Investigating ecological questions at the scale of individual organisms is necessary to understand and predict the biological consequences of environmental conditions. For small organisms this can be challenging because we need tools with the appropriate accuracy and resolution to record and quantify their ecological interactions. Unfortunately, many of our existing tools are only appropriate for medium to large organisms or those that are wide ranging, inhibiting our ability to investigate the ecology of small organisms at fine scales.
In Chapter 1, I tested a novel workflow for recording animal movements at very fine spatial and temporal scales. The workflow combined direct observation and the mapping of locations onto high-resolution uncrewed aerial vehicle (UAV) imagery loaded on hand-held digital devices. Observers identified landscape features they recognized in the imagery and estimated positions relative to those features. I found this approach was approximately twice as accurate as consumer-grade GPS devices with a mean and median error of 0.75 m and 0.30 m, respectively. I also found that performance varied across landscape features, with accuracy highest in areas that had more visual landmarks for observers to use as reference points. In addition to sub-meter accuracy, this method was cost-effective and practical, requiring no bulky equipment and allowing observers to easily record locations away from their own location. While this workflow could be used to record locations in a variety of situations, it will be most cost-effective when also using high-resolution environmental data from a UAV.
In Chapter 2, I used the workflow described in Chapter 1 to investigate blunt-nosed leopard lizard (Gambelia sila) thermoregulation at fine-scales. Recent research has suggested that the spatial distribution of temperatures is important to consider for ectotherm thermoregulation, but this work has been limited to simple artificial environments. My goal was to investigate this idea in a complex natural system for the first time. I tracked lizard movement and body temperatures at a desert site from May to July 2021. I used machine learning to combine high-resolution environmental data from a UAV with microclimate temperature data to create a model of the spatial distribution of environmental temperatures over time. I found that including information about the spatial distribution of temperatures improved the models of lizard thermoregulatory accuracy and movement rate. Because these response variables are important aspects of ectotherm energetics, this suggests that the spatial distribution of temperatures may be an important, but often overlooked, component of habitat quality. Going forward, identifying better methods to quantify the spatial distribution of temperatures would provide insights into the specific responses of ectotherms to different spatial distributions.
In this work I used recent technological advances in UAVs to investigate ecological questions at the scale of a small organism. The methods developed here provide insights into the importance of the spatial distribution of temperatures for a small ectotherm. Further efforts to develop, test and utilize tools for fine-scale ecological research will advance our ability to understand species’ interactions with current conditions and predict their responses to future changes.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4188 |
| Date | 01 December 2022 |
| Creators | Axsom, Ian |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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