Similarly, the diversity of Californian vegetation and its variable sensitivity to Pc has not been considered: higher resolution studies considering differentials of vegetation susceptibility could provide a far more nuanced picture of the spatial extent of Pc, and could, for instance, eliminate areas of risk in primarily urbanized environments in the San Francisco Bay Area. However, in the absence of improved data regarding host–pathogen interactions for dominant vegetation types in the area, there is limited basis on which to make such a high-resolution suite of assumptions about vegetation susceptibility. Despite this missing information, the damped sensitivity of Pc range relative to changes in assumed host resistance across the region in conjunction with field observations confirming that a wide range of regional species are susceptible to Pc, suggests that the overall conclusions of the modeling study regarding climate sensitivity should be robust. Finally, we have assumed that the local environmental conditions experienced by Pc are determined solely by climate, square plastic plant pots which is not true in the irrigated agricultural areas in California’s Central and Imperial Valleys.
In these regions, irrigation regimes that are sufficiently frequent to sustain high water potentials in the root zone would alleviate the environmental water stress, as indicated by the observation of Pc occurrence within the Imperial Valley. If water stress is alleviated in these regions, temperatures are warm enough in both winter and spring to support high levels of Pc activity. Thus, irrigated agricultural land should be considered at risk of Pc infestation under both present and future scenarios. Overall, the modeling analysis suggests that Pc has a much larger potential range in the US southwest than could be inferred purely from current observations of the locations of infected sites. At present, despite the high awareness of Phytophthora ramorum and sudden oak death in the US southwest, there is limited public awareness, policy or scientific attention given to Pc . Sources of Pc are known to exist in agricultural and nursery settings in California , and appear to be the source of Pc infection in native ecosystems in several cases . However, no current phytosanitation certification programs, protocols for reducing soil movement from infected to clean sites, or recognized successful spot treatment approaches to minimizing Pc spread are in place in California . While the number of native species in California that are susceptible to Pc remains unclear, estimates in comparably biodiverse southwestern Australia suggest that over 3000 of the 5700 indigenous plant species are susceptible .
In the absence of detailed information about host susceptibility in California, the large ranges that appear to be supported by contemporary climates in conjunction with the severe effects of Pc in comparable ecosystems point to a critical need to improve risk assessment, phytosanitation and awareness of Pc disease. Despite its relatively simple ecology, Pc nonetheless displays non-monotonic responses to climatic warming at regional and local scales, with spatially distinct regions having opposite trends in Pc risk. Although the modeling framework presented accounts for many different aspects of climate, other changes including carbon fertilization due to enhanced atmospheric [CO2], changing land use and ecological thresholds have been neglected, and linking disease risk models to ecosystem outcomes remains challenging. For example, in Western Australia a 40 year drying trend has reduced Pc activity, but with the effect of replacing Pc induced mortality with drought stress stress . Considering that Pc mortality is linked to periods of drought stress that follow wet periods in which Pc causes root damage , limitation of Pc range due to drying, while beneficial for limiting expansion of the pathogen, may come at the expense of increased mortality for infected ecosystems.A previous set of studies using the medicine for back pain scenario found that most participants tended to perseverate, though about 7% of participants alternated consistently . In this task the underlying back pain function was autocorrelated, which caused participants who perseverated to have very high error rates, grossly over or underestimating the actual difference in the effectiveness of the two medicines. For example, if the baseline pain trend increased over time, participants often concluded that the first medicine worked much better than the second. Participants who alternated, either by choice or by instruction, were much more accurate. Furthermore, perseveration vs. alternation did not make a difference when the underlying function was random from day to day. Given that perseveration caused worse performance, why did most participants perseverate? One potential reason is that they were worried about TSDC effects and wanted to give each medicine enough time to exhibit these effects. A second reason is that they thought that back pain was random from day to day, in which case alternation would not be necessary. The current studies test whether people have the foresight to choose appropriate search strategies based on their beliefs about autocorrelation and TSDC. There are at least two other factors that may influence this search task. If participants really imagine themselves as the patient in the scenario, they might try to test the medicine that they think is currently working the best . This could lead to just a couple switches between the medicine rather than frequent alternating. A very similar strategy to exploiting is some sort of positive test strategy – to keep on testing the medicine that one thinks is working better because one erroneously thinks that testing this medicine is the best way to figure out which of the two medicines actually works best. Exploiting and positive testing are hard to empirically disentangle. The following two studies test whether people are able to use their beliefs about autocorrelation and TSDC effects to choose more optimal search strategies. In Study 1, I approached this question by creating cover stories for which participants had different pre-existing beliefs about autocorrelation and TSDC to see if they are able to make use of these beliefs. In Study 2, I directly manipulated participants’ beliefs about TSDC and autocorrelation. Both of these studies have strengths and weaknesses. The strength of Study 2 is that it has a high degree control. However, the weakness is that by manipulating people’s beliefs explicitly it cannot assess how people behave in situations for which their beliefs about TSDC and autocorrelation are internally generated. In addition, Experiment 2 used 15 different cover stories to examine information search across a variety of situations for external validity.Condition C had stories with low autocorrelation and low TSDC effects. A prototypical example is a doctor testing two back pain medicines on 14 sequential patients . There should be no autocorrelation or TSDC effects across 14 patients because there is no plausible way that one patient’s pain level or medicine should influence another patient’s pain level. Comparing Conditions A and C tests for an influence of autocorrelation beliefs on the testing strategy. There is no fourth condition because it is difficult to conceive of situations in which each observation is independent from the previous one yet an intervention at one time could have some TSDC effect at a later time.
It is not that such a case is impossible , square pot plastic but that it would be hard to devise a natural situation that participants would confidently interpret as having low autocorrelation and high TSDC effects. There were 3 reasons for having 5 stories per condition. First, if only one story was used per condition, any differences between condition could be due to the different story. Thus I took the approach of sampling from a broader range. Second, using a variety of stories introduces variability in the cover stories within and across conditions , which is useful for correlational analyses. Third, the cover stories allow for a degree of external validity not typically afforded to many reasoning studies. Procedures and Manipulation Checks Participants were randomly assigned to one of the 15 cover stories. After reading the story they answered two questions about whether the outcome was autocorrelated or not. Two measures were used because there are no validated instruments about autocorrelation beliefs, and autocorrelation beliefs can be queried multiple ways. Question 1 asked whether the outcome scores were closely related to the prior observation or not . Question 2 showed participants a graph with low, medium, and high autocorrelation and participants judged which graph reflected their beliefs about the outcome on a 1-9 scale. Even though the two measures were not strongly correlated, r=.27, p<.001, they behave similarly for all the analyses, so they are averaged for simplicity. The manipulation worked as intended. Participants believed that autocorrelation was higher in Condition A than C , t=7.01, p<.001, d=.98, and there was no difference between A and B , t<1. Then, participants in Condition B were asked to rate whether the causes would have TSDC effects. These four questions were not asked in Conditions A and C. For example, it does not make sense how one patient’s medicine would have a tolerance or carryover effect on another patient’s back pain . Asking participants to make such a judgment could encourage unintended beliefs about the scenario to accommodate the question. The only exception was that these questions were asked of the deodorant story in Condition A; this story was included in Condition A instead of B because it was guessed that deodorant would be viewed to have low TSDC effects. Beliefs about tolerance, sensitization, delay, and carryover were all significantly but weakly correlated; the only exception was that tolerance and delay were uncorrelated, r=-.06, p=.53. Even though they were weakly associated, they are all expected to have the same influence on alternation , so for conceptual convenience they were averaged. Participants were worried about the possibility of TSDC effects within Condition B; the average rating was 5.12, right at the middle of the 9-point scale, “somewhat likely”. Average ratings for individual scenarios ranged from 4.62 to 5.78. The deodorant story had an average rating of 3.35, verifying that it did belong in Condition A. Next, participants were tasked with figuring out which of the two options produced a better outcome. Participants received 14 sequential choices between the two options. After they chose one option they saw the outcome score . When they were ready they made the next choice. The outcome score after each choice was determined in the following way. There was a baseline function that participants did not know about. One of the options increased the score of the baseline function by exactly 5 points whenever it was chosen, and the other did not change the score from the baseline function. So, at any given choice, one option always worked exactly 5 points better than the other, but participants could not directly experience the 5 point difference because they had to choose between the two options. The outcome scores were given numerically, and disappeared when the next choice was made; they did not see a graphical plot over time. In Conditions A and B, the baseline function was a compilation of three sine waves with different amplitudes and frequencies. This function is highly autocorrelated and gradually fluctuates in unpredictable waves. In Condition C, 14 observations from the function were sampled, but then randomized so that the data would support the interpretation that the observations were independent, not autocorrelated. After making the 14 choices participants were instructed to identify the better option . They also rated how much better it was; 5 points was the correct answer counter factually. Participants knew in advance that they would earn a 20, 15, 10, or 5 cent bonus for a judgment within 2, 4, 6, or 8 points on either side of the correct answer, respectively. Finally, participants rated the extent to which they exploited and used a positive test strategy. They were asked: “When I thought that one medicine was working better than the other, I would continue to use that medicine”…“in order to reduce my pain during the 14 days” and “in order to figure out whether it really works better or not to choose the best medicine for the future” .Exploitation and Positive Testing had a correlation of .70, and were averaged to create one composite measure . The reason for asking these questions was to understand why certain participants perseverated. However, there is a challenge in interpreting these sorts of questions in which subjects introspect about their reasons for behaving in a particular way; it is possible that they use the questions to justify their behavior even if it was not actually the cause of the behavior.