Analysis of seasonal and long-term population dynamics for modeling populations at low density: Experience with light traps

Abstract

Monitoring and population forecast of major pest species are important tasks for pest management in both agriculture and forestry. In current study we suggest the models for both flight-initiation within a season and multisession population dynamics at low population densities of most important Palearctic folivore species conifer silk moth Dendrolimus superans (Lepidoptera: Lasiocampidae). The analysis uses light trap adult catch data collected over 21 years, from 2005 to 2025. Three models of adult flight are considered: a flight-initiation model driven by weather factors, an autoregressive model of long-term catch dynamics, and a binary model of seasonal catch. For the flight-initiation model, we propose estimating the Sum of the Active Temperatures from the date when the first derivative of the Normalized Difference Vegetation Index (NDVI) monitored by remote sensing becomes positive until the date of the first adult capture of the season. Sum of the Active Temperatures is shown to be sufficiently stable across all years of observation, with flight each year beginning after this temperature sum is reached. The second model demonstrates that the long-term light trap catch time series is well described by a second-order autoregressive model AR(2), in which the catch of the current year depends on catches from the two preceding years. This long-term series is compared with a previously studied larval population density series of the Siberian silk moth; both are shown to be AR(2) series with similar coefficient values, which suggesting that adult catch data may serve as a proxy for absolute larval population density. In the third model, we describe the transition from absolute-scale seasonal catch dynamics (number of adults per day) to a binary scale (0, 1), where 0 denotes days on which no adults were attracted to the trap, and 1 denotes days on which at least one individual was captured.