Data from: Estimating occupancy using spatially and temporally replicated snow surveys ...
Occupancy modelling is increasingly used to monitor changes in the spatial distribution of rare and threatened species. Occupancy methods have traditionally relied on temporally replicated surveys to estimate detection probability. Recently, occupancy models with spatial replication have been used t...
| Main Authors: | , , , , |
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| Format: | Dataset |
| Language: | English |
| Published: |
Dryad
2015
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5061/dryad.v4p20 https://datadryad.org/stash/dataset/doi:10.5061/dryad.v4p20 |
| Summary: | Occupancy modelling is increasingly used to monitor changes in the spatial distribution of rare and threatened species. Occupancy methods have traditionally relied on temporally replicated surveys to estimate detection probability. Recently, occupancy models with spatial replication have been used to estimate detection probabilities over large geographic areas that are difficult to survey repeatedly. We developed occupancy models that combine spatially and temporally replicated data and applied them to snow-tracking surveys of six species including wolverine Gulo gulo and Canadian lynx Lynx canadensis. We surveyed thirty-nine 100 km2 cells and used one km trail segments within cells as spatial replicates. We surveyed 56% of the cells once and 44% of the cells between two and 14 times resulting in a total of 872 km surveyed. We compared four occupancy models that incorporated spatial correlation in detection probability and hierarchically estimated occupancy at two spatial scales: cell occupancy and segment ... : Banff_Occupancy_2012Banff_2012.csv ... |
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