High-resolution spatial modelling of greenhouse gas emissions from land-use change to energy crops in the United Kingdom

Funded by Energy Technologies Institute EPSRC-Supergen. Grant Number: EP/M013200/1

ELUM: a spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK

Acknowledgements This work is based on the Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial (ELUM) project, which was commissioned and funded by the Energy Technologies Institute (ETI). The authors are grateful to Niall McNamara (Centre for Ecology & Hydrology, Lancaster) for coordinating the project and to Dagmar Henner (University of Aberdeen) for project assistance. We are also grateful to staff at the ETI, particularly to Geraldine Newton-Cross, Geraint Evans and Hannah Evans for constructive advice and feedback, and to Jonathan Oxley for project support. The ELUM Software Package contains Ordnance Survey data © Crown copyright and database right 2012.

State-space modelling of geolocation data reveals sex differences in the use of management areas by breeding northern fulmars

We thank Orkney Islands Council for access to Eynhallow and Talisman Energy (UK) Ltd and Marine Scotland for fieldwork and equipment support. Handling and tagging of fulmars was conducted under licences from the British Trust for Ornithology and the UK Home Office. EE was funded by a Marine Alliance for Science and Technology for Scotland/University of Aberdeen College of Life Sciences and Medicine studentship and LQ was supported by a NERC Studentship. Thanks also to the many colleagues who assisted with fieldwork during the project, and to Helen Bailey and Arliss Winship for advice on implementing the state-space model.

State-space modelling of geolocation data reveals sex differences in the use of management areas by breeding northern fulmars

We thank Orkney Islands Council for access to Eynhallow and Talisman Energy (UK) Ltd and Marine Scotland for fieldwork and equipment support. Handling and tagging of fulmars was conducted under licences from the British Trust for Ornithology and the UK Home Office. EE was funded by a Marine Alliance for Science and Technology for Scotland/University of Aberdeen College of Life Sciences and Medicine studentship and LQ was supported by a NERC Studentship. Thanks also to the many colleagues who assisted with fieldwork during the project, and to Helen Bailey and Arliss Winship for advice on implementing the state-space model.

Strong inference from transect sign surveys : combining spatial autocorrelation and misclassification occupancy models to quantify the detectability of a recovering carnivore

Funding — Forest Enterprise Scotland and the University of Aberdeen provided funding for the project. The Carnegie Trust supported the lead author, E. McHenry, in this research through the award of a tuition fees bursary.