Pemberdayaan Masyarakat Pesisir Pulau Untungjawa Dalam Upaya Meningkatkan Kesadaran Hukum Dan Kemandirian Nelayan

Kelurahan Pulau Untung Jawa merupakan salah satu dari enam kelurahan di wilayah kepulauan Seribu atau satu di antara tiga kelurahan di wilayah kecamatan Kepulauan Seribu Selatan. Penduduk Pulau Untung Jawa sebagian besar adalah masyarakat pribumi yang silsilahnya berasal dari Pulau Untung Jawa dan perpindahan masyarakat Pulau Ubi Besar tanggal 13 Februari 1954. Kelurahan Pulau Untung Jawa merupakan kawasan andalan Wisata Pemukiman yang mempunyai peranan penting dalam mewujudkan visi kabupaten yaitu: Sebagai Taman dan Ladang Kehidupan Bahari yang Berkelanjutan Penelitian ini menemukan model pemberdayaan yang tepat bagi masyarakat pesisir pulau UntungJawa dan menganalisis upaya peningkatan kesadaran hukum dan kemandirian nelayan Pulau UntungJawa. Penelitian ini termasuk dalam penelitian deskriptif kualitatif dengan pendekatan sosiologis, atau dalam penelitian hukum biasa disebut normatif terapan/normatif empiris. Model pemberdayaan yang bertujuan membangun kemandirian nelayan pulau UntungJawa dapat dilaksanakan dengan kerjasama dan partisipasi masyarakat. Perlu sinergitas antara peran pemerintah baik aparat kelurahan dan instansi terkait lainnya, LSM yang peduli atau pun Perusahaan, kampus maupun masyarakat nelayan itu sendiri. Terdapat faktor pendukung dan faktor penghambat yang harus diperhatikan dan dicarikan solusinya. Faktor pendukung antara lain sudah terbangun konsep kesadaran dalam melakukan segala macam kegiatan yang sesuai dengan hukum yang ada, potensi wisata dan produksi perikanan, aparat Kelurahan yang cukup aktif dan kesiapan SDM untuk memotivasi diri dan menerima pendampingan serta berbagai pelatihan. Adapun faktor penghambatnya antara lain: terbatasnya modal, faktor alam, sarana prasarana, kurangnya gairah wisata, daya minat beli dan daya minat permainan air, tidak adanya penghasillan rutin, honor pekerja yang kurang dari UMP, SDM, dan kurangnya kesadaran hukum, bantuan hukum dan perlindungan hukum

A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control

Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed

Is the French SIRE equine information system a good basis for surveillance and epidemiological research? Quality assessment using two surveys

International audienceAccurate demographic knowledge of the equine population is needed to assess and model equine health events. France is one of the few European countries with an operational centralized database (SIRE) recording individual data on all declared equines living in France and on their owners and keepers. Our study aimed to assess SIRE database quality concerning the updating of information by equine owners and keepers with a view to its improvement and use in surveillance and research. Two online surveys were conducted with the participation of 6244 registered keepers and 13,869 owners. Results showed some inconsistencies between SIRE records and survey responses. The inconsistency rate for equines whose castration and death were not registered in the database was 28.7% and 5.9% respectively. Concerning owners, 11% of respondents did not own the reference equine selected considered by the survey, 33% had changed address without updating it in the SIRE. Concerning premises hosting equines, the keeper survey's inconsistency rate was 7.3%, of which 57 respondents had closed and 32 had opened premises without reporting it. Comparatively, the owner survey's inconsistency rate was 40.7% including respondents who owned and hosted an equine without reporting these equine premises, and owners who did not keep any equines on their premises. In conclusion, the SIRE database proved to be a valuable and reliable source for epidemiological research as long as some bias is taken into account. On the contrary, its use in surveillance is currently limited due some shortcomings in updating and/or reporting by owners and keepers

Performance comparison between electrochemical and semiconductors sensors for the monitoring of O₃

International audienceAs part of the Quality of Life and Urban Mobility (MouVIE) Chair, an individual mobile sensor designed as an adaptable and scalable "platform" is being developed within the LATMOS (Atmospheres Space Observations Laboratory). This sensor must contribute to answering problems related to the exposure of individuals to air pollution and their impact on health. In this context, its adaptable and scalable nature will allow the insertion of new consumer measurement components available ("low cost" micro-sensors).In this paper we present a laboratory evaluation of commercially sensors for the monitoring of ozone (O3). Two type of sensors are tested: electrochemical and semiconductors sensors. Theses sensors are tested at different temperatures, humidity and at ppb level. The voltage response and their dependence on ambiant temperature and humidity are evaluated. The time drift effect on electrochemical sensors was also evaluated during 4 months of use

Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent

Unravelling the ecological structure of emerging plant pathogens persisting in multi-host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space and host species, and may lead to biases of perception. The biased perception of pathogen ecology may be exacerbated by hidden fractions of the whole host population, which may act as infection reservoirs. We designed a mechanistic-statistical approach to help understand the ecology of emerging pathogens by filtering out some biases of perception. This approach, based on SIR (Susceptible-Infected-Removed) models and a Bayesian framework, disentangles epidemiological and observational processes underlying temporal counting data. We applied our approach to French surveillance data on Xylella fastidiosa, a multi-host pathogenic bacterium recently discovered in Corsica, France. A model selection led to two diverging scenarios: one scenario without a hidden compartment and an introduction around 2001, and the other with a hidden compartment and an introduction around 1985. Thus, Xylella fastidiosa was probably introduced into Corsica much earlier than its discovery, and its control could be arduous under the hidden compartment scenario. From a methodological perspective, our approach provides insights into the dynamics of emerging plant pathogens and, in particular, the potential existence of infection reservoirs

A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control.

Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed

Approche combinée d’analyses de séries temporelles et génomiques / exemple de la détection d’une augmentation de la présence de Salmonella Goldcoast en filière avicole

International audienceCombined approach of time series and genomic analyses: example of the detection of an increase in the occurrence of Salmonella Goldcoast in the French poultry sector Salmonella, an ubiquitous bacteria, is the second-most frequent cause of bacterial food poisoning in France as well as in Europe. In this context, ANSES monitors the food chain via the Salmonella network, which has been centralizing serotyping results for non-human Salmonella for more than 20 years. A statistical tool for time series analysis has been developed for these data, enabling the early detection of unusual increases in specific serotypes at the national or regional level, or in a given sector. Coupling this statistical approach with the genomic analysis of strains makes it possible for these events to be characterized with a very high degree of precision. This article describes an example of this combined approach used for an unusual increase in Salmonella Goldcoast during the 2018-2019 period in France.Les salmonelles, bactéries ubiquitaires, représentent la deuxième cause la plus fréquente de toxi-infections alimentaires bactériennes en France et en Europe. Dans ce contexte, l'Anses exerce une activité de surveillance de la chaîne alimentaire via le réseau Salmonella qui centralise, depuis plus de vingt ans, des résultats de sérotypage de salmonelles d'origine non humaine. Un outil statistique d'analyse de séries temporelles a été développé pour analyser ces données de surveillance. Il permet de détecter précocement des augmentations inhabituelles de la présence de certains sérovars aux niveau national, régional ou encore dans une filière spécifique, susceptible de présenter un risque pour le consommateur. Le couplage de cette approche statistique et de l'analyse génomique des souches permet de caractériser finement ces évènements inhabituels d'un point de vue épidémiologique. Cet article décrit un exemple de cette approche combinée déployée suite à l'augmentation inhabituelle de la détection de Salmonella Goldcoast au cours de la période 2018-2019 en France. Les analyses épidémiologiques et génomiques ont mis en évidence un cluster majoritaire lié à la filière avicole

The Four Clusters or the Yearly Honey Bee Colony Mortality for EPILOBEE <i>second</i> year.

<p>They are illustrated by a map (a) and a dendogram (b) for EPILOBEE and broken into the winter (c) and the seasonal (d) mortality rates. The vertical segments represent the 95% confidence intervals. BE = Belgium; DE = Germany; DK = Denmark; EE = Estonia; ES = Spain; FI = Finland; FR = France; GR = Greece; HU = Hungary; IT = Italy; LT = Lithuania; LV = Latvia; PL = Poland; PT = Portugal; SE = Sweden; SK = Slovakia.</p