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One sentence summary: 2Previously unreported forest areas in dryland biomes increase current estimates of the 3 global forest cover by at least 9 %. 4 List of authors:

The long-term trend in tropical forest area receives less scrutiny than the tropical deforestation rate. We show that constructing a reliable trend is difficult and evidence for decline is unclear, within the limits of errors involved in making global estimates. A time series for all tropical forest area, using data from Forest Resources Assessments (FRAs) of the United Nations Food and Agriculture Organization, is dominated by three successively corrected declining trends. Inconsistencies between these trends raise questions about their reliability, especially because differences seem to result as much from errors as from changes in statistical design and use of new data. A second time series for tropical moist forest area shows no apparent decline. The latter may be masked by the errors involved, but a ''forest return'' effect may also be operating, in which forest regeneration in some areas offsets deforestation (but not biodiversity loss) elsewhere. A better monitoring program is needed to give a more reliable trend. Scientists who use FRA data should check how the accuracy of their findings depends on errors in the data.global environmental monitoring ͉ sustainability indicators ͉ tropical deforestation

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The Global Drylands Observing System proposed in this issue should reduce the huge uncertainty about the extent of desertification and the rate at which it is changing, and provide valuable information to scientists, planners and policy-makers. However, it needs careful design if information outputs are to be scientifically credible and salient to the needs of people living in dry areas. Its design would benefit from a robust, integrated scientific framework like the Dryland Development Paradigm to guide/inform the development of an integrated global monitoring and assessment programme (both directly and indirectly via the use of modelling). Various types of dryland system models (e.g. environmental, socioeconomic, land-use cover change, and agent-based) could provide insights into how to combine the plethora of monitoring information gathered on key socioeconomic and biophysical indicators to develop integrated assessment models. This paper shows how insights from models can help in selecting and integrating indicators, interpreting synthetic trends, incorporating cross-scalar processes, representing spatio-temporal variation, and evaluating uncertainty. Planners could use this integrated global monitoring and assessment programme to help implement effective policies to address the global problem of desertification.

This paper suggests how the United Nations Convention to Combat Desertification (UNCCD) community can progressively make use of a flexible framework of analytical approaches that have been recently developed by scientific research. This allows a standardized but flexible use of indicator sets adapted to specific objectives or desertification issues relevant for implementing the Convention. Science has made progress in understanding major issues and proximate causes of dryland degradation such that indicator sets can be accordingly selected from the wealth of existing and documented indicator systems. The selection and combination should be guided according to transparent criteria given by existing indicator frameworks adapted to desertification conceptual frameworks such as the Dryland Development Paradigm and can act as a pragmatic entry point for selecting area-and theme-specific sets of indicators from existing databases. Working on different dryland sub-types through a meaningful stratification is proposed to delimit and characterize affected areas beyond the national level. Such stratification could be achieved by combining existing land use information with additional biophysical and socio-economic data sets, allowing indicator-based monitoring and assessment to be embedded in a framework of specific dryland degradation issues and their impacts on key ecosystem services.

The candidate confirms that the work submitted is her own and that appropriate credit has been given where reference has been made to the work of others. i ACKNOWLEDGEMENTSThe first thanks must go to the National Environment Research Council, who were kind enough to fund the first three years of this project. My supervisors Oliver Phillips and Alan Grainger deserve the strongest possible thanks for all their ideas, feedback and support. Thanks too to Steve Carver, a helpful member of the supervisory team in the first phase of the project. I am grateful for the constructive criticism of RSG members Mike Kirkby and Steve Compton.On the climate modelling side, Richard Betts and Peter Cox from Hadley Centre were supportive with advice, elucidation and most importantly, climate change scenario outputs. In the standard impact scenario (SIS), future population processes are simulated over 100 years, with changes in the variables governing cell suitability being applied annually according to anomalies from a selected GCM. The run is repeated for each species using anomalies of half that magnitude, as a reduced-impact scenario (RIS).