The Influence of Environmental Factors and Fishing Pressure on Catch Rates of Diplodus vulgaris Vânia Baptista, Carlos J. A. Campos & Francisco Leitão
Estuaries and Coasts Journal of the Coastal and Estuarine Research Federation ISSN 1559-2723 Estuaries and Coasts DOI 10.1007/s12237-015-9990-y
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Author's personal copy Estuaries and Coasts DOI 10.1007/s12237-015-9990-y
The Influence of Environmental Factors and Fishing Pressure on Catch Rates of Diplodus vulgaris Vânia Baptista 1 & Carlos J. A. Campos 2 & Francisco Leitão 1
Received: 22 July 2014 / Revised: 9 April 2015 / Accepted: 12 May 2015 # Coastal and Estuarine Research Federation 2015
Abstract The variability of coastal marine resources has been linked to environmental variability but the impacts of artisanal nearshore fishing activity and environmental factors interact in different ways. Time series, Min/Max Autocorrelation Factor, Generalised Least Squares and Dynamic Factor Analyses models were applied to examine the role of environmental factors (SST, NAO Index, Upwelling Index, Wind Magnitude, Easterly Wind Component, Northerly Wind Component, Coastal River Discharges) and fisheries effort on commercial catch rates of Diplodus vulgaris in the Northwest, Southwest and South Portuguese coast. Environmental factors were found to affect short-term variations of catch rates with a time lag of 2 years, according to the regions and seasons. In the Northwest, autumn wind magnitude and summer river discharges were positively correlated with D. vulgaris catch rates. In the Southwest, D. vulgaris catch rates were negatively associated with variations in winter sea surface temperature. In the South, catch rates were positively associated with yearly westerly wind component and yearly, winter and autumn river discharges whilst negatively associated with the North Atlantic Oscillation (NAO) Index. These results indicate that both large-scale climatic patterns and local hydrological factors can have an influential role in determining the abundance of D. vulgaris stocks. Fishery assessment should therefore Communicated by Karin Limburg * Francisco Leitão
[email protected] 1
Centro de Ciências do Mar, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
2
Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, Barrack Road, The Nothe, Weymouth, Dorset DT48UB, UK
incorporate information on ecosystem and environmental effects to help managers to make informed decisions on how to best regulate coastal fisheries. Keywords Fish-environment relationships . Environmental variability . Portuguese coast (ICES IXa) . Regional analyses . Multi-model inference
Introduction Fisheries activity and fish population dynamics are closely linked to climate dynamics and weather patterns (Lehodey et al. 2006). Resolving the impact of climate changes on fish populations is however complicated because climatic variables influence multiple environmental factors which may, in turn, influence different processes at different levels of biological organisation (Lehodey et al. 2006; Ottersen et al. 2001). For instance, temperature, salinity, upwelling, winds, tidal currents and oceanic circulation, precipitation and river runoff and nutrients are known to influence productivity, abundance and distribution of fishing stocks at both regional and oceanic scales (e.g. Baptista et al. 2014; Baptista and Leitão 2014; Brander 2007, 2010; Borges et al. 2003; Erzini 2005; Lehodey et al. 2006; Planque and Frédou 1999; Santos et al. 2001). All of these parameters have been implicated on changes in timing of reproduction, population dynamics, abundance, distribution and inter-specific relationships such as competition and predator–prey interactions (Ottersen et al. 2001). However, the impacts of fishing activity and climate variability interact in different ways. The direct effects include alterations in abundance and distribution of exploited species (Brander 2007, 2010; Lehodey et al. 2006). The indirect effects relate to changes in productivity, structure and composition of marine ecosystems (Brander 2007, 2010). Therefore,
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the environmental changes may reduce, or in some cases improve, the productivity of fisheries stocks since they influence production, survival, growth and offspring (Brander 2007). According to Cushing (1996), the larval period is a major cause of variability in marine fish production. Houde (1987) analysed the impact of variations in lifestage duration or mortality from the egg, larval and juvenile stages and concluded that for most species the larval stages have the greatest influence in regulating year-class size. This reinforces the importance of larval survival in the dynamics of recruitment. Studies linking environmental variability to fisheries have been based on a wide range of fishing biological Bindicators^ including recruitment (Solow 2002), spawning stock biomass (Gröger and Fogarty 2011), landings (Erzini 2005) and catch rates data (Baptista et al. 2014). Over recent decades, many studies have increased our understanding of the impacts of environmental factors on commercial fisheries, including those of pelagic fishes (Lehodey et al. 2006; Santos et al. 2001; Ullah et al. 2012), demersal fishes (Gröger and Fogarty 2011; Planque and Frédou 1999; Solow 2002), cephalopods (Ullah et al. 2012), bivalves (Baptista et al. 2014; Baptista and Leitão 2014) and crustaceans (Herraiz et al. 2009). However, most studies are limited to specific geographical areas and do not evaluate in detail the combined impact of climatic factors on fisheries variability (Ullah et al. 2012). Diplodus vulgaris (Sparidae), the two-banded sea bream (Geoffroy Saint-Hilaire 1817), have distribution in the Mediterranean areas and along the eastern Atlantic coast from France to Senegal, including the Madeira, the Azores and the Canaries Archipelagos (Bauchot and Hureau 1986). Owing to the intense exploitation in the Mediterranean Sea, a variety of management tools have been adopted to maintain the sustainability of the fishery such as daily bag limitations or gear restrictions, a minimum legal size of 180 mm (MoralesNin et al. 2010). This euryhaline species inhabits rocky and sandy bottoms to a maximum depth of 160 m (Gonçalves et al. 2003). The Portuguese continental shelf is the northern distribution limit for nursery areas (Vinagre et al. 2010) because this species does not tolerate temperatures lower than 15 °C. D. vulgaris is particularly sensitive to alterations in temperature during the initial stages of the life cycle with adults spawning near the coast and the larvae migrating to nursery and shallow coastal areas to continue their development (Vinagre et al. 2009). However, D. vulgaris is a rocky associated resident species (Leitão et al. 2009), remaining in a specific area if favourable habitat conditions for growth and survival are found (Harmelin-Vivien et al. 1995). Success of recruitment in Diplodus spp. has been mostly attributed to specific requirements of early juveniles for settlement and on the availability of suitable habitats, which is known to determine
population structure and dynamics (Harmelin-Vivien et al. 1995). Despite progress in understanding the complex processes involved in the variability of fish stocks, especially at short and medium time scales, our ability to predict abundance and catches is limited (Fréon et al. 1993). The published literature and experience in the fisheries-environmental relationship sciences can be used to help formulate a set of a priori candidate models (Burnham and Anderson 1998). Several time series models can be used for analysing yearly or monthly fisheries data (for review see: Keyl and Wolff 2008). Most of them allow smoothing of non-stationary data such as fisheries data (Zuur et al. 2003a, b, 2007). Other examples of commonly used techniques include forecasting statistics (surplus production models, Fréon et al. 1993; autoregressive moving average, Lloret et al. 2001) or model fitness [dynamic factorial analyses, min/max autocorrelation factor analyses and generalized linear models (Lloret et al. 2001)]. Most studies exploring the relationships between environmental and fisheries variability use single model analyses (Zuur et al. 2003a) and only a few use complementary or multi-model approaches (Baptista et al. 2014; Erzini 2005; Lloret et al. 2001). Although the use of complementary approaches (several statistics models) is common practice in ecological studies (Zuur et al. 2007), these routines have not been used in time series analyses of fisheries data (Loots et al. 2011; Planque et al. 2011). The use of multiple modelling techniques can be regarded as a more accurate measure of the strength of the relationships between these types of datasets (Baptista et al. 2014; Erzini 2005; Leitão et al. 2014a; Zuur et al. 2007). The Portuguese coast has experienced substantial changes in climatic events impacting on ocean circulation, namely increasing sea surface temperatures (Relvas et al. 2009), changing wind patterns (Baptista et al. 2014), intensification of upwelling (Relvas et al. 2009) and frequency of negative and positive phases of the North Atlantic Oscillation (NAO) Index (Hurrell 1995). Climate change projections indicate that these modifications could have significant impacts on fishing grounds in this biogeographical area (Relvas et al. 2009). Therefore, it is anticipated that the population dynamics of many species is likely to respond to these pressures in the near future (Santos and Miranda 2006; Teixeira et al. 2014), particularly along the coastal region off western and southern Portugal (IPCC 2001). D. vulgaris is a valuable commercial species in the context of the inshore fisheries around the coast of Portugal. Most fishing vessels operating from ports in this area are fibreglass open deck boats
