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- Influence of seagrass meadows on nursery and fish provisioning ecosystem services delivered by Ria Formosa, a coastal lagoon in PortugalPublication . Erzini, Karim; Parreira, Filipe; Sadat, Zineb; Castro, Margarida; Bentes, Luis; Coelho, Rui; Gonçalves, Jorge Manuel Santos; Lino, Pedro G.; Martínez-Crego, Begoña; Monteiro, Pedro; Oliveira, Frederico; Ribeiro, Joaquim; de los Santos, Carmen B.; Santos, RuiThis study is the first to evaluate the fish provisioning services of a whole transitional landscape (Ria Formosa lagoon, Portugal), in parallel with the enhancement of growth, survival and production of single cohorts of the most important commercial fish species by vegetated and unvegetated sub-tidal habitats. Based on monthly beach seine samples, total density and biomass of 96 species of fishes were 1.89 and 3.03 times greater in vegetated habitats than unvegetated habitats, respectively. Vegetated habitat enhanced survival in six of eight commercial species for which survival could be estimated in both habitats. The total production of all 12 commercially important species within vegetated habitat was approximately double that of unvegetated habitat, with production enhancement in 7 of 12 species ranging from 1.8 to 169-fold for the vegetated habitats. Within the lagoon, vegetated sub-tidal habitat covers an area 5-fold smaller than unvegetated habitat, yet it accounts for 27.1 % of fish production. Estimated total lifetime economic values of the single cohorts of the 12 commercial species were between 30 million and 59 million EUR. An exceptionally strong year class of the European seabass (Dicentrarchus labrax), a species with higher density and biomass in unvegetated habitat, accounts for the higher overall values per hectare for unvegetated habitat (Low natural mortality (M): EUR 32,844 ha-1; High M: EUR 16,751 ha-1) than for vegetated habitat (Low M: EUR 22,028 ha-1; High M: EUR 10,700 ha-1). These results highlight the enormous importance of temperate coastal lagoons as a nursery and source of recruits for coastal fisheries. Our evaluation of fish provisioning services based on data for individual cohorts of fish for a whole transitional landscape is a stronger and more valid approach for estimating future biomass and value than previous studies based on mean densities and biomasses of fish that did not distinguish between cohorts.
- Influence of wind, rainfall, temperature, and primary productivity, on the biomass of the bivalves Spisula solida, Donax trunculus, Chamelea gallina and Ensis siliquaPublication . Bento De Almeida, Joana Maria; Gaspar, Miguel; Castro, Margarida; Rufino, Marta M.Coastal bivalve populations are characterized by large temporal fluctuations, driven by recruitment success dependent on environmental conditions. The present study evaluated the relationship of rainfall, chlorophyll-α, sea surface temperature (SST) and wind, with the biomass of four commercial bivalve species (Spisula solida, Donax trunculus, Chamelea gallina and Ensis siliqua) that occur both along the Barlavento (western region), and the Sotavento (eastern region), along the South coast of Portugal, between 1999 and 2011. Multiple regression models were used to test two hypotheses: a) the association between environmental conditions three months prior to the spawning period and the biomass in the following year (response variable) and b) the association between environmental conditions during the spawning season plus one month and the biomass in the following year. Wind was not included in the model to test a). The environmental variables considered were not important for E. siliqua. In the period prior to spawning, temperature was significant for S. solida (both study areas), rain for D. trunculus and C. gallina (western area) and chlorophyll-α for C. gallina (western area). All the correlations were positive. During the spawning period, rain was significant for all three species but with different trends. The correlation with rain in months early in the spawning season was positive for S. solida (both areas) and D. trunculus (western area). The correlation with the rain in months towards the end of the spawning period was negative for D. trunculus, and C. gallina (western area). Chlorophyll-α in months at later stages of spawning was significant and positive for D. trunculus (both areas) and C. gallina (western area). The wind, expressed as an index developed to indicate the overall occurrence of winds blowing parallel to the coast (assumes to favour the retention of larvae in appropriate environments), was negatively correlated with biomass for mid to late spawning season for S. solida (eastern area) and D. trunculus (western area). For C. gallina there was a positive correlation with wind early in the spawning season. The SST was not an important variable during the spawning period with the exception of D. trunculus where the temperature at mid spawning season was significant with a negative correlation in the western coast and positive in the eastern coast. This study highlighted the annual spatial distribution of four commercially important bivalves and how environmental factors are likely to play an important role on both biomass and location of the fishing beds. Monitoring of the environmental variables could provide information to predict biomasses of bivalve species, an important tool for its management, as well as predict trends and plan strategies in response to global environmental changes.
- Predicting gear used in a multi-gear coastal fleetPublication . Leitão, Pedro; Campos, Aida; Castro, MargaridaKnowledge of the gear used in multi-gear fisheries is crucial for supporting fisheries management. Still, the high complexity and lack of data in the Portuguese multi-gear coastal fleet compromise this task. The present study developed a method to predict main fishing gear used in each fishing trip for the Portuguese multi-gear coastal fleet based on landing records (species caught, port, and month of landing). Landing records were used to predict gear (available for part of the fleet with electronic logbooks) using a machine learning model (random forest). This model was then applied to the remaining trips of the fleet, without electronic logbooks, to predict the gear used. A total of six gear types were considered: bivalve dredges, traps, gillnets, trammel nets, drifting longlines, and bottom longlines. The overall model prediction error was 14 %; bivalve dredges and longlines had the lowest errors, and trammel nets and gillnets were the highest. The study sheds new light on important aspects of the dynamics of this fleet, namely a decreasing trend in the use of longlines, poor electronic logbook coverage for some gear types, and greater diversity in the catches obtained with nets compared to other gear types.