Introduction

Ecological integrity has enjoyed wide usage in ecosystem monitoring in recent time. Though opinion still defers on its definition as is common with most scientific concepts, there are agreement on intrinsic elements of an ecosystem that defines its level of integrity. These attributes are: structure, composition and function, which are important for ecological resilience and biodiversity conservation (Mitchell et al. 2014). Various definitions of ecological integrity exist and some of them are: “Ecological integrity is the degree to which the physical, chemical and biological components (including composition, structure and process) of an ecosystem and their relationships are present, functioning and maintained close to a reference condition reflecting negligible anthropogenic impacts” (Schallenberg et al. 2011). “An ecological system has integrity, or a species population is viable, when its dominant ecological characteristics (e.g., elements of composition, structure, function and ecological processes) occur within their natural ranges of variation and can withstand and recover from most perturbations imposed by natural environmental dynamics or human disruptions” (Parrish et al. 2003). ‘‘Ecological integrity comprises naturalness, ecosystem health, stability, wildness, biodiversity and beauty. In a narrow but easily quantifiable sense, it incorporates physical, chemical and biological integrity. A holistic index must factor in the different constituents of ecological integrity. Structure, composition and function are also essential features of ecosystems’’ (Andreasen et al. 2001). ‘‘Ecological integrity is a measure of the composition, structure and function of an ecosystem in relation to the system’s natural or historical range of variation, as well as perturbations caused by natural or anthropogenic agents of change’’ (Tierney et al. 2009). From the above definitions, it is obvious that in order to determine the integrity of an ecosystem, its physical, chemical and biological components need to be assessed against some standards. The physico-chemical variables (temperature, pH, alkalinity, turbidity, etc.) of an ecosystem determine the viability of organisms in that environment as well as its carrying capacity. These variables play crucial roles in the physiology of plants and animals in the environment, which then affects reproduction, food availability, survival and sustainability. The biological component of the ecosystem as a measure of ecological integrity can be assessed by ecological indices (Shannon’s diversity index, Simpson’s dominance index, etc.) (Krebs 1999), length–weight relationship and condition factor.

Physico-chemical variables represent important indices by which the integrity of an aquatic ecosystem can be evaluated (O’Brien et al. 2016). Ecosystem provides services that humans depend on for sustainability. These ecosystem services are placed in four distinct groups: regulating services (carbon storage, climate regulation and disease control), provisioning services (food and water production), cultural services (aesthetic inspiration, recreational, spiritual benefits and cultural identity) and supporting services (oxygen production, soil formation, biomass production and nutrient cycles). Some of these services like the provision of food (fish) and drinking water (Keeler et al. 2012), biodiversity maintenance and nutrient recycling (Millennium Ecosystem Assessment 2005) are contingent on the prevalence of optimum abiotic conditions (dissolved oxygen, pH, temperature, alkalinity, turbidity, etc.). A number of these water quality variables affect enzyme-mediated physiological processes, which determine the quality and quantity of food produced in aquatic environment, reproductive successes, survival rate, resistance to disease, growth rate, etc. These are some of the indices that determine the status of water bodies. In addition, the distribution of pollutants in sediment, water, plant and animal is controlled by physical and chemical processes (precipitation, sorption, dilution and diffusion), as well as other processes like uptake and elimination, which involves aquatic organisms (Oyewo 1998).

Biodiversity indices are one of the major components of ecological integrity assessment (Willamette Partnership 2011; Vickerman and Kagan 2014). The measurement of biodiversity has evolved over time from its simplest form as species richness or abundance to a more complex heterogeneity (information theory) measure, which combines richness and evenness to produce a single index of biodiversity. The latter is more reliable than the former because it is devoid of the bias associated with sample size. The commonly used information theory indices for measuring biodiversity are Shannon’s index and Shannon–Wiener index (Krebs 1999). Shannon’s diversity index is calculated from natural logarithm (Loge) while Shannon–Wiener index is based on logarithm 10 (Log10). However, both indices show similar results and are actually related by a simple linear equation. Other measures of ecological diversity are Simpson’s dominance index, Berger–Parker, McNemar’s test, etc. Organisms tend to populate environment that is conducive for their growth and survival. In the same vein, healthy ecosystem attracts variety of species. Therefore, biodiversity correlates with ecological integrity.

Length–weight relationship (LWR) and condition factor (K) are metrics that can also be used to assess the wellbeing of aquatic ecosystems but have not been applied frequently for this purpose. The knowledge of LWR is an essential tools for fish stock assessment, which is a critical component of fisheries management (King 2007). LWR is useful in comparing fish population at all scales: local, inter-regional and inter-continental. It is equally important for calculating standing crop of biomass, assessing function of fish population and age structure and evaluating growth pattern in fish stock or population (Morato et al. 2001; Ndimele et al. 2021; Ozaydin et al. 2007). The slope or growth coefficient (b) calculated from LWR gives an impression of the appetite of the fish species and gonad content (Pervin and Mortuza 2008). Fish gain weight when they make use of the available food resources in their environment for energy and growth (Kamaruddin et al. 2012; Ndimele et al. 2021). The growth coefficient may also change due to the following factors: sex, stomach content and growth phase (Hossain et al. 2006; Leunda et al. 2006). The condition factor (K) is a crucial biological parameter that identifies whether a particular body of water is suitable for fish growth and also serves as an indicator of species average size (Alam et al. 2014). The physiological characteristics of fish, particularly life cycle, spawning, environmental factors, maturity and the availability of food in a body of water, all affect the value of this index (Ujjania et al. 2012; Dan-Kishiya 2013). High condition factor value means that the fish species is in a near perfect or good condition, which indicates that its environment is healthy.

Badagry Lagoon is part of a network of lagoons described as Lagos Lagoon Complex. The lagoons originally called Western Nigerian Lagoons stretches from Republic of Benin to Nigeria. The Lagos Lagoon Complex consists of a system or network of nine (9) lagoons—Ologe, Kuramo, Yewa, Badagry, Lekki, Iyagbe, Epe, Lagos and Marhin (Uduma-Olugu and Oduwaye 2010). These lagoons have been studied extensively in terms of physico-chemistry (Ladipo et al. 2011; Lawson 2011), heavy metals pollution (Kumolu-Johnson et al. 2010; Lawson 2011; Ndimele et al. 2011; Adeyemi et al. 2019) and fisheries biology (Agboola and Anetekhai 2008; Lawson et al. 2011). However, there is no study yet on the use of multi-metric indices to ascertain the integrity of these aquatics. Multi-metric indices are helpful in that they can reduce a large amount of data and parameters to a consistent scale that can be quickly used to make management decisions, but they cannot be used across borders or regions. Therefore, this study evaluates the ecological integrity of Badagry Lagoon using water quality parameters, biodiversity indices, length–weight relationship and condition factors of some fish species from the water body.

Methodology

Study area

This research was done in Badagry Lagoon (Fig. 1) located in Lagos, Nigeria, and one of the aquatic habitats which form the Lagos Lagoon Complex. It has mangrove swamps, floodplains and deltaic distributaries. Nigeria's 960 km of coastline are directly connected to the Lagoon (Lawson et al. 2011). It is located between latitudes 6° 22′ N and 6° 42′ N and longitudes 2° 42' E and 3° 42′ E. In addition to providing water for domestic and industrial use, transportation, logging and sand dredging, it also supports a thriving artisanal fishing industry (Anetekhai et al. 2003). Lagune de Porto-Novo (Benin Republic); Rivers Owo and Yelwa; and Ologe and Lagos Lagoons (Nigeria) are some of the water sources that feed the Lagoon. The two air masses, north-eastern wind from the Sahara and south-western from the Atlantic Ocean, in conjunction with the oscillations of the Inter-Tropical Discontinuity (ITD), result in heavy rainfall. The weather is tropical, with rainy seasons from April to November and dry season from December to March. Fishing is the main source of income for the Badagry people. With buyers coming from the city of Lagos and the Yoruba hinterland, this has a significant impact on the Egun people's economy and nutrition. Other sources of livelihood in the area are farming, transportation and lumbering. Akarakumo, Topo and Ajido were chosen as the three sampling stations because they are fishing communities.

Fig. 1
figure 1

Map of Badagry Creek. Sampling stations are marked with red triangles (Scale: 1:150,000)

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Collection of water samples

Water samples were collected monthly from the surface at a depth of 0–6 m from each sampling location (Topo, Akarakumo and Ajido). The sampling lasted 10 months (February to November, 2018) spanning wet and dry seasons. Water samples were collected with acid-clean 1.5 Litre sampling bottles. One hundred ml from the water sample collected was measured out using measuring cylinder and acidified with 0.5 mL of concentrated nitric acid to preserve the integrity of the water sample. Before laboratory analysis, water samples were kept chilled at 4 °C. The following water quality parameters were measured using standard methods and procedures (APHA 1998): temperature, pH, turbidity, total dissolved solids (TDS), acidity, total alkalinity, chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO) and salinity.

Collection of fish sample

Fish specimens used for this study were randomly sampled from the catches of the fisherfolks at the three landing/sampling sites (Akarakumo, Topo and Ajido) (Fig. 1) from February to November, 2018. The fishermen who work in this lagoon use cast nets, seine nets, hook and lines, surface and bottom-set gillnets, as well as fish aggregating devices. The fish samples were immediately transported to the research laboratory of Department of Fisheries, Faculty of Science, Lagos State University, Ojo, Lagos State, Nigeria in plastic coolers containing ice blocks/cubes to prevent spoilage. Fish species were identified using keys from Boulenger (1909), Pellegrin (1912), Reed et al. (1967) and Leveque et al. (1990) before length and weight measurements were made. Using a top loading Metler balance, body weight (± 0.01 g) was determined and total length (± 0.01 cm) of each fish were measured from the tip of the snout (mouth closed) to the extended tip of the caudal fin (Kumolu-Johnson and Ndimele 2011).

The relationship between the lengths and weights of the fish species was assessed using the function:

$$W = aL^{b} \quad \left( {{\text{Ricker}}\;\;{1973}} \right)$$
(1)

where W = Weight of fish species (g), L = Total length of fish species (cm), a = Intercept on the vertical (y) axis or the initial growth coefficient, b = Slope of the graph or the growth coefficient.

After linearizing Eq. (1) by logarithmic transformation, the estimates of the constants "a" and "b" were obtained by least square linear regression method (Zar 1984) to give:

$${\text{Log}}W \, = {\text{ Log}}a \, + \, b*{\text{Log}}L$$
(2)

Prior to performing linear regression analysis of LogW on LogL, length and weight values were plotted as Log–Log plots to visually check for outliers (Froese 2006). Extreme outliers caused by data error were not included in the analyses. The following equation was used to calculate the 95% confidence interval, or CI of b:

$${\text{CI }} = \, b \, \pm \, \left( {1.96 \, \times {\text{ SEr}}} \right)$$
(3)

where SEr is the standard error of b.

The t-test was used in accordance with the equation given by Sokal and Rohlf (1987) to determine whether the b values (growth coefficients) obtained in the linear regression models were significantly different from the isometric value of 95% CI of b at α = 0.05:

$$t_{s} = \, \left( {b - 3} \right)/{\text{SE}},$$
(4)

where ts is the value of the t-test, b is the slope and SE is the standard error of the slope (b).

Condition factor was calculated by the formula:

$${\text{Condition factor }}\left( K \right) \, = { 1}00\;{\text{W}}/{\text{L}}^{{3}} \quad \left( {{\text{Pauly}}\;\;{1983}} \right)$$
(5)

Fish diversity studies

Fisher alpha (a), Berger–Parker (d), Simpson's index of dominance (D), Simpson’s reciprocal index (1/D), Simpson's diversity index (1 − D), Shannon index of diversity (H′), evenness index (E′), Margalef's index of species abundance (n), Menhinick's index of species richness (m) and equitability (J) were all used to estimate the fish assemblage structure for each site.

This formula forms the basis of Simpson's dominance index:

$${\text{Simpson's index of dominance }}\left( D \right) \, = \sum\limits_{i = 1}^{s} {\frac{n_{i}(n_{i} - 1)}{{N(N - 1)}}} \;\;\;\left( {{\text{Simpson}}\;{1949}} \right)$$
(6)

where ni = the number of individuals of a particular (ith) species; N = the total number of individuals of all the species considered, S = the total number of species

Simpson's dominance index (D) was converted to Simpson's indices of reciprocal (1/D) and diversity (1 − D) by dividing 1 by index of dominance and deducting dominance index from 1, respectively.

$${\text{So}},\;{\text{Simpson's diversity index }} = { 1 }{-} \, D$$
(7)
$${\text{Simpson's reciprocal index }} = { 1}/D$$
(8)

The equation below serves as the foundation for Shannon's diversity index:

$${\text{Shannon's index of diversity }}\left( {H^{{1}} } \right) \, = \, - \sum\limits_{i = 1}^{s} {(Pi \cdot {\text{Log}}_{e} Pi} )\;\;\left( {{\text{Shannon and Wiener}}\;\;{1949}} \right)$$
(9)

where Pi = ni/N; ni, N and S are as previously defined above.

$${\text{Evenness index }}\left( E \right) \, = \frac{{H^{1} }}{{{\text{Log}}_{e} S}}\quad \left( {{\text{Pielou}}\;\;{1966}} \right)$$
(10)

Margalef's and Menhinick's indices were used to assess the sites' fish species richness or abundance.

$${\text{Menhinick's index of species richness }}\left( m \right) \, = \frac{S}{\sqrt N }\quad \left( {{\text{Whittaker}}\;\;{1977}} \right)$$
(11)
$${\text{Margalef's index of species abundance }}\left( n \right) \, = \frac{S - 1}{{{\text{Log}}_{e} N}}\quad ({\text{Margalef}}\;\;{1968)}$$
(12)

where N is the total number of individuals of all the species considered and S is the total number of species.

$${\text{Equitability index }}\left( {{J}} \right) \, = \frac{H}{{H_{\max } }}$$
(13)

where H = Brilloun's index and Hmax = LogeS

Brillouin’s index (H) is defined as:

$$H = \frac{1}{n}\log \frac{n!}{{\prod\nolimits_{i = 1}^{k} {n_{i} !} }} = \frac{{\log n! - \sum\nolimits_{i = 1}^{k} {\log n_{i} !} }}{n}$$
(14)

where ni is the number of observations from the sample in the ith of k (non-empty) categories and n is the sample size.

The formula below provides an implicit definition of Fisher's alpha diversity index (a):

$$S \, = \, a*{\text{Log}_e}\left( {1 + n/a} \right)$$
(15)

where S is the total number of species and N is the total number of individuals of all the species considered.

The Berger–Parker index is an easy way to quantify the importance of the most numerous species.

$$d \, = \, N_{\max } / \, N\quad \left( {{\text{Berger and Parker}}\;\;{197}0} \right)$$
(16)

where N is the overall sample size or total number of individuals in the samples and Nmax is the number of individuals in the most numerous species.

Statistical analysis

ANOVA was used to determine whether there were any significant variations between the sampling stations. The Fisher’s least significant difference (LSD) test was used to compare means. The relationship between the total length and body weight of the fish species was investigated using regression analysis (Pearson's product–moment correlation). The significance level for each statistical analysis was set at 5% (p < 0.05).

Results

Physico-chemistry of Badagry Lagoon

The physico-chemical parameters of Badagry Lagoon are presented in Table 1. Four of the water quality variables (total alkalinity, chemical oxygen demand, biological oxygen demand and salinity) showed significant (p < 0.05) spatial variation. The highest values of these parameters (total alkalinity, 64.42 ± 16.48 mg/L; biological oxygen demand, 278.85 ± 65.86 mg/L; chemical oxygen demand, 878.46 ± 150.45 mg/L; salinity, 1.94 ± 0.28 ppt) occurred in Ajido while the lowest values (total alkalinity, 45.89 ± 9.20 mg/L; biological oxygen demand, 160.57 ± 38.86 mg/L; chemical oxygen demand, 520.45 ± 89.20 mg/L; salinity, 0.67 ± 0.12 ppt) were obtained in Topo.

Table 1 Physico-chemical variables of water samples collected from the sampling stations (Topo, Akarakumo and Ajido)
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Ecological indices of the sampling stations

Table 2 shows the ecological indices of the three sampling stations. Simpson’s dominance index (0.056–0.057), Simpson’s index of diversity (0.94), Simpson’s reciprocal index (17.54–17.86), Shannon diversity index (3.11–3.13) and evenness index (0.77–0.79) for the three sampling stations were similar. The highest values of Menhinick’s index of species abundance (m) (1.12) and Margalef’s index of species richness (n) (4.31) were reported in Ajido while their lowest values (m = 0.80; n = 3.90) occurred in Topo. Equitability values were similar for the three sampling stations, ranging from 0.92 in Topo and Ajido to 0.93 in Akarakumo. Berger–Parker indices for the stations are the same while Fisher’s alpha diversity indices varied from 5.24 in Topo to 6.19 in Ajido.

Table 2 Ecological indices of the sampling sites (Topo, Akarakumo and Ajido)
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Length–weight relationship and condition factor of fish species collected from the sampling sites

This study analyzed 2930 individuals from 17 families, 26 genera and 29 species. Table 3 lists the families, genera, species, number of specimens (N), parameters for the length–weight relationship (a and b), 95% confidence interval for b, growth type (isometric or allometric) and condition factor (K) of the fish species. The initial growth coefficient (a) in this study varied within a very narrow range (0.01–0.04) while the growth coefficient had wider range (2.14–3.16). The correlation between the total length and body weight of the fish was strong and positive except in few instances: Hyperopisus bebe occidentalis (0.77), Brevimyrus niger (0.75), Sphyraena afra (0.76), Trachinotus goreensis (0.68), Lutjanus agennes (0.76), Coptodon zilli (0.76) and Sarotherodon galilaeus (0.78). The condition factor (K) of the fish species varied from 0.14 in Polydactylus quadrifilis to 9.46 in Trachinotus goreensis. Growth pattern was predominantly negative allometry except in five species, three of which was isometric (Neochelon falcipinnis, Hemichromis fasciatus and Pelmatolapia mariae) and two positively allometric (Sarotherodon melanotheron and Chrysichthys auratus).

Table 3 Length–weight relationships of fish species collected from the sampling sites (Topo, Akarakumo and Ajido)
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Discussion

Physico-chemistry of water body as an index for assessing ecological integrity

The physico-chemistry of the aquatics is one of the factors used to evaluate aquatic ecosystem integrity (O'Brien et al. 2016). Water quality variables affect many processes in aquatic environments and must be kept within a certain range for the aquatic ecosystem to function properly (Kumolu-Johnson et al. 2010; Agboola et al. 2016). In this study, the majority of the water quality variables fall within the range of values that are ideal for the development and survival of fish in tropical waters. The dissolved oxygen recorded in this study range from 4.76 ± 0.86 to 4.94 ± 0.94 mg/L while temperature varied from 27.45 ± 0.89 to 27.87 ± 0.54 °C. For tropical aquatic organisms, Boyd (1998) recommended dissolved oxygen levels of 3–4 mg/L and temperatures of 20–30 °C. World Health Organization (1989) recommended pH of 5.5–9.0 for sustainable aquaculture, and the values (7.67 ± 0.69 to 7.78 ± 0.57) obtained in this study are within this range. Four (total alkalinity, chemical oxygen demand, biological oxygen demand and salinity) of the parameters exhibited significant (p < 0.05) spatial variation but two (turbidity and biological oxygen demand) of them have exceeded the limits proposed by World Health Organization (WHO 1989) for fish culture. The high turbidity recorded in the sampling stations might be due to intense sand extraction activities in this water bodies. Clarke et al (2019) reported that sand mining caused increase in turbidity and reduction in chlorophyll a (primary productivity) in Lagos Lagoon. Increase in turbidity caused by sand extraction leads to increase in extinction coefficient of aquatic ecosystems. This situation results in reduced light penetration, which brings down the rate of primary productivity causing low fish output. The water quality parameters of the sites showed that most tropical fish species can exist, survive, grow and reproduce in the water bodies. This implies that the integrity of the water bodies is still good enough to support the processes that ensure proper functioning of the ecosystems and their carrying capacities.

Ecological indices of aquatic ecosystem as metrics for evaluating ecological integrity

Another set of parameters that can be used to measure the integrity of an aquatic ecosystem are ecological indices. Biodiversity has frequently been cited as a crucial element of ecological integrity assessment in earlier studies on the subject (Faber-Langendoen et al. 2012; Vickerman and Kagan 2014). According to Chiu et al. (2011), they have been employed as single-metric or multi-metric indices to assess ecosystem integrity or health and direct management decisions. Ecological integrity and biodiversity are related (Andreasen et al. 2001). Parrish et al. (2003) claimed that “ecological integrity measures biodiversity status, tracks major ecological properties of focal biodiversity and determines the response of biodiversity status to anthropogenic stressors.” Most of the ecological indices measured in this study did not show spatial variation. The ranges of diversity indices recorded in this study are high indicating that the sampling sites are healthy: Shannon diversity index (3.11–3.13), Simpson’s reciprocal index (17.54–17.86) and Simpson’s index of diversity (0.94 for each sampling station; maximum for this index is 1.0). Simpson’s dominance index is inversely proportional to diversity. The Simpson’s dominance indices for the three stations were very low (~ 0.06 for each site), which implies that the fish species diversities in those water bodies were high. Further alluding to the healthy status of the three sampling sites. The ecological indices obtained in this study are similar to previously reported values (Jewel et al. 2018; Lawson and Olusanya 2010) except for Simpson’s indices for dominance, reciprocal and diversity as well as Shannon’s diversity index. The values gotten for these four indices in this study are not within the same range as those reported by Lawson and Olusanya (2010). They got 0.20–0.26, 3.89–6.45, 0.74–0.85 and 1.90–2.02 for Simpson’s dominance, reciprocal, diversity and Shannon diversity indices, respectively, for River Ore, South-west, Nigeria, which is contiguous to Badagry Lagoon. The difference in fish species diversity could be attributed to intensity of anthropogenic stressors (fishing activities, industrial pollution, sand mining, etc.) and the number of individual specimen collected. Lawson and Olusanya (2010) opined that the increase in diversity indices in Badagry Lagoon compared to River Ore might be as a result of migration of fish to the former due to high temperature, poor water quality, salinity and reduced water level in the latter water body during the dry season.

The number of individuals sampled play an important role in determining diversity indices (Ndimele et al. 2021). The more individuals sampled, the greater the probability of encountering more species, and the greater the diversity. The number of fish species (29) reported in this study is lower than the values (35) obtained by Agboola and Anetekhai (2008) from the same water body about 12 years ago. However, Kumolu-Johnson and Ndimele (2010) encountered 21 fish species in Ologe Lagoon, which is adjacent to the present sampling locations. The variation of the fish species in the present study area and adjacent water bodies in the last 15 years does not allow for easy deduction to be made as the trend is not consistent. However, it would be safe to say that the processes governing the sustainability of fisheries in these areas within this period have been fairly constant. Otherwise, the reduction in the number of fish species would have been more significant given the intensity of industrial activities in this region. According to Ndimele et al. (2017), since 2013, industrial activities have increased in Lagos and Ogun States, which has resulted in a rise in the quantity of industrial effluent discharged into Badagry and Ologe Lagoons from Agbara Industrial Estate. About 45 companies with a minimum investment of US$100 million each started operations in the estate. Untreated industrial wastes emptied into water bodies pose serious danger to the health of coastal communities who depend on aquatics for source of drinking water, fishing, navigation, etc. These pollutants alter the physico-chemistry of the aquatic ecosystem, which affects their capacity to sustain life. One of these pollutants is heavy metal. They are non-biodegradable and persist in the environment for a long time, increasing their possibility of entering the food chain. Once they get into the food chain, they move through the trophic levels by bioaccumulation and eventually reach man, who is at the apex of the pyramid. These metals could cause illnesses such as cancer, congenital malformation and genetic distortion. Although, there are laws in Nigeria regulating the discharge of industrial effluent into water bodies, but they are not properly enforced and so, compliance is low. Companies that violate existing regulations should be made to bear the full wrath of the law to act as deterrent to others.

Length–weight relationship and condition factor of fish as measures for assessing ecological integrity

Two important indices for assessing aquatic ecosystem integrity are length–weight relationship (LWR) and condition factor (K) of fish species, although they might not be commonly used as would have been expected. Many fish population dynamic models rely on length and weight data (Ndimele et al. 2022). The health status or well-being of fish can be inferred from the growth coefficient (b) derived from LWR, which is an indication of the content of the fish gonad, appetite for food, adaptation to prevailing environmental conditions, etc. (Pervin and Mortuza 2008). Fish gain weight as a result of using available food for energy and growth (Kamaruddin et al. 2012). Other factors that could affect growth coefficient are sex, gut contents, growth phase and gonad development (Hossain et al. 2006). The b estimates of the fishes from the three sampling sites used for this study fell within the expected range of 2.5–3.5 (Froese 2006) except in four species (Schilbe mystus, Trachinotus goreensis, Citharinus citharius and Drepane africana), suggesting that the water bodies are of good integrity. Jisr et al (2018) opined that variation in the values of b could be due to the condition of the fish, its phenotype or its geographical location and hence its environment. Other factors that can affect b are food availability, season and sex (Ali et al. 2016).

The correlation coefficients (r) obtained in the LWR in this study varied from 0.68 to 0.98 with about 76% showing strong, positive and significant correlation (p < 0.05). These strong correlation coefficients imply that the variation in the dependent variable (body weight of fish) is largely accounted for by changes in the independent variable (total length). This means that the quality of prediction that would be obtained from the linear regression of total length against body weight would be high for the analyzed fish species. Furthermore, it can be inferred that future catches of fish of those sizes from those specific geographic locations can be extrapolated. There was significant correlation (p < 0.05) for all fish species except Trachinotus goreensis and Lutjanus agennes. The negative allometric growth recorded in about 83% of analyzed fish species (b < 3, t-test, p < 0.05) suggests that most of the fish species grow slowly and have thinner bodies. However, Neochelon falcipinnis, Hemichromis fasciatus and Pelmatolapia mariae exhibited isometric growth (b = 3, t-test, p > 0.05), which may be related to the condition of their habitat or their particular phenotype (Tsoumani et al. 2006). The fish's level of wellbeing in their preferred environment is also reflected by the condition factor (K). The K values of most of the fish species in this study is outside the range (2.9–4.8) suggested by Bagenal and Tesch (1978) as appropriate for adult freshwater fish. There was about 38% conformity with this standard. However, a lot of the K are greater than 1.0 except Polydactylus quadrifilis and Chrysichthys auratus. K values greater than 1.0 indicate that the fish lived in good and healthy environment with optimum water quality variables, adequate food, high immunity, low or absence of predators, high feeding intensity, sustainable fishing practices, appropriate carrying capacity and self-regulatory systems while K values less than 1.0 means the direct opposite (Abdul et al. 2016; Ogunola et al 2018; Ndimele et al 2022). The high K values of the fish species suggest that the environment of the fish is in good condition. This corroborates the earlier assertion that the physico-chemical parameters of the water bodies are still good to support freshwater fisheries and so, the integrity of the sampling sites are still good.

Conclusion

Badagry Lagoon is part of the network of lagoons that form the Lagos Lagoon Complex and they support the coastal communities especially in the provision of cheap and affordable fish protein. The continuous performance of these roles is dependent on the integrity of these aquatics. This study showed that the physico-chemistry of Badagry Lagoon, fish diversity indices and condition factor are within the limits that is favorable for tropical fish species, suggesting that the water body is healthy. The correlation of these parameters implies that they might give an objective measure of the ecological integrity of aquatic ecosystems. Thus, they can be combined as multi-metric indices to assess the integrity of aquatic environment.