Modelling the effect of habitat composition and roads on the occurrence and number of moose damage at multiple scales

• Published in: Silva Fennica
• Main Authors: Ari Nikula, Vesa Nivala, Juho Matala, Kari Heliövaara
• Format: Article in Journal/Newspaper
• Language: English
• Published: Finnish Society of Forest Science 2019
• Subjects: Alces alces; forest damage; forest plantation; forestry; damage probability; habitat selection; habitat modelling; zero-inflated negative binomial distribution; SD1-669.5; Lapland
• Online Access: https://doi.org/10.14214/sf.9918; https://doaj.org/article/a66a76f8c8fa4f668bc8079bc36f4c42

We modelled the effect of habitat composition and roads on the number and occurrence of moose (Alces alces L.) damage in Ostrobothnia and Lapland using a zero-inflated count model. Models were developed for 1 km2, 25 km2 and 100 km2 landscapes consisting of equilateral rectangular grid cells. Count models predict the number of damage, i.e. the number of plantations and zero models the probability of a landscape being without damage for a given habitat composition. The number of moose damage in neighboring grid cells was a significant predictor in all models. The proportion of mature forest was the most frequent significant variable, and an increasing admixture of mature forests among plantations increased the number and occurrence of damage. The amount of all types of plantations was the second most common significant variable predicting increasing damage along with increasing amount of plantations. An increase in thinning forests as an admixture also increased damage in 1 km2 landscapes in both areas, whereas an increase in pine-dominated thinning forests in Lapland reduced the number of damage in 25 km2 landscapes. An increasing amount of inhabited areas in Ostrobothnia and the length of connecting roads in Lapland reduced the number of damage in 1 and 25 km2 landscapes. Differences in model variables between areas suggest that models of moose damage risk should be adjusted according to characteristics that are specific to the study area.