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Movement behavior is an essential element of fundamental ecological processes such as competition and predation. Although intraspecific trait variation (ITV) in movement behaviors is pervasive, its consequences for ecological community dynamics are still not fully understood. Using a newly developed individual-based model, we analyzed how given and constant ITVs in foraging movement affect differences in foraging efficiencies between species competing for common resources under various resource distributions. Further, we analyzed how the effect of ITV on emerging differences in competitive abilities ultimately affects species coexistence. The model is generic but mimics observed patterns of among-individual covariation between personality, movement and space use in ground-dwelling rodents. Interacting species differed in their mean behavioral types along a slow-fast continuum, integrating consistent individual variation in average behavioral expression and responsiveness (i.e. behavioral reaction norms). We found that ITV reduced interspecific differences in competitive abilities by 5-35% and thereby promoted coexistence via an equalizing mechanism. The emergent relationships between behavioral types and foraging efficiency are characteristic for specific environmental contexts of resource distribution and population density. As these relationships are asymmetric, species that were either 'too fast' or 'too slow' benefited differently from ITV. Thus, ITV in movement behavior has consequences for species coexistence but to predict its effect in a given system requires intimate knowledge on how variation in movement traits relates to fitness components along an environmental gradient.
Estimates of present and future extreme sub-hourly rainfall are derived from a daily spatial followed by a sub-daily temporal downscaling, the latter of which incorporates a novel, and crucial, temperature sensitivity. Specifically, daily global climate fields are spatially downscaled to local temperature T and precipitation P, which are then disaggregated to a temporal resolution of 10 min using a multiplicative random cascade model. The scheme is calibrated and validated with a group of 21 station records of 10-min resolution in Germany. The cascade model is used in the classical (denoted as MC) and in the new T-sensitive (MC+) version, which respects local Clausius-Clapeyron (CC) effects such as CC scaling. Extreme P is positively biased in both MC versions. Observed T sensitivity is absent in MC but well reproduced by MC+. Long-term positive trends in extreme sub-hourly P are generally more pronounced and more significant in MC+ than in MC. In units of 10-min rainfall, observed centennial trends in annual exceedance counts (EC) of P > 5 mm are +29% and in 3-yr return levels (RL) +27%. For the RCP4.5-simulated future, higher extremes are projected in both versions MC and MC+: per century, EC increases by 30% for MC and by 83% for MC+; the RL rises by 14% for MC and by 33% for MC+. Because the projected daily P trends are negligible, the sub-daily signal is mainly driven by local temperature.
Niche-based species distribution models (SDMs) play a central role in studying species response to environmental change. Effective management and conservation plans for freshwater ecosystems require SDMs that accommodate hierarchical catchment ordering and provide clarity on the performance of such models across multiple scales. The scale-dependence components considered here are: (a) environment spatial structure, represented by hierarchical catchment ordering following the Strahler system; (b) analysis grain, that included 1st to 5th order catchments; and (c) response grain, the grain at which species respond most, represented by local and upstream catchment area effects. We used fish occurrence data from the Danube River Basin and various factors representing climate, land cover and anthropogenic pressures. Our results indicate that the choice of response grain local vs. upstream area effects and the choice of analysis grain, only marginally influence the performance of SDMs. Upstream effects tend to better predict fish distributions than corresponding local effects for anthropogenic and land cover factors, in particular for species sensitive to pollution. Key predictors and their relative importance are scale and species dependent. Consequently, choosing proper species dependent spatial scales and factors is imperative for effective river rehabilitation measures.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Changes in mid-latitude circulation can strongly affect the number and intensity of extreme weather events. In particular, high-amplitude quasi-stationary planetary waves have been linked to prolonged weather extremes at the surface. In contrast, analyses of fast-traveling synoptic-scale waves and their direct influence on heat and cold extremes are scarce though changes in such waves have been detected and are projected for the 21st century. Here we apply regression analyses of synoptic activity with surface temperature and precipitation in monthly gridded observational data. We show that over large parts of mid-latitude continental regions, summer heat extremes are associated with low storm track activity. In winter, the occurrence of cold spells is related to low storm track activity over parts of eastern North America, Europe, and central-to eastern Asia. Storm tracks thus have a moderating effect on continental temperatures. Pronounced storm track activity favors monthly rainfall extremes throughout the year, whereas dry spells are associated with a lack thereof. Trend analyses reveal significant regional changes in recent decades favoring the occurrence of cold spells in the eastern US, droughts in California and heat extremes over Eurasia.