More winners than losers over 12 years of monitoring tiger moths (Erebidae: Arctiinae) on Barro Colorado Island, Panama

Data used in the manuscript:

Column Name: Description

Taxon.Code: this code represents the unique identification
number used during monitoring for each species of there past 12 years

Species.name: This is the initial species name based on
morphological and DNA barcoding data

Proposed.new.name: Recent changes in DNA barcoding data and
results from taxonomist suggest these new proposed species names

Tribe: The tribe that each Arctiinae is within

Subtribe: The subtribe that each species is within

numYearObs: The number of years that each species was
observed, a maximum of 12 and a minimum of 6

sumAbund: The total number of individuals that were
collected over the 12 year period

NumZeroObs: The number of sample that the species was not
observed, with a maximum of 47 and minimum of 0

n: Total number of sample days over the 12 year period. Four
sample days per year, but we omitted one sample period due to covid

pctZero: the proportion of sample periods that the species
was not observed

pctObs: the proportion of sample periods that the species
was observed

NumZeroObsYear: the number of years that each species was
not observed. As long as a species was observed once out of the 4 sample days,
it was counted as observed for that year

pctZeroYear: the proportion of years that species was not
observed

pctObsYear: the proportion of years that species was
observed

numyear.vec: the mean of the posterior probability
distribution for Year in the Negative Binomial Bayesian regression. This served
as our measurement of population trend

postCI2.5.numyear.vec: The lower 2.5% of the 95% credible
interval from the posterior probability distributon for year

postCI97.5.numyear.vec: The upper 2.5% of the 95% credible
interval from the posterior probability distributon for year

p.value.KSTest: p-value from the DHARMa package checking for
uniformity of siulated residuals from the bayesian regression. Uses the
Komogorov-Smirnov Test (KS Test). P-values greater than 0.05 suggest no
deviation from uniformity

p.value.ZInfTest: p value from DHARMa's zero inflation test
which compares observed number of zeros with zeros expected from simulations.
Values greater than 0.05 suggest no evidence of zero inflation

p.value.DispTest: p-value form DHARMa's dispersion test,
which compares the variance of the observed raw residuals against the variance
of the simulated residuals. Values greater than 0.5 suggest no evidence of over
dispersion

p.value.TempAutoCor: p-value from DHARM's temporal
autocorrelation test, which used the Durbin-Watson test on the uniformly scaled
residuals. If values are greater than 0.05, it suggest no temporal
autocorrelation

looic: leave-one-out cross validation information criteria
from "loo" package

r2Bayes: R2 for each Bayesian model was estimated as the
variance of the residuals for a given mode divided by the total variance, which
is the sum of the variance of fitted vales and the residual variance. The value
was subtracted from 1

essyear: Effective sample size, which measure the amount of
independent information there is in an autocorrelated chain (Kurschke 2015)

probgreat1: Probability that the trend is greater than one,
or the species is increasing based on the Bayesian "degree-of-belief"

Forewing_length: Average forewing length for each species.
Averaged across sexes

Thorax_width: Average thorax width for each species.
Averaged across sexes

Wing_load: ratio of thorax width to wing length

Wing_tip_angle: Angle of the tip of the forewing

Lightness: Lightness of the dorsal wing

Redness: Redness of the dorsal wing

Greenness: Greenness of the dorsal wing

Blueness: Blueness of the dorsal wing

mean.red: mean red value of dorsal wings

mean.green: mean green value of dorsal wings

mean.blue: mean blue value of dorsal wings

maxtemp.vec: the mean of the posterior probability
distribution for average maximum temperature during the sampling month in a Negative Binomial Bayesian regression.
This served as our measurement of sensitivity to average maximum temperature

postCI2.5.maxtemp.vec: The lower 2.5% of the 95% credible
interval from the posterior probability distribution for the maximum
temperature predictor

postCI97.5.maxtemp.vec: The upper 2.5% of the 95% credible
interval from the posterior probability distribution for the maximum
temperature predictor

mintemp.vec: the mean of the posterior probability distribution
for average miniumun temperature during the sampling month in a Negative
Binomial Bayesian regression. This served as our measurement of sensitivity to
average maximum temperature

postCI2.5.mintemp.vec: The lower 2.5% of the 95% credible
interval from the posterior probability distribution for the minimum
temperature predictor

postCI97.5.mintemp.vec: The upper 2.5% of the 95% credible
interval from the posterior probability distribution for the minimum
temperature predictor

avgprecip.vec: the mean of the posterior probability
distribution for average precipitation duing the sampling month in a Negative
Binomial Bayesian regression. This served as our measurement of sensitivity to
average maximum temperature

postCI2.5.avgprecip.vec: The lower 2.5% of the 95% credible
interval from the posterior probability distribution for the average monthly
precipitation predictor

postCI97.5.avgprecip.vec: The upper 2.5% of the 95% credible
interval from the posterior probability distribution for the average monthly
precipitation predictor

CVAbund: Coefficient of variation in abundance across each
sample period (n=47)

Geographic_range: a count of the number of countries with
records for each species logged on internet databases including funet, BOL,
EOL, GBIF, and Google scholar

Iridescence: presence/absence of iridescence

Aposematism: presence/absence of aposematism

Clearwing_coverage: % clearwing coverage

Seasonality: a seasonal decomposition of time series as the
proportion of standard deviation in seasonal component to standard deviation in
detrended time series

Spatial_aggregation: Lloyd index of patchiness (Lloyd,
1967), based on the dispersion of individuals captured in the ten traps across
BCI

Final.group: functional grouping based on multivariate and
phylogenetic analyses

本文献中使用的数据集包括以下列：
列名：描述
Taxon.Code：此代码代表过去12年监测中每个物种的唯一识别号码
Species.name：基于形态学和DNA条形码数据的初始物种名称
Proposed.new.name：基于DNA条形码数据的最新变化以及分类学家的研究结果，提出的新物种名称
Tribe：每个Arctiinae所属的族
Subtribe：每个物种所属的亚族
numYearObs：每个物种被观察的年数，最长为12年，最短为6年
sumAbund：在12年期间收集到的总个体数
NumZeroObs：物种未被观察到的样本数量，最多为47，最少为0
n：12年期间的总样本天数。每年4个样本日，但由于新冠疫情，我们省略了一个样本周期
pctZero：物种未被观察到的样本周期比例
pctObs：物种被观察到的样本周期比例
NumZeroObsYear：物种未被观察到的年数。只要物种在4个样本日中的任何一天被观察到，即计为该年观察到
pctZeroYear：物种未被观察到的年数比例
pctObsYear：物种被观察到的年数比例
numyear.vec：在负二项式贝叶斯回归中，Year后验概率分布的均值。这作为我们测量种群趋势的指标
postCI2.5.numyear.vec：后验概率分布对于年的95%可信区间下限的2.5%部分
postCI97.5.numyear.vec：后验概率分布对于年的95%可信区间上限的2.5%部分
p.value.KSTest：DHARMa包检查贝叶斯回归模拟残差的均匀性的p值。使用Kolmogorov-Smirnov测试（KS测试）。p值大于0.05表示没有偏离均匀性
p.value.ZInfTest：DHARMa的零膨胀测试的p值，该测试比较观察到的零数与模拟中预期的零数。p值大于0.05表示没有零膨胀的证据
p.value.DispTest：DHARMa的离散度测试的p值，该测试比较观察到的原始残差的方差与模拟残差的方差。p值大于0.5表示没有过度离散的证据
p.value.TempAutoCor：DHARM的时序自相关测试的p值，该测试使用Durbin-Watson测试对均匀缩放的残差进行操作。如果p值大于0.05，则表示没有时序自相关
looic：来自“loo”包的留一法交叉验证信息标准
r2Bayes：每个贝叶斯模型的R2值是通过给定模式的残差方差除以总方差估计的，总方差是拟合值方差和残差方差的和。该值从1中减去
essyear：有效样本大小，它衡量自相关链中独立信息的数量（Kurschke 2015年提出
probgreat1：趋势大于一的或物种增加的概率，基于贝叶斯‘信念度’
Forewing_length：每个物种的平均前翅长度。跨性别平均
Thorax_width：每个物种的平均胸宽。跨性别平均
Wing_load：胸宽与翅长的比率
Wing_tip_angle：前翅尖的角度
Lightness：背翅的亮度
Redness：背翅的红色
Greenness：背翅的绿色
Blueness：背翅的蓝色
mean.red：背翅的平均红色值
mean.green：背翅的平均绿色值
mean.blue：背翅的平均蓝色值
maxtemp.vec：在负二项式贝叶斯回归中，采样月份平均最高温度后验概率分布的均值。这作为我们测量对平均最高温度敏感性的指标
postCI2.5.maxtemp.vec：后验概率分布对于最高温度预测器的95%可信区间下限的2.5%部分
postCI97.5.maxtemp.vec：后验概率分布对于最高温度预测器的95%可信区间上限的2.5%部分
mintemp.vec：在负二项式贝叶斯回归中，采样月份平均最低温度后验概率分布的均值。这作为我们测量对平均最低温度敏感性的指标
postCI2.5.mintemp.vec：后验概率分布对于最低温度预测器的95%可信区间下限的2.5%部分
postCI97.5.mintemp.vec：后验概率分布对于最低温度预测器的95%可信区间上限的2.5%部分
avgprecip.vec：在负二项式贝叶斯回归中，采样月份平均降水量后验概率分布的均值。这作为我们测量对平均最高温度敏感性的指标
postCI2.5.avgprecip.vec：后验概率分布对于平均月降水预测器的95%可信区间下限的2.5%部分
postCI97.5.avgprecip.vec：后验概率分布对于平均月降水预测器的95%可信区间上限的2.5%部分
CVAbund：每个样本周期（n=47）的丰度变异系数
Geographic_range：记录在互联网数据库（包括funet、BOL、EOL、GBIF和Google Scholar）中每个物种的国家记录数
Iridescence：是否存在彩虹色
Aposematism：是否存在警戒色
Clearwing_coverage：透明翅覆盖率
Seasonality：时间序列的季节分解，作为季节成分标准差与去趋势时间序列标准差的比例
Spatial_aggregation：基于BCI十个陷阱中捕获的个体分布的Lloyd斑状指数（Lloyd，1967年提出
Final.group：基于多元和系统发育分析的功能分组