Daniel Chapman is a Co Editor, Writer & Podcaster at The Metropolis Planet. He specializes in football, with a particular focus on Leeds United, providing insights through match recaps, player analysis, and team performance commentary. Daniel's work has been featured in The Metropolis Planet newspaper, The Square Ball, Wiley, Berkeleyside, and PLOS.
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Leeds, United Kingdom (Local)
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With a local focus on Leeds and a strong emphasis on football (soccer) match recaps, team performance, and managers at Leeds United, Daniel Chapman would likely appreciate pitches relating to exclusive interviews with players or management staff of the club. Providing insights into the behind-the-scenes aspects of team strategies and performances could be well-received.
Additionally, given his coverage attributes that include press releases and government announcements related to crime and sports, he may also be interested in receiving information about initiatives aimed at addressing crime within sports or community-oriented programs involving Leeds United.
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By: David E. L. Cooke, Ana Perez-Sierra, Giles Hardy, Sarah Green, Nari Williams, Peter Scott, Treena I. Burgess, Daniel Chapman
Estimates of invasion risk can support prioritisation of future threats from non-native species. Greater risk of invasion is expected when species occur in connected source regions and possess trait...
By: Flavia Occhibove, Juan A. Navas-Cortes, Steven M. White, Daniel Chapman, James M. Bullock, Pieter S. A. Beck
Author summary Mathematical and computational models are crucial role for understanding plant disease outbreaks and evaluating the effectiveness of disease management. A case in point is Xylella fastidiosa, one of the world’s deadliest plant pathogens. Since its European discovery in olives in Puglia, Italy in 2013, there remain key knowledge gaps that undermine landscape-scale containment efforts of the outbreak, most notably concerning the year of introduction, the rate of spread and control efficacy. We developed a simulation model for the outbreak coupled to a simulation of surveillance and containment measures. We used Approximate Bayesian Computation to fit the model to surveillance and remote-sensing infection data. The model suggests that the bacterium may have been introduced earlier than previous estimates and that containment measures may have had some impact on slowing down the spread of the pathogen. Our results highlight the need for stronger biosecurity and surveillance for earlier detection of emerging plant pathogens, and suggest that stronger measures will be required to contain the Xylella fastidiosa outbreak fully.
