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Storm Miller

Publications and source records attributed to Storm Miller.

3 recordsLinked to original sources

Predator hunting modes induce varied selection pressures on prey personality

Consistent individual differences in animal behavior—ie, personalities—have been documented in many taxa to have important fitness consequences in the context of predator-prey interactions. Multiple mechanisms have been suggested to maintain variation in prey personality traits, including life-history tradeoffs and divergent selection pressures from predators with different hunting strategies. However, empirical evidence for these hypotheses has been limited by a lack of data originating from complex multipredator systems in situ. To address this gap and evaluate these 2 mechanisms, we collected hispid cotton rats (Sigmodon hispidus) in the Florida Everglades and quantified 3 behavioral traits in the field. After releasing rats at their point of capture, we monitored their survival in their natural habitat where they faced threats from a diversity of ambush (snakes) and active predators (mammals; birds). Using Cox proportional hazards regression models and competing risks models, we assessed the effect of boldness, activity, and docility on rodent predation risk. Contrary to conventional predictions that bolder and more active individuals would face higher predation risk due to life-history tradeoffs, we found that the impact of these traits on prey survival varied with the predators' hunting modes: shyer, more active individuals were more likely to be killed by active predators, whereas less active rats appeared more likely to be killed by ambush predators. Our study supports the hypothesis that a diversity of predator hunting strategies may help maintain trait variation in prey populations.

Florida↗

Mammalian scent lures fail to increase detections of invasive Burmese pythons (Python bivittatus)

Burmese pythons ( Python bivittatus ) are large constricting snakes native to Southeast Asia that have invaded the Greater Everglades Ecosystem in South Florida, USA. Pythons have caused precipitous declines in native mammals and are exceedingly difficult to detect using traditional methods such as scout snakes, detection dogs, and visual surveys. Live mammal lures have previously been used to attract pythons, with rabbits outperforming rodents in increasing detection. While live mammal lures can increase python detection and identify hotspots of python activity, ensuring animal welfare and logistical challenges limit their utility. As part of this study, field experiments were conducted to determine if mammalian lures, derived from rabbits (feces, urine, and hair) could replace live mammals and increase the detection of pythons. We ran trials for 84 days from June to September 2022 for 21 paired plots across three study sites in the Greater Everglades. We monitored two groups, the treatment (i.e., rabbit scent) and control (i.e., soil) with camera traps on a 1-minute time lapse. We detected 11 pythons during our study, but there was no difference between controls with soil ( n = 7) and treatments (n = 4). However, we did find that scent lures increased native snake detection. This pattern was best explained by an increased number of rodents at the scent lures. Our experiment indicates that mammalian scent lures alone are insufficient to attract pythons. Since live mammals can attract pythons, but scent lures cannot, future studies could examine if a multi-faceted lure combining several stimuli (i.e., heat, visual, movement) might increase python detection.

Florida↗

Object detection-assisted workflow facilitates cryptic snake monitoring

Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.

Florida↗