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Intrinsic traits, social context, local environment, and home range size and fidelity data from a field study of sleepy lizards, 2009 - 2017


Payne, Eric et al. (2022), Intrinsic traits, social context, local environment, and home range size and fidelity data from a field study of sleepy lizards, 2009 - 2017, Dryad, Dataset,


Home ranges, the region within which animals interact with their environment, constitute a fundamental aspect of their ecology. Home range (HR) sizes and locations commonly reflect costs and benefits associated with diverse social, biotic and abiotic factors. Less is known, however, about how these factors affect intra-specific variation in HR size or fidelity (the individual’s tendency to maintain the same HR location over time), or if variation in these features emerge from consistent differences among individuals or among the sites they occupy. To address this gap we used an extensive GPS-tracking dataset of a long-lived lizard, the sleepy lizard (Tiliqua rugosa) that included repeated observations of multiple individuals across years. We tested how three categories of predictors: 1) lizard characteristics (sex, aggressiveness, and parasitic tick counts), 2) environmental characteristics (precipitation, food, and refuge quality), and 3) social conditions (conspecific overlap and number of neighbors) affected HR size and fidelity. We found that individuals differed consistently in the size and fidelity of annual HRs (with repeatability of 0.58 and 0.33, respectively), and that all three categories of predictors affected both HR size and fidelity. For example, HRs were smaller in areas with more food and males had larger HRs than females. In addition, more aggressive lizards tended to have larger HRs. Conspecific overlap and number of individuals that a lizard interacted with (social network degree) had an interactive effect on HR size where individuals whose HRs overlapped more with neighbors had larger HRs, and this effect was particularly strong for individuals that interacted with more neighbors. HR fidelity declined over time (HR locations drifted from year to year), but individuals differed consistently in this rate of drift. The fact that HR size was consistent despite drifting locations suggests that lizard HRs reflect individual traits (e.g., habitat choice criteria that differ among individuals), rather than simple heterogeneity among sites. Overall, these findings demonstrate 1) both strong, long-term, within-individual consistency and between-individual differences in space use, and 2) combined effects of individual traits, social conditions, and environmental characteristics on animal HRs, with implications for diverse ecological processes.


The following description is from Payne et al. (Ecological Monographs, 2022) but modified to focus on data acquistion and derivation. See the paper for a more detailed description of the statistical analyses that we performed with our metrics (e.g., home range size and fidelity repeatability, predictors of home range size, and null model randomizations for evaluating effects of certain predictors).

Sleepy lizard life history and field site information

Sleepy lizards are large-bodied (adults are 400-950 g, snout-vent length 25-35 cm), long-lived (up to 50 years), and socially monogamous skinks (Bull 1995, Bull et al. 1998, 2017). At our field site, they are primarily active from September to December (Bull 1987). Overnight and during periods of daytime heat stress, they shelter in shaded refugia (usually large shrubs, logs, or burrows) (Kerr et al. 2003). Past studies suggest that sleepy lizards exhibit some HR site fidelity (Bull and Freake 1999), with males tending to have larger HRs than females (Spiegel et al. 2018). Conspecific interactions may involve both direct (e.g., fights) and indirect (e.g., scent marking) interactions (Bull and Lindle 2002, Leu et al. 2016, Spiegel et al. 2016, 2018).

Our field site was an ~ 1.2 km2 area near Bundey Bore Station (33.888240° S, 139.310718° E), South Australia. The broader region has a semi-arid Mediterranean climate, and the local site is dominated by chenopod shrubs (e.g., Maireana and Atriplex spp.), with various annual plants (e.g., Ward’s weed, Carrichtera annua, which is a preferred food item) growing between and under these shrubs. In most years, food is abundant earlier in the spring when conditions are relatively cool and wet, and much less abundant later when conditions are hotter and drier. The site also lies within the parapatric boundary between two lizard-specific hard tick species, Bothriocroton hydrosauri and Amblyomma limbatum, and lizards in the site harbor both (Godfrey and Gardner 2017, Norval and Gardner 2020).

Tracking data and home range metrics

As part of a long-term monitoring study, we collected information on adult lizard movement during their active season from 2009 through 2017, excluding 2012. In 2009 through 2014, GPS units (Technosmart Gipsy 4; horizontal precision +/- 6 m (Leu et al. 2010)) took one GPS fix per 10 minutes, while in 2015 through 2017, GPS units took one fix per two minutes. We therefore subsampled the 2015 through 2017 data to follow the same schedule as the earlier years. We removed GPS errors according to fix accuracy (e.g., horizontal dilution greater than three), using an algorithm modified from Bjorneraas (2010) – which identifies errors via displacement, speed, and turning angle – and with manual inspection of GPS tracks for obvious errors. Using these GPS data, we determined lizards’ home range (HR) size and fidelity. This Dryad dataset contains these derived home range products, rather than the underlying GPS coordinates.

To calculate HR size, we first approximated lizard HRs by calculating utilization distributions for each lizard for each year (Van Winkle 1975) using the R package adehabitatHR (Calenge 2006). Specifically, we used kernel density estimation with a bivariate normal kernel, an ad hoc smoothing method (“href”), grid set to 500, and extent set to one. From these utilization distributions, extracted lizard HR size in hectares, at both the 50% and 95% utilization distributions.

To measure HR fidelity, we calculated two metrics. The first was an HR overlap metric, based on the HR utilization distributions mentioned above. We used the utilization distribution overlap index (UDOI), also calculated using adehabitatHR, to evaluate the extent to which an individual’s utilization distribution overlapped with itself across years (Fieberg and Kochanny 2005). Hereafter, we refer to this index as ‘HR fidelity.’ An HR fidelity value of zero means no overlap (no fidelity), a value of one implies perfect overlap between two uniform utilization distributions (i.e., if an animal uses its space uniformly, and the animal uses that same space in two years, then the HR fidelity for that home range in those two years would be one), and values greater than one indicate overlap between non-uniform utilization distributions. The exact value will depend on the nonuniformity of the utilization distribution. The HR fidelity dataset included all year-year dyads for a focal lizard, such that if a lizard was observed in 2011, 2013, and 2014, then this lizard would have a HR fidelity value for 2011/2013, 2013/2014, and 2011/2014, and the time differences for these HR fidelities would correspond to 2, 1 and 3 years.

Complementing UDOI, a simple indicator of relative HR fidelity is binomial presence or absence. Assuming that an individual was observed in the site for a year (e.g., 2009), we asked whether that individual was ever observed in the site again. If the individual was ever observed again in the future (e.g., 2014), even if it was missing for intervening years (missing in 2010 through 2013), then this individual would have a binomial success for the focal year (2009). Because adult sleepy lizards have high annual survival (~90% or higher) and longevity (up to 50 years, Bull 1995, Bull et al. 2017), we assumed that an animal’s disappearance from our study site was more likely due to dispersal (i.e., low HR fidelity) than death. Further, we assumed that lizards observed on our site across non-consecutive years (e.g., 2014, 2015, and 2017) were present in intervening years. Lizards often exhibited high site fidelity across non-consecutive years (personal observation). Because sleepy lizards were often inactive in refuge and cryptically colored (i.e., they can be on site but unseen), rather than assume that lizards left the site in their missing years, and then returned in a future year, we assumed that field crews simply missed these individuals.

Predictor variables

In analyzing home range size and fidelity, we considered several explanatory variables. We divided these variables into lizard trait-, social pressure-, and habitat- related categories. Lizard intrinsic variables were tick counts, mass, and aggressiveness. Social pressure variables were metrics of lizards’ social network and HR overlap. Habitat variables assessed environmental features of the field site.

For lizard-intrinsic variables, we recaptured lizards periodically throughout the field season using radio tracking. To assess lizards’ tick counts (of both species) and mass, we recaptured lizards approximately every two weeks (i.e., approximately eight times per lizard per season). To evaluate lizard aggressiveness, we captured lizards approximately three times per season, at which time we ranked lizards’ responses to an attempted capture by an observer (1 – 11 scale, with higher ranks meaning greater aggression). The methods for this assay have been published previously (see Godfrey et al. 2012 for details, and Spiegel et al. 2015, Payne et al. 2021). Note that we did not assess aggression in 2009 and 2013.

To evaluate social pressure for each lizard, we included both the number of interacting conspecifics for that lizard (its social network degree) and its average interaction potential (mean probability of home range overlap, PHR). Social network degree was calculated as the number of different individuals an individual interacted with at least once over the course of an activity season, with interactions defined as occurring whenever two lizards were less than 14m from each other at simultaneous GPS fixes (Leu et al. 2010, 2011, Godfrey et al. 2012, Spiegel et al. 2016). For HR overlap among-individuals, we calculated each lizard’s mean non-zero 95% probability of home range overlap (PHR, i.e., the average extent to which a lizard’s home range was overlapped by the utilization distribution of other lizards within a year, excluding lizards with zero overlap with the focal lizard, since distant individuals cannot overlap). The PHR calculated with the adehabitatHR package ranges from 0-1. We used mean (rather than sum) PHR to prevent bias from focal lizards with untagged neighbors, which would have resulted in reduced apparent total overlap for those focal lizards. Note that we used 95% PHR for all analyses, as lizards in our site tend to exclude others from their core home ranges (Kerr and Bull 2006).

For habitat metrics, we obtained data on several environmental variables. Because year-year variation in rainfall has large effects on food availability for these lizards, for each year, we calculated the precipitation sum (“rainfall” in mm) and the number of days with precipitation (“rainy days”), extracted from the Australian Bureau of Meteorology Bower weather station (~ 26km from the field site). Rainfall and the number of rainy days were not significantly correlated (Kendall’s rank tau correlation, 0.07, p = 0.90). We elected to not include year-year variation in temperature both because it was not as clear (as with precipitation) how that might affect food availability, and because it was not clear which of many metrics (e.g., mean daily max or min, average daily, average nightly, etc.), if any, should be most important for lizard HRs. In a subset of years (2015-2017), we additionally assessed spatial variation in habitat quality in 123 quadrats distributed in a grid over the site (similar methods as in Spiegel et al. 2015). For each quadrat, we surveyed a 20m radius around a central point and gave habitat quality ranks (on a 1 – 5 scale, with higher numbers indicating better quality). In 2015 through 2017, we ranked the availability of moist food (i.e., primarily their most commonly consumed food, Ward’s weed). In 2015, we additionally assessed refuge quality (i.e., sites with large, dense bushes or burrows received a higher refuge rank than sites with small, porous bushes or no burrows). Because the size and location of these large shrubs and burrows was very stable from year to year, we applied the 2015 refuge ranks to 2016 and 2017 data.


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