DOI: 10.5061/dryad.wpzgmsbnd
title: Analyzing time-energy constraints to understand the links between environmental change and local extinctions in terrestrial ectotherms

description: Accelerated extinction rates have prompted an increased focus on the
 interplay between environmental change and species response. The effects
 of environmental change on thermal opportunity are typically considered
 through a climate change context. However, habitat alteration can also
 have strong effects on the thermal environment. Additionally, habitat
 alteration is considered a leading factor of species extinction, yet few
 studies address the influence of habitat alteration on thermal opportunity
 and time-energy budgets in at-risk species. Here we show the strong
 effects that habitat degradation can have on thermal opportunity, time
 energy-budgets, and life history demographics of local populations. In the
 Ozark Mountains of northern Arkansas, woody vegetation encroachment has
 resulted in a shift in life history traits that appears to play an
 important role in recent extirpations of Eastern Collared Lizards
 (Crotaphytus collaris). Populations in degraded habitats experienced a
 decline in thermal opportunity and less time-at-body temperatures
 (time-at-Tb) suitable for digestion compared to those in intact habitats.
 We used our data to model the effect of reduced time-at-Tb on the net
 assimilated energy available for growth and reproduction. Our model
 predicts a ca. 46% decline in annual fecundity of individuals – which is
 similar to empirical observations of reproduction of C. collaris
 populations in degraded habitats (~49%). We conclude that C. collaris in
 degraded habitats experienced reduced growth and reproduction primarily as
 a result of constrained thermal opportunity leading to a decline in
 digestive processing rates. Our study applies an under-appreciated
 approach to identify the biophysical and time-energy effects of habitat
 alteration.
description: <i>Study Sites</i>: We sampled <i>C.
 collaris</i> populations from six sites and designated each
 glade-site as either intact, with low levels of woody vegetation density
 and shade cover (<i>n</i> = 3), or encroached, with high
 levels of woody vegetation density and shade cover (<i>n</i> =
 3; see Brewster et al. 2018 for summary data describing glade types). All
 glade-sites occurred within an 80 km radius along the White and Buffalo
 Rivers, in the Ozark-St. Francis National Forest in northern Arkansas.
 Sampling for most statistical comparisons (early May – mid July) were made
 across the reproductive season (vitellogenesis begins upon emergence in
 late April – early May; copulations from early May – early July;
 oviposition from late May – mid July) of <i>C. collaris</i> in
 northern Arkansas (Trauth 1978; Brewster et al. 2018).
 Biophysical Comparisons
 <i>Prey Availability</i>: If dense woody vegetation
 in encroached glades results in reduced prey availability, this could lead
 to time-energy constraints in <i>C. collaris </i>(fig.1). We
 used linear observational transects to estimate arthropod densities per
 square meter sampled. All estimates included only numbers of arthropods
 within seven suborders known to represent 95 % of <i>C.
 collaris</i> diet: Orthoptera, Coleoptera, Lepidoptera, Hymenoptera,
 Araneida, Hemiptera, and Diptera (McAllister 1985). Sampling was conducted
 by walking along ten, 20m transects per site, and counting the number of
 arthropods observed from the ground to waist height, and within 0.5m on
 either side of the transect. Sampling was standardized by time of day
 (between 11:00am and 3:00pm), on sunny days, and only when <i>C.
 collaris</i> were active. We conducted linear transect sampling in
 May, June and July (10 replicates/month/site) for three consecutive
 summers (2014-2016). Data analyses of prey densities between glade types
 were conducted using a mixed model (nlme package in R; Fox and Weisberg
 2011), with glade-type (intact vs. encroached) and month (May, June or
 July) as fixed effects, and glade-site (6 total sites repeatedly sampled)
 as random effects. <i>Operative</i>
 <i>Environmental Temperature</i>: A reduction in thermal
 opportunity in encroached glades as a result of an increase in dense woody
 vegetation could also lead to time-energy constrains in <i>C.
 collaris</i> (fig.1). We used two metrics to compare thermal
 environments between encroached and intact glades: 1) percent of operative
 environmental temperature (<i>Te</i>; Bakken 1992) within the
 range of voluntary active <i>Tb</i>s of <i>C.
 collaris</i>, and 2) thermal quality index (<i>de</i>;
 Hertz et al. 1993). A recent study suggested that <i>C.
 collaris</i> uses postural adjustments as a means to behaviorally
 thermoregulate, and this behavior can have a major influence on the
 predicted available activity-time and <i>de </i>in this
 species (Brewster and Beaupre 2019). Thus, we used the same
 <i>Te</i> modeling procedures as Brewster and Beaupre (2019;
 “OTM<sub>IDEAL</sub>”) that accounts for postural adjustments
 by <i>C. collaris</i> to estimate <i>de</i> and
 the frequency distribution of <i>Te</i> in this study.
 Briefly, we used three different types of operative temperature models
 (OTM) modified from a 12cm (2.5cm diameter) length of hollow copper pipe,
 and painted to match the reflective properties of <i>C.
 collaris</i>: 1) stilted OTM that mimics the biophysical properties
 of <i>C. collaris</i> in an elevated posture, 2) unmodified
 OTM that mimics the biophysical properties of <i>C.
 collaris</i> in an intermediate posture, and 3) compressed OTM that
 mimics the biophysical properties of <i>C. collaris</i> in a
 prostrate posture. All three OTM types were validated against
 live<i> C. collaris </i>individuals in the corresponding
 posture in the field (see fig.4<i>B</i>, Brewster and Beaupre
 2019). For each glade-site, we used a total of 75 OTMs to estimate the
 <i>Te</i> distribution of 25 randomly selected microsites. At
 each microsite we placed three OTMs (one of each type; stilted,
 unmodified, or compressed) side-by-side, oriented in the same direction.
 We suspended a single iButton<sup>TM</sup> temperature data
 logger (Maxim Integrated Products) in the center of each OTM using a piece
 of aluminum screening and set loggers to record temperature every 20 min
 (OTM time constant ~3.5min). We logged thermal environment estimates
 simultaneously between one encroached and one intact glade for a minimum
 of five days and alternated among glade sites across the
 activity-season. Each glade to glade comparison included three sunny days
 in May, June and July (9 days total for each glade-glade
 comparison). To compare thermal environments between
 encroached and intact glades we estimated the percentage of microhabitats
 (n = 25 for each site) within the voluntary active <i>Tb</i>
 range (T<sub>ACT</sub>) of<i> C. collaris. </i>We
 used the central 99 % of all <i>Tb</i>s recorded on
 surface-active <i>C. collaris</i> in our study populations to
 estimate T<sub>ACT</sub> (31.2 - 42.6˚C). Using the
 posture-specific modeling procedure described above
 (OTM<sub>IDEAL</sub>; Brewster and Beaupre 2019), we estimated
 the percent of microhabitats at a given time point within
 T<sub>ACT</sub>. Percentages were calculated every 20 min from
 7:00am to 9:00pm, and comparisons were made across days (n = 3 per
 glade-site) for May, June and July (9 days total per
 glade-site). We also compared thermal environments by
 estimating the thermal quality index (<i>de</i>) between
 glade-types. The thermal quality index provides the average absolute
 deviation between <i>Te</i> and the set-point <i>Tb
 </i>range (T<sub>SET</sub>; Hertz et al. 1993). We used
 the central 60% of all <i>Tb</i>s selected by <i>C.
 collaris</i> in a laboratory gradient (34.8–38.1˚C; Firth et al.
 1989) as our metric for T<sub>SET</sub> (Brewster and Beaupre
 2019). Using OTM<sub>IDEAL</sub>, we estimated the average
 absolute deviation between <i>Te</i> and
 T<sub>SET</sub> at a given time point. Average absolute
 deviations were calculated every 20 min from 7:00am to 9:00pm, and
 comparisons were made across days (n = 3 per glade-site) for May, June and
 July (nine days total per glade-site). We used a linear mixed model (nlme
 in R) to compare <i>de</i> by glade-type. We designated
 glade-type and season as fixed effects, and glade-site (sampled
 repeatedly) as a random effect. We made no formal statistical comparisons
 of percentage of <i>Te </i>within T<sub>ACT</sub>
 between glade-types. Instead we use this comparison to more effectively
 visualize the thermal environment in each glade type.
 Time-Energy Budget Comparisons
 <i>Movement Rates: </i>If <i>C.
 collaris</i> in encroached glades have greater movement rates from
 increased thermoregulatory effort (i.e. shuttling among basking sites;
 Huey and Slatkin 1976; Withers and Campbell 1985; Basson et al. 2017) this
 could result in greater proportions of energy allocated to activity,
 leaving less energy available for growth and reproduction (fig.1,
 <b>a<sub>2,4</sub></b>; Brewster et al. 2013). To
 determine if <i>C. collaris</i> in encroached glades allocate
 more energy to locomotion, we estimated movement rates of individuals.
 Because of time constraints (all field sampling in this study was
 conducted by one of the coauthors) we were only able to estimate movement
 rates on a sub-sample of individuals from one encroached and one intact
 glade. We opportunistically chose five males and three females from both
 glades to estimate movement rates. Subjects were observed through
 binoculars from a distance of 30–70m (so as to not impact behavior), for
 10min time blocks, and only on days and times when multiple individuals
 were surface-active. All subjects had been previously captured within the
 previous 1–4 days and had been temporarily marked on their dorsum with a
 white paint pen (i.e. a single letter) to allow visual identification of
 the individual by the observer. We sampled movement rates of individuals
 over a ten-day period in May 2015, with a total of five observations
 (50min) per subject. We estimated the total distance moved (when animals
 moved greater than 10m at a time, we used a laser rangefinder;
 Nikon<sup>TM</sup> model AL-11, estimated to the nearest 1m)
 per 10min time block. Data analysis of movement rates were conducted using
 mixed models (nlme package in R; Fox and Weisberg 2011). We designated
 glade-type and sex as fixed effects, and subject ID (individuals sampled
 repeatedly) as a random effect. <i>Surface Active
 Tb: </i>If <i>C. collaris</i> in encroached glades
 experience reduced surface-active <i>Tb</i>, this could
 suggest that individuals suffer reduced physiological performance leading
 to reduced processing rates (<b>b<sub>3</sub></b>,
 fig.1; Huey 1982; Congdon 1989; Beaupre et al. 1993a; Brewster et al.
 2020) and/or reduced harvesting rates
 (<b>b<sub>2</sub></b>, fig.1; Huey and Stevenson
 1979; Avery et al. 1982; Smith and Ballinger 2001). To determine if
 <i>C. collaris</i> in encroached glades experience lower
 <i>Tb</i>s during surface activity, we used
 <i>Tb</i> data collected from 2013-2017. Animals were captured
 with a pole-and-lasso from late April through late July, and cloacal
 <i>Tb</i>s<i> </i>were recorded using a quick-read
 digital thermometer (Model HH800, Omega Engineering). We only used
 <i>Tb</i> data on animals known to be surface-active for a
 minimum of two thermal time-constants (time for the temperature of an
 object to change by∼63% of the differential between the initial
 temperature and the ambient temperature; <i>C. collaris</i>
 time constant ~6.5 mins; Grigg et al. 1979) to ensure they were near
 equilibrium <i>Tb</i>, and where <i>Tb</i> was
 recorded within 2 min after capture. Comparisons of <i>Tb</i>s
 between glade-types were made using a mixed model (nlme package in R; Fox
 and Weisberg 2011). We designated glade-type, sex and month as fixed
 effects, and subject ID (individuals were sampled repeatedly and at
 variable time-points) as a random effect.
 <i> Time Surface Active: </i>If <i>C.
 collaris</i> in encroached glades spend less time surface-active
 this could leave them with less time-at-<i>Tb</i>s where
 digestion is optimal (Dunham et al. 1989), resulting in reduced processing
 rates (<b>c<sub>3</sub></b>, fig.1). Similarly,
 reduced activity-time could leave less time available for foraging (Grant
 and Dunham 1988; Adolph and Porter 1993), resulting in reduced harvesting
 rates (<b>c<sub>2</sub></b>, fig.1). To compare
 total daily surface-activity times between glade types, we made focal
 observations on <i>C. collaris</i> surface activity. A pilot
 study in 2014 suggested that surface-activity (percentage of animals in
 the population on the surface) was stable (&gt;50% of animals) during
 mid-day (11:00am-3:00pm), assuming typical climatic conditions during the
 activity season. We estimated total activity-hours per day by recording
 the onset of surface activity in the morning, and end of surface activity
 in the evening. We conducted observational transects on 10–20min intervals
 starting at local sunrise until the onset of activity was observed, and
 then conducted hourly observational transects through the midday. Within
 two hours of local sunset, we again returned to 10-20min intervals between
 observational transects until lizards were no longer observed surface
 active. Activity-time estimates (total number of hours animals were
 observed surface active) were made on three days for each glade-site, in
 May, June, and July (9 days total per glade-site) in 2015–2016. Estimates
 were made only on days with typical climatic conditions for a given month.
 We used a mixed-linear model (nlme package in R; Fox and Weisberg 2011)
 for statistical analyses of hours active between glade-types. We
 designated glade-type and month as fixed effects, and glade-site
 (repeatedly sampled) as a random effect.
 <i>Frequency of Recent Meal: </i>If <i>C.
 collaris </i>in encroached glades spend more time with an empty
 stomach as a result of low absolute prey availability
 (<b>d<sub>1</sub></b>, fig.1) and/or reduced
 capture performance (<b>b<sub>2</sub></b>; fig.1),
 this could result in reduced harvesting rates
 (<b>d<sub>3</sub></b>, fig.1). To test for
 differences between glade-types in the frequency of a recent meal, we used
 palpation data collected on <i>C. collaris</i> captured from
 2013-2017. Upon capture, we physically palpated the abdomen of individuals
 to feel for the presence of a recent meal. In our experience, females in
 late gravidity (those with shelled oviductal eggs) typically had an empty
 stomach, suggesting that they stopped foraging until after oviposition.
 Thus, we excluded females from our palpation dataset if they contained
 large-soft follicles or oviductal eggs (reasonably distinct in shape and
 texture from food items). To analyze presence/absence data we used a
 general linear mixed model (glmer in R; Bates et al. 2015), fit with the
 binomial (logit) function. We designated glade-type and sex as fixed
 effects, and subject ID (individuals sampled repeatedly) as a random
 effect. Net Assimilated Energy (NAE)
 Model We used the results from our
 statistical comparisons to model their effects on the NAE available for
 growth and reproduction of <i>C. collaris</i> in our study
 populations. The goal of the NAE model was to predict the annual NAE and
 the corresponding number of eggs produced by <i>C.
 collaris</i> female individuals in intact and encroached glades,
 given the specific conditions identified in our statistical comparisons
 (operational environments, <i>Tb</i>s, activity-times,
 movement rates, and meal frequencies). Detailed methods and
 parameterization of the NAE model are provided in the “Net Assimilated
 Energy (NAE) Model Parameterization” section of the appendix. Briefly, the
 NAE model uses data on variation in time (hours active and inactive per
 day) and <i>Tb</i>s of individuals in encroached and intact
 glades to predict their effects (time-at-<i>Tb</i>) on
 digestive processing rates. We used general principles provided by Congdon
 et al. (1982) to estimate the various components of <i>C.
 collaris</i> energy budgets assuming: NAE = ME –
 <i>M</i> Where NAE is net assimilated
 energy, ME is metabolizable energy (energy consumed minus energy lost in
 feces minus energy lost in uric acid; C – F – U) and <i>M</i>
 is metabolic maintenance cost; all in kJ
 d<sup>-1</sup>. To estimate
 <i>Tb</i>s of <i>C. collaris</i> during inactivity
 periods, we assumed that while inactive, the mean and variance in
 <i>Tbs</i> of animals would approximate the mean and variance
 of under-rock refugia (based on biophysical principles; Porter and Gates
 1969). We calculated the hourly mean and variance of n = 7 deep crevice
 microsites used by <i>C. collaris</i> at each of the six
 glades during May, June, and July (see <i>Operative</i>
 <i>Environmental Temperature</i>). We wrapped a single
 iButton<sup>TM</sup> temperature data logger (Maxim Integrated
 Products) with aluminum foil and attached a length (~1.5m) of surveyor
 marking tape to the unit. We used a 1m dowel (2cm diameter) to guide the
 iButton<sup>TM</sup> unit into the refugia (~15-30cm deep),
 leaving the remaining length of surveyor tape on the surface for ease of
 retrieval. iButtons logged temperature on 20-minute intervals, over the
 same time periods as the surface <i>Te</i> estimates at a
 given site. Refugia sites were chosen opportunistically, based on previous
 observations of individuals using these sites. We did not make any formal
 statistical analyses on refugia temperatures. Instead, we used estimates
 of refugia temperatures to provide a 24hr <i>Tb</i> profile of
 <i>C. collaris</i> in our populations and to parameterize the
 NAE model. We did not deploy temperature loggers in April and August, thus
 for these months our model used mean hourly operative temperatures for May
 and July, respectively. During activity time each month we assumed lizards
 effectively thermoregulated to their mean field body temperature for a
 given month. During inactivity, hourly lizard body temperature was taken
 as the average hourly refuge temperature. To parameterize the NAE model we
 combined temperature specific digestive processing data from Brewster et
 al. 2020, lizard bioenergetic data in the literature and estimates from
 our statistical comparisons in this study. We provide a glossary of
 acronyms, terms, equations, estimates, and citations used in the NAE model
 in table S1. We computed model estimates using a script
 written in R 3.6.2 (R Core Team, 2019). We obtained estimates of annual
 NAE and predicted egg production of one-year-old (1YO; 15g) and
 two-year-old (2YO; 25g) female <i>C. collaris</i> individuals
 from intact and encroached sites by iterating the model 1000 times. Two
 primary variables were allowed to vary randomly within the bounded values
 for each model iteration: Consumption on an empty stomach
 (C<sub>ES</sub>) and Metabolic scope
 (M<sub>SCOPE</sub>). We chose to vary these two estimates
 because these variables were assumed to strongly influence our model
 predictions and because of the substantial observed variation
 (C<sub>ES</sub> and activity-season length; Brewster 2019), or
 unknown variation (M<sub>SCOPE</sub>) in <i>C.
 collaris</i> for these estimates. We conducted model sensitivity
 analyses by re-running models each fixing these parameters at specific
 values and examining variation in model-predicted egg production and
 annual NAE (Appendix, fig. S1 a-d). In all analyses, assumptions of
 normality and homogeneity of slopes were met.
description: See README files 1 & 2 for details on data and
 model.