Figure 48: Total Live Biomass by Site
• Figure 49: Total Dead Biomass by Site
• Figure 50: Mean Total Biomass by Site
• Figure 51: Mean Live Biomass by Site
• Figure 52: Mean Dead Biomass by Site
• Figure 53: Mangrove Plot Map of Monkey River 1
• Figure 54: Mangrove Plot Map of Monkey River 2
• Figure 55: Mangrove Plot Map of Monkey River 3
• Figure 56: Mangrove Plot Map of Deep River 4
• Figure 57: Mangrove Plot Map of Deep River 5
• Figure 58: Mangrove Plot Map of Deep River 6
• Figure 59: Regression of Old Growth Biomass by Relative Distance from River
• Figure 60: Regression of Number of Shoots by Relative Distance from River
• Figure 61: Regression of Number of Blades by Relative Distance from River