Hierarchy theory implies that regional- and landscape-scale drivers of change may not operate independently of each other (Turner et al. 2001), and the processes at one scale may constrain patterns and processes at another scale (Turner et al. 2001, Peters et al. 2007). Only by investigating interactive effects can we reach a clear understanding of how these drivers affect responses at the focal scale (Cash and Moser 2000).
Interactions occur when the influence of one explanatory variable on the response variable differs with the level of another explanatory variable. Within-scale interactions (WSIs) occur when these interacting variables are operating at the same spatial scale, whereas cross-scale interactions (CSIs) occur when the variables are operating at different spatial scales (Soranno et al. 2014). Identification of such interactions can help ecologists decipher mechanisms from observed patterns (McMahon and Diez 2007), and knowledge of interactions can be important in conservation, yet little is known about how interactions affect ecological responses (Soranno et al. 2014).
Most multiscale research on bird-environment relationships has focused on analyzing variables as additive (main) effects (Battin and Lawler 2006) and not on interactions. A thorough literature search indicated that studies involving birds have examined WSI effects of habitat, weather, and climate (e.g., Mantyka-Pringle et al. 2012, Cox et al. 2013, Ferger et al. 2017), and we are aware of one CSI study involving birds that focused on the effects of habitat variables (Vergara and Armesto 2009). But because of the anthropogenic nature of the drivers of environmental change, much can be gained by studying ecological relationships from a social-ecological perspective (Collins et al. 2011), and WSI and CSI effects of climate and socioeconomic factors on birds have not been examined separately or together.
Climate refers to long-term (≥ 20 years) meteorological averages, whereas weather refers to shorter term meteorological patterns (Reside et al. 2010). Climate can affect trends in bird abundances across sites, regions, and entire ranges (Brotons and Jiguet 2010, Illán et al. 2014). Even a small (~ 2 °C) change in average temperature can affect food availability and thus avian breeding success (Sanz et al. 2003). A landscape with favorable temperatures may have more resources (i.e., food and vegetation) for a species and result in higher dispersal of the species into and within a landscape. However, if the region surrounding the landscape has a less-favorable temperature, there may be fewer individuals in the region to disperse into the landscape. Regional climate may therefore affect landscape-scale avian population densities (Anders and Post 2006).
The socioeconomics of local human populations also can affect avian species (Lerman and Warren 2011). Tens of millions of American bird enthusiasts spend about four billion dollars annually on bird food and an additional billion dollars annually on feeders, bird houses, nest boxes, and bird baths (U.S. Department of Interior et al. 2014, 2018). These expenditures on food, feeders, and nest boxes are conservation-related because some bird species are likely to benefit from the supplemental food, nest sites, and water. This information indicates that population income has the potential to have an important effect on bird abundance. In addition, older aged, female, higher income, and college-educated Americans participate in birding more often than do those who are not in these categories (Carver 2013), and conservation support from these groups may be important as well. For these reasons, we were interested in how these four socioeconomic variables may interact with climate variables to influence forest-bird abundance.
If human populations belonging to specific socioeconomic categories can, through their direct (e.g., bird feeders) and indirect (e.g., home ownership and backyard vegetation) choices affect food and habitat availability for birds, these socioeconomic characteristics also may affect avian abundance in their neighborhoods and surrounding landscapes. Women are more involved in birding around their homes than are men (Cooper and Smith 2010), and women start birding at a significantly older age (mean = 32 years) than do men (mean = 23 years; Moore et al. 2008). Birdwatchers are four times more likely to exhibit proenvironmental behavior such as donating money to conservation, supporting habitat management, and participating in environmental groups than are people who are not involved in wildlife-related recreation (Cooper et al. 2015). Human populations that associate with the natural environment tend to participate in activities that support nature (Mayer and Frantz 2004), and human populations engaged in activities that support nature (e.g., bird feeding) are more likely to take action to conserve birds in their backyards (Dayer et al. 2019). Groups involved in birdwatching (older people, wealthier households, more-educated people, and older women; Cooper and Smith 2010, Carver 2013) and bird feeding (older people and women) have a higher propensity to contribute to local bird conservation (Cooper et al. 2015, Dayer et al. 2019). Furthermore, age, income, and education can affect whether a person lives in rented or owner-occupied housing (U.S. Census Bureau 2013). Income and home ownership are important predictors of vegetation cover in residential yards (Boone et al. 2010), and the amount and type of vegetation cover in yards may affect resources available for birds (Narango et al. 2018).
Effects of climate and socioeconomic conditions have considerable potential to differ between groups of birds based on their habitat associations (forest interior or edge) and wintering location (Nearctic or Neotropical region). During the breeding season, forest-interior species are unlikely to occupy forest edges, whereas forest-edge species use the perimeter of the forest, adjacent fields, and clearings within forests (Freemark and Collins 1992). Because of their affinity to open areas with low canopy cover, forest-edge species may be subjected to higher temperatures than may forest-interior species. We used zoogeographical regions (Cox 2001) to define Nearctic-wintering species as those that are resident or that migrate short distances to winter within the Nearctic region, and Neotropical-wintering species as those that use the Neotropical region during winter. In addition to stressors on the breeding grounds, long-distance migrants such as Neotropical-wintering species face stressors during extensive migrations (Newton 2006), are affected by various impacts on their wintering grounds (Sæther and Engen 2010), and may therefore be more vulnerable to climate and human socioeconomic conditions than may Nearctic-wintering species. Since the North American Breeding Bird Survey (BBS) began in 1966, forest (Robbins et al. 1989, Sauer and Link 2011) and Neotropical migrant species (Robbins et al. 1989, Sauer et al. 2017) have exhibited declines across the eastern United States. We studied eight species as examples of how WSIs and CSIs may affect the abundance of forest-interior or forest-edge species and Nearctic- or Neotropical-wintering species that breed in this region. We used our results for these small sets of species to provide initial information about patterns that may be important to consider in subsequent analyses involving larger numbers of species.
Our focal climate variable was the 30-year mean daily maximum temperature during the breeding season. Environmental temperature can affect human spending (Murray et al. 2010) and altruistic behaviors (Van de Vliert et al. 2004). If breeding-season temperature (temperature hereafter) alters the effects of socioeconomic variables on birds, there is potential for WSIs and CSIs involving these variables to affect avian abundance. Consider, for example, a scenario involving temperature and age. Older-aged human populations participate more often in bird feeding (Lepczyk et al. 2012, Dayer et al. 2019), and people are more likely to put out bird feeders when temperatures are colder (Dayer et al. 2019), such as during winter (Lepczyk et al. 2004). Investment in backyard bird feeders may thus be more common in areas with cooler breeding seasons and older aged populations, and less common in areas with warmer breeding seasons and younger aged populations. For these reasons, temperature and age have the potential to interact within or across scales to affect a species’ abundance. Conceivably, temperature also may influence conservation-associated behavior in human populations that are wealthier, that have a higher percent of females ≥ 30 years old, or that have a higher percent of college-educated individuals differently than it influences behavior in populations that are not in these categories. If so, such differences may result in WSI and CSI effects of temperature and socioeconomic factors on avian abundance.
One of our overarching goals was to identify potential influences of WSIs and CSIs involving temperature and socioeconomic factors on broad-scale patterns of avian abundance. To do so effectively, we studied these various effects while accounting for environmental variables (e.g., weather and land cover) already known to affect bird populations. Our second overarching goal was to provide results that can be used to help steer subsequent research on patterns (relationships) and associated underlying processes. Discernment of patterns is a necessary first step for identifying important relationships and setting the stage for follow-up studies of related mechanisms.
For this initial pattern assessment, we conducted a comparative analysis of the effects of WSIs, CSIs, their component additive (main) effects, weather variables, and habitat variables by addressing three questions. First, was there evidence that avian abundance was associated with WSIs and CSIs involving temperature and socioeconomic variables? Second, were the relative influences of WSIs and CSIs higher than those for the additive effects of temperature and socioeconomic variables, or higher than those for habitat and weather variables? And third, were there differences in associations with WSIs, CSIs, or temperature and socioeconomic variables between habitat groups (interior vs. edge species) and between wintering groups (Nearctic-wintering vs. Neotropical-wintering species) that warranted further investigation? These significant but understudied issues have important ramifications for broad-scale avian conservation.
The study area (Fig. 1) lies within the Eastern Temperate Forest Ecoregion, a U.S. Environmental Protection Agency (EPA) Level I ecoregion in the eastern United States (Commission for Environmental Cooperation 1997, Omernik and Griffith 2014). This region’s climate is generally warm and humid, but varies latitudinally and seasonally; the vegetation is primarily characterized by broadleaf deciduous and conifer forests (Commission for Environmental Cooperation 1997). Within this EPA Level I ecoregion, our study area occupied 14 EPA Level III ecoregions (Omernik and Griffith 2014). We used these 14 ecoregions because each contained at least 10 BBS routes that were active during the study period, and all of the regions were within the breeding ranges of all of our study species (BirdLife International and Handbook of the Birds of the World 2016; Appendix 1).
We used data from BBS routes (Pardieck et al. 2016, Sauer et al. 2017) that were within the study ecoregions and had been run using standard BBS protocol and the single-observer method for three consecutive years (2010–2012). We compiled a list of species whose breeding ranges substantially overlapped large parts of the Eastern Temperate Forest Ecoregion. From this list, we chose as examples four forest-interior species and four forest-edge species, and each of these two groups included two Neotropical-wintering and two Nearctic-wintering species. The species’ names, breeding-season habitat, wintering region, and the number of BBS routes used are in Table 1. A BBS route consists of 50 stops spaced 0.8 km apart, and at each stop the observer records all birds heard or seen during a 3-minute period (Sauer et al. 2013). For our study, the response variable (relative abundance) was calculated as the total number of individuals of a species detected on a BBS route during the 3-year study period (2010–2012).
The landscape- and regional-scale mean daily maximum temperature of the breeding season, four landscape-scale socioeconomic variables, and the WSIs and CSIs involving temperature and socioeconomic variables were the focal explanatory variables (Table 2; Appendix 1). Landscape-scale variables were measured within species-specific buffers established around each BBS route; a species’ median natal dispersal distance was used to define buffer size (Gutzwiller et al. 2015; Appendix 1). Regional-scale variables were measured within each Level III EPA ecoregion. As covariates, we also considered 11 landscape- and regional-scale explanatory variables to control for factors that may have affected avian relative abundance and that may have been associated with our focal explanatory variables (Table 2). To control for potential sources of variation in BBS data, five additional covariates were used: mean day of the year, area within 400 m of a route, number of years of assistant use, number of years involving first-time observers, and number of stops with excessive noise (Table 2). The rationale for using these covariates was explained in Appendix 1.
We used PRISM data (PRISM Climate Group 2016) to compute landscape- and regional-scale 30-year (1981–2010) mean daily maximum temperature (LndMaxTemp and RegMaxTemp) for the breeding season (May–July). LndMaxTemp values were binned into 14 groups, equal to the number of study regions (Appendix 1). Area-weighted averages (Appendix 1) of the percentage of the population that was female and ≥ 30 years old (PercFemale), the percentage of the human population ≥ 25 years old with ≥ 4 years of college (PercColleg), median age (MedianAge), and median income (MedianInc) were the landscape-scale socioeconomic variables. These variables were derived from American Community Survey data for the 5-year period, 2009–2013 (U.S. Census Bureau 2015; Appendix 1).
We calculated percent cover for deciduous forest, mixed forest, and developed land from the 2011 National Land Cover Data (NLCD; Homer et al. 2015) using FRAGSTATS v4.2.1 (McGarigal et al. 2012). The four types of 2011 NLCD developed land-cover classes (21 [developed, open space], 22 [low-intensity developed], 23 [medium-intensity developed], and 24 [high-intensity developed]) were combined in ArcGIS 10.1 (Environmental Systems Research Institute 2012) to measure percent cover for developed land. Weather involves short-term (< 20 years) meteorological patterns (Reside et al. 2010), and three-year averages for two weather variables (minimum temperature of the two coldest months, and precipitation during the spring; Table 2) were obtained from the PRISM Climate Group (2016).
Statistical analyses were carried out using various functions in the R base package version 3.6.1 (R Core Team 2019) except where stated otherwise below and in Appendix 1. All explanatory variables were standardized by subtracting the mean from each observation and then dividing by one standard deviation. For all eight species, we provided Kendall’s tau b correlation matrices (Appendix 2) for all of the explanatory variables used in the analyses. This correlation coefficient was provided because some of the variables (regional-scale) had many tied observations (Xu et al. 2013). Histograms (Appendix 1, Fig. A1.1) and scatterplots (Appendix 1, Fig. A1.2) for the set of routes used for the Ruby-throated Hummingbird (Archilochus colubris) were provided to illustrate the frequency and geographic distributions of the four socioeconomic variables, and Pearson (r) correlation matrices for the socioeconomic variables and geographic coordinates are provided in Appendix 1. This species was used because it had the largest number of observations (n = 390 routes) among the eight species in the analysis. The histograms, scatterplots, and correlations for the socioeconomic variables for the remaining seven species (not provided) were very similar because many of the BBS routes used were the same among species.
We fitted negative binomial (NB) regression models using R package MASS (Venables and Ripley 2002) for all eight species because the relative abundance values (counts) followed a negative binomial distribution. Model fitting was carried out in two stages. In the first stage we identified covariates (from among the 16 nonfocal explanatory variables) that individually were associated with at least 10% of the variation (Pearson 1993, Gutzwiller et al. 2015) in a species’ relative abundance. To identify these variables for each species, each individual candidate covariate was regressed against relative abundance. R²COR values (squared correlation between actual and fitted values; Cameron and Trivedi 2013) ≥ 10% were used to identify covariates for a base model. We used the R package car (Fox and Weisberg 2011) to compute variance inflation factors (VIFs) for each covariate in the base model (Table 3). Covariates with VIFs ≥ 3 (Zuur et al. 2010) were removed one at a time until all variables in the base model had VIFs < 3 (Appendix 1). Steps taken in this stage reduced multicollinearity and the risk of spurious results associated with using a larger number of potential variables in the second (model-selection) stage of the analyses. All covariates selected in stage one were included as base variables in stage two of the analyses.
In the second stage, we compared nine competing models (Table 3) separately for each combination of the eight species and four socioeconomic variables; this process involved assessment of 32 sets of models. Models within a set were compared using Akaike’s Information Criterion adjusted for small sample size (AICc; Burnham and Anderson 2002), and AICc values were calculated and ranked using R package MuMIn (Bartoń 2018). For each of the 32 model sets, the model with the lowest AICc value (best-supported model) was interpreted (Burnham and Anderson 2002).
If spatial autocorrelation was detected in the residuals of the best-supported model, spatial eigenvectors that reduced Moran’s I for the residuals were added to the model one at a time until the absolute value of Moran’s I for the model’s residuals was ≤ 0.1 for all distance classes (Gutzwiller et al. 2015; Appendix 1). Standard techniques (Appendix 1) were used to confirm that all statistical assumptions of the analyses were met. We used methods in Hilbe (2014) to confirm that NB models were better suited for our data than were Poisson models. To characterize the amount of variation in the response variable that was associated with explanatory variables, we computed R²COR for the best-supported models.
Assessment of interaction effects and relative effect sizes of variables was integral to addressing our research questions. We used the general approach of assessing the difference in fit between a full and a reduced model (Neter et al. 1989) instead of using the sum of weights (SW; Burnham and Anderson 2002). Specifically, we computed the change in AICc (∆AICc) between the best-supported model and the reduced model in which the variable of interest was removed (Coppes et al. 2017). Within a model, a variable with a larger ∆AICc value was more influential than was a variable with a smaller ∆AICc because removing the former resulted in more degradation of model fit than did removing the latter. A negative ∆AICc value indicated that the reduced model had a better fit than did the best-supported model, implying that the associated variable was not influential.
Model weights (wi) from AICc-based model selection (Burnham and Anderson 2002) have often been used to estimate the relative influence of explanatory variables in regression models. But when variable influence is assessed using the SW, interactions can inflate the SW values associated with the explanatory variables involved in the interactions (Galipaud et al. 2014 and references therein). In addition, the SW has been found to be a poor measure of the relative influence of a variable (Galipaud et al. 2014) because SW values reflect the relative influence of models, or perhaps the occurrence of a variable among multiple models, but not the relative contribution of a variable in a given model (Cade 2015). SW values also can indicate levels of variable influence that are not consistent with the magnitudes of regression estimates or how much the variables contributed to maximizing model likelihoods (Cade 2015).
We computed 85% confidence intervals for regression coefficients because they are more consistent with AICc-based model selection (Arnold 2010) than are other (e.g., 90% or 95%) confidence intervals. If a confidence interval for a regression coefficient did not include zero, the variable was considered to be informative (Josefsson et al. 2018). We calculated VIFs to identify the degree of association among explanatory variables in the best-supported model. To help interpret the statistical results for the best-supported models when VIFs were ≥ 3, we followed advice from the literature. Specifically, high VIFs between nonfocal variables are not considered a problem because they do not affect the interpretation of the focal variables (Wooldridge 2003). And if focal explanatory variables with high VIFs are informative, high VIFs do not affect conclusions about the effects of those variables (Allison 1999, 2012).
A leave-one-out algorithm (Harrell 2001) was used to cross-validate each best-supported model; we compared the mean absolute error (MAE) values for the best-supported model (fitted) to the MAE for the cross-validated model. Additional details about statistical analyses and figure production are in Appendix 1.
A best-supported model was identified for each of 32 model sets (8 species × 4 socioeconomic variables per species). For each combination of species and socioeconomic variable (i.e., each set of nine models) separately, two interactions (one WSI and one CSI; Table 2) were studied. Of the 32 best-supported models (Table 4; supporting statistics in Appendix 1, Tables A1.1 and A1.2), 12 contained interactions (9 WSIs and 3 CSIs), relative abundances for 5 of the 8 species were associated with informative interactions (Table 5; Appendix 1, Table A1.2), and 10 interaction effects were informative (85% CI did not include zero). To illustrate how a species’ relative abundance may have been associated with variables involved in interactions, we considered in some detail a WSI involving the Yellow-breasted Chat (Icteria virens; Table 5). Relative abundance of the Yellow-breasted Chat (forest-edge species) was associated with the WSI involving landscape-scale 30-year (1981–2010) mean daily maximum breeding-season temperature (LndMaxTemp) and median income of the population (MedianInc). Yellow-breasted Chat relative abundance was greater in landscapes with higher LndMaxTemp; increasing MedianInc had a positive effect on relative abundance in the landscapes with the highest LndMaxTemp values and a negative effect on relative abundance in the remaining landscapes (Fig. 2).
An individual socioeconomic variable (e.g., MedianInc) and its interaction with temperature were candidate variables in a maximum of eight sets of competing models (one set for each species), whereas the other variables considered were candidate variables in all 32 sets of competing models. We therefore calculated the occurrence of variables in the best-supported models as a percentage of possible best-supported models in which the variables could have potentially occurred. Within the context of a best-supported model, variables with positive ∆AICc values were considered to be influential. On the basis of information in Table 6, the occurrence of influential CSIs associated with the four socioeconomic variables in the best-supported models was 0% for CSIs involving percentage of the population ≥ 25 years old with ≥ 4 years of college (PercColleg) and 13% for CSIs involving the remaining three socioeconomic variables. Occurrence of influential WSIs ranged from 0% for WSIs involving median age of the population (MedianAge) to 50% for WSIs involving MedianInc. In contrast, the occurrences of influential additive effects of socioeconomic variables (38% to 100%) and climate variables (16% for 30-year mean daily maximum temperature during the breeding season at the regional scale [RegMaxTemp] and 50% for LndMaxTemp) were higher in models. Only two covariates, landscape-scale percent of deciduous forest (LndPercDec) and landscape-scale daily minimum temperature of the two coldest months (LndWinTemp), were in the best-supported models for more than one species.
The relative influence of interactions varied among species and with the types of variables involved in the interaction. Among the 12 best-supported models with interaction effects, the relative influences (based on ∆AICc values) of interactions were less than were those for the additive effects of LndMaxTemp and RegMaxTemp in 10 models, and less than were those for the additive effects of socioeconomic variables in 9 models (Table 6). Among socioeconomic variables, the influences of the additive and interactive effects of PercColleg and MedianInc were generally higher than were those for percent of the population that was female and ≥ 30 years old (PercFemale) and MedianAge (Table 6). When present in the same best-supported models, interactions tended to be less influential than was LndPercDec (Hairy Woodpecker, Picoides villosus) and more influential than was LndWinTemp (Yellow-breasted Chat; Table 6).
There were no clear differences between forest-interior and forest-edge species in the number of best-supported models with WSIs (5 vs. 4 models), with temperature and socioeconomic variables (11 vs. 8 models; Table 7), or in the relative influences of WSIs (Table 6). CSIs (three models) were only associated with the Brown-headed Cowbird (Molothrus ater; forest-edge, Nearctic-wintering species). Furthermore, there were no clear differences in the additive associations of socioeconomic variables between the Neotropical-wintering and Nearctic-wintering groups (Table 6). LndMaxTemp was informative in more models (5 WSIs and 5 additive models) for Neotropical-wintering species than it was in models for Nearctic-wintering species (2 WSIs and 2 additive models). RegMaxTemp was informative in fewer CSIs (0) and more additive models (4) for Neotropical-wintering species than it was for Nearctic-wintering species (3 CSIs and 0 additive models; Table 5; Appendix 1, Table A1.2). Overall, the additive effects of temperature were more influential (higher ∆AICc) on the relative abundance of Neotropical-wintering species than they were on the relative abundance of Nearctic-wintering species.
The explanatory abilities of our best-supported models varied considerably (R²COR range: 11.0–47.2%; Appendix 1, Table A1.2), and the low R²COR values (< 15%) for some models (e.g., for Kentucky Warbler, Geothlypis formosa) imply that the factors considered by this study did not strongly affect some species’ relative abundance. The low MAE values of most fitted models indicated a good fit, and fitted and cross-validated MAE values were similar in magnitude (Appendix 1, Table A1.2), indicating that our models generalized well to independent data. The low VIFs (< 3) for most of the variables (Table 5; Appendix 1, Table A1.2) suggested that individual explanatory variables in most models had largely independent effects on a species’ relative abundance. In models for the Yellow-breasted Chat (Appendix 1, Table A1.2), our conclusion about the effects of focal climate variables with high VIFs (>3) was not affected by collinearity because the variables’ confidence intervals for the regression coefficients did not include 0. In models for the Northern Mockingbird (Mimus polyglottos) and the White-breasted Nuthatch (Sitta carolinensis; Appendix 1, Table A1.2), focal climate variables with high VIFs had confidence intervals for the regression coefficients that included 0. For these latter models, the collinearity prevented us from drawing clear conclusions about whether the focal climate variables were informative. The histograms for the socioeconomic variables indicated that there was ample variation in the data, and the scatterplots and Pearson correlations of socioeconomic variables against longitude and latitude implied that there were no strong geographical gradients in our socioeconomic data (Appendix 1). Kendall’s tau b correlation matrices (Appendix 2) indicated that, apart from variables for which associations would be expected (e.g., between additive effects and their related interactions, and between some temperature variables) very few of the variables were strongly correlated with one another.
Interactions between temperature and socioeconomic variables were present in more than one-third of the 32 best-supported models (4 models for each of 8 species), and most were informative. Interactions were not associated with the Acadian Flycatcher (Empidonax virescens), White-breasted Nuthatch, or the Northern Mockingbird and had low influence in some species’ models (Table 6). The relative influence of interactions for other species (e.g., Kentucky Warbler ∆AICc = 14.30 and Brown-headed Cowbird ∆AICc = 11.18; Table 6) indicated, however, that not exploring WSIs and CSIs of temperature and socioeconomic variables on forest birds may miss information crucial for management decisions and lead to ineffective use of limited conservation resources.
To follow up on our Yellow-breasted Chat example, here we summarize one possible explanation (see additional details in Appendix 1) for the relationship between this species’ relative abundance and the WSI involving LndMaxTemp and MedianInc (Fig. 2). Higher temperatures can lead to higher forest productivity (Raich et al. 2006) and more food for the Yellow-breasted Chat during the breeding season, which may have led to the progressively greater relative abundance of this species with higher LndMaxTemp (Fig. 2). The Yellow-breasted Chat’s association with increasing MedianInc varied with LndMaxTemp (Fig. 2), and we hypothesize that this variation arose from the effect of maximum breeding-season temperature on human contributions to the conservation of natural places. Higher income populations are associated with proenvironmental behavior (Theodori and Luloff 2002), but such behavior may be affected by the amount of contact with nature (Schuttler et al. 2018). Environmental temperature can affect human behaviors (Van de Vliert et al. 2004), and we suggest that people in the warmest landscapes had less contact with nature (went out less often because of uncomfortably high temperatures), resulting in less contribution to the conservation of natural places. Landscapes with less support for conservation may have had less contiguous (more fragmented) forest and hence more edge habitat for the Yellow-breasted Chat; thus, in the warmest landscapes, relative abundance increased with increasing MedianInc (Fig. 2). But, as LndMaxTemp decreased, populations may have had increased contact with nature (went out more often), and increased proenvironmental behavior as MedianInc increased may have resulted in more contiguous forest and less edge habitat for the Yellow-breasted Chat. Consequently, as LndMaxTemp declined and MedianInc increased, less and less edge habitat may have been available, resulting in lower Yellow-breasted Chat relative abundance (Fig. 2).
Environmental factors such as habitat and food are important in the regulation of bird populations (Newton 1991), and these factors are affected by climate (Crick 2004). Climate variables (RegMaxTemp, LndMaxTemp) had higher ∆AICc values than did the interactions, implying that additive climate effects had a greater influence on avian abundance. The greater relative influences of socioeconomic variables and LndPercDec compared to interactions (Table 6) suggest that the interactions involving temperature and landscape-scale human socioeconomic factors we studied were influential in some cases but were not as influential for forest birds overall as were the additive effects of socioeconomic, climate (LndMaxTemp, RegMaxTemp), and habitat variables. Yet, the greater relative influence of interactions compared to the relative influence of either the climate or the socioeconomic variable in some models (Table 6) underscores the need to consider interactions.
Generally, PercColleg and MedianInc had higher relative influences (Table 6) than did PercFemale and MedianAge, perhaps because education (positive relationship in three of five models) and income (positive relationship in all models) can dictate home ownership (Gyourko and Linneman 1997) and vegetation composition of backyards and neighborhoods (Melles 2005), and vegetation composition of yards can influence food availability for birds (Narango et al. 2018). In addition, households with higher education and income participate more actively in community groups (Matarrita-Cascante and Luloff 2008), including grassroots environmental movements (Weber 2000), and thus may contribute more to decisions that affect natural bird habitats at the landscape-scale. Appendix 1 contains additional discussion of the effects of LndPercDec and LndWinTemp on avian abundance.
We considered eight species as examples of forest-interior, forest-edge, Nearctic-wintering, and Neotropical-wintering groups of birds. Our findings that models for only one species included associations with CSIs, and that the relative influence of WSIs did not differ greatly between habitat groups or wintering groups, suggest that responses to interactions may be species-specific rather than group-specific (see Cox et al. 2013). These results emphasize the need to assess the influence of interactions on individual species, and not necessarily groups of species that share a habitat or wintering region.
Galitsky and Lawler (2015) found that avian species’ responses to environmental factors at different scales were group-specific. Our results suggest that the response of breeding forest birds in the eastern United States to temperature may have been group-specific, with Nearctic-wintering species being less associated with climate than were Neotropical-wintering species. Nearctic-wintering species may have to cope with colder winter temperatures than may Neotropical-wintering species. Compared to Neotropical-wintering species, Nearctic-wintering species that breed in our study area may have developed greater capacity to tolerate a wider range of temperatures. Hence, in the eastern United States, Nearctic-wintering forest bird species may be less sensitive to temperature overall compared to Neotropical-wintering forest bird species.
Recognizing the limitations of single-scale analyses, landscape ecologists have advocated for hierarchical multiscale studies (Turner et al. 2001, Cushman and McGarigal 2002). The present study involving WSI and CSI effects of social-ecological conditions on avian abundance is to our knowledge the first of its kind. We demonstrated that avian abundance was associated with both WSIs and CSIs, and that these interactions can be more influential for some species than can additive influences of the interacting variables alone. For example, influences of a WSI was greater than the influence of PercFemale for the best-supported Hairy Woodpecker model, and the influences of CSIs were greater than the influences of RegMaxTemp for two best-supported Brown-headed Cowbird models (Table 6). We also found that different habitat and wintering groups of species were similarly susceptible to such interaction effects. If either landscape- or regional-scale variables exacerbate or ameliorate the effects of other variables, results of studies that do not consider interactions may be misleading. Factors already known to strongly influence a species of conservation concern may be good starting points for managers and researchers interested in incorporating WSIs and CSIs into analyses to improve conservation outcomes. Managers also may be interested in direct and indirect responses of species to human population demographics. For example, wealthier communities in some regions may have vegetation structure with lower value for native bird species (Loss et al. 2009), whereas in other regions native plants in backyard vegetation in wealthy communities may be vital habitat for birds (Warren et al. 2019).
Results that document important effects of social-ecological factors, and lack thereof, are essential for characterizing the full range of conditions that affect avian abundance. Based on our findings involving interactions, additional research on the ways in which climate, education, age, gender, and income affect avian abundance is warranted. The observed associations with socioeconomic variables appear to arise because of these variables’ effects on bird habitat and food. For example, females are associated more with contributing money to conservation (Cooper et al. 2015).
The broad-scale nature of our study enabled us to identify patterns involving interactions of climate and human socioeconomics. At present, the lack of fine-scale data on relevant factors (e.g., backyard habitats, bird baths, bird feeders, expenditures on conservation, and conservation-friendly decisions by local committees) across large areas makes it difficult to discern the mechanisms involved. Fine-scale data for food, habitat, and other factors for birds may become available in the near future with new technologies or wider use of available technologies like light detection and ranging (LiDAR) imagery, for example. LiDAR currently has an approximate horizontal resolution as good as 0.5 m (Chang 2008) and vertical resolution as good as 0.1 m (Bradbury et al. 2005), and these resolutions are likely to improve in the future. Therefore, LiDAR imagery may enable assessment of backyard vegetation, presence of bird baths, or other fine-scale variables across large geographic areas. Such data hold promise for helping to disentangle the drivers of the patterns we uncovered.
For a broader assessment of the prevalence and effect size of interactions involving climate and socioeconomic factors, researchers need to consider a greater diversity of species in the analyses. The models for the species whose abundance was associated with interactions can be viewed as working hypotheses about such effects, and follow-up analyses with new data can be used to test hypotheses about the processes that underlie the patterns we observed. Grouping species based on their functional traits (Galitsky and Lawler 2015) rather than habitat groups may reveal group-specific responses not identified by the present study. Interactions can be nonlinear, include feedbacks, involve thresholds (Peters et al. 2004, Liu et al. 2007, Raffa et al. 2008, Soranno et al. 2014), and may have legacy effects, and identifying such patterns and their underlying mechanisms constitutes an important research frontier for avian ecologists and conservationists.
We thank S. Alexander, T. A. Pinney, J.-J. Song, J. D. White, and J. C. Yelderman for advice about this research and an early draft of the paper; B. S. Cade for information on statistical modeling and response-surface plotting; T. Arnold, L. Bosco, and J.-J. Song for statistical information; F. Eigenbrod, B. J. Payne, and B. Rimal for recommendations that improved the manuscript; and BirdLife International for species distribution maps. We are grateful to Baylor University for funding.
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