Roads, forestry, and wolves interact to drive moose browsing behavior in Scandinavia

Abstract

As wild ungulate densities increase across Europe and North America, plant-herbivore interactions are increasingly important from ecological and economic perspectives. These interactions are particularly significant where agriculture and forestry occur and where intensive grazing and browsing by wild ungulates can result in economic losses to growing crops and trees. We studied plant-herbivore interactions in a moose (Alces alces)-dominant system where forestry is a primary economy, the primary and secondary road networks are extensive, and wolves (Canis lupus) are recolonizing. Wolves and humans use low-traffic, secondary roads, yet roadsides provide high-quality and quantity browse for moose. Foraging theory predicts that moose will respond to riskier landscapes by selecting habitats that reduce predation risk, sacrificing feeding time or food quality. As food becomes limiting, however, animals will accept higher predation risk in search of food. We predicted that road avoidance behavior would be strongest within wolf territories. In areas without wolves, moose should select roadsides for their high forage availability. To test these predictions, we measured moose browsing and counted pellet groups as a proxy for habitat use each spring in Norway and Sweden between 2008 and 2018, in areas with and without wolves and at different distances from primary and secondary roads. We used generalized linear mixed models to evaluate drivers of the probability of browsing occurrence and browsing pressure. We found that browsing occurrence increased closer to secondary roads but decreased closer to primary roads. We also found browsing patterns to vary among tree species. For Scots pine (Pinus sylvestris), the browsing occurrence was two times higher in young forests relative to non-young forests and decreased further from secondary roads. Wolf territory presence and probability had neutral or positive effect on browsing occurrence and pressure for all species. However, wolf territory presence had negative effects on browsing occurrence and pressure when interacting with secondary roads, young forest, or snow cover. We showed that roads can influence browsing patterns in Norway and Sweden. However, further research is needed, particularly in the face of continued infrastructure development in Scandinavia.

Effects of camera‐trap placement and number on detection of members of a mammalian assemblage

“…Several species had different detection probabilities at microsites that were defined by different features (forest roads, wildlife trails, or cliff sides), likely due to differences among species in their microsite selection. For example, moose often select forest roads for movement and foraging (Loosen et al 2021), whereas these might be avoided by red squirrels to reduce predation (Andrén and Delin 1994). We also found contrasting results across species of the effects of visibility in front of the CT on detection probabilities.…”

“…For example, moose often select forest roads for movement and foraging (Loosen et al. 2021), whereas these might be avoided by red squirrels to reduce predation (Andrén and Delin 1994). We also found contrasting results across species of the effects of visibility in front of the CT on detection probabilities.…”

Effective conservation decisions require models designed for purpose: a case study for boreal caribou in Ontario’s Ring of Fire

“…The original RSFs used road density as a proxy for other anthropogenic disturbances (e.g., harvest) and did not distinguish among road types (Hornseth and Rempel, 2016). Evidence from other locations and contexts suggests that road type and traffic volume is important for wildlife, and metrics that include this information may be more informative (D’Amico et al, 2016; Jaeger et al, 2005; Leblond et al, 2013; Loosen et al, 2021). Although projecting impacts in a unique and previously undisturbed region is inherently challenging, we are optimistic that more informative models can be developed.…”

The importance of evaluating standard monitoring methods: Observer bias and detection probabilities for moose pellet group surveys

“…As part of a long-term monitoring project [ 44 , 45 ], we have plots (n = 992) nested within quadrats at three sites, which were named Gravberget, Plassen, and Ljørdalen. Sites were on average 30 km apart (SD = 17.8).…”

“…Additionally, we assigned a forest maturity index (cutting class), field-layer composition [ 47 ], tree density, and dominant canopy species from a separate dataset, which was collected in 2018 [ 45 ]. These covariates should not change within one or two years unless the area was clearcut or thinned, which was indicated in the ‘forestry actions’ variable collected in 2019 and 2020 ( Table 1 ).…”