Members of the Boreal Ecosystem Recovery and Assessment (BERA) project held their final synthesis webinar on April 30, 2020. While the event was originally planned as a full-day, in-person workshop, the COVID-19 pandemic necessitated a change to a two-hour webinar format. This meant that we had to cut a lot of planned activities and shorten up remaining presentations, but the event went ahead regardless!
BERA Co-Investigators presented the main findings and contributions of BERA researchers from the five-year “Phase 1” period of BERA (2015-2020). The emphasis was on the knowledge gained and planning tools designed to assist researchers and resource managers engaged in boreal restoration activities.
The webinar started with an introduction to BERA and it’s guiding question – When is human footprint in the boreal forest no longer “footprint”? – and an overview of the specific research questions addressed by the team. Team leaders then presented short synthesis talks highlighting lessons learned from each of the five BERA groups: (i) remote sensing, (ii) soils and ecohydrology, (iii) vegetation, (iv) wildlife, and (v) internet of things.
A recording of the full webinar is provided below. We also invite you to browse the 43 project summaries at the bottom of the page.
We want to thank our partners and collaborators for their active engagement and support. We are excited to present the findings from our first phase of BERA, and look forward to more great research in BERA 2.
Remote Sensing Team
- The Forest Line Mapper: an open-source tool for mapping linear disturbances
- Leaf-off imagery with 5cm pixels are required for establishment surveys of evergreen seedlings
- Simple drone workflow finds conifer seedlings in forest harvest areas
- Coarse woody debris can be mapped effectively over large areas
- Deep-learning algorithms show promise for detecting conifer seedlings
- Drone photogrammetry can measure height of establishment-aged seedlings
- LiDAR still the best strategy for mapping forest canopy openings
- LiDAR shows promise for measuring understory vegetation attributes
- Airborne data can be used to map groundwater levels
- Multi-source data provides good foundation for wetland classification
- UAV metrics complement field measurements, but key differences remain
- UAVs provide effective platforms for mapping individual trees
- Drones can measure vegetation height on seismic lines
- Cloud computing and satellite data streams are transforming large-area mapping and monitoring projects
- Satellite time series provide broad-brush mapping tool for well sites
- Satellite time series can map regeneration on forest-harvest areas
Soils and Ecohydrology Team
- Soil disturbance on seismic lines leads to compaction, wetter conditions and organic matter loss
- Mounding alters nutrient cycling in peatlands that may change competition
- Seismic line disturbances change rates of peatland carbon cycling
- Fire erases seismic lines in jack pine forests
- Mounding promotes tree regeneration on seismic lines
- Fire promotes recovery on seismic lines in peatlands
- Fire promotes recovery on seismic lines in mesic forests
- Trajectory models predict site and landscape patterns
- Seismic lines simplify microtopography in peatlands
- Fire, seismic lines promote blueberry production
- Fire promotes tree regeneration on exploratory well pads
- Mounding alters understory vegetation communities in restored peatlands
- Seismic line geometry affects microclimate and tree regeneration
- OSE results in post-fire lichen refugia
- ARUs decrease costs of monitoring wildlife use of small-scale energy disturbances
- ARUs track hunting activity
- LIDAR improves understanding of habitat selection by birds
- Bird use of reclaimed wellsites
- Canada warbler response to vegetation structure on regenerating seismic lines
- Understory protection harvesting improves bird habitat quality
- Songbirds and chronic industrial noise
- Can songbirds adapt their song in response to chronic industrial noise?
- Multi-source remote sensing improves bird-habitat models
Internet of Things (IoT) Team