Featuring 10 phenological estimators using simulated data

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Do growing degree days predict phenology across butterfly species (2015, background): “Peak abundance is generally less sensitive to variation in sampling size and imperfect detectability (Moussus et al. 2010).”
Odonata as candidate macroecological barometers for global climate change (2015, background): “Percentile sighting dates are more robust estimators of phenological variation than absolute sighting dates (Moussus et al. 2010).”
French citizens monitoring ordinary birds provide tools for conservation and ecological sciences (2012, methodology): “The method was further refined, compared to other usual metrics in phenology (Moussus et al., 2010), while the phenological flexibility is to be compared among species life history traits e species displaying a high plasticity for habitat selection and/or climatic conditions might be also those…”
Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature (2014, background): “…to estimate for species with multiple annual generations of which there are several in our data set, and the GAM burn-in intervals and inclusion of abundance covariates in phenological models (which we will describe) serve to mitigate biases in estimating first appearance (Moussus et al. 2010).”