The Nile perch invasion in Lake Victoria: cause or consequence of the haplochromine decline?

We review alternative hypotheses and associated mechanisms to explain Lake Victoria’s Nile perch takeover and concurrent reduction in haplochromines through a (re)analysis of long term climate, limnological and stock observations in comparison with size-spectrum model predictions of co-existence, extinction and demographic change. The empirical observations are in agreement with the outcomes of the model containing two interacting species with life-histories matching Nile perch and a generalized haplochromine. The dynamic interactions may have depended on size related differences in early juvenile mortality: mouth-brooding haplochromines escape predation mortality in early life stages, unlike Nile perch that have miniscule planktonic eggs and larvae. In our model predation on the latter by planktivorous haplochromine fry act as a stabilizing factor for co-existence, but external mortality on the haplochromines would disrupt this balance in favor of Nile perch. To explain the observed switch, mortality on haplochromines would need to be much higher than the fishing mortality that can be realistically re-constructed from observations. Abrupt concomitant changes in algal and zooplankton composition, decreased water column transparency, and widespread hypoxia from increased eutrophication most likely caused haplochromine biomass decline. We hypothesize that the shift to Nile perch was a consequence of an externally caused, climate triggered, decrease in haplochromine biomass and associated recruitment failure rather than a direct cause of the introduction

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

High Order Volterra Series Analysis Using Parallel Computing

INTRODUCTION The Volterra series technique has been used extensively in various applications in the area of nonlinear circuit analysis and optimization (see e.g. references [1]--[28]). Examples are in the (i) analysis of intermodulation in small signal amplifiers [6]--[12], (ii) determination of oscillation frequency and amplitude in near sinusoidal oscillators [3]--[5], (iii) analysis of mixers with moderate local oscillator levels [13, 14], analysis of communication systems [14]--[18], and (v) analysis of noise in nonlinear networks [24]--[28]. The use of the Volterra series technique basically involves two steps: (i) first, from specified input signal frequencies to determine all relevant Volterra transfer functions of the network, and (ii) next, to determine the output response from the non-linear network based on specified amplitudes of the input signals. One limitation in the use of Volterra series is that the determination of Volterra transfer functions is usually lim