A Priori Prediction of Population Growth of Parnassius smintheus.

Kehinde Oniyide , Chethana Chathuprabha Belpa Godage, Stephen Matter

A priori testing of a model, i.e., predicting an event before it happens, is the gold standard for model validation. This model testing approach steps up ecological modeling beyond simply explaining the past to proactive predictive modeling by challenging the ability of theories to make accurate forecasts before new data are collected, a litmus test of how well we understand the systems we study. The present research aims to test a model based on temperature at ground level by predicting population growth (year-to-year adult population size among 17 sub-populations) of the Rocky Mountain Apollo Butterfly Parnassius smintheus, from 2025 to 2026. We built a generalized linear model using subpopulation density and accumulated warm (> 6°C) ground temperatures from November to December (early overwintering period) from 2010 to 2024 as predictors of subpopulation growth rate (F1,70 =6.615619, P=1.22e-02). Adult subpopulation growth rate was calculated by using rt = ln (N_(t+1)/N_t ), where Nt+1 is the current year's peak population while Nt is the peak population size of the previous year. While the model significantly explains past population growth, its predictive ability remains untested. Despite the critical need to accurately predict the effects of climate change on populations, population models have rarely been tested in this manner.