The yield penalty of high amylose crops may be alleviated by picking an ideal AP/AM ratio through a coordinate change in the relative balance of starch biosynthetic enzymes. In the case of potato, it is plausible that downregulation of SBEs not only produces healthy fiber-starch, but also lessens the CIS severity and acrylamide problem . However, the sugars derived from starch during CIS may be an adaptive mechanism to enhance plant chilling tolerance. Rapid sugar accumulation upon cold stress have been reported in fruit. The sugars freed from starch may promote metabolic activity and serve as an osmoprotectant, thus alleviating chilling injury. The major functional SBEs were found to be upregulated in cold-stressed banana fruit, potato tuber, and Arabidopsis leaf, which may facilitate the ‘sugaring’ process. Modulating SBE activities may alter the rate of sugar released from the highly digestible starch polymers, thus changing the fruit/tuber cold responses. In fruiting species, the importance of ‘transitory-storage starch’ may be underestimated due to the lack of enough direct knowledge of its function, gained from experimental data. Tomato serves as a functional genomics model for fleshy fruit, as it is easily transformed and genetically manipulated. The putative function of ‘transitory-storage starch’ in fruit ripening, respiration, and sweetness enhancement may be revealed by engineering AP/AM ratio through over expression or suppression of SBEs. We hypothesize that high amylose, resistant starch tomato fruit may have reduced available starch, sugars, square plant pot and changes in fruit ripening and other processes that are dependent on starch as a carbon supply and source of energy postharvest.
Tomato SBEs may not reflect the functionality of all fruit SBEs, but it would produce fundamental knowledge and expand our understanding of species-, organ- and developmental-specific regulations of the core starch biosynthetic enzymes.The potential for warmer temperatures to expand pathogen ranges and alter epidemiology is an important consequence of global climate change for human populations and the environment . Plant pathogens influence large-scale forest mortality events, so understanding their future range and impacts will assist conservation planning . Plant pathogens also impact agricultural production, meaning their response to climate change threatens global and regional food security . Pathogens are sensitive to multiple climatic and environmental factors, as reflected in the ‘disease triangle’ , a conceptual model that states that disease is the outcome of the presence of a virulent pathogen, a susceptible host, and suitable environmental conditions. Theoretical and empirical studies addressing climate change impacts on plant disease tend to focus on individual environmental factors such as temperature , elevated atmospheric carbon dioxide concentrations and water availability , despite the likelihood that climate change will alter temperature, precipitation, potential evaporation, and ecological regimes simultaneously. Retrospective analyses show that multiple environmental drivers and their interactions influence expansion of disease ranges . Understanding these interactions remains an area of outstanding research need . Climatic and edaphic factors could limit Phytophthora cinnamomi range in several ways. First, Pc is sensitive to cold temperatures . Exposure to sufficiently cold temperatures for sufficiently long durations during winter will kill Pc .
Temperatures warm enough to permit survival of Pc may still be cold enough to suppress its ability togrow, reproduce, and cause disease to hosts . Previous modeling studies considering temperature effects on Pc range in Europe suggest the potential for considerable expansion in warming climates . However, Pc growth rates also display a threshold-like response to soil moisture in laboratory conditions. Dry soils also inhibit reproduction, survival, dispersal, and development of symptoms in host plants . Soil moisture conditions experienced by the pathogen themselves arise from interactions among the precipitation regime, soil depth, drainage, and atmospheric evaporative demand, and thus reflect the interplay of edaphic and climatic conditions. Finally, Pc disease is also often suppressed in rich soils where organic matter content exceeds 5% , probably because of predation by other soil organisms in the diverse microfaunal communities sustained in these soils . Projections of potential future risk therefore require techniques to assess the impact of multiple environmental changes and their interactions on pathogen range and epidemiology. Here, we apply a mechanistic modeling approach to explore how climate change could impact pathogen range and activity. We explore how simultaneous changes in temperature, precipitation, snow-pack extent, and evaporative demand might impact the range of a well-characterized pathogen under different climate scenarios. To do this we couple two existing models that describe controls on the range of the generalist root pathogen Pc in the state of California and surrounding regions in the states of Oregon, Nevada, and Arizona in the southwest USA. Pc occurs in this region but its range is poorly delineated. In other warm climates such as southern Australia and Hawaii, Pc has had a devastating effect on timber production, natural forests and agriculture € . Modeling the climatic and edaphic limits on its potential range in the US southwest will help determine the risks posed by this pathogen, particularly since there is not as yet a detailed understanding of the susceptibility of native species to Pc infection.The effects of a warming climate on Pc risk vary depending on the risk factors and specific climate scenario being assessed. Warming changes winter survival in a straightforward fashion: under the A2 scenario survival increases dramatically so that the region in which more than half of a Pc population would survive the winter increases from 43% to 72% of the study area. More modest increases in winter-survival probabilities arise under the B1 climate scenario, in which Pc winter survival becomes probable over 65% of the study area . The effects of climate change on soil moisture and Pc spring activity are more complex. At the regional scale, climate change reduces the risk posed by Pc across the majority of the study area. However, the changes are spatially variable. Pc risk declines markedly in the Central Valley. It is largely unchanged in coastal northern California and Oregon, where rainfall levels are projected to remain high. Its range is also unchanged in the south-eastern part of the region, which is significantly water limited under contemporary scenarios and projected to remain so. Pc risk increases in the north-eastern extent of the study area. The increase in Pc risk in this area is greatest in the high emissions scenario. In the lower emissions scenario, comparable increases in Pc extent occur inland in the southern extent of the range. In both cases, these increases indicate an interaction of warmer temperatures with unchanged or slightly enhanced rainfall. The potential complexity of the interactions between changing water and temperature in one of these southern locations is illustrated in Fig. 5 for a site in the southwestern part of the region. In this location, Pc risk increases under the B1 scenario but decreases under the A2 scenario. Figure 5 shows a decomposition of the projected changes into those due to temperature and those due to changes in soil moisture. As shown, square pot increasing temperatures increase Pc risk from the baseline case for both A2 and B1 scenarios, but in the A2 scenario, a decrease in soil moisture more than offsets the effect of warmer spring conditions.
Conversely, under the B1 conditions, the slight increase in soil moisture increases pathogen risk at this location, but only when both temperature and soil moisture increase together does the large predicted increase in pathogen risk occur. While these threshold-dynamics are not general across the study range, they illustrate the potential for highly nonlinear pathogen responses to interactions in changing temperature and moisture conditions, and highlight the importance of considering the impacts of synchronous changes in climate on pathogen dynamics. As summarized in Table 2 and illustrated in Fig. 2, water limitation reduces Pc risk over a range of 340 000 km2 across the region for the high emissions scenario, with an average decrease in the Pc risk of 0.28. Pc risk is reduced over 40 000 km2 for the low emissions scenario, by 0.26 on average. Alleviation of thermal limitations on Pc growth rates increased the predicted Pc risk in an 470 000 km2 area for the high emissions scenario with a projected mean increase of 0.08. For the low emissions scenario, Pc risks increase over a 390 000 km2 area for the low emissions scenario, with an average 0.05 increase in the Pc risk. Overall, the total area where Pc risks exceed 0.5 declines to approximately 164 000 km2 or 13% of the study region for the B1 scenario. The decline in spatial extent under a high emissions scenario is actually slightly less pronounced, with the predicted Pc range being 170 000 km2 or 13.7% of the region. This occurs even though the area where there is sufficient water for high spring Pc activity declines more for the high emissions scenario than for a low emissions scenario. The results reflect the importance of a tradeoff between increased winter survival range and decreased spring water availability. At the scale of the Bay Area, future climate projections contain considerable uncertainty associated with climatic downscaling. Broadly, however, the B1 scenario represents a case with warmer spring temperatures but without considerable reduction in rainfall,while the A2 scenario represents a situation with reduced rainfall and warmer temperatures. As shown in Fig. 3, the effects are quite striking: an increase in temperature without a large decrease in rainfall results in an increase in the area that is vulnerable to Pc by approximately 20% . Conversely, a decline in rainfall and increase in temperature result in a large decline in Pc risk, to approximately 3800 km2 , about half of its contemporary range.The modeling study indicated four different controls on modeled Pc range, which interacted to generate complex spatial patterns of Pc response to projected climatic changes. The first control was the proportion of soil organic carbon, which provided a static template of areas in which Pc would not establish. The second control was winter temperatures which impede pathogen over-winter survival. The range over which the pathogen survived winter expanded sequentially from contemporary conditions to the B1 and then A2 climate scenarios. The third and fourth controls on pathogen range lay in spring temperature and rainfall which together controlled spring Pc activity. Declining precipitation in the B1 and A2 scenarios inhibited Pc growth. The implications of drier climatic conditions in the study area varied depending on the absolute local Pc risk in a given area. Thus, little change in Pc risk was predicted in wet regions such as the northern coast, where drier spring conditions under B1 and A2 climate futures were still wet enough to support Pc activity. In the driest limits of the current Pc range, contemporary rainfall was too limited to support Pc activity, and further drying of the climate under B1 and A2 scenarios did not alter the pathogen risk. In mesic regions, progressively dry springs reduced overall Pc risk under the B1 scenario, relative to contemporary conditions. Despite further reductions in rainfall under the A2 scenario, however, only minor further decreases in Pc spring risk were predicted compared to the B1 scenario. This we attribute to increasing spring temperatures enhancing the rate of Pc expansion during spring, which increased disease risk over part of the model range. The spatial locations where warmer spring temperatures under the B1 and A2 scenarios caused increases in Pc risk were spatially separated from the locations where decreases in rainfall reduced Pc risk, so the overall pattern of Pc risk during spring under contemporary, B1 and A2 scenarios differs: the B1 case primarily reflects reductions in the contemporary area of high risk, while the A2 scenario continues this reduction but also leads to expanded regions of moderate Pc risk in the north-eastern part of the study region. The interactions of these controls lead to a decrease in Pc range under both B1 and A2 scenarios compared to contemporary conditions, but, surprisingly, less of a reduction in total Pc range under the more extreme warming scenario. The modeling study ignored several factors that impact dynamic Pc risk – that is, the risk of Pc developing in uninfected areas. Proximity to roads, streams, soil disturbance, innoculum sources, and high vehicle or pedestrian traffic are highly likely to impact these risks.