DOI: 10.7291/d1m67x
title: Data for: Canopy coverage, light, and moisture affect thermoregulatory trade-offs in an amphibian breeding habitat

description: When amphibians thermoregulate, they face a fundamental trade-off between
 the ability to maintain activity and an increased rate of dehydration at
 higher temperatures. Canopy coverage affects both the thermal and hydric
 conditions of the environment and can therefore influence amphibian
 thermoregulation. Frogs require proper conditions to thermoregulate to
 successfully grow, survive, and reproduce. But while we know how canopy
 and environmental variables typically affect operative temperature, less
 is known about the effects on amphibian water loss rates. In this study,
 we measure the effect of canopy coverage on the conditions available for
 thermoregulation at a breeding pond of the California red-legged
 frog, Rana draytonii. We use agar frog models to estimate the
 thermal and hydric capacities frogs would experience in locations with
 different canopy coverage and microhabitats. At each site, we deployed
 models under four microhabitat treatments: wet/sun, wet/shade, dry/sun,
 and dry/shade. We modeled how environmental variables affected operative
 temperature and evaporative water loss from agar frogs. We found positive
 effects of air temperature, the sun treatment, and reduced canopy cover on
 operative temperature, and negative direct or indirect effects of these
 variables on evaporative water loss, consistent with the hypothesized
 trade-off between thermoregulatory behavior to increased temperature and
 the increased desiccation risk due to higher water loss. Additionally, our
 results indicate that the availability of wet microhabitats can allow
 frogs to reduce water loss, potentially mitigating the risk of desiccation
 when thermoregulating to achieve higher operative temperatures. Our
 findings suggest, that with access to proper microhabitats, amphibians can
 mitigate the fundamental trade-off and receive benefits of
 thermoregulating at high temperatures.
description: <em>Study
 Design</em> To measure the thermoregulatory
 conditions at the pond, we divided up the pond into 8 wedges, each with an
 area of approximately 51.28m<sup>2</sup>. We deployed agar
 frogs in each site’s shoreline for 24-hour periods from March to
 September. We used natural differences in canopy coverage to test how
 percent canopy coverage affects EWL and T<sub>e</sub> on agar
 frogs. For a list of variables and their definitions, see Table
 1. To measure the full
 range of thermal and hydric conditions an amphibian could experience, agar
 frogs were placed in different microhabitats using a 2 x 2 factorial
 design with two levels of light treatment (sun vs shade) and two levels of
 water treatment (wet vs dry). We tracked T<sub>e</sub> with
 internal dataloggers, EWL from mass changes in agar frogs, and other
 environmental conditions. With this design, we tested how percent canopy
 coverage affects agar frogs’ T<sub>e</sub> and EWL
 individually. Then, we tested the bi-directional interaction between
 T<sub>e</sub> and EWL using structural equation modeling.
 Finally, we tested a real-life example of thinning excess vegetation and
 its effect on EWL and T<sub>e</sub>. <em>Canopy
 Coverage</em> Holding a camera at 1 meter height
 and pointing the lens up vertically during the daytime, we took
 photographs of the canopy overstory at each site to calculate the percent
 canopy coverage. We took two photos at each site: one at the densest
 canopy coverage where shade agar frogs were deployed and the second at the
 thinnest canopy coverage where sun agar frogs were deployed (approximately
 5m apart). We analyzed photos in Adobe Photoshop software and converted
 pixels of the sky to white and pixels of vegetation to black following the
 procedure in Stewart et al. 2007. Using ImageJ software (Rasband
 1997-2018), we calculated the number of black vs white pixels estimated
 percent canopy coverage by comparing the number of pixels of vegetation to
 the total pixels. The average percent canopy coverage between the two
 photos was calculated for each site. <em>Agar Creation and
 Validation</em> Agar frogs are regularly used to
 estimate the T<sub>e</sub> and EWL an amphibian could
 potentially access in its environment. The free evaporation from agar
 captures the evaporative properties experienced by live frogs, including
 the cooling aspects caused by EWL, since we are unable to use live frogs
 due to their threatened status. Agar EWL has been validated in comparison
 to living frogs, and agar models exhibit internal temperatures that match
 live amphibians. While there has been recent debate about using agar
 models to compare to live thermoregulating amphibians, we minimized
 identified limitations by using agar frogs that represent live frogs in
 size, shape, posture, and inactivity, and replace models facing extreme
 dehydration to avoid inaccurate readings of EWL. We also acknowledge that
 agar frogs cannot replicate live frog behavior and movements, so we
 instead use agar frogs to study the available microhabitats with different
 thermal and hydric opportunities for frogs, as opposed to simulating what
 a live frog would experience. We made agar frog models in latex molds,
 which were initially created with plaster casts from museum specimens
 spanning the size range of anuran in the Pacific Northwest: two
 large-bodied frogs (15-48 grams),<em> Bufo boreas and Rana
 draytonii, </em>one small frog (6-22 grams)<em>, Pseudacris
 regilla,</em> and a small ellipse shape (3 cm diameter, 5-16 grams).
 We filled molds with agar (4.74 g in 100 ml) tinted green with acrylic
 paint and allowed them to solidify around a thermocouple probe in the
 center. The agar formula we used was denser than previous studies to
 achieve a similar density to live amphibians, obtain more accurate
 T<sub>e</sub>, and be a better proxy for simulating
 evaporative properties. <em>Agar Frog
 Deployment</em> We deployed the agar frogs for
 24 hours every two weeks during the 2017 breeding season March-September
 (N = 13 total weeks of deployment). For the first 7 deployment weeks, agar
 frogs were deployed at all 8 sites. However, as the pond water levels
 naturally went down, agar was only deployed at sites still holding water,
 with four sites measured in weeks 8 and 9, three sites in weeks 10-12, and
 one site in week 13. Within each of the 8 wedges, one small (5–22 g) and
 one large (15–48 g) agar frog were deployed under four microhabitat
 treatments: wet/sun (N = 432 deployment periods), wet/shade (N = 438),
 dry/sun (N = 428), and dry/shade (N = 448). Agar frogs in the sunlight
 treatment were placed in the site under the location of lowest canopy
 coverage and shade light treatment frogs were placed under fallen branches
 to be fully shaded. Agar frogs in the dry water treatment were placed on a
 dry substrate near the pond bank (1-2 meters from the water’s edge), and
 wet water treatment frogs were placed on the water line, so half the agar
 was in the water and half was on the saturated substrate. As the season
 progressed and the water level changed, we adjusted the wet treatment
 location to keep agar frogs on the water line, but dry treatment locations
 remained in the original location. Every agar frog was deployed with an
 internal thermocouple (Onset HOBO ProV2 U23-003) that measured internal
 model temperature at one-minute intervals to calculate
 T<sub>e</sub>. To determine EWL for each deployment
 period, agar frogs were initially weighed with a Pesola PPS200 digital
 scale at each site location. Agar frogs were then deployed in the evening
 (15:00-19:30 hours) and reweighed three times in situ the following day:
 morning (between 07:30-11:00 hours), noon (between 11:00-15:00 hours) and
 evening (between 14:00-20:00 hours). This creates three deployment periods
 each day (overnight, morning, and afternoon), defined as the 4–14 hour
 period between models being set in the field and the time when the next
 weighing occurred (weigh point). The first 15 minutes after each weigh
 point were removed from calculations to ensure agar frogs had achieved
 equilibrium again following handling. Any agar frogs that had lost 10% or
 more of their body weight were replaced with fresh agar frogs because agar
 that has lost more than 10% mass no longer maintains consistent EWL with
 live amphibians. Cages, constructed from chicken wire, were placed over
 agar frogs to prevent damage from birds. A relative humidity datalogger
 (Onset HOBO ProV2 U23-001) was placed at the main site 2 meters above the
 ground in shade conditions to log ambient air temperature and relative
 humidity at one-minute intervals. <em>EWL and T<sub>e</sub>
 Calculations</em> EWL was measured as percent
 water loss per hour, calculated as the change in agar mass over the
 deployment period. Operative temperature, T<sub>e</sub>, was
 calculated from the internal thermocouple data. We calculated the average,
 minimum, and maximum temperature for each weigh period as well as the
 standard deviation and full range difference of temperatures. The ambient
 data logger was used to calculate air temperature and relative humidity
 for each weight period. We calculated the average, minimum, maximum,
 standard deviation, and range of both air temperature and relative
 humidity. <em>Effects of Vegetation
 Trimming</em> At one wedge site along the pond,
 site managers trimmed the canopy and removed dead vegetation to simulate
 historical management. We used this site in our study, but we additionally
 compared how this artificially trimmed canopy affected EWL and
 T<sub> e</sub>, compared to sites with naturally thin canopy
 coverage. We aimed to test if artificial thinning can return potential
 thermoregulatory benefits, such as increased T<sub>e</sub>,
 that naturally thin canopy provides. <em>Statistical
 Analysis</em> <em>Environmental Variable Effects on
 Average T<sub>e</sub> and EWL</em>
 To examine how percent canopy coverage, water treatment, and
 light treatment impacted agar frogs, we fit a linear mixed model using the
 <em>lmer </em>package in R ver 3.6.1 (www.r-project.org). This
 modeling framework allowed us to examine the effect of many environmental
 variables (i.e., fixed effects) on multiple agar response variables and
 include two nested random effects of “session/period” and
 “site/microhabitat." We created two models to test how these
 environmental variables influenced two primary response variables: 1) EWL
 and 2) average T<sub>e</sub>. We started
 with a base model that includes the nested random effects above and the
 planned experimental variables as fixed effects: percent canopy coverage,
 water treatment, and light treatment. We chose these variables to start
 because percent canopy coverage is the predictor variable of interest, and
 the water and light treatments were manipulated in our experimental
 design. These nested effects and three fixed effects stayed constant in
 each following step of model creation. Next, we added
 additional predictor variables using a traditional forward selection
 procedure, in each step adding the predictor that contributed most to the
 model (lowest p-value). Predictors of interest used in model selection
 were relative humidity, air temperature, day condition, agar mass, agar
 surface area, and agar shape. Additionally, we used the predictor of
 T<sub>e </sub>for the EWL model and EWL for the
 T<sub>e</sub>model to start understanding the causal direction
 of the relationship between T<sub>e</sub> and EWL. We did not
 use any interaction terms. We continued to add predictors and stopped when
 no predictors significantly improved the model (p&gt;0.05). The
 significance of the fixed effect was assessed by using an <em>F
 </em>test in the <em>ANOVA </em>function in R, by
 sequentially adding the variable of interest and comparing it with the
 previous model, which did not include the effect. To reduce problems with
 multicollinearity, we only included the best single predictor variable
 (average, minimum, maximum, standard deviation, or range) in each of the
 following categories of highly correlated predictors: air temperature,
 agar operative temperature, and relative humidity. After one predictor was
 added from each category, other variables from that category were removed
 from consideration as future predictors. We visually assessed the model assumptions
 of normality and homoscedasticity with Q-Q plots and plotted the model
 residuals against the fitted values. We also measured normality with a
 Shapiro-Wilk statistic test. We calculated the variance inflation factor
 to check for multicollinearity between predictor variables in the final
 models and used a variance inflation factor of less than 5 as our
 threshold for variable inclusion. <em>Interaction between
 T<sub>e</sub> and EWL</em> To
 evaluate hypothesized causal pathways linking the EWL and average
 T<sub>e</sub> of the agar frogs, including a potentially
 bidirectional relationship between T<sub>e</sub> and EWL, we
 used piecewise structural equation modeling (SEM). This statistical
 technique estimates the strengths of the effects using the linear
 relationships between variables and can account for nested random effects.
 Structural equation modeling has been used as a strong technique to
 discern the bidirectional effects of two biological variables, which are
 each individually affected by additional variables. For discrete
 variables, such as light treatment and water treatment, we set them as
 binary values (0 and 1) to model as numeric variables. The hypothesized
 causal links between variables were determined by the LMMs created in the
 previous section for EWL and average T<sub>e</sub>. To fit our
 models, we used the piecewiseSEM package in R.
 <em>Effects of Vegetative Trimming</em>
 To discern the effects of the artificial trimming on EWL and
 T<sub>e</sub>, we used a 1 sample t-test. With this test, we
 tested how the distribution of EWL and T<sub>e</sub> of the
 artificially trimmed site compared to the predicted values of our
 model.
description: # Data for: Canopy coverage, light, and moisture affect thermoregulatory
 trade-offs in an amphibian breeding habitat --- This file includes the
 main data set for how environmental variables, specifically canopy
 density, affect the thermal and hydric measurements of agar frogs. The
 code for linear mixed models and structural equation modeling is also
 included. ## Description of the Data and file structure ### Spranger et
 al. Water loss and operative temperature response variables This data
 frame has all the predictor and response variables measured in the
 experiment. See R markdown for analyses. * Spranger_et_al._dataset.csv *
 Metadata_Spranger_et_al._dataset.docx - contains all variables and units
 in Spranger_et_al._dataset.csv ### Spranger et al. Linear Mixed Models R
 markdown analyzing environmental variable effects on agar operative
 temperature and water loss. This R markdown has the code that uses forward
 selection to build and run linear mixed effects models. *
 Spranger_et_al._Linear_Mixed_Models.Rmd ### Spranger et al. Piecewise SEM
 R markdown with structural equation modeling. *
 Spranger_et_al._Piecewise_SEM.Rmd