The history of environmental change and adaptation in eastern Saloum–Senegal—Driving forces and perceptions

Environmental change in the Sahel–Sudan zone of West Africa has been a major issue in development debates over the last decades. Using remote sensing based land cover change analysis, archival data, national and international statistical data, and household interviews, we analyze the drivers of environmental change in Eastern Saloum in Central East Senegal as well as the local perceptions of these changes and adaptation. Being part of the ground nut basin, Eastern Saloum has witnessed rapid environmental degradation caused by the conversion of forest and savanna areas to agricultural land during the last 20–30 years and by a combination of decline in precipitation, soil degradation, a diversity of policies with little concern for the environment, fluctuating markets and population pressure. Farmers perceive the environmental change mainly as land degradation and poor soil fertility, though recent extensification of agriculture counters this effect and has led to increased vegetation cover in marginal areas. They identified erratic climate, agricultural policies, insufficient food production and desire to increase income as the main drivers of change in the area. We conclude that while climate variability has influenced environmental change in the area, various types of State interventions in agriculture and global market fluctuations appear to have been the main underlying causes of environmental degradation.     

Assemblages of phytoplankton pigments along a shipping line through the North Atlantic and tropical Pacific

A set of phytoplankton pigment measurements collected on eight quarterly transects from France to New Caledonia is analyzed in order to identify the main assemblages of phytoplankton and to relate their occurrence to oceanic conditions. Pigment concentrations are first divided by the sum [monovinyl chlorophyll a plus divinyl chlorophyll a] to remove the effect of biomass, and second are normalized to give an equal weight to all pigments. The resulting 17 pigments × 799 observations matrix is then classified into 10 clusters using neural methodology. Eight out of these 10 clusters have a well marked regional or seasonal character, thus evidencing adapted responses of the phytoplankton communities. The main gradient opposes two clusters with high fucoxanthin and chlorophyll c₁₊₂ in the North Atlantic in January, April and July, to three clusters in the South Pacific Subtropical Gyre with high divinyl chlorophyll a, zeaxanthin and phycoerythrin. One of the clusters in the South Pacific Subtropical Gyre has relatively high zeaxanthin and phycoerythrin contents and dominates in November and February (austral summer), while another with relatively high divinyl chlorophylls a and b dominates in May and August (austral winter). The third one in the South Pacific is characterized by high carotene concentration and its occurrence peaks in February and May. In the equatorial current system, one cluster, rich in chlorophylls b and c₁₊₂, is strictly located in a narrow zone centred at the equator, while another with relatively high violaxanthin concentration is restricted to the high nutrient - low chlorophyll waters in only the southern part of the South Equatorial Current. One cluster with relatively high prasinoxanthin content has a spatial distribution spanning the entire South Equatorial Current. Two clusters have a ubiquitous distribution: one in the equatorial Pacific, the Carribbean Sea and the North Atlantic during summer has pigment concentrations close to the average of the entire dataset, and the other in the South Pacific Subtropical Gyre, the Carribbean Sea and the North Atlantic during autumn clearly has an oligotrophic character. Many of the differences between clusters are caused by diagnostic pigments of nano- or picoflagellates. While the space and time characteristics of the clusters are well marked and might correspond to differences in physical and chemical forcing, knowledge of the ecological requirements of these flagellates is generally lacking to explain how the variability of the environment triggers these clusters.     

Otolith reading and multi-model inference for improved estimation of age and growth in the gilthead seabream Sparus aurata (L.)

Accurate knowledge of fish age and growth is crucial for species conservation and management of exploited marine stocks. In exploited species, age estimation based on otolith reading is routinely used for building growth curves that are used to implement fishery management models. However, the universal fit of the von Bertalanffy growth function (VBGF) on data from commercial landings can lead to uncertainty in growth parameter inference, preventing accurate comparison of growth-based history traits between fish populations. In the present paper, we used a comprehensive annual sample of wild gilthead seabream (Sparus aurata L.) in the Gulf of Lions (France, NW Mediterranean) to test a methodology improving growth modelling for exploited fish populations. After validating the timing for otolith annual increment formation for all life stages, a comprehensive set of growth models (including VBGF) were fitted to the obtained age–length data, used as a whole or sub-divided between group 0 individuals and those coming from commercial landings (ages 1–6). Comparisons in growth model accuracy based on Akaike Information Criterion allowed assessment of the best model for each dataset and, when no model correctly fitted the data, a multi-model inference (MMI) based on model averaging was carried out. The results provided evidence that growth parameters inferred with VBGF must be used with high caution. Hence, VBGF turned to be among the less accurate for growth prediction irrespective of the dataset and its fit to the whole population, the juvenile or the adult datasets provided different growth parameters. The best models for growth prediction were the Tanaka model, for group 0 juveniles, and the MMI, for the older fish, confirming that growth differs substantially between juveniles and adults. All asymptotic models failed to correctly describe the growth of adult S. aurata, probably because of the poor representation of old individuals in the dataset. Multi-model inference associated with separate analysis of juveniles and adult fish is then advised to obtain objective estimations of growth parameters when sampling cannot be corrected towards older fish.