Abundance of adverse environmental conditions during critical stages of crop production in Northern Germany.

BACKGROUND: Understanding the abundance of adverse environmental conditions e.g. frost, drought, and heat during critical crop growth stages, which are assumed to be altered by climate change, is crucial for an accurate risk assessment for cropping systems. While a lengthening of the vegetation period may be beneficial, higher frequencies of heat or frost events and drought spells are generally regarded as harmful. The objective of the present study was to quantify shifts in maize and wheat phenology and the occurrence of adverse environmental conditions during critical growth stages for four regions located in the North German Plain. First, a statistical analysis of phenological development was conducted based on recent data (1981-2010). Next, these data were used to calibrate the DSSAT-CERES wheat and maize models, which were then used to run three climate projections representing the maximum, intermediate and minimum courses of climate development within the RCP 8.5 continuum during the years 2021-2050. By means of model simulation runs and statistical analysis, the climate data were evaluated for the abundance of adverse environmental conditions during critical development stages, i.e. the stages of early crop development, anthesis, sowing and harvest. RESULTS: Proxies for adverse environmental conditions included thresholds of low and high temperatures as well as soil moisture. The comparison of the baseline climate and future climate projections showed a significant increase in the abundance of adverse environmental conditions during critical growth stages in the future. The lengthening of the vegetation period in spring did not compensate for the increased abundance of high temperatures, e.g. during anthesis. CONCLUSIONS: The results of this study indicate the need to develop adaptation strategies, such as implementing changes in cropping calendars. An increase in frost risk during early development, however, reveals the limited feasibility of early sowing as a mitigation strategy. In addition, the abundance of low soil water contents that hamper important production processes such as sowing and harvest were found to increase locally.

Model-based analysis of causes for habitat segregation in Idotea species (Crustacea, Isopoda).

On the shore of the rocky island of Helgoland (North Sea) two closely related isopod species, Idotea balthicaPallas, 1772, and Idotea granulosaRathke, 1843, share a similar fundamental niche but inhabit well-separated habitats. Idotea balthica inhabits floating algae at the sea surface and accumulations of decaying algae on the seafloor, whereas I. granulosa primarily occurs in intertidal macroalgal belts. In laboratory experiments on individually reared isopods I. balthica outperformed I. granulosa with regard to growth, reproduction, and mortality in both a fully inundated habitat and in a tidal habitat with 5 h of daily emergence. We hypothesized that habitat segregation in the two isopod species is driven by one or multiple types of biotic interactions: (1) no interaction, (2) cannibalism, (3) intraguild predation, and (4) terrestrial predation. In order to evaluate how habitat segregation can be explained by each of these interaction types we employed a size-structured population model to account for the body-size-dependent predation. Net population growth rates were fitted to the simulations as a measure of population fitness. Experimental results served as database for parameter and process identification. As predation rates were unknown, we performed a sensitivity analysis for these. We found that below 5 h of daily tidal emergence either cannibalism or terrestrial predation sufficed to explain habitat segregation. Intraguild predation, in contrast, advantaged I. balthica in any case. From linear extrapolation of the effects occurring under conditions of 5 h of daily tidal emergence, we predict that contrasting physiological responses in I. balthica and I. granulosa would cause segregation even without any interaction if emergence lasted long enough.

Rainfed winter wheat cultivation in the North German Plain will be water limited under climate change until 2070.

BACKGROUND: We analysed regionalised ECHAM6 climate data for the North German Plains (NGP) in two time slots from 1981 to 2010 and 2041 to 2070. RESULTS: The annual mean temperature will increase significantly (by about 2 °C) that will result in shorter growing periods since the sum of degree days until harvest will be reached earlier. Even if the amount of total precipitation does not change there appears to be a shift towards increased winter precipitation and thus noticeable reduced summer precipitation. CONCLUSIONS: Through the example of winter wheat we show a future limitation of water availability if yields are to be maintained or even increase.

Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: a review and introduction to the G-RaFFe model.

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model "G-RaFFe" generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.