Michael Hilton

Studies concerning the social behaviours of non-avian reptiles have generally lagged behind other taxa, yet many reptiles are among the most globally threatened animal groups, and their behaviours are key to conservation successes. Herein, we utilized a captive-reared cohort of headstart Gopher Tortoises (Gopherus polyphemus) to test if these animals first discern soil type by social familiarity and/or tortoise exposure and then we tested how social familiarity and sibling status affected social behaviours between individuals. We found that headstart Gopher Tortoises preferentially chose familiar soil over soil that had never been in contact with a Gopher Tortoise, but they also preferred soil that had been in contact with non-familiar individuals over familiar soil. Tortoises displayed the social behaviour of sniffing disproportionately to non-familiar individuals, regardless of sibling status, over familiar individuals. Other social behaviours of nipping, chasing, headbobbing, and colliding were performed independently of social familiarity or sibling status. Taken together, this set of experiments demonstrates that Gopher Tortoises have a high degree of social nuance that is built upon familiarity, and these results could have direct effects on how to optimize headstarting protocols for restoring wild populations.

Studies concerning the social behaviours of non-avian reptiles have generally lagged behind other taxa, yet many reptiles are among the most globally threatened animal groups, and their behaviours are key to conservation successes. Herein, we utilized a captive-reared cohort of headstart Gopher Tortoises (Gopherus polyphemus) to test if these animals first discern soil type by social familiarity and/or tortoise exposure and then we tested how social familiarity and sibling status affected social behaviours between individuals. We found that headstart Gopher Tortoises preferentially chose familiar soil over soil that had never been in contact with a Gopher Tortoise, but they also preferred soil that had been in contact with non-familiar individuals over familiar soil. Tortoises displayed the social behaviour of sniffing disproportionately to non-familiar individuals, regardless of sibling status, over familiar individuals. Other social behaviours of nipping, chasing, headbobbing, and colliding were performed independently of social familiarity or sibling status. Taken together, this set of experiments demonstrates that Gopher Tortoises have a high degree of social nuance that is built upon familiarity, and these results could have direct effects on how to optimize headstarting protocols for restoring wild populations.
•Using a cohort of Gopher Tortoises temporarily raised in captivity for conservation, we found that individual tortoises preferentially chose to be on soil from non-familiar tortoises over their own soil and also tortoises avoided soil that had not come into contact with any Gopher Tortoise.•By separating clutch-mates at hatching, we isolated effects of social familiarity from maternal sibling status and found that Gopher Tortoises disproportionately displayed the social behaviour of sniffing non-familiar individuals, regardless of sibling status.•We found that non-familiar sibling pairs did not display any social behaviours differently than non-familiar non-sibling pairs, suggesting that sibling status is not a key social metric that assorts wild Gopher Tortoises into social groups.•Together, these results suggest that Gopher tortoises are both spatially and socially curious yet cautious in both navigating new environments (e.g., novel soil) and interacting with new individuals (e.g., non-familiar members of the same cohort).

Social network analyses are sparse, despite having great potential to illuminate intricate details of wildlife behavioral ecology and to inform basic conservation practices. Using social interactions recorded during 1 year of 5-second interval photography, we conducted social network analyses of Gopher Tortoises (Gopherus polyphemus). G. polyphemus are charismatic and declining mid-sized tortoises that are habitat specialists endemic to the southeastern United States. We also conducted a simultaneous radio-telemetric study of tortoises contained within our study population to ascertain whether home range location is consistent with membership in distinct tortoise social network communities. We found strong statistical support for the presence of nonrandom social networks that were derived from male-female mating relationships. The most parsimonious social network included two distinct “cliques” that were spatially segregated. Each clique contained a similar number of males and females. Understanding this basic aspect of tortoise behavior should be key in basic population biology, not only of turtles but also other reptiles. Our results should influence protocols for successful conservation of this keystone species.

The overlap between spatial and physiological ecology is generally understudied, yet both fields are fundamentally related in assessing how individuals balance limited resources. Herein, we quantified the relationships between spatial ecology using two parameters of home range (annual home range area and number of burrows used in one year) and four measures of physiology that integrate stress and immunity (baseline plasma corticosterone concentration [CORT], plasma lactate concentration, heterophil:lymphocyte ratio [H:L], and bactericidal ability [BA]) in a wild free-ranging population of the gopher tortoise (Gopherus polyphemus) to test the hypothesis that space-usage is correlated with physiological state. We also used structural equation modeling (SEM) to test for causative relationships between the spatial and physiological parameters. We predicted that larger home ranges would be negatively correlated to traditional biomarkers of stress and positively correlated with immunity, consistent with our hypothesis that home ranges are determined based on individual condition. Males had larger home ranges, used more burrows, and higher baseline CORT than females. We found significant negative correlations between lactate and home range (r = -0.456, df = 21, P = 0.029). CORT was negatively correlated with number of burrows used in both sexes (F = 7.322, df = 2,20, P = 0.003, Adjusted R² = 0.383). No correlations were observed between space use and BA or, notably, H:L. SEM models suggested that variation in number of burrows used was a result of variation in baseline corticosterone. The lack of a relationship between H:L and home range suggests that home range differences are not associated with differences in chronic stress, despite the pattern between baseline CORT and number of burrows used. Rather, this study indicates that animals balance tradeoffs in energetics, likely by way of baseline corticosteroid, in such a way as to maintain function across continuously variable home range strategies.

Camera trap time-lapse recordings can collect vast amounts of data on wildlife in their natural settings. Transforming these data into information useful to ecologists is a major challenge. Machine learning techniques show promise for becoming important tools in the cost-effective analysis of camera trap data, but only if they become readily available to researchers without requiring advanced computing skills and resources. We present a new suite of software tools that reduce the amount of human effort needed to segment time-lapse, camera trap recordings in preparation for analysis. The tools incorporate a convolutional neural network trained to detect a focal species and to generate a draft video segmentation indicating the ranges of time when the focal species is present. We evaluated the utility of our neural network by comparing manual and automatic segmentations of 64 time-lapse recordings of gopher tortoise (Gopherus polyphemus) burrows, recorded in Pinellas County, Florida, USA between 25 November 2020 and 30 November 2020. The neural network correctly found 130 of the 145 segments containing tortoises (89.7%), whereas student graders found 135 segments (93.1%). A year of experience using the new software suite in an ongoing study of gopher tortoises deploying 12 camera traps indicates one person, assisted by machine learning algorithms, can segment a week's worth of time-lapse recordings-11.5 hours of standard-speed video-in under 3 hours. We concluded that the use of machine learning algorithms is practical and allows researchers to process large volumes of time-lapse data with minimal human effort.