DOI: 10.5061/dryad.n8pk0p344
title: Personality, space use, network, and tick infestation data from a field study of sleepy lizards, 2010 and 2014 - 2017

description: Host personality can markedly affect parasite transmission. Especially for
 parasites with indirect transmission through the environment, the effects
 of consistent among-individual differences in behavior may have both
 direct and indirect components. For example, personality may mediate both
 how hosts respond to infected individuals and the likelihood that hosts
 indirectly interact with infected conspecifics (e.g., by visiting patches
 infected hosts have previously contaminated). Integrating parasites,
 personality, and these different kinds of interaction networks constitutes
 a key step towards understanding transmission in natural systems. In the
 original manuscript for this dataset (Payne et al. Ecological Monographs
 2024), we evaluated these elements using a five-year field study of a wild
 population of sleepy lizards, Tiliqua rugosa, and their tick parasites,
 which transmit among lizards through lizards’ shared use of refuges. Using
 Bayesian models, we evaluated (1) predictors of lizard infestation
 probability and intensity (i.e., average tick count when infested) and (2)
 relationships among the predictors. We used the latter set of models to
 assess indirect relationships between the predictors and the infestation
 metrics. As predictors, we used lizards’ infestation ‘risk’ (derived from
 a time-lagged refuge-sharing transmission network), traits (sex, mass, and
 the personality axes aggression and boldness), space use (number of unique
 refuges used and home range overlap with other lizards), and measures of
 synchronous social interactions (i.e., edge weight and degree). We found
 both indirect and direct connections between our predictors and tick
 infestation. For example, boldness was positively directly associated with
 infection intensity and indirectly positively associated with both
 infestation probability and intensity via intermediary connections with
 social network interaction and risk. Using more unique refuges, on the
 other hand, was indirectly negatively associated with infestation
 probability (via reduced risk), but directly positively associated with
 infestation probability, indicating a potential trade-off in the
 anti-parasite benefits of using more refuges. Our results emphasize that
 (1) multiple aspects of host behavior may be associated with parasite
 infection, (2) these components may proceed through both direct and
 indirect pathways, and (3) multiple pathways should be considered together
 because the pathways may have compounding or counteracting effects.
description: The following description comes the Methods section of Payne et
 al. (Ecological Monographs, 2024), modified slightly to focus on data
 acquisition and derivation. See the paper for a more detailed description
 of the statistical analyses that we performed with our metrics.
 <strong>Study System</strong> We
 studied sleepy lizards, a skink common in South Australia (Cogger 2014),
 within an approximately 1.2km<sup>2</sup> field site roughly
 centered on 33.887997° S, 139.311814° E. Sleepy lizards are large (400-950
 g as adults) and long-lived (up to 50 years; Bull 1995, Reinke et al.
 2022). At our field site, which has a Mediterranean climate and is
 dominated by chenopod shrubs, sleepy lizards are primarily active during
 the Austral spring, September through December (Bull 1987), which
 constitutes our field season. During their activity season, lizards
 overnight in refugia, such as dense bushes (Kerr et al. 2003).
 Sleepy lizards at the site harbor two reptile-specific tick
 species, <em>Amblyomma limbatum</em> and
 <em>Bothriocroton hydrosauri</em> (Bull 1991). Due to sleepy
 lizards’ relatively large size and abundance at the site, they are the
 principal hosts for the two tick species (Bull et al. 1981). Both ticks
 have a three-host life cycle, infesting a host at the larval, nymph, and
 adult stages (Smyth 1973). Each tick stage typically detaches from the
 lizard host while the host is in a refuge (e.g., into the litter
 underneath a bush), as ticks have low survival outside of refuges (Bull
 and Smyth 1973, Chilton and Bull 1993a). While detached from a host,
 neither tick species quests (Petney et al. 1983). Thus, tick transmission
 occurs primarily in lizard refuges (Petney et al. 1983, Chilton and Bull
 1993b). <strong>Data
 collection</strong> During the activity seasons
 of 2010 and 2014 through 2017, we assayed lizard aggression and boldness,
 tracked lizard movement via GPS, and counted lizard ticks at regular
 intervals. We monitored 61 lizards in 2010, 49 in 2014, 76 in 2015, 76 in
 2016, and 73 in 2017. In 2014, we had fewer lizards because monitoring
 effort was split between the main and an accessory site; in this work, we
 use data from the main site. To identify lizards, we permanently marked
 them with toe clips (described in Leu et al. 2015). Toe clipping causes
 relatively low stress in these lizards and is less stressful than other
 techniques, such as microchipping, for similar species (Langkilde and
 Shine 2006, Perry et al. 2011). Lizard sex was identified once per year
 based on lizard morphology (Bull and Pamula 1996). We
 assayed lizard aggression and boldness approximately three times per
 lizard per year. The lizard boldness and behavioral assays have been
 described elsewhere (Godfrey et al. 2012, Spiegel et al. 2015, Payne et
 al. 2021, 2023). Briefly, in the aggression assay, we ranked a lizard’s
 response to a simulated grab. More aggressive responses (e.g., attempting
 to bite the observer) received higher ranks. In the boldness assay, we
 ranked a lizard’s response to a preferred food item (a banana) in a
 perceived situation of risk (a looming observer). Bolder behavior (e.g.,
 immediately approaching and eating the banana, which required approaching
 the observer) received higher ranks. Since these metrics have been shown
 to be repeatable (Payne et al. 2021), and we needed a single value per
 year to align with our other metrics, we averaged boldness and aggression
 scores within year. Our GPS monitoring of lizards has
 been previously described (Leu et al. 2010a, e.g., Godfrey et al. 2012,
 Spiegel et al. 2015, 2018). At the beginning of each activity season
 (year), we attached GPS units and radio-transmitters with surgical tape to
 lizards’ tails. Tracking units were removed at the end of each year. We
 have never observed adverse effects of this protocol, such as skin
 irritation or changes in lizard behavior (Leu et al. 2010a, e.g., Godfrey
 et al. 2012). GPS fix frequency varied among years, but was always at
 least one fix per every 10 minutes. GPS data corresponding to lizard
 handling was removed, as were errors identified by manual inspection and
 an automated filtering algorithm (Bjorneraas et al. 2010).
 Each year, we relocated lizards via radio-tracking approximately
 every two weeks to download GPS data, replace GPS batteries, count ticks,
 and weigh lizards. These assessments were usually done in the morning to
 avoid interfering with daily lizard activity (Kerr et al. 2004). We
 visually inspected lizards to count the number of larvae, nymphs, and
 adults of each tick species. For this study, we used only larval counts,
 combined across species. We used larvae because in 2010, 2016, and 2017,
 we experimentally added larvae to several lizards in the site. After
 molting, these added ticks would bias our counts of the nymph and adult
 stages on all lizards, not just the treated ones. However, removing
 lizards that received the added larvae removes the bias from the larval
 counts. We combined tick species because larvae are difficult to reliably
 identify to species in the field and the two species both transmit
 indirectly through refuges (Leu et al. 2010b). Since larvae may stay on a
 lizard for two to four weeks, we filtered non-zero larval tick count
 observations to those separated by at least 28 days (Payne et al. 2020);
 zero tick counts were treated as independent. At each tick count, we
 recorded lizard mass with a digital scale. We averaged mass within year
 due to defecation, seasonal fat accumulation, and measurement
 error. <strong>Derivation of social network and
 space use metrics</strong> We calculated two
 social network metrics for each lizard within each year: degree and edge
 weight sum. Following our previous work in this system, we defined an edge
 to occur between two lizards whenever they were within 14 m of each other
 at the same time (Leu et al. 2010a), which is based on GPS error but well
 within lizard to lizard detection ability (Auburn et al. 2009). Degree was
 calculated as a proportion: the number of different individuals (partners)
 with which a focal individual ever formed an edge divided by the total
 number of possible partners, where possible partners was the number of
 lizards that were ever observed at the same time as the focal lizard. We
 calculated degree as a proportion (P|Degree in figures) because the number
 of possible partners varied among focal lizards. Edge weight was
 calculated as the simple ratio index (Farine and Whitehead 2015), and we
 summed all edge weights within a focal lizard. For
 space use, we calculated each lizard’s distance from the center of the
 site, home range overlap with other lizards, and the number of unique
 refuges that it used within a year. For the former two metrics, we first
 estimated each lizard’s home range utilization distribution (Van Winkle
 1975) for each year, calculated using a bivariate kernel with the ad hoc
 smoothing method in the R adehabitatHR package (Calenge 2006). Distance
 from the center of the site was then calculated as the distance in meters
 between the center of the field site (33.8879971726° S, 139.311814433° E)
 and the center of a lizard’s 95% home range polygon (i.e., the smallest
 polygon encompassing 95% of the lizard home range utilization distribution
 probability). Home range overlap (Fieberg and Kochanny 2005) was
 calculated as the total extent to which a focal lizard’s 95% home range
 polygon was overlapped by the utilization distributions other lizards
 (i.e., the probability of finding another lizard in the focal’s home
 range, hence <em>P</em>HR, and then summed across all
 nonfocals). Number of unique refuges was the number of different overnight
 refuges that a lizard used within a year divided by the number of a nights
 a lizard’s overnight refuge was determined (e.g., if a lizard was observed
 for 20 nights and used 15 different refuges, then the value would be 15 /
 20). We calculated number of unique refuges as a proportion (P|Refuges in
 figures) because the number of nights a refuge could be determined varied
 among lizards. We discuss the full details of our nightly and unique
 refuge derivation process in Appendix S1. Essentially, we identified a
 refuge as a coherent cluster of GPS points using density-based clustering
 (Ester et al. 1996, Campello et al. 2013). Unique refuges were then
 derived by clustering refuges; all refuges belonging to a single cluster
 were assumed to represent a single unique refuge.
 <strong>Derivation time-lagged network
 metrics</strong> After ticks are deposited in a
 refuge, they have an infectious interval controlled by their developmental
 period and survival times off host. Based on previous work in this system,
 we assume an infectious interval of 9 through 39 days post-deposition in
 the refuge (Leu et al. 2010b, Wohlfiel et al. 2013, Wohlfeil et al. 2020).
 The total risk that a recipient lizard acquires ticks should stem from (1)
 how often the recipient uses refuges 9 – 39 days after those refuges were
 used by a donor lizard, and (2) how often donor lizards used the
 recipient’s refuge, in that greater use by donors should translate to a
 greater probability of depositing ticks at a refuge (Leu et al.
 2010b). Given these components of risk and following
 Leu, Kappeler, & Bull (2010b), we calculated two metrics: cross
 risk and self risk. Cross risk describes a focal individual’s time-lagged
 refuge sharing with other lizards, whereas self risk refers to a focal
 lizard’s lagged interaction with itself. Briefly, for a given
 donor-recipient lizard dyad, we counted the number of times that the donor
 used each of the recipient’s nightly refuges 9 – 39 days prior to the
 recipient’s use of each refuge. We then divided this count by a
 standardization factor (the sum of the number of nights where only the
 recipient was observed, only the donor was observed, and both were
 observed) to account for different observation extents among
 donor-recipient dyads (see the Appendix to the main manuscript, Section
 S3.2), analogously to the simple ratio index (Farine and Whitehead 2015).
 Cross risk was the sum of these weighted values from each of a recipient
 lizard’s non-self donors, whereas self risk was the recipient’s single
 weight into itself. For more details, see the Appendix to the main
 manuscript, Section S3. <strong>Overview of
 provided dataset</strong> Based on the
 aforementioned data collection and metric derivation, the dataset provided
 in this repository contains one row for each lizard-year observation
 (e.g., if a lizard were observed in 2010, 2014, and 2015, then that lizard
 would have three rows in the dataset). Columns identify the year and the
 lizard for each row. The dataset also contains columns for lizard traits
 (sex, mass, behavior), space use metrics (center site distance, home range
 overlap, number unique refuges used), social network metrics (degree, edge
 weight), transmission network metrics (cross risk, self risk), and tick
 infestation (number of tick counting events, number of tick counting
 events where a lizard was observed with larvae, average larval counts when
 a lizard was infested). The ReadMe more fully describes each column in the
 dataset.
 <strong>References</strong>
 Auburn, Z. M., C. M. Bull, and G. D. Kerr. 2009. The visual
 perceptual range of a lizard, Tiliqua rugosa. Journal of Ethology
 27:75–81. Bjorneraas, K., B. Van Moorter, C. M.
 Rolandsen, and I. Herfindal. 2010. Screening Global Positioning System
 Location Data for Errors Using Animal Movement Characteristics. Journal of
 Wildlife Management 74:1361–1366. Bull, C. M. 1987. A
 Population Study of the Viviparous Australian Lizard,
 <em>Trachydosaurus rugosus</em> (Scincidae). Copeia
 1987:749–757. Bull, C. M. 1991. Ecology of Parapatric
 Distributions. Annual Review of Ecology and Systematics
 22:19–36. Bull, C. M. 1995. Population Ecology of the
 Sleepy Lizard, <em>Tiliqua rugosa</em>, at Mt-Mary,
 South-Australia. Australian Journal of Ecology 20:393–402.
 Bull, C. M., and Y. Pamula. 1996. Sexually dimorphic head sizes
 and reproductive success in the sleepy lizard Tiliqua rugosa. Journal of
 Zoology 240:511–521. Bull, C. M., R. D. Sharrad, and T.
 N. Petney. 1981. Parapatric boundaries between Australian reptile ticks.
 Pages 95–107. Bull, C. M., and M. Smyth. 1973. The
 distribution of three species of reptile ticks, <em>Aponomma
 hydrosauri</em> (Denny), <em>Amblyomma albolimbatum</em>
 Neumann, and <em>Amb. limbatum</em> Neumann II. Water balance
 of nymphs and adults in relation to distribution. Australian Journal of
 Zoology 21:103–110. Calenge, C. 2006. The package
 “adehabitat” for the R software: A tool for the analysis of space and
 habitat use by animals. Ecological Modelling 197:516–519.
 Campello, R. J. G. B., D. Moulavi, and J. Sander. 2013.
 Density-Based Clustering Based on Hierarchical Density Estimates. Pages
 160–172 <em>in</em> J. Pei, V. S. Tseng, L. Cao, H. Motoda,
 and G. Xu, editors. Advances in Knowledge Discovery and Data Mining.
 Springer Berlin Heidelberg. Chilton, N. B., and C. M.
 Bull. 1993a. A Comparison of the Off-Host Survival Times of Larvae and
 Nymphs of 2 Species of Reptile Ticks. International Journal for
 Parasitology 23:693–696. Chilton, N. B., and C. M.
 Bull. 1993b. Interspecific Differences in Microhabitat Choice by 2 Species
 of Australian Reptile Tick. International Journal for Parasitology
 23:1045–1051. Ester, M., H.-P. Kriegel, and X. Xu.
 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial
 Databases with Noise. Pages 226–231 Knowledge Discovery and Data Mining.
 Institute for Computer Science, University of Munich.
 Farine, D. R., and H. Whitehead. 2015. Constructing, conducting
 and interpreting animal social network analysis. J Anim Ecol
 84:1144–1163. Fieberg, J., and C. O. Kochanny. 2005.
 Quantifying home-range overlap: The importance of the utilization
 distribution. Journal of Wildlife Management 69:1346–1359.
 Godfrey, S. S., J. K. Bradley, A. Sih, and C. M. Bull. 2012.
 Lovers and fighters in sleepy lizard land: where do aggressive males fit
 in a social network? Animal Behaviour 83:209–215. Kerr,
 G. D., C. M. Bull, and D. Burzacott. 2003. Refuge sites used by the
 scincid lizard Tiliqua rugosa. Austral Ecology 28:152–160.
 Kerr, G. D., C. M. Bull, and D. Mackay. 2004. Human Disturbance
 and Stride Frequency in the Sleepy Lizard (Tiliqua rugosa): Implications
 for Behavioral Studies. Journal of Herpetology 38:519–526.
 Langkilde, T., and R. Shine. 2006. How much stress do researchers
 inflict on their study animals? A case study using a scincid lizard,
 <em>Eulamprus heatwolei</em>. Journal of experimental biology
 209:1035–1043. Leu, S. T., J. Bashford, P. M. Kappeler,
 and C. M. Bull. 2010a. Association networks reveal social organization in
 the sleepy lizard. Animal Behaviour 79:217–225. Leu, S.
 T., D. Burzacott, M. J. Whiting, and C. M. Bull. 2015. Mate Familiarity
 Affects Pairing Behaviour in a Long-Term Monogamous Lizard: Evidence from
 Detailed Bio-Logging and a 31-Year Field Study. Ethology
 121:760–768. Leu, S. T., P. M. Kappeler, and C. M.
 Bull. 2010b. Refuge sharing network predicts ectoparasite load in a
 lizard. Behavioral Ecology and Sociobiology 64:1495–1503.
 Payne, E., D. L. Sinn, O. Spiegel, M. G. Gardner, and A. Sih.
 2023. A field experiment reveals reciprocal effects of host personality
 and parasitism in wild lizards. Behavioral Ecology 35.
 Payne, E., D. L. Sinn, O. Spiegel, S. T. Leu, M. G. Gardner, S.
 S. Godfrey, C. Wohlfeil, and A. Sih. 2021. Consistent after all:
 behavioural repeatability in a long-lived lizard across a 6-year field
 study. Animal Behaviour 174:263–277. Payne, E., D. L.
 Sinn, O. Spiegel, S. T. Leu, C. Wohlfeil, S. S. Godfrey, M. G. Gardner,
 and A. Sih. 2020. Consistent individual differences in ecto-parasitism of
 a long-lived lizard host. Oikos 129:1061–1071. Perry,
 G., M. C. Wallace, D. Perry, H. Curzer, and P. Muhlberger. 2011. Toe
 Clipping of Amphibians and Reptiles: Science, Ethics, and the Law. Journal
 of Herpetology 45:547–555. Petney, T. N., R. H.
 Andrews, and C. M. Bull. 1983. Movement and Host Finding by Unfed Nymphs
 of 2 Australian Reptile Ticks. Australian Journal of Zoology
 31:717–721. Reinke, B. A., H. Cayuela, F. J. Janzen,
 J.-F. Lemaître, J.-M. Gaillard, A. M. Lawing, J. B. Iverson, D. G.
 Christiansen, I. Martínez-Solano, G. Sánchez-Montes, J.
 Gutiérrez-Rodríguez, F. L. Rose, N. Nelson, S. Keall, A. J. Crivelli, T.
 Nazirides, A. Grimm-Seyfarth, K. Henle, E. Mori, G. Guiller, R. Homan, A.
 Olivier, E. Muths, B. R. Hossack, X. Bonnet, D. S. Pilliod, M. Lettink, T.
 Whitaker, B. R. Schmidt, M. G. Gardner, M. Cheylan, F. Poitevin, A.
 Golubović, L. Tomović, D. Arsovski, R. A. Griffiths, J. W. Arntzen, J.-P.
 Baron, J.-F. Le Galliard, T. Tully, L. Luiselli, M. Capula, L. Rugiero, R.
 McCaffery, L. A. Eby, V. Briggs-Gonzalez, F. Mazzotti, D. Pearson, B. A.
 Lambert, D. M. Green, N. Jreidini, C. Angelini, G. Pyke, J.-M. Thirion, P.
 Joly, J.-P. Léna, A. D. Tucker, C. Limpus, P. Priol, A. Besnard, P.
 Bernard, K. Stanford, R. King, J. Garwood, J. Bosch, F. L. Souza, J.
 Bertoluci, S. Famelli, K. Grossenbacher, O. Lenzi, K. Matthews, S.
 Boitaud, D. H. Olson, T. S. Jessop, G. R. Gillespie, J. Clobert, M.
 Richard, A. Valenzuela-Sánchez, G. M. Fellers, P. M. Kleeman, B. J.
 Halstead, E. H. C. Grant, P. G. Byrne, T. Frétey, B. Le Garff, P.
 Levionnois, J. C. Maerz, J. Pichenot, K. Olgun, N. Üzüm, A. Avcı, C.
 Miaud, J. Elmberg, G. P. Brown, R. Shine, N. F. Bendik, L. O’Donnell, C.
 L. Davis, M. J. Lannoo, R. M. Stiles, R. M. Cox, A. M. Reedy, D. A.
 Warner, E. Bonnaire, K. Grayson, R. Ramos-Targarona, E. Baskale, D. Muñoz,
 J. Measey, F. A. de Villiers, W. Selman, V. Ronget, A. M. Bronikowski, and
 D. A. W. Miller. 2022. Diverse aging rates in ectothermic tetrapods
 provide insights for the evolution of aging and longevity. Science
 376:1459–1466. Smyth, M. 1973. The distribution of
 three species of reptile ticks, <em>Aponomma hydrosauri</em>
 (Denny), <em>Amblyomma albolimbatum</em> Neumann, and
 <em>Amb. limbatum</em> Neumann I. Distribution and hosts.
 Australian Journal of Zoology 21:91–101. Spiegel, O.,
 S. T. Leu, A. Sih, S. S. Godfrey, and C. M. Bull. 2015. When the going
 gets tough: behavioural type-dependent space use in the sleepy lizard
 changes as the season dries. Proceedings of the Royal Society B:
 Biological Sciences 282:20151768. Spiegel, O., A. Sih,
 S. T. Leu, and C. M. Bull. 2018. Where should we meet? Mapping social
 network interactions of sleepy lizards shows sex-dependent social network
 structure. Animal Behaviour 136:207–215. Van Winkle, W.
 1975. Comparison of Several Probabilistic Home-Range Models. The Journal
 of Wildlife Management 39. Wohlfeil, C. K., S. S.
 Godfrey, S. T. Leu, J. Clayton, and M. G. Gardner. 2020. Spatial proximity
 and asynchronous refuge sharing networks both explain patterns of tick
 genetic relatedness among lizards, but in different years. Austral Ecology
 45:493–501. Wohlfiel, C. K., S. T. Leu, S. S. Godfrey,
 and C. M. Bull. 2013. Testing the robustness of transmission network
 models to predict ectoparasite loads. One lizard, two ticks and four
 years. International Journal for Parasitology: Parasites and Wildlife
 2:271–7.
description: # Personality, space use, network, and tick infestation data from a field
 study of sleepy lizards, 2010 and 2014 - 2017 Dataset DOI:
 [https://doi.org/10.5061/dryad.n8pk0p344](https://doi.org/10.5061/dryad.n8pk0p344) **Provided data file** * lizard_data.csv * Includes the tick count, infestation risk, and other variables used in the tick transmission network analysis of sleepy lizards (*Tiliqua rugosa*) reported in Payne et al. 2024 (Ecological Monographs). * Columns in the dataset are described below. **Columns** * lizard: the unique individual to which the current row refers (the focal lizard). * year: the year of the observation. * sex: the sex of the lizard. Female = 0. Male = 1. * mass.avg: the lizard's average mass within a given year (in grams). Lizard mass was recorded at each tick count event. * obsthreat.avg: the lizard's average aggression score within a given year. Aggression scores come from an observer threat assay in which an observer attempted to grab the lizard. Higher ranks correspond to more aggressive responses. More details on this assay can be found in Godfrey et al. 2012 (Animal Behaviour), Payne et al. 2021 (Animal Behaviour), and Payne et al. 2023 (Behavioral Ecology). * bananaboldness.avg: the lizard's average boldness score within a given year. Boldness scores come from a banana boldness assay in which a lizard was offered a piece of banana, but choosing to obtain the banana meant coming closer to the looming observer (a presumed threat). Higher ranks correspond to bolder responses. More details on the boldness assay can be found in Payne et al. 2021 (Animal Behaviour) and Payne et al. 2023 (Behavioral Ecology). Note that boldness averages should be and were standardized within year prior to use in models because boldness ranks differed among years. * center.site.dist.95: the distance (in meters) between the center of the lizard's 95% home range polygon (HR) and the center of the field site. A lizard's HR polygon was derived from its utilization distribution (UD), which was calculated using the kernalUD function from the adehabitatHR package, using a bivariate normal kernel, an ad hoc smoothing method, and a grid set to 500. * max.degree: the number of other lizards with which the focal lizard could have potentially interacted (i.e., the number of other lizards that were ever observed at the same time as the focal lizard within the year of the row). * degree: the number of other lizards with which the focal lizard interacted in the given year, where an interaction was defined as the focal being within 14 m of another lizard at the same time as the other lizard. * degree.std: degree divided by max.degree (degree / max.degree). * sn.edge.sri.sum: the focal lizard's social network edge weight sum (i.e., the sum of a focal lizard's edge weights with all other lizards in a given year), where edge weight was calculated as the simple ratio index. See Farine and Whitehead 2015 (Journal of Animal Ecology). An edge between two lizards occurred when they were within 14 m of each other at the same time. * phr.overlap.95.sum: PHR refers to the proportion of a focal lizard's home range (HR) polygon that is overlapped by the utilization distribution (UD) of another animal, which can be thought of as the probability of finding the other animal within the HR polygon of the focal animal. PHR was calculated as the proportion of the focal lizard's 95% HR polygon that was overlapped by the 95% UD of another animal. Then, we summed a given focal lizard's PHR overlaps with other lizards, producing the total extent to which the focal lizard's 95% HR was overlapped by other lizards. PHR values were calculated with the kerneloverlap function in the adehabitatHR package, using a bivariate normal kernel, an ad hoc smoothing method, a grid set to 500, and the same grid for all animals within a given year, which is necessary for deriving overlap. * nights.observed: the number of nights for which a refuge location was able to be identified. More details on the refuge derivation process can be found in the manuscript associated with this dataset. * num.unique.refuges.4m: the number of unique refuges that a lizard used within a given year. * num.unique.refuges.4m.std: the number of unique refuges that a lizard used divided by the number of nights for which a refuge could be determined (num.unique.refuges.4m / nights.observed). * cross.risk: an estimate of a lizard's risk of acquiring infestation from other lizards, approximated as how much a lizard interacted with other lizards via time-lagged shared refuge use (i.e., using the same refuge at time point X as another lizard did at an earlier time). * self.risk: an estimate of a lizard's risk of self-infestation, approximated as how much a lizard interacted with itself via time-lagged shared refuge use (i.e., using the same refuge at time point X as it did at an earlier time). * trials: the number of times that a lizard was assessed for ticks in a given year. * success: the number of times that a lizard was observed with tick larvae in a given year. For example, if a lizard's ticks were counted 4 times in a year, and the lizard had tick larvae at 3 of those counting events, then trials would be 4 and success would be 3. * tick.avg: the average of a lizard's non-zero larval counts. For instance, if a lizard's ticks were counted 4 times, and the lizard had 0, 15, 0, and 5 larvae at each counting, respectively, then the non-zero average would be 20/2 = 10. * tick.total: the total number of larvae that a lizard had across all of its tick counting events within a given year. In the previous example, the lizard's larval total would be 20.