Tuesday, February 28, 2012

Does monitoring make the man?

AliĆ©nor Chauvenet and co-authors published an interesting article in Animal Conservation last week. They used mark-recapture data on Hihi to parameterize a stochastic population model and evaluate the benefits of supplemental feeding of a translocated population. This is a really solid piece of work: an interesting species with a nice simple management decision. This will definitely make it into my Population Dynamics course next year as an example. They have this to say about Adaptive Management in the introduction:
In situ food experiments (on–off or temporal and/or spatial variation in quantity) can help assess the consequences of altering management actions (Armstrong & Perrott, 2000). However, managers rarely take this risk as translocated populations are generally small (Shaffer, 1981)
and such experiments could result in the loss of precious translocated individuals. Alternatively, models can be used to study past and future variation in management regimes and assess the importance of such variation on a species’ survival and/or reproductive rates. The goal of this type of modelling exercise is to inform and update management decisions as an iterative process, that is, perform adaptive management (Holling, 1978; Walters & Hilborn, 1978; Walters, 1986). Ideally, adaptive management requires an a priori development of possible management options, which are evaluated and refined following targeted monitoring (Ewen & Armstrong, 2007). In many cases, however, new management options arise well into a project. If relevant monitoring has been ongoing, then population modelling can inform the likely response of populations based on past data, and new management can be incorporated into the adaptive management framework (Williams, 2010).
They assume that adaptive management requires experimentation, and seem to believe that introducing new management actions into an AM process is a relatively new idea. It isn't. At least in the Decision Theoretic school, the possibility of changes to the available actions or shifts in objectives is considered regularly as part of the iterative cycle - so called "double loop learning". Such double loop learning is also not dependent on monitoring data - you may learn things outside of any monitoring program, e.g. from independent research, changes in policies enabling new actions etc.
So, a long term monitoring dataset, clear management decision, nice models for forecasting the future. But is it Adaptive Management? I have to say I can't tell. It is clear that at least one decision, to cap the ad libitum feeding program in 2010, was made by trading off one objective - high adult survival, against another objective - cost. What isn't clear is whether the models developed by Chauvenet et al were used to evaluate the future consequences of that decision. One quote makes me think not:

Investigating other management scenarios, such as ones looking at the impact of reducing or increasing supplemental feeding by x% would be highly informative but data did not allow such models to be built. However, a new management regime has been put in place on Kapiti Island recently. In late 2010, managers reached the end of their ad libitum capacity and were forced to make a decision as to the future of management for the population. They came to the conclusion that capping the quantity of supplemental food to 75% of the 2009 amount was the best solution for both hihi and managers. As a result there may be a possibility for further model parameterization, that is, new scenarios, in the near future.

So they suggest that the model cannot be used to evaluate the effect of the new management regime until after the new regime has been in place sufficiently long to have data on its effectiveness. Hogwash, I say! They know the parameters of the model in the absence of feeding, and with ad libitum feeding. Surely a reasonable null hypothesis draws a line between those two points to get an idea of how capping feeding will affect population size. By making that prediction prior to changing management, or even now, they would be able to use subsequent observations of the population to test the validity of their population model.
So, I have to say that it doesn't look like Adaptive Management, although I think it is clearly one of those decisions that could benefit from a rigourous Decision Theoretic approach to AM.

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