THE INFLUENCE OF SPECIFIC ATMOSPHERIC VARIABLES ON FALL BAT ACTIVITY VARIES AMONG GEOGRAPHIC REGIONS AND SPECIES

Transcription

1 THE INFLUENCE OF SPECIFIC ATMOSPHERIC VARIABLES ON FALL BAT ACTIVITY VARIES AMONG GEOGRAPHIC REGIONS AND SPECIES Lauren Hooton, Crissy Sutter, Allison Costello, and Greg Forcey Wind Wildlife Research Meeting IX 212 Photo: J.S. Altenbach

2 BACKGROUND Correlations between bats and weather e.g.,baerwald and Barclay (211),Weller and Baldwin (211)

3 BACKGROUND The need for predictive activity and mortality modeling Understanding mechanisms of mortality at WEFs Focusing curtailment

4 OBJECTIVES To investigate the broad-scale relationships between bat activity and atmospheric variables Species-based Predict bat activity based solely on atmospheric conditions

5 LARGE DATASET National, multi-year monitoring consistent dataset Enabled us to develop broad-scale, regional predictive activity models

6 METHODS Regions Midwest (29-21) Iowa (3) Missouri (2) West (21-211) California (4) Nevada (1) Arizona (2) 2 detectors/tower 15m, 6m

7 METHODS Species Midwest Hoary bat Eastern red bat Silver-haired bat West Hoary bat Mexican free-tailed bat Silver-haired bat

8 METHODS Fall Activity July 1 October 31, Bat activity bat passes/night 1 night = 5pm-5am local standard time

9 METHODS Bat activity Bat Activity Number of Bats but there is an assumed positive relationship: more calls captured ~ likely more bats present Activity Mortality

10 METHODS Weather variables Met towers Temperature ( o C) Wind speed (m/s) Wind direction (sine and cosine of radian value) Weather stations Relative humidity (%) Temperature + Wind Speed + Wind Direction + Relative Humidity Sine Direction Cosine Direction NB: These variables were standardized prior to modeling

11 METHODS Generalized Linear Mixed Model glmmadmb package in R All models were controlled for the random effects of Year, Weeks since July 1 st, Tower Bolker et al. 28, Generalized linear mixed models: a practical guide for ecology and evolution

12 RESULTS MIDWEST Hoary ~ Temperature + Wind Speed Eastern red ~ Temperature Silver-haired ~ Temperature + Wind Speed + Wind Direction + Relative Humidity

13 RESULTS WEST Hoary ~ Wind Speed + Wind Direction* Mexican free-tailed ~ Temperature + Wind Speed + Wind Direction + Relative Humidity Silver-haired ~ Temperature + Wind Speed + RH

14 RESULTS COMPARISON Midwest Temperature most important predictor of activity of all three species Likely explanation the mean nightly temp (15 vs 18 o C) is low enough to affect activity via energetics

15 Eastern red bat passes/night Silver haired bat passes/night Temperature ( o C) Photo: E. Fraser Temperature ( o C) Hoary bat passes/night Temperature ( o C)

16 3 1 Silver haired bat passes/night Hoary bat passes/night Temperature ( o C) Temperature ( o C) Eastern red bat passes/night Temperature ( o C)

17 25 3 Hoary bat passes/night Eastern red bat passes/night Temperature ( o C) Temperature ( o C) Silver haired bat passes/night Temperature ( o C)

18 RESULTS COMPARISON Midwest Temperature most important predictor of activity of all three species Likely explanation the mean nightly temp (15 vs 18 o C) is low enough to affect activity via energetics West Wind speed most important predictor of activity of all three species Likely explanation the higher wind speeds in the west (max 23.5 m/s vs 12.5 m/s) are high enough to effect activity via energetics

19 9 16 Mexican free-tailed bat passes/night Silver-haired bat passes/night Wind Speed (m/s) Photo: E. Fraser Wind Speed (m/s) Hoary bat passes/night Wind Speed (m/s)

20 Silver-haired bat passes/night Hoary bat passes/night Wind Speed (m/s) Photo: J.S. Altenbach Mexican free-tailed bat passes/night Wind Speed (m/s) Wind Speed (m/s)

21 9 3 Mexican free-tailed bat passes/night Hoary bat passes/night Wind Speed (m/s) Silver-haired bat passes/night Wind Speed (m/s) Wind Speed (m/s)

22 MODEL VALIDATION MIDWEST Validation - Tests how accurately the models predict activity based solely on atmospheric variables. Validation needed for each model - Midwest Used data from an independent site (3 towers)

23 MODEL VALIDATION Hoary Bat Predicted Hoary bat passes/night Observed Hoary bat passes/night 91% of data R 2 =.45, p <.1

24 MODEL VALIDATION Eastern red bats % of data Predicted Eastern red bat passes/night R 2 =.24, p < Observed Eastern red bat passes/night

25 MODEL VALIDATION Silver-haired bats 25 Predicted silver-haired bat passes/night R 2 =.1, p > Observed silver-haired bat passes/night

26 IMPLICATIONS Bats are reacting differently to weather in different geographic regions Could not use model from one region to predict activity in another Differences among species Cannot lump species together

27 APPLICATIONS Two main levels of specificity: 1. Site screening use the regional (e.g., Midwest) models to get predictions of relative activity, based solely on weather data 2. Curtailment schemes use site-specific models, using collected bat and weather data, to determine how weather influences activity at each site, and using that to assess curtailment.

28 NEXT STEPS Further model refinement More data for model development Consider other variables Moon %, broad weather fronts, habitat features Extend model to include mortality Mortality surveys, add to model

29 QUESTIONS? Photo: Y. Attila