Assessment of the status of Chambo (Oreochromis species) and Other Shallow Water Fish Stocks in the Southwest Arm and Southeast Arm of Lake Malawi, Malawi

doi:10.21203/rs.3.rs-2075924/v1

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Assessment of the status of Chambo (Oreochromis species) and Other Shallow Water Fish Stocks in the Southwest Arm and Southeast Arm of Lake Malawi, Malawi

https://doi.org/10.21203/rs.3.rs-2075924/v1

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There is overwhelming evidence that the shallow water stocks of Lake Malawi have been noticeably overexploited by both the artisanal and commercial trawl fishers. However, there have been some recent initiatives to restore these areas such as enhancing monitoring, control and surveillance, revamping local fisheries management authorities and establishments of community managed fish protected areas. Yet, information evaluating the impacts of such interventions on the fish stocks is limited. This information is critical for fisheries management and planning. Therefore, a study to reveal the current status of Chambo (Oreochromisspecies) and other shallow water fish stocks of the southwest arm (SWA) and southeast arm (SEA) of Lake Malawi was conducted. The study compared the status of fish stocks in the two arms using stock abundance and biodiversity indicators. Results indicated similar fish species diversity in the two arms. Overall, Chambo (Oreochromis spp.) was among the top ten fish that contributed a significant amount of weight to the total sampled catch. The SWA and SEA reported average catch rates of 77.4±22.6Kg/0.5hr and 177.7±18.4Kg/0.5hr, respectively whereas Chambo registered mean catch rates of 61.0±8.1Kg/0.5hr and 68.0±13.7Kg/0.5hr, respectively. Overall fish densities for all fish species in the SWA and SEA were estimated at 2.9tons/km2and 3.5tons/km2, respectively. Chambo density estimates for both SWA and SEA were calculated as 1.1tons/km2 and 1.2tons/km2, respectively. A total biomass of 10, 722 tons was estimated in both regions with the SEA contributing 57% while the biomass estimates of Chambo was 3,964 tons of which 54% was registered in the SEA. A decline in fish stocks in the shallow waters was observed and masks the efforts that the Government is doing to ensure the restoration and sustainability of fish stocks in the areas. It is therefore strongly believed thatregulating mesh sizes, limiting fishing effort and protecting critical breeding habitats are critical in reversing the observed trends.

Conservation Biology

Cichlids

trawling

catch rates

spatial distribution

management measures

For years, Chambo, a general local name for all Oreochromis species has been considered as one of the most economically important fish in the country (Bell, et al., 2012). With ever-increasing demand for the fish, Chambo landings have reached a record low with many researchers considering its fishery as collapsed (FAO, 1993; Palsson, et al., 1999; Hara, 2006; Bell, et al., 2012; Makwinja & M'balaka, 2017). Palsson et al(1999) reported that the average length of a landed Chambo fish had decreased pointing further to the declining of the stock. Chambo is found throughout Lake Malawi, Upper Shire River and Lake Malombe, generally in shallow waters of less than 50m deep (Konings, 2016). Chambo stock assessment studies previously conducted found that the fish was severely overfished and to rebuild the stocks would require significant decrease in fishing effort as well as enforcement of the existing management regulations (Banda, et al., 2005; Bell, et al., 2012). In addition, protection of habitat for breeding and juveniles would similarly be essential to aid in the stock restoration (Bulirani, 2005). Unfortunately, these recommendations were not followed and in turn, the Chambo fishery has continued to decline particularly in the shallow waters including the southwest arm (SWA) and southeast arm (SEA) of Lake Malawi (Hara, 2006; Bell, et al., 2012; Hara & Njaya, 2016).

Management of the fish exploitation in the SWA and SEA of Lake Malawi is therefore crucial to avoid further losses in the biodiversity and fish production. The paramount reason in focusing much of the effort in the SWA and SEA is the fact that these two arms are considered to be the most productive parts of the lake (Weyl, et al., 2010). One of the reasons for its productivity is the moderate shallowness of the regions (Turner, 1996) which massively boosts primary production. These important attributes correspondingly lead to higher densities of fish attract increased interest in fishing activities in the region. It is therefore very important to understand the distribution of the Oreochromis species stocks particularly the potential hotspots that can be used to setup proper sustainable management strategies. Biologically, Oreochromis species renders itself prone to overexploitation as the fish is known for slow growth, low fecundity and its shallow water preference especially during breeding (FAO, 1993). Currently, limited information exists that provide hotspots for Oreochromis species and other important shallow water fish stocks in the southwest and southeast arms of Lake Malawi, hence the core reason of carrying out the study.

Survey Area, Equipment and tools

The survey covered the southwest arm and southeast arm of Lake Malawi. The total surface area for the study sites were estimated at 3,300km² combining 1,575 km² for SWA and 1,725 km² for SEA. A mixture of old and new stations located in the shallow waters (less than 50 m deep) totalling to 21 in the SWA and 30 in the SEA were targeted as shown in Fig. 1. A research vessel Ndunduma, belonging to the Department of Fisheries was used in carrying out the surveys using Guttorpur bottom trawl net with a recommended cod-end mesh size of 38 mm when stretched. An on-board Global Positioning System (GPS) receiver was used to locate the predetermined sampling stations.

Sampling procedure

The trawling speed for the vessel was limited to 4.6km/hour while the trawling duration was 30minutes. Once the fish were moved from the net into the receiving bin, a protocol recommended by Sparre et al. (1989) was applied.

Data Analysis

Size Structure

Length frequency distribution of Oreochromis spp and other selected fish species with numerical dominance were modelled with a probability density function for determining the likelihoods for gamma distribution (Haddon, 2011). The gamma distribution function is the most preferred as it is known to be very flexible since it follows the behaviour of the size distribution. Because of this, gamma distribution function is one of the most useful simulation tools in fisheries science (Haddon, 2011). The size-at-capture for three selected fish species sampled were modelled using the logistic model (Haddon, 2011). The logistic model was used to determine the probability of a fish to being retained by the encountered trawl net. Data for the size structure was processed in Microsoft Excel 2016 then imported in SigmaPlot 12.5 for generation of the graphs.

Fish species diversity

Species composition, richness and abundance have been used in many studies to describe and assess fish community and diversity (Hewitt et al., 2008). Species diversity indices for the two regions were calculated using the Shannon and Weiner (1963) relationship. Data on fish species diversity analysis was prepared in Microsoft Excel 2016.

Fish Biomass Estimation

Fish biomass from trawl surveys is estimated by the swept area method. This study adopted the same approach and the swept area, a, was estimated by an equation provided by Sparre et al, (1989). Biomass estimation accuracy was enhanced by application of geostatistical techniques which combined ordinary kriging with grid-based analysis in ArcGIS version 10.5.

Experimental variograms were initially computed from sample points and then fitted with a spherical variogram model after which three variogram parameters (Range Nugget and Sill) were estimated. Ordinary kriging was used generate the variogram parameters and to interpolate the catch per unit effort (CPUE) by region after which grid-based analysis was used to apportion the interpolated CPUE by pixel. The Geometric mean (GM) was used for calculating mean CPUE instead of the arithmetic mean because previous studies have showed that trawl data has an asymmetric distribution therefore skewed (Kanyerere & M'balaka, In Press; M'balaka & Kanyerere, 2019a). The asymmetry was tested by subjecting the data to a normality test in Sigmaplot 12.5 and in all cases failed the Shapiro-Wilk test (p= 0.05). Before applying the geospatial techniques, the Global Position System data collected in decimal degrees were projected to Universal Transverse Mercator (UTM) for uniformity. Depth based pixels which indicatively showed CPUE differences per sampling station were generated and used to estimate fish abundance in the study zones. Biomass was then estimated by multiplying the interpolated CPUE per pixel by the ratio of the total region to the area of each pixel. The final biomass estimate was obtained by summing all the pixel values within a particular region.

Spatial interpolation

Ordinary kriging was used to produce fish density maps using standardised catch per unit area (CPUA) data (Kaluzny, et al., 1998). Similar maps for bathymetry were also produced using georeferenced trawl depth data which were supplemented by charts where necessary. The bathymetric maps were used during map algebra to apportion catch rates and their subsequent biomass to the 50m water depth. The study made use of data collected during 2020 biomass trawl survey to enhance distribution and resolution of the maps as recommended by Kaluzny et al., (1998). Where sampling points are less than 30, it is recommended that dummy stations with no catch or depth data be added especially on the edges of the water body where trawling would be impossible. This was the case in both the SWA and the SEA particularly in all untrawlable areas.

Table 1: Parameters used during the shallow water assessment surveys in the SWA and SEA of Lake Malawi

Parameter	Values
Velocity (km/hr)	2.5 (4.63km/hr)
t (hrs)	0.5
hr (nm)	0.01241
x2	0.639
Vulnerability	0.5

Fish species composition

The SWA biomass assessment survey reported a total of 91 different fish species, 39 fish genera and 6 fish families. The family Cichlidae comprising about 82% of the total landed catch was dominant. The Cichlidae dominance, was followed by Claridae, Mochokidae, Cyprinidae and Bagridae with 9%, 4%, 3% and 2%, respectively (Fig. 2). The family Mormyridae had the lowest contribution of less than 1%. The SEA survey on the other hand recorded 91 fish species, 38 genera and 6 fish families. The family Cichlidae that contributed about 89% of the total landed catch was the most dominant followed by Claridae, Bagridae and Cyprinidae with 7%, 2% and 2%. Family Mastacembelidae and Mochokidae with 0.12% and 0.03% were the last two.

At genera level, the genus Oreochromis spp (Chambo) was among the top ten to contribute a significant amount of weight to the sampled fish. In SWA, the category was led by the shallow water Nyassachromis with 16% (470kg) followed by Copadichromis (328kg), Lethrinops (276kg), Buccochromis (276kg), Trematocranus (239kg), Bathyclarias (227kg) and Oreochromis (174kg) representing 11%, 10%, 10%, 8% and 6%, respectively (Fig. 3). Placidochromis (61kg), Otopharynx (42kg) and Stigmatochromis (32kg) were the least among the genera that contributed more than 1% as they individually contributed 2%, 1% and 1%, respectively. In the SEA, Nyassachromis (23%) Oreochromis (9%), Copadichromis (9%), Trematocranus (8%), Otopharynx (7%) and Lethrinpos (7%), were the most common and contributed more than 60% of the sampled fish (Fig. 3).

In the category of fish that contributed less than 1%, the genera Ctenopharynx (0.7%), Clarias (0.6%), Alticorpus (0.5%), Pseudotropheus (0.4%), Chilotilapia (0.4%) and Diplotaxodon (0.4%) were common as they registered at least 0.4% each (Figure 4). The least contributors were the genera Tramitichromis, Taeniochromis, Hemitilapia, Hemitaeniochromis and Aristochromis with 0.05%, 0.04% and 0.02%, 0.01% and 0.01%, respectively. In the SEA, Stigmatochromis (0.96%) and Clarias (0.76%) were the most common as they individually contributed close to 1% each. Chilotilapia, Hemitilapia, Aulonocara and Scienochromis followed with 0.52%, 0.44%, 0.28% and 0.28%, respectively (Fig. 4). Caprichromis, Taeniochromis and Dimidiochromis had 0.01% each whereas Coptodon and Corematodus contributed 0.004% apiece.

Figure 5 shows contribution of different fish species to the sampled fish. The dominant fish species was Nyassachromis argyrosoma (16%) followed by Buccochromis nototaenia, Trematocranus placadon, Copadichromis virginalis and Oreochromis species with 8%, 6%, 6% and 6%, respectively. Within this category; Lethrinops altus, Protomeras hennydaviesae and Placidochromis long were the least with 1% apiece. N. argyrosoma with 319kg (12%) was also dominant in the SEA but was closely followed by Oreochromis spp with 290kg representing a 11% contribution. T. placadon, C. virginalis, Bathyclarias spp and Otopharynx argyrosoma followed with 8%, 7%, 6% and 6% contributions, respectively. Taeniolethrinops furcicauda, P. hennydaviesae and Copadichromis eucinostomus were the least with 1% each.

Size Structure of selected fish species in SWA

The selected three fish species based on numerical advantage were Oreochromis spp, N. argyrosoma and Engraulicypris sardella in the SWA. In the SEA, the following were selected; N. argyrosoma, Oreochromis spp and C. virginalis. The fishes were analysed at two levels; using gamma distribution function to determine their modal length and logistic model for selectivity. Table 2 shows the solver fitted parameters that this study used to plot graphs for both gamma distribution and gear selectivity modelling.

Table 2: Solver fitted parameters for Gamma distribution and Logistic Models for the selected fish species

Site	Fish Species	Gamma Distribution		Logistic		Sample n	Length at maturity (Literature)	Remarks
Site	Fish Species	Phi	Sigma	L₅₀	Sigma	Sample n	Length at maturity (Literature)	Remarks
SWA	Nyassachromis argyrosoma	0.8	105	79	0.15	989	114	Immature
	Engraulicypris sardella	0.72	93	65	0.2	417	Unknown	Unknown
	Oreochromis spp.	1.54	115	177	0.04	404	290	Immature
SEA	Nyassachromis argyrosoma	0.94	93	82	0.17	581	114	Immature
	Copadichromis virginalis	0.89	94	80	0.13	525	99	Immature
	Oreochromis spp.	2.00	93	202	0.03	413	290	Immature

Figure 6a to 6c show the modelled size structure and selective ogive for N. argyrosoma, Oreochromis spp and E. sardella in the SWA. The smallest N. argyrosoma measured 40mm and the largest was 120mm with a mean of 79±0.52mm (Fig. 6a1). The modal length of the fish was 85mm while 79mm was estimated as the value for L₅₀ of the fish (Fig. 6a2.) The total length for Oreochromis spp ranged from 101mm to 330mm with a mean of 204±1.22mm. A modal length value of 176mm was estimated (Fig. 6b1) whereas the length at which the fish had 50% chance of being retained by the trawl net was 177mm (Fig. 6b2). The smallest E. sardella measured 40mm and the largest measured 120mm with an average total length of 67±2.01mm. Modal length was estimated at 67mm (Fig. 6c1). The length at which E. sardella had 50% chance of being retained by the 38mm cod-end mesh was 65mm (Fig. 6c2).

Figure 7d to 7f show the modelled size structure and selective ogive for N. argyrosoma, Oreochromis spp and C. virginalis in the SEA. The study registered the smallest N. argyrosoma of 51mm and the largest being 130mm with an overall mean total length of 88±0.43mm. The modal length value was estimated as 87mm (Fig. 7d1). The length at which the fish had 50% chance of being retained by the gear was 82mm (Fig. 7d2). The size of Oreochromis spp ranged from 97mm to 369mm with an average total length 203±2.51mm. The modal length for the fish was 230mm (Fig. 7e1) and the value of 202mm was a total length at which the fish had 50% chance of being retained by the 38mm cod-end (Fig. 7e2). The smallest C. virginalis was 50mm and the largest was 131mm with a mean total length of 85±1.23mm. The sampled fish had a gamma distribution model estimate of 82mm as modal length (Fig. 7f1). The value for L₅₀ for C. virginalis was estimated at 80mm (Fig. 7f2).

Fish species diversity

An assessment of the fish diversity indicated similar indices of 3.64 in both the SWA and SEA. The effective number of species (ENS) which represents the real diversity was 38.

Kriging parameters

Semi-variogram parameters used in the interpolation process are shown in Table 3. As observed, the value for range was greater in the SWA (17,819 and 17,954) than in the SEA (9,446 and 9,744) for both overall sampled fish and Chambo, respectively. On contrary, the sill values were higher in the SEA (84,935 and 93,132) than in the SWA (15,420 and 17,408) for all fish species and Chambo, respectively. The nugget values did not follow the pattern of either range or sill as the values for the overall fish species was higher in SWA than in the SEA which was the opposite with Chambo values where 5,467 in SEA was higher than 2,923 recorded in the SWA.

Table 3: Parameters for the semi-variogram model used for interpolation of catch rates.

Region	Size (km²)	Level	Range (m)	Sill	Nugget
SWA		Overall	17,819	15,420	9,006
SWA		Chambo	17,954	17,408	2,923
SEA		Overall	9,446	84,935	6,769
SEA		Chambo	9,744	93,132	5,467

Mean catch rates, productivity and biomass

Results for mean catch rates, productivity and biomass estimates for the SWA and SEA are shown in Table 4. The overall catch rates for SWA and SEA ranged from 20.0kg/0.5hr to 204.1kg/0.5hr and 28.1kg/0.5hr to 377.1kg/0.5hr, respectively. Regarding Chambo catch rates, SWA registered a range of 11.1kg/0.5hr to 182.3kg/05Hr whereas the SEA ranged from 12.4kg/0.5hr to 215.5kg/0.5hr. The results further indicate that the SWA and SEA reported averages of 77.4±22.6kg/0.5hr and 177.7±18.4kg/0.5hr, respectively for all the fish species sampled. Chambo registered mean catch rates of 61.0±8.1kg/0.5hr and 68.0±13.7kg/0.5hr for SWA and SEA, respectively. Just like with the overall catches, Chambo was more abundant in the SEA than in the SWA.

Table 4: Catch rates, Productivity and Biomass estimates from the two regions

Year	Level	Catch Rate Range	Mean Catch Rate	Fish Density (tons/km²)	Biomass (tons)
SWA	Overall	20.0-204.1	77.4±22.6	2.9	4,651
	Chambo	11.1-182.3	61.0±8.1	1.1	1,826
SEA	Overall	28.1-377.1	177.7±18.4	3.5	6,071
SEA	Chambo	12.4-215.5	68.0±13.7	1.2	2,138

Overall fish density also known as abundance for all fish species in the SWA and SEA were estimated at 2.9 tons/km² and 3.5 tons/km², respectively (Table 4). During the same survey, Chambo density estimates for both SWA and SEA were calculated as 1.1 tons/km²and 1.2 tons/km², respectively. The table further shows fish biomass estimates for the for both Chambo and all fishes. A total of 10, 722 tons of fish was estimated in the two regions with the SEA reporting higher estimate of 6,071 tons (57%) as compared to 4,651 tons (43%) estimated in the SWA. Out of a total of 3,964 tons of Chambo, about 54% (2,138 tons) was contributed by the SEA while the SWA with 1,826tons contributed the remaining 46%.

Spatial Distribution of fish stocks in the SWA

Figure 8 shows the spatial distribution of all fish stocks and Chambo in the SWA of Lake Malawi. It was observed that fish density was low in the shallow water and increased towards the deeper part. This pattern was observed throughout the SWA. High fish densities were also observed around Linthipe River mouth and stretched southwards beyond Chipoka area. High fish densities were likewise seen close to the shores of Msaka and Kasankha fishing communities. Chambo stocks were observed in high densities around the 50m boundary. Higher Chambo catch rates were distributed between Linthipe River mouth and Chipoka area with other smaller pockets being observed around Chitukula and the northern side of Msaka.

Spatial Distribution of fish stocks in the SEA

Figure 9 displays the spatial distribution of all the sampled fish against the Chambo in the SEA. The total sampled fish concentrated in a number of pockets with the largest being observed around the south-eastern part of the lake and around Lugola River mouth. However, relatively lower and smaller fish densities were observed around Nkhudzi Bay and in the southern part of Makanjira. Similar distribution was noted in Chambo stocks having a denser pocket in the southern Makanjira while relatively smaller size was observed around Nkhudzi Bay and Kadango North. A medium sized density of Chambo stocks were observed around Maldeco Fisheries cage site.

Lake Malawi despite facing a number of compliance and environmentally related challenges(Bulirani, 2005; Weyl, et al., 2010; Jamu, et al., 2021) continues to boast about having the highest freshwater fish diversity in the world. The latest information generated by this study has revealed that the southern part of Lake Malawi is still striving and its species robustness previously reported by Jamu et al.,(2021) has also been demonstrated. A continued dominance of the Cichlids in lake Malawi just like in Lake Victoria(Meyer, et al., 1990; Marshall & Nkumbo, 2011; Masai, et al., 2022) has equally been noticed by the study and scientists have a number of theories that are associated with these phenomena (Liem, 1974; Holzberg, 1978; Liem, 1980; Danley & Kocher, 2001). It is also impressive to note that Oreochromis species are still available in the shallow waters of Lake Malawi notwithstanding the increased anthropogenic, climatic and environmental stresses in the lake (Weyl, et al., 2010; M'balaka & Kanyerere, 2019a; Makwinja & M'balaka, 2017). Despite lacking information on the economic losses of such impacts on local scale, global illegal fishing alone is reported to cost about USD10-23.4 million annually (Petrossian, 2019). The study has equally reported a prominent availability of smaller haplochromine species attesting to their wide spreading(Turner, 1996; Konings, 2016) at the expense of the overexploited bigger fishes(Sayer, et al., 2019; GoM, 2021). Recently studies in Lake Malawi,(Jamu, et al., 2021) and Lake Malombe(Kanyerere & M'balaka, 2017) reported similar results. Catch statistics from both small-scale and commercial trawl fisheries is also full of smaller and low economically valued fishes providing the much-needed nutritional and economic security to the developing nation (Sayer, et al., 2019; GoM, 2021). The continued ascendancy of smaller fishes extends to other tropical water bodies like Lake Victoria, Tanganyika and Kariba (Sayer, et al., 2019). Rather than being worried, Sayer et al.,(2019) provided many benefits of harvesting smaller fishes instead of the mostly preferred bigger fishes and encouraged custodians of these tropical lakes to consider making some policy shifts that favour exploitation of the smaller fishes. Nevertheless, aquatic systems like Lake Malawi that are multi-species in nature always face fisheries conservation dilemma particularly on fishing gear regulations. Challenges like harvesting of immature fish reported by this study will always test decision makers’ resolve with regards to fisheries conservation priorities. These challenges highlight the importance of embracing the ecosystems approach to fisheries management (EAFm) which principally consider the welfare of both the communities and the aquatic ecosystems with governance and regulations overarching them (FAO, 2018).

The study has also revealed that most of the fish stocks are no longer concentrated in the shallow waters as it was the case in the early 1960s. The current trend agrees to the findings of Kanyerere and M’balaka (In Press) who reported that the shallow water fish stocks were no longer in good health. The significant concentration of fish stocks around river mouths is credited to the high productivity potential of such areas as they are nutrient rich from the uplands agro-industrial activities. The tributary rivers are also reported to provide refuge from various stresses in the main water body (Kimirei, et al., 2008). The high concentration of fish in the north-eastern of the southeast side could be attributed to the good management initiatives around the area and reported by FISH (2018). Most parts experienced a resuscitation of local fisheries management authorities (LFMAs) like Beach Village Committees (BVCs) and Fisheries Association (FAs) (FISH, 2018) to ensure enhanced decentralised fisheries resource management. The establishment of locally-owned fish protected areas could not go unnoticed as suggested by Banda et al., (2005) and echoed by Bell et. al., (2012). The concentration of fish stocks including Oreochromis species around the Maldeco Fisheries Company site could be attributed to a number of factors primarily the presence of aquaculture cages provides easy sources of feed and protection to the wild fishes. Unlike what was reported by M’balaka and Kanyerere (2019a), the current survey has seen shift from the north-eastern of Area A to the western. Whether this shift means that the high fishing efforts from illegal beach seines and trawlers has been reduced remains to be appreciated.

The overall good performance of the southeast arm of the lake has a number of underlying reasons which are well stipulated by Turner (1996) and Konings (2016). It is also believed that the increased trawlable areas in the western side could also subject the region to over-exploitation by the trawl fishers (DoF, Unpublished Data) and other actively operated fishing gears (GoM, 2021). Still more, both arms appear to have more fish stocks in the depth boundaries which only shows that fish stocks are still located in the offshores (Kanyerere & M'balaka, In Press) not much in the over-exploited inshore zones of Lake Malawi.

Finally, the status of Chambo and fish stocks in the shallow waters of the southwest and southeast arms of Lake Malawi has been reported where a continued fish resource decline in these shallow waters has been observed. Spatial shifts in the fish stocks towards the deeper waters have also been noted and this is primarily in response to the ever-increasing fishing pressures and environmental degradation. With the current status quo, calls for continued adoption of stringent management measures like sticking to the recommended licenses and ensuring compliance to all regulations to ensure regeneration of the stocks are being made. Special attention should seriously be channelled towards curbing the use of beach seines as they cause both growth and recruitment overfishing of Chambo and other economically important fish stocks. Recent efforts by the DoF such as the introduction of VMS, Closed Season for the commercial operators, engagement and empowering of LMFAs, community fish sanctuaries and ban on the importation of monofilament gillnets are greatly acknowledged and encouraged (DoF, Unpublished Data). A continuous monitoring of the exploited fish stocks is nevertheless highly suggested. Additionally, the adoption of ecologically friendly techniques like Hydroacoustic to assess the fish stocks rather than the environmentally damaging bottom trawling is extremely recommended. To ensure that the Chambo is fully and sustainable resuscitated, a research studies to review the life history parameters of Chambo in southern Lake Malawi, the Upper Shire and Lake Malombe is urgently required as it is strongly believed that most of the life history parameters of the fish might have changed in response to externally stresses. Equally important is the urgent need to expand the research spectrum to provide maturity information for other equally important fishes especially the E. sardella which currently commands a lion’s share in the national total landing statistics (GoM, 2021).

Competing Interest

Mwamad S. M’balaka, Maxon Ngochera, Geoffrey Z. Kanyerere and Heals Kabowa have no financial interest in the study. Enert N. Chindevu is employed by the project as a Component Manager, hence has a financial interest in the study.

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Assessment of the status of Chambo (Oreochromis species) and Other Shallow Water Fish Stocks in the Southwest Arm and Southeast Arm of Lake Malawi, Malawi

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Abstract

Figures

Introduction

Materials And Methods

Survey Area, Equipment and tools

Sampling procedure

Data Analysis

Size Structure

Fish species diversity

Fish Biomass Estimation

Spatial interpolation

Results

Fish species composition

Size Structure of selected fish species in SWA

Fish species diversity

Kriging parameters

Mean catch rates, productivity and biomass

Spatial Distribution of fish stocks in the SWA

Spatial Distribution of fish stocks in the SEA

Discussion

Conclusion And Recommendation

Declarations

Competing Interest

References

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