Dynamic energy landscapes of predators and the implications for modifying prey risk
Data files
Nov 15, 2023 version files 178.32 KB
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Grey_reef_shark_depth_data.csv
176.38 KB
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README.md
1.94 KB
Abstract
Landscapes of fear describe a spatial representation of an animal's perceived risk of predation and the associated foraging costs, while energy landscapes describe the spatial representation of their energetic cost of moving and foraging. Fear landscapes are often dynamic and change based on predator presence and behavior, and variation in abiotic conditions that modify risk. Energy landscapes are also dynamic and can change across diel, seasonal, and climatic timescales based on variability in temperature, snowfall, wind/current speeds etc.
Recently, it was suggested that fear and energy landscapes should be integrated. In this paradigm, the interaction between the landscapes relates to prey being forced into areas of the energy landscape they would avoid if risk were not a factor. However, dynamic energy landscapes experienced by predators must also be considered since they can affect their ability to forage, irrespective of variation in prey behavior. We propose an additional component to the fear and dynamic energy landscape paradigm that integrates landscapes of both prey and predators, where predator foraging behavior is modulated by changes in their energyscape.
Specifically, we integrate the predators' energy landscape into foraging theory that predicts prey patch-leaving decisions under the threat of predation. We predict that as a predator's energetic cost of foraging increases in a habitat, then the prey's foraging costs of predation and patch quitting harvest rate will decrease. Prey may also decrease their vigilance in response to increased energetic foraging cost for predators, which will lower prey-giving-up densities.
We then provide examples in terrestrial, aerial and marine ecosystems where we might expect to see these effects. These include birds, sharks which use updrafts that vary based on wind and current speeds, tidal state, or temperature and terrestrial predators (e.g. wolves) whose landscapes vary seasonally with snow depth or ice cover which may influence their foraging success and even diet selection.
A predator perspective is critical to considering the combination of these landscapes and their ecological consequences. Dynamic predator energy landscapes could add a spatiotemporal component to risk effects which may cascade through food webs.
README: Dynamic energy landscapes of predators and the implications for modifying prey risk
https://doi.org/10.5061/dryad.7sqv9s4zv
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This is an acoustic telemetry dataset from a single grey reef shark (Carcharhinus amblyrhinchos) tagged in the south channel of Fakarava atoll, French Polynesia. The shark has the tag internally implanted and data from the transmitter was detected by an array of acoustic listening stations (VR2W, Innovaseas) located in the channel. We downloaded all the receivers after one year, so data is over a similar time frame. The transmitter also has a pressure sensor so it measures the swimming depth of the shark as well as the time, date and location of the animal when detected. Our main purpose here was to compare the swimming depth of the shark at night, throughout the year, but separated by tidal state (incoming and outgoing tides).
Description of the data and file structure
The data file is for one shark over a year and shows the time and date when the animal was detected, the receiver it was detected on, and its swimming depth. The transmitter sends out a signal at a semi-randomized delay of 100-160 seconds between transmissions. Note that if a shark was not within range of a receiver (approximate range is 50 m), then we don't have any data on the animal's location. Hence these data represent detections on receivers of known location, not the actual movement of the animals.
In the spread sheet you can see the transmitter number (we only used data from one animal so this doesn't matter much), and the number of the receiver ('receiver') where it was detected. The latitude and longitude of the receiver are given as well as the date/time of detection. The swimming depth in meters is also given. Detections have already been characterized based on time of day (day/night) and tidal state (incoming/outgoing) as well as moon phase.
Methods
The grey reef shark was caught and had an acoustic transmitter surgically implanted into the body cavity. The transmitter sent a signal which could be detected and stored by underwater receivers (Innovaseas, VR2W), anytime the shark was within range. The transmitter included a pressure sensor and recorded the shark depth which was also transmitted. Data includes the transmitter number, date/time of detection, shark swimming depth, and receiver number where the shark was detected.