Some Alternative Approaches To Wildlife Management
Learning From EngineersWildlife managers have repeatedly emphasized the need to have "good science" behind their management plans. Obviously this is essential, but what has been forgotten is that wildlife management almost always involves an attempt to maintain populations within certain boundaries. In this sense, wildlife management has the same needs as engineering has when designing control systems, such as a thermostat for a heating system. And yet wildlife managers are rarely, if ever, trained in control systems. Fuzzy systems in particular, which have been so successful in industrial and consumer products, offer a huge opportunity for control systems of wildlife populations.
The Elements of PreparednessIn his 2002 paper (The Canadian Veterinary Journal (43(4):265-267) Ted Leighton makes a number of important points that wildlife managers might not encounter, because of the journal in which they were published and the fact that his topic appears to be disease transmission. He points out that the policy that livestock must be protected at the expense of wildlife can no longer be taken for granted: "It is no longer certain that society will place a higher value on the livestock industry than it will on its wildlife resources, if the latter is threatened by the former."
He includes a good explanation of why feral pig populations can pose a huge risk to wildlife and livestock. The main point of his disease transmission section is that widespread infections of wildlife represent "an enormous and unacceptable economic risk to animal agriculture and all who derive their living from it." His solution is to implement the elements of preparedness: Information, Networking and Methods. Given that they are affordable and feasible, they should be implemented immediately on critical disease and invasive species issues.
Lessons From The PiggeryDave Fraser (Fraser, D. 1985. Piggery perspectives on wildlife management and research. Wildlife Society Bulletin 13:183-187) comments that wildlife managers tend to emphasize techniques, while pig farmers emphasize "systems". Fraser defines a system as something that has a beginning, a middle and an end. The beginning would be data gathering. The end would be a management decision. The "middle" is a rule of thumb, which explicitly relates data to action. One example would be that if a sow has two small litters in a row she is culled.
Wildlife management is often missing the "middle". There are almost no rules of thumb. The result is that data are gathered, but languish unused in reports. Or they are applied inconsistently, resulting in a loss in credibility with stakeholders, and repetitive debates during decision-making. Fraser's paper is a clear argument for using fuzzy systems in wildlife management, though he never refers to fuzzy systems. But the rules in a fuzzy expert system are nothing more than rules of thumb, gathered into an integrated package.
The Three Minute GridIt has become easy to georeference almost all data in wildlife management now. What is less easy is deciding how to summarize and link georeferenced data. When data have to be grouped into discrete areas, a choice has to be made about sample units: how big should they be, and how should their boundaries be defined? In big game management in Manitoba, the three minute grid has proven extremely flexible and expandable. It presents an opportunity for widespread standardizion of big game sampling.
In central North America, a cell that is three minutes of latitude by three minutes of longitude is about 20 square kilometers: approximately 3.5 km wide and 5.5 km tall. It slightly trapezoidal, narrower to the north as the lines of longitude converge toward the pole. At first, a sample unit which is either square or rectangular would seem to offer more benefits, but that is not necessarily the case. With rectangular sample units, often defined by UTM coordinates, any study area that straddles a UTM zone boundary will lose its coordinate system. Sample units in adjacent zones will be at an angle to one another and there will be slivers of sample units along the boundary. Also, with UTM coordinates or section/township grids, any transect lines that are used in surveys will either not be due north-south, or will not be parallel to the sides of the sample unit, creating partial or overlapping strips.
For any large-scale study, such as on a national or continental scale (or even for a large state or province), georeferencing must fbe in latitude and longitude. Assembling data using any other system often presents difficulties during data analysis that can consume large amounts of staff time, often in demoralizing manual corrections.
Some data already exist in standardized lat/long grids which nest well with the three minute grid. The three minute grid is used by USGS for some shoreline data. Bird banding data are aggregated by 10 minute grids or degree grids. Data are also combined in 7.5 minutes and 15 minute grids by USGS. In these cases, assembling and combining data can be extremely efficient.
Very Simple ModelsIn Wildlife Management, it is tempting to create complex models. Biologists who have explored the complexity of natural systems quite naturally want to incorporate their knowledge as completely as possible. There are advantages to simple models, however. The biggest advantage, to my mind, is the ease with which they can be explained to colleagues and stakeholders who are not comfortable with sets of equations or programming languages. In Manitoba, I have found that over the past three decades, complex models are ignored and simple models are explored.
If a model consists of a list of understandable events, such as reproduction, mortality from hunting or mortality from predation, and if they are displayed in chronological order, through a small number of years, everyone understands what is being modelled. Also, with simple models, if discussion in a workshop session or public forum reveals that modifications are needed, programming changes can often be made in real time. (I'm not talking about changing an input variable here. I'm talking about changing how the model works.) As far as building credibility with team members or the public is concerned, nothing can match making a change immediately after it is suggested, and seeing the consequences.
Simple models, are, of course, less precise in many situations. But every model has a purpose. If that purpose is to find and/or support a management decision, it often transpires that the model must only select from alternatives such as "increase", "decrease" or "no change". In those cases, simple models are the best choice across the board: there is no penalty from a coarser level of prediction, and there is a huge benefit in the area of team-building.
Personal DatabasesSpreadsheets are wonderful tools for many purposes, but storing data is not one of them. You can do it, and obviously far more people put data into spreadsheets than databases, but you take a risk. If your data have to be sent to another program, if you have a range of possible analytical methods that you might use, if you need to clean up noisy data, if you need to combine data, ......... all these criteria (and more) demand that you learn how to make and use your own small database applications.
This is a topic dear to my heart, and so I'll be writing more here. Come back some time and check it out.
Simple Questions, Big SamplesQuestionnaires........... "If you're going to take the trouble to mail them out, you might as well get all the information you can." Right? Wrong. It's just the opposite. Never ask a question unless you absolutely must have the data. Why? Because a high response rate and a big sample will get you reliable data. And simple questionnaires get answered. If you mail complex questionnaires, most of them are heading for the recyling bin.
This is another topic dear to my heart, and so I'll be writing more here, too. Maybe I can convince you.