Dataset Information

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions.

ABSTRACT: Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster capacity building around a common threat. Regional or international databases curated for mNIS can inform local monitoring programs and can foster real-time information exchange on mNIS of concern. When combined, local species reference libraries, publicly available mNIS databases, and predictive modeling can facilitate the development of biosecurity programs in regions lacking baseline data. Biosecurity programs should be practical, feasible, cost-effective, mainly focused on prevention and early detection, and be built on the collaboration and coordination of government, nongovernment organizations, stakeholders, and local citizens for a rapid response.

SUBMITTER: Carvalho S

PROVIDER: S-EPMC10408360 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions.

Carvalho Susana S Shchepanik Hailey H Aylagas Eva E Berumen Michael L ML Costa Filipe O FO Costello Mark John MJ Duarte Sofia S Ferrario Jasmine J Floerl Oliver O Heinle Moritz M Katsanevakis Stelios S Marchini Agnese A Olenin Sergej S Pearman John K JK Peixoto Raquel S RS Rabaoui Lotfi J LJ Ruiz Greg G Srėbalienė Greta G Therriault Thomas W TW Vieira Pedro E PE Zaiko Anastasija A

Bioscience 20230701 7

Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster c ...[more]

PMID: 37560322

Similar Datasets

Project description:Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Project description:Background and objectivesThis paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.MethodsWe used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.ResultsIt was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.ConclusionsDespite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Project description:Commercial poultry is often farmed in high-density facilities, therefore, predisposing exposure to threats of infectious diseases. Studies suggest that it is likely that farmers have little motivation to practise on-farm biosecurity. In Taiwan, where high-density intensive poultry production is commonplace, unfortunately, several avian influenza outbreaks have occurred over the past decade despite the establishment of biosecurity procedures. To develop effective interventions, it is essential to understand the determinants of farmers' biosecurity mindset through systems thinking. In this qualitative study, we directly explored the opinions of Taiwan's chicken farmers, and a grounded theory analysis was performed. The study revealed that farmers allocate resources based on their justification for the optimisation of resource utilisation, and biosecurity is the most concerning challenge. Farmers focus on the economic aspects of their production systems, particularly when the implementation of biosecurity increases production costs, and there are multifaceted, complex barriers to implementing on-farm biosecurity. Although the participant farmers accepted to take major responsibility for disease management, paradoxically, some farmers blamed the practicality of government regulations and government employees' attitudes. Additionally, the farmers rejected the government's intentions to ask farmers to take major responsibility for the outbreaks of avian influenza while some of them intended to ignore the perceived risks. Government interventions that were considered not directly related to biosecurity also negatively influenced farmers' willingness to improve biosecurity. Using the interview results together with information in the scientific literature, we constructed a modified six-level social-ecological model to explain the complex influences of macro socio-economic conditions on farmers' biosecurity mindset. The novelty of this research lies in its wider relevance to Taiwan's chicken production industry in that it provides first-hand evidence-based knowledge to demonstrate a wide number of determinants of farmers' biosecurity mindset. This social-ecological model highlights the importance of systems thinking for the development of behavioural interventions and allows adaptation to the local context. The findings of this study have relevance to Taiwan's chicken production industry and potentially to similar systems in other countries in the wider region and should result in more effective animal health management at the farm level.

Dataset Information

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions.

Publications

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets