Research Paper

INTRODUCTION

For birds, nesting is a critical life stage under selective pressures to optimize fitness. In addition to influencing evolutionary trajectories, nest survival often drives population demographics and predation is often the primary cause of nest failure (Ricklefs 1969, Martin 1995). Human induced landscape change is affecting the magnitude and effect of nest predation geographically and temporally in association with changing dynamics of nest predator distribution, abundance, and foraging efficiency (Rodewald and Kearns 2011, DeGregorio et al. 2016). Understanding the effect of human induced landscape change on nest survival is critical to understanding species evolution, ecology, and avian management and conservation (Chalfoun et al. 2002, Sih et al. 2011, Hethcoat and Chalfoun 2015a).

Human induced landscape change in the form of oil and gas development reduces nest survival of birds across a range of species (Chalfoun et al. 2002, Liebezeit et al. 2009, Hethcoat and Chalfoun 2015b). Songbird nest predation increased with habitat loss and disturbance caused by natural gas development in Wyoming, USA (Hethcoat and Chalfoun 2015a). Nest success for grouse species in this region has also been found to decline in association with oil and gas development (Kirol et al. 2015, 2020, Burr et al. 2017). In Canada’s Boreal Forest, oil and gas development has caused the direct loss of nesting habitat or destruction of nests during construction for passerines (Van Wilgenburg et al. 2013). Elsewhere in the Boreal Forest, nest survival increased near forest edges but was not different between fragmented and non-fragmented forests for ground nesting passerines (Ball et al. 2008).

The Boreal Forest is one of the most extensive and intact forests remaining globally (Schmiegelow and Mönkkönen 2002, Haddad et al. 2015). In Alberta, Canada, the Boreal Forest has undergone rapid oil and gas development in recent decades (Schneider and Dyer 2006, Carlson and Browne 2015). Oil and gas exploration and extraction results in a footprint from industrial block features, such as well pads and mines, and linear features, including roads, pipelines, and seismic lines (Northrup and Wittemyer 2013, Pickell et al. 2015, Dabros et al. 2018). The Boreal Forest is also an important, but understudied, breeding area for North American ducks, supporting 12–15 million pairs annually (Slattery et al. 2011, Roy et al. 2019) and declining trends have been identified for upland nesting ducks in association with industrial development (Singer et al. 2020). Declines in association with industrial features might suggest a demographic response, with decreased nest success as the primary hypothesized mechanism explaining declines (Slattery et al. 2011).

In Prairie ecosystems, landscape change as a result of conversion to agriculture has altered the community composition, distribution, abundance, and foraging efficiency of nest predators, resulting in reduced nest success for ducks (Batt et al. 1992, Pasitschniak-Arts and Messier 1995, Stephens et al. 2005, Pieron and Rohwer 2010). However, few studies have investigated the effects of oil and gas development on duck nest survival to date. In the Prairies of North Dakota, oil and gas activities had no effect on nest survival across species, but nest density declined in association with oil and gas development, suggesting avoidance during nest-site selection (Skaggs et al. 2020). In southern Alberta’s Prairies, oil and gas development had weak effects on duck nest success (Ludlow and Davis 2018). These studies suggest that upland nesting ducks may be resilient to current levels of industrial development; however, in the Boreal Forest, where landscape, climate, and predator communities differ, the response may be different.

We investigated the effects of land cover and oil and gas development on nest survival of upland nesting ducks in the Boreal Forest. Habitat selection occurs at multiple scales and the two finest scales of selection are the home range of the species (i.e., third order) and the selection of habitat components, such as a nest site (i.e., fourth order; Johnson 1980). The influence of habitat components can vary across these levels (Bauder et al. 2018, Smith et al. 2020, Dyson et al. 2022) and, therefore, a multi-scale approach can result in greater insights into factors influencing nest survival. If ducks were making behavioral changes to increase survival (Clark and Shutler 1999, Chalfoun and Schmidt 2012), we expected that features avoided by ducks (Dyson et al. 2019, 2022) would be associated with lower nest survival at the scale of the nest (i.e., fourth order) and at broader spatial scales (i.e., third order) in relation to the distribution and abundance of predator communities (Johnson 1980, Stephens et al. 2005). Our study provides novel insights into boreal duck ecology that can help conservation practitioners understand the effect of oil and gas development on duck nest survival and revise current assumptions associated with duck management and conservation in this region.

METHODS

Study area and site selection

We conducted our study north of Slave Lake, Alberta, Canada, near Utikuma Lake in Alberta’s Boreal Forest natural region within Canada’s Boreal Plains ecozone; hereafter the Western Boreal Forest (WBF; Fig. 1). This working landscape is a mosaic of industrial development and deciduous, mixed-wood, and coniferous forests interspersed by extensive wetland complexes and lakes. Industrial development has created high density linear features (e.g., seismic lines, roads, pipelines) and large block features (e.g., well pads, pumping stations, industrial sites) on the landscape (Schneider and Dyer 2006, Fisher and Burton 2018).

To select our study sites, we used a hierarchical selection criterion guided by spatial layers provided by Ducks Unlimited Canada (DUC) and the Alberta Biodiversity Monitoring Institute (ABMI; ABMI 2017; Dyson 2020). We used a 2.5 by 2.5 km sampling grid to consider cumulative energy development, duck pair density, accessibility, and land cover type. To reduce confounding effects from other disturbances, including forestry and wildfire, we did not consider grid cells that consisted of areas that were recently logged or burned (< 20 years). We selected grid cells that were representative of the gradient of industrial development density (e.g., low, medium, and high) and had a predicted duck density greater than the median for the region (Ducks Unlimited Canada 2014). We selected one site within each grid cell by identifying areas associated with waterbodies that were greater than 1 ha in size and were accessible (i.e., within ~ 3 km of a vehicle accessible road). We aimed to search as many sites as possible across our study area given logistical considerations.

Field methods

We searched 16 sites in 2016, 24 sites in 2017, and 25 sites in 2018 between 01 May and 31 July (Fig. 1; Dyson et al. 2019). We searched at the same sites annually, except for one site that was only searched in 2016 and one site that was not searched in 2017. We completed a minimum of two searches of each site in 2016, two to three searches of each site in 2017, and three searches of each site in 2018. We separated searches by 15–25 days and searched on foot with teams of 3 - 6 searchers approximately 20 m apart systematically searching around wetlands (Klett et al. 1986). Our focal species were upland-nesting ducks (i.e., we did not target overwater or cavity nesting species). We estimated the mean total area searched at each site as 27.5 ± 12.2 ha (SD) by using GPS tracks of searchers with a 20 m buffer. We searched for nests starting at least 3 hours after sunrise (~ 0800h) until 1600h (Gloutney et al. 1993). We located an additional 3 radio-tagged mallard nests in 2018 with VHF telemetry as part of a pilot study. We included these marked birds in our sample, because nests were found in similar habitat to unmarked birds.

At each nest, we determined species and estimated the stage of incubation through a combination of egg candling and floating (Weller 1956, Klett et al. 1986). We estimated nest initiation date by subtracting the estimated incubation stage from the date the nest was found and assumed an interval of one egg laid per day plus one skipped day during laying (Batt et al. 1992, Emery et al. 2005). We monitored nests every 7–10 days to determine fate. We installed a camera trap (n ~ 20 each year; Moultrie 1100i, Moultrie Feeders, Birmingham, AL, USA) at nests suitable for installation (i.e., able to install inconspicuously without disturbing nesting cover) to detect predation events and identify predator species (Dyson et al. 2020). We used the presence of eggshell membranes to determine if an egg had hatched and considered a nest successful if > 1 egg hatched (Klett et al. 1986).

We measured overhead cover using a cover grid placed in the nest bowl, lateral cover using a Robel pole, and estimated vegetation species composition within 1 m of the nest bowl (Guyn and Clark 1997, Dyson et al. 2019). We took microhabitat measurements within 5 days of hatch or predicted hatch date (Dyson et al. 2019). We did not include nests that were abandoned (e.g., flooded or failed because of investigator disturbance) or were found depredated or hatched in our sample because the process by which these nests failed (depredation vs other) would be different and possibly confound results.

Analytical approach

Animal habitat use is commonly conceptualized across four orders of selection in which the fourth order represents selection of particular habitat features such as vegetation where a nest is placed and third order represents broader habitat such as selection of a nest-site within an individual’s home range (Johnson 1980, Eichholz and Elmberg 2014). Nest survival can be influenced by features associated with the nesting habitat at both the fourth (hereafter microhabitat) and third (hereafter macrohabitat) orders of habitat use (Stephens et al. 2005, Howerter et al. 2008). Therefore, we evaluated the daily survival rate (DSR) of nests at the microhabitat level with habitat data collected in the field and at the macrohabitat level with habitat data obtained from a geographic information system (GIS). We considered microhabitat and macrohabitat models separately; however, we accounted for the effect of the non-habitat variables (e.g., nest age, year) with a baseline model consistent across both hierarchical levels of selection. To accommodate for the effect of scale on variation in macrohabitat survival, we first investigated the effect of variables at multiple spatial scales and identified the top-ranked scale for each variable (Fisher et al. 2011, McGarigal et al. 2016, Dyson et al. 2022) prior to developing a multi-scale macrohabitat model explaining nest DSR.

Model variables

We considered a suite of variables that have been previously found important for duck nest survival and additional variables relevant to our specific hypotheses about industrial land use. Variables that were not captured by micro or macrohabitat were treated as baseline variables (e.g., site, year, nest age). We investigated the effect of site and year to account for spatial and annual variation in predation pressure or environmental conditions (Ringelman et al. 2018). We considered species as a random effect to account for potential differences in survival related to species-specific variation in nest-site selection strategies and only included species with at least 5 nests found in our analysis (Dyson et al. 2019). We tested for the effect of nest camera presence, because previous studies have shown a positive association of cameras with nest survival, potentially due to predator neophobia (Richardson et al. 2009). Next, we considered variables associated with nesting phenology, which included age of nest when found and nest initiation date (Shaffer 2004, Pieron et al. 2012). Nest vulnerability can change with nest age as a function of hen attendance and nests initiated earlier often experience a greater probability of success because they are initiated by more experienced hens (Klett and Johnson 1982, Stephens et al. 2005, Devries et al. 2008). We also considered the quadratic effect of initiation and nest age to account for nonlinear responses (Webb et al. 2012, Setash et al. 2020, Skaggs et al. 2020).

We predicted the direction of each effect for microhabitat and macrohabitat variables on nest survival would be similar to our previously reported nest-site selection analyses (Dyson et al. 2019, 2022), because nest-site selection should be adaptive (Clark and Shutler 1999, Chalfoun and Schmidt 2012, Setash et al. 2020). These microhabitat variables and their measurements were previously described in greater detail in Dyson et al. (2019). We investigated the effect of overhead and lateral cover, vegetation height, and the proportion of graminoid, forb, and shrub cover. We predicted that upland nesting ducks would have greater nest DSR at nests with greater overhead and graminoid cover, and lower nest DSR at nests with greater forb cover consistent with features that we found ducks previously selected (Dyson et al. 2019).

At the macrohabitat scale, we previously described and quantified a suite of land cover and land use variables hypothesized to affect nest-site selection of boreal ducks (Dyson et al. 2022). These land cover variables represent the surface cover related to vegetation on the ground and we included mineral wetland, peatland, upland forest, and open water, which were derived from Ducks Unlimited Canada’s Enhanced Wetland Classification layer (Smith et al. 2007, Dyson 2020). We also included land use variables related to anthropogenic modification of the landscape derived from the 2016 Alberta Biodiversity Monitoring Institute’s Human Features Inventory (ABMI 2017) and quantified the area of block features, such as borrow pits and well pads and the length of linear features, such as roads, pipelines, and seismic lines (Dyson et al. 2022). Borrow pits are small excavated areas of soil and substrate to create roads and well pads and often become flooded; industrial features include any block feature not captured by our other categories (e.g., gravel pit, processing plant); and well pads include both active and abandoned wells. Linear features included roads, which were any paved, gravel road, or vegetated road or trail. Pipelines included above and below ground infrastructure and were maintained (i.e., vegetation removed) at a variety of widths across the study area. Seismic lines were relatively thin lines (2–5 m, but up to 10 m) and are created at high densities for oil deposit exploration purposes and exist across the landscape at various levels of regeneration (Lee and Boutin 2006). We predicted that DSR would be greater at nests with a greater proportion of mineral wetlands, borrow pits, and greater lengths of roads; and lower at nests with greater proportions of peatlands, upland forest, and greater lengths of pipelines and seismic lines because these features were previously found to be selected or avoided, respectively (Dyson et al. 2022).

We created 30 m rasters for our land cover and land use variables and developed spatial covariates by summarizing the landscape using moving windows at six spatial scales (i.e., radii: 30, 90, 300, 1000, 2500, 5000 m) associated with species biology or management relevance (Hagen-Zanker 2016, Dyson et al. 2022).

Model structure

We modeled nest DSR with the logistic exposure model in a generalized linear mixed effects model framework (Shaffer 2004). We used a two-step variable inclusion approach to evaluate microhabitat and macrohabitat nest survival models separately (Webb et al. 2012). The first step involved evaluation of the top baseline model, which we then carried forward for analysis of microhabitat and macrohabitat additive effects on nest survival. We included species as a random intercept, but we did not consider year as a random effect because we only had 3 years of data and random effects perform better with greater than 5 levels; therefore, we retained year as a fixed effect (Bolker et al. 2009).

We screened variables for the baseline model by first testing them in a univariate framework and used AIC_c to include only variables that performed better than the intercept-only model in our candidate set (Burnham and Anderson 2002). Next, we considered all combinations of the baseline variables and used AIC_c to find the top-ranked model to move forward to the next step. We built a global microhabitat model using the baseline model in addition to all microhabitat variables. For macrohabitat, we used pseudo-optimization (sensu McGarigal et al. 2016) to determine the top-ranked scale for each variable (Fisher et al. 2011). We retained the variables included the baseline model during the pseudo-optimization procedure to account for variation in nest DSR not explained by macrohabitat features (Dyson et al. 2022). We did not consider variables in our macrohabitat additive models if the baseline model ranked better than any variable scale we considered based on AIC_c. We then moved the top-ranked scale for each variable forward in an additive framework with the baseline model to create a global multi-scale macrohabitat model for nest survival.

We standardized continuous covariates ( - x /SD) to promote convergence and comparison across covariates measured on different scales. We then used AIC_c on all combinations of our global models for each step to select the best combination of predictor variables that explained nest survival. We present candidate model sets for each step with more complex variants of the top-ranked model removed because these models create model selection uncertainty in association with an all combinations approach by including uninformative parameters (Anderson 2001, Richards 2008, Arnold 2010, Doherty et al. 2012). We present estimates of nest DSR and nest success rate for all nests and by species across years with 85% confidence intervals (Arnold 2010). We calculated nest success for each species by raising the DSR to the power of the estimated age of the nest at hatch, which we derived for each species using information on average clutch size and incubation duration from Birds of the World (Anteau et al. 2020, Drilling et al. 2020, Johnson et al. 2020, Mini et al. 2020, Rohwer et al. 2020). We performed all analysis in R version 3.6.2 “Dark and Stormy Night” (R Core Team 2019) and used package lme4 for mixed-effect survival models (Bates et al. 2016).

RESULTS

We located 136 active nests of upland-nesting ducks and removed nests not suitable for survival analysis (e.g., found depredated, abandoned, flooded). Our final sample for survival analysis included 96 nests of 5 different species, including 39 Blue-winged Teal (Spatula discors), 11 American Wigeon (Mareca americana), 24 Mallard (Anas platyrhynchos), 14 Green-winged Teal (Anas crecca), and 8 Lesser Scaup (Aythya affinis). We excluded two nests of Northern Shoveler from our analysis because of small sample size (< 5 nests). We also removed two nests for microhabitat analysis because of missing microhabitat data (n = 94); but we retained all nests for macrohabitat analysis (n = 96).

The mean DSR across all nests was 0.957 (85% CI: 0.948 to 0.965) and an estimated nest success rate of 0.212 (85% CI: 0.152 to 0.282; Fig. 2). Green-winged Teal had the greatest DSR of 0.974 (85 % CI: 0.945 to 0.988; Fig. 2) and greatest estimated nest success rate of 0.425 (85% CI: 0.155 to 0.667). In contrast, Lesser Scaup had the lowest DSR of 0.943 (85% CI: 0.863 to 0.977; Fig. 2) and an estimated nest success of 0.128 (85% CI: 0.006 to 0.450). Mallard also had a low DSR of 0.945 (85% CI: 0.911 to 0.966; Fig. 2) and an estimated nest success rate of 0.137 (85% CI: 0.039 to 0.297). American Wigeon had a DSR of 0.963 (85% CI: 0.916 to 0.998; Fig. 2) and an estimated nest success rate of 0.284 (85% CI: 0.055 to 0.584). Blue-winged Teal had a DSR of 0.955 (85% CI: 0.934 to 0.969; Fig. 2) and an estimated nest success rate of 0.209 (85% CI: 0.098 to 0.348).

Our top baseline model explaining nest DSR included the age of the nest when found (AgeFound) and year (Table 1). Nests that were older when found had a greater DSR and we observed an increasing trend in DSR across years (Table 4, Fig. 3). We used this model structure to account for unexplained variation not related to habitat for both the microhabitat and macrohabitat models. Our top microhabitat DSR model included proportion of grass, forb, and shrub cover in addition to the baseline model (Table 2). We observed nest survival to increase with the proportion of vegetative cover at a nest, regardless of functional group (Table 4; Fig. 4).

The pseudo-optimization procedure identified predictive scales for mineral wetland (2500 m), peatland (5000 m), and forest (5000 m) landcover variables and road (30 m), pipeline (90 m) and borrow pit (300 m) land use variables to affect nest DSR (Fig. 5). The remaining variables had AICc units lower than the intercept-only model and confidence intervals that overlapped zero across scales suggesting that inclusion of those variables did not improve upon the baseline model (Fig. 5). We detected high negative correlation between peatland and upland cover (-0.94) and did not let these variables compete in the same model. All spatial scales for each variable ranked within 5 AIC_c units of the top-ranked scale or baseline model suggesting some uncertainty in scale selection. Subsequently, the top-ranked multi-scale model explaining macrohabitat variation in nest DSR included mineral wetland, forest, roads, and pipelines in addition to the baseline model (Table 3; Fig. 6). Nest DSR decreased with increasing proportion of mineral wetland habitat at the 2500 m scale, increased with greater proportion of forest at the 5000 m scale, and increased with lengths of roads and pipelines at the 30 m and 90 m scale, respectively (Table 4; Fig. 6).

DISCUSSION

Nest survival of upland nesting ducks from Canada’s Western Boreal Forest is similar to ducks elsewhere in North America, including the Prairie Pothole Region (PPR), and exceeded an estimate of 15% needed to sustain populations (Cowardin et al. 1985, Hoekman et al. 2002). We did not find any negative effects of oil and gas development on nest survival based on the suite of variables we considered over the duration of our study. Nest survival varied annually and nests found later in incubation were more likely to survive. We found evidence of adaptive selection for greater graminoid and shrub cover at the nest and greater road densities at the landscape scale (Dyson et al. 2019, 2022).

We found that non-habitat variables explained the greatest amount of variation for nest survival, including year and the age of the nest when found. Annual conditions can affect nest survival through variation in water availability, temperature, or changing predator populations (Walker et al. 2013). In a long-term study on nest success of Prairie ducks, large-scale environmental variables often outperformed local variables at the nest-site scale (e.g., vegetation concealment) suggesting that annual environmental conditions can have a relatively large effect on nest survival compared to variables specific to the nest site (Ringelman et al. 2018). Nest survival increasing with nest age is ubiquitous for waterfowl across their breeding range (Stephens et al. 2005, Ludlow and Davis 2018, Setash et al. 2020). Older nests have a higher probability of hatching (less exposure time remaining) and nest defense should increase for older nests (Forbes et al. 1994, Gunness and Weatherhead 2002). Our nest searching approach precluded us from detecting an adequate number of nests in the laying stage because nests are more difficult to locate when females are absent. Therefore, if nest survival during laying differs from incubation (Setash et al. 2020), then our estimates may be biased high (Devineau et al. 2014, Miller et al. 2017).

At the microhabitat level ducks likely maximize nest concealment to avoid detection by predators (Ringelman et al. 2018, Moraga et al. 2019). We found that DSR increased with a greater proportion of vegetative cover, consistent with nests having increased survival in thicker vegetation (Ringelman et al. 2018). Evidence for increased nest survival with more graminoid cover is consistent with other research (Livezey 1981, Clark and Shutler 1999, Setash et al. 2020). Graminoid cover likely provides concealment for nesting ducks and Blue-winged Teal prefer nest sites in graminoid cover, which composed approximately 40% of our sample (Gloutney and Clark 1997, Baldassarre 2014, Dyson et al. 2019, Rohwer et al. 2020). We found greater survival at nests that had more forb cover, despite hens avoiding this habitat during nest-site selection (Dyson et al. 2019). We suspect that females likely tradeoff between increased vegetative concealment at the nest site and their ability to detect incoming predators (Götmark et al. 1995).

At the macrohabitat level we did not detect any negative effects of oil and gas development on duck nest survival, contrary to our predictions but consistent with other recent studies (Ludlow and Davis 2018, Skaggs et al. 2020). Nest success was greater with increasing density of pipelines (90 m) and roads (30 m). Nest-site selection in areas of higher road densities and associated survival benefits has been observed for other duck species across breeding biomes (Pasitschniak-Arts et al. 1998, Raquel et al. 2015, Roy 2018, Skaggs et al. 2020). Collectively, these results are consistent with the hypothesis that roads may create refuges from nest predators because some duck predators avoid them (Pasitschniak-Arts et al. 1998, Tucker et al. 2018). We also observed greater survival for nests associated with higher densities of pipelines, which was opposite of our prediction based on observed avoidance of pipelines during nest-site selection (Dyson et al. 2022). Here, greater nest survival occurred for greater pipeline densities within 90 m, while ducks avoided selecting nests with greater pipeline densities within 2500 m (Dyson et al. 2022). Therefore, a scale effect may be responsible for the differing responses to pipelines. In our study area, pipelines are often wider than seismic lines and predators may use them more functionally like roads because vegetation is often removed regularly on pipeline corridors, so they provide relatively unimpeded travel compared to some overgrown seismic lines. Alternatively, pipelines may be used as travel corridors for oil and gas maintenance and monitoring, possibly providing a refugia effect through predator avoidance. Ducks selected nest sites and had higher survival at nests in association with higher road densities, and therefore, we hypothesize that ducks likely received a direct predator refuge benefit (i.e., lower probability of encounter with nest predator) from roads. Unfortunately, we did not have direct evidence of how predators used roads from this study.

We found that a greater proportion of mineral wetland landcover within 2500 m of a nest reduced nest survival. While this result was counterintuitive, we highlight the importance of the scale effect. For example, ducks select nest sites with mineral wetland habitat at the 300 m scale, which could imply a survival benefit (Dyson et al. 2022). We observed that ducks avoided mineral wetlands during nest-site selection at the 2500 m scale; however, it was not the best scale of response based on AICc (Dyson et al. 2022). Reduced nest survival associated with greater mineral wetland cover at the 2500 m scale may relate to effects of wetland size, where nests in large or dense wetland complexes may have lower survival than in areas with smaller wetlands interspersed through forest habitat. If large or dense wetland complexes are highly productive, it may concentrate prey and predators on this landscape and lead to a greater risk of nest failure for ducks. We also found a positive effect of forest cover within 5000 m of a nest, which was inconsistent with avoidance at a 300 m scale (Dyson et al. 2022). Forest cover is the most dominant landcover type in the Boreal Forest and nests were often associated with forest cover, but forest cover was used less than expected compared to its availability (Dyson et al. 2022). Consistent with our interpretation of the relationship with mineral wetlands, greater forest cover at coarse scales is most often associated with smaller interspersed wetlands that might be the most beneficial for upland nesting ducks. Alternatively, forest and peatland were negatively correlated in our study sites and peatland was avoided during nest-site selection and negatively affected survival (though forest outperformed its explanatory power for survival). Understanding the effect of the composition of landcover types (e.g., patch size) or at finer thematic resolution (e.g., within mineral wetland classes) on nest survival may benefit future conservation efforts.

In the Alaskan Boreal Forest, flooding was a prominent source of nest failure in a study of nest survival (Petrula 1994, Walker et al. 2005). We did not include flooded nests (n = 10) in our analysis because we assumed flooding is a separate process from predation. In our study area, flooding occured mid-season due to precipitation after most early nests had already been initiated (M. Dyson, personal observation). We suspect that environmental variation in precipitation, beaver activity (Nummi et al. 2013, Lapointe St-Pierre et al. 2017), and industrial development interact in the Boreal Forest to create a dynamic hydrological landscape. Understanding whether flooding is compensatory or additive to the predation process presents an opportunity for future inquiry in this system.

Our results improve our understanding of duck nesting ecology in the Boreal Forest, including evidence to suggest that at current levels of industrial development, there appears to be no negative effects on nest success. Given these results, effective management interventions will likely be best realized through conservation of nesting habitat (Dyson et al. 2022). Continued research in this important breeding area is critical for developing better understanding and management of Boreal ducks. For example, at what level might oil and gas development result in negative demographic consequences and how does that interact with predicted changes in duck abundance in the Boreal Forest under climate change scenarios (Drever et al. 2012)? How do forestry practices and natural disturbances like wildfire interact with these relationships (Borger and Nudds 2014, Lewis et al. 2016)? Are there any time-lagged responses to landscape and climate change by predators or prey that could alter these observed relationships (Ringelman et al. 2018)? Continuing to advance our understanding of species-specific processes and relationships across different life-history phases, including settling, nest-site selection, survival, and brood-rearing, will likely provide the greatest benefit to conservation of ducks in the Boreal Forest.

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