DOI: 10.5061/dryad.mgqnk9988
title: Is climate change to blame? Increased rainfall reduces emergence of Taiwanosemia hoppoensis (Hemiptera: Cicadidae) in coastal windbreak forests

description: We found that increased rainfall reduced exuvial numbers and impacted
 populations of Taiwanosemia hoppoensis from coastal regions, likely due to
 prolonged soil flooding harming the subterranean nymphs. As climate change
 advances, long-term monitoring is essential to track cicada populations
 across taxa and regions, given their important ecological roles.
description: <strong>Exuvial sampling and weather
 data</strong> The study was conducted in the
 Chengxi windbreaks, dominated by Australian pines (<em>Casuarina
 equisetifolia</em>). We establishing four plots, spaced about 500
 meters apart. In 2015 and 2016, we selected 12 same Australian pines per
 plot for exuvial sampling. In 2019 and 2022, we retained the original
 trees if they remained alive; if any had fallen or died, we replaced them
 with nearby Australian pines and increased the sample size to 15 trees per
 plot. Surveys were conducted between mid-April and mid-August of the
 selected year (45 surveys in total: 11 in 2015, 10 in 2016, 13 in 2019 and
 11 in 2022). Each session occurred every 5 to 17 days and lasted about 4
 hours. Exuviae were collected and counted by hand from the trees and
 within about a 1-meter radius on the ground. Although we occasionally
 found exuviae on the ground, their numbers were minimal.
 We obtained daily air temperature, relative humidity, daily
 accumulated rainfall, and monthly rainy day counts from the Anping weather
 station (22°59'34" N, 120°09'07" E, about 9 km
 southeastern of the study area) for the period between August 2012 and
 July 2022. Although exuvial surveys began in 2015, weather data from 2012
 onward provided a baseline for understanding pre-study weather conditions,
 particularly rainfall. <strong>Emergence season
 scale</strong> To assess the impact of weather
 conditions during emergence periods on exuvial numbers, we calculated mean
 air temperature (mean T<sub>a</sub>), mean relative humidity
 (mean RH), total accumulated rainfall (TAR) and simple daily intensity
 index (SDII, TAR divided by the numbers of rainy days) between two exuvial
 survey periods (n = 45 for all). To account for irregular survey
 intervals, we standardized exuvial numbers, TAR, and SDII by dividing them
 by the number of days between surveys, yielding standardized numbers of
 exuviae (STD_NE), standardized TAR (STD_TAR), and standardized SDII
 (STD_SDII), respectively. The STD_NE were rounded to whole numbers for
 GLMM (generalized linear mixed model) analysis. For
 GLMM analysis, we began by screening the variables through Spearman's
 rank correlation between STD_NE and each meteorological factor (mean
 T<sub>a</sub>, mean RH, STD_TAR and STD_SDII). Relative
 humidity was excluded from the GLMM due to its low correlation with STD_NE
 (Spearman’s rho = -0.24, p = 0.12, n = 45). In the GLMM, we modeled STD_NE
 as a function of three fixed-effect variables (mean
 T<sub>a</sub>, STD_TAR, STD_SDII) and one random-effect
 variable (year) (i.e., the full model). Given the count nature of our
 data, we used a Poisson family with log links and included
 observation-level random effects to address overdispersion (=
 3.36) (Harrison, 2014). We excluded the interaction terms (mean
 T<sub>a</sub> ´ STD_TAR, mean T<sub>a</sub> ´
 STD_SDII and STD_TAR ´ STD_SDII) to prevent multicollinearity, as these
 variables were significant correlated, which could lead to convergence
 issues and unreliable results (Izenman, 2013). Multicollinearity was
 further assessed using variance inflation factors (VIF) (Dormann et al.,
 2013), finding high multicollinearity between STD_TAR (VIF= 31.26) and
 STD_SDII (VIF= 31.84). We retained only STD_SDII in the final model due to
 its stronger correlation with STD_NE (Spearman's rho= -0.47, p =
 0.001, n = 45) compared to STD_TAR (Spearman's rho= -0.38, p = 0.01,
 n = 45) (Figure S5). While only the final model results were reported in
 the main text, both models were presented in Table S1 for reference. The
 GLMM analysis was performed in R-4.1.1 using the lme4 package.
 <strong>Interannual scale</strong>
 We calculated total exuviae collected in 2015, 2016, 2019, and
 2022, We also calculated one- and two-year values for mean
 T<sub>a</sub> (MT<sub>a</sub>
 <sub>one-year</sub>, MT<sub>a</sub>
 <sub>two-year</sub>), one-year and two-year accumulated
 rainfall (A_TAR<sub>one-year</sub>,
 A_TAR<sub>two-year</sub>), and one-year and two-year simple
 daily intensity index (A_SDII<sub>one-year</sub>,
 A_SDII<sub>two-year</sub>) for the period from previous August
 (and the August of the two preceding years) to July across the years
 2013/2014 to 2022 (e.g., A_TAR<sub>one-year</sub> in 2015
 represents the total accumulated rainfall from August 2014 to July 2015).
 We selected this timeframe because twilight cicada nymphs typically begin
 development in August (Chang et al., 2021), with a possible 2-3 year
 nymphal stage (or even shorter). Moreover, their emergence (early May to
 early August) coincides with Taiwan’s rainy season (Chang et al., 2021),
 when heavy rains impact both emerging adults and underground
 nymphs. We also calculated one-year accumulated
 rainfall and SDII for the rainy (August – September, May – July)
 (A_TAR<sub>rainy season</sub>, A_SDII<sub>rainy
 season</sub>) and dry (October – April) (A_TAR<sub>dry
 season</sub>, A_SDII<sub>dry season</sub>) seasons
 during the exuviae collection periods (August 2014 – July 2015, August
 2015 – July 2016, August 2018 – July 2019 and August 2021 – July 2022) as
 an example to assess the effects of dry and rainy seasonal rainfall on
 cicada emergence, despite their 2-3 year nymphal stage.
 We calculated the percentage decrease in the annual total exuviae
 collected in 2016, 2019, and 2022 relative to the total counts in 2015.
 Changes in
 A_TAR<sub>one-year</sub>/A_TAR<sub>two-year</sub>
 and
 A_SDII<sub>one-year</sub>/A_SDII<sub>two-year</sub> from 2016 to 2022 were expressed as quotients, with each year's values divided by those from 2015, when our exuvial survey began. Similarly, changes in A_TAR<sub>rainy season</sub>, A_TAR<sub>dry season</sub>, A_SDII<sub>rainy season</sub> and A_SDII<sub>dry season</sub> for 2016, 2019, and 2022 were expressed as quotients, using the 2015 rainy season values as the baseline. Data were expressed as means ± 1 SE (standard error), with significance level (α) of 0.05. References Chang, Y. M., Jong, J. J. & Lin, S. F. (2021). <em>Taiwan Twilight Cicada</em>.<em> </em>Taijiang National Park Headquarters. Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B. & Leitão, P. J. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. <em>Ecography,</em> <em>36</em>, 27-46. Harrison, X. A. (2014). Using observation-level random effects to model overdispersion in count data in ecology and evolution. <em>PeerJ,</em> <em>2</em>, e616.
description: # Is climate change to blame? Increased rainfall reduces emergence of
 Taiwanosemia hoppoensis (Hemiptera: Cicadidae) in coastal windbreak
 forests
 [https://doi.org/10.5061/dryad.mgqnk9988](https://doi.org/10.5061/dryad.mgqnk9988) ## Description of the data and file structure We collected exuviae of *Taiwanosemia hoppoensis* in the Chengxi windbreaks of Taijiang (Tainan, Taiwan) throughout the emergence seasons across four years (2015, 2016, 2019, 2022). We also obtained data on air temperature, relative humidity, accumulated rainfall, and the number of rainy days from the Anping weather station for the period between August 2012 and July 2022. Our study aimed to: (1) assess how weather conditions (temperature, humidity, rainfall) during emergence periods influence exuvial numbers at the emergence season scale, and (2) analyze interannual variations in total annual exuvial counts and identify the weather factors influencing these numbers at the interannual scale. ### Files and variables #### File: Annual\_total\_exuviae\_\_mean\_temperature\_\_accumulated\_rainfall\_and\_simple\_daily\_intensity\_index.xlsx **Description:** The data of this file were used to analyze interannual variations in total annual exuvial counts and identify the weather factors influencing these numbers. This file contains data on total exuviae collected in 2015, 2016, 2019, and 2022. It also includes unprocessed raw data for mean monthly air temperatures, monthly accumulated rainfalls, monthly numbers of rainy days downloaded from the Anping weather station and processed monthly simple daily intensity index (monthly accumulated rainfall divided by the monthly numbers of rainy days). Based on these weather data, we calculated one- and two-year values for mean air temperatures, one-year and two-year accumulated rainfall, and one-year and two-year simple daily intensity index (SDII, total accumulated rainfall divided by the numbers of rainy days). These calculations cover the period from the previous August (and the August of the two preceding years for two-year valuess) to July for each year from 2013/2014 to 2022. For example, the one-year accumulated rainfall in 2015 represents the total accumulated rainfall from August 2014 to July 2015. #### File: Emergence\_season\_scale.xlsx **Description:** The data of this file were used to assess the impact of weather conditions during emergence periods on exuvial numbers using the GLMM (generalized linear mixed model) analysis. The file contains data on the total exuviae collected in each survey in 2015, 2016, 2019, and 2022. It also includes unprocessed raw data for daily mean air temperatures, daily mean relative humidity, daily accumulated rainfalls, numbers of rainy days downloaded from the Anping weather station and processed daily simple daily intensity index (SDII, total accumulated rainfall divided by the numbers of rainy days). Based on these weather data, we calculated mean air temperature, mean relative humidity, total accumulated rainfall and simple daily intensity index between two exuvial survey periods. To account for irregular survey intervals, we standardized exuvial numbers, total accumulated rainfall, and SDII by dividing them by the number of days between surveys, yielding standardized exuvial numbers, standardized total accumulated rainfall, and standardized simple daily intensity index, respectively. For the GLMM analysis, the weather data were further processed by subtracting their means and dividing by their standard deviations. The standardized exuvial numbers were rounded to whole numbers before analysis.