Combinations of reproductive, individual, and weather effects best explain torpor patterns among female little brown bats (Myotis lucifugus)

Abstract

Heterothermic mammals can use torpor, a state of metabolic suppression, to conserve energy during times of limited food and poor environmental conditions. Females may use torpor throughout gestation and lactation; however, there are associated physiological and ecological costs with potential fitness consequences. Previous studies have controlled for, but not quantified the impact of interindividual variation on torpor patterns and understanding this may provide insight on why certain thermoregulatory responses are employed. The objective of this study was to identify and quantitatively characterize the intrinsic variables and weather conditions that best explain variation in torpor patterns among individual female little brown bats, Myotis lucifugus. We used temperature-sensitive radio-transmitters affixed to females to measure skin temperature patterns of 35 individuals roosting in bat boxes in the spring and summer. We used Bayesian multi-model inference to rank a priori-selected models and variables based on their explanatory power. Reproductive condition and interindividual effects best explained torpor duration and depth, and weather best explained torpor frequency. Of the reproductive conditions, lactating females used torpor for the shortest durations and at shallower depths (i.e., smallest drop in minimum T _sk), while females in early spring (i.e., not-obviously-pregnant) used torpor for the longest and deepest. Among individuals, the greatest difference in effects on duration occurred between pregnant individuals, suggesting interindividual variation within reproductive condition. Increases in precipitation and wind were associated with a higher probability of torpor use. Our results provide further support that multiple variables explain torpor patterns and highlight the importance of including individual effects when studying thermoregulatory patterns in heterothermic species.

Open research badges: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5061/dryad.c04tj85.

Keywords: Chiroptera; Myotis lucifugus; ecophysiology; reproduction; thermoregulation; torpor.

Figure 1

Little brown bat, Myotis lucifugus, flying out of a bat box at Salmonier Nature Park, Newfoundland, Canada. Photo credit: Cody Fouts

Figure 2

Skin temperature (T _sk) patterns of the same individual female Myotis lucifugus during (a) pregnancy and (b) lactation, two pregnant individuals (c) #13 and (d) #17 on June 30, 2017, and the same pregnant individual on (e) July 3, 2017 and (f) July 4, 2017. Data were collected from bats roosting in bat boxes at Salmonier Nature Park, Newfoundland. The black line represents T _sk (°C), the dashed gray line represents the torpor onset threshold (°C), and the gray line represents ambient temperature (°C). Black bars above the x axis represent night

Figure 3

Difference in torpor (a) duration and (b) depth for pregnant (n = 11, N = 30), lactating (n = 11, N = 43), post‐lactating (n = 8, N = 37), nonreproductive (n = 2, N = 7), and not‐obviously pregnant (n = 4, N = 22) female Myotis lucifugus in Newfoundland from June to August 2016 and 2017. The top and bottom of each box show the upper and lower quartiles and the dashed vertical lines represent the maximum and minimum values. The black bars represent the median, the gray dots represent the mean, and open circles represent outliers. Above the boxes are the posterior distributions for the estimates of each reproductive condition from the highest ranked candidate model. L: lactating; n: number of individual bats; N: number of bat days; NOP: not‐obviously‐pregnant; NR: nonreproductive; P: pregnant, PL: post‐lactating

Figure 4

Torpor frequency (i.e., the probability of using torpor on a given day) for female Myotis lucifugus in Newfoundland as mean daily (a) maximum wind speed and (b) precipitation increase. Curves are logistic regressions (y = exp(β ₀ + β ₁)/(1 + exp(β ₀ + β ₁))) based on the model‐averaged posterior estimates. The posterior distribution of each variable for the highest ranked model is adjacent to the plot

Figure 5

The effects of daily maximum wind speed on torpor depth in female Myotis lucifugus in Newfoundland from June to August 2016 and 2017. The black line is a linear regression based on the model‐averaged posterior estimates of the slope and intercept (y = −0.09x + 25.45). The posterior distribution of maximum wind speed for the highest ranked model is adjacent to the plot