An official website of the United States government
Abstract One objective of eco‐evolutionary dynamics is to understand how the interplay between ecology and evolution on contemporary timescales contributes to the maintenance of biodiversity. Disturbance is an ecological process that can alter species diversity through both ecological and evolutionary effects on colonization and extinction dynamics. While analogous mechanisms likely operate among genotypes within a population, empirical evidence demonstrating the relationship between disturbance and genotypic diversity remains limited. We experimentally tested how disturbance altered the colonization (gain) and extinction (loss) of genets within a population of the marine angiospermZostera marina(eelgrass). In a 2‐year field experiment conducted in northern California, we mimicked grazing disturbance by migratory geese by clipping leaves at varying frequencies during the winter months. Surprisingly, we found the greatest rates of new colonization in the absence of disturbance and that clipping had negligible effects on extinction. We hypothesize that genet extinction was not driven by selective mortality from clipping or from any stochastic loss resulting from the reduced shoot densities in clipped plots. We also hypothesize that increased flowering effort and facilitation within and among clones drove the increased colonization of new genets in the undisturbed treatment. This balance between colonization and extinction resulted in a negative relationship between clipping frequency and net changes in genotypic richness. We interpret our results in light of prior work showing that genotypic diversity increased resistance to grazing disturbance. We suggest that both directions of a feedback between disturbance and diversity occur in this system with consequences for the maintenance of eelgrass genotypic diversity.
more »
« less
Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
More Like this
-
-
This paper considers the active recognition scenario, where the agent is empowered to intelligently acquire observations for better recognition. The agents usually compose two modules, i.e., the policy and the recognizer, to select actions and predict the category. While using ground-truth class labels to supervise the recognizer, the policy is typically updated with rewards determined by the current in-training recognizer, like whether achieving correct predictions. However, this joint learning process could lead to unintended solutions, like a collapsed policy that only visits views that the recognizer is already sufficiently trained to obtain rewards, which harms the generalization ability. We call this phenomenon lingering to depict the agent being reluctant to explore challenging views during training. Existing approaches to tackle the exploration-exploitation trade-off could be ineffective as they usually assume reliable feedback during exploration to update the estimate of rarely-visited states. This assumption is invalid here as the reward from the recognizer could be insufficiently trained.To this end, our approach integrates another adversarial policy to constantly disturb the recognition agent during training, forming a competing game to promote active explorations and avoid lingering. The reinforced adversary, rewarded when the recognition fails, contests the recognition agent by turning the camera to challenging observations. Extensive experiments across two datasets validate the effectiveness of the proposed approach regarding its recognition performances, learning efficiencies, and especially robustness in managing environmental noises.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.rse.2020.112244
