Predictive power of remote sensing versus temperature‐derived variables in modelling phenology of herbivorous insects

Abstract

Application of remote sensing datasets in modelling phenology of heterotrophic animals has received little attention. In this work, we compare the predictive power of remote sensing versus temperature-derived variables in modelling peak flight periods of herbivorous insects, as exemplified by nocturnal moths. Moth phenology observations consisted of weekly observations of five focal moth species (Orthosia gothica, Ectropis crepuscularia, Cabera exanthemata, Dysstroma citrata and Operophtera brumata) gathered in a national moth monitoring scheme in Finland. These species were common and widespread and had peak flight periods in different seasons. Temperature-derived data were represented by weekly accumulating growing degree days (GDD) calculated from gridded temperature observations. Remote sensing data were obtained from three sources: (1) snow melt-off date from the MODIS daily snow maps, (2) greening date using the NDWI from MODIS data and (3) dates of start, maximum and end of growing season based on the JRC FAPAR products. Peak phenology observations of moths were related to different explanatory variables by using linear mixed effect models (LMM), with 70% of the data randomly selected for model calibration. Predictive power of models was tested using the remaining 30% of the data. Remote sensing data (snow melt-off and vegetation greening date) showed the highest predictive power in two moth species flying in the early and late spring, whereas in the three other species none of the variables showed reasonable predictive power. Flight period of the spring species coincides with natural events such as snow melt or vegetation greening that can easily be observed using remote sensing techniques. We demonstrate the applicability of our methodology by predictive spatial maps of peak flight phenology covering the entire Finland for two of the focal species. The methods are applicable in situations that require spatial predictions of animal activity, such as the management of populations of insect pest species.

Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century

“…Insects are expected to show a tighter correlation with the spatial gradient than vertebrates, simply because the physiology and behaviour of these ectotherms are more tightly driven by changes in temperature than for endotherms (Thackeray et al, ). Our findings are supported by research on the phenology of Finnish moths for which latitudinal relationships were shown to be very variable and sometimes of poor predictive power, despite strong relationships with snow melt and leafing date: only two of the five moth species studied showed any relationship with latitude and when compared, they had opposing relationships with latitude ( Orthosia gothica positive; Operophtera brumata negative Pöyry et al, ). Clearly, even at the species level, moths have a complex relationship with space that is not easily resolvable by simple spatial terms and requires further study.…”

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

“…The lack of spatial data on ecologically relevant factors driving species distribution constraints the full potential of SDMs for management and monitoring purposes [11][12][13]. In particular, RS can provide critical information on species habitat requirements such as soil moisture (measured through e.g., normalized difference of water indices [14]), microclimatic conditions (e.g., land-surface temperature [15]) or vegetation productivity (e.g., spectral vegetation indices [16]), thus improving species niche characterization and SDMs predictions [11]. In addition, the high spatiotemporal resolution of EO data obtained from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) (from TERRA/AQUA satellites) and MultiSpectral Instrument (MSI) (from Sentinel-2 mission) allows derivation of different descriptors of ecosystem functioning (also known as remotely sensed ecosystem functioning attributes; hereafter RS-EFAs) [17,18].…”

Herbivory in a changing climate—Effects of plant genotype and experimentally induced variation in plant phenology on two summer‐active lepidopteran herbivores and one fungal pathogen

“…O . gothica overwinters as pupae, adults emerge and oviposit in spring, and larvae feed on foliage at the start of the growing season (Figure 1 ; Elmquist et al, 2011 ; Pöyry et al, 2018 ). In contrast, P .…”