Diversity, Structure and Dynamics of a Mangrove Forest: a Case Study

Article (PDF Available) · September 2014with 169 Reads
DOI: 10.15835/nsb.6.3.9339
Cite this publication
Abstract
The intertidal vegetation along tropical and subtropical coast is defined as mangrove vegetation. India has a long coast line measuring 7516 km. The ecology of mangrove forest is relatively less studied. Mangrove systems are known to be one of the most productive systems in the world. The study aimed to estimate the carbon sequestration potential of a relatively protected sacred grove along the western coast of India, in Kagekanu, Kumta, Karnataka. One hectare permanent plot was established, with all woody stems > 1 cm dbh (diameter at breast height), which were marked and identified. Repeated measurements were made to register the growth and other parameters. Allometric equation was used to estimate the biomass, out of which 50% was considered as carbon content. A total of 1100 stems > 1 cm dbh, belonging to 4 species, were enumerated. There was an overall decline of 13.9% stems during the study period. Mean mortality rate was found to be 5.83 ± 1.85% and there was no recruitment. The biomass increased from 155.53 tons/ha to 164.28 tons/ha. There was a net gain of 4.38 tons. Avicinnia officinalis was found to contribute significantly to carbon sequestration.
Figures - uploaded by Raman Sukumar
Author content
All content in this area was uploaded by Raman Sukumar
Content may be subject to copyright.
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
Diversity, Structure and Dynamics of a Mangrove Forest:
a Case Study
Suresh S. HEBBALALU
1
, Dattatray M. BHAT
1
, Ravindranath H. NIJAVALLI
2
,
Sukumar RAMAN
1
*
1
Center for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India; rsuku@ces.iisc.ernet.in (*corresponding author)
2
Center for Sustainable Technologies, Indian Institute of Science, Bangalore 560012, India
Abstract
The intertidal vegetation along tropical and subtropical coast is defined as mangrove vegetation. India has a long coast line
measuring 7516 km. The ecology of mangrove forest is relatively less studied. Mangrove systems are known to be one of the most
productive systems in the world. The study aimed to estimate the carbon sequestration potential of a relatively protected sacred
grove along the western coast of India, in Kagekanu, Kumta, Karnataka. One hectare permanent plot was established, with all
woody stems > 1 cm dbh (diameter at breast height), which were marked and identified. Repeated measurements were made to
register the growth and other parameters. Allometric equation was used to estimate the biomass, out of which 50% was considered
as carbon content. A total of 1100 stems > 1 cm dbh, belonging to 4 species, were enumerated. There was an overall decline of
13.9% stems during the study period. Mean mortality rate was found to be 5.83 ± 1.85% and there was no recruitment. The biomass
increased from 155.53 tons/ha to 164.28 tons/ha. There was a net gain of 4.38 tons. Avicinnia officinalis was found to contribute
significantly to carbon sequestration.
Keywords: Avicinnia officinalis, biomass, carbon sequestration, growth, mortality, recruitment, Rhizophora mucronata
Available online at www.notulaebiologicae.ro
Print ISSN 2067-3205; Electronic 2067-3264
Not Sci Biol, 2014, 6(3):300-307
Introduction
Mangrove forests have attracted attention of humans
historically by their special adaptation for surroundings,
their economical utilities and their ecology (Lugo and
Snedekar, 1974). The intertidal forested vegetation in
tropical estuarine zones is defined as “mangrove” (Mooney
et al., 1995; Valiela et al., 2001); they consist of salt tolerant
species with complex dynamics (Lugo and Snedekar, 1974;
Tomlinson, 1986; Duke et al. 1998) and are estimated to
cover about 240 x 103 km
2
area (Lugo et al., 1990; Mandal
and Naskar, 2008). Valiela et al. (2001) found that at least
35% of the mangrove habitats were lost during the last two
decades. Giri et al. (2008) opine that mangrove forests in
India and Bangladesh have remained largely unchanged
during 1975-2005, and even more, it gained a small
percentage area. Although mangrove forests cover small
geographical area, they have a unique and significant
contribution to the carbon geochemistry (Mitra et al., 2011)
and provide a wide range of ecosystem services (Badola and
Hussain, 2005; Donato et al., 2011). The floristics of
mangrove forests has been reviewed globally (Duke et al.,
1998) and at local scale, for example Sundarbans (Gopal and
Chauhan, 2006), but the studies on the patterns of diversity,
structure and dynamics of mangrove are scarce. The land
forests including tropical aseasonal (Lee et al 2002), seasonal
dry (Mc Shea et al. 2011), and temperate forests, have been
extensively studied for several ecological aspects. The large
consortium of large plots network coordinated by Center for
Tropical Forest Science (CTFS) has been monitoring
tropical forests for a long time (Condit, 1998). There are
other networks, such as “rainfor”, that monitor forests. But
with all this, there is no such organized effort to monitor
mangrove forests.
India has a long coast line of 7516 km, including island
territories. Recent estimation of mangrove forest cover is of
4639 km
2
, which is about 3% of the global mangrove forest
area (FSI, 2011), including in this surface also Sundarbans,
the land shared between India and Bangladesh, which is
probably the largest wet land in the world (Gopal and
Chauhan, 2006). Indian mangroves have been classified as
“tidal wetlands, woody vegetation”, under a hierarchical
system of classification that considers factors such as
location, salinity, physiognomy and duration of flooding
(Gopal and Sah, 1995). Indian mangrove vegetation has
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
three distinct zones
:
1. East coast habitats, having a coast
line of 2700 km, facing the Bay of Bengal; 2. West coast
habitats, having a coast line of 3000 km, facing the Arabian
sea; 3. Island territories with 1816 km of coast line (Mandal
and Naskar, 2008). Mandal and Naskar (2008) also
recognized three habitat types with the Indian mangroves,
which include: a. Deltaic mangrove habitat (east coast
mangrove and gulfs of Gujarat), b. Coastal mangrove
habitat (west coast mangroves) and c. Island mangrove
habitat (Lakshadweep, Andaman and Nicobar islands).
Characteristics and status of mangroves along the Indian
coast have been reviewed by Selvam (2003). Studies in
Indian mangroves have been along the lines of documenting
biodiversity (Gopal and Chauhan, 2006), biomass (Mitra et
al., 2011), nutrient dynamics (Kumar et al., 2011) and other
conservation aspects, such as review of status (Jagtap et al.,
1993; Gopal and Chouhan, 2006). Biodiversity studies in
mangrove forests have focused on describing different
species of both true mangrove and associates of mangrove
formations in Sundarbans (Joshi and Ghose, 2003; Gopal
and Chouhan, 2006), Godavari delta (Azariah et al., 1992),
Bidarkarnika (Upadhyay and Mishra, 2008) or Andamans
(Singh et al., 1987). There are hardly any studies on the
quantitative aspects of vegetation diversity and growth rates
on mangrove species, based on repeated stem
measurements.
This paper describes the diversity and structure in a one-
hectare permanent vegetation plot of a mature mangrove
sacred grove along the western coast of Karnataka. The
present study describes the dynamics of the forest, including
population changes, demographical changes based on size
class distribution, patterns of mortality, growth and carbon
sequestration over a period of three years. This is probably
the first permanent plot based systematic and an innovative
and elaborate study on the mangrove forest in India.
Study area
This study was undertaken in Uttara Kannada district
(14° 63’ N Lat and 781’ E Long) of Karnataka state,
south India. Uttara Kannda is characterized
terrain with fertile valleys. There is a considerable variation
in the altitude, ranging from sea level to as high as 1000
meters ASL. Rainfall is mainly from the southwest
monsoon, which is active from June to September. Mean
annual rainfall during 1990-2010 is 3629.6±521 mm, with
June and July being the rainiest months (Fig. 1). The
vegetation varies from tropical seasonal evergreen forests
and its variants towards the west to moist deciduous forests
on the eastern part (Pascal, 1986). Administratively, the
forests in the district are classified into the following
categories: a. Reserve forest (exclusively under state control
and highly regulated), b. Minor forests (extraction of
biomass and fuel wood is allowed to meet the demands of
people) and c. Leaf manure forests, or locally called “
Soppina betas” (forest area allotted to areca nut farmers
under certain privileges for extraction of leaf and dry wood).
Further, detailed description of the study area can be found
in Daniels (1989) and Bhat et al. (2011). In Uttara
Kannada district mangrove patches are seen in estuarine
zones of important westward flowing rivers such as Kali,
Bedthi, Aghanashini, Sharavathi and Vektapura. Mangroves
are also present along the estuaries of small creeks and
rivulets. Among these, Kali and Aghanashini estuaries
support larger isolated mangrove patches. River
Aghanashini runs in thick forests and amid cultivated lands,
mainly paddy and Areca orchards, forms two waterfalls,
covers a total distance of about 121 km before joining
Arabian sea at Tadri and Aghanashini villages. The estuary
is 13 km long and 2-6 km broad. This is the only river in
Uttara Kannada district so far not dammed and with no
major townships and industries polluting the water (Gadgil
et al. 1993). Therefore, it is supposed to be least polluted.
Before joining the sea, it forms many islands in the estuarine
zone, varying in area. These islands are locally called by
names such as Masurkurve, Gunda, Kekanakodi,
Keppekuve, Tanneerahonda, Chowlihonda. Among these,
Masurkuve is the largest, comprising about 32 ha. Though
part of the land area of the island is cultivated by the local
farmers, there is a good mangrove patch along the fringe.
This patch is conserved and protected over many years in
the name of a deity ‘Bobbrudevaru’, which is housed in a
temple in the island. On the account of this, local
community considers the patch as sacred and do not extract
from this mangrove patch for personal use. However,
collection and use of dead and fallen wood is allowed for
performing religious rituals for the deity. This particular
forest patch is called Kagekanu (14° 42’ N Lat and 74° 40’ E
Long). Majority of the islands are used for prawn culture
after the harvest of the paddy crop.
Material and Methods
We established one-hectare (100 x 100 meters)
permanent plot in the sacred groove. Caution was taken in
laying the plot to avoid edge effect. For easy enumeration we
temporarily divided this plot into smaller blocks of 20 X 20
meters, by laying ropes. Within these smaller blocks, all
woody individuals > 1.0 cm DBH (diameter at breast
height) were enumerated for species, measured for size and
marked with a unique tag number. Point of measurement
was marked with paint for successive measurements.
Multiple stems were given the same number and measured
301
0
200
400
600
800
1000
1200
Jan Feb Mar Ap r May Jun J ul Aug Sep Oct Nov Dec
Rainfall (mm)
Months
Mean RF
Fig. 1. Rainfall pattern in the study area (Kumta). Data from
District met office, Karwar, for the period of 1990-2010
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
for size. Any deformity on the stems, such as stem breakage,
bark stripped, top broken and branches pulled, were noted.
Sizes of surviving stems were measured annually at the
same point of measurement. Dead stems were noted, and
recruitment of new stems of 1.0 cm DBH was also marked.
Sizes of the largest stems were considered for growth
analysis for individuals with multiple stems. All stems were
included in basal area estimation. Mortality rate was
estimated as the proportion of dead stems with respect to
surviving stems and expressed as percentage. Recruitment
was defined as appearance of new stems >1 cm DBH in
accordance with CTFS protocols (Condit, 1998). Growth
rate was calculated as difference in size between census
periods over time elapsed between census period.
Above ground biomass (AGB) was calculated using
allometric equation based on diameter, developed by Chave
et al. (2005) for wet mangrove forest patches:
AGB = ρ*exp (-1.349+1.980*ln(D)+0.207*(ln(D))^2-
0.0281(ln(D))^3),
where ρ = wood specific gravity (grams/cm
3
), ln =
natural logarithm and D = dbh (cm).
There are several allometric equations developed for the
estimation of AGB (Chave et al., 2005; Brown et al., 1989;
FRI, 1970). The current equation is precise and also used in
estimation of biomass by CTFS global network of
permanent forest dynamics plots (Chave et al., 2005). A
universal mean value of 0.6 was used as wood specific
gravity, as many mangrove species specific values are not
available. 50% of AGB was considered as C stocks,
according to IPCC standards.
Results
One-hectare permanent plot at Kagekanu had 1100
individuals >1.0 cm dbh (diameter at breast height)
belonging to 4 species. Rhizophora mucronata
(Rhizophoraceae) was the dominant specie, with 368
individuals, followed by Avicinnia officnalis (Verbenaceae,
273 individuals), Sonneratia alba (Sonnaratiaceae, 233
individuals) and Kandelia kandel (Rhizophoraceae, 233
individuals). Rhizophora mucronata and Avicinnia officinalis
accounts for more than 58% of the stand composition (Tab.
1). Vegetation diversity of the plot was low. The probability
estimation of species diversity, Fisher’s alpha, was also very
low (0.52). The distribution of individuals among various
species was uniform, as shown by the high evenness index
(0.98).
Size class distribution of individuals followed the typical
inverted “J” shape (Fig. 2). There was a large concentration
of individuals in 5-10 cm size class. Over 62% of the
individuals were in 0-10 cm size class (Fig. 2). Among
species, Avicinnia had more or less a uniform distribution of
individuals in each size class (Fig. 3) and was significantly
302
Species
(Family)
Number of
Individuals
Rel.
Abundance
(%)
Cum.
Abundance
(%)
Rhizhophora
mucronata
(Rhizhophoraceae)
368 33.45 33.45
Avicinnia
officinalis
(Verbenaceae)
273 24.81 58.27
Sonnaratia alba
(Sonnaratiacae) 233 21.18 79.45
Kandlia kandel
(Rhizhophoraceae) 226 20.54 100
Tab. 1. Abundances of different species in the Kagekanu permanent
vegetation plot
of picking two individuals (Simpson’s index) though was
relatively high (0.73), while the heterogeneity index
(Shannon-Weiner’s H) was low (1.36). Non-parametric
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8 9 10 11 12 13
Size class
%
I
n
d
i
v
i
d
u
a
l
s
0
2
4
6
8
10
12
14
%
b
a
s
a
l
a
r
e
a
bas ar ea
si ze
Fig. 2. Size class distribution of individuals and basal area in
Kagekanu permanent vegetation plot
0
10
20
30
40
50
60
4.99
14.99
24.99
34.99
44.99
54.99
60
Size class (cm)
% Indiiduals
avicinnia
kandelia
rhizophora
sonnaratia
Fig. 3. Distribution of individuals in various size classes
different from all species (KS test, p>0.05). Other species
did not show significant difference among themselves (KS
test NS). There were 339 stems (30.9%) that had multiple
stems. Population of Avicinnia officinalis had much larger
stems with mean dbh of 21.4±15 cm, followed by
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
303
Sonnaratia alba
(10.6±4.3 cm),
Rhizophora mucronata
(7.1±4.4 cm) and Kandelia kandel (6.1±4.5 cm).
Total basal area of the one hectare plot was 24.4 m
2
.
Avicinnia officinalis accounts for 69.5% (16.9 m
2
) of the
total basal area, followed by Rhizophra mucronata (14.1%,
3.4 m
2
), Sonnaratia alba (11.5%, 2.8 m
2
) and Kandelia
kandel (4.7%, 1.1 m
2
). Basal area was more or less uniformly
distributed across size classes and did not show
concentration in higher size class as observed with several
other mature forests (Fig. 2). Total biomass of the plot was
155.53 tons. Avicinnia officinalis accounted for 77.4% of
total biomass, followed by Rhizophora mucronata (10.8%),
Sonnaratia alba (8.3%) and Kandelia kandel (4.1%).
Dynamics of the forest
Population change
There was a decline of the population from 1100
individuals in 2008 to 971 in 2011, resulting in an overall
decline of 13.9%. All species have shown decline, but
Kandelia kandel had the maximum decline of 16.5% (Tab.
2). Mean decline observed in all species was in the range of
4.27% to 5.73% (Tab. 2). There was a major decline in the
lower size classes (Tab. 3). However, there is a positive trend
in higher size classes, as a result of growth from smaller size
class.
Mortality and recruitment
Mean mortality rate of the community was 5.83±1.85%
(range =4.27% - 7.88%, N =3). Among the different
species, Avicinnia officinalis had a mean rate of 4.95±1.62%,
Kandelia kandel had 6.79±3.06%, Rhizophora mucronata
had 5.13±2.33% and Sonnaratia alba had 7.10±2.02%
mortality rates. There was no significant difference in
mortality rates between species (t test, NS).
There was a declining trend in mortality rates with
increasing sizes (Fig. 4); however, there were elevated
mortality rates in the size classes of 20-25 cm, 35-40 cm, 50-
55 cm dbh (Fig. 4). Mean mortality rate for stems <30 cm
Tab. 2. Population changes observed in Kagekanu permanent vegetation plot
Species
(Family)
Percent change
(2008-2009) (N 2008)
Percent change
(2009-2010) (N 2009)
Percent change
(2010-2011) (N 2010)
Percent change
(2009-2011)(N 2011)
Rhizhophora mucronata
(Rhizhophoraceae) -5.70 (368) -4.32 (347) -3.01 (332) -12.5 (322)
Avicinnia officinalis
(Verbenaceae) 2.56 (273) -12.5 (280) -2.85 (245) -12.8 (238)
Sonnaratia alba
(Sonnaratiacae) -8.58 (233) -6.10 (213) -1.00 (200) -15.02 (198)
Kandlia kandel
(Rhizhophoraceae) -5.75 (226) 9.39 (213) 2.07 (193) -16.3 (189)
Total population -4.27 (1100) -7.88 (1053) -2.37 (970) -13.9 (947)
Tab. 3. Size class specific population changes in Kagekanu permanent vegetation plot
Size Pop 2008 Pop 2009 Pop 2010 Pop 2011 Change 08-09 Change 09-10 Change 10-11 Change 08-11
4.99 293 236 182 142 -19.45 -22.88 -21.97 -51.53
9.99 397 382 346 331 -3.77 -9.42 -4.33 -16.62
14.99 215 225 223 242 4.65 -0.88 8.52 12.55
19.99 43 60 69 71 39.53 15.0 2.89 65.11
24.99 29 27 32 36 -6.89 18.51 12.5 24.13
29.99 31 24 20 28 -22.58 -16.66 40.0 -9.67
34.99 32 36 32 31 12.5 -11.11 -3.12 -3.12
39.99 22 23 23 22 4.54 0.0 -4.34 0.0
44.99 12 13 15 13 8.33 15.38 -13.33 8.33
49.99 15 14 12 15 -6.66 -14.28 25.0 0.0
54.99 9 11 14 14 22.22 27.27 0 55.55
59.99 2 2 2 2 0 0 0 0
0
1
2
3
4
5
6
7
8
9
10
4.99
9.99
14.99
19.99
24.99
29.99
34.99
39.99
44.99
49.99
54.99
59.99
Size clas s (cm )
% Mortality
Mean mortality
Fig. 4. Size class specific mortality observed in Kagekanu
permanent plot
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
dbh was 6.06±2.02% and for the stems >30 cm it was
2.05±2.06%. There was no recruitment observed during the
study period into 1 cm dbh class.
Growth and carbon sequestration
Mean growth rate in Avicinnia officinalis was found to
be 0.6±1.08 cm per annum, with maximum growth of 3.71
cm and shrinkage of 3.2 cm. It was 0. 43±0.44 cm in
Kandelia kandel, which had the maximum growth of 6.5 cm
and shrinkage of 0.9 cm. Mean growth rate in Rhizophora
mucronata was 0.49±0.76 cm, with growth as high as 9.1 cm
and shrinkage of 1.2 cm. Sonnaratia alba recorded a mean
growth of 0.45±0.62 cm, with maximum growth of 2.8 cm
and shrinkage of 3 cm. There was high variability in growth
rates among species.
There was no pattern in growth rates in each species
across different size classes (Tab. 4). However, there was
high variability in mean growth rates in each size class (Tab.
4). The mean growth rates among different species in the
size class 0-5 cm dbh were not significant (t test, p>0.05,
NS). Mean growth rate of Avicinnia officinalis with other
species in the size 5-10 cm dbh was significantly different (t
test, p<0.05) and also between Kandelia kandel and
Rhizophora mucronata (t test, p<0.05). Growth rate of
Sonnaratia alba with Kandelia kandel and Rhizophora
mucronata was not different (t test, p>0.05, NS). Among
the stems of 10 -15 cm dbh, Kandelia kandel had significant
difference with Rhizophora mucronata and Avicinnia
officinalis (t test, p<0.05), but rest of the combinations were
not significant (t test, p>0.05, NS).
There was a net gain of 1.36 m
2
(5.57%) of basal area
during the study period of three years. However, the gain
was not uniform. During the first year, it was 0.56 m
2
(2.31%), in the second year it was negligible (0.0014 m
2
,
0.005%) and in the third year there was a significant gain of
0.79 m
2
(3.18%) (Tab. 5). Kandelia kandel lost 12.7% basal
area during the study period, while other species gained
basal area in the range of 5-6%.
During the study period, in all censuses Avicinnia
officinalis contributed with over 75% to the AGB pool,
followed by
Rhizhophora mucronata
(10%),
Sonnaratia a
lba
(8%) and Kandelia kandel (2%). AGB changed from 155.53
tons/ha to 164.28 tons/ha, resulting in a net accumulation
of 8.76 tons. Avicinnia officinalis (8.61 tons) and Sonnaratia
alba (1.41 tons) gained biomass, while both Kandelia kandel
(1.25 tons) and Rizhophora mucronata (0.01 tons) lost
biomass.
The carbon stock of the plot ranged from 77.7 to 82.1
tons of C. There was a net gain of 4.38 tons during the study
period. The mean annual increment in C stocks over three
years was 1.46±1.02 tons. However, there is a great
variability for this parameter. In the Kagekanu plot, large
amount of carbon has been locked up in Avicinnia officinalis
and accounts for more than 70% of total C stocks. Aviccinia
officinalis also contributes significantly for the sequestration
of C.
Discussion and conclusions
Mangroves are unique ecosystems of the world with
adaptations to halophytic conditions. Mangrove ecosystems
are known to provide great services to both human beings
and others organisms, including fishes and water birds (Ewel
et al., 1998; Bridgewater and Cresswell, 1999; Badola and
Hussain, 2005; Donato et al., 2011). But mangrove forests
are facing serious threat as a consequence of human activity
(Valiela et al., 2001; Upadhyay et al., 2002).
Mangrove forests across the globe consists of species
poor compared to either aseasonal rain forests or tropical
dry forests (Condit, 1998). Mangrove forest of Kagekanu is
also species poor. Pattern is similar to species richness
reported from other mangrove patches along the western
coast of India (Suresh et al., 2010). Less number of species
was observed in pure mangrove stands off the coast of
French Guiana (Fromard et al., 1998). Avicinnia officinalis
dominates the floristics of Kagekanu plot. Similar pattern of
dominance of Avicinnia sp. was also observed in Sundarbans
(Joshi and Ghose, 2003), but domination of different
species of Avicinnia was determined by salinity (Joshi and
Ghose, 2003). Dominance of Avicinnia was also seen in
pure stands of French Guiana (Fromard et al., 1998).
304
Tab. 4. Mean growth rates (± SD) (cm) in various size classes for different species in Kagekanu permanent vegetation plot
Size class (dbh cm) Avicinnia officinalis Kandelia kandel Rhizophora mucronata Sonnaratia alba
0-4.99 0.49±0.44 0.54±0.70 0.52±0.84 0.53±0.49
5-9.99 0.80±0.70 0.32±0.39 0.43±0.44 0.41±0.52
10-14.99 0.86±1.07 0.28±0.33 0.55±0.58 0.49±0.31
15-19.99 0.68±1.19 1.43±2.24 0.66±0.79 0.52±0.65
20-24.99 0.66±0.74 0.26±1.53 1.64±4.36 0.34±0.34
25-29.99 0.40±0.57 NA 0.04±0.03 -3.77±5.72
30-34.99 0.3±0.41 NA 0.70±0.43 1.36±1.84
35-39.99 0.52±0.58 NA 0.40±0.21 NA
40-44.99 0.86±0.95 NA NA NA
45-49.99 0.50±0.71 NA NA NA
50-54.99 0.31±0.84 NA NA NA
55-59.99 0.29±0.03 NA NA NA
Tab. 5. Basal area changes (%) in the Kagekanu permanent vegetation plot
Year Size 0-10 cm dbh Size 10-20 cm dbh Size 20-30 cm dbh Size >30 cm dbh Total
2008-2009 -4.98 19.09 -14.44 8.91 5.22
2009-2010 -9.83 4.60 4.14 -0.57 -0.08
2010-2011 -5.81 4.21 13.76 1.38 2.80
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
However, Jayatissa
et al
.
(2002) reported 8
-
16 true
mangrove species from different patches at Sri Lanka. The
complexity index of Kagekanu plot (6) is considerably low
compared to mature mangrove stands of French Guiana
(18, Fromard et al., 1998), which could be attributed to the
height of the forest. Mean height of Kagekau plot was 5.58
meters, while in French Guiana it was 19.6 meters. Total
number of species recorded in Indian mangroves is 33
(Selvam, 2003). Region-wise diversity of mangrove species,
which include both true and associated species in India, is
given in Mandal and Naskar (2008). A total of 13 species
was reported from mangrove forests of Godavari region
(Azariah et al., 1992).
Azriah et al. (1992) also reported a gradient in species
diversity, with inland mangroves being highly diverse.
Relative mangrove diversity of Kagekanu region, as
estimated by Mandal and Naskar (2008), is also low
compared with other mangrove regions in India. Kagekanu
plot, being a shoreline mangrove, is having a low species
diversity, which could be attributed to high salinity.
Size class distribution of individuals followed the typical
inverted “J” shaped curve, which is seen in most tropical
(Sukumar et al., 1992) and other mangrove forest patches
(Jimenez et al., 1991; Cox and Allen, 1999; Khan et al.;
2009). A bell shaped curve is reported for Rhizophora
plantation from Kenya (Kairo et al., 2008) and for natural
Rhizophora forest of Malaysia (Eong et al., 1995). Density of
stems at Kagekanu plot was considerably low compared to
stands in Odisha (Upadyay and Mishra, 2008). Kairo et al.,
(2008) reports a density of 5132 individuals in a hectare of
12 year old Rhizophora plantation. Similarly, Engo et al.
(1991) reports a stand density of little over 4000 stems from
Malaysia. Exceptionally high density of 47000 stems over
2.5 cm dbh was recorded in mangrove stands of Puerto Rico
(Pool et al., 1977).
There is a great variation in basal area reported for
mangrove forests across tropics. Pool et al. (1977) reports an
area as high as 96.4 m
2
/ha to as low as 6.0 m
2
/ha from
Central America. Komiyama et al. (2008) reports values
ranging from 2.5 m
2
/ha to 43.8 m
2
/ha for mangrove forest
patches across globe. Basal area of Kagekanu plot is in the
range of values reported for tropical dry forest (Sukumar et
al., 1998) and is higher than the values reported from
Sundarbans (Joshi and Ghosh, 2003).
Mangrove forests are dynamic systems. The dynamism
in these forests is influenced both by extreme natural events
such as hurricane, cyclone and tsunami, as well as normal
processes including diseases and pests, resulting in natural
mortality and recruitment (Jamenez et al., 1985).
Other human mediated factors, such as erosion and
flooding, could also result in mangrove tree mortality
(Jamenez et al., 1985). Kagekanu plot is not exposed to any
of the natural extreme events. Our results indicate the
decline in total population as shown in dry forests of
Mudumalai initially (Sukumar et al., 2005). However, long-
term data is required to understand the dynamics of a forest.
Mortality rates reported in the literature (Jamenez et al.,
1985) include small seedlings. Hence, the rates observed in
Kagekanu are not comparable. There are hardly any studies
on the dynamics of mangrove forest as they are done in
either dry or moist land forests (Losos and Leigh, 2004).
Carbon sequestration potential
Mangrove forests are one of the carbon rich forests in
the tropics (Donato et al., 2011), however most carbon is
locked up in the soil. There is a great variation in AGB
across different geographical areas. AGB of mangrove forest
at a global scale varies from as low as 7.9 tons/ha to as high
as 460 tons/ha (Fromard et al., 1998; Komiyama et al.,
2008; Khan et al., 2009). One hectare of mangrove forest in
Kagekanu plot has 155.53 tons, which is in the range of
values reported for other forests, such as tropical dry forests
(Sukumar et al. unpublished results), mangrove forest
patches in Andaman islands (Mall et al., 1991), Rhizophora
plantations in Kenya (Kairo et al., 2008) and mangrove
patches in French Guiana (Fromard et al., 1998). There is
also a considerable variation in biomass storage across
species, along with total biomass. At Kagekanu plot,
Avicinnia officinalis accounts for >70% of total biomass.
Sonnaratia alba in Kagekanu plot accounts for 8% of
biomass, while Sonnaratia apetala has significant amount of
biomass in Sundarban mangroves (Mitra et al., 2011). A
detailed analysis of carbon content in different forests across
different districts in India has been carried out (Chhabra et
al., 2002). According to them, India had a total phytomass
C pool of 3874.3 TgC in 1994. According to Ravindrnath
et al. (2008) total forest carbon stocks in India was 10.01
GtC. Chhabra and Dadhwal (2004) based on growing
stock–volume approach, estimated the Indian forest
phytomass in the range of 3.8-4.3 PgC. A hectare of mature
forest such as Kagekanu is estimated to sequester 1.46 tons
of carbon per annum, which is higher than the reported
value of 0.535 tons/ha (Lal and Singh, 2000). The above
ground carbon sequestration rate is comparatively lower
than most forest types in the world; most forest are reported
to sequester carbon in the range of 1.40 to 8 tons/ha (Jina et
al. 2008). A hectare of Rhizophora plantation is estimated to
accumulate 11.0 tons (equivalent to 5.5 tC) (Kairo et al.
2008). Putz and Chan (1986) record mean increment of 6.7
tons/ha/year of biomass in mangrove forest of Malaysia.
India has 306400 hectares of mature (dense) and
moderately dense mangrove stand (FSI, 2011). Assuming
the above values, mature stands of Indian mangroves
sequester 447,344 tons of C per annum. These values are
comparatively low with other mangrove stands across the
globe. Dense mangroves form 0.44% of total forest cover.
Degraded mangroves cover an area of 157500 hectares.
Reclamation of these mangroves and development into
dense mangroves would result in additional 229,950 tons of
carbon per annum. Total carbon sequestered by above
ground mangrove vegetation would be 677,294 tons per
annum. Contribution of mangrove forests to the total
carbon pool of the country is on the lower side, as mangrove
forests cover relatively less geographical area. But we need to
have a pragmatic approach to conservation and
development of mangrove forests, as they store very high
levels of soil carbon, and also offer immense ecosystem
services. Therefore, there is an urgent need to make a proper
assessment of potential area available for the development of
mangrove vegetation through assisted propagation and
planting of mangrove species along traditional coastal
agricultural bunds, which would help in not only mitigation
of impacts of climate change, but also provide several
305
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
ecosystem services to communities that are traditionally
dependent on mangroves.
Acknowledgements
We thank Ministry of Environment and Forests,
Government of India for funding this research. We thank
Forest department, Karnataka State for their help during
the research. We thank Sridhar Patagar, M. Govinda
Praveen Dube, Mr. Deepak Shetty, Mr. G.T. Hegde and
other for their help during the field survey. We thank the
Kagekanu temple authorities for their help and cooperation.
References
Azariah JH, Azariah S, Gunasekaran, Selvam V (1992).
Structure and species distribution in Coringa mangrove
forest, Godavari Delta, Andhra Pradesh, India.
Hydrobiologia 247:11-16.
Badola R, Hussain SA (2005). Valuing ecosystem functions:
an empirical study on the storm protection function of
Bhitarkanika mangrove ecosystem, India. Environ
Conservat 32:85-92.
Bhat DM, Hegde GT, Shetti DM, Patgar SG, Hegde GN,
Furtado RM, Shastri CM, Bhat PR, Ravindranath NH
(2011). Impact of disturbance on compositrion, structure
and floristics of tropical moist forests in Uttara Kannada
district, Western Ghats, India. Ecotropica 17:1-14.
Bridgewater PB, Cresswell ID. (1999). Biogeography of
mangrove and saltmarsh vegetation: implications for
conservation and management in Australia. Mangroves and
Salt Marshes 3:117-125.
Brown S, Gillespie A, Lugo AE. (1989). Biomass estimation
methods for tropical forests with application to forest
inventory data. For Sci 35:881-902.
Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ,
Eamus D, Folster H, Fromard F, Higuchi N, Kira T,
Lescure J-P, Nelson BW, Ogawa H, Puig H, Riera B,
Yamakura T (2005). Tree allometry and improved
estimation of carbon stocks and balance in tropical forests.
Oecologia 145: 87-99.
Chatutvedi RK, Tiwari R, Ravindranath NH (2008). Climate
change and forests in India. Intern Fores Rev 256-268.
Chhabra A, Palria S, Dadhwal VK. (2002). Spatial distribution
of phytomass carbon in Indian forests. Global Chan Biol
8:1230-1239.
Condit R (1998). Tropical forest census plots. Springer-Verlag.
Cox EF, Allen JA. (1999). Stand structure and productivity of
the introduced Rhizophora mangle in Hawaii. Estuaries.
22:276-284.
Daniels RJR (1989). Conservation strategy for the birds of the
Uttara Kannada district. PhD Thesis submitted to Indian
Instit Sci Bangal.
Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S,
Stidham M, Kanninen M (2011). Mangroves among the
most carbon-rich forests in the tropics. Nature Geoscience
4(5):293-297.
Duke NC, Ball MC, Ellison JC (1998). Factors influencing
biodiversity and distributional gradients in Mangroves.
Global Ecol Biogeog Letters 7:27-47.
Ewel KC, Zheng S, Pinzon ZS, Bourgeois JA (1998).
Environmental effects of canopy gap formation in high-
rainfall mangrove forests. Biotropica 30:510-518.
Forest Research Institute and Colleges, Dehradun (1970).
Growth and yield statistics of common Indian timber
species. Volume II. Forest Research Institute and colleges.
DehraDun. India.
Forest Survey of India (2011). State of Forest Report (2011).
Forest Survey of India. Ministry of Environment and
Forests. DehraDun. India.
Fromard F, Puig H, Mougin E, Marty G, Betoulle JL,
Cadamuro L (1998). Structure, above-ground biomass and
dynamics of mangrove ecosystems: new data from French
Guiana. Oecologia 115:39-53.
Gadgil M, Berkes F, Folke C (1993). Indigenous knowledge for
biodiversity conservation. Ambio 22:151-156.
Giri C, Zhu Z, Tieszen LL, Singh A, Gillette S, Kelmelis JA
(2008). Mangrove forest distributions and dynamics
(1975-2005) of the tsunami-affetced region of Asia. J
Biogeog 35:519-528.
Gopal B, Sah M (1995), Inventory and classification of
wetlands in India. Vegetatio 118:39-48.
Gopal B, Chauhan M (2006). Biodiversity and its conservation
in the Sundarban mangrove ecosystem. Aqua Sci 68:338-
354.
Haripriya GS (2003). Carbon Budget of the Indian Forest
ecosystem. Climate Change 56:291-319.
Jana BK, Biswas S, Majumdar M, Roy PK, Majumdar A.
(2009). Carbon sequestrtation rate and above ground
biomass carbon potential of four young species. J Ecoland
Natural Environ 2:15-24.
Jayatissa LP, Dahdouh-Guebas F, Koedam N (2002). A review
of the floral composition and distribution o mangroves in
Sri Lanka. Biol J Linn Soci 138:29-43.
Jimenez JA, Lugo AE (1985). Tree mortality in mangrove
forests. Biotropica17:177-185.
Jina BS, Sah P, Bhatt MD, Rawat YS (2008). Estimating
carbon sequestration rates nad total carbon stockpilein
degraded and non-degrade sites of oak and pine forest of
Kumaun central Himalaya. Ecoprint 15:75-81.
Joshi H, Ghose M (2003). Forest structure and species
composition along soil salinity and pH gradient in
mangrove swamps of the Sundarbans. Tropical Ecology
44(2):195-204.
Kairo JG, Langat JKS, Dahbouh-Guebas F, Bosire J, Karachi
M (2008). Structural development and productivity of
replanted mangrove plantations in Kenya. Fores Ecoland
Managemen 255:2670-2677.
Khan MNI, Suwa R, Hagihara A (2009). Biomass and above
ground net primary production in a subtropical mangrove
stand of Kandelia obovata (S.L.) Yong at Manko wetland
Oknawa, Japan. Wetlands Ecology Managem 17:585-599.
306
Hebbalalu S.S. et al./ Not Sci Biol, 2014, 6(3):300-307
Komiyama A, Ong JE, Poungparn S (2008). Allometry,
Biomass, and productivity of mangrove forests: A review.
Aquatic Bot 89:128-137.
Kumar IJN, Sajish PR, Rita NK, Basil G, Shailendra V (2011).
Nutrient dynamics in an Avicennia marina (Forsk.) Vierh.
Mangrove forest in Vamleshwar, Gujarat, India. Not Sci
Biol 3:51-56.
McShea WJ, Davies SJ, Bhumpakphan N (2011). The ecology
and conservation of seasonally dry forests in Asia. (Eds.)
Smithsonian Institution Scholarly Press. Washington DC,
USA.
Lal M, Singh R (2000). Carbon sequestration potential of
Indian forests. Environ Monit Assess 60:315-327.
Lee HS, Davies SJ, LaFrankie JV, Tan S, Itoh A, Yamakura T,
Okhubo T, Ashton PS (2002). Floristic and structural
diversity of 52 hectares of mixed dipterocarp forest in
Lambir Hills National Park, Sarawak, Malaysia. J Trop
Forest Sci 14(3):379-400.
Leigh E, Losos E (2004). The Global Network of Large Forest
plots. University of Chicago Press, Chicago, USA
Lugo AE, Snedakar SC (1974). The Ecology of Mangroves.
Ann Rev Ecol System 5:39-64.
Lugo AE, Brown S, Brinson MM (1990). Concepts in wetland
ecology. Pp 53-85. In: Lugo AE, Brinson MM, Brown S
(eds.), Ecosystems of the world 15, Forested wetlands.
Elsevier, Amsterdam.
Mall LP, Singh VP, Garge A (1991). Study of biomass, litter
fall, litter decomposition and soil respiration in
monogeneric mangrove and mixed mangrove forests of
Andaman Islands. Trop Ecol. 32:144-152.
Mandal BN, Naskar KR (2008). Diversity and classification of
Indian mangroves: a review. Trop Ecol 49:131-146.
Mitra A, Sengupta K, Banerjee K (2011). Standing biomass
and carbon storage of above-ground structures in
dominant mangrove trees in the Sundarbans. Forest Ecol
Manag 261:1325-1335.
Putz FE, Chan HT (1986) Tree growth, dynamics and
productivity in a mature mangrove forest in Malaysia.
Forest Ecol Manag 17:211-230.
Ravindranath N H, Somashekar BS, Gadgil M (1997). Carbon
flows in Indian forests. Climate Change 35:297-320.
Ravindranath N H, Chaturvedi RK, Murthy IK (2008). Forest
conservation, afforestation and reforestation in India:
Implications for forest carbon stocks. Current Sci 95:216-
222.
Ravindranath N H, Murthy IK (2010). Greening India
Mission. Current Sci 99:444-449.
Selvam V (2003). Environmental classification of mangrove
wetlands of India. Current Sci 84:757-765.
Singh VP, George A, Pathak SM, Mall LP (1987). Pattern and
process in mangrove forests of the Andaman Islands.
Vegetatio 71:85-188.
Sukumar R, Dattaraja HS, Suresh HS, Radhakrishnan J,
Vasudeva R, Nirmala S, Joshi NV (1992). Long term
monitoring of vegetation in a tropical deciduous forest in
Mudumalai, southern India. Current Sci 62:608-616.
Sukumar R, Suresh HS, Dattaraja HS, Joshi NV (1998).
Dynamics of a tropical deciduous forest: Population
changes (1988 through 1993) in a 50-hectare plot at
Mudumalai, southern India. In: Dallmeier F, Comiskey JA
(eds.), Forest Biodiversity Research, Monitoring and
Modelling: Conceptual Background and Old World Case
Studies. UNESCO, Paris and The Parthenon Publishing
Group, Man and The Biosphere Series, Volume 20,
Chapter 28, p. 495-506.
Suresh HS, Bhat DM, Ravindranath NH, Sukumar R (2010).
Structure and diversity of Mangrove forest patches along
coast of Karnataka. In Zoological Surv. India. Mangroves
in India: Biod, Protection Environ Serv 185-191.
Tomlinson PB (1986). The Botany of Mangroves. Cambridge
University Press. Cambridge.
Upadhyay VP, Ranjan R, Singh JS (2002). Human-mangrove
conflicts: The way out. Current Sci 83:1328-1336.
Upadhyay VP, Mishra PK (2008). Population status of
mangrove species in estuarine regions of Orissa coast, India.
Trop Ecol 49:183-188.
Valiela I, Bowen JL, York JK (2001). Mangrove forests: one of
the world’s threatened major tropical environments.
Bioscience 51:807-815.
WB (The World Bank) (1998). A practical guidance
document for the assessment of project level greenhouse
gas emissions. Greenhouse gas assessment handbook.
World Bank 64:168.
307
  • Article
    Full-text available
    Utilization of remote sensing techniques, particularly from high resolution airborne laser scanning could be an effective tool in describing forest structural features. The study aims to assess LiDAR's capability in characterizing mangrove forest stand using the available LiDAR dataset in the Philippines. Characterization of the structural attributes between mangrove families is done through separability and variability analysis of the point density distribution and height values at different levels. On a hectare scale, point densities of two mangrove families were extracted and graphed to determine separability. The point density parameter was further processed through image classification to come up with LiDAR-based Point Density Distribution Curves (PDDC) for Rhizophoraceae and Avicenniaceae mangroves. The result yielded an overall accuracy of 77.43% with a Kappa coefficient value of 0.42. Variability between and across families were analyzed using point density clustering at a finer scale of 5 by 5 meter plots. The sample plots have homogenous families, wherein fifteen (15) are composed of Rhizophoraceae and fifteen (15) Avicenniaceae, respectively. At finer scales of 5x5m, variability within and across families were evident. LiDAR point density data can serve as an important tool to structurally characterize the two mangrove families by utilizing the height and point density parameters. However, exploring other ways to statistically describe point density distribution per plot should be done as well to be able to improve the analysis.
  • Article
    Full-text available
    Reviews the following: estimation of wetland areas; global role of wetlands; organic-carbon export from wetlands; forested wetlands and animal life; large-scale reduction of forested wetland areas; hydroperiod; influence of water on forested wetlands; xeromorphism in wetland plants for water conservation; energy language; the energy signature; zonation and succession; wetlands on slopes in Puerto Rico; wetland stressors; wetland values; sediment and peat accumulation; definitions of concepts dealing with organic-matter dynamics; tolerance of trees to flooding; anaerobic processes and detritus processing; response of wetlands to stressors; peat loss rates; and the influence of forested wetlands on water and soil. -P.J.Jarvis
  • Article
    Impact of disturbance on forest stand density, basal area , dbh class distribution of density and basal area, species richness, species diversity and similarity index was assessed through monitoring six, one-hectare, permanent forest plots after a period of 24 years in tropical moist forests of Uttara Kannada district, Western Ghats, India. It was observed that all sites lost trees due to removal by people and mortality. Loss of trees was more in sites that are easily accessible and closer to human habitation. In spite of a decrease in tree density, an increase in basal area was observed in some forest plots, which could be on account of stimulatory growth of surviving trees. Decrease in basal area in other sites indicates greater human pressure and overexploitation of trees. Preponderance of lower girth class trees, and a unimodal reverse 'J-shaped' curve of density distribution as observed in majority of the sites in the benchmark year, was indicative of regenerating status of these forests. The decrease in number of species in all forest sites was due to indiscriminate removal of trees by people, without sparing species with only a few individuals, and also due to mortality of trees of rare species. Higher species richness and diversity in the lowest dbh class in most of the sites in the benchmark year is indicative of the existence of favorable conditions for sylvigenesis. The decrease in the similarity index suggests extirpation of species, favoring invasion and colonization by secondary species. To minimize human pressure on forests and to facilitate regeneration and growth, proper management planning and conservation measures are needed.
  • Article
    Indian mangrove vegetation covers about 6,749 km2 along the 7516.6 km long coast line, including Island territories. The entire mangrove habitats are situated in three zones: (1) East Coast, about 4700 km2, (2) West Coast, about 850 km2, and (3) Andaman & Nicobar Islands about 1190 km2, with Lakshadweep Atoll. These three zones have been further categorized into Deltaic, Coastal, and Island habitats following Thom's classification of estuarine habitats. Estimates of the number of species considered mangrove in the world, range from 48 to 90, and in India from 50-60. We estimate 82 species of mangroves distributed in 52 genera and 36 families from all the 12 habitats in India. The relative mangrove diversity (HMD) of each of the 12 habitats is calculated as, RMD=100 × [(Fn+Gn+Sn)/N], where Fn, Gn and Sn are respectively, numbers of families, genera and species, and N = 170 (sum of numbers of families, genera and species in mangrove vegetation of all the 12 habitats in India). Sundarbans recorded the maximum RMD (90%) and Lakshadweep Atoll the minimum (9.4%). The inter tidal vegetation is classified into three categories: 'Major mangroves,' 'Mangrove associates,' and 'Back mangal', on the basis of their morpho-anatomical characters representing adaptation to halophytic condition.
  • Article
    A 52-ha permanent forest plot was established in Lambir Hills National Park Sarawak, Malaysia to begin the long-term study of factors controlling the origin and maintenance of tree diversity. Stand structure and floristic composition of the plot are described. Lambir was found to have the highest diversity of trees anywhere on earth. In the 52-ha plot there were 356 501 trees with a total basal area of 2252 m2, comprising 1173 tree species in 286 genera and 81 families. The Euphorbiaceae (125 species) and the Dipterocarpaceae (87 species) were the mostspecies-rich families. The Dipterocarpaceae dominated the forest with 42% of the basal area and 16% of the trees. The Burseraceae, Anacardiaceae and Euphorbiaceae were the next most important large trees in the plot. Shorea was the most important genus with 53 species and the highest basal area and stem number. As with other Asian tropical forests there were many speciose genera in the plot; 21 genera had ≤ 12 species. In addition to Shorea, Dipterocarpus and Dryobalanops were important basal area contributors and Dryobalanops was the second most abundant genus. Dryobalanops aromatica and Dipterocarpus globosus were the most important canopy trees. Fordia splendidissima was the most abundant understorey tree in the 52 ha. An important finding is the change in floristic composition and stand structure across the 52-ha plot in relation to soil and topographic variation.
  • Article
    Twenty-eight worldwide reports of massive mangrove tree mortalities are reviewed. Massive mortality is defined as tree mortalities that occur in response to rapid environmental change and affect all size classes. Massive mortality occurs in addition to normal tree mortality. Normal tree mortality was described using structural data from 114 mangrove stands. This mortality is density dependent, follows orderly time dependent patterns dictated by stand maturation (related to average tree diameter), and usually occurs in the smaller diameter size classes. Disease and other biotic factors do not appear to be primary causes of massive mangrove mortalities. Instead, these factors appear to attack forests weakened by changes in the physical environment. Mangrove environments are dynamic and cyclical and mangrove associations adapt to such environments by both growing and dying fast. Mangrove species' characteristics such as the capacity to produce large quantities of propagules that take advantage of dispersal agents, sharp species zonations, and even-aged populations contribute to the rapid growth-mortality cycles in mangroves. Humans may tilt the balance towards higher mortality rates by introducing chronic stressors that inhibit regeneration mechanisms.
  • Article
    The Government of India has announced the Greening India Mission (GIM) under the National Climate Change Action Plan. The Mission aims to restore and afforest about 10 mha over the period 2010-2020 under different sub-missions covering moderately dense and open forests, scrub/grasslands, mangroves, wetlands, croplands and urban areas. Even though the main focus of the Mission is to address mitigation and adaptation aspects in the context of climate change, the adaptation component is inadequately addressed. There is a need for increased scientific input in the preparation of the Mission. The mitigation potential is estimated by simply multiplying global default biomass growth rate values and area. It is incomplete as it does not include all the carbon pools, phasing, differing growth rates, etc. The mitigation potential estimated using the Comprehensive Mitigation Analysis Process model for the GIM for the year 2020 has the potential to offset 6.4% of the projected national greenhouse gas emissions, compared to the GIM estimate of only 1.5%, excluding any emissions due to harvesting or disturbances. The selection of potential locations for different interventions and species choice under the GIM must be based on the use of modelling, remote sensing and field studies. The forest sector provides an opportunity to promote mitigation and adaptation synergy, which is not adequately addressed in the GIM. Since many of the interventions proposed are innovative and limited scientific knowledge exists, there is need for an unprecedented level of collaboration between the research institutions and the implementing agencies such as the Forest Departments, which is currently non-existent. The GIM could propel systematic research into forestry and climate change issues and thereby provide global leadership in this new and emerging science.
  • Article
    Full-text available
    Despite an undeserved reputation for being dull and homogenous systems, mangal and saltmarsh in Australia have highly complex patterns and processes. Their role as key ‘edge’ systems between land and sea has implications for many species which have larval stages in mangal and saltmarsh, but spend adult life as benthic, pelagic or demersal species. Many such species are also important commercially. Mangal and saltmarsh are both highly dynamic systems, reacting rapidly to changes in hydrological condition and sedimentation. In many areas of the world mangal and saltmarsh are threatened systems, especially near human habitation. Appropriate management strategies for mangal and saltmarsh are therefore critical for both conservation and sustainable use, the two key objectives of the Convention on Biological Diversity. Clearing and associated development, invasion of alien species, pollution effects and poor management are the key threats to these systems. Management at a bioregional level, including the development of a comprehensive system of protected areas, is identified as the key management strategy which will ensure an adequate future for these dynamic systems.