2013 5 vegunitsbiomass report1 ncd · 2018. 5. 10. · involving their training, support and...

MESCAL

Preliminary assessment of biomass and carbon content of mangroves in

Solomon Islands, Vanuatu, Fiji, Tonga and Samoa

May 3, 2013

MESCAL Mangrove Biomass Report 3 May 2013

2

MESCAL

Preliminary assessment of biomass and carbon content of mangroves in

Solomon Islands, Vanuatu, Fiji, Tonga and Samoa

May 3, 2013

Prepared by Dr Norman Duke Project Advisor on Mangrove Habitats and their Assessment

With the assistance of the five country teams and MESCAL PMU

Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) James Cook University, Townsville Q 4811

Tel: 07-4781-6448 Fax: 07-4781-5589 Mob: 04 1967 3366 www.jcu.edu.au/TropWATER

Emails: [email protected]


3

KEY FINDINGS

1) This report documents important findings from the program of works for 2012-2013 directed by Dr Norm Duke with each of the five MESCAL country teams involving their training, support and consultation, prescription of methodology and approach, as well as the compilation and preliminary assessment of data received.

2) The bulk of data presented in this report were collected from the field by five different work teams for the respective countries. This was a notable challenge from the beginning, but the outcomes are measured not just by the veracity of results presented here, but also by the increased capacity and confidence instilled in the people responsible in each country. The results presented are an exceptional and unprecedented compilation of essential information about mangroves in five countries. This project is a triumph of the hard work and care taken by all involved.

3) The chief data output of the project includes observations on the structure of the dominant mangrove vegetation units (based on upper canopy species) in each of the five countries. For each, there are now baseline primary data on local mangrove stands of stand biomass, diversity, density, basal area and canopy height. From these measures, it has been possible to derive realistic, preliminary estimates of stand carbon content for the specific vegetation units being quantified in the mapping component of the MESCAL project.

4) For this scaling up from the mapping work, an equation was derived for scaling up from the anticipated mapping vegetation units, coupled with mean observed estimates of canopy height. This equation may be used in both demonstration sites and for each national inventory. The equation is the significant linear relationship, Total Mangrove Carbon (as living above ground biomass plus below ground biomass) = 21.721 * Height – 4.1833, where carbon is dry weight in t.ha-1, and height is the weighted canopy measure (Lorey’s height) in metres.

5) Data have been collected for 7 dominant vegetation assemblages of mangrove stands of Solomon Islands, Vanuatu, Fiji, Tonga and Samoa. All measured and derived estimates of stand biomass, density, height and basal area with this project compare exceptionally well with literature values for similar mangrove stands in Asia and elsewhere in the World. The structure and biomass of mangrove forests in the five countries depends on the species present, and especially on the canopy height of the climax stands surveyed.

6) For Solomon Islands, mangrove biomass data were collected from 12 plots for 3 dominant mangrove vegetation assemblages, Bruguiera species, Rhizophora species and Ceriops tagal. Stand heights ranged from 12-25 m with a total living mangrove biomass of 478-1059 t.ha-1.

7) For Vanuatu, mangrove biomass data were collected from 26 plots for 5 dominant mangrove vegetation assemblages, Bruguiera gymnorhiza, Rhizophora species, Ceriops tagal, Xylocarpus granatum and Avicennia marina. Stand heights ranged from 4-19 m with a total living mangrove biomass of 155-747 t.ha-1.

8) For Fiji, mangrove biomass data were collected from 43 plots for 3 dominant mangrove vegetation assemblages, Bruguiera gymnorhiza, Rhizophora species and Xylocarpus granatum. Stand heights ranged from 4-17 m with a total living mangrove biomass of 327-950 t.ha-1.


4

9) For Tonga, mangrove biomass data were collected from 25 plots for 4 dominant mangrove vegetation assemblages, Bruguiera gymnorhiza, Rhizophora species, Excoecaria agallocha and Lumnitzera littorea. Stand heights ranged from 3-9 m with a total living mangrove biomass of 94-326 t.ha-1.

10) For Samoa, mangrove biomass data were collected from 11 plots for 2 dominant mangrove vegetation assemblages, Bruguiera gymnorhiza, and Rhizophora samoensis. Stand heights ranged from 4-11 m with a total living mangrove biomass of 188-520 t.ha-1.

11) Bruguiera gymnorhiza assemblages were sampled in all five countries, Solomon Islands, Vanuatu, Fiji, Tonga and Samoa. Stand heights ranged from 5-22 m with a total living mangrove biomass of 326-747 t.ha-1.

12) Rhizophora species assemblages were sampled in all five countries, Solomon Islands, Vanuatu, Fiji, Tonga and Samoa. Stand heights ranged from 4-17 m with a total living mangrove biomass of 94-817 t.ha-1.

13) Ceriops tagal assemblages were sampled in two countries, Solomon Islands and Vanuatu. Stand heights ranged from 7-13 m with a total living mangrove biomass of 282-478 t.ha-1.

14) Xylocarpus granatum assemblages were sampled in two countries, Vanuatu and Fiji. Stand heights ranged from 7-10 m with a total living mangrove biomass of 380-500 t.ha-1.

15) Excoecaria agallocha assemblages were sampled in one country, Tonga. Stand heights ranged ~7 m with a total living mangrove biomass of ~289 t.ha-1.

16) Lumnitzera littorea assemblages were sampled in one country, Tonga. Stand heights ranged ~6 m with a total living mangrove biomass of ~223 t.ha-1.

17) Avicennia marina assemblages were sampled in one country, Vanuatu. Stand heights ranged ~5 m with a total living mangrove biomass of ~483 t.ha-1.


5

PROJECT OVERVIEW

This report adds to previous progress reports summarising new findings and observations about biodiversity, structure and condition of mangrove ecosystems in the five MESCAL countries, Fiji, Samoa, Tonga, Vanuatu and Solomon Islands. These new data specifically provide for the derivation of estimates of living biomass and preliminary carbon values in all five countries. While prior reports addressed biodiversity of mangrove plants and the discovery of new species for each country, this report focuses on the biomass and potential carbon stores present in key vegetation units of mangrove habitats of the region. As outlined previously, this component of the MESCAL project has 4 key activities in each of the five countries – mapping and verification (A), floristics and biodiversity (B), biomass and carbon evaluation (C), and shoreline health monitoring (D). This combination of activities makes up an important part of this Coastal Health Archive and Monitoring Program for the region. This biomass and carbon component of work has only been possible after receipt of sufficient information collected by participants from all five countries, with significant primary data received up to April 2013. These data have now been carefully assessed and processed with considerable effort made in checking data quality and its veracity, as far as practical. For example, issues, errors and incorrect data entry included, the occasional use of inches on field tape measures for measuring circumference – and, the use of a diameter tape measure. Such matters have been largely resolved, with feedback from the respective country teams. Such matters can be minimised in future where all data parameters might be more fully described and annotated with the field data sheets. Despite such issues, the total data compiled here represents a significant and invaluable resource of baseline information on the mangroves of five major Pacific island countries. Key observations and derivations from these data are summarised in this report. A triumph for citizen science and better management of threatened mangrove ecosystems. Field surveys of 7 dominant mangrove vegetation assemblages have been evaluated for five countries, Solomon Islands, Vanuatu, Fiji, Tonga and Samoa. This has been achieved by people with previously limited knowledge of mangrove ecosystems, and for making assessments of biomass structure, before receiving specific training and instruction – starting with the field component. The intention with this program has been to develop both local capacity and personal skill levels for the conduct of scientific investigations with implications for major environmental management outcomes. This effort has been an outstanding success and all responsible need to be acknowledged and congratulated.


6

INTRODUCTION Measuring mangrove stand structure and biomass – the context for using the long plot methodology Mangroves have the distinction of forming a unique marine habitat that is both forest and wetland. As such, they form an important component of a number of international conventions that recognize their uniqueness and immense value to both coastal and marine communities, and mankind in general (eg. Duke et al. 2007). It is essential that the assessment of such a valuable resource be conducted in a rigorous and practical way. The long plot methodology was developed to specifically meet this need. The method quantifies biomass of mangrove forests in a way that is scientifically reliable, accurate, low cost, low skilled, simple, pragmatic and relevant. The essential distinguishing characteristic of the long plot method is that it uses a set number of trees rather than a fixed plot area. The method takes in measures sequentially so the measures created can be evaluated to show accumulative consistency and covariance in the data. And, these observations can be readily verified and checked after collection in the field. The aim of this methodology has been to demonstrate plot homogeneity with statistical rigor. This has been a primary question in the collection of data from different mangrove vegetation units, or zones – as observed in the field and from remote sensing – to make sure a sufficient number trees and area of habitat are measured. Definition of mangrove habitat – essential knowledge for site selection The method does rely on at least one observer present having a reasonable knowledge of what is, and what is not a mangrove stand. It cannot be assumed that there is a clear distinction between mangroves and other plant habitats. While it maybe surprising, it is not always clear exactly what is a mangrove or what vegetation is considered mangrove habitat. So, it is important to review briefly the definition of mangroves. Mangroves are the tidal wetland habitat comprised of salt-tolerant trees and shrubs that inhabit the elevation zone from above mean sea level up to the highest tidal level. And, as with rainforests, mangroves have a mixture of plant types from a diverse array of plant families. Specifically, the definition of a mangrove is a tree, shrub, palm or ground fern, generally exceeding one half metre in height, that normally grows above mean sea level in the intertidal zone of marine coastal environments and estuarine margins. Mangroves share this intertidal niche with saltmarsh plants, as well as a number of dedicated epiphytes and parasitic plants plus some occasional associates from neighbouring upland habitats. Mangrove plants mostly consist of trees and shrubs of varying stature and structure. And, since the larger plants as trees form the substantive part of any stand structure, the description of mangrove stands has many similarities with upland forestry methods.


7

However, there are notable differences as evidenced by the distinctive structural features of their above ground roots as well as their often extraordinary stem structures. Both these characteristics of mangrove forest stands have significant implications for the way mangrove trees and shrubs are measured. Weighted stand canopy height – Lorey’s Height Estimates of the weighted mean canopy height (see: http://fennerschool-associated.anu.edu.au/) – especially since these canopy trees are the ones observed in remote sensing imagery for mapping of vegetation units. Lorey's mean height weights the contribution of trees to the overall stand height by their basal area. Lorey's mean height is calculated by multiplying tree height (H) by their basal area (as the cross section area of stems per unit area), and then dividing the sum of this calculation by the total stand basal area (G). Estimating biomass and carbon content As with general forestry practice, allometric equations offer a convenient and non-destructive way of sampling forest biomass. Such equations have long been used to estimate mangrove forest biomass from field measures of density, stem diameter and height (Snedaker & Snedaker 1984). Over the years, improvements have been made with the reporting of more specific equations for different species (e.g. Clough & Scott 1989), and for a greater range of climatic regions and mangrove habits (e.g. Clough et al. 1997). The bulk of these estimates relate to the relatively easy to measure biomass above-ground (also see Fromard et al. 1998). However, there are a limited number of equations that also include below-ground biomass (Poungparn et al. 2002; Ong et al. 2004; Komiyama et al. 2005; Comley & McGuinness 2005). Ong et al. (2004), working with Rhizophora apiculata, observed that his allometric equations were not very site specific. And, partitioning of biomass was quite variable for smaller trees (diam. <15 cm) but relatively constant for larger trees. The ratio of all the components, except for trunk, to total biomass was larger and more variable for the smaller trees. For larger trees, 4.5% was allocated to below-ground roots, 12.5% to stilts, 71.7% to trunk, 8.1% to branches, twigs and fruits and 3.2% to leaves, i.e. 17% is apportioned to roots and 11.3% to the canopy (branches, twigs, leaves, flowers and fruits), which is a bottom-heavy stable structure. Komiyama et al. (2008) reviewed 72 published articles to elucidate further characteristics of biomass allocation and productivity of mangrove forests. The biomass of mangrove forests varies with age, dominant species, and locality. In primary mangrove forests, the above-ground biomass tends to be relatively low near the sea and increases inland. On a global scale, mangrove forests in the tropics have much higher above-ground biomass than those in temperate areas. Mangroves often accumulate large amounts of biomass in their roots, and the above-ground biomass to below-ground biomass ratio of mangrove forests is significantly low compared to that of upland forests. Litter fall production is generally high in mangrove forests. Moreover, in many mangrove forests, the rate of soil respiration is low, possibly because of the anaerobic soil conditions. These trends in biomass allocation, net primary production, and soil respiration will result in high net ecosystem production, making mangrove


8

forests highly efficient carbon sinks in the tropics. Komiyama et al. (2005) identified common allometric equations, ideally applicable to key mangrove species depending on their stem wood density. These equations can be used to estimate the weight of trunk, leaf, other above ground parts, and roots. The allometric relationships for these weights were attained, when stem diameter was selected as the independent variable. The value of allometric equations depends on the strength of the relationship between component dry weight (W) and stem diameter (D), and/or tree height (H). Stem diameter (often derived from stem girth or circumference) is measured more conveniently and more easily than height, so the latter is less often used in biomass estimations. Choosing allometric equations for this study For calculating the biomass of individual trees (above and below ground) with this project, it was essential to first review available allometric equations. This was considered essential in the absence of local country equations for the dominant species present. There are a number of equations from studies elsewhere (see Appendix 1; Komiyama et al. 2008). These quantify the relationship between simple structural measures (like stem diameter of trees) and dry weight of above and below ground biomass (AGB and BGB, respectively). Equations vary between species. By summing the amounts of biomass for respective species and individual plants present, this amount equates directly to amounts of carbon stored in various mangrove plots and zones. Importantly, because there are a number of species to consider, there are common equations that apply to multiple species (Komiyama et al. 2005; Chave et al. 2005). These become specific-specific with the input of cited measures of specific wood density for particular species (see Appendix 2). The choice therefore is to either use an equation for each species (these preferably are developed locally), or to use the common equation that uses these species-specific values of wood density (p). The current approach is to use a common equation, but the actual choice requires additional justification and validation. The common equations for biomass estimates of mangrove forests include: WAGB 1 = 0.251pD^2.46 (Komiyama et al. 2005) WAGB 2 = 0.168pD^2.47 (Chave et al. 2005) WAGB/H = 0.0509pD^2.H (Chave et al. 2005) WBGB = 0.199p^0.899D^2.22 (Komiyama et al. 2005) Note: a) Explanation of equation parameters - where WAGB is above ground dry weight in kg, WBGB is below ground dry weight in kg, D is stem diameter in cm, and p is wood density for individual species in t/m3 (see Appendix 2). Wood densities ρ have been calculated from the ratio of WS/VS, where WS is trunk (stem) dry weight in t (=tonnes =kg/1000), and VS is trunk (stem) wet wood volume in m3. Height (H) is included in one equation, and this is considered more realistic across a variety of climatic zones. There is uncertainty in the literature as to whether WAGB should include Rhizophora prop roots, or not. While it may seem logical that prop roots would be part of WBGB often this is either not stated or inconsistent. For this treatment, it is assumed that WAGB


9

does not include prop roots, and WBGB does. Hence, the ratio of WAGB to WBGB will represent the Stem to Root ratio often used in forest descriptions. Note: b) There is another serious question surrounding the protocol for measuring stem diameter (D). Slight differences in this measure can create considerable differences and errors in biomass and carbon calculations when using allometric equations. The terrestrial forestry standard has stem diameter at 1.3 m above the ground – called, diameter at breast height (DBH). However, while a standard rule for all trees is understandable, important and desirable, unfortunately there are considerable difficulties in applying the DBH rule to mangrove plants – as a collection of ferns, a palm, shrubs and trees. In Appendix 3, stem characteristics of most Indo West Pacific mangroves are compared and assessed for their relevance in using DBH. In more than 50% of species listed, DBH cannot be strictly applied to the measurement of stem diameter, where these either do not have stems, or they are too short, or they are shrubs with low branching, or they have highly placed prop roots or buttresses. For the remaining 21 species, the DBH rule applies only for a minority of occasions. Therefore, in the majority of instances when stem diameter is measured in mangroves, amendments to the DBH rule are applied. This is strong justification for questioning why the DBH standard is applied at all. And, it is strong justification for the adoption of a more appropriate and unequivocal standard guideline. A more realistic protocol for appropriate measurement of stem diameter for mangrove plants is needed. While alternate measurement rules have been proposed (see Komiyama et al. 2005), in general, these have not been widely adopted, and there is incongruity and inconsistency between studies. This is of concern when applying someone else’s equation, as this requires the use of their method – and often this is not clearly stated. The importance of this distinction has been greatly elevated by recent requirements for more precise and reproducible measures of sequestered carbon in mangrove forests. In this study, while we will collect data applicable to existing equations, it is our intention to also measure stem girth using a more pragmatic standard in an effort to be part of a process of refinement in this methodology. For this reason, stem girth has been measured, where possible, above the highest prop root and below the lowest branch. The application of this rule to each species and form is shown in Appendices 3 and 4. Note: c) In addition, for those plants with multiple stems at their base (a common occurrence in mangrove habitats), the rule is that each stem will be measured, while making reference to their other branches. Note: d) It is recognised further that there are some mangrove stands, like stunted Rhizophora thickets, that defy all attempts to apply some standard measures, and to non-destructively quantify their structural characteristics. In some tangled thickets, it is sometimes impossible to tell where stems differ from branches or above ground roots, and even where different individual trees begin and end. In these cases, the only solution maybe to physically cut up and sample representative area plots and equate these to canopy height as the proxy for biomass. Calculations of biomass and carbon content of mangrove vegetation units After estimating the biomass (W in kg) for each individual tree in both above and below ground components, the sum these was taken for each sample plot. Calculation


10

was then made of the total biomass per unit area, dividing the biomass by the plot area (A in m2). Make comparable estimates for each set of plots in each vegetation unit, in each study area. Calculation of the mean carbon stock for above and below ground biomass of each component is made by converting plant dry weight estimates to amounts of carbon. This is a primary goal - to determine the amount of carbon accumulated in a mangrove stand. For this calculation, the volume of carbon as dry biomass is quantified for the various forest components, including: woody stems, branches, leaves – using the conversion coefficients 0.4535, 0.4800, 0.5025, respectively (note that these coefficients vary and require local confirmation for particular species). Carbon accumulation is calculated by multiplying the dry weight biomass estimates (W) by the carbon coefficients. The total carbon accumulation of trees is the total of all the component parts. For overall calculation of carbon amounts in total biomass, the carbon coefficient is usually roughly averaged at 0.5. However, this calculation would benefit from using a more accurate coefficient for the particular stand. A subsequent step might also be to calculate the absorption of carbon dioxide by forests by converting carbon estimates to carbon dioxide equivalents. This is calculated by the method of NIRI (Institute Nissho Iwai-Japan) where the CO2 absorbed equals the carbon accumulation times 44/12, where 1 ton of carbon is equal to 3.67 tons of CO2. Remote estimation of mangrove forest carbon – something for the future It seems likely that in the future, biomass and carbon estimates made from field locations will be verified and scaled up from remotely operated sensors on either satellites or from specially equipped aircraft. These are not expected to replace field assessments, but such tools would be useful for, a regional collation of plot inventories, monitoring plot condition and general review for on-going compliance and plot maintenance. There are at least two approaches described: 1) spatial quantification based on geographic area of vegetation units – use normal wavebands in high definition satellite imagery; and 2) direct estimates of biomass based on stand height and ground truth – using LIDAR as radar wavebands. The latter are expensive but this is a tool that may be used by national and regional groups to verify claims made by community groups as custodians of the forested areas. The first method, by Proisy et al. (2007), used Fourier-based textural ordination (FOTO) to estimate mangrove forest biomass from very high resolution (VHR) IKONOS images. The method identified and characterized high biomass tropical forest from standardized measures of canopy grain characteristics. In the case study presented, multiple linear regression using the three main textural indices yielded accurate predictions of mangrove total aboveground biomass over 8000 ha of unexplored, poorly accessible mangrove. The other method, described by Simard et al. (2006), produced a landscape scale map of mangrove tree height using elevation data from the Shuttle Radar Topography Mission (SRTM). This data was calibrated using airborne LIDAR (X-ray) data and a high


11

resolution USGS digital elevation model. The authors further used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves, and the total mangrove standing biomass. For the MESCAL project, it is not possible to utilize these techniques, but it is planned to apply intermediate remote assessments as those being conducted using geo-referenced video with the Shoreline Video Assessment Methodology (S-VAM). With S-VAM, it has been demonstrated in Solomon Islands and others, that observations of biomass and carbon can be quantified shorelines surveyed in this way. The technique has the benefit of linking mapping and field verification to gain not only a spatial quantification with measures of structure and biodiversity, but also to measure habitat condition and key drivers of change. To date, there has been significant video filming accomplished by each country team and these surveys will be reported on in another month or so. It is expected that these data will build further on the overall assessment of mangrove ecosystems being undertaken in this MESCAL program. Field data collection strategy Mangrove stands and particularly the distinct zones, are often relatively narrow. The location and orientation of plots was standardised to accommodate these characteristics. Plots were kept narrow and orientated along tidal contours – notably laid out parallel to the sea or channel edge. This had the desired effect of minimising variability of species composition and structural assemblages dependant largely on inundation frequency and elevation. Specific plot sites were referenced using GPS coordinates for later location on remote sensing images ranging from aerial photographs to those on Google Earth, or others. Study areas were selected within each countries demonstration sites. For each demonstration site, a number of plots were sampled for their forest structural characteristics. The number of plot measured depended on the number of mangrove zones (species assemblage types) within the respective areas. The objective was to measure, where possible, up to 4 replicate plots for each dominant vegetation type of each site. Our primary objective was to rapidly and accurately determine key biodiversity and structural characteristics of selected mangrove stands for the calculation of biomass and carbon content. Specific characteristics included: species, stem density, canopy height and stem diameter. The basic assessment unit was the plot of sampled trees, along with the record of accompanying information, like species dominating the canopy – as observed from remote sensing imagery – plus the condition of trees in each plot. A standard field data sheet was used. The transect layout of the long plot methodology was developed specifically, to accommodate the special features of mangrove forests. Plots of 4 or 10 metre wide were laid out as prescribed – within single mangrove vegetation zones. The length of the plots depended on the density of trees, and the zone’s extent. The number of trees sampled was around 30-50. Tree height was determined using height measuring poles.


12

Data management following field data collection Once data were collected from the field, they were then input into a dedicated, pre-formatted Excel spreadsheet with fields and columns matching the field data sheet. These fields have all the necessary information for determination of plot area (A), plus stem diameter (D) and tree height (H) as well as details of the location markers, like GPS coordinates, plus places to add comments about tree condition, like live or dead condition, whether they are unhealthy, and how trees died. All data was prepared in this way by the country team with assistance from the MESCAL PMU. This data was then submitted to Dr Duke for this analysis and synthesis of results (see: Appendices 5 & 6).


13

RESULTS OVERALL FINDINGS In total, for all 5 countries, 111 long plots were established and measured. This equates to the measurement of 4661 tree stems covering a total area of 25,085 m2, or 2.51 ha of mangrove forests. These data (see Appendices 5 & 6) for the first time provide measures of biomass and structure for 7 dominant vegetation units, as key zones of mangrove assemblages present in the SW Pacific region. Two assemblage zones of Bruguiera gymnorhiza and unspecified Rhizophora species were sampled in all 5 countries, reflecting the common dominance of these vegetation types throughout the region. Two other species assemblages, Ceriops tagal and Xylocarpus granatum were sampled in two of the western-ranging countries. Three other species assemblages, Avicennia marina, Excoecaria agallocha and Lumnitzera littorea were sampled in only one each of the five countries.

Table 1. Numbers of specific mangrove vegetation units sampled and numbers of plots measured in the Solomon Islands, Vanuatu, Fiji, Tonga and Samoa by the respective country teams in 2012-2013. Dominant Mangrove Vegetation Assemblage

Solomon Islands

Vanuatu

Fiji

Tonga

Samoa

TOTAL per Veg

Unit Bruguiera gymnorhiza 2 2 29 5 9 47 Rhizophora species 6 10 5 12 2 35 Ceriops tagal 3 7 - - - 10 Avicennia marina - 5 - - - 5 Xylocarpus granatum - 2 4 - - 6 Excoecaria agallocha - - - 4 - 4 Lumnitzera littorea - - - 4 - 4 TOTAL per Country 11 26 38 25 11 111

These data provide for the more detailed assessments of forest biomass for each of the 7 dominant vegetation assemblages, for each country, and grouped across all countries. VEGETATION UNIT RESULTS DOMINANT VEGETATION UNIT - BRUGUIERA GYMNORHIZA Perhaps the most accessible of the dominant vegetation units was Bruguiera gymnorhiza. This species has regular, singular stems with modest trunk buttressing, making the sampling of these forests relatively straight forward compared with other


14

species. They also form a dominant component of the mangrove ecosystems of all five countries. In Table 2, canopy heights are shown to vary noticeably in each country sample from smaller 4 m stands in Tonga to moderately tall 18 m stands in Solomon Islands. It follows that these stands have corresponding higher basal areas of 74 m2.ha-1 and densities of 4669 stems.ha-1 in Tonga, while those in Solomon Islands have 34 m2.ha-1 and 852 stems.ha-1, respectively. Table 2. For Bruguiera gymnorhiza derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Solomon islands, Vanuatu, Fiji, Tonga and Samoa.

Country MeanHgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2.ha-1)

Samoa 7.2 9.0 700.2 269.9 485.2 249.7 58.8 Tonga 4.1 5.1 476.9 125.3 328.7 200.6 73.7 Fiji 9.9 11.0 552.0 247.5 384.1 187.5 42.2 Vanuatu 11.7 14.6 803.7 463.0 557.5 283.7 69.5 Solomons 18.4 21.7 415.4 400.6 287.7 148.8 34.4

Taller stands generally have higher amounts of biomass. This is shown in Table 2 and further in Figure 1 where plots from all countries are considered together. The equation used in this figure was that of Chave et al. 2005 using stem diameter and height, but the trend changed little between common equations. Likewise, this applies also with the use of Lorey’s Height or the mean stand height. These parameters are presented as they are considered the more realistic in their quantification of biomass and canopy height.

Fig. 1. Above ground biomass of Bruguiera gymnorhiza for different height stands across all five countries. AGB derived from common equation using diameter and height by Chave et al., 2005.


15

These data describe a significant linear relationship between biomass and canopy height. This shows how canopy height can be used as a proxy for stand biomass, and later for living carbon content. The proviso in this, relates to the range of stands presented in these plots, based on their range of canopy heights and stem diameters. DOMINANT VEGETATION UNIT - RHIZOPHORA SPECIES The Rhizophora species are perhaps the most difficult to sample because of their tangles of above ground roots and their multi-stemmed structure. These vegetation assemblages are also dominant across all five countries. And, together with the Bruguiera species, these two are expected to cover the bulk of mangrove habitats. But, this must be confirmed with the mapping of these habitats – at least for the demonstration sites. In Table 3, canopy heights again are shown to vary noticeably in each country sample from smaller 4 m stands in Tonga to moderately tall 17 m stands in Solomon Islands. However, for this assemblage there are corresponding basal areas of 16 m2.ha-1 and 60 m2.ha-1 while densities are opposite with 5474 stems.ha-1 in Tonga while those in Solomon Islands have 1464 stems.ha-1. Table 3. For Rhizophora species derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Solomon islands, Vanuatu, Fiji, Tonga and Samoa.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Samoa 4.2 4.3 213.6 79.0 145.7 109.1 32.7 Tonga 4.0 4.4 109.8 39.1 75.0 54.4 16.3 Fiji 9.2 9.5 317.8 192.4 218.6 134.3 34.5 Vanuatu 6.4 7.4 171.0 79.6 117.2 75.7 20.7 Solomons 14.3 17.3 730.8 553.8 506.1 262.8 59.5

Again, taller stands have higher amounts of biomass, as shown in Table 3 and Figure 2. The same equations were used in these presentations, as before. These data describe a significant linear relationship between biomass and canopy height for Rhizophora species assemblages. This shows further how canopy height can be used as a proxy for stand biomass, and later for living carbon content. The proviso in this, relates to the range of stands presented in these plots, based on the range of canopy heights and stem diameters.


16

Fig. 2. Above ground biomass of Rhizophora species for different height stands across all five countries. AGB derived from common equation using diameter and height by Chave et al., 2005. DOMINANT VEGETATION UNIT - CERIOPS TAGAL Ceriops tagal vegetation assemblages are relatively open stands of straight slender stems with moderate low buttressing. They can be prone to multi-stemmed structures in more arid regions. Such stands are less common in the region, especially as this species occurs only in Vanuatu and Solomon Islands. In Table 4, canopy heights are varied in each country with smaller 7 m stands in Vanuatu to taller stands of 11 m in Solomon Islands. It follows that these stands have corresponding higher basal areas of 32 m2.ha-1 and densities of 4670 stems.ha-1 in Vanuatu while those in Solomon Islands have 42 m2.ha-1 and 1380 stems.ha-1, respectively. Table 4. For Ceriops tagal derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Solomon islands and Vanuatu.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Vanuatu 6.3 7.3 308.6 148.8 212.1 133.4 32.4 Solomons 11.3 13.2 477.0 296.1 329.4 181.8 41.9

The taller stands have higher amounts of biomass. This is shown in Table 4 and further in Figure 3 where plots from the two countries are considered together. The equation used in this figure was that of Chave et al. 2005 using stem diameter and height, but the trend changed little between common equations. Likewise, this applies also with the use of Lorey’s Height as representing mean stand height. As before, these


17

parameters are presented as they are considered the more realistic in their quantification of biomass and canopy height.

Fig. 3. Above ground biomass of Ceriops tagal for different height stands in Vanuatu and Solomon Islands. AGB derived from common equation using diameter and height by Chave et al., 2005. These data further describe a significant linear relationship between biomass and canopy height. This shows how canopy height can be used as a proxy for stand biomass, and later for living carbon content. The proviso in this, relates to the range of stands presented in these plots, based on their range of canopy heights and stem diameter. DOMINANT VEGETATION UNIT - XYLOCARPUS GRANATUM Xylocarpus granatum assemblages were measured in just two of the five countries were they occur. This reflects a relative lack of dominance of this species further to the east. Trees are usually straight stemmed but they often have large sinuous buttresses, especially in larger individuals. These larger trees are also prone to hollowing of stems making estimates of biomass using stem diameter alone somewhat fraught. In Table 5, the trends observed in other species assemblages with canopy height do not exist in this one. Stem densities vary from 1913 stems.ha-1 in Vanuatu while those in Fiji have 1066 stems.ha-1. Table 5. For Xylocarpus granatum derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Vanuatu and Fiji.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Fiji 5.9 6.9 948.8 201.2 661.6 298.2 75.9 Vanuatu 7.6 9.8 509.4 193.1 353.0 186.7 52.7


18

The lack of a relationship for this species assemblage cannot be fully evaluated from these limited data. DOMINANT VEGETATION UNIT - AVICENNIA MARINA Avicennia marina assemblages were measured in only one of the two countries where they occur. In Vanuatu, these stands are often relatively uniform with sinuous simple stems with no obvious buttresses. In Table 6, canopies are notably single layered in the Vanuatu plots, since mean canopy height equals the weighted Lorey’s canopy height. Stand stem density is around 1226 stems.ha-1. And, basal area is relatively high at 68 m2.ha-1. It is possible this species plot, like Xylocarpus granatum, may have non-functional stem diameters where trunks are sometimes hollowed. Table 6. For Avicennia marina species derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Vanuatu.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Vanuatu 5.4 5.4 844.1 189.5 585.4 294.0 68.3

Fig. 4. Above ground biomass of Avicennia marina for different height stands in Vanuatu. AGB derived from common equation using diameter and height by Chave et al., 2005. Nethertheless, plots with taller canopy heights have higher amounts of biomass. This is shown in Figure 4, where all plots from Vanuatu are considered. The equation used in this figure was that of Chave et al. 2005 using stem diameter and height, but the trend changed little between common equations. Likewise, this applies also with the use of


19

Lorey’s Height or the mean stand height. As before, these parameters are presented as they are considered the more realistic in their quantification of biomass and canopy height. These data further describe a significant linear relationship between biomass and canopy height. This shows how canopy height may be used as a proxy for stand biomass, and later for living carbon content. The proviso in this, relates to the range of stands presented in these plots, based on their range of canopy heights and stem diameters. DOMINANT VEGETATION UNIT - EXCOECARIA AGALLOCHA Assemblages of Excoecaria agallocha occur across the western Pacific from the Solomons, Vanuatu, Fiji and Tonga. Only in Tonga were these stands sampled and characterised during current surveys. Stands are somewhat clumped and distinguished by multiple, spreading stems with minimal buttressing. In Table 7, the trends observed in other more dominant assemblages do not exist in this one. Stem densities vary around 4669 stems.ha-1 in Tonga. And, basal areas are around 68 m2.ha-1. Table 7. For Excoecaria agallocha derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Tonga.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Tonga 4.3 7.0 390.7 128.3 269.8 160.9 67.8

The lack of a relationship for this species assemblage cannot be fully evaluated from these limited data. DOMINANT VEGETATION UNIT - LUMNITZERA LITTOREA Assemblages dominated with Lumnitzera littorea occur across the western Pacific from the Solomons, Vanuatu, Fiji and Tonga. Only in Tonga were these stands sampled and characterised during current surveys. Stands are open and distinguished by erect simple stems with minimal buttressing. In Table 8, the trends observed in other more dominant assemblages with canopy height did not exist in this one. Stem densities vary around 6561 stems.ha-1 in Tonga. And, basal areas are around 43 m2.ha-1.


20

Table 8. For Lumnitzera littorea derived mean estimates of average tree height (Hgt), weighted canopy height (CanW Hgt), biomass (in t.ha-1) as AGB from Komiyama, AGB/H from Chave et al. 2005, and AGB from Chave et al. 2005, BGB from Komiyama et al. 2005, and Stand Basal Area (BA) for long plots in Tonga.

Country Mean Hgt(m)

CanW Hgt(m)

AGB Komiyama

AGB/H Chave

AGB Chave

BGB Komiyama

BA (m2/ha)

Tonga 5.2 6.0 265.2 102.7 182.1 120.6 43.0 The lack of a relationship for this species assemblage cannot be fully evaluated from these limited data. COUNTRY RESULTS BIOMASS AND CARBON EVALUATION – SOLOMON ISLANDS Mangrove biomass data were collected from 12 long plots from along 2 estuaries in the demonstration site. Data sheets were processed and the Excel spreadsheet for Solomon Islands are available as a separate file for further reference (see Appendices 5 & 6). These data show biomass estimates for three dominant vegetation types (number of long plots) in the demonstration site area – Bruguiera gymnorhiza (2), Rhizophora species (6) and Ceriops tagal (3). These estimates can now be used with the areas derived from the mapping for development of local and national estimates of biomass and carbon bound up in living mangrove forest vegetation units (Table 9). There were issues with data checking, missing data, and possible errors that have been mostly resolved. It is advisable to check the Excel spreadsheets to validate or correct the respective data elements. Table 9. Solomon Islands mangrove vegetation unit data, showing derived estimates of carbon (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from the allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass Solomons & Structure Mean SE X1 Bruguiera gymnorhiza AGB Komiyama 207.7 AGB/H Chave 200.3 AGB Chave 143.9 BGB Komiyama 74.4 Hgt(m) 18.4 CanWHgt(m) 21.7 BA(m2/ha) 34.4


21

Rhizophora sp. AGB Komiyama 365.4 54.0 AGB/H Chave 276.9 45.1 AGB Chave 253.0 37.6 BGB Komiyama 131.4 17.3 Hgt(m) 14.3 1.2 CanWHgt(m) 17.3 1.1 BA(m2/ha) 59.5 7.0 Ceriops tagal AGB Komiyama 238.5 43.4 AGB/H Chave 148.1 15.4 AGB Chave 164.7 30.0 BGB Komiyama 90.9 15.6 Hgt(m) 11.3 0.7 CanWHgt(m) 13.2 1.2 BA(m2/ha) 41.9 6.7

BIOMASS AND CARBON EVALUATION – VANUATU Mangrove biomass data were collected from 27 long plots in the 2 demonstration areas – with 14 in Malekula, and 13 in Efate. Data sheets were processed and the Excel spreadsheets for Vanuatu are available as a separate file for further reference (see Appendices 5 & 6). These data show biomass estimates for five dominant vegetation types (number of long plots) in the two demonstration site areas – Avicennia marina (5), Bruguiera gymnorhiza (2), Rhizophora species (10) and Ceriops tagal (8) and Xylocarpus granatum (2). These estimates can now be used with the areas derived from the mapping for development of local and national estimates of biomass and carbon bound up in living mangrove forest vegetation units (Table 10). There were issues with data checking, missing data, and possible errors that have been mostly resolved. It is advisable to check the Excel spreadsheets to validate or correct the respective data elements. Table 10. Vanuatu mangrove vegetation unit data, showing derived estimates of carbon (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from the allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass Vanuatu & Structure average SE X1 Bruguiera gymnorhiza AGB Komiyama 401.9 AGB/H Chave 231.5 AGB Chave 278.7 BGB Komiyama 141.8 Hgt(m) 11.7


22

CanWHgt(m) 14.6 BA(m2/ha) 69.5 Rhizophora sp. AGB Komiyama 85.5 12.1 AGB/H Chave 39.8 4.7 AGB Chave 58.6 8.3 BGB Komiyama 37.8 4.9 Hgt(m) 6.4 0.5 CanWHgt(m) 7.4 0.6 BA(m2/ha) 20.7 2.6 Xylocarpus granatum AGB Komiyama 254.7 AGB/H Chave 96.5 AGB Chave 176.5 BGB Komiyama 93.3 Hgt(m) 7.6 CanWHgt(m) 9.8 BA(m2/ha) 52.7 Ceriops tagal AGB Komiyama 154.3 19.0 AGB/H Chave 74.4 10.4 AGB Chave 106.1 13.1 BGB Komiyama 66.7 8.1 Hgt(m) 6.3 0.4 CanWHgt(m) 7.3 0.4 BA(m2/ha) 32.4 4.6 Avicennia marina AGB Komiyama 422.0 40.9 AGB/H Chave 94.7 17.7 AGB Chave 292.7 28.4 BGB Komiyama 147.0 14.7 Hgt(m) 5.4 0.4 CanWHgt(m) 5.4 0.4 BA(m2/ha) 68.3 7.0

BIOMASS AND CARBON EVALUATION – FIJI Mangrove biomass data was collected from 43 long plots mostly along the Rewa River estuary demonstration site area. Data sheets were processed and the Excel spreadsheet for Fiji are available as a separate file for further reference (see Appendices 5 & 6). These data show biomass estimates for three dominant vegetation types (number of long plots) in the demonstration site area – Bruguiera gymnorhiza (29), Rhizophora species (5) and Xylocarpus granatum (4). The estimates can now be used with the areas derived from the mapping for development of local and national estimates of biomass and carbon bound up in living mangrove forest vegetation units (Table 11). There were issues with data checking, missing data, and possible errors that have been mostly resolved. Most data were received through February and March 2013. The original data sheets and reporting lists around 116 plots but mangroves were not


23

present in all – and others contained mixed associations, indicative of long plots crossing mangrove vegetation zones (= vegetation units). Plots were also incorrectly described as 10m X 10m when this appears to have been not the case. It is advisable to check the Excel spreadsheets to validate or correct the respective data elements. The relatively large number of 29 Bruguiera forest plots were most useful in exploring the relationship between biomass and forest canopy height for this vegetation unit. This is discussed above. It is likely, forest camopy height will have immense importance for remote and rapid assessments of biomass and carbon in the future. Table 11. Fiji mangrove vegetation unit data, showing derived estimates of carbon (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from the allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass Fiji & Structure average SE X1 Bruguiera gymnorhiza AGB Komiyama 276.0 25.0 AGB/H Chave 123.8 11.5 AGB Chave 192.1 17.6 BGB Komiyama 93.8 7.5 Hgt(m) 9.9 0.4 CanWHgt(m) 11.0 0.4 BA(m2/ha) 42.2 3.0 Rhizophora sp. AGB Komiyama 158.9 55.4 AGB/H Chave 96.2 32.1 AGB Chave 109.3 38.2 BGB Komiyama 67.1 22.0 Hgt(m) 9.2 1.3 CanWHgt(m) 9.5 1.4 BA(m2/ha) 34.5 10.5 Xylocarpus granatum AGB Komiyama 474.4 244.2 AGB/H Chave 100.6 37.0 AGB Chave 330.8 170.9 BGB Komiyama 149.1 70.3 Hgt(m) 5.9 0.7 CanWHgt(m) 6.9 0.8 BA(m2/ha) 75.9 33.5 Annona glabra AGB Komiyama 344.0 (NB: invasive in AGB/H Chave 190.8 mangroves) AGB Chave 238.3 BGB Komiyama 124.3 Hgt(m) 11.4


24

CanWHgt(m) 13.2 BA(m2/ha) 67.1

BIOMASS AND CARBON EVALUATION – TONGA Mangrove biomass data were collected from 25 long plots from around the demonstration site area of Nukuhetulu and Veitongo. Data sheets were processed and the Excel spreadsheets for Tonga are available as a separate file for further reference (see Appendices 5 & 6). These data show biomass estimates for four dominant vegetation types (number of long plot samples) in the demonstration site area – Bruguiera gymnorhiza (5), Rhizophora species (12), Excoecaria agallocha (4) and Lumnitzera littorea (4). These estimates can now be used with the areas derived from the mapping for development of local and national estimates of biomass and carbon bound up in living mangrove forest vegetation units (Table 12). There were issues with data checking, missing data (like dead trees), and possible errors that have been mostly resolved. It is advisable to check the Excel spreadsheets to validate or correct the respective data elements. Table 12. Tonga mangrove vegetation unit data, showing derived estimates of carbon (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from the allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass Tonga & Structure average SE X1 Bruguiera gymnorhiza AGB Komiyama 238.5 50.1 AGB/H Chave 62.7 11.0 AGB Chave 164.3 34.8 BGB Komiyama 100.3 18.5 Hgt(m) 4.1 0.1 CanWHgt(m) 5.1 0.4 BA(m2/ha) 73.7 15.6 Rhizophora sp. AGB Komiyama 54.9 5.6 AGB/H Chave 19.5 2.3 AGB Chave 37.5 3.8 BGB Komiyama 27.2 2.6 Hgt(m) 4.0 0.2 CanWHgt(m) 4.4 0.2 BA(m2/ha) 16.3 1.6 Excoecaria agallocha AGB Komiyama 195.4 35.4 AGB/H Chave 64.1 8.1


25

AGB Chave 134.9 24.4 BGB Komiyama 80.5 14.3 Hgt(m) 4.3 0.2 CanWHgt(m) 7.0 0.5 BA(m2/ha) 67.8 10.6 Lumnitzera littorea AGB Komiyama 132.6 4.2 AGB/H Chave 51.3 3.4 AGB Chave 91.0 2.8 BGB Komiyama 60.3 3.3 Hgt(m) 5.2 0.4 CanWHgt(m) 6.0 0.3 BA(m2/ha) 43.0 3.0

BIOMASS AND CARBON EVALUATION – SAMOA Mangrove biomass data were collected from 11 long plots from along the demonstration site shoreline. Data sheets were processed and the Excel spreadsheet for Samoa are available as a separate file for further reference (see Appendices 5 & 6). These data show biomass estimates for three dominant vegetation types (number of long plots) in the demonstration site area – Bruguiera gymnorhiza (9) and Rhizophora samoensis (2). These estimates can now be used with the areas derived from the mapping for development of local and national estimates of biomass and carbon bound up in living mangrove forest vegetation units (Table 13). There were issues with data checking, missing data, and possible errors that have been mostly resolved. It is advisable to check the Excel spreadsheets to validate or correct the respective data elements. Table 13. Samoa mangrove vegetation unit data, showing derived estimates of carbon (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from the allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass Samoa & Structure average SE X1 Bruguiera gymnorhiza AGB Komiyama 350.1 29.0 AGB/H Chave 134.9 12.9 AGB Chave 242.6 20.2 BGB Komiyama 124.8 9.7 Hgt(m) 7.2 0.3 CanWHgt(m) 9.0 0.5 BA(m2/ha) 58.8 4.4


26

Rhizophora samoensis AGB Komiyama 106.8 AGB/H Chave 39.5 AGB Chave 72.8 BGB Komiyama 54.5 Hgt(m) 4.2 CanWHgt(m) 4.3 BA(m2/ha) 32.7


27

DISCUSSION MANGROVE BIOMASS MEASURED ELSEWHERE There are a limited number of published measures of above ground biomass, below ground biomass, canopy height and basal area in mangrove forests comparable with those in the SW Pacific. In Table 14, measures have been compiled from 4 of the comparable dominant vegetation units measured in SE Asia. Differences between vegetation assemblages are largely insignificant considering the differences in canopy height represented within these data. In these data, the root to shoot (AGB:BGB) ratios vary from 2.4-3.7, 1-3.3, 1-5.7 and 0.7-2.8, respectively. Table 14. Published estimates of measured biomass of four dominant mangrove vegetation units, Bruguiera gymnorhiza, Rhizophora species, Ceriops tagal and Avicennia marina, for various locations in SE Asia (summarised from Komiyama et al. 2008, plus updates by Pandey et al. 2013).

Dominant Mangrove Vegetation Assemblage

Height (m)

AGB (t.ha-1)

BGB (t.ha-1)

BA (m2.ha-1)

Bruguiera gymnorhiza 22-26 281-436 106-181 31-36 Rhizophora species 2-30 13-619 12-306 3-44 Ceriops tagal 2-5 14-92 3-88 ~15 Avicennia marina 1-16 29-341 36-160

The measures compare closely with the above ground biomass estimates derived from the MESCAL plots with the common equation of Chave et al. (2005) using stem diameter and height (Table 15). In these data, the root to shoot (AGB:BGB) ratios vary from 0.6-2.7, 0.7-2.1, 1.1-1.6, 0.6, 0.8, 0.9 and 0.7-1.0, respectively. Again, these MESCAL survey estimates compare well with published measures from elsewhere. Table 15. Derived estimates of biomass of four dominant mangrove vegetation units, Bruguiera gymnorhiza, Rhizophora species, Ceriops tagal and Avicennia marina, for long plots from the 2012-2013 MESCAL surveys.

Dominant Mangrove Vegetation Assemblage

Lorey’s Height

(m)

AGB/H (t.ha-1)

BGB (t.ha-1)

BA (m2.ha-1)

Bruguiera gymnorhiza 5-22 125-463 149-284 34-74 Rhizophora species 4-17 39-554 54-263 16-60 Ceriops tagal 7-13 149-296 133-182 32-42 Avicennia marina 5 190 294 68 Excoecaria agallocha 7 128 161 68 Lumnitzera littorea 6 103 121 43 Xylocarpus granatum 7-10 193-201 187-298 53-76


28

The reason for most differences in these parameters appears more to do with the relationship between biomass and canopy height (see Fig. 5), than with any other factor. For example, note how the published estimates for Ceriops tagal mostly represent shorter stands, where those in this study were much taller. A similar interpretation can be made of the other vegetation assemblages surveyed.

Fig. 5. The relationships between weighted height (Lorey’s Height) and above ground biomass (using common equation, AGB/H of Chave et al., 2005) for the current study of four dominant vegetation units of Bruguiera gymnorhiza, Rhizophora species, Ceriops tagal and Avicennia marina. These deductions were only possible because of the broad array of plots sampled in the current study. The veracity of data collected in the current study is also confirmed where estimates derived from the current study are so closely comparable with data collected elsewhere. It is useful also to note how closely each of the vegetation assemblages compare with each other when presented together. ESTIMATION OF NATIONAL MANGROVE CARBON RESOURCES FROM MAPPING The derived estimates from these long plot surveys reasonably provide for the estimation of living biomass and carbon in mangrove forest stands across all countries. All that is required to make these estimates are the areas of mangroves present and area


29

representative field observations to affirm average canopy heights. For the species present, there appears to be little difference in their respective biomass estimates compared to the notable differences in stand canopy heights.

Fig. 6. Total carbon biomass in mangrove living forests above and below ground compared with canopy height across all plots and countries. This relationship is based on climax forests of the dominant mangrove vegetation assemblages. Overall and for each country there are notable trends with canopy height, where each trend clearly depends on the range of stand heights included in the sample. The overall trend across all countries can be summarised in the formula of the linear best fit (r2 = 0.4775; n = 117) as: Total Carbon (AGB+BGB) = 21.721*L-Hgt – 4.1833, where carbon is dry weight in t.ha-1, and canopy height is in metres. It appears the full relationship is curvi-linear, especially for trees larger than 20 metres in height. This is shown notably in the trend for plots in Solomon Islands where trees are considerably larger than the other five countries. However, the noted linear relationship is applicable for the bulk of mangrove forests observed. There are also likely to be detrimental influences of cutting and harvesting – affecting these estimates. More precise data on these influences and their effects are needed to quantify the changes in biomass and carbon accumulation. These data can be readily collected with long plot measurements in the field. Preliminary observations on this aspect have been collected by some country teams but it is not yet sufficient for a


30

comparable quantitative assessment. This is highly recommended for future long plot sampling. NOTES AND SPECIFIC RECOMMENDATIONS The long plot method has been implemented as the standard assessment method for this project because of its proven acceptance and application by previously unskilled participants. This practical methodology has the advantage of being relatively simple to learn and use, being rapidly undertaken, with easy follow-up for uploading data into the standard assessment format. Field operations are relatively easy and predictable, measuring a set number of stems rather than a fixed plot area. Furthermore, the incremental accumulation of plot measures provides the means to test statistical variance to affirm attainment of sufficient numbers of stems for calculation of biomass for each plot. Some on-going practical feedback for country teams with specific comments, as follows:-

1) Long plot biomass data must be collected, where possible, for at least 4 of each dominant vegetation types of mangrove stands present in each country; 2) Ensure start and end trees are marked with permanent tags – and labelled for future reference (plot number, start/end); and GPS coordinates taken; 3) Include scores of both live and dead trees, where possible, measuring stem diameter of both, listing species where possible and cause of death or damage; 4) Two future tasks can be implemented with future visits in May-June 2013. The additional tasks include a) depth of peat measures, and b) carbon content and bulk density of biomass/carbon in sediments; 5) Estimates of biomass and carbon will characterize and quantify each dominant vegetation type, along with appropriate error terms – for scaling up from areas derived from the mapping of these same vegetation units to national estimates; 6) It is considered important to undertake country-specific allometric studies (involving cutting down and weighing 15-30 trees from smallest to largest, for each dominant species) to develop and affirm allometric equations for each country. This requires supervised field surveys with experienced technical support of the JCU project team.


31

REFERENCES Boland, D. J., M. I. H. Brooker, G. M. Chippendale, N. Hall, B. P. M. Hyland, R. D.

Johnston, D. A. Kleinig, and J. D. Turner. 1984. Small-stilted Mangrove. Pages 610. Forest Trees of Australia. Thomas Nelson and CSIRO, Melbourne. 610.

Cause et al. 1989. In Saenger 2002. Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Fölster, F.

Fromard, N. Higuchi, T. Kira, J.-P. Lescure, B. W. Nelson, H. Ogawa, H. Puig, B. Riéra, and T. Yamakura. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 1432-1939.

Clough, B. F., and K. Scott. 1989. Allometric relationship for estimating above-ground biomass in six mangrove species. For. Ecol. & Manag. 27: 117 - 128.

Clough, B. F., P. Dixon, and O. Dalhaus. 1997. Allometric relationships for estimating biomass in multi-stemmed mangrove trees. Aust. J. Botany 45: 1023-1031.

Comley, B. W. T., and K. A. McGuinness. 2005. Above- and below-ground biomass and allometry of four common northern Australian mangroves. Australian Journal of Botany 53: 431–436.

Duke, N. C., and K. A. Burns. 1999. Fate and effects of oil and dispersed oil on mangrove ecosystems in Australia. Pages Main Report, 212 pages; Executive Summary, 23 pages. Australian Institute of Marine Science and CRC Reef Research Centre.

Duke, N. C., J.-O. Meynecke, S. Dittmann, A. M. Ellison, K. Anger, U. Berger, S. Cannicci, K. Diele, K. C. Ewel, C. D. Field, N. Koedam, S. Y. Lee, C. Marchand, I. Nordhaus, and F. Dahdouh-Guebas. 2007. A World Without Mangroves? Science 317: 41-42.

Fromard, F., H. Puig, E. Mougin, G. Marty, J. T. Betoulle, and L. Cadamuro. 1998. Structure, above-ground biomass and dynamics of mangrove ecosystems: new data from French Guiana. Oecologia 115: 39-53.

Imbert, D., and B. Rollet. 1989. Phytomasse aérienne et production primaire dans la mangrove de Grand Cul-de-Sac Marin (Guadelope, Antilles françaises). Bulletin of Ecology 20: 27-39.

Komiyama, A., H. Moriya, S. Prawiroatmodjo, T. Toma, and K. Ogino. 1988. Forest primary productivity. Pages 97–117 in K. Ogino and M. Chihara, eds. Biological System of Mangrove. Ehime University. 97–117.

Komiyama, A., S. Havanond, W. Srisawatt, Y. Mochida, K. Fujimoto, T. Ohnishi, S. Ishihara, and T. Miyagi. 2000. Top/root biomass ratio of a secondary mangrove (Ceriops tagal (Perr.) C. B. Rob.) forest. Forest Ecology and Management 139: 127–134.

Komiyama, A., S. Poungparn, and S. Kato. 2005. Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology 21: 471–477.

Komiyama, A., J. E. Ong, and S. Poungparn. 2008. Allometry, biomass, and productivity of mangrove forests: A review. Aquatic Botany 89: 128-137.

Nam, Vien Ngoc 2009. Personal communication (Thu Duc Univ. HCM). Ong, J. E., W. K. Gong, and C. H. Wong. 2004. Allometry and partitioning of the

mangrove, Rhizophora apiculata. Forest Ecology and Management 188: 395–408.


32

Pandey, C. N., R. Pandey and M. Mali. 2013. Carbon sequestration by mangroves of Gujarat. Gandhinagar, Gujarat, India, Gujarat Forest Department & GEER Foundation.

Panshin, A. J. 1932. An anatomical study of the woods of the Philippine mangrove swamps. Philippine Journal of Science 48: 143-208.

Philips & Watson 1959. In Saenger 2002. Poungparn, S., A. Komiyama, V. Jintana, S. Piriyayaota, T. Sangtiean, P. Tanapermpool,

P. Patanaponpaiboon, and S. Kato. 2002. A quantitative analysis on the root system of a mangrove, Xylocarpus granatum Koenig. Tropics 12: 35–42.

Proisy, C., P. Couteron, and F. Fromard. 2007. Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sensing of Environment 109: 379-392.

Rumbold, D. G., and S. C. Snedaker. 1994. Do Mangroves Float? Journal of Tropical Ecology 10: 281-284.

Saenger, P. J. 2002. Mangrove ecology, silviculture and conservation. Kluwer Academic Publishers, Dordrecht, Netherlands.

Sattar, M. A., and D. K. Bhattacharjee. 1983. Strength properties of keora (Sonneratia apetala). Pages 6 pp. Bangladesh Forest Research Institute.

Sattar, M. A., and D. K. Bhattacharjee. 1987. Physical and mechanical properties of sundri (Heritiera fomes) and baen (Avicennia alba). Pages 8 pp. Bangladesh Forest Research Institute.

Simard, M., K. Zhang, V. H. Rivera-Monroy, M. S. Ross, P. L. Ruiz, E. Castañeda-Moya, R. R. Twilley, and E. Rodriguez. 2006. Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogrammetric Engineering & Remote Sensing 72: 299-311.

Snedaker, S. C., and J. G. Snedaker, eds. 1984. The mangrove ecosystem: research methods. UNESCO, Paris.

Tamai, S., T. Nakasuga, R. Tabuchi, and K. Ogino. 1986. Standing biomass of mangrove forests in southern Thailand. Journal of the Japanese Forestry Society 68: 384-388.


33

Appendix 1. Allometric equations for various mangroves based on stem diameter (D in cm) and the relationships with total above ground dry weight (WAGB in kg) and below ground dry weight (WBGB in kg). Modified from Komiyama et al., 2008.

Mangrove Species

AGB Above ground weight (WAGB in kg)

AGB Source Reference

BGB Below ground weight (WBGB in kg)

BGB Source Reference

Avicennia alba

WAGB = 0.129D*2.41; r2 = 0.99, n = 28, Dmax = 30 cm

Nam (2009)

Avicennia germinans

WAGB = 0.140D*2.40; r2 = 0.97, n = 45, Dmax = 4 cm

Fromard et al. (1998)a

WAGB = 0.0942D*2.54; r2 = 0.99, n = 21, Dmax: unknown,

Imbert and Rollet (1989)a

Avicennia marina

WAGB = 0.308D*2.11; r2 = 0.97, n = 22, Dmax = 35 cm

Comley and McGuinness (2005)

WBGB = 1.28D*1.17; r2 = 0.80, n = 14, Dmax = 35 cm


Laguncularia racemosa

WAGB = 0.102D*2.50; r2 = 0.97, n = 70, Dmax = 10 cm




Rhizophora apiculata

WAGB = 0.235D*2.42; r2 = 0.98, n = 57, Dmax = 28 cm c.f., Wstilt = 0.0209D*2.55; r2 = 0.84, n = 41

Ong et al. (2004)

WBGB = 0.00698D*2.61; r2 = 0.99, n = 11, Dmax = 28 cm

Ong et al. (2004)

Rhizophora mangle



Rhizophora stylosa

WAGB = 0.067D*2.79; r2 = 0.99, n = 9, Dmax = 26 cm

Duke & Burns (1999)

WBGB = 0.261D*1.86; r2 = 0.92, n = 5, Dmax = 15 cm



34

c.f., Wstilt = 0.017D*2.87; r2 = 0.99, n = 8

Rhizophora spp.

WAGB = 0.128D*2.60; r2 = 0.92, n = 9, Dmax = 32 cm


WBGB = 0.00974(D2H)1.05; r2: unknown, n = 16, Dmax = 40 cm

Tamai et al. (1986)

WAGB = 0.105D*2.68; r2 = 0.99, n = 23, Dmax = 25 cm

Clough and Scott (1989)a

Bruguiera exaristata

WBGB = 0.302D*2.15; r2 = 0.88, n = 9, Dmax = 10 cm


Bruguiera gymnorrhiza

WAGB = 0.186D*2.31; r2 = 0.99, n = 17, Dmax = 25 cm


Bruguiera parviflora

WAGB = 0.168D*2.42; r2 = 0.99, Dmax = 25 cm n = 16,


Bruguiera spp.

WBGB = 0.0188(D2H)*0.909; r2: unknown, n = 11, Dmax = 33 cm

Tamai et al. (1986)

Ceriops australis

WAGB = 0.189D*2.34; r2 = 0.99, n = 26, Dmax = 20 cm


WBGB = 0.159D*1.95; r2 = 0.87, n = 9, Dmax = 8 cm


Ceriops tagal WAGB = 0.856D*1.53; r2 = 0.92, n = 32, Dmax = 9 cm

Nam (2009)

Ceriops zippeliana

WAGB = 0.207D*2.41; r2 = 0.97, n = 35, Dmax = 7 cm

Nam (2009)

Lumnitzera racemosa

WAGB = 0.163D*2.37; r2 = 0.99, n = 35, Dmax = 12 cm

Nam (2009)

Xylocarpus granatum

WAGB = 0.0823D*2.59; r2 = 0.99, n = 15, Dmax = 25 cm


WBGB = 0.145D*2.55; r2 = 0.99, n = 6, Dmax = 8 cm

Poungparn et al. (2002)

Common equations

WAGB = 0.251pD*2.46; r2 =

Komiyama et al. (2005)

WBGB = 0.199p*0.899D*2.22;

Komiyama et al. (2005)


35

(AGB/BGB Komiyama)

0.98, n = 104, Dmax = 49 cm

r2 = 0.95, n = 26, Dmax = 45 cm

Common equation (AGB Chave)

WAGB = 0.168pD*2.47; r2 = 0.99, n = 84, Dmax = 50 cm

Chave et al. (2005)

Common equation (AGB/H Chave)

WAGB = 0.0509pD*2.H

Chave et al. (2005)

Dstilt: the weight of prop root of Rhizophora sp. Dmax: the upper range of samples. p: a constant based on wood density, usually species specific, see Table 2.


36

Appendix 2. Mean wood density (ρ in kg.m-3) of 30 mangrove species (key sources: Saenger 2002; Komiyama et al. 2005).

Species Mean Wood

Density Source

References Aegiceras corniculatum 700 7 Avicennia alba 543 3, 9 Avicennia marina 765 1, 6, 7 Bruguiera cylindrica 816 7, 9, 10 Bruguiera gymnorhiza 780 1, 7, 8, 9, 10, 11 Bruguiera parviflora 790 7, 8 Bruguiera sexangula 885 7 Camptostemon schultzii 473 5, 7 Cerbera manghas 600 7 Ceriops australis 752 8 Ceriops zippeliana (=C.decandra) 975 7 Ceriops tagal 872 1, 7, 9, 12 Dolichandrone spathacea 500 7 Excoecaria agallocha 418 1, 5, 7 Heritiera fomes 1010 3 Heritiera littoralis 848 1, 7 Laguncularia racemosa 759 2 Lumnitzera littorea 640 7 Lumnitzera racemosa 650 7 Osbornia octodonta 850 7 Rhizophora apiculata 827 7, 8, 9, 10, 11 Rhizophora mangle 1011 2 Rhizophora mucronata 867 1, 7, 9 Rhizophora stylosa 855 6, 8 Scyphiphora hydrophylacea 900 7 Sonneratia alba 638 7, 9, 11 Sonneratia apetala 570 4 Sonneratia caseolaris 520 7, 9 Xylocarpus granatum 605 1, 7, 8, 9, 11 X. moluccensis (=X.mekongensis) 647 1, 7, 9, 11

References: 1. Cause et al. 1989; 2. Rumbold & Snedaker 1994; 3. Sattar & Bhattacharjee 1987; 4. Sattar & Bhattacharjee 1983; 5. Philips & Watson 1959; 6. Boland et al. 1984; 7. Panshin 1932; 8. Clough & Scott 1989; 9. Komiyama et al. 2005; 10. Tamai et al. 1986; 11. Komiyama et al. 1988; 12. Komiyama et al. 2000.


37

Appendix 3. Stem diameter for 43 mangrove species, and the overall relevance of diameter measured at breast height (DBH). Also see Appendix 4 for further detail. Indo West Pacific Mangrove species

#

DBH Yes or No

Habit - position of trunk, stem structures and branches in relation to breast height (BH)

Stem Diameter - recommended position to measure girth

Acanthus ebracteatus AE NO Herbaceous shrub – short, liane-like stem, <BH

0.1m above ground – but mostly uniform ~1 cm

Acanthus ilicifolius AI NO Herbaceous shrub – short, liane-like stem, <BH

0.1m above ground – but mostly uniform ~1 cm

Acrostichum aureum AU NO Ground fern – short, fibrous trunk, no stem

Not possible, whole sampling needed

Acrostichum speciosum AS NO Ground fern – short, no trunk, no stem Not possible, whole sampling needed Aegiceras corniculatum AC NO Shrub – short, low, multiple branching,

<BH 0.1m above trunk thickening at base

Aegiceras floridum AF NO Shrub – short, low, multiple branching, <BH

0.1m above trunk thickening at base

Avicennia alba AA variable Tree - sometimes short, low branching, <BH


Avicennia marina AM variable Tree - sometimes short, low branching, <BH


Avicennia officinalis AO variable Tree - sometimes short, low branching, <BH


Avicennia rumphiana (=A.lanata)

AR variable Tree - sometimes short, low branching, <BH


Barringtonia racemosa BR variable Tree - sometimes short, low branching, <BH


Bruguiera cylindrica BC variable Tree - sometimes short, low branching, <BH

0.1m above trunk buttresses at base

Bruguiera gymnorhiza BG variable Tree - sometimes short, low branching, 0.1m above trunk buttresses at base


38

<BH Bruguiera hainesii BH variable Tree - sometimes short, low branching,

<BH 0.1m above trunk buttresses at base

Bruguiera parviflora BP variable Tree - sometimes short, low branching, <BH


Bruguiera sexangula BS variable Tree - sometimes short, low branching, <BH


Bruguiera X rhynchopetala

BX variable Tree - sometimes short, low branching, <BH


Ceriops zippeliana (=C.decandra)

CZ NO Tree - short, low branching, <BH 0.1m above trunk buttresses at base

Ceriops tagal CT NO Tree - short, low branching, <BH 0.1m above trunk buttresses at base Cynometra iripa CN NO Tree - short, low branching, <BH 0.1m above trunk thickening at base Dolichandrone spathacea DS variable Tree - sometimes short, low branching,

<BH 0.1m above trunk thickening at base

Excoecaria agallocha EA variable Tree - sometimes short, low branching, <BH


Heritiera littoralis HL NO Tree – often with high buttresses, >BH 0.1m above trunk buttresses at base Lumnitzera littorea LL variable Tree, shrub - sometimes short, low

branching, <BH 0.1m above trunk thickening at base

Lumnitzera racemosa LR NO Tree, shrub - often short, low branching, <BH


Lumnitzera X rosea LX NO Tree, shrub - often short, low branching, <BH


Nypa fruticans NF NO Palm – trunkless, no stem, below ground Not possible, whole sampling needed Pemphis acidula PA NO Shrub - short, low branching, <BH 0.1m above trunk thickening at base Rhizophora X annamalayana

RY NO Tree - high prop roots, >BH 0.1m above prop root juncture

Rhizophora apiculata RA NO Tree - high prop roots, >BH 0.1m above prop root juncture Rhizophora X lamarckii RL NO Tree - high prop roots, >BH 0.1m above prop root juncture


39

Rhizophora mucronata RM NO Tree - high prop roots >BH 0.1m above prop root juncture Rhizophora stylosa RS NO Tree - high prop roots, >BH 0.1m above prop root juncture Scyphiphora hydrophylacea

SH NO Shrub – short, low, multiple branching, <BH


Sonneratia alba SA variable Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia X hainanensis SN NO Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia caseolaris SC variable Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia lanceolata SL variable Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia ovata SO NO Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia X urama SU variable Tree - often short, low branching, <BH 0.1m above trunk thickening at base Sonneratia X gulngai SG variable Tree - large, low branching, <BH 0.1m above trunk thickening at base Xylocarpus granatum XG variable Tree - often with high buttresses, >BH 0.1m above trunk buttresses at base X. moluccensis (=X.mekongensis)

XM variable Tree - sometimes short, low branching, <BH



40

Appendix 4. Positions for the measurement of stem diameter on mangrove trees and shrubs of various types, forms shapes and sizes. The default reference is taken at 1.3m above the ground (as DBH). And, note also that the centre position of measurement is the reference point for decisions regards that stem being in or out of the plot.


41

Appendix 5. Listings of the spreadsheet working files that accompany this report, containing all data as transcribed from the long plot field data sheets and supplied to Dr Duke. See Appendix 6 for a summary extract of these data sets.

Filename

MESCAL Country Content

1_ALL LongPlot1 NCD.xlsx All Summary data 2_LongPlot_Fiji1 NCD.xlsx Fiji 16 plots, 1 template 2_LongPlot_Fiji2 NCD.xlsx Fiji 16 plots, 1 template 2_LongPlot_Fiji3 NCD.xlsx Fiji 11 plots, 1 template 2_LongPlot_SAMOA NCD.xlsx Samoa 11 plots, 1 template 2_LongPlot_Solomons NCD.xlsx Solomons 12 plots, 1 template 2_LongPlot_TONGA1 NCD.xlsx Tonga 13 plots, 1 template 2_LongPlot_TONGA2 NCD.xlsx Tonga 12 plots, 1 template 2_LongPlot_VANUATU crab NCD.xlsx Vanuatu 13 plots, 1 template 2_LongPlot_VANUATU eratap NCD.xlsx Vanuatu 13 plots, 1 template


42

Appendix 6. Summary of dominant mangrove vegetation assemblage data for Samoa, Tonga, Fiji, Vanuatu and the Solomons, showing derived estimates of biomass (t.ha-1) in living above ground biomass (AGB) and below ground biomass (BGB). Estimates were made from allometric common equations by Komiyama (2005) and Chave (2005) based on stem diameter. ‘AGB/H’ refers to Chave’s equation that includes total average tree height (Hgt). Those in bold were the ones presented in the report tables and figures. ‘CanWHgt’ refers to the weighted canopy height, termed Lorey’s height. ‘BA’ refers to stand basal area.

Dominant Veg. Types Biomass (tC/ha) Samoa Tonga Fiji Vanuatu Solomons

average SE X1 average SE X1 average SE X1 average SE X1 average SE X1

Bruguiera gymnorhiza AGB Komiyama 700.2 58.0 476.9 100.2 552.0 50.0 803.7 415.4 AGB/H Chave 269.9 25.7 125.3 22.1 247.5 22.9 463.0 400.6 AGB Chave 485.2 40.3 328.7 69.5 384.1 35.1 557.5 287.7 BGB Komiyama 249.7 19.5 200.6 37.1 187.5 15.0 283.7 148.8 Hgt(m) 7.2 0.3 4.1 0.1 9.9 0.4 11.7 18.4 CanWHgt(m) 9.0 0.5 5.1 0.4 11.0 0.4 14.6 21.7 BA(m2/ha) 58.8 4.4 73.7 15.6 42.2 3.0 69.5 34.4 Rhizophora sp. AGB Komiyama 213.6 109.8 11.1 317.8 110.8 171.0 24.2 730.8 108.0 AGB/H Chave 79.0 39.1 4.7 192.4 64.1 79.6 9.5 553.8 90.3 AGB Chave 145.7 75.0 7.6 218.6 76.4 117.2 16.6 506.1 75.1 BGB Komiyama 109.1 54.4 5.2 134.3 44.1 75.7 9.8 262.8 34.7 Hgt(m) 4.2 4.0 0.2 9.2 1.3 6.4 0.5 14.3 1.2 CanWHgt(m) 4.3 4.4 0.2 9.5 1.4 7.4 0.6 17.3 1.1 BA(m2/ha) 32.7 16.3 1.6 34.5 10.5 20.7 2.6 59.5 7.0 Excoecaria agallocha AGB Komiyama 390.7 70.8 AGB/H Chave 128.3 16.1 AGB Chave 269.8 48.9 BGB Komiyama 160.9 28.6 Hgt(m) 4.3 0.2 CanWHgt(m) 7.0 0.5 BA(m2/ha) 67.8 10.6


43

continued…


average SE X1 average SE X1 average SE X1 average SE X1 average SE X1

Lumnitzera littorea AGB Komiyama 265.2 8.3 AGB/H Chave 102.7 6.7 AGB Chave 182.1 5.6 BGB Komiyama 120.6 6.5 Hgt(m) 5.2 0.4 CanWHgt(m) 6.0 0.3 BA(m2/ha) 43.0 3.0 Xylocarpus granatum AGB Komiyama 948.8 488.3 509.4 AGB/H Chave 201.2 73.9 193.1 AGB Chave 661.6 341.7 353.0 BGB Komiyama 298.2 140.6 186.7 Hgt(m) 5.9 0.7 7.6 CanWHgt(m) 6.9 0.8 9.8 BA(m2/ha) 75.9 33.5 52.7 Annona glabra AGB Komiyama 687.9 AGB/H Chave 381.6 AGB Chave 476.6 BGB Komiyama 248.6 Hgt(m) 11.4 CanWHgt(m) 13.2 BA(m2/ha) 67.1


44

continued…


average SE X1 average SE X1 average SE X1 average SE X1 average SE X1 Ceriops tagal AGB Komiyama 308.6 38.0 477.0 86.8 AGB/H Chave 148.8 20.8 296.1 30.8 AGB Chave 212.1 26.2 329.4 60.1 BGB Komiyama 133.4 16.2 181.8 31.3 Hgt(m) 6.3 0.4 11.3 0.7 CanWHgt(m) 7.3 0.4 13.2 1.2 BA(m2/ha) 32.4 4.6 41.9 6.7 Avicennia marina AGB Komiyama 844.1 81.9 AGB/H Chave 189.5 35.4 AGB Chave 585.4 56.7 BGB Komiyama 294.0 29.5 Hgt(m) 5.4 0.4 CanWHgt(m) 5.4 0.4 BA(m2/ha) 68.3 7.0 Total Long Plot #s 11 25 43 26 12

2013 5 vegunitsbiomass report1 ncd · 2018. 5. 10. · involving their training, support and...

Documents