Publications

The global challenges of food security and biodiversity are rarely addressed together, though recently there has been an increasing awareness that the two issues are closely related. The majority of land available for agriculture is already used for food production, but despite the productivity gains, one in nine people worldwide are classified as food insecure. There is an increasing risk that addressing food insecurity through methods such as agricultural expansion or intensification could lead to biodiversity loss through destruction of habitats important for conservation. This analysis uses various indicators of biodiversity at a global scale, including biodiversity hotspots, total species richness, and threatened and endemic species richness. Areas where high biodiversity coexists with high food insecurity or a high risk of agricultural expansion, were examined and found to mainly occur in the tropics, with Madagascar standing out in particular. The areas identified are especially at risk of biodiversity loss, and so are global priorities for further research and for policy development to address food insecurity and biodiversity loss together.

We compared output from 3 dynamic process-based models (DMs: ECOSSE, MILLENNIA and the Durham Carbon Model) and 9 bioclimatic envelope models (BCEMs; including BBOG ensemble and PEATSTASH) ranging from simple threshold to semi-process-based models. Model simulations were run at 4 British peatland sites using historical climate data and climate projections under a medium (A1B) emissions scenario from the 11-RCM (regional climate model) ensemble underpinning UKCP09. The models showed that blanket peatlands are vulnerable to projected climate change; however, predictions varied between models as well as between sites. All BCEMs predicted a shift from presence to absence of a climate associated with blanket peat, where the sites with the lowest total annual precipitation were closest to the presence/absence threshold. DMs showed a more
\nvariable response. ECOSSE predicted a decline in net C sink and shift to net C source by the end of this century. The Durham Carbon Model predicted a smaller decline in the net C sink strength, but no shift to net C source. MILLENNIA predicted a slight overall increase in the net C sink. In contrast to the BCEM projections, the DMs predicted that the sites with coolest temperatures and greatest total
\nannual precipitation showed the largest change in carbon sinks. In this model inter-comparison, the greatest variation in model output in response to climate change projections was not between the BCEMs and DMs but between the DMs themselves, because of different approaches to modelling soil organic matter pools and decomposition amongst other processes. The difference in the sign of the
\nresponse has major implications for future climate feedbacks, climate policy and peatland management.
\nEnhanced data collection, in particular monitoring peatland response to current change, would significantly improve model development and projections of future change.

Abstract Croplands account for ~ one‐third of global anthropogenic nitrous oxide (N 2 O) emissions. A number of recent field experiments found substantial fallow‐period N 2 O emissions, which have been neglected for decades. However, the global contribution of the fallow‐period emissions and the associated drivers remain unclear. Based on 360 observations across global agroecosystems, we simulated the ratio of the fallow to the whole‐year N 2 O emissions (R fallow ) by developing a mixed‐effect model and compiling cropping‐system‐specific input data. Our results revealed that the mean global gridded R fallow was 44% (15%–75%, 95% confidence interval), with hotspots mainly in the northern high latitudes. For most cropping systems, soil pH was the dominant driver of global variation in R fallow . Global cropland emission factors (i.e., the percentage of fertilizer N emitted as N 2 O, EFs) in EF‐based models doubled to 1.9% when the fallow‐period N 2 O emissions were included in our simulation, similar to EFs estimated by process‐based and atmospheric inversion models (1.8%–2.3%). Overall, our study highlights the importance of fallow‐period N 2 O emissions in annual totals, especially for single cropping systems and croplands in acidic areas. To accurately estimate N 2 O emissions for national greenhouse gas inventories, it is crucial to update current EFs with full consideration of the fallow‐period N 2 O emissions in the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method.

We assessed the vulnerability of blanket peat to climate change in Great Britain using an ensemble of 8 bioclimatic envelope models. We used 4 published models that ranged from simple threshold models, based on total annual precipitation, to Generalised Linear Models (GLMs, based on mean annual temperature). In addition, 4 new models were developed which included measures of water deficit as threshold, classification tree, GLM and generalised additive models (GAM). Models that included measures of both hydrological conditions and maximum temperature provided a better fit to the mapped peat area than models based on hydrological variables alone. Under UKCIP02 projections for high (A1F1) and low (B1) greenhouse gas emission scenarios, 7 out of the 8 models showed a decline in the bioclimatic space associated with blanket peat. Eastern regions (Northumbria, North York Moors, Orkney) were shown to be more vulnerable than higher-altitude, western areas (Highlands, Western Isles and Argyle, Bute and The Trossachs). These results suggest a long-term decline in the distribution of actively growing blanket peat, especially under the high emissions scenario, although it is emphasised that existing peatlands may well persist for decades under a changing climate. Observational data from long-term monitoring and manipulation experiments in combination with process-based models are required to explore the nature and magnitude of climate change impacts on these vulnerable areas more fully.

Abstract Process‐based models can be classified into: (a) terrestrial biogeochemical models (TBMs), which simulate fluxes of carbon, water and nitrogen coupled within terrestrial ecosystems, and (b) dynamic global vegetation models (DGVMs), which further couple these processes interactively with changes in slow ecosystem processes depending on resource competition, establishment, growth and mortality of different vegetation types. In this study, four models – RHESSys, GOTILWA+, LPJ‐GUESS and ORCHIDEE – representing both modelling approaches were compared and evaluated against benchmarks provided by eddy‐covariance measurements of carbon and water fluxes at 15 forest sites within the EUROFLUX project. Overall, model‐measurement agreement varied greatly among sites. Both modelling approaches have somewhat different strengths, but there was no model among those tested that universally performed well on the two variables evaluated. Small biases and errors suggest that ORCHIDEE and GOTILWA+ performed better in simulating carbon fluxes while LPJ‐GUESS and RHESSys did a better job in simulating water fluxes. In general, the models can be considered as useful tools for studies of climate change impacts on carbon and water cycling in forests. However, the various sources of variation among models simulations and between models simulations and observed data described in this study place some constraints on the results and to some extent reduce their reliability. For example, at most sites in the Mediterranean region all models generally performed poorly most likely because of problems in the representation of water stress effects on both carbon uptake by photosynthesis and carbon release by heterotrophic respiration ( R h ). The use of flux data as a means of assessing key processes in models of this type is an important approach to improving model performance. Our results show that the models have value but that further model development is necessary with regard to the representation of the some of the key ecosystem processes.