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Moose Alces alces is abundant throughout the boreal zone. However, in the landscapes occupied by moose the density of predators and human influence often vary considerably, as do the arrival of spring and phenology of vegetation – all crucial factors for moose, especially during their calving period. During calving, female moose are faced with a dilemma between choosing a habitat offering high quality forage or one offering protection for the vulnerable calf. This study examined the timing of calving and habitat structure of female moose during and after calving in two areas of Finland with different predator and human densities. Data from 14 GPS-collared moose were integrated with data on landscape composition and forest structure from airborne Lidar. In both study areas, calves were born in May and mostly in drained forests (with ditches) on peatland soils. After giving birth, females in the more human-influenced landscape moved to more fertile forests on mineral soils, whereas the females in the landscape with little human influence stayed mostly on the drained and pine-dominated peatland forests, which also had less shrub vegetation. Moose were also seen giving birth near human settlements (farms) and staying close to them (<500 m) throughout summer. Thus, females in both study areas illustrated a high degree of adaptability by selecting the highly modified drained landscapes or areas near humans for calving sites over other available landscapes. [ABSTRACT FROM AUTHOR]

Abstract In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters. Highlights • We quantify the effect of varying resolution in the context ALS forest inventories. • Irregular point pattern of ALS data hamper achieving resolution invariance. • Very small resolution effect in most real-world cases • Resolution invariance is most relevant in large area strategic inventories. [ABSTRACT FROM AUTHOR]