FOSTER-An R package for forest structure extrapolation.

The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates.

Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore.

Changing climates are altering wildlife habitats and wildlife behavior in complex ways. Here, we examine how changing spring snow cover dynamics and early season forage availability are altering grizzly bear (Ursus arctos) behavior postden emergence. Telemetry data were used to identify spring activity dates for 48 individuals in the Yellowhead region of Alberta, Canada. Spring activity date was related to snow cover dynamics using a daily percent snow cover dataset. Snow melt end date, melt rate, and melt consistency explained 45% of the variation in spring activity date. We applied this activity date model across the entire Yellowhead region from 2000 to 2016 using simulated grizzly bear home ranges. Predicted spring activity date was then compared with a daily spring forage availability date dataset, resulting in "wait time" estimates for four key early season forage species. Temporal changes in both spring activity date and early season forage "wait times" were assessed using non-parametric regression. Grizzly bear activity date was found to have either remained constant (95%) or become earlier (5%) across the study area; virtually no areas with significantly later spring activity dates were detected. Similarly, the majority of "wait times" did not change (85%); however, the majority of significant changes in "wait times" for the four early season forage species indicated that "wait times" were lessening where changes were detected. Our results show that spring activity date is largely dictated by snow melt characteristics and that changing snow melt conditions may result in earlier spring activity. However, early season food stress conditions are likely to remain unchanged or improve as vegetation phenology also becomes earlier. Our findings extend the recent work examining animal movement in response to changing phenology from migratory birds and ungulates to an apex predator, further demonstrating the potential effects of changing climates on wildlife species.

Uncovering regional variability in disturbance trends between parks and greater park ecosystems across Canada (1985-2015).

We assess the protective function of Canada's parks and protected areas (PPAs) by analyzing three decades of stand-replacing disturbance derived from Landsat time series data (1985-2015). Specifically, we compared rates of wildfire and harvest within 1,415 PPAs against rates of disturbance in surrounding greater park ecosystems (GPEs). We found that disturbance rates in GPEs were significantly higher (p < 0.05) than in corresponding PPAs in southern managed forests (six of Canada's 12 forested ecozones). Higher disturbance rates in GPEs were attributed to harvesting activities, as the area impacted by wildfire was not significantly different between GPEs and PPAs in any ecozone. The area burned within PPAs and corresponding GPEs was highly correlated (r = 0.90), whereas the area harvested was weakly correlated (r = 0.19). The average area burned in PPAs/GPEs below 55° N was low (0.05% yr-1) largely due to fire suppression aimed at protecting communities, timber, and recreational values, while the average burn rate was higher in northern PPAs/GPEs where fire suppression is uncommon (0.40% yr-1 in PPAs/GPEs above 55° N). Assessing regional variability in disturbance patterns and the pressures faced by PPAs can better inform policy and protection goals across Canada and the globe.

Remotely-sensed productivity clusters capture global biodiversity patterns.

Ecological regionalisations delineate areas of similar environmental conditions, ecological processes, and biotic communities, and provide a basis for systematic conservation planning and management. Most regionalisations are made based on subjective criteria, and can not be readily revised, leading to outstanding questions with respect to how to optimally develop and define them. Advances in remote sensing technology, and big data analysis approaches, provide new opportunities for regionalisations, especially in terms of productivity patterns through both photosynthesis and structural surrogates. Here we show that global terrestrial productivity dynamics can be captured by Dynamics Habitat Indices (DHIs) and we conduct a regionalisation based on the DHIs using a two-stage multivariate clustering approach. Encouragingly, the derived clusters are more homogeneous in terms of species richness of three key taxa, and of canopy height, than a conventional regionalisation. We conclude with discussing the benefits of these remotely derived clusters for biodiversity assessments and conservation. The clusters based on the DHIs explained more variance, and greater within-region homogeneity, compared to conventional regionalisations for species richness of both amphibians and mammals, and were comparable in the case of birds. Structure as defined by global tree height was also better defined by productivity driven clusters than conventional regionalisations. These results suggest that ecological regionalisations based on remotely sensed metrics have clear advantages over conventional regionalisations for certain applications, and they are also more easily updated.

A thirty year, fine-scale, characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade.

Fire as a dominant disturbance has profound implications on the terrestrial carbon cycle. We present the first ever multi-decadal, spatially-explicit, 30 meter assessment of fire regimes across the forested ecoregions of Canada at an annual time-step. From 1985 to 2015, 51 Mha burned, impacting over 6.5% of forested ecosystems. Mean annual area burned was 1,651,818 ha and varied markedly (σ = 1,116,119), with 25% of the total area burned occurring in three years: 1989, 1995, and 2015. Boreal forest types contained 98% of the total area burned, with the conifer-dominated Boreal Shield containing one-third of all burned area. While results confirm no significant national trend in burned area for the period of 1985 to 2015, a significant national increasing trend (α = 0.05) of 11% per year was evident for the past decade (2006 to 2015). Regionally, a significant increasing trend in total burned area from 1985 to 2015 was observed in the Montane Cordillera (2.4% increase per year), while the Taiga Plains and Taiga Shield West displayed significant increasing trends from 2006 to 2015 (26.1% and 12.7% increases per year, respectively). The Atlantic Maritime, which had the lowest burned area of all ecozones (0.01% burned per year), was the only ecozone to display a significant negative trend (2.4% decrease per year) from 1985 to 2015. Given the century-long fire return intervals in many of these ecozones, and large annual variability in burned area, short-term trends need to be interpreted with caution. Additional interpretive cautions are related to year used for trend initiation and the nature and extents of spatial regionalizations used for summarizing findings. The results of our analysis provide a baseline for monitoring future national and regional trends in burned area and offer spatially and temporally detailed insights to inform science, policy, and management.

Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar.

The structure and productivity of boreal forests are key components of the global carbon cycle and impact the resources and habitats available for species. With this research, we characterized the relationship between measurements of forest structure and satellite-derived estimates of gross primary production (GPP) over the Canadian boreal. We acquired stand level indicators of canopy cover, canopy height, and structural complexity from nearly 25,000 km of small-footprint discrete return Light Detection and Ranging (Lidar) data and compared these attributes to GPP estimates derived from the MODerate resolution Imaging Spectroradiometer (MODIS). While limited in our capacity to control for stand age, we removed recently disturbed and managed forests using information on fire history, roads, and anthropogenic change. We found that MODIS GPP was strongly linked to Lidar-derived canopy cover (r = 0.74, p < 0.01), however was only weakly related to Lidar-derived canopy height and structural complexity as these attributes are largely a function of stand age. A relationship was apparent between MODIS GPP and the maximum sampled heights derived from Lidar as growth rates and resource availability likely limit tree height in the prolonged absence of disturbance. The most structurally complex stands, as measured by the coefficient of variation of Lidar return heights, occurred where MODIS GPP was highest as productive boreal stands are expected to contain a wider range of tree heights and transition to uneven-aged structures faster than less productive stands. While MODIS GPP related near-linearly to Lidar-derived canopy cover, the weaker relationships to Lidar-derived canopy height and structural complexity highlight the importance of stand age in determining the structure of boreal forests. We conclude that an improved quantification of how both productivity and disturbance shape stand structure is needed to better understand the current state of boreal forests in Canada and how these forests are changing in response to changing climate and disturbance regimes.