Michael Hilton

Large-scale Gopher Tortoise translocations are a widespread practice to remove tortoises from imminent dangers of habitat loss for human development. However, translocation for explicit conservation goals remains an infrequently-utilized and untested tool to conserve this at-risk species. Given the effects of density on tortoise movement and population viability, we conducted a conservation effort to increase the population density of a low-density tortoise population on a public property in southern Alabama. Efforts included consolidating the resident tortoises from a fire-suppressed landscape into a temporary enclosure located in a high-quality restored sandhill. We also headstarted two cohorts of hatchlings from the site for one and two years. In total, 100 resident tortoises were consolidated into the enclosure and 98 headstarted juvenile tortoises were released into the nearby area into both soft- and hard-release conditions. Using radiotelemtry of a sub-population of consolidated adults tracked for two seasons (before and after the enclosure was removed), we found that site fidelity of the adults was 69% in the area enclosed by the pen and 93% in the wider-managed site. Adult home range area was not significantly different between the years that adult tortoises were enclosed in the pen and the year after the pen was removed. A subpopulation of two-year old headstarts was also radiotracked. Radiotracked headstarts had 100% site fidelity, regardless of release condition, and home ranges significantly decreased over the study period. Mortality was extremely low in all groups. We further discuss how this research may contribute to effective strategies for the demographic management of low-density tortoise populations, particularly in the species’ distributional periphery.

Recent technological advances have allowed wildlife ecologists to begin to understand the complexity of relationships among individuals within populations. Given gopher tortoises have high site fidelity, naturally exist in high population densities, and are long lived, their population social structures may be among the most complex within Reptilia. Prior studies have demonstrated that gopher tortoises form non-random interactions with each other, described as “cliques” in a southern Georgia population of tortoises. Herein, we tested for the presence of this complex social structure at Boyd Hill Nature Preserve in Pinellas County, FL, using camera traps placed at 12 active tortoise burrows and set to record every 5 seconds for an entire year during daylight hours. We found that cliques are not present within this population when we only considered social interactions as co-occurrence of two individuals. However, when we only considered interactions to be positive, namely burrow sharing between same- or opposite-sex pairs and burrow chasing between opposite-sex pairs, we found that cliques were significantly present. This study has implications for basic understanding of sociality in turtles and also conservation efforts for this intensively-managed species. 

Camera traps are increasingly being used as a research tool in behavioral ecology to observe animals in the field. One challenge of continuous data collection by camera traps is that the amount of data produced can be overwhelming to process manually. Machine learning techniques show promise for becoming important tools to meet this challenge in a time- and cost-effective way. Over the past year, we collected images of over 950 social interactions among gopher tortoises from twelve active tortoise burrows at Boyd Hill Nature Preserve in St. Petersburg, Florida. The individual tortoises in each interaction must be identified to comprehensively analyze this data. To automate the re-identification step, we developed a machine learning algorithm to distinguish between tortoises. It takes a large amount of data to train the machine learning algorithm to recognize patterns. Therefore, we describe how our training dataset was collected and created. We also recount how we trained a Siamese neural network to identify individual tortoises based on their carapace markings in a manner similar to how facial recognition is used on humans. This research demonstrates that machine learning has the potential to be a powerful tool to aid data processing and analysis in behavioral ecology. 

A new open-source image processing pipeline for analyzing camera trap time-lapse recordings is described. This pipeline includes machine learning models to assist human-in-the-loop video segmentation and animal re-identification. We present some performance results and observations on the utility of this pipeline after using it in a year-long project studying the spatial ecology and social behavior of the gopher tortoise.

Camera trap time-lapse recordings can collect vast amounts of data on wildlife in their natural habitat. Transforming these data into information useful to ecologists is a major challenge. Machine learning techniques show promise for becoming important tools to meet this challenge in a cost-effective way. Over the past year, we recorded 5-second interval time-lapse video of twelve active gopher tortoise burrows at Boyd Hill Nature Preserve in St. Petersburg, Florida, generating more than 100 Terabytes of data in the process. Herein, we describe a suite of open-source software tools we developed to manage the collection and analysis of these data, and present preliminary results on tortoise activity levels at this study site. The tools incorporate a convolutional neural network trained to detect gopher tortoises and to generate a draft video segmentation marking when tortoises are present. These tools allow a single human grader to review and refine the draft segmentations for a week’s worth of time-lapse recordings (11.5 hours of video if played back at standard speed) in under 3 hours.  This research demonstrates that the tools developed can facilitate future studies across research groups to assess key population features as well as to remotely monitor wildlife populations efficiently.

Large-scale Gopher Tortoise translocations are a widespread practice to remove tortoises from imminent dangers of habitat loss for human development. However, translocation for explicit conservation goals remains an infrequently-utilized and untested tool to conserve this at-risk species. Given the effects of density on tortoise movement and population viability, we conducted a conservation effort to increase the population density of a low-density tortoise population on a public property in southern Alabama. Efforts included consolidating the resident tortoises from a fire-suppressed landscape into a temporary enclosure located in a high-quality restored sandhill. We also headstarted two cohorts of hatchlings from the site for one and two years. In total, 100 resident tortoises were consolidated into the enclosure and 98 headstarted juvenile tortoises were released into the nearby area into both soft- and hard-release conditions. Using radiotelemtry of a sub-population of consolidated adults tracked for two seasons (before and after the enclosure was removed), we found that site fidelity of the adults was 69% in the area enclosed by the pen and 93% in the wider-managed site. Adult home range area was not significantly different between the years that adult tortoises were enclosed in the pen and the year after the pen was removed. A sub-population of two-year old headstarts was also radiotracked. Radiotracked headstarts had 100% site fidelity, regardless of release condition, and home ranges significantly decreased over the study period. Mortality was extremely low in all groups. We further discuss how this research may contribute to effective strategies for the demographic management of low-density tortoise populations, particularly in the species’ distributional periphery.