Mortality index as an Indicator to Examine Overall Selectivity of a Multi-Species and Multi-gear Fishery

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Mortality index as an Indicator to Examine Overall Selectivity

of a Multi-Species and Multi-gear Fishery

Abstract

Lake Mweru –a multi-species and multi-gear fishery in Africa- has been experiencing with the increase in number of effort and variability of stock over decades. It was assumed that in a multi-gear fishery with all fishing gears that is selective in species-size and target all species in total fish community would lead to the non-selective fishery that can conserve the relative structure of fish community. It was hypothesized that the total fisheries in Lake Mweru would generate an overall non-selectivity fishing pattern. Mortality index as a part of life history characteristics that correlated to maximum length was considered to be analyzed in order to see its changes that reflecting the responses of fish community to fishing pressures. The experimental data of 13-mesh size gillnets that collected from 1970s to 1990s were used and completed by gathering and categorizing the maximum length data. By using the ANCOVA, the relationship between maximum length and mortality index was obvious to see how the mortality index of each species category change by maximum length over time as the impacts of fishing pressures. The mortality index analysis showed that the mortality index and the productivities increase for all species over decades whilst the increase of mortality index was more rapid for the large species. Therefore, the main hypothesis which was saying the condition of Lake Mweru can be generating an overall non-selective fishery was partly accepted.

Keywords: mortality index, maximum length, multi-gear, multi-species, non-selective fishery, fishing pressures.

Introduction

Lake Mweru is a highly productive lake which has variable stock levels of its important species (Jul-Larsen et al., 2002b). Change in fish yield is related to change in lake productivity (Jul-Larsen et al. (2002a). The changes of fish stock can be addressed to resilience concepts. Resilience is the capacity of a system to absorb disturbance and reorganize while going through with changes so as to still retain essentially the same function, structure, identity, and feedbacks (Walker et al., 2004). Subject to fish community, resilience of species to fishing is defined as recovery capacity of a species after the population is depleted (Feitosa et al., 2008).

To explain the stock changes, there are several pressure factors that should be taken into account. There are three elements need to be considered (Jul-Larsen and Zwieten, 2002). The first is the changes in water levels which are related to climatic changes. The second is the increase in fishing effort. The third element is species resilience and susceptibility. Fishing can raise greater variability in exploited populations and diminish resilience to fishing for several species as well. It is structurally and functionally threats the fish population and manifest directly in abundance of target species, habitat destruction and decrease in mean size, indirectly to change in fish community structure (Hsieh et al., 2006; Yemane et al., 2005). The change in community structure as fishing impact on ecosystem is due to the fact that fishing gears are selective in species and size (Jul-Larsen et al., 2002a).

The selectivity of fishing gear is important for fisheries management programme with the aim of protecting the resource. On the other hand, the non-selective targeting fish is assumed by single-species perspective, as carrying the harmful or destructive and lead to growth-over-fishing. However, the selectivity also raises much more problem for industrialized fisheries subject to their discards and problem defining the “right” mesh size in multi-species fishery (Jul-Larsen et al., 2002a).

Concerning to the selectivity of gears in multi-species and multi-gear fishery, it is assumed that the fishing gears are selective in species and size (Jul-Larsen et al., 2002a) meaning that one gear catches a certain set of species over a certain length range, while another gear or the same gear employed and modified in the different methods also catches another certain set of species with another length range. However, if they are all together catch all the whole community, can we still name the fishery as “selective fishery” or can we name them as “non-selective fishery”?

According to Jul-Larsen et al. (2002a), as regard to ecosystem point of view, if all fishing gears target all species in total fish community at rates proportional to their natural mortality pattern, they are non-selective fishery. They will conserve the fish community since the relative structure of the ecosystem would be preserved and only small parts of them would be reduced. The Figure 1 below shows how the non-selectivity fishery mechanism.

In order to know whether the fishery has selective or non-selective fishing pattern, the impacts of fisheries to the fish community need to be clarified. The impacts of fisheries can be explained by the species resilience and susceptibility. The susceptibility of species to gears can describe how the fish stocks react to fishing (Stevens et al., 2000; Jul-Larsen et al., 2002a). To define susceptibility and vulnerability of fish community to fishing gears, the analysis of life history characteristics are developed (Stobutzki et al., 2001; O’Malley, 2010).

In this study, in order to know whether the stock variability and total fisheries in Lake Mweru will generate the selective or non-selective fishery, mortality index as a part of life history characteristics that correlated to maximum length was considered to be analyzed in order to see its changes that represent the changes and variability of fish community as a response to fishing pressure (Welcomme, 1999; Stobutzki et al., 2001; King and McFarlane, 2003; Patrick et al., 2009).

According to Milton (2001), mortality index are used to be an estimation of relative mortality (Z) of each species. The species whose mean length in the catch is closer to the smallest length caught are considered to have a larger percentage of population removed and higher mortality. This research used the analysis of relationship between mortality index and theoretical maximum length[1] performed over decades to see how the mortality index of each species change by maximum length over time. From this analysis, the changes in mortality rate of fish community in Lake Mweru were obvious to observe. Therefore, the aim of this study was to assess the change in mortality rate as one of kind life-history characteristics of a fish community to fishing pressures.

Materials and Methods

1. Materials

Lake Mweru is a freshwater lake on the longest side of Africa’s second-longest river, the Congo. It is located on the border of Northern-Zambia and the Democratic Republic of Congo in the Luapula valley. This lake is a productive lake where production is significantly dependent on nutrient pulse. In such a system, the exploitation rate can be high. It potentially recovers fast and has high and variable yield; however the susceptibility to increased fishing pressure is low (Jul-Larsen et al., 2002a).

The experimental fishing survey gathered in Lake Mweru was conducted to provide the information over time regarding the changes occur through changes in fishing effort. The surveys which were employed a fleet of multifilament gillnets ranging from 25 mm to 178 mm stretched mesh with 13 mm increases were collected by the Department of Fisheries during 1970-1972, 1982-1985 and 1993-1999. The mesh sizes used for the experimental fishing were shown in Table 1.

2. Methods

2.1 Species categorization

Species categorization was done in order to see the changes of the whole fish community in Lake Mweru where the 73 species were assembled into 26 groups by putting each species into the species categorization mechanism. In the next step, each species was specified into three maximum length categories that analyzed from data distribution of all species. The groups of species were categorized as:

1. Small species (L-max value was less than 31.06)

2. Big species (L-max value was more than 47.97)

3. Medium species (L-max value in between 31.06 and 47.97).

2.2 Calculation of Mortality Index

Mortality index is related with recovery capacity. It was calculated by using: theoretical maximum length (L-max) of species that was collected from fishbase.org; average length of species from the catches of each decade; and minimum length of species that was collected from catches of overall decades. The index of mortality was calculated by the following formula:

Mortality index = (Lmax – L ave)/(Lave-Lmin)

where Lmax is the max length a species, L ave is the mean length at capture in the fishery, and Lmin is the smallest length in the catch.

The closer the average length is to the maximum length, the lower fishing mortality in the population. Mortality indicates the increase in fishing effort so that in this case, the average length of species in a population closes to the smallest length. This index was influenced by the previous and current fishing effort. Also, this index is under assumption that catchability and mortality is constant across the whole length ranges caught (Stobutzki et al., 2001).

2.3 The mortality index (MI) over theoretical maximum length (L-max)

There were three analyses taken regarding the relationship MI and L-max as the following:

  1. To answer the question whether MI increase over LM and to explore the relationship of the mortality index and maximum length, the comparison between the separate slopes & intercepts and the overall slope & intercept were examined. Therefore, the full model appears as follows:

MIij= a + b.LM + Decadeij+ ci.LM(Decadeij) + εij

Where MI = mortality index;

LM = theoretical maximum length (cm);

a= overall intercept;

b= overall slope;

i= the decades 1970s, 1980s, and 1990s; and

j= the degree of freedom used.

  1. To analyze whether the increases occur each decade, the analyses were focused on comparing intercepts of each decade. The ANCOVA models were defined as:

MIij= a +b.LM + Decadeii + εij

  1. To have significance whether the increases differ per decade, the analysis was performed by taking into account the previous model. Therefore, the model was presented as follows:

MIij= a+ b.LM + ci.LM (Decadeij) + εij

According to this ANCOVA model, the separate slopes and the overall slope were compared.

Results

The relationship of mortality index and maximum length over decades were taken to see how the mortality index of each species changed by maximum length over time. To check whether there was the systematic change in overall mortality index over the maximum length over decades, the regression line through natural log of mortality index and maximum were performed in order to normalize the data as follows (10 log-transformed values):

The graph above used three parameters of linear regression for respectively the 1970s, 1980s, and 1990s, they are as follows (10 log-transformed values): intercept = -0.1789, -0.0631, 0.2048; slope= -0.0196, 0.2895, 0.5161; r2= 0.00033, 0.0726, and 0.1401. All of coefficient determinations are relatively small. Besides, the significances in slopes were available for 1990s. While for intercept, they were significant in the 1970s and 1990s.

The data points used is normally distributed. The residuals of data were tested by using Shapiro-Wilk (W) test for respectively the 1970s, the 1980s and 1990s: 0.9706, 0.9663 and 0.9522. To prove normality of the data, these W values should be close to 1 otherwise it will lead to the rejection of normality. Their p-value shows significance of each W-value.

According to the graph above, it can be noticed that there are changes of slopes and intercepts over decades. There is an increase of intercept over time between 1970s and 1990s. These indicate that during those times the mortality index increased for both the smaller species and the bigger species (the indication of mortality index of smaller and bigger species are noted in Appendix 9).

To check the significances of the slopes and intercepts above, the stepwise examining a series of regression models were taken. These will examine the reduction in variability in regression models where slopes and intercepts were fitted. Subsequently, the overall reduction of variability represented by slope and intercept analysis was presented as follow

From Table 2, the amount of variability explained by the differences between slopes (r2=0.032) and intercept (r2=0.188) in the full model are relatively low. The full model gave the result that all intercepts were significantly different, while the slopes were significantly different only for 1970s and 1990s, with the 1990s having both highest slope and intercept. In the slope analysis, the slope in 1970s and 1980s resembled to each other. These two slopes were significantly different compared to the 1990s. The slopes significantly increased in 1990s.

All intercepts were significant. It increased significantly over decades. This represented that the mortality index increased for all individuals over decades. Meanwhile, the slopes analysis indicated that during 1970s and 1980s the change in mortality could not be shown, the increase of mortality occurred during 1980s to 1990s and it was more rapid for the large species.

According to Pauly (1980), there is a relationship between mortality and length along with growth and temperatures. In this study, this relationship between mortality and length were examined. The results showed that there was an overall increase of mortality index over decades. This indicated that the fishing pressure were not only targeted the bigger species, but also the medium ones. As stated by Welcomme (1999), the mullti-gear fishery has the wider selectivity range. Therefore, this fishery was able to exploit all fish assemblages. The result also said that the rapidity of mortality index was higher for the bigger species. Since: (1) the bigger species are more vulnerable to fishing pressure; (2) the multi-gear and multispecies fisheries targets the bigger species; and (3) the reproductive strategy of many bigger species is slow-growing (low-Rmax), therefore it was logical to state that the mortality index increased for all species over decades and it was faster for the bigger species.

The increase of mortality occurs during 1980s to 1990s might be caused by the fishing activities that recently more intensive in Lake Mweru. It is noted that the massive increase in fishing effort for gillnet fishery in Lake Mweru since the early 1990s (Jul-Larsen et al., 2002b). However, fishing and species susceptibility (life-history indicators) are not the only one cause of these changes. It might be also result of the environmental variability in Lake Mweru. The declines in total catch rates are related with periods of extremely low water levels. The catch rates will stabilize when the water level rise again (Jul-Larsen and Zwieten, 2002).

To identify the changes of fish assemblages as response to fishing, there are many ways to be taken into account. One of them is the analysis of critical parameter. Actually, there are still several life histories that can be considered, such as trophic level (trophic functioning), age/length maturity and reproductive strategies. Therefore, the reflections from those indicators are required for further analysis.

Conclusion

It was summarized that the mortality index increased for all species (small –medium-large) over decades where it was more rapid for the large species. Therefore, the main hypothesis which was saying the condition of Lake Mweru could be generating an overall non-selective fishery was partly accepted. This can be seen from Figure 4.


[1] The value of theoretical maximum length can be collected from www.fishbase.org

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