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Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas

Citation

Ortega Chinchilla, Jesús Eduardo et al. (2022), Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas, Dryad, Dataset, https://doi.org/10.5061/dryad.t76hdr83v

Abstract

We investigated how thermoregulatory behaviors affect hydro-thermoregulation in anurans, using agar models as a sampling unit, simulating four behaviors related to behavioral fever and sickness behavior. We collected data in two forest environments (Wet forest and transitional forest) in the Parque Estadual Intervales (PEI), an Integral Conservation Unit of the Atlantic Forest (24°12' - 24°25' S; 48°03 - 48°30' W). The Wet forest is a mature Atlantic Forest, and the Transitional forest is a young secondary forest adjacent to open areas.  We measured operational temperatures (temperatures of inanimate objects comparable to real frog species in size and shape) of agar models across 8 replicates in the two forest environments. We also measured agar models' temperature and water loss in different behavior simulations. In each transect, we used eight sampling unit (called tetrad) that was composed of two sets of four sensor-fit agar models. One of the sets was used to collect the operational temperature of the forest areas. These agar models were fitted with a 170 cm HOBO® Data Logger (U12-008) sensor programmed to record the temperature every 15 min.  The second set of tetrads was used to collect the agar models' body temperature and water loss in different behavior simulations. The behavioral simulations were defined as: (a) strong behavioral fever (SBF); (b) apathy behavior (AB); (c) single thermoregulatory event (STE); and (d) control model (CO).

After the temperature data were collected at 0600 h, three of the agar models (SBF, AB, and STE) were moved immediately, each one according to their corresponding protocol (SBF: Warmest Neighboring Site, AB: closest shelter, mainly small burrows, or accumulations of leaf litter; STE: Alternative Warmest Neighboring site). The selection of the places was made by using a FLIR TG165 Thermal Imaging Thermometer. One hour later, at about 0700 h, and hereafter hourly, a similar procedure was repeated, but only the SBF model required movement. We placed each model within 5 cm of another in this set to ensure similar initial thermal conditions. These tetrads were left undisturbed overnight, and no manipulation occurred after 2000 h. On the next day, at 0600 h, we measured the surface temperatures of the models and immediately applied the corresponding behavioral rule. This procedure was performed hourly until 2000 h. To analyze water loss, we recorded the mass of each model at 0600 h just after measuring temperature and repeated this every two hours. We used a portable balance (A&D Newton EJ-123, 0.01g accuracy) and calculated water loss rates from the difference between the initial model mass and mass measured at each subsequent 2-hour period. We express water loss as a percentage of maximum hydration.

Methods

To collect data on the temperature of the two forest areas, we used a tetrad sampling unit. This tetrad consisted of 4 agar models fitted with a 170 cm HOBO® Data Logger (U12-008) sensor programmed to record the temperature every 15 min. We placed one tetrad across 8 different replicates in a transects in each forest environment. The distance between tetrads was from 5 to 7 m, and the distance between models within a tetrad was based on sensor length (about 250 cm). We collected operational temperature data continuously over the sampling period using eight tetrads, all of which were set to collect data for five consecutive days. We sampled Wet Forest first (23 to 27 October 2016), then Transitional Forest (29 October to 2 November 2016). To collect data on body temperature and water loss of agar models, we used eight similar tetrad in the same transects explained above. However, for the behavior data, we did not use temperature sensors. Instead, we collected body temperature using a Raytek Pro Infrared Thermometer placed 2 cm above a model's surface to measure model temperature. As those behavior models were moved constantly depending on their behavior, we used a FLIR TG165 Thermal Imaging Thermometer to visualize the thermograms of the soil surface to select the Warmest Neighboring Site and Alternative Warmest Neighboring Site. For water loss measures, we weighted each model at 0600 h after measuring temperature and repeated this every two hours. We used a portable balance (A&D Newton EJ-123, 0.01g accuracy) and calculated water loss rates from the difference between the initial model mass and mass measured at each subsequent 2-hour period. We express water loss as a percentage of maximum hydration.

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2016/15297-7