The importance of fieldwork over predictive modeling in quantifying predation events of carnivores marked with GPS technology

1. What they did

Since the early 2000s, researchers have estimated how often large carnivores kill (kill rates) by fitting animals with Global Positioning System (GPS) collars and looking for “GPS clusters”, places where an animal’s locations bunch together. Field teams visit some of these clusters to see whether there was a kill, then build a statistical model to guess which of the unvisited clusters were kills. This saves time and money, but it can go wrong when a carnivore eats prey of different sizes or is pushed off a kill early. The authors tested how reliable this shortcut is.

They studied pumas in three places with different prey: Chilean Patagonia (guanaco, huemul deer and domestic sheep), western Colorado (mule deer and elk) and northern California (black-tailed deer). Pumas were caught mainly with hounds, and also with box traps and foot snares, and fitted with GPS collars that took a location every 30 minutes to 2 hours. The team tried to visit every cluster in the field, searching a 30 m circle around each location for prey remains, and classed each cluster as a kill or not.

Using this nearly complete field record as the “truth”, they then pretended they had done less fieldwork in two ways:

• Selectively: leaving out short clusters (under 12 hours, or under 24 hours), as many studies do.
• Randomly: leaving out a random share of clusters, repeated 10,000 times.

For each version they built a model, predicted the number of kills, and compared it with what was found on the ground. They also compared the variety of prey in the pumas’ diets.

2. What they found

Fieldwork

• They captured 26 pumas and used 24. They visited 1,896 clusters; after removing scavenging events, clusters not visited within a year, and kills of prey under 5 kg, 1,752 clusters remained (California 355, Colorado 1,036, Patagonia 361).
• On average they visited 73 clusters per puma, about 38 days after the puma had left.
• 973 prey were found. After small prey were removed, diets were 88% guanaco, 9% sheep and 3% huemul in Patagonia; 80% mule deer and elk and 20% medium-sized prey in Colorado; and 96% black-tailed deer in California.

The models

• In all three areas, the time a puma spent at a cluster was the best and only reliable sign that it was a kill.
• Models built on all the visited clusters came closest to the true number of kills.
• Leaving out short clusters made the models look better by the usual tests, yet they gave wrong answers. In California and Colorado they underestimated kills. In Patagonia, leaving out clusters under 12 hours greatly underestimated kills, and leaving out clusters under 24 hours greatly overestimated them.
• In Patagonia, pumas spent much less time at sheep kills (about 9 hours) than at guanaco kills (about 41 hours), so models missed 72% of sheep kills. Guanaco kills in open grassland were also often abandoned quickly to Andean condors.
• In Colorado and California, black bears often pushed pumas off their kills, shortening the time spent there.
• Randomly leaving out clusters mostly made the estimates less precise. Visiting only 75% of clusters tripled the spread of estimates in California and doubled it in Patagonia.

Diet

Leaving out short clusters made pumas’ diets look less varied and overstated large prey. The effect was biggest in California, where diets included the most small-bodied prey: diversity dropped by 45% (under 12 hours removed) and 57% (under 24 hours removed). In Colorado it dropped by 9% and 16%.

3. What they concluded

• Predictive models of GPS clusters should be treated with caution. How long a carnivore stays at a kill depends on prey size, kleptoparasitism (food stealing by competitors), fear of people or other animals, and local ecology, and only fieldwork captures this.
• Skipping short clusters skews the data towards kills and hides small prey and quick kills.
• Until models improve, the authors recommend:
• Sample intensively for set periods (at least 60 days), with cluster rules suited to the species, and visit clusters from all times of day and night.
• Sample different individuals and seasons.
• When models are used, publish the raw data, including how many clusters were and were not visited.
• Also visit a random set of single GPS locations, or use other methods such as snow tracking along random sections of trail, to measure how many kills cluster methods miss.
• For now, cluster models may be best used to decide which clusters to visit in the field.

4. How tracking was or was not used

Tracks and signs identification was central to the fieldwork. At each GPS cluster, field teams searched for prey remains and identified prey from hair, skin, rumen contents and bone fragments. They read how the carcass lay and which parts had been eaten, and looked for the presence and position of bite marks to decide whether the puma had killed or scavenged the prey. Clusters without a kill were classed as non-predation events, such as beds. No trailing by people is reported (pumas were treed by hounds for capture). The authors suggest snow tracking along random sections of trail as a way to check how many kills cluster methods miss.

5. Tracker or observer certification

Certified. The paper states that all personnel doing field investigations were certified through CyberTracker to ensure reliable and standardized prey identification, citing Evans et al. (2009). The level of certification is not given. The authors also acknowledge that even well-trained observers can miss prey remains.

6. Other technologies, skills or innovations

• Argos and Iridium GPS collars (Lotek 7000SAW and Lotek IridiumTrack M), sending locations by satellite.
• Clusters plotted in ArcGIS or Google Earth, then loaded onto handheld GPS units for field searches.
• The authors point to accelerometer or activity data from modern collars as a promising way to tell feeding animals (moving their heads) from resting ones.

7. Implications for conservation (from the paper)

• Kill rates and prey selection feed into understanding predator and prey dynamics and wildlife management, so biased estimates can mislead decisions. Inaccurate predictions can also bias kill rates for rare or threatened prey.
• Capturing and handling carnivores carries risk, so researchers should gather as much as possible from every collared animal rather than handle more animals than needed.
• The authors cite a case where a study based on modelled kill rates and a later diet study of the same pumas reached opposite conclusions about kills near housing.