Redefining Adaptive Management

Welcome to the first LIVE blog direct from the Epsom 3 room at the Skycity convention center in Auckland. This is a special session redefining Adaptive Management organized by Craig Groves and Jensen Montambault from The Nature Conservancy. Definitions are good, so I’m looking forward to adding a new school of thought to my pantheon. I’m sitting here with Mike Runge (USGS), waiting eagerly to hear how our lives will be different. Well, maybe that’s just me.

Mike has just offered a perspective that what non-decision theoretic AM people worry about are the unknown unknowns only – the surprises that are unanticipated.

Here comes Craig Groves.

Survey of AM from conservation measures partnership

Of 7000 projects, 5% of projects do the full cycle, although 2500 have plans.

Why? AM is too complicated, and there is no mandate from senior management.

Overcoming Barriers

Use risk and leverage to guide investments in AM. Invest in projects that are high risk, with potential to generalize to other projects. Need to have the best statistics to be able to say that things are actually happening the way. They have a nice little decision tree that leads to diagnosing when an experimental approach (AM?) would be needed.

Focus AM on addressing questions that managers need to answer – This seems obvious, but it isn’t clear that he means which decision to make. Mike says “Looks like evaluation monitoring”, and I agree. www.conservationgateway.org is the place to go for the details, apparently.

Stop reinventing the wheel – yes! There’s 60 years of literature on decision analysis! This is a good idea, collaborate with other agencies and analyze data across projects within the TNC – but not AM.

Get senior managers to support the idea – yep, hard to disagree with that. Another signal that it is evaluation monitoring in disguise, is that they “peer review” their plans for evaluating effects.

Summary

Not all projects need scientifically rigorous AM, some do not.

Training and tools matter but so does leadership

And we need more success stories.

It’ll be interesting to see how they define success? Getting around the Plan, Do, Check, Adapt cycle? He didn’t define AM :(. 

Congressing

I'm fortunate enough to be down in Auckland NZ this week for the International Congress on Conservation Biology. This is the first of the new bi-annual format meetings of the Society for Conservation Biology. So far, I've not been blown away by anything, but its fun to catch up with people. I've run into alot of people from my time in Oz, which is harder to do at conferences in North America. There was a good session yesterday on modelling future responses to climate change, which, for once, included a talk that expressed some skepticism of the utility of static species distribution models for this purpose (John Leathwick, NIWA). Walter Jetz (Yale), gave a remote talk describing his labs work on building models of every bird species on the planet - bold stuff (see www.mappinglife.org). There was some talk of testing predictions from these models, but no discussion of how much accuracy is enough, or of what these models would be used for. Luckily, I also saw Helen Regan's (UC Santa Barbara) talk where she laid a stochastic population model on top of climate affected future habitat distributions. That was sufficient antidote to residual frustration from earlier in the day. Although I worry about using downscaled point predictions of climate in this way - the uncertainty in these predictions is huge.

Not predicting the future

I came across the following quote in an old USFWS report today:

In essence, then, mathematical models applied to real-world situations can be used only as a tool to guide management decisions having future effects on an ecosystem. In contrast, models cannot be used to tell a manager what the future will look like.

Say whut?!? How can you do the first without doing the second? Can someone explain this to me please?

Ignoring the evidence?

A big part of what I call the "Decision Theoretic School" of AM focuses on using models to predict future outcomes. However, before you can predict the future, you have to fit the models to existing data, and that's what Skalski et al (2011) did in a very nice article demonstrating the use of population reconstruction methods for age-at-harvest data on American Marten in Michigan. This approach is gaining a lot of ground in terrestrial wildlife management, although its old hat in oceanic fisheries work. There's an abundance of age-at-harvest data in state agency archives just waiting to be put to work. However, these methods require fairly substantial mathematical/statistical/computational know-how to put to work, which is why most agencies still rely on population indices of various sorts. Skalski et al. are critical of this approach:

Use of statistical population reconstruction suggests that the population of martens has been in general decline in Michigan’s UP, a finding not clearly evidenced using more traditional indices of harvest.

They then give examples of 3 harvest indices, two of which are partially or completely consistent with declining populations, and then further conclude that:

Inconsistencies between these traditional harvest indices and the statistical population reconstruction results emphasize the importance of reliable and defensible population estimates, including estimates of precision.

Except that they are not inconsistent! Only the sex ratio index is not indicative of a female bias, and I'm not sure why that would lead to a declining population anyway ... I'd better go back to Skalski et al's great book on wildlife demography and read up on that. Juv/Adult ratios and CPUE seem much more relevant, and they clearly are consistent with a declining population. So it seems that Michigan DNR had data indicating that Marten populations were declining, but failed to do anything about it. Now that they have a "better" analysis, complete with confidence limits, will they act? I suspect not:

Season lengths, harvest quotas, and registered harvests for martens and fishers in Michigan are generally conservative when compared to nearby jurisdictions with harvest seasons.

So harvesters are already more limited in Michigan than elsewhere, and the evidence in favor of a decline is actually not that strong. I've replotted the data in their Table 4 below; they have something like this in Figure 2, but it appears to be incorrect data or typos on the Y-axis.

As you can see, the confidence limits on the abundance are huge, and quite consistent with a population that isn't decreasing at all, or even increasing. The seven models they tested all assume that natural survival and harvest vulnerability are constant across time, so model selection doesn't provide the "population decreasing" evidence. They calculated a value for the population growth rate lambda = 0.94, but provided no confidence limit for this estimate. So, the uncertainty in the abundance has been quantified, and very nicely, but how will managers respond to that uncertainty? The real question for me is why has harvest effort increased 5-fold in 7 years? They mention nothing that suggests a big change in management conditions - an extension of the season from 10 days to 14 days in 2002 is all?

The need to include parameter uncertainty

One of the themes in Population Viability analysis that's been echoing around for a bit is the distinction between sampling variability and environmental variability in vital rate estimates. For instance, if you measure reproductive output for Piping Plovers over 5 years, the variance in reproductive output includes two components - variation between years due to environmental and biotic differences, and pure sampling error due to the fact that you can only measure reproductive output for a sample of nests. Conor McGowan and coauthors have a nice article in the latest issue of biological conservation "Incorporating parametric uncertainty into population viability analysis models", which directly demonstrates the dramatic impact of failing to distinguish between these two sources, and/or to incorporate both of them. Here's the "killer figure":

The top two panels are what you get if you either A) separate temporal and sampling variance, but ignore sampling variance, or B) leave sampling and temporal variance combined as "process variance". The bottom panel shows the impact of separating temporal and sampling variance, and then using them independently in the predictions. The expected trajectory isn't much different. But the variance in the trajectory is much, much bigger in case C. I saw this exact same pattern in regional models of Piping Plover and Interior Least Tern prepared for the USACE on the Missouri River:

This is the distribution of population sizes in 2015, forecast under the "Business as usual" habitat selection strategy, and including sampling variability in the vital rate parameters. The vertical red bar indicates the Recovery Plan target, which is met less than 50% of the time. The trouble with these predictions is that they end up including POSITIVE trajectories as well as negative ones. This tends to make them controversial, because obviously plovers can't increase in the absence of substantial modifications to their habitat, they're threatened. They have to decrease. Don't they?

The need for theory

hmmm, that doesn't rhyme quite as well. Ben Bolker brought the following quote from Efron and Tibshirani (1986; "Bootstrap methods for standard errors ...") to my attention:

An important theme of what follows is the substitution of computing power for theoretical analysis. This is not an argument against theory, of course, only against unnecessary theory.

I've often thought of the need for theory as falling along a continuum of 1/n, so when your sample size is small you need strong theory to make predictions, and when large you can get away with less theory. In either case it helps if your theory is well tested in other cases, or you risk making predictions that are completely bogus.

Wolf Management reprise

On The Wildlife Society Blog Michael Hutchins criticized Deborah Peter's article in the Huffington Post on the current wolf harvest. One section in particular emphasizes why wolf management will be political, not scientific, and thus not a good candidate for AM:

I hate the fact that Congress intervened in the ESA with regard to wolf management. Management and conservation should be in the hands of scientists and professional managers and not in the hands of politicians. But why did this happen? Precisely because extreme animal rights proponents (and some extreme environmentalists)–unwilling to acknowledge that wolves have indeed recovered, pushed things too far, arguing for no control what-so-ever.

The reason it is political is precisely because different groups hold different values for wolves - ranchers vs. cool headed wildlife scientists vs. extreme animal rights proponents. Last time I looked, people are allowed to have different values, and when they do, politics, not science, will carry the day.