Beavers (Castor canadensis) can be a significant prey item for wolves (Canis lupus) in boreal ecosystems due to their abundance and vulnerability on land. How wolves hunt beavers in these systems is largely unknown, however, because observing predation is challenging. We inferred how wolves hunt beavers by identifying kill sites using clusters of locations from GPS-collared wolves in Voyageurs National Park, Minnesota. We identified 22 sites where wolves from 4 different packs killed beavers. We classified these kill sites into 8 categories based on the beaver-habitat type near which each kill occurred. Seasonal variation existed in types of kill sites as 7 of 12 (58%) kills in the spring occurred at sites below dams and on shorelines, and 8 of 10 (80%) kills in the fall occurred near feeding trails and canals. From these kill sites we deduced that the typical hunting strategy has 3 components: 1) waiting near areas of high beaver use (e.g., feeding trails) until a beaver comes near shore or ashore, 2) using vegetation, the dam, or other habitat features for concealment, and 3) immediately attacking the beaver, or ambushing the beaver by cutting off access to water. By identifying kill sites and inferring hunting behavior we have provided the most complete description available of how and where wolves hunt and kill beavers.
Wolf (Canis lupus L., 1758) diet is commonly estimated via scat analysis. Several researchers have concluded that scat collection method can bias diet estimates, but none of these studies properly accounted for interpack, age class, and temporal variability, all of which could bias diet estimates. We tested whether different scat collection methods yielded different wolf diet estimates after accounting for these other potential biases. We collected scats (n = 2406) monthly from four packs via three scat collection methods (at home sites, at clusters of GPS locations, and opportunistically) in and adjacent to Voyageurs National Park, Minnesota, USA, during April–October 2015. Diet estimates were not affected by scat collection method but did vary temporally, among packs, and by age class. To more accurately estimate wolf population diets, researchers should collect 10–20 adult scats/pack per month from home sites and (or) opportunistically from packs that are representative of the population of interest. Doing so will minimize the potential biases associated with temporal, interpack, and age-class variability.
Investigation of bird migration has often highlighted the importance of external factors in determining timing of migration. However, little distinction has been made between short‐ and long‐distance migrants and between local and flight birds (passage migrants) in describing migration chronology. In addition, measures of food abundance as a proximate factor influencing timing of migration are lacking in studies of migration chronology. To address the relationship between environmental variables and timing of migration, we quantified the relative importance of proximate external factors on migration chronology of local American woodcock (Scolopax minor), a short distance migrant, using event‐time analysis methods (survival analysis). We captured 1,094 woodcock local to our study sites in Michigan, Minnesota, and Wisconsin (USA) during autumn 2002–2004 and documented 786 departure dates for these birds. Photoperiod appeared to provide an initial proximate cue for timing of departure. Moon phase was important in modifying timing of departure, which may serve as a navigational aid in piloting and possibly orientation. Local synoptic weather variables also contributed to timing of departure by changing the rate of departure from our study sites. We found no evidence that food availability influenced timing of woodcock departure. Our results suggest that woodcock use a conservative photoperiod‐controlled strategy with proximate modifiers for timing of migration rather than relying on abundance of their primary food, earthworms. Managing harvest pressure on local birds by adjusting season lengths may be an effective management tool with consistent migration patterns from year to year based on photoperiod.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.