• Energy Research

Satellite remote sensing provides a rapid and broad-scale means for monitoring vegetation phenology and its relationship with fluctuations in air temperature. Investigating the response of plant communities to climate change is needed to gain insight into the potentially detrimental effects on ecosystem processes. While many studies have used satellite-derived land surface temperature (LST) as a proxy for air temperature, few studies have attempted to create and validate models of forest understory temperature (Tust), as it is obscured from these space-borne observations. This study worked to predict instantaneous values of Tust using daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST data over a 99,000 km2 study area located in the Rocky Mountains of western Alberta, Canada. Specifically, we aimed to identify the forest characteristics that improve estimates of Tust over using LST alone. Our top model predicted Tust to within a mean absolute error (MAE) of 1.4 °C with an overall model fit of R2 = 0.89 over two growing seasons. Canopy closure and the LiDAR-derived standard deviation of canopy height metric were found to significantly improve estimations of Tust over MODIS LST alone. These findings demonstrate that canopy structure and forest stand-type function to differentiate understory air temperatures from ambient canopy temperature as seen by the sensor overhead.

Satellite remote sensing provides a rapid and broad-scale means for monitoring vegetation phenology and its relationship with fluctuations in air temperature. Investigating the response of plant communities to climate change is needed to gain insight into the potentially detrimental effects on ecosystem processes. While many studies have used satellite-derived land surface temperature (LST) as a proxy for air temperature, few studies have attempted to create and validate models of forest understory temperature (Tust), as it is obscured from these space-borne observations. This study worked to predict instantaneous values of Tust using daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST data over a 99,000 km2 study area located in the Rocky Mountains of western Alberta, Canada. Specifically, we aimed to identify the forest characteristics that improve estimates of Tust over using LST alone. Our top model predicted Tust to within a mean absolute error (MAE) of 1.4 °C with an overall model fit of R2 = 0.89 over two growing seasons. Canopy closure and the LiDAR-derived standard deviation of canopy height metric were found to significantly improve estimations of Tust over MODIS LST alone. These findings demonstrate that canopy structure and forest stand-type function to differentiate understory air temperatures from ambient canopy temperature as seen by the sensor overhead.

AbstractPeatlands are globally significant stores of soil carbon, where local methane (CH4) emissions are strongly linked to water table position and microtopography. Historically, these factors have been difficult to measure in the field, constraining our capacity to observe local patterns of variability. In this paper, we show how remote sensing surveys conducted from unmanned aerial vehicle (UAV) platforms can be used to map microtopography and depth to water over large areas with good accuracy, paving the way for spatially explicit estimates of CH4 emissions. This approach enabled us to observe—for the first time—the effects of low‐impact seismic lines (LIS; petroleum exploration corridors) on surface morphology and CH4 emissions in a treed‐bog ecosystem in northern Alberta, Canada. Through compaction, LIS lines were found to flatten the observed range in microtopographic elevation by 46 cm and decrease mean depth to water by 15.4 cm, compared to surrounding undisturbed conditions. These alterations are projected to increase CH4 emissions by 20–120% relative to undisturbed areas in our study area, which translates to a total rise of 0.011–0.027 kg CH4 day−1 per linear kilometer of LIS (~2 m wide). The ~16 km of LIS present at our 61 ha study site were predicted to boost CH4 emissions by 20–70 kg between May and September 2016.

AbstractPeatlands are globally significant stores of soil carbon, where local methane (CH4) emissions are strongly linked to water table position and microtopography. Historically, these factors have been difficult to measure in the field, constraining our capacity to observe local patterns of variability. In this paper, we show how remote sensing surveys conducted from unmanned aerial vehicle (UAV) platforms can be used to map microtopography and depth to water over large areas with good accuracy, paving the way for spatially explicit estimates of CH4 emissions. This approach enabled us to observe—for the first time—the effects of low‐impact seismic lines (LIS; petroleum exploration corridors) on surface morphology and CH4 emissions in a treed‐bog ecosystem in northern Alberta, Canada. Through compaction, LIS lines were found to flatten the observed range in microtopographic elevation by 46 cm and decrease mean depth to water by 15.4 cm, compared to surrounding undisturbed conditions. These alterations are projected to increase CH4 emissions by 20–120% relative to undisturbed areas in our study area, which translates to a total rise of 0.011–0.027 kg CH4 day−1 per linear kilometer of LIS (~2 m wide). The ~16 km of LIS present at our 61 ha study site were predicted to boost CH4 emissions by 20–70 kg between May and September 2016.

Individual body growth is controlled in large part by the spatial and temporal heterogeneity of, and competition for, resources. Grizzly bears (Ursus arctos L.) are an excellent species for studying the effects of resource heterogeneity and maternal effects (i.e. silver spoon) on life history traits such as body size because their habitats are highly variable in space and time. Here, we evaluated influences on body size of grizzly bears in Alberta, Canada by testing six factors that accounted for spatial and temporal heterogeneity in environments during maternal, natal and 'capture' (recent) environments. After accounting for intrinsic biological factors (age, sex), we examined how body size, measured in mass, length and body condition, was influenced by: (a) population density; (b) regional habitat productivity; (c) inter-annual variability in productivity (including silver spoon effects); (d) local habitat quality; (e) human footprint (disturbances); and (f) landscape change.We found sex and age explained the most variance in body mass, condition and length (R(2) from 0.48-0.64). Inter-annual variability in climate the year before and of birth (silver spoon effects) had detectable effects on the three-body size metrics (R(2) from 0.04-0.07); both maternal (year before birth) and natal (year of birth) effects of precipitation and temperature were related with body size. Local heterogeneity in habitat quality also explained variance in body mass and condition (R(2) from 0.01-0.08), while annual rate of landscape change explained additional variance in body length (R(2) of 0.03). Human footprint and population density had no observed effect on body size.These results illustrated that body size patterns of grizzly bears, while largely affected by basic biological characteristics (age and sex), were also influenced by regional environmental gradients the year before, and of, the individual's birth thus illustrating silver spoon effects. The magnitude of the silver spoon effects was on par with the influence of contemporary regional habitat productivity, which showed that both temporal and spatial influences explain in part body size patterns in grizzly bears. Because smaller bears were found in colder and less-productive environments, we hypothesize that warming global temperatures may positively affect body mass of interior bears.

Individual body growth is controlled in large part by the spatial and temporal heterogeneity of, and competition for, resources. Grizzly bears (Ursus arctos L.) are an excellent species for studying the effects of resource heterogeneity and maternal effects (i.e. silver spoon) on life history traits such as body size because their habitats are highly variable in space and time. Here, we evaluated influences on body size of grizzly bears in Alberta, Canada by testing six factors that accounted for spatial and temporal heterogeneity in environments during maternal, natal and 'capture' (recent) environments. After accounting for intrinsic biological factors (age, sex), we examined how body size, measured in mass, length and body condition, was influenced by: (a) population density; (b) regional habitat productivity; (c) inter-annual variability in productivity (including silver spoon effects); (d) local habitat quality; (e) human footprint (disturbances); and (f) landscape change.We found sex and age explained the most variance in body mass, condition and length (R(2) from 0.48-0.64). Inter-annual variability in climate the year before and of birth (silver spoon effects) had detectable effects on the three-body size metrics (R(2) from 0.04-0.07); both maternal (year before birth) and natal (year of birth) effects of precipitation and temperature were related with body size. Local heterogeneity in habitat quality also explained variance in body mass and condition (R(2) from 0.01-0.08), while annual rate of landscape change explained additional variance in body length (R(2) of 0.03). Human footprint and population density had no observed effect on body size.These results illustrated that body size patterns of grizzly bears, while largely affected by basic biological characteristics (age and sex), were also influenced by regional environmental gradients the year before, and of, the individual's birth thus illustrating silver spoon effects. The magnitude of the silver spoon effects was on par with the influence of contemporary regional habitat productivity, which showed that both temporal and spatial influences explain in part body size patterns in grizzly bears. Because smaller bears were found in colder and less-productive environments, we hypothesize that warming global temperatures may positively affect body mass of interior bears.

Allometric equations for estimating aboveground biomass (AGB) from easily measured plant attributes are unavailable for most species common to mid-continental boreal peatlands, where shrubs comprise a large component of the vegetation community. Our study develops allometric equations for three dominant genera found in boreal fens: Alnus spp. (alder), Salix spp. (willow) and Betula pumila (bog birch). Two different types of local equations were developed: (1) individual equations based on genus/phylogeny, and (2) a general equation that pooled all individuals regardless of genera. The general equation had a R2 = 0.97 (n = 82), and was not significantly different (p > 0.05) than any of the phylogenetic equations. This indicated that a single generalized equation is sufficient in estimating AGB for all three genera occurring in our study area. A closer look at the performance of the general equation revealed that smaller stems were predicted less accurately than larger stems because of the higher variability of leafy biomass found in small individuals. Previously published equations developed in other ecoregions did not perform as well as our local equations.