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Abstract Road mortality may contribute to the population decline of eastern monarch butterflies (Danaus plexippus L.). We estimated autumn monarch roadkill rates within the primary Oklahoma to Mexico southern migration corridor (i.e., Central Funnel). Dead monarchs were surveyed along Texas roadsides during four weeks of autumn migration in 2016 and 2017. Roadkill averaged 3.4 monarchs per 100 m transect, reaching 66 per 100 m in a roadkill hotspot in southwestern Texas. Extrapolations of Central Funnel roadkill based on survey data and road types were 3.6 and 1.1 million in 2016 and 2017, respectively. Spatial distribution of roadkill across the Central Funnel was projected from Texas survey data using 30 m resolution MaxEnt niche models. Highest roadkill probability was linked to arid climate and low human population density. The latter variables may not be directly related to roadkill, but instead represent indirect correlates of increased densities of monarchs where the migration corridor narrows southwards. The higher roadkill projected in southwest Texas and Mexico by MaxEnt models agrees with previously reported monarch roadkill hotspots. MaxEnt-based 2016–2017 projections for annual roadkill rates throughout the Central Funnel averaged 2.1 million. This figure is similar to the result by simple extrapolation, and represents about 3% of the overwintering monarch population for these years. Mitigation at roadkill hotspots in the Central Funnel could reduce monarch roadkill mortality during migration and contribute towards conservation efforts for the monarch butterfly. Graphical abstract Unlabelled Image Highlights • Texas autumn monarch roadkill averages 3.4/100 m, reaching 66/100 m in hotspots. • Central Funnel estimated roadkill ranged from 0.2 to 2 million in 2017 and 2016. • MaxEnt projected 1.3 million overall roadkill, mainly in West Texas and Mexico. • Central Funnel roadkill may represent 2–3% of monarch overwintering population. • MaxEnt models can guide mitigation efforts needed to reduce monarch roadkill. [ABSTRACT FROM AUTHOR]

Climate-related extended outbreaks and range shifts of destructive bark beetle species pose a serious threat to urban boreal forests in North America and Fennoscandia. Recent developments in low-cost remote sensing technologies offer an attractive means for early detection and management of environmental change. They are of great interest to the actors responsible for monitoring and managing forest health. The objective of this investigation was to develop, assess, and compare automated remote sensing procedures based on novel, low-cost hyperspectral imaging technology for the identification of bark beetle infestations at the individual tree level in urban forests. A hyperspectral camera based on a tunable Fabry-Pérot interferometer was operated from a small, unmanned airborne vehicle (UAV) platform and a small Cessna-type aircraft platform. This study compared aspects of using UAV datasets with a spatial extent of a few hectares (ha) and a ground sample distance (GSD) of 10–12 cm to the aircraft data covering areas of several km 2 and having a GSD of 50 cm. An empirical assessment of the automated identification of mature Norway spruce ( Picea abie s L. Karst.) trees suffering from infestation (representing different colonization phases) by the European spruce bark beetle ( Ips typographus L.) was carried out in the urban forests of Lahti, a city in southern Finland. Individual spruces were classified as healthy, infested, or dead. For the entire test area, the best aircraft data results for overall accuracy were 79% (Cohen’s kappa: 0.54) when using three crown color classes (green as healthy, yellow as infested, and gray as dead). For two color classes (healthy, dead) in the same area, the best overall accuracy was 93% (kappa: 0.77). The finer resolution UAV dataset provided better results, with an overall accuracy of 81% (kappa: 0.70), compared to the aircraft results of 73% (kappa: 0.56) in a smaller sub-area. The results showed that novel, low-cost remote sensing technologies based on individual tree analysis and calibrated remote sensing imagery offer great potential for affordable and timely assessments of the health condition of vulnerable urban forests. [ABSTRACT FROM AUTHOR]