Як уникнути підйому рівня води?

East Africa’s Lake Victoria provides resources and services to millions of people on the lake’s shores and abroad. In particular, the lake’s fisheries are an important source of protein, employment, and international economic connections for the whole region. Nonetheless, stock dynamics are poorly understood and currently unpredictable. Furthermore, fishery dynamics are intricately connected to other supporting services of the lake as well as to lakeshore societies and economies. Much research has been carried out piecemeal on different aspects of Lake Victoria’s system; e.g., societies, biodiversity, fisheries, and eutrophication. However, to disentangle drivers and dynamics of change in this complex system, we need to put these pieces together and analyze the system as a whole. We did so by first building a qualitative model of the lake’s social-ecological system. We then investigated the model system through a qualitative loop analysis, and finally examined effects of changes on the system state and structure. The model and its contextual analysis allowed us to investigate system-wide chain reactions resulting from disturbances. Importantly, we built a tool that can be used to analyze the cascading effects of management options and establish the requirements for their success. We found that high connectedness of the system at the exploitation level, through fisheries having multiple target stocks, can increase the stocks’ vulnerability to exploitation but reduce society’s vulnerability to variability in individual stocks. We describe how there are multiple pathways to any change in the system, which makes it difficult to identify the root cause of changes but also broadens the management toolkit. Also, we illustrate how nutrient enrichment is not a self-regulating process, and that explicit management is necessary to halt or reverse eutrophication. This model is simple and usable to assess system-wide effects of management policies, and can serve as a paving stone for future quantitative analyses of system dynamics at local scales

Trophic niche-space imaging, using resource and consumer traits

Abstract The strength of trophic (feeding) links between two species depends on the traits of both the consumer and the resource. But which traits of consumer and resource have to be measured to predict link strengths, and how many? A novel theoretical framework for systematically determining trophic traits from empirical data was recently proposed. Here we demonstrate this approach for a group of 14 consumer fish species (Labeobarbus spp., Cyprinidae) and 11 aquatic resource categories coexisting in Lake Tana in northern Ethiopia, analysing large sets of phenotypic consumer and resource traits with known roles in feeding ecology. We systematically reconstruct structure and geometry of trophic niche space, in which link strengths are predicted by the distances between consumers and resources. These distances are then represented graphically resulting in an image of trophic niche space and its occupancy. We find trophic niche to be multi-dimensional. Among the models we analysed, one with two resource and two consumer traits had the highest predictive power for link strength. Results further suggest that trophic niche space has a pseudo-Euclidean geometry, meaning that link strength decays with distance in some dimensions of trophic niche space, while it increases with distance in other dimensions. Our analysis not only informs theory and modelling, but may also be helpful for predicting trophic link strengths for pairs of other, similar species

Determination of the xyf-SOM map size.

<p>Relationship between number of nodes (xyf-SOM size) and prediction percentage (blue circles), and distance between objects inside the xyf-SOM (red circles).</p

Fish groups and species caught by the small-scale fleet of San Pedro port in Tabasco, Mexico.

<p>Total weight percentage and catch rates (kg/ day) are presented by species and gear combination.</p

Hierarchical cluster analysis of fish groups’.

<p>Cluster was based on weights from the general xyf-SOM using Ward’s method.</p

Graphical summary of the methodology.

<p>a) Vessel arrival identified by fishermen nickname of vessel name, b) data acquisition in logbook, c) ordering and classifying data in electronic format, d) defining the importance of factor by ctree methodology, e) definition the size of xyf-SOM grid, f) xyf-SOM training, g) generation of confidence intervals for xfy-SOM predictions, h) testing for species arrangement based on xyf-SOM weights using cluster analysis, i) catch rate analyses using beanplots on xyf-SOM weights.</p

Seasonal catch rates (kg/ trip) for the main species associated to vertical line plus shark bottom longline (VL+SBL) based on weights derived from the seasonal SOM analysis.

<p>Cold seasons are in blue and warm seasons are in red. Dotted line indicates the overall mean catch rate; long, thick lines indicate the mean values by season, short lines indicate individual catch rates by node, as derived from the xyf-SOM. Areas correspond to kernel density shapes.</p

Confusion matrix in percentage, reporting the performance of xyf-SOM in classifying new observations.

<p>BL fleet in blue, VL+SSBL in green, and VL in red; cold season is in light tones and warm season is in dark tones.</p

Supervised Self-Organizing Map (xyf-SOM).

<p>At the general xyf-SOM map, color indicates gear type: bottom longline (BL) is represented in blue, vertical line + shark bottom longline (SBL) in green and vertical line (VL) in red; light tones indicate the cold season, and dark tones the warm season. Thick lines delineate years. At the species-specific xyf-SOM maps, lines indicate the predicted areas for BL, VL and SBL, whereas the color scale corresponds to the catch rate in kg/trip (blue is low, red is high).</p

Study area.

<p>The light-grey polygon indicates the fishing area of the small-scale fleet from San Pedro Port in the southern Gulf of Mexico.</p