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Find video protocols related to scientific articles indexed in Pubmed.
Spatial Segregation between Invasive and Native Commensal Rodents in an Urban Environment: A Case Study in Niamey, Niger.
PUBLISHED: 01-01-2014
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Invasive rodents have been responsible for the diffusion worldwide of many zoonotic agents, thus representing major threats for public health. Cities are important hubs for people and goods exchange and are thus expected to play a pivotal role in invasive commensal rodent dissemination. Yet, data about urban rodents' ecology, especially invasive vs. native species interactions, are dramatically scarce. Here, we provide results of an extensive survey of urban rodents conducted in Niamey, Niger, depicting the early stages of rodent bioinvasions within a city. We explore the species-specific spatial distributions throughout the city using contrasted approaches, namely field sampling, co-occurrence analysis, occupancy modelling and indicator geostatistics. We show that (i) two species (i.e. rural-like vs. truly commensal) assemblages can be identified, and that (ii) within commensal rodents, invasive (Rattus rattus and Mus musculus) and native (Mastomys natalensis) species are spatially segregated. Moreover, several pieces of arguments tend to suggest that these exclusive distributions reflect an ongoing native-to-invasive species turn over. The underlying processes as well as the possible consequences for humans are discussed.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.