DOI: 10.5061/dryad.sj3tx964r
title: Population dynamics of little brown bats (Myotis lucifugus) at summer roosts: apparent survival, fidelity, abundance, and the influence of winter conditions

description: White-nose syndrome (WNS) has caused the death of millions of bats, but
 the impacts have been more difficult to identify in western North America.
 Understanding how WNS, or other threats, impact western bats may require
 monitoring other roosts, such as maternity roosts and night roosts, where
 bats aggregate in large numbers. Little brown bats (Myotis lucifugus) are
 experiencing some of the greatest declines from WNS. Estimating survival
 and understanding population dynamics can provide valuable data for
 assessing population declines and informing conservation efforts. We
 conducted a 5-year mark-recapture study of two M. lucifugus roosts in
 Colorado. We used the robust design model to estimate apparent survival,
 fidelity, and abundance to understand population dynamics, and
 environmental covariates to understand how summer and winter weather
 conditions impact adult female survival. We compared the fidelity and
 capture probability of M. lucifugus between colonies to understand how
 bats use such roosts. Overwinter survival increased with the number of
 days with temperatures below freezing (β &gt; 0.100, SE = 0.003), and
 decreased with the number of days with snow cover (β &lt; -0.40, SE
 &lt; 0.13). Adult female fidelity was higher at one maternity roost
 than the other. Overwinter and oversummer adult female survival were high
 (&gt;0.90), and based on survival estimates and fungal-swabbing
 results we believe these populations have yet to experience WNS. Recapture
 of M. lucifugus using antennas that continuously read passive integrated
 transponder tags allows rigorous estimation of bat population parameters
 that can elucidate trends in abundance and changes in survival. Monitoring
 populations at summer roosts can provide unique population ecology data
 that monitoring hibernacula alone may not. Because few adult males are
 captured at maternity colonies, and juvenile males have low fidelity,
 additional effort should focus on understanding male M. lucifugus
 population dynamics.
description: We conducted this study at two roosts within the Yampa Valley of
 northwest Colorado (Figure 2). One colony (herein called “house roost”) is
 in a 120-m<sup>2</sup> ranch house at the Rehder Ranch
 (elevation 2,150 m) built in 1900. The house roost is adjacent to
 Catamount Lake, and approximately 17 km south of Steamboat Springs,
 Colorado. Bats roost in the spaces between the interior walls and exterior
 metal roofing material. The other colony (herein called “barn roost”,
 elevation 1,930 m) is in the Carpenter Ranch barn, which was built in
 1903, is adjacent to the Yampa River, and is approximately 7 km east of
 Hayden, Colorado. Bats roost between timbers in the hayloft and between
 timbers and the metal roof. We captured bats using harp
 traps and mist nets around the outside of the house roost, and within the
 barn roost. We captured bats in early summer (June) and when
 young-of-the-year (juveniles) were believed to be volant, but had not
 dispersed (late summer: late July/early August). We captured and marked
 bats twice a year from 2014 – 2018, except for late summer of 2017 at the
 house roost. Because the timing of <em>M. lucifugus</em>
 parturition varies each year, the timing of the late-summer capture event
 was adjusted to optimize juvenile captures. For each bat captured, we
 recorded the mass, sex, age as juvenile or adult (Brunet-Rossinni
 & Wilkinson, 2009), palpated and assessed the individual’s
 reproductive condition, and marked it with HPT9 (9 mm x 2 mm) 134.2 kHz
 passive integrated transponder (PIT) tag (Biomark, Inc., Boise, Idaho). We
 inserted tags subcutaneously below the scapula, and we sealed insertion
 sites with a biomedical glue (VetBond Tissue Adhesive, 3M Science, St.
 Paul, Minnesota). We conducted facial and wing swabbing and guano
 collection to assess the presence of the fungus
 (<em>Pseudogymnoascus destructans</em>;
 <em>Pd</em>) that causes WNS (analysis conducted by U.S.
 Geological Survey, National Wildlife Health Center, Madison,
 WI). At the house roost, we detected tagged bats using
 a 7.6-m cord PIT-tag antenna system (IS1001, Biomark, Inc., Boise, Idaho;
 herein called “antenna”) that was stretched under the eaves. We installed
 this antenna in the summer of 2015, and it was disrupted for several weeks
 late in summer of 2015 and early 2016 because the solar panel became
 disconnected from the battery. At the barn roost, we varied the PIT-tag
 reading system’s configuration and placement over time. In 2014, we placed
 two 0.6 m x 0.6 m window-style PIT-tag readers (FS2001, Biomark, Inc.,
 Boise, Idaho) at the top of the primary entrance (3.5 m x 3.5 m barn door
 opening), while netting was stretched along the lower section of the
 entrance to channel bat flight through or near the window-style readers.
 This arrangement produced few detections (&lt; 60 detections/2 weeks),
 so in 2015, we installed a cord antenna system like that used at the house
 roost. We weaved the cord antenna across the barn door opening with
 approximately 0.25-m gap, then in August 2015 moved the antenna within the
 hayloft to reduce conflicts with on-going cattle operations. At both
 locations, we acquired additional detections using handheld PIT-tag
 readers to scan roosting bats in the hayloft of the barn roost, and at bat
 houses near the house roost. We created encounter
 histories based on weeks of the year when individuals were captured or
 detected, thus there were 52 encounter periods throughout the year. We
 structured the data into weekly intervals because it allowed multiple
 reencounter (recapture) occasions throughout the summer. These multiple
 opportunities to reencounter tagged animals increase the opportunities to
 detect bats and improve estimates of detectability. We analyzed
 mark-recapture data using a Huggins robust design model in Program MARK
 (Kendall, 2001). The robust design model allows estimation of parameters,
 such as abundance, during the closed sessions when we assume there are no
 births, deaths, immigration, or emigration. Also, the robust design model
 allows estimation of other parameters, such as survival, immigration,
 emigration, and fidelity, during open sessions. Because the fate of
 juvenile bats may be strongly associated with the fates of their mothers,
 we estimated an overdispersion scaling parameter (<em>ĉ</em>),
 from the global model in a Cormack-Jolly-Seber analysis (White &
 Burnham, 2001). We compared competing models using quasi-likelihood
 Akaike’s information criterion with small sample size bias correction
 (QAIC<em><sub>c</sub></em>) and the probability of
 a model being the most-parsimonious model
 (QAIC<em><sub>c</sub></em> weight;
 <em>w<sub>i</sub></em> – Burnham &
 Anderson, 2002). We estimated oversummer and overwinter
 survival, because if WNS was impacting <em>M. lucifugus</em>
 populations, we expected overwinter survival to be lower than oversummer
 survival. To estimate oversummer survival, we structured the encounter
 data such that there was an open season between summer tagging events. To
 estimate overwinter survival, we used the open session between late summer
 and early summer tagging events. The two periods of population closure
 each year were based on the arrival of a substantial number of individuals
 (&gt;30 individuals/week) after hibernation, and the departure of most
 bats before hibernation (&lt;30 individuals/week remaining at the
 roost). The selection of 30 individuals is arbitrary, but chosen to
 designate when we believed bats were returning to the roost. The
 “early-summer closed period” began after the week when greater than 30
 individuals were detected (late April to mid-May) and extended until the
 week after the capture and tagging event in early June. The time between
 early-summer (June) and late-summer (late July/early August) capture
 events was considered open because young were being born. The “late-summer
 closed session” began after the July/August capture event and ended the
 week when fewer than 30 individuals were detected in early fall
 (mid-August to mid-September). Using the robust design
 model, we estimated capture probability (<em>p</em>),
 recapture probability (<em>c</em>), overwinter and oversummer
 apparent survival (<em>φ</em>), abundance (N), temporary
 immigration (1 – γ’), temporary emigration (γ”), and site fidelity (1 –
 γ”; the probability of being detected at time interval
 <em>i</em>, given you were detected in time interval
 <em>i</em> - 1). As a modeling approach, we first modeled
 capture and recapture probability, keeping apparent survival and movement
 parameters (γ’, γ”) as varying temporally. Once the best set of models of
 <em>p</em> and <em>c</em> were identified, we used
 those <em>p</em> and <em>c</em> configurations to
 model <em>φ</em>, γ’, γ”. Seasonal parameter estimates were
 transformed based on weekly estimates, and variances were estimated using
 the Delta method (Powell, 2007). Abundance at each roost was estimated for
 each of the two closed periods per year. For estimating juvenile
 <em>φ</em> we transitioned juveniles into adults the first
 year after they were captured, when they would be sexually mature
 (Humphrey & Cope, 1976). We ran 27 models for
 <em>p</em> and <em>c</em>, and we ran 89 models
 for <em>φ</em> and the movement parameters using the most
 parsimonious model of <em>p </em>and
 <em>c</em>. Because weather can impact bat
 activity and survival, we used weather data from a Steamboat Springs
 weather station (National Oceanic and Atmospheric Administration National
 Centers for Environmental Information, Station No. GHCND:USCOOO57936;
 ncdc.noaa.gov) to model capture and recapture probabilities, and monthly
 weather summaries to model seasonal survival. We used number of days with
 precipitation &gt;0.25 cm and &gt;2.5 cm, number of days with
 maximum temperature &gt;21°C and &gt;32°C, and total monthly
 precipitation (in) to model oversummer <em>φ</em>. We used
 measures of winter severity, including number of days with low temperature
 &lt; 0°C, number of days with low temperature &lt; -32°C, number
 of days with high temperature &lt; 0˚C, number of days with
 precipitation &gt; 0.25 cm, number of days with precipitation
 &gt;2.5 cm, number of days with snow depth ≥ 2.5 cm, and total
 precipitation as covariates for modeling overwinter survival.
 Additionally, we used the variance of winter covariates to assess how the
 variability of winter severity impacted parameters. We
 used individual covariates of mass and body condition index (BCI: forearm
 length/mass) as covariates for seasonal survival (Pearce et al., 2008).
 Mass was measured at each capture event, but we did not begin measuring
 forearm lengths until 2016.