A harmonized database of European forest simulations under climate change.

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

Updated incidence trends in cardia and non-cardia gastric adenocarcinoma in Sweden.

BACKGROUND: The aim of this study was to provide an update of the recent incidence trends of cardia and non-cardia gastric adenocarcinoma in Sweden. METHODS: Temporal trends in the age-standardised incidence were assessed separately for cardia and non-cardia gastric adenocarcinoma in 1970-2014 among all people in Sweden aged ≥50 years. Data were retrieved from the Swedish Cancer Registry. The log-linear joinpoint regression method was used to identify change points in the incidence trends. The annual percent changes with 95% confidence intervals (CI) were calculated for each segment before and after change points. RESULTS: The overall incidence of cardia adenocarcinoma increased during the earlier period of 1970-1988, but was stable during the later period of 1989-2014 (annual percent change: -0.3%, 95% CI: -0.7 to 0.2%). In contrast, in women aged 50-69 years the incidence of cardia adenocarcinoma increased by 6.6% annually (95% CI: 1.9 to 11.5%) during the period 2005 to 2014. The incidence of non-cardia gastric adenocarcinoma decreased by 4.4% per year (95% CI: -4.6 to -4.2%) in 1984-2014 and the decrease was stronger in men aged 70 years or older compared to other groups. CONCLUSION: The incidence of cardia adenocarcinoma is seemingly rapidly increasing in younger women, while it has been stable in other groups during recent years in Sweden. The incidence of non-cardia gastric adenocarcinoma continues to decrease, particularly in older men.

Does canopy mean nitrogen concentration explain variation in canopy light use efficiency across 14 contrasting forest sites?

The maximum light use efficiency (LUE = gross primary production (GPP)/absorbed photosynthetic photon flux density (aPPFD)) of plant canopies has been reported to vary spatially and some of this variation has previously been attributed to plant species differences. The canopy nitrogen concentration [N] can potentially explain some of this spatial variation. However, the current paradigm of the N-effect on photosynthesis is largely based on the relationship between photosynthetic capacity (A(max)) and [N], i.e., the effects of [N] on photosynthesis rates appear under high PPFD. A maximum LUE-[N] relationship, if it existed, would influence photosynthesis in the whole range of PPFD. We estimated maximum LUE for 14 eddy-covariance forest sites, examined its [N] dependency and investigated how the [N]-maximum LUE dependency could be incorporated into a GPP model. In the model, maximum LUE corresponds to LUE under optimal environmental conditions before light saturation takes place (the slope of GPP vs. PPFD under low PPFD). Maximum LUE was higher in deciduous/mixed than in coniferous sites, and correlated significantly with canopy mean [N]. Correlations between maximum LUE and canopy [N] existed regardless of daily PPFD, although we expected the correlation to disappear under low PPFD when LUE was also highest. Despite these correlations, including [N] in the model of GPP only marginally decreased the root mean squared error. Our results suggest that maximum LUE correlates linearly with canopy [N], but that a larger body of data is required before we can include this relationship into a GPP model. Gross primary production will therefore positively correlate with [N] already at low PPFD, and not only at high PPFD as is suggested by the prevailing paradigm of leaf-level A(max)-[N] relationships. This finding has consequences for modelling GPP driven by temporal changes or spatial variation in canopy [N].