There are many preventive methods to ensure reduced wolf-livestock conflict risk.
As I have been reading through some pack case studies, I thought that perhaps a system (a type of database) could be created where the wildlife managers would be able to enter basic wolf population monitoring data.
These data would then be subjected to processing by algorithms based on research that studies the averages of wolf population situations (i.e., social or environmental conditions affecting packs at a given time) that can lead to increased conflict risk.
If the data of a given pack conform to these averages at the specific time, a message would be sent to the farmers whose livestock operations lie adjacent or within the particular pack’s territory in order to caution on the high risk period.
Participation by the farmers would be voluntary (if they wish to be notified, they allow for their contacts and locations to become entered in the system).
Some of the messages might even be formed on the basis of the local livestock operation (e.g., if they submit their calving/lambing data, they might get annual reminders to keep watch when the calving/lambing season in their specific area approaches).
Similarly, if there are packs with traditional denning, rendevouz site, travelling, migratory etc. behaviour, notifications could be issues to those farmers who are of concern during a specific time (e.g., if the livestock operation is located near to a known den site, a message could be sent prior to denning period in order to remind the livestock owner that wolves are likely going to focus their activity near his/her ranch).
However, mainly the system would be based on fundamental monitoring data that is routinely collected in many countries, regions, states, provinces regarding local wolf packs (confirmation of denning, pup count, winter pack count, changes in breeders, pack disruption, pack displacement etc.).
Research would be conducted in order to determine which of the typical situations that a wolf pack faces in most of the populations constitute a higher risk period with respect to conflict rates.
For example, as I have been reading through some case studies, it appears to me that a great number of older subordinates (yearlings, two-year-olds) in the pack during the spring/summer period can lead to higher depredation risk because not all of these helpers are needed to assist the breeding pair and some of them might engage in pre-dispersal forays or temporarily detach from the core unit roaming on their own, exploring and taking advantage of opportunistic prey.
Similarly, known dispersals (e.g., by radiocollared individuals) might trigger notifications because dispersing wolves tend to ‘get into trouble’ more often due to their foraging behaviour, due to necessity to avoid established pack territories, due to the use of suboptimal habitats, due to lack of connectivity between habitats etc.
Social disruptions (change in breeders, intraspecific strife that results in territorial changes, pack dissolution etc.) can also lead to higher conflict risk.
Social changes can be anticipated, e.g., when the breeding pair has reached a certain age (> 9 years old).
Pup mortality might be a factor, as well, because packs that fail to reproduce or the pups of which suffer high mortality (none surviving until fall) would roam more widely even during summer/fall period when typically their activity is centered around dens/rendevouz sites.
There could be permanent factors that are of significance but that are case-specific.
For example, some packs may have prey that migrate out of their range or that aggregates only in one part of their range.
During these periods, the pack might have difficulties hunting or it might localize their activity in a certain area where conflict risk could be higher if livestock is grazed in the wilderness.
These are examples that I have noticed while reading about wolf packs but averages should be estimated in order to establish which events are worth giving a notice to the nearby farmers.
I believe that such a system would benefit the livestock farmers because they might be able to watch out during high vulnerability phases.
Also, ‘personalized notifications’ would enable the local farmers to connect with the resident wolves and to truly understand their lives and the events that can affect their behaviour.
For example, the emotional reaction and attitude is rather different if an anonymous wolf suddenly takes a sheep or if the farmer is aware that this wolf which took the sheep perhaps lost its family in a tragic event and is lonely, on its own, confused and unable to feed itself properly.
A deeper understanding of wolf pack life would minimize fear and prejudice.
The conflicts would no longer appear to have arisen without a cause.
A sense of causality, personal stories of the wolves and knowledge of wolf ecology would empower the local community through creating a connection and being able to foresee events.
Climatic or prey population variables might be included, as well.
Anything truly that is relevant to risk rates and that data is being gathered for anyway.
The system itself might be designed to facilitate (and perhaps standardize for research purposes) the wolf population monitoring data collection and even processing.
For example, in order to follow the age structure dynamics in the population, algorithms could be applied that calculate the most probable age distribution in a pack in the given year (the number of yearlings and two-year-olds based on known pup production data over the last years, known dispersal/mortality and winter counts) as well as the age of the breeders (in cases when the year in which the breeder assumed the breeding position is known).
It might be of assistance to the staff involved in monitoring and management but also to researchers later using the data in their studies.
Notes by Ieva 