https://orcid.org/0000-0002-6043-8763
Figures
Abstract
Logging is the main human disturbance impacting biodiversity in forest ecosystems. However, the impact of forest harvesting on biodiversity is modulated by abiotic conditions through complex relationships that remain poorly documented. Therefore, the interplay between forest management and climate change can no longer be ignored. Our aim was to study the expected long-term variations in the assemblage of bird and beetle communities following modifications in forest management under different climate change scenarios. We developed species distribution models to predict the occurrence of 88 species of birds and beetles in eastern Canadian boreal forests over the next century. We simulated three climate scenarios (baseline, RCP4.5 and RCP8.5) under which we varied the level of harvesting. We also analyzed the regional assemblage dissimilarity by decomposing it into balanced variations in species occupancy and occupancy gradient. We predict that forest harvesting will alter the diversity by increasing assemblage dissimilarity under all the studied climate scenarios, mainly due to species turnover. Species turnover intensity was greater for ground-dwelling beetles, probably because they have lower dispersal capacity than flying beetles or birds. A good dispersal capacity allows species to travel more easily between ecosystems across the landscape when they search for suitable habitats after a disturbance. Regionally, an overall increase in the probability of occupancy is projected for bird species, whereas a decrease is predicted for beetles, a variation that could reflect differences in ecological traits between taxa. Our results further predict a decrease in the number of species that increase their occupancy after harvest under the most severe climatic scenario for both taxa. We anticipate that under severe climate change, increasing forest disturbance will be detrimental to beetles associated with old forests but also with young forests after disturbances.
Citation: Bouderbala I, Labadie G, Béland J-M, Tremblay JA, Boulanger Y, Hébert C, et al. (2023) Long-term effect of forest harvesting on boreal species assemblages under climate change. PLOS Clim 2(3): e0000179. https://doi.org/10.1371/journal.pclm.0000179
Editor: Asif Qureshi, Indian Institute of Technology Hyderabad, INDIA
Received: August 23, 2022; Accepted: February 14, 2023; Published: March 15, 2023
Copyright: © 2023 Bouderbala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Flying and ground-dwelling beetles occurrence datasets as well as the used predictor variables for the analysis are available from the datadryad.org repository:https://doi.org/10.5061/dryad.1jwstqjz4. Birds datasets were provided by BirdsCanada (https://www.birdscanada.org).
Funding: This work was supported by the Sentinel North program of Laval University, funded by the Canada First Research Excellence Fund (sentinellenord.ulaval.ca) awarded to A.A., D.F., and C.H.. The project was also supported by the Natural Sciences and Engineering Research Council of Canada (NSERC; A.A.: 2019-05183, P.D.: 2019-06887; D.F.: RGPIN-06790-2020, C.H.: RGPIN-2018-06202). Also, I.B. received a salary from Sentinel North program as a postdoctoral fellow. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Biodiversity is facing an unprecedented rate of change due to human activities [1, 2]. For terrestrial ecosystems, land-use changes through forest harvesting, agriculture or urbanization are considered the main causes of biodiversity losses and reductions in ecosystem services [1, 3–5]. Human exploitation of natural resources can induce animal and plant mortality, force species displacement, increase biotic interactions, modify their life-history traits, change their morphology and physiology, and even their polymorphic forms [6]. Moreover, the exploitation of natural habitats by humans results in habitat losses and biotope fragmentation, which constrains the size of animal and plant populations [7]. For instance, by returning stands to an early successional stage, forest harvesting homogenizes forest landscape, which results in an overrepresentation of early-succession forests at the expense of the older states [8, 9]. Forest harvesting targets old-stands, which are economically more profitable but these stands are not homogeneous as they present structural diversity and can be distinguished into different groups [10]. Moreover, these stands are the habitat of a biodiversity indicator species, the Black-backed Woodpecker Picoides arcticus), associated with old-growth attributes. Thus, forest harvesting may threaten vertebrate species associated with old-growth forest attributes [11] but also organisms (insects, fungi, non-vascular plants) that have low-dispersal capacity and for show extinction risk increases rapidly when ecological continuity is broken [12].
Climate change is the second most important driver of change in biodiversity after land use, for the terrestrial ecosystems [1, 13]. Climate change may impact forest landscapes indirectly by triggering natural disturbances such as wildfire, which strongly alter forest age structure and composition [14–16], and to which are associated a unique biodiversity. Climate change may also directly impact tree growth and alter competitive abilities of boreal tree species [16]. These alterations are predicted to be detrimental to biodiversity especially those associated with older mixedwood and coniferous forests [17, 18]. It may also impact species directly by changing their occurrence or abundance, when climatic conditions go out of their tolerance limits, due to modifications in temperature and precipitation conditions [19, 20], which might reshape community composition by species turnover [20]. For example, the displacement of species to higher elevations is one of the predicted consequences of climate change to overcome the potential increase in temperature [21]. However, climate change could have implications on the abundance of species with low dispersal capacity that are unable to follow the climate conditions to which they are currently adapted [21, 22] and could lead to an increase in extinction risks [23].
Although modelling biodiversity changes is becoming essential to predict the effects of human activities on the global environment, many challenges remain because there are many related issues that need to be integrated [24]. There are urgent needs to identify the most appropriate forest harvesting scenarios to achieve management objectives, such as species recovery [25] or habitat restoration [26]. This would help identify more targeted conservation actions under climate change such as vegetation restoration.
Despite the number of studies made to better understand the cumulative effects of the different types of disturbances on biodiversity [1], there are considerable uncertainties over the magnitude of changes in future animal communities [27]. The cumulative effect of anthropogenic disturbances could induce complex effects on biodiversity through the adjustment in species’ ecological traits, which may have cascading repercussions from individual changes to community-level responses [6]. However, most literature evoking the modelling of biodiversity changes considered the assumption of no interactions among the various drivers [1]. In this work, we investigate how the interaction between forest harvesting and climate change would impact biodiversity. Specifically, we examine future implications of forest harvesting levels on the variation of bird and beetle assemblages in Québec’s boreal forest under different climate change scenarios. We aim first to quantify the extent of assemblages’ alteration by increasing harvest levels under different climate change scenarios and second to determine the main drivers. Moreover, we study how species associated with different types of habitat classes will be affected by the combined effects of forest harvesting and wildfire.
Materials and methods
Study area and occurrence data
The study area was located in the Côte-Nord region of Québec, Canada (48°N to 53°N, 65°W to 71°W) within an area of 114,118 km2 (Fig 1). This region is part of the eastern spruce-moss subdomain and is dominated by black spruce (Picea mariana [Mill.] BSP) with balsam fir (Abies balsamea [L.] Miller). Forest harvesting had been the main source of anthropogenic disturbances since the late 1990s [28]. Moreover, This area is mainly affected by two major natural disturbances: spruce budworm outbreak (SBW) (Choristoneura fumiferana) and frequent wildfires [9, 29].
Base layer of the region was from ArcGIS (https://www.arcgis.com/apps/mapviewer/index.html?layers=1768e8369a214dfab4e2167d5c5f2454).
We used presence-absence data collected between 2004 and 2018 to model the species distribution of two taxa: birds and beetles. For birds, we used the data from the Second Atlas of Breeding Birds of Québec [30], based on species occurrence detected using unlimited distance 5-minute point counts [31] collected during the breeding season, from late May to mid-July. For beetles, we merged different databases collected in 2004, 2005, 2007, and 2011 [4, 32, 33]. We added 54 sites sampled in 2018 in the northern part of the territory, along the northeast principal road going to Labrador. Insect sampling involved one multidirectional flight-interception trap per site to capture the flying beetles, and four meshed pitfall traps per site to sample beetles that move on the soil surface [4, 32]. Data from the sites heavily impacted by the SBW outbreak [34] were removed to focus only on the effect of the wildfires. We used for this purpose a cumulative index of defoliation severity [29] (we removed 4% and 7% of sampling sites for beetle and bird databases, respectively).
The forest management scenarios
Three harvest levels were considered: no harvest activity (NoHarvest), 8% of the areas harvested over 10 years (Harvest)—the typical harvest rate for the study area—and half this level (i.e., 4%, Harvest0.5). The harvest levels were combined with three climate scenarios: baseline level, radiative forcing associated with the Representative Concentration Pathway scenario (RCP) 4.5, which involved a 3°C increase in mean temperature from 2000 to 2100; and RCP 8.5 associated with a 7.5°C increase over the same period. We compared the response of species occurrence and occupancy between different harvest intensities under three climate scenarios, which explains why we had three reference scenarios (Table 1). Also, we took into consideration in our simulations the northern limit of territorial forest attributes of harvest activities. Beyond this limit, no forest management was authorized by the Ministry of Forests, Wildlife, and Parks [34].
We considered three of them for reference (Climate = Baseline, Harvest = NoHarvest; Climate = RCP 4.5, Harvest = NoHarvest; Climate = RCP 8.5, Harvest = NoHarvest) and compared them to six other scenarios. Under each climate scenario (Baseline, RCP 4.5 and RCP 8.5) we compared NoHarvest to Harvest0.5 and Harvest, which makes six comparisons in total.
Forest landscapes simulation
We simulated forest succession across landscapes using LANDIS-II [35], a forest landscape model that captures forest succession across landscapes as an emergent property of both stand and landscape-scale processes [36]. Specifically, we used the LANDIS-II Biomass Succession extension v5.0, Base Fire v4.0, BDA v4.0, together with the Biomass Harvest v5.0 [35] to simulate forest succession, fire [37] and spruce budworm outbreak disturbances as well as harvesting in each 250-m cell at a 10-year time step. Outbreaks of SBW were simulated using the LANDIS-II Biological Disturbance Agent (BDA) extension v3.0 [38, 39]. The Biomass Succession extension simulates modifications in cohort aboveground biomass (AGB) over time by taking into consideration cohort age, life-history traits, and land type responses of individual tree species. A random forest model was used to predict crown closure covariate by using Canadian National Forest Inventory (NFI) forest attribute maps [40]. These maps are a k-Nearest Neighbours interpolation of the NFI photoplot data acquired in 2001 and are depicting over 130 forest attributes including species-specific biomass, stand age and crown closure at a 250-m resolution (see [40]). We, therefore, build a random forest model predicting cell-level crown closure in NFI products from NFI species-specific biomass as well as stand age. This model had a very high goodness of fit (R2 = 0.86). The model was then applied on LANDIS-II outputs to predict crown closure all along the simulation, for each cell by using simulated species-specific biomass and stand age [41]. Using species group and predicted crown closure, we created five land cover classes from the Earth Observation for Sustainable Development of Forests (EOSD) Land Cover Classification Legend ([42]; see Table 2). See [41] for more details regarding the spatially-explicit forest simulation model with LANDIS-II.
The predictor variables
Land cover associated with sampling sites was determined from the Canadian National Forest Inventory referenced in the year 2001 [40], updated yearly for subsequent fires and cuts. The landscape was split into 11 land cover types. Specifically, we considered land covers with stand age greater than 50 years, i.e., closed-canopy conifer forest, open-canopy mature conifer forest, mixed forest, and open area (see Table 2), together with six disturbance classes, i.e, fire and forest harvesting subdivided by age classes: 0–10, 11–20 and 21–50 years [29]. The category of the other types of habitat was not included in the models to avoid collinearity. In the same region of study and forest types, [32] showed that ground-dwelling and flying beetles were influenced by landscape variables within radius around sample plots of 400 and 800 m respectively, while [4] used a 1 km radius for birds. Therefore, we used for ground-dwelling beetles 20 pixels (231 m resolution) centered on the focal pixel (i.e., 21 pixels in total) to calculate the frequencies of the land cover classes with a radius of 2.5 pixels (∼0.58 km). For flying beetles and birds, we used 37 pixels with a radius ∼0.81 km to calculate land cover frequencies. We also considered the distance to the nearest burned areas as well as the stand age as potential predictor variables.
Association between bird species and habitat type
We considered three land cover classes to calculate species winning conditional probability (WCP), i.e., an increase in the probability of species occupancy from uncut to post-harvest in 2100, conditional to species habitat (see Eq 2). We calculated WCP for (1) generalist bird species; (2) species associated with early-to-mid succession forest (EMSF), which included the following land covers: wetland, deciduous, mixed-wood (young to mid-age), and coniferous habitat (young to mid-age); and (3) species associated with late succession forest (LSF), which included the following land covers: mixed-wood (mature to old), deciduous (mature to old) and coniferous habitat (mature to old). Stand age was classified as follows: young (stand age < 30 years), mid-age (stand aged between 30 and 60 years), mature (stand aged between 60 and 80 years), and old (stand age greater than 80 years). This classification was used only for birds because information regarding beetles’ associated land cover was unavailable at the moment of the study. Bird-associated land cover has been based on habitat associations from [43, 44].
Relationship between beetles and forest disturbances
A literature review was done on every beetle taxon to characterize two important functional traits: habitat preference and dispersal capacity. First, we identified species that respond positively to forest harvesting or burned forests, the remainder being considered as species associated with undisturbed natural forests. Then, in order to discriminate beetle taxa that mainly disperse by flight from those that mainly walk on the forest floor, we calculated abundance ratios from the data collected. For each beetle taxon, we pooled the total abundance (from 2004 to 2018) per flight interception trap and divided by the total abundance per pitfall trap. This ratio provides an index of beetle proficiency to fly or to walk on the ground. As the ratio provides continuous estimates, we kept only beetles with a ratio higher than 2 as flying beetles and beetles with a ratio lower than 3 as ground-dwelling beetles. Beetle species with ratios between 2 and 3 were removed from the study.
Modelling framework
We developed species distribution models (SDMs) [45–47] to estimate single-species occurrence probability based on extensive field surveys of 88 species (39 flying beetles, 15 ground-dwelling beetles and 34 bird species (see S1 File)). The methodology here is described in details in [20] as Habitat-Only-Based Models (HOBMs). We used generalized linear mixed models to predict species occurrence probability (package ‘lme4’; [48]). A random intercept was added to include any differences between sampling years based on three full potential models deferring only in the fixed effects of the models. We partitioned the modelling strategy into several steps starting from data pre-processing to the species occurrence probability projections. SDMs were estimated only for species recorded at least at 1% and 5% of sampling sites for birds and beetles, respectively. We used land cover variables extracted from LANDIS forest landscape simulations for the simulated scenarios (see S1 Table for predictor variables description). We also standardized the predictor variables just before any model calibration to facilitate the convergence of the models [49]. We removed highly correlated variables (|r| > 0.60, where r represented pairwise Pearson correlation coefficient) and kept the 5 most important potential predictor variables according to the Akaike information criterion (AIC) calculated on a univariate generalized linear model (GLM) with linear and quadratic terms for each variable [20, 50].
We suggested three full potential models, the first one contained only linear forms of the predictor variables. However, the second and third full models included also quadratic transformations of the covariates (see S2 Table). The best models were selected for each full model based on a 10-fold cross-validation procedure by minimizing the AIC criterion (package ‘MuMIn’; [51]). For each species, we kept only the models with AUC ≥ 0.7 [52, 53]. In addition, we analyzed the assemblage structure based on continuous occurrence probabilities instead of binarizing the outputs [46, 54].
We calculated the percentage change in the regional occupancy probability (ROP) between the reference (Ref) and the simulated (Sim) scenarios [4] as follows:
(1)
where
denotes the average of the ROP over all the species under the scenario s and is given by
with
and where Nspecies, Npixel and Ps,i,j respectively represent the number of species, the number of pixels in the study area, and the probability of occurrence of the species i for the scenario s at the cell j.
Winning conditional to habitat or to relation to disturbance probability was calculated as
(2)
where
is the indicator function and Hi is the associated habitat for bird species i (H ∈ {G, EMSF, LSF}) or the relation to disturbance for beetle species (H ∈ {Fire, Harvest, Fire − Harvest, None}). In addition, we used the regional Bray-Curtis dissimilarity index (RBCSim,Ref) to measure the degree of dissimilarity in assemblage composition between the compared scenarios [55, 56]. The RBCSim,Ref between the scenarios Sim and Ref was defined as
(3)
We split the Bray-Curtis dissimilarity index into two additive components:
where BC_gradSim,Ref is the occupancy gradient and BC_balSim,Ref is the species balanced variation in occupancy. The latter was defined as
where
To quantify the main source of assemblage dissimilarity, we performed a decomposition of the regional Bray-Curtis dissimilarity, based on the probability of occurrence, into balanced variation in species occupancy and occupancy gradient, which are the generalizations of species turnover and the nestedness respectively in the context of continuous output [56] and occurrence probabilities in our case [20]. This decomposition of the dissimilarity measure computed based on scenarios helped us to characterize the mechanisms that drive variation in species composition. Hereafter the terms species turnover and nestedness will be used as equivalents of balanced variation in species occupancy and occupancy gradient respectively. We then computed the ratio
(4)
The case BC_ratioSim,Ref > 0.5 then indicated that the community change was mostly caused by nestedness, whereas BC_ratioSim,Ref < 0.5 indicated the dominance of species turnover in the assemblage dissimilarity. We used the package ‘betapart’ [57] for the assemblage analysis.
Results
Of 61, 105 and 42 of candidate species of birds, flying beetles and ground-dwelling beetles, 34, 39 and 15 were selected for the projection with AUC ≥ 0.7 (Fig 2a–2c) respectively. Stand age was the most frequently selected predictor variable for the two taxa (Fig 2d). Moreover, ground-dwelling beetles were more closely associated with old undisturbed forests than flying beetles. However, the response of flying beetles was much greater to the distance from burned forests than ground-dwelling-beetles, which showed a weaker response that decreased with time after wildfire (Fig 2d).
Performance metrics (area under the curve (AUC), sensitivity (Sens), specificity (Spec) and the true skill statistic (TSS)) of the selected models (a, b, c). Percentage of the predictor variables that were included in the species regressions (d). Abbreviations: distance to the nearest fire (dist_pert_Fire), stand age (Age), frequency of conifer dense (FreqConDens), frequency of conifer open (FreqConOpen), frequency of mixed wood (FreqMixed), frequency of open habitat (FreqOpen), frequency of disturbance by fire (FreqFire), frequency of disturbance by cut (FreqCut).
Changes in disturbance level following climate change
Changing climate from the baseline to RCP 8.5 scenarios in 2100 increased the proportion of burned stands at each harvest scenario. For example, the proportion of burned stands was 17.2% under the baseline-Harvest scenario, 24.7% under RCP 4.5-Harvest scenario, and 31.6% under RCP 8.5-Harvest scenario (Table 3). Furthermore, the increase either in wildfire or in harvest levels decreased the proportion of coniferous forests (dense, open and mixed wood). Moreover, climate change as well as forest harvesting decreased significantly stand age. For instance, under RCP 4.5 the mean stand age decreased from 90 to 46 years from NoHarvest to Harvest scenarios. Also, the mean stand age under Harvest0.5 went from 67 to 49 years from baseline to RCP 8.5 (Table 3).
Abbreviations: conifer dense (ConDens), conifer open (ConOpen), mixed wood (Mixed), open habitat (Open), Other, disturbance by fire (Fire), disturbance by cut (Cut) and Fire+Cut (Disturbance).
Assemblage analysis
Species assemblage dissimilarity was higher in NoHarvest-Harvest than in NoHarvest-Harvest0.5, under the three climate scenarios in 2100 (Fig 3). Under RCP 4.5, Bray-Curtis dissimilarity increased from 0.08 to 0.12 for birds and 0.06 to 0.09 for flying beetles and from 0.08 to 0.11 for ground-dwelling beetles when comparing NoHarvest-Harvest0.5 to NoHarvest-Harvest scenarios. Compared to birds and ground-dwelling beetles, flying beetles had lower dissimilarity in assemblages regardless of climate scenarios (Fig 3). However, the dissimilarity in species assemblage over harvest gradient decreased with climate change (see the difference in Bray-Curtis dissimilarity between baseline and RCP 8.5 in Fig 3). This result was also observed in the structure of land cover under RCP8.5 compared to RCP4.5 and baseline by varying harvest intensity (see S4 Fig).
At each climate change scenario (Baseline, RCP 4.5, RCP 8.5), we computed the dissimilarity between Harvest0.5 and Harvest vs. NoHarvest in 2100. Under Baseline, we compared respectively Baseline-NoHarvest to Baseline-Harvest0.5 and Baseline-NoHarvest to Baseline-Harvest. Under RCP 4.5 we compared respectively RCP 4.5-NoHarvest to RCP 4.5-Harvest0.5 and RCP 4.5-NoHarvest to RCP 4.5-Harvest. Finally, under RCP 8.5 we compared respectively RCP 8.5-NoHarvest to RCP 8.5-Harvest0.5 and RCP 8.5-NoHarvest to RCP 8.5-Harvest. The abbreviation “None” represents the case of species associated to closed forests.
Occupancy analysis
We observed a change in the regional occupancy between the scenarios of forest harvesting (Harvest0.5 and Harvest vs. NoHarvest) under all three climate change scenarios. More precisely, we observed an increase in regional occupancy in the bird community from nonharvest to harvest (passage from 13% to 15% in the regional occupancy from nonharvest to harvest scenarios under RCP 4.5). The opposite behaviour was observed in the beetle community (for instance a decrease in regional occupancy from 31% to 27% was observed for flying beetles by comparing the same scenarios). These changes in the overall occupancy of birds, and the decrease for beetles from NoHarvest to Harvest cases was visible over 114,118 km2 of the study area (Fig 4) and also regionally (Fig 5a). Post-harvesting changes in species assemblages were mainly caused by species turnover (BC_ratioSim,Ref < 0.5) for the two taxa with a strongest turnover for ground-dwelling than flying beetles. Under each climate scenario, the occupancy of bird species associated with young habitats together with generalist species increased significantly contrary to species associated with mature and old forests, when we compared harvested to unharvested scenarios (Fig 5b). Under baseline and RCP 4.5, 50% of bird species associated with late succession forest increased in occupancy from no harvested to harvested scenarios (Fig 6a), whereas 82% of species associated with early to mid succession forest increased in occupancy (see S1 Fig for the variation at the species scale). By comparing RCP 8.5 to RCP 4.5, the proportion of winner species (from non-harvested to harvested landscapes) that were associated with early to mid-succession forests decreased from 82% to 73%. Under the same conditions, species associated with late succession forests increased from 50% to 63% (Fig 6a). When we compared non-harvested to harvested scenarios for flying beetles, species that were associated with burned stands had the weakest increase in occupancy compared to species associated with harvested stands or closed forests (Fig 5c). The percentage of winning flying beetles associated with closed forests decreased from 53% to 47% from baseline to RCP 8.5 under a forest harvest scenario (Fig 6b). Winning species of flying beetles associated with harvested stands even decreased from 42% to 33% under the same conditions. Among flying beetles associated with burned forests, the percentage of winning species went from 40% to 0%. Regarding ground-dwelling beetles, the percentage of winning species associated with closed forests decreased from 64% to 57% under the harvest scenario under climate change (baseline to RCP 8.5) (Fig 6c).
The black line represents the northern limit for forest harvesting activities.
(a) Regional Bray-Curtis dissimilarity ratio (BC_ratio) with the percentage of change in the regional occupancy probability (). At each climate change scenario (Baseline, RCP 4.5, RCP 8.5), we computed the change in the ROP (Eq 1) and Bray-Curtis ratio (Eq 4) between Harvest0.5 and Harvest vs. NoHarvest in 2100. (b) Percentage of change in the regional occupancy probability between Harvest and Noharvest under the baseline and RCP 8.5 for bird species. Abbreviations: late succession forest (LSF), early-to-mid succession forest (EMSF), generalist species (G).(c-d) Percentage of change in the regional occupancy probability between Harvest and Noharvest under the baseline and RCP 8.5 for flying and ground-dwelling beetles species with relation to a disturbance.
Under Baseline, we compared Baseline-NoHarvest to Baseline-Harvest. Under RCP 4.5 we compared RCP 4.5-NoHarvest to RCP 4.5-Harvest. Finally, under RCP 8.5 we compared RCP 8.5-NoHarvest to RCP 8.5-Harvest.
Discussion
Forest harvesting can impact significantly forest biota and may have lasting effects on biodiversity [58]. We aimed to quantify how the interaction between forest harvesting and climate change would impact biodiversity. We analyzed how species assemblages can be expected to change under different forest management scenarios and climate conditions. Our models suggest that forest harvesting will cause pronounced changes in species assemblages for birds and beetles by 2100 under all climate change scenarios. Our result is supported by many empirical studies [4, 59] evoking the noticeable effect of harvesting on the assemblage of common species and predicted that the effect could be stronger by including rare species [4]. Moreover, [17] projects a drastic reduction in the habitat of the Black-backed Woodpecker (Picoides arcticus), a focal species associated with deadwood and representative of old-growth forest biodiversity in eastern Canada [60]. The simulated change in assemblage dissimilarity was mainly dominated by species turnover. This implies that the change in the assemblage composition from NoHarvest to Harvest scenarios was dominated by an increase in occupancy for a subset of species that was balanced mostly by a decrease in occupancy for the other subset of species.
It is important to underline that the effects of forest harvesting were studied under each climate scenario. So, the difference between the less disturbed scenario (baseline-NoHarvest, i.e. 15% of disturbance) and the most disturbed scenario (RCP8.5-Harvest, i.e. 55% of disturbance) on species occurrence could be different from our results based on comparing different harvest levels within the same climate scenario. Our results predict an increase in occupancy in the majority of bird species associated with Early-to-Mid succession forests contrary to species associated with Late succession forests. This result is coherent with previous studies [4, 61]. However, under the extreme climate scenario, there is one more late-succession species increasing in occupancy when we compared harvested to unharvested scenarios, while this is the opposite for early-to-mid succession species which decreased by one species. This difference could be explained by the proportion of burned stands on the harvestable stands under each climate scenario. Indeed, we observed a decrease in the harvested stands with climate change contrary to the burned stands. Those interactions between forest harvesting and future climate change could impact the occupancy of species that depend on disturbed stands. For example, on the one hand, we observed that Yellow Warbler (YEWA, Setophaga petechia), a bird species associated with early-to-mid succession forest and negatively affected by both harvested forest and stand age regressed under RCP 8.5 contrary to the baseline climate scenario. On the other hand, Swainson’s Thrush (SWTH, Catharus ustulatus) that was associated with late succession forest and negatively affected by burned forests increased under RCP 8.5 (see S1 Fig). The increase in wildfires under the extreme climate scenario could affect negatively species that prefer harvested stands if forest managers increase salvage logging. It has been shown that bird trait assemblages differ between burned, post-fire salvage, and traditionally logged habitats in North American forests [62].
Our results on beetles confirm that the dispersal capacity of taxa is an important functional trait to understand future extinction risks for this order of insects. As they are less mobile, ground-dwelling beetles are mainly associated with local conditions [32]. In our study, 93% of them were associated with mature and undisturbed forests (see S2 and S3 Figs). Nevertheless, the frequency of young harvested stands had some support in the models, suggesting that ground-dwelling beetle communities found in old boreal forest persist some years in recently harvested stands, as shown for carabids by [63] in western Canada. For instance, two common carabids, Platynus decentis and Pterostichus adstrictus, selected in our models were “winners” under each climate change scenario. Post-disturbance successions are usually characterized by a deciduous stage, which should favor P. decentis as it is usually found in deciduous and mixed forest [64]. Moreover, P. adstrictus is favored by wildfire [65] and is not affected by forest management [64]. Two species of Cryptophagus (Cryptophagidae), a mycetophagous family usually associated with mature and undisturbed forests, also showed a positive response to harvest intensification. They have low dispersal capacities and their persistence in harvested landscapes suggest that suitable conditions could be maintained as the area of study is covered by a matrix of old-growth forests growing in a humid climate that favor fungal growth. Certain ground-dwelling beetles also respond to recently burned stands. For instance, some carabids are known to be attracted to recently burned stands [66]. In a recent study, [67] suggested that pyrophily in insects have evolved to take advantage of unique post-fire environmental conditions. Indeed, heat-sterilized ovipositing substrates are used by carabids of the genus Sericoda to increase their reproductive output. These conditions are ephemeral and Sericoda disappears rapidly after fire. This could result from suboptimal reproductive conditions. Many other insects are known to respond in such a way to wildfire [68].
We showed that species turnover was the dominant source of dissimilarity in species assemblages for the two studied taxa but ground-dwelling beetles registered the highest rate. This might be due to their lower moving capacity. They cannot escape easily new conditions generated by disturbances. As seen earlier, they can persist for some years after harvesting, but the new habitat conditions end up inducing a higher species turnover. In the same boreal region, [69] showed that post-logging beetle assemblages during forest succession were mostly driven by interspecific interactions than by habitat attributes. It was probably a consequence of the presence of a pristine forest matrix in the surrounding landscape in which local communities differ as they have evolved together over a long time. Our results on ground dwelling beetle occupancy showed a reduction of the rove beetles (Staphylinidae), which are mainly associated with mature and undisturbed forests. They represent 83% of the ground-dwelling “losers” regardless of the climatic change scenario. As the rove beetles modelled were all predators and thus at the top of the food chain, interspecific interactions are critical, and they might be the first to disappear in such turnovers.
In comparison, beetle communities that fly to colonize burned trees after wildfire are much more structured according to habitat characteristics [70]. Among modelled flying beetles in our study, only 44% of species were associated with undisturbed forests. Most were saproxylic beetles associated with burned forests. The time window for successful colonization of burned trees could be narrow for some saproxylic beetle as phloeophagous species may simply benefit from the high-quality sub-cortical tissues of freshly burned trees, usually for 1–2 years only [71–75]. After tree felling, woody debris decay for several decades, allowing great persistence for late-succession beetles [75, 76]. The complexity of the different saproxylic beetle feeding guilds is expressed in the analysis of the change in occupancy for flying beetles. Even though 26% were associated with fire, the “winners” among them dropped from 40% under base scenario to 20% under the RCP8.5 scenario. For instance, in the literature, the six bark beetle species modelled were associated with fire, harvest or both disturbances, but were all “losers” under both climate change scenarios. [77] showed the preference of these beetles to colonize large diameter burned trees, showing the importance of this forest attribute for maintaining biodiversity. However, it is probably the first attribute to be negatively affected by harvesting and climate change. Moreover, wildfire [68, 70], salvage logging [78] and traditional logging [33] rapidly homogenize stands and biodiversity at the landscape level. On the long-term, this lowers landscape heterogeneity as well as biodiversity at the landscape level. As observed for ground-dwelling beetles, changes in occupancy are also most visible for predators in flying beetles. While the proportion of “losers” increases from 50 to 63% between climate change scenarios for predatory ground dwelling beetles, it increases from 44 to 77% for predatory flying beetles. The scenarios tested integrate burned forests interacting with increased harvesting and the resulting landscapes suggest that an erosion in the flying beetle food chain is likely to impact all trophic levels.
Our results were based only on the most common species, the response of rare specialists and threatened species might decrease the regional occupancy with both harvest activities and climate conditions [25]. Therefore, it will be interesting to analyze the response of the rare species to disturbance. One possible approach could be through the development of a probabilistic model which combines the occurrence probability of rare species with indicator species.
Conclusion
In conclusion, forest harvesting activities are predicted to modify the composition of beetle and bird species assemblages under all climate scenarios. We also indicated that the consequences of maintaining the current level of forest harvesting under the worst climate change scenario could modify the species-habitat association. Ideally, we aim to generate a situation where both early and late successional stages can coexist by managing forests. Interestingly, we observed opposite responses in occupancy between birds, which was generally positive, and beetle, which was negative, in our study area. The perfect separation between the two taxonomic groups regarding the change in occupancy could reflect the impact of ecological traits such as movement capacity and diet differences between boreal species. In any case, forest management must be adapted to achieve conservation objectives under all climate scenarios. Our simulations show that harvest activities must be reduced. Even when it maximizes biodiversity, harvest could be harmful to taxa with low moving capacity (beetles in our case) which characterize old-growth forests.
Supporting information
S1 File. The accepted beetle and bird species.
https://doi.org/10.1371/journal.pclm.0000179.s001
(XLSX)
S1 Fig. Change in ROP for bird species with the associated habitat.
https://doi.org/10.1371/journal.pclm.0000179.s004
(TIFF)
S2 Fig. Change in ROP for flying beetles species with the relation to a disturbance.
https://doi.org/10.1371/journal.pclm.0000179.s005
(TIFF)
S3 Fig. Change in ROP for ground-dwelling beetles species with the relation to a disturbance.
https://doi.org/10.1371/journal.pclm.0000179.s006
(TIFF)
S4 Fig. The distribution of the land cover under the simulated management scenarios in 2100.
https://doi.org/10.1371/journal.pclm.0000179.s007
(TIFF)
Acknowledgments
We acknowledge Calcul Québec and Compute Canada for their technical support and computing infrastructures. We also thank the Québec Breeding Bird Atlas for supplying data. We would also like to thank the following partners: Regroupement QuébecOiseaux, Environment and Climate Change Canada, and Birds Canada, as well as all of the volunteer participants who gathered data for the project. We are grateful to Louis-Paul Rivest for his valuable statistical suggestions. Special thanks are due to Alexandre Terrigeol for his valuable efforts regarding data collection, as well as to Georges Pelletier and Nicolas Bédard for beetle identification.
References
- 1. Sala OE, Chapin FS, null III, Armesto JJ, Berlow E, Bloomfield J, et al. Global Biodiversity Scenarios for the Year 2100. Science. 2000;287(5459):1770–1774. pmid:10710299
- 2. Newbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA, et al. Global effects of land use on local terrestrial biodiversity. Nature. 2015;520(7545):45–50. pmid:25832402
- 3. Naeem S, Duffy JE, Zavaleta E. The Functions of Biological Diversity in an Age of Extinction. Science. 2012;336(6087):1401–1406. pmid:22700920
- 4. Bichet O, Dupuch A, Hébert C, Le Borgne H, Fortin D. Maintaining animal assemblages through single-species management: the case of threatened caribou in boreal forest. Ecological Applications. 2016;26(2):612–623. pmid:27209799
- 5. Matuoka MA, Benchimol M, de Almeida-Rocha JM, Morante-Filho JC. Effects of anthropogenic disturbances on bird functional diversity: A global meta-analysis. Ecological Indicators. 2020;116:106471.
- 6. Sergio F, Blas J, Hiraldo F. Animal responses to natural disturbance and climate extremes: a review. Global and Planetary Change. 2018;161:28–40.
- 7. Coelho AJP, Magnago LFS, Matos FAR, Mota NM, Diniz ÉS, Meira-Neto JAA. Effects of anthropogenic disturbances on biodiversity and biomass stock of Cerrado, the Brazilian savanna. Biodiversity and Conservation. 2020;29(11):3151–3168.
- 8. Cyr D, Gauthier S, Bergeron Y, Carcaillet C. Forest management is driving the eastern North American boreal forest outside its natural range of variability. Frontiers in Ecology and the Environment. 2009;7(10):519–524.
- 9. Boucher Y, Perrault-Hébert M, Fournier R, Drapeau P, Auger I. Cumulative patterns of logging and fire (1940–2009): consequences on the structure of the eastern Canadian boreal forest. Landscape Ecology. 2017;32(2):361–375.
- 10. Martin M, Fenton N, Morin H. Structural diversity and dynamics of boreal old-growth forests case study in Eastern Canada. Forest Ecology and Management. 2018;422:125–136.
- 11. Martin M, Tremblay JA, Ibarzabal J, Morin H. An indicator species highlights continuous deadwood supply is a key ecological attribute of boreal old-growth forests. Ecosphere. 2021;12(5):e03507.
- 12. Nordén B, Appelqvist T. Conceptual problems of ecological continuity and its bioindicators. Biodiversity & Conservation. 2001;10(5):779–791.
- 13. Dobson A, Rowe Z, Berger J, Wholey P, Caro T. Biodiversity loss due to more than climate change. Science. 2021;374(6568):699–700. pmid:34735233
- 14. Boulanger Y, Gauthier S, Burton PJ. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research. 2014;44(4):365–376.
- 15. Boulanger Y, Taylor AR, Price DT, Cyr D, McGarrigle E, Rammer W, et al. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Landscape Ecology. 2017;32(7):1415–1431.
- 16. Boulanger Y, Puigdevall JP. Boreal forests will be more severely affected by projected anthropogenic climate forcing than mixedwood and northern hardwood forests in eastern Canada. Landscape Ecology. 2021;36(6):1725–1740.
- 17. Tremblay JA, Boulanger Y, Cyr D, Taylor AR, Price DT, St-Laurent MH. Harvesting interacts with climate change to affect future habitat quality of a focal species in eastern Canada’s boreal forest. PLoS ONE. 2018;13(2):1–25. pmid:29414989
- 18. Cadieux P, Boulanger Y, Cyr D, Taylor AR, Price DT, Sólymos P, et al. Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada. Diversity and Distributions. 2020;26(6):668–682.
- 19. Micheletti T, Stewart FEC, Cumming SG, Haché S, Stralberg D, Tremblay JA, et al. Assessing pathways of climate change effects in SpaDES: An application to bBoreal Landbirds of Northwest Territories Canada. Frontiers in Ecology and Evolution. 2021;9:654.
- 20. Bouderbala I, Labadie G, Béland JM, Boulanger Y, Hébert C, Desrosiers P, et al. Effects of global change on animal biodiversity in boreal forest landscape: an assemblage dissimilarity analysis. bioRxiv. 2022.
- 21. Davis MB, Shaw RG. Range Shifts and Adaptive Responses to Quaternary Climate Change. Science. 2001;292(5517):673–679. pmid:11326089
- 22. Muluneh MG. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agriculture & Food Security. 2021;10(1):1–25.
- 23. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, et al. Extinction risk from climate change. Nature. 2004;427(6970):145–148. pmid:14712274
- 24. Ceaușu S, Apaza-Quevedo A, Schmid M, Martín-López B, Cortés-Avizanda A, Maes J, et al. Ecosystem service mapping needs to capture more effectively the biodiversity important for service supply. Ecosystem Services. 2021;48:101259.
- 25. Cadieux P, Boulanger Y, Cyr D, Taylor AR, Price DT, Tremblay JA. Spatially explicit climate change projections for the recovery planning of threatened species: The Bicknell’s Thrush (Catharus Bicknelli) as a case study. Global Ecology and Conservation. 2019;17:e00530.
- 26. Jones GM, Keyser AR, Westerling AL, Baldwin WJ, Keane JJ, Sawyer SC, et al. Forest restoration limits megafires and supports species conservation under climate change. Frontiers in Ecology and the Environment. 2022;20(4):210–216.
- 27. Almeida-Rocha JM, Soares LASS, Andrade ER, Gaiotto FA, Cazetta E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: A global meta-analysis. Molecular Ecology. 2020;29(24):4812–4822. pmid:33058295
- 28. Bouchard M, Pothier D. Long-term influence of fire and harvesting on boreal forest age structure and forest composition in eastern Québec. Forest Ecology and Management. 2011;261(4):811–820.
- 29. Labadie G, McLoughlin PD, Hebblewhite M, Fortin D. Insect-mediated apparent competition between mammals in a boreal food web. Proceedings of the National Academy of Sciences of the United States of America. 2021;118(30). pmid:34282006
- 30.
Atlas des oiseaux nicheurs du Québec. Données obtenues en réponse à une demande présentée aux bureaux de l’Atlas (www.atlas-oiseaux.qc.ca). Regroupement QuébecOiseaux; Service canadien de la faune d’Environnement Canada; Études d’Oiseaux Canada. Québec, QC, Canada; 2018.
- 31.
Bibby CJ, Burgess ND, Hill DA, Hillis DM, Mustoe S. Bird census techniques. Elsevier; 2000.
- 32. Janssen P, Fortin D, Hébert C. Beetle diversity in a matrix of old-growth boreal forest: influence of habitat heterogeneity at multiple scales. Ecography. 2009;32(3):423–432.
- 33. Légaré JP, Hébert C, Ruel JC. Alternative silvicultural practices in irregular boreal forests: Response of beetle assemblages. Silva Fennica. 2011;45(5):937–956.
- 34.
Ministère des Forêts de la Faune et des Parcs. Aires infestées par la tordeuse des bourgeons de l’épinette au Québec en 2018, 20 p. Québec, gouvernement du Québec, Direction de la protection des forêts; 2018.
- 35. Scheller RM, Mladenoff DJ. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecological Modelling. 2004;180(1):211–229.
- 36. Scheller RM, Domingo JB, Sturtevant BR, Williams JS, Rudy A, Gustafson EJ, et al. Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecological Modelling. 2007;201(3):409–419.
- 37.
Scheller RM, Domingo JB. LANDIS-II Base Fire Extension (v1. 0) User Guide; 2005.
- 38. Sturtevant BR, Gustafson EJ, Li W, He HS. Modeling biological disturbances in LANDIS: a module description and demonstration using spruce budworm. Ecological Modelling. 2004;180(1):153–174.
- 39. Sturtevant BR, Miranda BR, Shinneman DJ, Gustafson EJ, Wolter PT. Comparing modern and presettlement forest dynamics of a subboreal wilderness: Does spruce budworm enhance fire risk? Ecological Applications. 2012;22(4):1278–1296. pmid:22827135
- 40. Beaudoin A, Bernier PY, Guindon L, Villemaire P, Guo XJ, Stinson G, et al. Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. Canadian Journal of Forest Research. 2014;44(5):521–532.
- 41. Labadie G, Hardy C, Boulanger Y, Vanlandeghem V, Hebblewhite M, Fortin D. Global change impacts a threatened species through alteration of predator-prey dynamics; 2022. Research Square. Available from: https://doi.org/10.21203/rs.3.rs-1317079/v1.
- 42. Beaubien J, Cihlar J, Simard G, Latifovic R. Land cover from multiple thematic mapper scenes using a new enhancement-classification methodology. Journal of Geophysical Research: Atmospheres. 1999;104(D22):27909–27920.
- 43.
Gauthier J, Aubry Y. Les oiseaux nicheurs du Québec: Atlas des oiseaux nicheurs du Québec méridional. Association québécoise des groupes d’ornithologues; Société québécoise de protection des oiseaux; Service canadien de la faune; Environnement Canada, région du Québec; 1995.
- 44.
Robert M. Deuxième atlas des oiseaux nicheurs du Québec méridional. Regroupement QuébecOiseaux; Environnement et changement climatique Canada; Études d’oiseaux Canada; 2019.
- 45. Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecological Modelling. 2000;135(2):147–186.
- 46. Guisan A, Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecology Letters. 2005;8(9):993–1009. pmid:34517687
- 47.
Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, et al. Ecological niches and geographic distributions (MPB-49). vol. 49. Princeton University Press; 2011.
- 48.
Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4; 2015. https://cran.r-project.org/web/packages/lme4.
- 49.
MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey L, Hines JE. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier; 2017.
- 50. Zurell D, Zimmermann NE, Gross H, Baltensweiler A, Sattler T, Wüest RO. Testing species assemblage predictions from stacked and joint species distribution models. Journal of Biogeography. 2020;47(1):101–113.
- 51.
Barton K. Package ‘MuMIn’; 2015. https://cran.r-project.org/web/packages/MuMIn.
- 52. Araújo MB, Pearson RG, Thuiller W, Erhard M. Validation of species–climate impact models under climate change. Global Change Biology. 2005;11(9):1504–1513.
- 53.
Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression, third edition. John Wiley & Sons; 2013.
- 54. Grenié M, Violle C, Munoz F. Is prediction of species richness from stacked species distribution models biased by habitat saturation? Ecological Indicators. 2020;111:105970.
- 55. Baselga A. Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography. 2010;19(1):134–143.
- 56. Baselga A. Separating the two components of abundance-based dissimilarity: balanced changes in abundance vs. abundance gradients. Methods in Ecology and Evolution. 2013;4(6):552–557.
- 57. Baselga A, Orme CDL. betapart: an R package for the study of beta diversity. Methods in Ecology and Evolution. 2012;3(5):808–812.
- 58.
Lindenmayer DB, Franklin JF. Conserving forest biodiversity: a comprehensive multiscaled approach. Island Press; 2002.
- 59. Baker SC, Halpern CB, Wardlaw TJ, Kern C, Edgar GJ, Thomson RJ, et al. A cross-continental comparison of plant and beetle responses to retention of forest patches during timber harvest. Ecological Applications. 2016;26(8):2495–2506. pmid:27787926
- 60.
Tremblay JA, Dixon RD, Saab VA, Pyle P, Patten MA. Black-backed Woodpecker (Picoides arcticus), version 1.0. In Birds of the World (P. G. Rodewald, Editor). Cornell Lab of Ornithology, Ithaca, NY, USA; 2020.
- 61.
Fortin D, Hébert C, Légaré JP, Courbin N, Swiston K, Hodson J, et al. Partial harvesting in old-growth boreal forests and the preservation of animal diversity from ants to woodland caribou. EB Wallace (Ed), Woodlands: Ecology, Management and Conservation, Nova Science Publishers, Inc, Hauppauge, NY, USA. 2011.
- 62. Bognounou F, Venier LA, Van Wilgenburg SL, Aubin I, Candau JN, Arsenault A, et al. Early avian functional assemblages after fire, clearcutting, and post-fire salvage logging in North American forests. Canadian Journal of Forest Research. 2021;51(3):393–407.
- 63. Niemela J, Langor D, Spence JR. Effects of Clear-Cut Harvesting on Boreal Ground-Beetle Assemblages (Coleoptera: Carabidae) in Western Canada. Conservation Biology. 1993;7(3):551–561.
- 64. Work TT, Koivula M, Klimaszewski J, Langor D, Spence J, Sweeney J, et al. Evaluation of carabid beetles as indicators of forest change in Canada. The Canadian Entomologist. 2008;140(4):393–414.
- 65. Cobb TP, Langor DW, Spence JR. Biodiversity and multiple disturbances: boreal forest ground beetle (Coleoptera: Carabidae) responses to wildfire, harvesting, and herbicide. Canadian Journal of Forest Research. 2007;37(8):1310–1323.
- 66. Holliday NJ. Species responses of carabid beetles (Coleoptera: Carabidae) during post-fire regeneration of boreal forest. The Canadian Entomologist. 1991;123(6):1369–1389.
- 67. Bell AJ, Calladine KS, Wardle DA, Phillips ID. Rapid colonization of the post-burn environment improves egg survival in pyrophilic ground beetles. Ecosphere. 2022;13(8):e4213.
- 68. Boucher J, Hébert C, Ibarzabal J, Bauce É. High conservation value forests for burn-associated saproxylic beetles: an approach for developing sustainable post-fire salvage logging in boreal forest. Insect Conservation and Diversity. 2016;9(5):402–415.
- 69. Le Borgne H, Hébert C, Dupuch A, Bichet O, Pinaud D, Fortin D. Temporal dynamics in animal community assembly during post-logging succession in boreal forest. PLOS ONE. 2018;13(9):1–18. pmid:30235333
- 70. Azeria ET, Ibarzabal J, Hébert C. Effects of habitat characteristics and interspecific interactions on co-occurrence patterns of saproxylic beetles breeding in tree boles after forest fire: null model analyses. Oecologia. 2012;168(4):1123–1135. pmid:22057900
- 71. Muona J, Rutanen I. The short-term impact of fire on the beetle fauna in boreal coniferous forest. Annales Zoologici Fennici. 1994;31(1):109–121.
- 72.
Wikars LO. Effects of fire and ecology of fire-adapted insects. Introductory research essay, Department of Zoology, University of Uppsala, Uppsala, Sweden; 1994. 12.
- 73. Saint-Germain M, Drapeau P, Hébert C. Landscape-scale habitat selection patterns of Monochamus scutellatus (Coleoptera: Cerambycidae) in a recently burned black spruce forest. Environmental entomology. 2004;33(6):1703–1710.
- 74. Saint-Germain M, Drapeau P, Hébert C. Xylophagous insect species composition and patterns of substratum use on fire-killed black spruce in central Quebec. Canadian Journal of Forest Research. 2004;34(3):677–685.
- 75. Boulanger Y, Sirois L. Postfire succession of saproxylic arthropods, with emphasis on Coleoptera, in the north boreal forest of Quebec. Environmental Entomology. 2014;36(1):128–141.
- 76. Jonsell M, Weslien J. Felled or standing retained wood—it makes a difference for saproxylic beetles. Forest Ecology and Management. 2003;175(1-3):425–435.
- 77. Saint-Germain M, Drapeau P, Hébert C. Comparison of Coleoptera assemblages from a recently burned and unburned black spruce forests of northeastern North America. Biological Conservation. 2004;118(5):583–592.
- 78. Norvez O, Hébert C, Bélanger L. Impact of salvage logging on stand structure and beetle diversity in boreal balsam fir forest, 20 years after a spruce budworm outbreak. Forest Ecology and Management. 2013;302:122–132.