Michael Hilton

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.