Bayesian estimation of population viability

Alejandro Ruete and colleagues have a paper in the Journal (100:2) titled “Hierarchical Bayesian estimation of the population viability of an epixylic moss“.  Read it here.

The authors have provided a motivation for their study, a brief description of their major finding below, and a picture of their study organism.

“I know that I know nothing”. Socrates means that one cannot know anything with absolute certainty but can feel confident about certain things. Regardless of any philosophical connotation, scientists need to acknowledge uncertainties in their understanding of systems. In ecology, living systems are far from behaving in a simple fashion or being easily predictable. However, with the rise of more powerful computation capabilities and using an old probabilistic theory (Bayesian theorem, 1763), it is now straightforward to acknowledge uncertainties. This allows us to know with more certainty, although it sounds contradictory, how a biological system works. For example, uncertainties can be now integrated throughout the models that describe the fluctuations of populations’ abundances, and retrieve probabilities for all possible outcomes given known environmental (e.g. weather) conditions. Conservation biologists can now attach probabilities to statements about the viability of populations in the long term. With this approach, Ruete and collaborators showed for a moss species categorized as ‘Vulnerable’, that the conclusions and decisions to be made based on population viability analysis could be dangerously misleading if uncertainties are not taken into account.


2 thoughts on “Bayesian estimation of population viability

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s