Generalism drives abundance: A computational causal discovery approach
Fig 3
We apply the causal discovery method based on formal logic to detect the causal direction in an empirical dataset of 25 hummingbird-plant communities. In each panel, the x axis shows the two categories (rare and generalized species versus abundant and species species), and the y axis shows the mean proportion of species in that category. Each point denotes a different empirical community. Each line connects two points in the same empirical community. If the line is going up (meaning there are more species being abundant and specialized), it indicates that generalism drives abundance, and vice versa. The original method (panels C and D) suggests that generalism drives abundance in most communities (this is expected because the marginal distribution of abundance is more skewed than that of generalism). In contrast, our refined method has removed the bias regarding skewed marginal distributions, and it (panels A and B) suggests that generalism drives abundance in most communities. The qualitative patterns are similar among hummingbirds (panel A) and plants (panel B).