> We tested eight annual and seasonal climate variables, including temperature, precipitation, frost days, and atmospheric CO2 concentrations in a mixed-effects model framework to account for city-to-city variation. We found that mean annual temperature was the strongest predictor of these four pollen metrics (P < 0.0001 for all metrics; Fig. 2 and SI Appendix, Table S4). The full mixed-effects models explained 51–90% of the variance (i.e., conditional R^2) in pollen metrics, and mean annual temperature alone (i.e., marginal R^2) explained 14–37% of the variance in pollen metrics (SI Appendix, Figs. S4 and S5 and Table S4). Notably, while atmospheric CO2 concentrations were sometimes included in the group of the most parsimonious models, the variation explained was often quite low (e.g., annual integral R^2 marginal = 0.01).
I'm not sure what I expected, but temperature fluctuations seem like a sensible primary driver of pollen count. Other variables that I think might be interesting include wind speed and relative humidity.
More pollen may translate to more fruits/seeds, which may translate to more saplings.
The first step would require a corresponding increase in pollinators, but pollinators (mostly insects) have reportedly been declining for decades.
I’m curious about the “amb a 1 allergen” which has been increasing according to a link in the article. Do we know what role it plays for the ragweed in reproduction, if any? What’s the advantage here?
Correlation is not causation. More co2 and favorable conditions lead to more vegetation. Large ecologies may have dynamic mechanisms that tend toward homeostasis, but the systems are chaotic and fundamentally unpredictable. That climate change produces one result or another is a secondary consequence of complex systems interacting. There is no earth thing "fighting" climate change.
> We tested eight annual and seasonal climate variables, including temperature, precipitation, frost days, and atmospheric CO2 concentrations in a mixed-effects model framework to account for city-to-city variation. We found that mean annual temperature was the strongest predictor of these four pollen metrics (P < 0.0001 for all metrics; Fig. 2 and SI Appendix, Table S4). The full mixed-effects models explained 51–90% of the variance (i.e., conditional R^2) in pollen metrics, and mean annual temperature alone (i.e., marginal R^2) explained 14–37% of the variance in pollen metrics (SI Appendix, Figs. S4 and S5 and Table S4). Notably, while atmospheric CO2 concentrations were sometimes included in the group of the most parsimonious models, the variation explained was often quite low (e.g., annual integral R^2 marginal = 0.01).
I'm not sure what I expected, but temperature fluctuations seem like a sensible primary driver of pollen count. Other variables that I think might be interesting include wind speed and relative humidity.