Data from: Prey encounters and spatial memory influence use of foraging patches in a marine central place forager
Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterise how marine predators integrate memory with recent prey encounters to ad...
| Main Authors: | , , , , , , |
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| Format: | Dataset |
| Language: | unknown |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://zenodo.org/record/6464063 https://doi.org/10.5061/dryad.6q573n601 |
| Summary: | Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterise how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (Phoca vitulina) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' Area Restricted Search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas, and archived accelerometry data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov Models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine scale movement and this ability should be accounted for in movement models. The data consist of 18 files and include the datasets and data packages required to repeat the analyses. Files from 1 to 7 represent raw data necessary to start the analysis. Within the output folder, files from 8 to 18 are processed outputs generated from the R codes. Outputs are provided so that all the R codes can be run starting from any point.The R codes to be used with this datasets can be found here. A full description of each datasets is provided in the Readme.txt file: 1. pv64-2017_seal_summary.txt 2. pv64-2017_trip_summaries.txt 3. pv64-2017_gps_data_with_haulout_&_trip_info.txt 4. pv64-2017_dive.txt 5. Accelerometer_data.zip 6. Moray_Firth_1km_grid_shapefile.zip 7. Coastline_UTM30.zip Within the ... |
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