Modelling the effect of moose Alces alces population density and regional forest structure on the amount of damage in forest seedling stands

• Published in: Pest management science Vol. 77; no. 2; pp. 620 - 627
• Main Authors: Nikula, Ari, Matala, Juho, Hallikainen, Ville, Pusenius, Jyrki, Ihalainen, Antti, Kukko, Tuomas, Korhonen, Kari T
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.02.2021; Wiley Subscription Services, Inc
• Subjects: Alces alces; Animals; Bayes Theorem; Bayesian analysis; Damage; Deer; Dependent variables; Food; Food resources; Forest management; Forest resources; forestry; Forests; In Focus: s; Land area; Modelling; Moose; moose damage risk; Plantations; Population Density; Population levels; Seedlings; Variables; Wolves; population density; moose; forestry; Alces alces; moose damage risk
• ISSN: 1526-498X; 1526-4998
• DOI: 10.1002/ps.6081

BACKGROUND Moose (Alces alces L.) populations and moose damage in forests are debated in Nordic countries with dense moose populations. Moose populations and food resources vary greatly, both spatially and temporally, and reliable data covering both variables simultaneously at the same scale have seldom been available. We modelled the effect of moose population density and forest resources on the area of moose damage at regional scale, referring to moose management areas (MMA). Forest data and moose damage data originated from the Finnish National Forest Inventory, and the moose population data came from a Bayesian moose model. For modelling, average values of moose population, damage and forest variables were calculated for the periods 2004–2008 and 2009–2013 for each MMA. The MMAs were further classified into one of four larger geographical zones. The area of moose damage was used as a dependent variable, and the proportions of different types of forests and moose population densities per land area or area of seedling stands as explanatory variables. The relationships were modelled with a linear mixed‐effects model with an exponential spatial correlation structure. RESULTS The area of moose damage was best explained by total forest area, proportions of plantations and mature forests, and moose population density per land area or the proportion of plantations. There were differences among the biogeographical zones in how different variables explained the amount of damage. CONCLUSION The results provide tools for analyzing the regional effects of moose population density and the amount of food resources on the amount of moose damage. This information can be used in reconciling sustainable moose population levels and the amount of damage. We modelled the amount of moose damage with linear mixed effects models by using regional forest structure parameters. The model can be used as a tool in moose management. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.