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Analyzing time-energy constraints to understand the links between environmental change and local extinctions in terrestrial ectotherms

Citation

Brewster, Casey; Gifford, Matthew; Ortega, Jason; Beaupre, Steven (2021), Analyzing time-energy constraints to understand the links between environmental change and local extinctions in terrestrial ectotherms, Dryad, Dataset, https://doi.org/10.5061/dryad.wpzgmsbnd

Abstract

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.

Methods

Study Sites: We sampled C. collaris populations from six sites and designated each glade-site as either intact, with low levels of woody vegetation density and shade cover (n = 3), or encroached, with high levels of woody vegetation density and shade cover (n = 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 C. collaris in northern Arkansas (Trauth 1978; Brewster et al. 2018).

Biophysical Comparisons

Prey Availability: If dense woody vegetation in encroached glades results in reduced prey availability, this could lead to time-energy constraints in C. collaris (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 C. collaris 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 C. collaris 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.

Operative Environmental Temperature: 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 C. collaris (fig.1). We used two metrics to compare thermal environments between encroached and intact glades: 1) percent of operative environmental temperature (Te; Bakken 1992) within the range of voluntary active Tbs of C. collaris, and 2) thermal quality index (de; Hertz et al. 1993). A recent study suggested that C. collaris uses postural adjustments as a means to behaviorally thermoregulate, and this behavior can have a major influence on the predicted available activity-time and de in this species (Brewster and Beaupre 2019). Thus, we used the same Te modeling procedures as Brewster and Beaupre (2019; “OTMIDEAL”) that accounts for postural adjustments by C. collaris to estimate de and the frequency distribution of Te 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 C. collaris: 1) stilted OTM that mimics the biophysical properties of C. collaris in an elevated posture, 2) unmodified OTM that mimics the biophysical properties of C. collaris in an intermediate posture, and 3) compressed OTM that mimics the biophysical properties of C. collaris in a prostrate posture. All three OTM types were validated against live C. collaris individuals in the corresponding posture in the field (see fig.4B, Brewster and Beaupre 2019). For each glade-site, we used a total of 75 OTMs to estimate the Te 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 iButtonTM 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 Tb range (TACT) of C. collaris. We used the central 99 % of all Tbs recorded on surface-active C. collaris in our study populations to estimate TACT (31.2 - 42.6˚C). Using the posture-specific modeling procedure described above (OTMIDEAL; Brewster and Beaupre 2019), we estimated the percent of microhabitats at a given time point within TACT. 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 (de) between glade-types. The thermal quality index provides the average absolute deviation between Te and the set-point Tb range (TSET; Hertz et al. 1993). We used the central 60% of all Tbs selected by C. collaris in a laboratory gradient (34.8–38.1˚C; Firth et al. 1989) as our metric for TSET (Brewster and Beaupre 2019). Using OTMIDEAL, we estimated the average absolute deviation between Te and TSET 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 de 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 Te within TACT between glade-types. Instead we use this comparison to more effectively visualize the thermal environment in each glade type.

Time-Energy Budget Comparisons

Movement Rates: If C. collaris 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, a2,4; Brewster et al. 2013). To determine if C. collaris 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; NikonTM 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.

Surface Active Tb: If C. collaris in encroached glades experience reduced surface-active Tb, this could suggest that individuals suffer reduced physiological performance leading to reduced processing rates (b3, fig.1; Huey 1982; Congdon 1989; Beaupre et al. 1993a; Brewster et al. 2020) and/or reduced harvesting rates (b2, fig.1; Huey and Stevenson 1979; Avery et al. 1982; Smith and Ballinger 2001). To determine if C. collaris in encroached glades experience lower Tbs during surface activity, we used Tb data collected from 2013-2017. Animals were captured with a pole-and-lasso from late April through late July, and cloacal Tbs were recorded using a quick-read digital thermometer (Model HH800, Omega Engineering). We only used Tb 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; C. collaris time constant ~6.5 mins; Grigg et al. 1979) to ensure they were near equilibrium Tb, and where Tb was recorded within 2 min after capture. Comparisons of Tbs 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.

 Time Surface Active: If C. collaris in encroached glades spend less time surface-active this could leave them with less time-at-Tbs where digestion is optimal (Dunham et al. 1989), resulting in reduced processing rates (c3, 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 (c2, fig.1). To compare total daily surface-activity times between glade types, we made focal observations on C. collaris surface activity. A pilot study in 2014 suggested that surface-activity (percentage of animals in the population on the surface) was stable (>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.

Frequency of Recent Meal: If C. collaris in encroached glades spend more time with an empty stomach as a result of low absolute prey availability (d1, fig.1) and/or reduced capture performance (b2; fig.1), this could result in reduced harvesting rates (d3, fig.1). To test for differences between glade-types in the frequency of a recent meal, we used palpation data collected on C. collaris 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 C. collaris in our study populations. The goal of the NAE model was to predict the annual NAE and the corresponding number of eggs produced by C. collaris female individuals in intact and encroached glades, given the specific conditions identified in our statistical comparisons (operational environments, Tbs, 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 Tbs of individuals in encroached and intact glades to predict their effects (time-at-Tb) on digestive processing rates. We used general principles provided by Congdon et al. (1982) to estimate the various components of C. collaris energy budgets assuming:

NAE = ME – M

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 M is metabolic maintenance cost; all in kJ d-1.

To estimate Tbs of C. collaris during inactivity periods, we assumed that while inactive, the mean and variance in Tbs 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 C. collaris at each of the six glades during May, June, and July (see Operative Environmental Temperature). We wrapped a single iButtonTM 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 iButtonTM 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 Te 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 Tb profile of C. collaris 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 C. collaris 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 (CES) and Metabolic scope (MSCOPE). 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 (CES and activity-season length; Brewster 2019), or unknown variation (MSCOPE) in C. collaris 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.

Usage Notes

See README files 1 & 2 for details on data and model.