Modelling Approach: most commonly used (maybe most uncertain?)

Estimate lambda (^) or rate of change. 1 = static; <1.0 is decreasing; >1.0 is increasing

Two types of models;

• Deterministic modeling: fixed birth and death rates for age classes, based on averages of actual field observations. Give same result fore any given pop size. Indicate incr or decr.

Once you have the rate of change, you make management decisions, like harvesting thru sport hunting, protection, eradication, etc.

• Stochastic modeling: allow for natural variation in some key factors like sub-adult survival; seed germination rates; etc.

The example in the text is a study of forest songbird populations in Wisconsin. It has two improvement son simple, deterministic modeling:

1. it includes a habitat quality variable: distance to edge or an "edge effect" where reproduction is reduced due to predation. And
2. It allows for stochastic or random variation in the parameter. Here they used a simple statistic of variation that is linked to the size of the average: the coefficient of variation, which is the standard deviation divided by the mean. They set this at 20% for fecundity and 30% for survival. We’re not told if the values come from empirical measurements.

The model yields different results each time it is run, even for the same initial population.

Fig. 7.4: discuss in class. They run for 15 years, get 5% of the runs with zero population or extinction.

SO: MVP of 15 gives a 95% chance of being around in 15 years.

Should be validated from data not use din the model.

Should be tested by sensitivity analysis to see how robust the model is.

Should not be taken as a single, infallible answer.

Each distinct population has a different lambda. Due to differences in their habitat, stochastic variations (e.g., a windstorm or fire), and other variation.

The regional populations’ lambda is determined by the sub-populations’ dynamics, and by dispersal.

Sink population: death rates greater than birth rates.

Source population: birth rates greater than death rates

Rescue effect: the effect of source populations re-colonizing sink populations, or helping them add population just when they appear to be at risk of local extinction .

So: a meta-population that is a sink might persist even though the demographic dynamics or population viability analysis says it is at risk, because its membership in a larger, regional population is not taken into account.

Read the example in the text: Florida scrub jay. Note that as a bird the Jay is likely a good disperser, so the sub-populations of the meta-population tend to persists. A mete-population can persist with a lot of habitat change, as long as sufficient source sub-populations exist and the transformations of landscape do not finally get so large as to block dispersal.

The information needs for good modeling are great: see Table 7.3. Do we have all or even most of these data for any species or suite of species in a habitat or ecosystem? Probably not.

In this way the problem of modeling ecosystems is like modeling any complex system, such as the atmosphere or the climate. The goal is to get more data and to build a model that can handle lots and lots of calculations on those data (a global climate model must calculate several atmospheric variables like wind, humidity, temperature and cloudiness, for thousands of grid cells, and several levels of the atmosphere at relatively short time-steps (maybe every hour). This requires a super-computer. Right now we put more energy into climate models than to ecosystems models.

Don’t be confused by the title of this section—this is not a return to the earlier discussions of traditional vs. ecosystems management. It is about managing communities of species, a process that still offers the potential for focussing on single species.

Chose to manage for species whose persistence helps to support (keystone) or protect (umbrella) or otherwise represent (indicator) a suite or broader community of species.

Manage not for species per se, but for processes, like predation, wildlife, etc---manage so that these processes occur in something like their historic range of spatial and temporal variability.

Focus on managing for landscape patterns of habitat, keeping landscape or habitat elements arrayed in their historic range of variability. If you’re accomplishing this, you are also likely providing the context for species persistence. We focus on this in Chap. 8