Data from: Mesopredators change temporal activity in response to a recolonizing apex predator ...
Apex predators can influence ecosystems through density and behaviorally mediated effects on herbivores and mesopredators. In many parts of the world, apex predators live in, or are returning to, landscapes that have been modified by people, so it is important to understand their ecological role in...
| Main Authors: | , , , , |
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| Format: | Dataset |
| Language: | English |
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Dryad
2019
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5061/dryad.60m1ps0 https://datadryad.org/stash/dataset/doi:10.5061/dryad.60m1ps0 |
| Summary: | Apex predators can influence ecosystems through density and behaviorally mediated effects on herbivores and mesopredators. In many parts of the world, apex predators live in, or are returning to, landscapes that have been modified by people, so it is important to understand their ecological role in anthropogenic landscapes. We used motion-activated game cameras to compare the activity patterns of humans and two mesopredators, coyotes (Canis latrans) and bobcats (Lynx rufus), in areas with and without an apex predator, the gray wolf (Canis lupus), in a multi-use landscape of the northwestern United States. In areas with wolves there was a significant increase in temporal niche overlap between the mesopredators owing to higher levels of coyote activity at all time periods of the day. Temporal overlap between mesopredators and humans also increased significantly in the presence of wolves. Coyotes exposed to wolves increased their activity during dawn, day, and dusk hours. The increase in coyote activity was ... : Shores_2019_code_Figure_4_Table_2_Mesopredators change temporal activity patterns in response to an apex predator_GLMMAnnotated R code to reproduce species activity models with free R statistical software andR package glmmTMBShores_2019_data_Figure_4_Table_2_Mesopredator and apex predator activity records from game cameras_GLMMData of recorded species observations from game cameras. Description of columns in .csv file: Location = unique identifier corresponding to each camera location; study_area: Categorical variable identifying 1 of 4 study areas, N = Nc’icn (wolf area), S = Strawberry (wolf area), B = Bonaparte (non-wolf area), A = Aeneas (non-wolf area); date: unique identifier corresponding to dates starting with 0 = May 1, 2013 and 1095 = April 30, 2016; time_cat: time category of either night, dawn, day or dusk; season: winter or summer; bobcat/coyote/human/wolf_activity: number of photographs of the identified species indexed by time category, date, and camera sampling location; day: fraction of a ... |
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