How are nitrogen availability,fine‐root mass,and nitrogen uptake related empirically? Implications for models and theory

Understanding the effects of global change in terrestrial communities requires an understanding of how limiting resources interact with plant traits to affect productivity. Here, we focus on nitrogen and ask whether plant community nitrogen uptake rate is determined (a) by nitrogen availability alone or (b) by the product of nitrogen availability and fine‐root mass. Surprisingly, this is not empirically resolved. We performed controlled microcosm experiments and reanalyzed published pot experiments and field data to determine the relationship between community‐level nitrogen uptake rate, nitrogen availability, and fine‐root mass for 46 unique combinations of species, nitrogen levels, and growing conditions. We found that plant community nitrogen uptake rate was unaffected by fine‐root mass in 63% of cases and saturated with fine‐root mass in 29% of cases (92% in total). In contrast, plant community nitrogen uptake rate was clearly affected by nitrogen availability. The results support the idea that although plants may over‐proliferate fine roots for individual‐level competition, it comes without an increase in community‐level nitrogen uptake. The results have implications for the mechanisms included in coupled carbon‐nitrogen terrestrial biosphere models (CN‐TBMs) and are consistent with CN‐TBMs that operate above the individual scale and omit fine‐root mass in equations of nitrogen uptake rate but inconsistent with the majority of CN‐TBMs, which operate above the individual scale and include fine‐root mass in equations of nitrogen uptake rate. For the much smaller number of CN‐TBMs that explicitly model individual‐based belowground competition for nitrogen, the results suggest that the relative (not absolute) fine‐root mass of competing individuals should be included in the equations that determine individual‐level nitrogen uptake rates. By providing empirical data to support the assumptions used in CN‐TBMs, we put their global climate change predictions on firmer ground.     

Modeling optimal responses and fitness consequences in a changing Arctic

Animals must balance a series of costs and benefits while trying to maximize their fitness. For example, an individual may need to choose how much energy to allocate to reproduction versus growth, or how much time to spend on vigilance versus foraging. Their decisions depend on complex interactions between environmental conditions, behavioral plasticity, reproductive biology, and energetic demands. As animals respond to novel environmental conditions caused by climate change, the optimal decisions may shift. Stochastic dynamic programming provides a flexible modeling framework with which to explore these trade‐offs, but this method has not yet been used to study possible changes in optimal trade‐offs caused by climate change. We created a stochastic dynamic programming model capturing trade‐off decisions required by an individual adult female polar bear (Ursus maritimus) as well as the fitness consequences of her decisions. We predicted optimal foraging decisions throughout her lifetime as well as the energetic thresholds below which it is optimal for her to abandon a reproductive attempt. To explore the effects of climate change, we shortened the spring feeding period by up to 3 weeks, which led to predictions of riskier foraging behavior and higher reproductive thresholds. The resulting changes in fitness may be interpreted as a best‐case scenario, where bears adapt instantaneously and optimally to new environmental conditions. If the spring feeding period was reduced by 1 week, her expected fitness declined by 15%, and if reduced by 3 weeks, expected fitness declined by 68%. This demonstrates an effective way to explore a species' optimal response to a changing landscape of costs and benefits and highlights the fact that small annual effects can result in large cumulative changes in expected lifetime fitness.     

Dissecting the nonlinear response of maize yield to high temperature stress with model‐data integration

Evidence suggests that global maize yield declines with a warming climate, particularly with extreme heat events. However, the degree to which important maize processes such as biomass growth rate, growing season length (GSL) and grain formation are impacted by an increase in temperature is uncertain. Such knowledge is necessary to understand yield responses and develop crop adaptation strategies under warmer climate. Here crop models, satellite observations, survey, and field data were integrated to investigate how high temperature stress influences maize yield in the U.S. Midwest. We showed that both observational evidence and crop model ensemble mean (MEM) suggests the nonlinear sensitivity in yield was driven by the intensified sensitivity of harvest index (HI), but MEM underestimated the warming effects through HI and overstated the effects through GSL. Further analysis showed that the intensified sensitivity in HI mainly results from a greater sensitivity of yield to high temperature stress during the grain filling period, which explained more than half of the yield reduction. When warming effects were decomposed into direct heat stress and indirect water stress (WS), observational data suggest that yield is more reduced by direct heat stress (?4.6 ± 1.0%/°C) than by WS (?1.7 ± 0.65%/°C), whereas MEM gives opposite results. This discrepancy implies that yield reduction by heat stress is underestimated, whereas the yield benefit of increasing atmospheric CO₂ might be overestimated in crop models, because elevated CO₂ brings yield benefit through water conservation effect but produces limited benefit over heat stress. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in agricultural management need to be better accounted for to adequately estimate the effects of heat stress.     

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to ?34% (?17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to ?37% loss by extreme drought (?32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.     

Risks of Biological Invasion on the Belt and Road

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