Better wildlife observation with new counting method

Are wildlife populations in Sweden increasing or decreasing? It is difficult to count wild animals, but the amount harvested through hunting gives an indication. Now, these statistics can be made clearer and more useful, thanks to a new model developed by Swedish researchers to count how many wild animals are hunted.

“We believe that this system will make the statistics clearer and more reliable. The idea is that this model is to be used from the autumn forward, for presenting official statistics about how many wild animals are harvested through hunting in Sweden,” says Tom Lindström, associate professor at the Department of Physics, Chemistry and Biology (IFM) at Linköping University, who has done the study in collaboration with Göran Bergqvist at the Swedish Association for Hunting and Wildlife Management.

Hunting is a way of getting an overview of the size of wild populations. For some species, it is the only indicator we have. Knowing how many wild animals are harvested through hunting every year is, therefore, an important part of wildlife monitoring, which needs to be adapted to changes in the ecosystem.

For example, the reporting of hunting of moose and large mammals is required by law, but for most species — everything from jays to wild boars — reporting is optional. The Swedish Association for Hunting and Wildlife Management is responsible for annual statistics around how many of these animals are harvested. Hunting teams report how much they have shot of around fifty species, and across how much land they have hunted. But because reporting is optional, and because reports are lacking for some of Sweden’s hunting grounds, statistical methods are used to calculate how many wild animals are harvested on these blind spots not covered by the hunting teams’ reports. One of the significant weaknesses with the analysis method that has been used until now is that it is very sensitive to low reporting — especially for species the hunting of which varies between hunting teams and are, generally, not so hunted. Tom Lindström gives an example:

“In 2015, the analysis appeared to show that many more beavers had been harvested than in previous years. However, when we analysed the data, it turned out that the big difference was because a single hunting team reported that they had shot a single beaver. Because this analysis method is so sensitive to single reports, it made it look like many thousands of beavers had been shot in that club.”

For this reason, the researchers developed a new analysis model which can give a better estimation as to how many wild animals of each species are harvested each year. The study, published in the journal Ecological Indicators, consists of two parts. In the first part, the researchers analysed various parameters and developed a model that is good at describing data. They then used the model to predict how many wild animals would be hunted on the area for which data were missing.

Analyses made with the new model can contribute to insights about hunting behaviour in Sweden. In the study, the researchers saw that the hunting teams that had greater areas to hunt in generally shot fewer animals per area. The correlation was similar for all species hunted. There may have been fewer animals in those areas, so a larger area is needed in order to have a chance of catching something — or there may be other explanations. Time is another piece of the puzzle. The researchers have done something called auto-regressive modelling, which means that the analysis of the hunting volume in one area takes account of the volumes from previous years.

This new statistical framework solves several problems.

“The model presents the uncertainty in these analyses in an honest way, and shows a range, instead of a definite figure. It is also less sensitive for individual hunting reports, and this reduces the uncertainty of the analysis,” says Tom Lindström.

The project was funded by Swedish Association of Hunting and Wildlife Management and The Swedish Environmental Protection Agency. Computation was executed on resources provided by the Swedish National Infrastructure for Computing (SNIC).

Story Source:

Materials provided by Linköping University. Original written by Karin Söderlund Leifler. Note: Content may be edited for style and length.


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