Supporting Spatial Management of Data-Poor, Small-Scale Fisheries With a Bayesian Approach

Marine conservation areas are an important tool for the sustainable management of multispecies, small-scale fisheries. Effective spatial management requires a proper understanding of the spatial distribution of target species and the identification of its environmental drivers. Small-scale fisheries, however, often face scarcity and low-quality of data. In these situations, approaches for the prioritization of conservation areas need to deal with scattered, biased, and short-term information and ideally should quantify data- and model-specific uncertainties for a better understanding of the risks related to management interventions. We used a Bayesian hierarchical species distribution modeling approach on annual landing data of the heavily exploited, small-scale, and data-poor fishery of Chwaka Bay (Zanzibar) in the Western Indian Ocean to understand the distribution of the key target species and identify potential areas for conservation. Few commonalities were found in the set of important habitat and environmental drivers among species, but temperature, depth, and seagrass cover affected the spatial distribution of three of the six analyzed species. A comparison of our results with information from ecological studies suggests that our approach predicts the distribution of the analyzed species reasonably well. Furthermore, the two main common areas of high relative abundance identified in our study have been previously suggested by the local fisher as important areas for spatial conservation. By using short-term, catch per unit of effort data in a Bayesian hierarchical framework, we quantify the associated uncertainties while accounting for spatial dependencies. More importantly, the use of accessible and interpretable tools, such as the here created spatial maps, can frame a better understanding of spatio-temporal management for local fishers. Our approach, thus, supports the operability of spatial management in small-scale fisheries suffering from a general lack of long-term fisheries information and fisheries independent data.En prens

Multivariable control of a grid-connected wind energy conversion system with power quality enhancement

This document is the Accepted Manuscript version of the following article: Kaddour Fouad, Houari Merabet Boulouiha, Ahmed Allali, Ali Taibi, and Mouloud Denai, ‘Multivariable control of a grid-connected wind energy conversion system with power quality enhancement’, Energy Systems, Vol. 9 (1): 25-57, February 2018. The final publication is available at Springer via: https://doi.org/10.1007/s12667-016-0223-7This paper proposes the design of a multivariable robust control strategy for a variable-speed WECS based on a SCIG. Optimal speed control of the SCIG is achieved by a conventional PI controller combined with a MPPT strategy. DTC-SVM technique based on a simple Clarke transformation is used to control the generator-side three-level converter in the variable speed WECS. The flow of real and reactive power between the inverter and the grid is controlled via the grid real and reactive currents and the DC link voltage using multivariable H∞ control. The overall WECS and control scheme are developed in Matlab/Simulink and the performance of the proposed control strategy is evaluated via a set of simulation scenarios replicating various operating conditions of the WECS such as variable wind speed and asymmetric single grid faults. The power quality of the WECS system under H∞ control control approach is assessed and the results show a significant improvement in the total harmonic distorsion as compared to that achieved with a classical PI control.Peer reviewedFinal Accepted Versio

Diversity Partitioning of Stony Corals Across Multiple Spatial Scales Around Zanzibar Island, Tanzania

The coral reefs of Zanzibar Island (Unguja, Tanzania) encompass a considerable proportion of the global coral-reef diversity and are representative of the western Indian Ocean region. Unfortunately, these reefs have been recently subjected to local and regional disturbances. The objectives of this study were to determine whether there are potentially non-random processes forcing the observed coral diversity patterns, and highlight where and at which spatial scales these processes might be most influential.A hierarchical (nested) sampling design was employed across three spatial scales, ranging from transects (<or=20 m), stations (<100 m), to sites (<1000 m), to examine coral diversity patterns. Two of the four sites, Chumbe and Mnemba, were located within Marine Protected Areas (MPAs), while the other two sites, Changuu and Bawe, were not protected. Additive partitioning of coral diversity was used to separate regional (total) diversity (gamma) into local alpha diversity and among-sample beta diversity components. Individual-based null models were used to identify deviations from random distribution across the three spatial scales. We found that Chumbe and Mnemba had similar diversity components to those predicted by the null models. However, the diversity at Changuu and Bawe was lower than expected at all three spatial scales tested. Consequently, the relative contribution of the among-site diversity component was significantly greater than expected. Applying partitioning analysis for each site separately revealed that the within-transect diversity component in Changuu was significantly lower than the null expectation.The non-random outcome of the partitioning analyses helped to identify the among-sites scale (i.e., 10's of kilometers) and the within-transects scale (i.e., a few meters; especially at Changuu) as spatial boundaries within which to examine the processes that may interact and disproportionately differentiate coral diversity. In light of coral community compositions and diversity patterns we strongly recommend that Bawe be declared a MPA

Episodic heterogeneous decline and recovery of coral cover in the Indian Ocean

Long-term changes in coral cover for the Caribbean and the Pacific/Southeast Asia regions (PSEA) have proven extremely useful in assessing the main drivers, magnitude and timescales of change. The one major coral reef region where such assessments have not been made is the Indian Ocean (IO). Here, we compiled coral cover survey data from across the IO into a database of similar to 2,000 surveys from 366 coral reef sites collected between 1977 and 2005. The compilation shows that the 1998 mass coral bleaching event was the single most important and widespread factor influencing the change in coral cover across the region. The trend in coral cover followed a step-type function driven by the 1998 period, which differs from findings in the Caribbean and the PSEA regions where declines have been more continuous and mostly began in the 1980s. Significant regional variation was observed, with most heterogeneity occurring during and after 1998. There was a significant relationship between cover and longitude for all periods, but the relationship became stronger in the period immediately after 1998. Before 1998, highest coral cover was observed in the central IO region, while this changed to the eastern region after 1998. Coral cover and latitude displayed a significant U-shaped relationship immediately after 1998, due to a large decrease in cover in the northern-central regions. Post-1998 coral cover was directly correlated to the impact of the disturbance; areas with the lowest mortality having the highest cover with India-Sri Lanka being an outlier due to its exceptionally high recovery. In 1998, reefs within Marine Protected Areas (MPAs) were more heavily impacted than unmanaged reefs, losing significantly greater total cover. MPA recovery was greater such that no differences were observed by 2001-2005. This study indicates that the regional patterns in coral cover distribution in the IO are driven mainly by episodic and acute environmental stress

Dynamic Model of Signal Fading due to Swaying Vegetation

In this contribution, we use fading measurements at 2.45, 5.25, 29, and 60&#x02009;GHz, and wind speed data, to study the dynamic effects of vegetation on propagating radiowaves. A new simulation model for generating signal fading due to a swaying tree has been developed by utilizing a multiple mass-spring system to represent a tree and a turbulent wind model. The model is validated in terms of the cumulative distribution function (CDF), autocorrelation function (ACF), level crossing rate (LCR), and average fade duration (AFD) using measurements. The agreements found between the measured and simulated first- and second-order statistics of the received signals through vegetation are satisfactory. In addition, Ricean K-factors for different wind speeds are estimated from measurements. Generally, the new model has similar dynamical and statistical characteristics as those observed in measurements and can thus be used for synthesizing signal fading due to a swaying tree. The synthesized fading can be used for simulating different capacity enhancing techniques such as adaptive coding and modulation and other fade mitigation techniques