Combined cytogenetic and molecular methods for taxonomic verification and description of Brassica populations deriving from different origins

Agriculture faces great challenges to overcome global warming and improve system sustainability, requiring access to novel genetic diversity. So far, wild populations and local landraces remain poorly explored. This is notably the case for the two diploid species, Brassica oleracea L. (CC, 2n=2x=18) and B. rapa L. (AA, 2n=2x=20). In order to explore the genetic diversity in both species, we have collected populations in their centre of origin, the Mediterranean basin, on a large contrasting climatic and soil gradient from northern Europe to southern sub-Saharan regions. In these areas, we also collected 14 populations belonging to five B. oleracea closely related species. Our objective was to ensure the absence of species misidentification at the seedling stage among the populations collected and to describe thereafter their origins. We combined flow cytometry, sequencing of a species-specific chloroplast genomic region, as well as cytogenetic analyses in case of unexpected results for taxonomic verification. Out of the 112 B. oleracea and 154 B. rapa populations collected, 103 and 146, respectively, presented a good germination rate and eighteen populations were misidentified. The most frequent mistake was the confusion of these diploid species with B. napus. Additionally for B. rapa, two autotetraploid populations were observed. Habitats of the collected and confirmed wild populations and landraces are described in this study. The unique plant material described here will serve to investigate the genomic regions involved in adaptation to climate and microbiota within the framework of the H2020 Prima project ‘BrasExplor’

Heavy metal accumulation in Mullus barbatus, Merluccius merluccius and Boops boops fish from the Aegean Sea, Turkey

The accumulation behaviour of copper, zinc, cadmium and lead concentrations in flesh, gills, liver and gonads of three commercial fish Mullus barbatus, Merluccius merliccius and Boops boops from the Aegean Sea in Turkey have been studied. Copper, zinc and lead concentrations in flesh were found low, while in gonads Cd was found high. Liver showed higher concentration of Cu than other fish organs. Gonads and liver recorded high concentrations of Cd. Liver and gills revealed high levels of Pb as compared to other fish organs. The organs of Boops boops revealed high accumulation of heavy metals as compared to other fishes. Heavy metals in the southern Aegean Sea were found to be higher around Cesme and Fethiye than the northern part. Concentration of heavy metals in the tested organs were within the allowable limit of world levels

Vulnerability of natural resources in Tunisian arid zones facing climate change and human pressure : toward better target actions to combat desertification

The main challenges of Tunisian arid areas is better understand desertification and better adapt decisions to uncertainties that arise in complex interactions between the socio-economic and biophysical dynamics at different spatial and temporal scales. This understanding is increasingly necessary to better target actions and improve coping strategies. The objective of this work is to assess the vulnerability of natural resources and the risk of desertification at the local scale, in a case study (Oum Zessar watershed) located in the south-east of Tunisia. The assessment is carried out through an analysis of different human and biophysical causes and driving forces, such as natural resources uses and climate change. It is based on geographical (landscape), integrated (socio-economy, biophysics) and multi-actors (research-decision) approaches, associated with reproducible methods and models to assess the risk of desertification, and to better target future combating actions. The work led to the development of desertification risk indexes (based on spatial integration of biophysical and socio-economic functioning through modeling), facing several scenarios of climate change and human pressure. It shows that the same level of risk in one place or another may have differentiated causes, which could help public policy in their fighting against desertification

Surveillance environnementale et développement. Acquis et perspectives : Méditerranée, Sahara et Sahel

La persistance/aggravation de la désertification dans les zones arides tunisiennes (Sghaier et al., 2007), ainsi que l'insuffisante connexion entre le monde scientifique et de la décision (Ouessar et al., 2006 ; Fetoui, 2011), ont fait émerger chez les acteurs du développement et les gestionnaires de ressources, une demande croissante en matière de développement d'outils opérationnels capables de produire des informations spatialisées adaptées à la prise en compte de la diversité des processus de désertification sur un même territoire. Or, la difficulté à comprendre la désertification dans ces zones se manifeste dans les interactions complexes entre les dynamiques socioéconomiques et biophysiques à différentes échelles spatiales et temporelles. L'apport de ce travail réside tant dans les avancées sur la compréhension des causes et processus, que dans la proposition d'approches systémiques (climat-homme-espace-ressources), géographiques (paysage) et multi-acteurs, associées à des méthodes (modèles et outils) reproductibles, pour l'évaluation et le suivi (indicateurs) des risques de désertification, en lien avec leurs déterminants interactifs, à une échelle territoriale. Le paysage, en tant que source d'information et instrument (Dérioz, 2008), est au coeur de ce travail. Ce dernier a conduit à i) l'élaboration des indices de risque de désertification par intégration spatiale des fonctionnements biophysiques et socioéconomiques à travers la modélisation ii) la compréhension et la comparaison entre types paysagers des risques et de la part respective prise par les causes socioéconomiques et biophysiques, et iii) la proposition de méthodes/outils visant à mieux évaluer les actions de lutte déjà mises en oeuvre, cibler les futures actions et suivre la désertification. Cet article valorise et synthétise les résultats obtenus dans le cadre du programme ROSELT/OSS (Loireau et al., 2004 ; Loireau et al., 2007) et de la thèse de Mondher Fetoui (Fetoui, 2011). Ces résultats ont traité le passage de la compréhension de la désertification (à travers des approches conceptuelles) vers la production d'outils d'aide à la décision (pour l'orientation d'actions et le suivi de la désertification). Ils ont tous été élaborés dans l'interdisciplinarité et avec la mobilisation des acteurs/décideurs de la gestion durable des territoires pour augmenter leur utilité dans les programmes de lutte contre la désertification et assurer un meilleur ancrage entre recherche et décision, pour valoriser les observatoires de l'environnement et leur donner un rôle d'appui aux politiques publiques

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in this paper, for the first time, as a recurrent Autoencoder (ML-ESNAE) to extract new features from original data representations. Further, the challenging and crucial task of optimally determining the ML-ESNAE architecture and training parameters is addressed, in order to extract more efficient features from the data. Traditionally, in a ML-ESN, the number of parameters (hidden neurons, sparsity rates, weights) are randomly chosen and manually altered to achieve a minimum learning error. On one hand, this random setting may not guarantee best generalization results. On the other, it can increase the network’s complexity. In this paper, a novel bi-level evolutionary optimization approach is thus proposed for the ML-ESNAE, to deal with these challenges. The first level offers Pareto multi-objective architecture optimization, providing maximum learning accuracy while maintaining a reduced complexity target. Next, every Pareto optimal solution obtained from the first level undergoes a mono-objective weights optimization at the second level. Particle Swarm Optimization (PSO) is used as an evolutionary tool for both levels 1 and 2. An empirical study shows that the evolved ML-ESNAE produces a noticeable improvement in extracting new, more expressive data features from original ones. A number of application case studies, using a range of benchmark datasets, show that the extracted features produce excellent results in terms of classification accuracy. The effectiveness of the evolved ML-ESNAE is demonstrated for both noisy and noise-free data. In conclusion, the evolutionary ML-ESNAE is proposed as a new benchmark for the evolutionary AI and machine learning research community

Novel single and multi-layer echo-state recurrent autoencoders for representation learning

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a specific task, in terms of: (1) high accuracy, (2) large short-term memory and (3) low execution time. The Echo-State Network (ESN) is a recent specific kind of a Recurrent Neural Networks (RNN), that presents very rich dynamics on account of its reservoir-based hidden layer. It is widely used in dealing with complex non-linear problems and has been shown to outperform classical approaches in a number of benchmark tasks. In this paper, the powerful dynamism and large memory provided by the ESN and complementary strengths of AEs in feature extraction are integrated, to develop a novel Echo-State Recurrent Autoencoder (ES-RA). In order to devise more robust alternatives to conventional reservoir-based networks, both single- (SL-ES-RA) and multi-layer (ML-ES-RA) models are formulated. The new features, once extracted from ESN’s hidden layer, are applied to various benchmark ML tasks including classification, time series prediction and regression. A range of evaluation metrics are shown to improve considerably compared to those obtained when applying original data features. An accuracy-based comparison is performed between our proposed recurrent AEs and two variants of ELM feed-forward AEs (Single and ML), for both noise free and noisy data. In summary, a comparative empirical study reveals the key contribution of exploiting recurrent connections in improving benchmark performance results

Linking spatialized indicators of desertification risks with observed land use/land cover change : an operational monitoring system of desertification

The need of useful information for decision-makers to fight against desertification in Tunisian dry zones leads to conceive assessing and monitoring systems that can supply synthetic indicators. These latter should integrate socioeconomic and environmental dimensions with their spatial and temporal diversity at the local scale. This paper proposes an example of information system entitled SIELO (information system for operational desertification monitoring at the local scale). This system attempts to create the link between i) spatialized indicators of desertification risks, built in connection with the systematic complexity of desertification and ii) observed Land Use/ Land Cover (LU/LC) Change. The first type of data arises from pre-existing environmental model: LEIS model (Local Environmental Information System). The second type of data is extracted from satellite images acquired according to regular time steps. The proposed approach is based also on the spatializing of knowledge, via the "landscape" tool in particular. We illustrate the feasibility and the operational effectiveness of a developed software prototype (SIELO v1.0) with an initial application in a Tunisian dry zone. This system showed that it has the capacity to feed an operational monitoring of desertification according to the state of LU/LC directly observed or measured in several dates. Then, it can be useful for the decision-makers in their programs of fighting against desertification, but also to manage uncertainties in southeastern Tunisia, especially climate variability and climate change

Linking spatialized indicators of desertification risks with observed land use/land cover change : an operational monitoring system of desertification

The need of useful information for decision-makers to fight against desertification in Tunisian dry zones leads to conceive assessing and monitoring systems that can supply synthetic indicators. These latter should integrate socioeconomic and environmental dimensions with their spatial and temporal diversity at the local scale. This paper proposes an example of information system entitled SIELO (information system for operational desertification monitoring at the local scale). This system attempts to create the link between i) spatialized indicators of desertification risks, built in connection with the systematic complexity of desertification and ii) observed Land Use/ Land Cover (LU/LC) Change. The first type of data arises from pre-existing environmental model: LEIS model (Local Environmental Information System). The second type of data is extracted from satellite images acquired according to regular time steps. The proposed approach is based also on the spatializing of knowledge, via the "landscape" tool in particular. We illustrate the feasibility and the operational effectiveness of a developed software prototype (SIELO v1.0) with an initial application in a Tunisian dry zone. This system showed that it has the capacity to feed an operational monitoring of desertification according to the state of LU/LC directly observed or measured in several dates. Then, it can be useful for the decision-makers in their programs of fighting against desertification, but also to manage uncertainties in southeastern Tunisia, especially climate variability and climate change