10.5061/DRYAD.MKKWH70W2
Cadieux, Philippe
0000-0001-6181-8509
Environment Canada
Boulanger, Yan
0000-0001-6181-8509
Natural Resources Canada
Cyr, Dominic
Environment and Climate Change Canada
Taylor, Anthony
Natural Resources Canada
Price, David
Natural Resources Canada
Solymos, Peter
AlbertaBiodiversity Monitoring Institute
Stralberg, Diana
University of Alberta
Chen, Han
Lakehead University
Brecka, Aaron
Lakehead University
Tremblay, Junior
Environment and Climate Change Canada
Projected effects of climate change on boreal bird community accentuated
by anthropogenic disturbances in western boreal forest, Canada
Dryad
dataset
2020
Wildfires
avifauna
forest landscape model
LANDIS-II
old boreal forest
2021-03-06T00:00:00Z
2021-01-20T00:00:00Z
en
https://doi.org/10.1111/ddi.13057
5324013 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Aim Climate change is expected to influence boreal bird communities
significantly, notably through changes in forest habitat (composition and
age structure), in the coming decades. How these changes will accumulate
and interact with anthropogenic disturbances remains an open question for
most species. Location Northeastern Alberta, Canada. Methods We used the
LANDIS-II forest landscape model to project changes in forest landscapes,
and associated bird populations (72 passerine species), according to three
climatic scenarios (baseline, RCP 4.5, RCP 8.5) and three forest
harvesting scenarios of differing intensity. Results Both forest
harvesting and climate-related drivers were projected to have large
impacts on bird communities in this region. As a result of climate-induced
increases in fire activity as well as decreased conifer productivity, our
simulations projected that an important proportion of Alberta’s boreal
forests would transition to treeless habitat (i.e. grass-, or
shrub-dominated vegetation) while many conifer-dominated stands would
likely be replaced by broadleaf tree cover. Consequently, the abundance of
bird species associated with open and deciduous habitats were projected to
increase. With a strong anthropogenic climate forcing scenario (RCP 8.5),
sharp declines in abundance of coniferous trees were also projected,
particularly in mature and old forest stands, triggering major declines
for bird species associated with coniferous and mixedwood forest types.
Main Conclusions As the most comprehensive simulation of climate change
and harvesting impacts on avian habitats in the North American boreal
region to date, our study reveals the importance of considering key
habitat characteristics like forest age structure and composition through
forest landscape modeling, and identifies 18 bird species particularly
sensitive to climate change. Our simulations suggest that a change in
forest management practices could play an important role in the
conservation of boreal bird species vulnerable to climate change. The
intensive forest harvesting simulated accelerated declines in bird
abundance compared to a “no harvesting” scenario.
We used LANDIS-II (Scheller & Mladenoff, 2004) to project future
forest attributes within our study area. In LANDIS-II, the forest
landscape is represented by a grid of interacting cells within which
stand-level forest processes (tree establishment, growth, competition and
mortality) occur while landscape-level processes, such as tree seed
dispersal and forest disturbances including fire and harvesting, generally
affect multiple cells in a spatially, interactive manner. In our
experiment, we set cell resolution to 250 m (6.25 ha) and simulations were
run at 10-year time steps across all activated extensions. Forest
composition and structure in each cell were initialized using forest
properties derived from the Alberta Biodiversity Monitoring Institute
(ABMI) cover products (as of the year 2010; ABMI 2012) and the Canadian
National Forest Inventory (NFI; nfi.nfis.org) and combined with stand age
cohort data derived from provincial forest inventory plots. Each of these
cells was then assigned to a ‘‘landtype’’ with homogeneous soil (Mansuy,
Thiffault, Paré, Bernier, Guindon, Villemaire, et al., 2014) and climate
conditions. A modified version of the LANDIS-II Biomass Succession
extension v 3.1 (Scheller et al., 2004) was used to simulate forest
succession. The Biomass Succession extension emulates succession at the
stand (cell) level by simulating the recruitment and growth of tree
cohorts (not individual trees). It permits multiple cohorts of tree
species to establish and interact within a cell through resource (i.e.,
“growing space” sensu Scheller et al. 2004) limitations based on
species-specific traits. The succession of each cell is driven by these
stand-level interactions, in addition to disturbance history and seed
source availability. Specific parameters that define basic life-history
traits are assigned to all species (see Table 1 for a full listing). To
account for the effects of climate change, the forest gap model PICUS
(version 1.5; http://picus.boku.ac.at) was used to develop the dynamic
tree species- and landtype-specific parameters required to operate
LANDIS-II. PICUS is an individual tree-based, spatially explicit forest
ecosystem model that simulates germination, establishment, growth, and
mortality of individual trees on 100 m2 patches of forest area (see
Boulanger, Taylor, et al. (2016) for more details). Hence, three dynamic
inputs of the Biomass Succession extension, namely maximum biomass
(maxAGB; g.m-2), maximum aboveground net primary productivity (maxANPP;
g.m-2.yr-1), and species establishment probability (SEP), were derived
from PICUS by running monospecific stand simulations for each combination
of species, climate conditions and landtype. Those parameters were allowed
to change during the course of the subsequent LANDIS simulations to
represent the effect of climate on each species’ potential growth. For a
complete description of the calibration, the validation procedures
regarding these parameters, and a description of how these dynamic inputs
were derived from the outputs of PICUS, refer to Tremblay et al. (2018).
We considered two natural disturbance agents, namely wildfires and
drought. Wildfire accounts for the majority of areas naturally disturbed
in the study area (Tymstra, Wang, & Rogeau, 2005) and is widely
recognized to have major impacts on Canada’s forest landscapes (Volney
& Hirsch, 2005; Price et al., 2013). Fire simulations were carried
out using the LANDIS-II Base Fire extension (He & Mladenoff,
1999), which simulates stochastic fire events dependent upon fire
ignition, initiation and spread. Fire regime data (annual area burned,
fire occurrence, and mean fire size) were first compiled into ‘‘fire
regions’’ corresponding to the Canadian Homogeneous Fire Regime (HFR)
zones (Boulanger et al. 2014). Baseline and future fire regime parameters
within each fire region were calibrated with models developed by Boulanger
et al. (2014) and they were updated to account for changing climate
conditions under the different RCP scenarios (Gauthier et al., 2015).
Drought-induced mortality was simulated by first modelling
species-specific mortality curves according to the climate moisture index
(CMI), calculated as the difference between annual precipitation and
potential evapotranspiration (see Brecka 2018 for more details).
Species-specific mortality was retrieved from undisturbed permanent sample
plots located within the Boreal Plains ecozone. This was used to construct
non-linear exponential regression models predicting the 10-yr proportion
of species biomass killed according to decadal CMI values (Chen, Luo,
Reich, Searle, & Biswas, 2016). Using the same climate datasets
described above, we projected future CMI values under all landtypes under
each climate scenario and each 30-year period (i.e. 2011-2040, 2041-2070;
2071-2100). Future species-specific drought-related mortality was then
projected using future CMI values and drought-related mortality models.
Projected drought-related species-specific mortality was then included in
the LANDIS-II simulations by removing biomass accordingly using the
Biomass Harvest extension (v3.0; Gustafson, Shifley, Mladenoff, Nimerfro
& He, 2000). Drought-induced mortality was applied equally to all
tree age cohorts. Forest harvesting was simulated using the
Biomass Harvest extension. Only clearcut harvesting was simulated as this
logging strategy is most frequently used in the study area
(https://alpac.ca/index.php/forest-sustainability/forest-planning). Only
stands in upland areas and that comprised cohorts older than 60 years old
were allowed to be harvested. When harvested, clearcutting was simulated
to remove of all age cohorts present except for the 0 – 10 year age
cohort. Mean harvested patch size and total harvested area were summarized
by forest management units. Harvesting parameters were held constant
throughout the simulations. Three harvesting scenarios were simulated
according to a gradient of harvesting pressure, from no harvesting (no
harvesting), to clearcutting with intensity similar to current management
practices (baseline harvesting - applied to 0.3% of the harvestable upland
area per year; ABMI, 2017a), to high-intensity clearcutting (high
harvesting - applied to 0.6% of the harvestable upland area per year).
Simulations were run for three climate scenarios (baseline, RCP 4.5 and
RCP 8.5) as well as under the three harvesting scenarios. Five replicate
simulations were run for 200 years, starting in the year 2000, with
10-year time steps. Except for scenarios involving the baseline climate,
fire regime parameters were allowed to change in 2010, 2040, and 2070
according to the average climate corresponding to each forcing scenario.
Dynamic growth and establishment parameters (SEP, maxANPP and maxAGB) as
well as drought mortality were allowed to change according to climate
scenarios following the same schedule but only for upland areas. Indeed,
our current understanding of the vulnerability of peatland systems to
climate change is very limited (e.g., Schneider, Devito, Kettridge
& Bayne, 2016). As such, lowland pixels were kept as “active” to
allow fire spread and seed dispersal, but growth parameters were kept
constant. A similar simulation strategy was used by Stralberg, Wang, et
al. (2018) in this area. As a result, future forest landscape, as well as
bird community results, were reported for uplands only. Boreal songbird
community To represent the boreal bird community, we selected passerines
with breeding ranges that overlapped with the study region. We further
limited this selection to 72 songbirds that were adequately modeled within
Northern Alberta by excluding species that were too rare (number of
detections < 5 x degrees of freedom in models) or for which model
goodness of fit was low (AUC < 0.6). These predictive models were
based on point count data, including surveys from the North American
Breeding Bird Survey (BBS; pwrc.usgs.gov/bbs/), Boreal Avian Modelling
Project (BAM; borealbirds.ualberta.ca), and the ABMI (abmi.ca). The models
were built following the methodology outlined in Ball, Sólymos,
Schmiegelow, Haché, Schieck, & Bayne (2016) and Sólymos, Azeria,
Huggard, Roy & Schieck (2019). Land cover associations were based
on the dominant landcover (native vegetation and human footprint) type
within a 150-m radius buffer around the points. Native vegetation classes
included deciduous, mixedwood, white spruce, pine, black spruce forest
stands, treed fen, shrub, grass/herb, graminoid fen, marsh, and swamp
cover types. Ages of forest stands (area-weighted average age at the year
of the survey) originating from natural disturbances or forest harvesting
were also assessed within the 150-m radius buffers. Survey counts were
modelled by Poisson generalized linear models with a logarithmic link. We
used the QPAD approach (Sólymos, Matsuoka, Bayne, Lele, Fontaine, Cumming,
Stralberg, Schmiegelow, & Song 2013) to account for differences in
sampling protocol and covariate effects on detectability via offsets in
the generalized linear models. This approach standardizes the estimates to
reflect density (number of singing individuals per ha) within the
different land cover type and stand age categories. As a result of these
models, ABMI provides expected density for the selected bird species for
each cover type, and data are available via the `cure4insect` R extension
package (R Core Team 2019, Sólymos, Allen, Azeria, White, ABMI &
BAM 2018; see model summaries at abmi.ca/data). ABMI bird habitat models
were built only for species that had enough samples and were adjusted for
the species detection distance (see ABMI (2017b) for modelling details).
We used expected density per species in conjunction with projected forest
cover types to estimate expected bird abundance. Aboveground biomass
density (t/ha) for each tree species as well as stand age and stand origin
as projected by LANDIS-II were used to derive habitat types (forest cover
types) using the same classification scheme used to define ABMI forest
cover types (see above). Stand-scale (250 m) forest cover information was
derived for each LANDIS-II simulation run at a 10-yr time step. We also
associated each bird species to the following habitat types (deciduous,
mixedwood, coniferous and treeless) and stand age classes (<30yr
(young); 30-60yr (closed); 60-80yr (mature); >80yr (old)). Analyses
Cumulative impacts of harvesting and climate change were assessed by
comparing temporal trends of tree species aboveground biomass and songbird
abundance under each climate and harvesting scenario over time. Outputs
from the five simulation replicates were averaged. Trends were assessed
using simulations where all disturbances (fire, drought and harvesting)
were considered. The impact of climate change and harvesting on simulated
songbird abundance was calculated as the percentage of change in simulated
songbird abundance relative to the proportion obtained under the baseline
harvesting and baseline climate scenario (hereafter referred to the
“reference scenario”) following: ((ProjAbundt / RefAbundt) -
1)*100 [1] where RefAbundt is
the abundance of a bird species under the reference scenario (baseline
climate and baseline harvesting), ProjAbundt is the projected abundance of
the same species for the given future time period, and t is time in years
(Cadieux et al., 2019). This method was used to get a direct assessment of
the effects of climate change and harvesting while controlling for forest
succession. Additional simulations were conducted to assess the
importance of a selected driver of change on bird abundance. Simulations
were conducted following a three-way factorial design according to
harvesting (no harvesting, 0.3% and 0.6%), fire (baseline fire, projected
fire) and climate change effects on dynamic biomass succession inputs and
drought. The relative contribution of each factor was assessed by
estimating the variance of songbird abundance that it explained using
omega-squared values (ω2) calculated following a 3-way factorial ANOVA. We
calculated ω2 for each driver of change, at each time step, as: ω2 =
[SSeffect – (dfeffect)*(MSerror)] / [MSerror +
SStot] [2] where SSeffect is the sum of
squares related to the driver of change (the effect), dfeffect is the
degree of freedom of the effect, MSerror is the mean square of the error
and SStot is the total sum of squares. ANOVA and ω2 calculations were
performed separately for each RCP scenario. Based on the relative
importance of drivers on bird species, we defined species sensitive to
climate change as those declining > 25% according to
climate-sensitive drivers (either Fire or Growth) under RCP 8.5 and the
current harvest scenario at 2100.