Coastal areas worldwide are characterized by an increasing population density, which strains inshore marine resources especially in countries such as tropical ones, where a large part of the coastal population may depend on marine fish and shellfish for subsistence (Salas et al. 2007, Allison and Ellis 2001). Moreover, as for many of the exploited fish stocks in the world oceans (Hutchings and Reynolds 2004, but see also Cardinale 2011 and Cardinale et al. 2013), coastal resources are also increasingly depleted (Davies et al. 2009). Hence, the assessment of the status of exploited coastal resources to be used for a sustainable management of marine resources and ecosystems is urgent.

In general, coastal areas of developing countries are mostly exploited by artisanal fisheries (Salas et al. 2007), but quantitative knowledge of the status of the exploited stocks is often incomplete (e.g. Salas et al. 2007). Artisanal fisheries in developing countries have been notoriuosly difficult to manage because the status and trends (in biomass or abundance) of marine resources have been difficult to assess. This is due to the complexity of artisanal fisheries, which are characterized by a great diversity of species and gears, as well as numerous landing sites that are difficult to access, making data collection difficult to conduct. Moreover, developing countries often have poorly developed management infrastructures. Thus, in developing countries little is generally known about the overall nature of the artisanal fisheries and the status of the resources they exploit.

Because of the difficulty of data collection in developing countries, catch and effort data form an important, and often the sole, source of data for fish stock assessment. Population parameters important for management are often estimated by fitting production models to standardized series of catch-per-unit-effort (CPUE) (Xiao 1998) or the stock status is simply assessed by analysing trends in standardized CPUE (e.g. Cardinale et al. 2009). Such basic information is notoriously hard to obtain for artisanal fisheries because of the spatial and temporal dynamics in effort, numerous landing sites and limited access (Pauly and Mines 1982, Munro 1983).

In Mozambique, artisanal fisheries targetting the coastal marine resources are highly important for a large sector of the population. At present there are more than 100000 fishers in Mozambique, distributed in 1217 fishing centres and villages, of which 70% are located along the marine coastline (IDPPE 2009). In a census performed in 2002, it was estimated that approximately 70000 of those fishers were involved in the artisanal fisheries in Mozambique (IDPPE 2004). The number of artisanal fisherman involved in marine fisheries approximately doubled between 2002 and 2007, while the number of boats increased by about 35% (IDPPE 2009). In Mozambique, more than 130000 people are directly dependent on coastal fisheries and the amount is steadily increasing (IDPPE 2009).

In Mozambique, a probability-based survey for the monitoring of the artisanal fisheries has been conducted in several coastal provinces since the 1997. The sampling frame in Mozambique is a matrix of landing sites and days. The fishery concerned operates from beaches, or in near coastal waters (generally within 5 km), and it is dominated by beach seines, hand lines and gillnets deployed by a mixed fleet of vessels less than 10 m in size. Vessels usually conduct daily fishing trips using one type of gear, but in some cases multiple gears are employed simultaneously. The vast majority of fishing trips can be accessed for sampling at a finite list of landings sites along the coast.

Despite more than a decade of regular data collection, no assessment has been conducted of the target species exploited by the artisanal fisheries along the whole Mozambiquan coastline. The CPUE trends published in Mozambican national official reports are usually given by gear and by fish family, and not by species or by area (but see Mualeque and Santos 2011), and are not standardized by effort (e.g. net length) and other changes in fleet features and behaviour (IIP 2007>). Moreover, fisheries-independent surveys of the coastal fisheries resources in Mozambique are not conducted regularly. Therefore, little information is available to assess the current status of the exploited resources against a historical baseline (but see Loureiro 2001).

In general, management of resources exploited by small-scale fisheries, which are based on a quantitative estimation of the impact of the fleet on the stock, are rare. For example, in the Mediterranean there is one single case for which the impact (in terms of partial fishing mortality exerted by the fleet) of the small-scale fisheries on common sole (Solea solea) is assessed (i.e. sole in the Adriatic Sea; STECF 2013), although management of this stock does not take the assessment results into consideration but is based solely on technical measures. In Vietnam, a TAC (Total Allowable Catches) management system is in place, although the TAC setting is not based on quantitative stock assessment and the real impact of the fleet on the stock is unkown (Pomeroy et al. 2009).

The main objectives of this paper were to collate and analyse a database on commercial artisanal fisheries collected between 1998 and 2009 along the Mozambican coast. An assessment of the trend in standardized CPUE was conducted for two of the most important commercial species landed by the small-scale fisheries, Hilsa kelee and Thryssa vitrirostris (Loureiro 2001, Mualeque and Santos 2011). Trend in CPUE from gillnets and beach seines from four provinces (Maputo, Inhambane, Nampula and Zambezia) were estimated. This is the first attempt to produce standardized time series of CPUE for the small-scale fisheries along the Mozambican coast.

Here we used the Mozambican data collection programme to illustrate both the difficulties and possibilities of making assessment of trends in stocks when data are limited in both quantity and quality, as is often the case for artisanal fisheries in developing countries. We also provide a novel trend analysis of the target species of the Mozambican artisanal fisheries, which is urgently needed for the management of Mozambican coastal marine resources.

The yearly average CPUE for all species caught declined from 1999 to 2009 (Fig. 2). Also, yearly average CPUE for HKE and TVI declined during the same period. The average net length employed per vessel for gillnet generally increased for the provinces of Maputo, Inhambane, Nampula and Zambezia over the time series (1999-2009) (Fig. 3A). For beach seines, a similar trend was present for Zambezia, while there was no increase in the length of the net for the other provinces (Fig. 3B).

For investigated species, provinces and gear, the full model was selected as the final model since it always resulted in the lowest GCV with all variables significant at p<0.0001, except for TVI caught by beach seines in Maputo province (Table 3). Deviance explained by the final models ranged between 1.6% and 57.5% (Table 3) with small uncertainties around the means. For TVI caught with gillnet in the Inhambane province, the model gives unrealistic results in terms of model fitting and it was excluded. Residuals were generally slightly right-skewed or normally distributed for the final models (data not shown). This implies that the final models are less able to predict very large catches.

The peak in CPUE for HKE caught by gillnet was observed in April-May or July-August, depending on the province (Fig. 4A). For HKE caught by beach seines, a peak between December and January was estimated in the Maputo and Zambezia province, while for Inhambane and Nampula the highest CPUE were between May and June (Fig. 4A).

For TVI caught by gillnet, a peak between November and December was shown in Maputo and Zambezia province, while for Nampula the highest CPUE was in July (Fig. 4B). CPUE of TVI caught by beach seines showed a peak in December-January for Inhambane and Zambezia province, and in May for Nampula (Fig. 4B).

The year effect of the models showed that the CPUE for some of the gear and provinces analysed hase declined, whereas for others there has been a stable trend or an increase compared with the beginning of the time series (Figs 5-6). For example, HKE from gillnet has declined in Maputo, Namupla and Zambezia but has increased in Inhambane, although the latter time series is much shorter (Fig. 5). The decline is more pronounced when the effect of the increased net length is accounted for. In the beach seine fisheries, a similar trend was evident for the analysed provinces (Fig. 5).

For TVI, CPUE from gillnets was stable in all provinces when net length was not considered, but showed a decline in Nampula and Zambezia when net length was taken into account in the analysis (Fig. 6). A declining trend was also shown for CPUE of TVI from beach seines in Nampula and, after net length standardization, also in Zambezia, while no clear trend was observed in Maputo and Inhambane (Fig. 6).

Artisanal fisheries that target stocks within the coastal ecosystems have been largely ignored by fisheries management agencies, despite their great importance for food security and livelihoods (Vincent et al. 2007). It has been shown that local artisanal fisheries with a relatively low level of effort and technological development can significantly impact the coastal environment and the coral reef ecosystem, and therefore the monitoring of their impact is important (Hardt 2009). From a socio-economic and food security perspective, artisanal fisheries are critically important worldwide, as it has been estimated that around 50 million artisanal fishermen are active in developing countries and more than 400 million people rely on artisanal fisheries for food, income and as a source of employment in the boat-building sector (Berkes et al. 2001, FAO 2004). In addition to the importance for the subsistence of a large portion of the coastal populations around the world (Onyango and Jentoft 2010), especially in Africa artisanal fisheries play a central role in alleviating household poverty (FAO 2005a,b). However, general knowledge about artisanal fisheries around the world is rather scarce and more information is urgently needed (e.g. Salas et al. 2007)

The analyses performed here are an attempt to quantify the changes for some of the species exploited by the artisanal fisheries along the Mozambican coast. The results show that the species analysed have declined in some of the provinces but have been stable or even increased in others. Nevertheless, both the combined CPUE of the target species, HKE and TVI, and the total CPUE of all the analysed species have significantly declined. However, it is important to note that for some of the models the deviance explained was rather low.

The monitoring programme of artisanal fisheries in Mozambique follows statistically sound on-shore catch sampling methods, as described in ICES (2013), with a spatio-temporal sampling frame based on site-days. The sampling frame, however, does not cover all landing sites along the coast, and this undercoverage may cause bias. Also, the multi-stage design, in which catch data are chosen from trips that are selected within selected landing sites and days, results in a complex hierarchical structure of the data. For this analysis we ignored one level of clustering for simplicity, and assumed that trips were representatively sampled in time and space. This assumption is reasonably well supported since the sampling effort was approximately proportional to fishing effort. Because of the wide spatial and temporal distribution of effort and minimal changes in fishing technology over time, we believe that our estimates of CPUE may realistically reflect changes in fish biomass over time.

For some provinces our analyses showed a large decline in the CPUE of the two species during the first period of our time series (from 1998 to 2002), while the decline after 2002 was minor. This might be partially explained by the fact that the period from 1996 to 1998 was characterized by drought conditions (Kolding et al. 2003), creating poor living conditions for the people engaged in the agricultural sector and causing a shift in work force from farming to fishing, with a subsequent increase in fishing effort that might have triggered the observed decline through overfishing. The sharp decline in CPUE between 1998 and 2002 might, however, also result from the fact that the sampling design started to be implemented at the beginning of the time series, and thus early estimates could be biased due to incomplete coverage (although the estimated model uncertainty is similar along the times series). Despite the uncertainty in the estimates in the first few years of the time series, it is also important to stress that the exploitation of the coastal species analysed here started at least two decades before the beginning of the collection of data used in our study (Loureiro 2001). For example, HKE supported a semi-industrial fishery in Maputo Bay from 1973 to 1990, and declined during the same period likely due to overexploitation (Loureiro 2001). Also for TVI, a decline in the standing stocks offshore and in the catches of this species by beach seines during 1973-1990 as a consequence of the HKE semi-industrial fishery was reported by Mualeque and Santos (2011). Thus, the decline of the CPUEs found in our study might have started long before 1997, and thus be more pronounced than is estimated in our analysis.

It is important to stress that for the analysed fish species there are no data on fishing mortality in relation to sustainable level of exploitation that can quantify the relative impact of the fisheries, other anthropogenic causes (pollution, coastal degradation, etc.) or climate. For example, massive floods in southern Mozambique from February to May 2000 caused extensive damage to coral reefs by sedimentation (Obura et al. 2002, Pereira and Gonalves 2004) and these events might have had a significant impacts on the stocks. Therefore, factors other than exploitation may also contribute to explain the observed trends, but the lack of data does not allow us to discriminate between the different causes.

The management of artisanal fisheries is crucial to allow them to continue supporting the local human communities who depend upon them (Davies et al. 2009). The knowledge of the status of the exploited stocks is the first step in the direction of sustainable exploitation and the analyses conducted here might provide managers with some of the basic instruments to initiate and promote the sustainable management of coastal marine resources in Mozambique. Our study also shows the importance of continuous monitoring of the artisanal fisheries in Mozambique. Assessment of the stock status based on data collected from the ongoing monitoring programme could be used as a set of indicators for deciding upon future management actions and it is therefore important for the programme to continue and be strengthened. Moreover, the use of indicators based on CPUE is a simple and cost-effective method to be used when data for implementing analytical assessment models are lacking (e.g. Cardinale 2009). Trend analyses based on CPUE triggered large reactions in the media and environmental stakeholders in Indonesia (Cardinale M., pers. comm.) and can therefore push managers to initiate management actions.