Evaluating a marine protected area in a developing country; Mafia Island Marine Park, Tanzania - Kamukuru AT, Mgaya YD, Öhman MC (2004)

Ocean & Coastal Management 47 (2004) 321–337
Evaluating a marine protected area in a
developing country: Mafia Island Marine Park,
Tanzania
Albogast T. Kamukurua,Ã, Yunus D. Mgayaa
,
Marcus C. O¨ hmanb
a
Faculty of Aquatic Sciences and Technology, University of Dar es Salaam, P.O. Box 60091,
Dar es Salaam, Tanzania
b
Department of Zoology, Stockholm University, 10691 Stockholm, Sweden
Abstract
The benefits of marine protected areas (MPAs) to fish productivity remain debated, and
comprehensive research projects have been suggested to assess MPA function. This study
evaluated MPA performance in a developing country in the context of local needs. We
compared density and size of the blackspot snapper, Lutjanus fulviflamma (Forsska˚ l 1775), in
Mafia Island Marine Park (MIMP), Tanzania, with adjacent intensively fished areas (IFA)
using underwater visual censuses (a total of 105, 50-m transects) as well as investigating the
catches in the local fishery. The target species was over four times more numerous, its biomass
six to ten times higher and individual sizes on an average 37% larger on reefs in MIMP
compared to the IFA. Fish numbers and biomass were negatively correlated with fishing
intensity and positively correlated with hard coral cover and structural complexity. This study
supports predictions that MPAs can play a key role in the conservation of habitats and
management of a fishery. It is suggested that for the purpose of management, it is possible to
evaluate MPA performance with limited resources by focusing on key information.
r 2004 Elsevier Ltd. All rights reserved.
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www.elsevier.com/locate/ocecoaman
0964-5691/$ - see front matter r 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ocecoaman.2004.07.003
ÃCorresponding author. Tel.: +468 164 022; fax: +468 167 715.
E-mail address: marcus.ohman@zoologi.su.se (M.C. O¨ hman).

1. Introduction
As coral reefs are important resources that provide humans with food and other
goods and services [1–4], the significance of protecting them is broadly recognised
[5–7]. A widely accepted management strategy in the conservation of reef-fish species
is the implementation of marine protected areas (MPAs) designated to protect fish
stocks and habitats [8–20]. The expectation is that the spatial closure will act as a
refuge for local reef fish communities, enhancing their densities and diversity. This
could then have a positive effect on fishery resources in the surrounding waters as
adult fish may migrate (spillover effect) and/or fish larvae might disperse beyond the
park [8,21–25].
How MPAs perform in terms of protecting fish stocks has been the focus of many
studies [6,26–35]. A common conclusion is that there is an increase in fish numbers in
MPAs compared to adjacent areas and/or compared to the situation before the
MPAs were established [36]. However, evidence that MPAs enhance fisheries in the
adjacent intensively fished areas is sparse [37]. In a paper assessing the effectiveness
of MPAs as tools for fishery management, Russ [11] concludes that we need more
information on how well they work, because many studies have had problems in
their research design. He suggests that a study on the effects of MPAs on fish
communities should include a before-after-control-impact-pairs design (BACIP)
with replicate sampling units in replicate sites duplicated regionally, and that the
investigation should be carried out over 5–20 years. In addition, habitat effects
should be factored out, fishing mortality measured, a capture–recapture study
conducted and large transects that traverse both controls and reserves taken. With
such a design we would attain a broad scientific understanding on how successful
MPAs are and general principles on marine conservation could be achieved.
On the contrary, as most MPAs are situated in developing countries mainly
protecting coral reefs, such large-scale studies that consider many aspects of marine
protection are not carried out due to limited resources. In addition, all that
information is usually not needed for the purpose of successful resource management
at a local scale in an area dominated by an artisanal fishery.
Along the coast of Africa lie some of the world’s poorest countries. In East Africa,
where most coral reefs are situated, there is a population growth rate of 4–7% per
annum [38]. In this area the information needed about MPA performance is less
academic and more about the subsistence of local fishermen. Most studies on MPA
function, so far, have mainly been carried out in Kenya [28,39–43]. Ecological
studies on the effects of MPAs in Tanzania and the other countries in the region are
limited (but see [6,44]).
The development of MPAs in Tanzania dates back to 1975 when eight areas were
designated as no-take marine reserves, but were subjected to continuous fishing
pressure, including blast fishing, and other abuses such as shell and coral collection,
anchor damage, coral mining and human trampling [45]. Lack of financial, technical,
and human resources caused the reserves to be left unmanaged. However, with the
Marine Parks and Reserves Act of 1994, the first guide on the institutional
mechanisms for the management of MPAs in Tanzania was provided. To date two
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A.T. Kamukuru et al. / Ocean & Coastal Management 47 (2004) 321–337322

functional marine parks and one marine reserve have been established in Tanzania
[46]. Unlike previous marine reserves, marine parks in Tanzania are now designed
for multiple usage applying a zoning scheme [47]. Like in adjacent areas where
fishing is usually carried out, extractive resource use is permitted within the park but
in a more regulated manner.
In this study, we investigated the effects of an MPA on fishery resources in the
context of local needs and resource limitations. This was done by focusing on a
selected set of data instead of covering all aspects of MPA performance. The
abundance, biomass and size structure of a target fish species in Mafia Island Marine
Park and adjacent intensively fished areas were compared. We also collected
information on catches as well as considered how the species may be influenced by
habitat structure.
2. Materials and methods
2.1. Study sites
Mafia Island Marine Park (MIMP) is situated south-east of Dar es Salaam in
Tanzania, about 20 km east of the Rufiji delta (Fig. 1). MIMP contains a variety of
biotopes that support a large number of species [48]. The park, which covers an area
of about 822 km2
, was established in 1995. It is a large, multiple-use area being
operated on the principles of integrated coastal management [46]. This ensures the
need to balance the protection of the natural resource base while maintaining the
local communities’ right to the resources. Through boat patrols and local
community participation, coral mining and destructive fishing techniques such as
beach seining and blast fishing have been greatly controlled [47]. This surveillance is
conducted on a regular basis and illegal fishers are either fined or have their gear and
boats confiscated. Although violations still occur, the fishing pressure is considerably
lower in MIMP than in intensively fished areas (IFA). This study was conducted on
patchy reefs at two sites in MIMP (Chole Bay and Jujima Bay) and at two sites
outside the park (Mfuruni and Tumbuju) (see Fig. 1) during September/October
2000.
2.2. Ecological data
The blackspot snapper, Lutjanus fulviflamma, is an important commercial reef-fish
species in Tanzania [49] and elsewhere in the Indo-West Pacific region [50]. Although
the fishery at Mafia Island is multispecific, snappers (Lutjanidae) along with
emperors (Lethrinidae) are preferred groups that contribute to about 42% of the
total marine catch [44].
Numbers and size structure of L. fulviflamma were estimated by applying the 50 m
belt-transect method in underwater visual censuses (UVC) [51]. Each transect was
taken by randomly laying a stretched fibreglass tape on the reef (2–15 m depth). The
observer swam along the tape at a constant speed (approximately 20 m per minute)
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A.T. Kamukuru et al. / Ocean & Coastal Management 47 (2004) 321–337 323

while counting and recording fish in a 5-cm total length (TL) class interval, 5 m on
either side of the tape for the 50-m length (i.e. within an area of 500 m2
). An initial
assessment of diver effects revealed that L. fulviflamma was not escaping nor
following the observer. Surveys were carried out between 10.00 h and 16.00 h and to
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KENYA
TANZANIA
Pemba
Is.
Shungu Mbili Is.
Chole Is.
Juani
Island
Jibondo
Island
Marine Park boundary
Ten metre isobath
Sampling site boundary
0
10 km
Bwejuu Is.
Utende
Barakuni Is.
Ras Mkumbi
7°45'
8°
39°45'
Ras
Mbisi
Ras
Murondo
Ras
Kisimani
Mange
Reef
Kitutia
Reef
TUMBUJU
SITE
Tumbuju
Mfuruni
Kilindoni
6.4
Fishers/km2
MFURUNI
SITE
CHOLE BAY
SITE
JUJIMA BAY
SITE
K
IS
IM
A
N
I
C
H
A
N
N
E
L
M
A
F
IA
C
H
A
N
N
E
L
8.6
Fishers/km
5.1
Fishers/km2
2
3.6
Fishers/km2
Mafia
Island
Unguja
Island
Dar es
Salaam
10°
0 50
km
38°E 40°
8°
6°S
I N D I A N
O C E A N
Rufiji River
Kinasi Pass
Fig. 1. Mafia Island and surroundings showing study sites.

avoid observer bias, all fish counts were conducted by the same person (A.T.K.).
Water visibility was greater than 10 m at all sites throughout the study period.
Biomass was calculated as follows:
Biomass ðgÞ ¼ aLb
; where L=mid class TL (cm) of fish with regression coefficients
obtained from Kamukuru [44] ða ¼ 1:165 Â 10À5
and b=3.049).
The substrate characteristics were assessed using the line intercept transect (LIT)
technique [52]. Substrate categories included live and dead coral, rock, rubble, sand,
macroalgae, seagrass, soft coral and sponge. To measure structural complexity, a 50-
m tape was laid on the bottom to conform to all irregularities and later pulled
horizontally tight to eliminate all the slack produced by the relief to obtain the
horizontal length. Structural complexity [R] was then calculated according to Grigg
[53]
R ¼ 10 À 10 Â
H1
50
;
where Hl is the horizontal length.
2.3. Fishery data
Fishery data (i.e. number of fishers, fishing boats and gear) were obtained from a
survey conducted by the Mafia Island District Fisheries Office in March 1999 (F.O.
Hemile, pers. comm.). Fishing grounds were assumed to extend from the shoreline to
the 10-m isobath based on the type of fishing gear, boats and methods currently
used. The area of each sampling site was estimated using a digital planimeter. The
measurements for each sampling area were redigitised five times and the average area
was used in the calculation of the fishing intensity index given as number of
fishermen per unit area (Table 1).
2.4. Data analysis
The Kolmogorov–Smirnov goodness of fit test was used to test the normality of
data collected from the underwater visual census. As the data did not conform to
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Table 1
Estimation of fishing intensity index of the study sites in shallow waters of Mafia Island. Data is based on
Mafia District annual fisheries census conducted in March 1999
Study sites
Jujima Bay Chole Bay Tumbuju Mfuruni
Fishing intensity index (fishermen kmÀ2
) 3.63 5.13 6.38 8.62
Fishing effort (fishermen vesselÀ1
) 3.20 2.61 2.85 6.22
Number of fishing vessels 209 142 201 99
Number of fishermen 669 370 572 616
Fished area by seine nets (km2
) 184.28 72.18 89.70 71.44
Seine nets stretched mesh size (mm) 50–64 50–64 19–38 19–38

normal distribution the Kruskal–Wallis rank test, the Dunn’s multiple comparisons
test and the Mann–Whitney U-test were applied [54,55]. To test for the effects of
habitat characteristics on fish abundance and biomass, the structural complexity and
hard coral cover were regressed against the natural logarithm of fish abundance and
biomass to obtain Pearson correlation coefficients. Because of incidence of zero
counts, the data were transformed (x+1) prior to loge transformation [56].
3. Results
3.1. Fish counts
There were over four times more L. fulviflamma (H=46.359, po0.001, df=3) and
their biomass was six to ten times higher (H=56.135, po0.001, df=3) within Mafia
Island Marine Park (MIMP) than in the intensively fished areas (IFA). There was a
negative relationship between fish abundance/biomass and fishing intensity (Fig. 2).
However, densities within a size range of 100–200 mm total length (TL) did not differ
significantly between the study sites (H=2.76, p=0.430, df=3). The highest
individual count among the 105 transects was 72 fishes per 500 m2
at Jujima Bay
compared to 2 fishes per 500 m2
at more than 50% of the transects within the IFA
(Fig. 3).
The mean (7 s.e.) visually assessed fish size was 229.071.5 mm TL in MIMP in
comparison to 167.272.6 mm TL in IFA. The length–frequency distribution of fish
recorded on transects was compared with that of fish caught in the fishery within the
census areas (Fig. 4). A similar trend of length–frequency distributions emerged with
a modal class centred at 175 and 225 mm TL for fish populations in IFA and MIMP,
respectively. There was no significant difference in mean fish sizes assessed visually
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Fig. 2. Mean density and biomass of Lutjanus fulviflamma estimated using underwater visual census
(UVC) in four study sites during September/October 2000. Values that are significantly different have
different letters for density and biomass following Dunn’s multiple comparisons test (error bars= 7 s.e.).
Values in parentheses indicate no. of fishermen/km2
for each site.

and caught in the fishery (Jujima Bay: U=77058, Z=À0.772, p=0.429; Chole Bay:
U=45718, Z=À1.016, p=0.307; Tumbuju: U=21066, Z=À1.497, p=0.134) except
at Mfuruni (U=20881, Z=3.541, po0.001), with larger sizes recorded visually.
3.2. Habitat characteristics
All habitat characteristics, except sand, differed significantly among study sites
(H=3.178, p=0.365, df=3) (See Table 2). Bare rock and rubble were the highest
substrate categories and accounted for 30–36% and 22–31%, respectively within the
IFA. Conversely, hard coral cover dominated in MIMP with a range of 26–28%
(Table 2). In terms of structural complexity of reefs Chole Bay had the highest
figures (3.0) and Mfuruni the lowest (1.3) (Table 2). There was a significant increase
in fish abundance and biomass with an increase in structural complexity and hard
coral cover (po0.001; Figs. 5 and 6).
4. Discussion
4.1. Fishery effects
This study examined fishery effects and MPA performance at Mafia Island,
Tanzania. This was achieved by studying the presence in the field of a target species
(blackspot snapper) and its catch composition in the fishery, and by collecting
ecological data on fish-habitat interactions. The results indicate significant
differences in abundance, biomass and population structure of the species. Within
the marine park there were four times more individuals of the species per unit area,
the biomass was six to ten times greater and the fishes were larger than in the
adjacent heavily fished areas. These observations conform to a number of studies
that have assessed differences in reef fish communities between MPAs (or less
disturbed areas) and more exploited locations, e.g. [28,30,33–35,57]. For example,
Watson & Ormond [28] reported from Kenya that the abundance of Lutjanus
fulviflamma and L. ehrenbergi (Peters, 1869) were 170 times higher in marine parks
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Fig. 3. Density–frequency distributions of Lutjanus fulviflamma visually assessed from shallow waters of
Mafia Island during September/October 2000.

than in adjacent fished areas. A similar pattern was observed from southern
Kenya and northern Tanzania where lutjanids were noted to have higher
biomass in protected areas [6]. In terms of size, e.g. Wantiez et al. [35] showed
that protection could increase the total length of the species by almost 30%.
This is comparable with our study where the blackspot snappers were 37% larger in
MIMP than in IFA. Consequently, our results indicate that there is an overall state
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(a)
(b)
(c)
(d)
Fig. 4. Comparison of length frequency distributions of Lutjanus fulviflamma caught in a fishery and
visually assessed from (a) Tumbujua
, (b) Mfurunia
, (c) Chole Bayb
and (d) Jujima Bayb
during September/
October 2000. Note: Sites that are significantly different have different superscript letters following Dunn’s
multiple comparisons test.

of overfishing, including growth overfishing [10], in the intensively fished areas of
Mafia Island.
How MIMP affects the surrounding areas where more fish are removed is an
important question. MPAs aim to sustain a critical mass of fish and it is expected
that this should have the secondary effect of boosting fish numbers in adjacent non-
protected waters [37]. This perspective has been criticised as there are limits to the
dispersal capabilities of reef fishes, and factors such as the hydrographic regime, the
distances separating reefs and the availability of suitable habitats, all have an impact
[58]. Nevertheless, there is increasing quantitative evidence that enhanced recruit-
ment and spillover (fish migration) are taking place [8,21,24]. Tupper and Rudd [25]
noted that there was a linear relationship between the numbers of a target species
and the distance to the centre of an MPA. In this study it was observed that Mfuruni,
the most intensively fished location closest to Jujima Bay (where fishing intensity was
the lowest) had a fairly higher fish abundance and biomass and larger mean size of
fish compared to Tumbuju, a site further away from MIMP. This could suggest that
fish spillover and larval settlement are occurring in the vicinity of the marine
protected area.
Habitat structure is an important factor in regulating fish numbers. The close
relationship between reef-fish communities and features of the reef habitat has been
well documented [31,48,59–65]. In a quantitative ecological study examining reef
structure, benthos and fish at Mafia Island Marine Park, it was shown that 92% of
the fish numbers were explained by habitat structure with live coral cover being the
foremost predictor [48]. As L. fulviflamma is a mid-sized predator [66], the refuge
availability that is common on reefs with high rugosity would be expected to
influence their survival. In addition, structurally complex reefs support preferred
prey such as various invertebrates and smaller fish [67–68].
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Table 2
Mean (7 s.e.) habitat characteristics at census sites listed in order of increasing fishing intensity index
Substrate Jujima Bay
(n = 22)
Chole Bay
(n = 14)
Tumbuju
(n = 15)
Mfuruni
(n = 14)
H p
Hard coral
(%)
26.073.7a
28.074.0ab
13.272.5ac
18.272.8abc
9.427 *
Rock (%) 19.173.8a
12.572.7a
30.174.4ab
36.175.4b
16.224 ***
Rubble (%) 9.072.2a
8.772.1a
31.174.0b
21.675.3ab
21.058 ***
Sand (%) 17.273.5a
8.572.2a
13.473.5a
11.472.9a
3.178 NS
Algae &
seagrass (%)
13.772.7a
15.372.4ab
4.971.7ac
5.171.8ac
15.699 ***
1
Others (%) 15.073.6a
27.176.0ab
7.472.8ac
7.573.0ac
10.501 **
Depth (m) 5.670.6a
9.270.8b
5.570.9a
4.470.6a
21.276 ***
Rugosity 2.270.2a
3.070.2ab
1.370.2ac
1.570.2c
17.829 ***
1
Others include dead coral, soft coral and sponge. Values that are significantly different have different
superscript letters in each row following Dunn’s multiple comparisons test.*** p o 0.001, ** p o 0.01, * p
o 0.05, NS = not significant p 4 0.05, df = 3.

4.2. MPA evaluation and local needs
Fishing is the most important human exploitative activity on coral reefs in
developing countries [10,69]. The bleak employment opportunities in Mafia Island
make fishing a relatively attractive occupation. In addition, most fishing is conducted
on nearshore reefs due to lack of seaworthy vessels and gear to retain large fish [83]
ultimately leading to a state of localised overexploitation. The situation is
exacerbated by an ever-increasing number of large boats equipped with ice boxes
trading in fish between Dar es Salaam and Mafia Island [70].
The most recognised research on MPAs seeks a general understanding of how
MPAs affect the organisms they host. However, there are great variations in reef
areas and how they are protected and utilised [24]. Consequently, every MPA needs
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0
1
2
3
4
5
Lndensity(fish.500m-2
)
y = 0.76x + 0.86
r = 0.77
n = 65
(a)
0
1
2
3
4
0
Rugosity
Lnbiomass(g.500m-2)
y = 0.61x - 0.23
r = 0.79
n = 65
(b)
1 2 3 4 5
Fig. 5. Relationships between rugosity and (a) ln density and (b) ln biomass of Lutjanus fulviflamma
assessed visually in shallow waters of Mafia Island during September/October 2000.

its own evaluation as results from one reef area may only in part hold information
that can be applied elsewhere. For example, McClanahan and co-workers [71–74] in
Kenya showed that the triggerfish–sea urchin interaction is an important factor in
reef ecology which is influenced by the fishery. On the contrary, in many other reef
areas in East Africa and elsewhere, sea urchins are not as common and consequently
the organism is of less importance to MPA performance and fisheries management.
On comparing MPAs with adjacent areas, it is found that the more the number of
MPAs and comparable sites that are included in the study, the more reliable are the
data [11]. Nevertheless, if the aim is to generate data at low cost that will give an
indication of MPA performance locally, a large-scale study may not be achievable or
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0
1
2
3
4
5
Lndensity(fish.500m-2)
(a)
y = 0.03x + 1.79
r = 0.39
n = 65
0
1
2
3
4
0 20 40 60 80
Hard coral (%)
Lnbiomass(g.500m-2)
y = 0.02x + 0.50
r = 0.41
n = 65
(b)
Fig. 6. Relationships between percent hard coral cover and (a) ln density and (b) ln biomass of Lutjanus
fulviflamma assessed visually in shallow waters of Mafia Island during September/October 2000.

necessary. In addition, there is an upper limit to the number of sites that can be
included because of limited numbers of comparable sites. In this study, we used two
plus two sites for comparing MIMP with adjacent IFAs. If other MPAs were to be
included, the nearest well-functioning park would be Chumbe Island Coral Park in
Zanzibar Island (Unguja), whose composition differs from that of MIMP [75–77].
In this study we focused on a target species, which may act as a quick indicator of
how well an MPA works. Depending on needs and available resources for additional
research, more exploited species can be examined. Moreover, certain species may
play a key role in regulating functional groups within coral reefs [78]. Taxa such as
Butterfly fishes have been used as indicator species for disturbance [34] although
they, and other smaller species such as pomacentrids, are of less importance for the
fishery in Mafia Island. Rare species may be more important to investigate than first
anticipated, as they could be the first to disappear. If time and resources permit, a
near-complete coverage of all species may show how the entire reef community is
affected. However, as mentioned above, the project may then become more academic
than necessary for the immediate local needs. It should further be noted that the
UVC-techniques used in this study have been criticised [79,80], one reason being that
it is difficult to count several species at the same time within a usually large area.
However, with few, or as in our case, a single species, visual surveys are more
accurate. It is further noticeable that the fishery-dependent data yielded the same fish
size structure with that of UVC. These observations are in agreement with Koslow et
al. [81] who compared trap catches and UVC, and reported that the two estimates
provided similar results of reef-fish sizes on Caribbean reefs.
In addition to fish data there are other factors that need to be considered when
examining MPAs. Reef-fish communities are influenced by their habitat
[31,34,48,60–65]. If this is not considered, differences between fished and non-fished
areas may be an effect of habitat only or a combination [47]. As there was a linear
relationship between fish and habitat structure in this study, it is difficult to separate
one from the other. Reefs in the IFA had a lower percentage coral cover and
structural complexity than in the MIMP. Hence, the differences between the two
areas is probably not only an effect of fish removal, but also determined by habitat.
This could, in part, also be the result of fishing practices that are caused by anchor
damage, and/or destructive fishing techniques, which occur more frequently in the
IFA.
5. Conclusions
In this study, we evaluated the performance of a marine protected area in
comparison with surrounding intensively fished areas. For a rapid assessment at low
cost that would provide useful data on the local communities, we limited the study to
one target species. The MPA had more individuals, higher biomass and larger fish.
This seemed to be an effect of fishing, as fewer and smaller-sized fish were seen in the
intensively fished areas. However, habitat was also an important aspect as fish data
correlated positively with habitat features.
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There is much debate on how well MPAs work. The point made here is that there
is a difference between evaluating MPAs as a protecting measure in general terms
and what the local needs are in a fishing village that depends on adjacent reefs for
food and income. For MPA evaluations a broad-scale quantitative study applying
various techniques including a BACIP design (Before-After-Control-Impact-Pairs)
makes a strong case in fishery science but if fishers and managers only want to know
if there is a difference between their MPA and the surrounding reefs, such an
endeavour becomes unnecessary for local needs.
This study gives information that should be considered in a management scheme
regulating the fishery at Mafia Island. The MPA seemed to be a useful strategy for
protecting fish stocks and conserving habitats. Our results suggested that the
intensively fished area was overfished, and of concern not only from its lower
numbers of fish but also their small size. The majority of the blackspot snappers
assessed visually and caught in the fishery within the IFA had mean sizes far below
the length of sexually mature fish [82]. This signifies that growth overfishing [10] is an
added measure of concern for the long-term sustainability of the fishery at the
current exploitation levels.
Acknowledgements
This study was funded by Sida/SAREC Bilateral Marine Science Programme
between Sweden and Tanzania for research in marine zoology (Sida=Swedish
International Development and Cooperation Agency). It was made possible through
the co-operation of the Mafia Island Marine Park (MIMP) administration in
granting permission to conduct research within the marine park. We are most
grateful to the Warden of MIMP Mr. George Msumi, the Technical Advisor of the
WWF-MIMP project Mr. Jason Rubens, the Natural Resources Officer of Mafia
District Mr. F.O. Hemile and their staff for their help and assistance during the
fieldwork. Thanks are also due to Mr. Nsajigwa Mbije at the Department of
Zoology and Marine Biology, University of Dar es Salaam (currently at the Sokoine
University of Agriculture, Morogoro, Tanzania), for his assistance during under-
water reef surveys. Finally, the manuscript benefited from the comments and
suggestions of anonymous reviewers, to whom we are grateful.
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Evaluating a marine protected area in a developing country; Mafia Island Marine Park, Tanzania - Kamukuru AT, Mgaya YD, Öhman MC (2004)

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