Insect Pests in Future Forests: More Severe Problems?

Growth and development of an invasive forest insect under current and future projected temperature regimes

Temperature and its impact on fitness are fundamental for understanding range shifts and population dynamics under climate change. Geographic climate heterogeneity, behavioral and physiological plasticity, and thermal adaptation to local climates make predicting the responses of species to climate change complex. Using larvae from seven geographically distinct wild populations in the eastern United States of the non-native forest pest Lymantria dispar dispar (L.), we conducted a simulated reciprocal transplant experiment in environmental chambers using six custom temperature regimes representing contemporary conditions near the southern and northern extremes of the US invasion front and projections under two climate change scenarios for the year 2050. Larval growth and development rates increased with climate warming compared with current thermal regimes and tended to be greater for individuals originally sourced from southern rather than northern populations. Although increases in growth and development rates with warming varied somewhat by region of the source population, there was not strong evidence of local adaptation, southern populations tended to outperform those from northern populations in all thermal regimes. Our study demonstrates the utility of simulating thermal regimes under climate change in environmental chambers and emphasizes how the impacts from future increases in temperature can vary based on geographic differences in climate-related performance among populations.

Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging

Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs.

Host plant phenology, insect outbreaks and herbivore communities - The importance of timing

Climate change may alter the dynamics of outbreak species by changing the phenological synchrony between herbivores and their host plants. As host plant phenology has a genotypic component that may interact with climate, infestation levels among genotypes might change accordingly. When the outbreaking herbivore is active early in the season, its infestation levels may also leave a detectable imprint on herbivores colonizing the plant later in the season. In this study, we first investigated how the spring phenology and genotype of Quercus robur influenced the density of the spring-active, outbreaking leaf miner Acrocercops brongniardellus. We then assessed how intraspecific density affected the performance of A. brongniardellus and how oak genotype and density of A. brongniardellus affected the insect herbivore community. We found that Q. robur individuals of late spring phenology were more strongly infested by A. brongniardellus. Conspecific pupae on heavily infested oaks tended to be lighter, and fewer heterospecific insect herbivores colonized the oak later in the season. Beyond its effects through phenology, plant genotype left an imprint on herbivore species richness and on two insect herbivores. Our results suggest a chain of knock-on effects from plant phenology, through the outbreaking species to the insect herbivore community. Given the finding of how phenological synchrony between the outbreak species and its host plant influences infestation levels, a shift in synchrony may then change outbreak dynamics and cause cascading effects on the insect community.