North American Breeding Bird Survey Research Articles

Demographic and potential biological removal models identify raptor species sensitive to current and future wind energy

AbstractA central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used three approaches to investigate the potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine‐caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or from a matrix model parameterized from literature‐derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management objectives. Different results among the methods reveal substantial gaps in knowledge and uncertainty in both demographic parameters and species‐specific estimates of fatalities from wind turbines. Our results suggest that, of the 14 species studied, those with relatively higher potential of population‐level impacts from wind turbine collisions included barn owl, ferruginous hawk, golden eagle, American kestrel, and red‐tailed hawk. Burrowing owl, Cooper’s hawk, great horned owl, northern harrier, turkey vulture, and osprey had a relatively lower potential for population impacts, and results were not easily interpretable for merlin, prairie falcon, and Swainson’s hawk. Projections of current levels of fatalities to future wind energy scenarios at 241 GW of installed capacity suggest some species could experience population declines because of turbine collisions. Populations of those species may benefit from research to identify tools to prevent or reduce raptor collisions with wind turbines.

Using temporal occupancy to predict avian species distributions

AbstractAimSpecies distribution models (SDMs) are ubiquitous in ecology to predict species occurrence throughout their range. Typically, SDMs are created using presence‐only or presence–absence data. We hypothesize that the continuous metric of temporal occupancy, the proportion of time a species is observed at a given site, provides more detail about species occurrence than binary presence‐based SDMs.LocationNorth America.MethodsWe compared SDMs for 189 focal species using four modelling methods to determine whether North American avian species distributions are better predicted using temporal occupancy over presence–absence. We used the North American Breeding Bird Survey and built SDMs based on all sites sampled consecutively between 2001 and 2015, as well as on a subset of only five time points within the 15‐year sampling window. Each model used the same environmental inputs to predict species range. Each SDM was cross‐validated temporally and spatially.ResultsSpecies distributions were generally better predicted using temporal occupancy rather than presence–absence when using either a five‐year or fifteen‐year sampling window. Species that occurred in a smaller proportion of their predicted range were particularly better predicted with SDMs using temporal occupancy. Temporal occupancy SDMs had lower false discovery and false‐positive rates but higher false‐negative rates than presence–absence models.Main conclusionsTemporal occupancy is a valuable metric that can improve predictions of species occurrence for birds and may improve conservation planning and design efforts.

Patterns of bird species richness explained by annual variation in remotely sensed Dynamic Habitat Indices

Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity (GPP), which can be estimated from satellite data.Our question was how temporal dynamics in GPP affect bird species richness. Specifically, we evaluated the potential of the Dynamic Habitat Indices (DHIs) derived from MODIS GPP data together with environmental and climatic variables to explain annual patterns in bird richness across the conterminous United States. By focusing on annual DHIs, we expand on previous applications of multi-year composite DHIs, and could evaluate lag-effects between changes in GPP and species richness.We used 8-day GPP data from 2003 to 2013 to calculate annual DHIs, which capture three aspects of vegetation productivity: (1) annual cumulative productivity, (2) annual minimum productivity, and (3) annual seasonality expressed as the coefficient of variation in productivity. For each year from 2003 to 2013, we calculated total bird species richness and richness within six functional guilds, based on North American Breeding Bird Survey data.The DHIs alone explained up to 53% of the variation in annual bird richness within the different guilds (adjusted deviance-squared D2adj = 0.20–0.52), and up to 75% of the variation (D2adj = 0.28–0.75) when combined with other environmental and climatic variables. Annual DHIs had the highest explanatory power for habitat-based guilds, such as grassland (D2adj = 0.67) and woodland breeding species (D2adj = 0.75). We found some inter-annual variability in the explanatory power of annual DHIs, with a difference of 5–7 percentage points in explained variation among years in DHI-only models, and 3–7 points for models combining DHI, environmental and climatic variables. Our results using lagged year models did not deviate substantially from same-year annual models.We demonstrate the relevance of annual DHIs for biodiversity science, as effective predictors of temporal variation in species richness patterns. We suggest that the use of annual DHIs can improve conservation planning, by conveying the range of patterns of biodiversity response to global changes, over time.

Combination of targeted monitoring and Breeding Bird Survey data improves population trend estimation and species distribution modeling for the Common Nighthawk

Abstract Standardized monitoring is critical for conservation because reliable survey data are used to inform the necessity, type, and effectiveness of conservation actions. Many of the avian monitoring data used for conservation are collected by “comprehensive” programs that survey for all species observed; however, there are some species that have low availability for detection during comprehensive surveys and are instead monitored with targeted programs. Unfortunately, those targeted programs are rarely evaluated relative to existing programs and management objectives to inform allocation of effort. We assessed the statistical performance of the comprehensive North American Breeding Bird Survey (BBS), the targeted Canadian Nightjar Survey (CNS), and the two programs combined for the Common Nighthawk (Chordeiles minor). First, we used parameters from the existing datasets to simulate population declines and determined the probability of detecting those declines. Analyses that combined both datasets resulted in higher probability of detecting a 30% population decline (BBS: 38%, CNS: 64%, combined: 69%). Next, we built probability of occurrence models and assessed the predictive performance of those models. Combined analyses had similar predictive performance to the CNS and moderated poor performance of the BBS in the north (mean Cohen’s kappa; BBS: 0.40, CNS: 0.46, combined: 0.50). Our results suggest that data from targeted monitoring is important for evaluation of Common Nighthawk population trend and habitat relationships but is best combined with BBS data. Comprehensive and targeted monitoring programs may be better when considered together, and exploration of data combination should become the rule, not the exception. We suggest that the framework we present can be used as a starting point for evaluating targeted monitoring programs relative to defined objectives and existing programs, with the potential to explore hypothetical management scenarios.

Strong differences in migratory connectivity patterns among species of Neotropical‐Nearctic migratory birds revealed by combining stable isotopes and abundance in a Bayesian assignment analysis

AbstractAimConservation planning for migratory species requires knowing how populations are connected throughout the annual cycle. We measured migratory connectivity for five migratory songbird species by combining stable‐hydrogen isotopes in feathers (δ2HF) with citizen science data on breeding range abundance.LocationCaribbean Basin and North America.TaxaNeotropical migratory birds: Mniotilta varia, Setophaga ruticilla, Seiurus aurocapilla, Setophaga americana, and Setophaga discolor.MethodsWe analyzed δ2HF in feathers grown on North American breeding grounds and sampled at 20 Caribbean Basin sites across most of the non‐breeding range of each species. We made Bayesian assignments to breeding origin by combining δ2HF values and North American Breeding Bird Survey abundance data. For each species, we used cluster analysis to group Caribbean sites into non‐breeding regions that shared birds with similar breeding ground assignment probabilities. We then mapped assignment probabilities for the non‐breeding regions of each species onto breeding ground δ2HF surfaces and estimated the strength of migratory connectivity.ResultsApart from some general similarities, the five species demonstrated different patterns of migratory connectivity. Species with larger non‐breeding ranges exhibited stronger migratory connectivity compared to those with smaller non‐breeding ranges. Despite geographical overlap, birds from each non‐breeding region had mostly unique breeding ground origins.Main conclusionsThe contrasting patterns of migratory connectivity shown by the five species suggest that geographic distribution during the non‐breeding period is a poor indicator of breeding ground origin. Our results indicate that full annual cycle conservation planning, which requires migratory connectivity information, should proceed on a species‐specific basis.

American Woodcock Singing-Ground Survey: Comparison of Four Models for Trend in Population Size

AbstractWildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.

Building a better baseline to estimate 160 years of avian population change and create historically informed conservation targets.

Globally, anthropogenic land-cover change has been dramatic over the last few centuries and is frequently invoked as a major cause of wildlife population declines. Baseline data currently used to assess population trends, however, began well after major changes to the landscape. In the United States and Canada, breeding bird population trends are assessed by the North American Breeding Bird Survey, which began in the 1960s. Estimates of distribution and abundance prior to major habitat alteration would add historical perspective to contemporary trends and allow for historically based conservation targets. We used a hindcasting framework to estimate change in distribution and abundance of 7 bird species in the Willamette Valley, Oregon (United States). After reconciling classification schemes of current and 1850s reconstructed land cover, we used multiscale species distribution models and hierarchical distance sampling models to predict spatially explicit densities in the modern and historical landscapes. We estimated that since the 1850s, White-breasted Nuthatch (Sitta carolinensis) and Western Meadowlark (Sturnella neglecta) populations, 2 species sensitive to fragmentation of oak woodlands and grasslands, declined by 93% and 97%, respectively. Five other species we estimated nearly stable or increasing populations, despite steep regional declines since the 1960s. Based on these estimates, we developed historically based conservation targets for amount of habitat, population, and density for each species. Hindcasted reconstructions provide historical perspective for assessing contemporary trends and allow for historically based conservation targets that can inform current management.

Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

Environmental heterogeneity enhances species richness by creating niches and providing refugia. Spatial variation in climate has a particularly strong positive correlation with richness, but is often indirectly inferred from proxy variables, such as elevation and related topographic heterogeneity indices, or derived from interpolated coarse‐grain weather station data. Our aim was to develop new remotely sensed metrics of relative temperature and thermal heterogeneity, compare them with proxy measures, and evaluate their performance in predicting species richness patterns. We analyzed Landsat 8's Thermal Infrared Sensor data, calculated two thermal metrics during summer and winter, and compared their seasonal spatial patterns with those of elevation and topographic heterogeneity. We fit generalized least squares models to evaluate each variable's effect in predicting seasonal bird richness using data from the North American Breeding Bird Survey. Generally speaking, neither elevation nor topographic heterogeneity were good proxies for temperature or thermal heterogeneity, respectively. Relative temperature had a non‐linear relationship with elevation that was negatively quadratic in summer, but slightly positively quadratic in winter. Topographic heterogeneity had a stronger positive relationship with thermal heterogeneity in winter than in summer. The magnitude and direction of elevation–temperature and topographic heterogeneity–thermal heterogeneity relationships in each season also varied substantially across ecoregions. Remotely sensed metrics of relative temperature and thermal heterogeneity improved the predictive performance of species richness models, and both thermal variables had significant effects on bird richness that were independent of elevation and topographic heterogeneity. Thermal heterogeneity was positively related to total breeding bird richness, migrant breeding bird richness and resident bird richness, whereas topographic heterogeneity was negatively related to total breeding richness and unrelated to migrant or resident bird richness. Because thermal and topographic heterogeneity had contrasting seasonal patterns and effects on richness, they must be carefully contextualized when guiding conservation priorities.

Can habitat suitability estimated from MaxEnt predict colonizations and extinctions?

AbstractAimMaxEnt has been widely used to model species’ geographic distributions as functions of environmental variables and to predict changes in distributions in response to environmental change. Here, we test the predictive ability of MaxEnt models through time by modelling colonizations and extinctions.LocationNorth America.MethodsUsing data for 21 species from the North American Breeding Bird Survey, we first related avian species’ geographic distributions to the spatial variation in environmental conditions. Then, we modelled site‐specific colonizations and extinctions between 1979 and 2009 as functions of MaxEnt‐estimated habitat suitability and neighbourhood occupancy.ResultsWe found that colonization and extinction probabilities were related to spatial variation in habitat suitability, and to neighbourhood occupancy, in the expected directions. However, change in habitat suitability (which is much smaller through time than through space) is a weak predictor of extinction and worse for colonization. This is because a) for most species and most sites, climatic suitability did not change dramatically between 1979 and 2009, and b) the relationship between colonization or extinction probability and change in climatic variables is very weak (r2 = 0.02). Most colonizations and extinctions are apparently unrelated to climate change.Main conclusionsMaxEnt models apparently capture a real effect of habitat suitability on North American bird species’ distributions, but over short and medium time scales, occupancy of neighbouring sites by conspecifics predicts changes in occupancy as well as, or better than changes in climatic habitat suitability, as characterized by MaxEnt. One would not expect species’ distributions to track climate change closely. Prediction of species’ responses to climate change should 1) recognize that the process of colonization and extinction are not equally well predicted by species distribution models and 2) account for the spatial structure of species’ distributions.

Mapping shifts in spatial synchrony in grassland birds to inform conservation planning.

Spatial synchrony, defined as the correlated fluctuations in abundance of spatially separated populations, can be caused by regional fluctuations in natural and anthropogenic environmental population drivers. Investigations into the geography of synchrony can provide useful insight to inform conservation planning efforts by revealing regions of common population drivers and metapopulation extinction vulnerability. We examined the geography of spatial synchrony and decadal changes in these patterns for grassland birds in the United States and Canada, which are experiencing widespread and persistent population declines. We used Bayesian hierarchical models and over 50 years of abundance data from the North American Breeding Bird Survey to generate population indices within a 2° latitude by 2° longitude grid. We computed and mapped mean local spatial synchrony for each cell (mean detrended correlation of the index among neighboring cells), along with associated uncertainty, for 19 species in 2, 26-year periods, 1968-1993 and 1994-2019. Grassland birds were predicted to increase in spatial synchrony where agricultural intensification, climate change, or interactions between the 2 increased. We found no evidence of an overall increase in synchrony among grassland bird species. However, based on the geography of these changes, there was considerable spatial heterogeneity within species. Averaging across species, we identified clusters of increasing spatial synchrony in the Prairie Pothole and Shortgrass Prairie regions and a region of decreasing spatial synchrony in the eastern United States. Our approach has the potential to inform continental-scale conservation planning by adding an additional layer of relevant information to species status assessments and spatial prioritization of policy and management actions. Our work adds to a growing literature suggesting that global change may result in shifting patterns of spatial synchrony in population dynamics across taxa with broad implications for biodiversity conservation.

Forest bird abundance can vary with cross-scale interactions involving climate, exurban cover and forest patch size

Abstract Context Climate and land use are among the most important drivers of global biodiversity change, and they may be operating at different spatial scales. The effects of cross-scale interactions (CSIs) between these drivers on avian abundance are poorly understood. Aims Our primary objective was to assess whether the abundances of eight forest bird species in the eastern United States were significantly associated with CSIs involving four subregional climate variables (breeding- and pre-breeding-season temperature and precipitation) and two landscape variables (percentage exurban cover and forest patch size). Methods For North American Breeding Bird Survey routes in six U.S. Environmental Protection Agency Level II ecoregions, we measured subregional climate variables within species maximum natal dispersal distances, and we measured landscape variables within species median natal dispersal distances. Using Akaike’s information criterion and negative-binomial regression, we compared the fits of 21 a priori competing models separately for each of the eight species, and separately for percentage exurban cover and forest patch size (8 × 2 = 16 model sets). Key results Total abundances during 2009–2013 of all eight species were associated with CSIs, which were informative in nine of the 16 best-supported models. The informative CSIs in the best-supported models involved all four subregional climate and both landscape variables. These results were evident after we accounted analytically for various methodological and environmental covariates, including within-scale interactions, that may otherwise have obscured the effects of CSIs. In some models, CSIs were more influential than were the associated additive effects, similar within-scale interactions, or other environmental variables, whereas in other models they were not. The associations between species abundances and CSIs were species-specific. Conclusions CSIs among global drivers of change may be common, and failure to identify CSI effects may result in misleading bird−landscape models. Implications Understanding how CSIs modify the effects of variables at different spatial scales may be crucial for effective broad-scale management of declining species. Conservation attention to species that are presently common but declining in abundance may help avoid extirpation in parts of their geographic ranges.

North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model

AbstractThe status and trend estimates derived from the North American Breeding Bird Survey (BBS) are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g., inferences about long-term change, annual fluctuations, population cycles, and recovery of once-declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAMs) for the BBS, which share information on the pattern of population change across a species’ range. We demonstrate the models and their benefits using data from a selection of species, and we run full cross-validation of the GAMs against 2 other models to compare the predictive fit. The GAMs have a better predictive fit than the standard model for all species studied here and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth component. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the International Union for the Conservation of Nature. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs. annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.

Assessing the efficacy of protected and multiple-use lands for bird conservation in the U.S.

Setting land aside has long been a primary approach for protecting biodiversity; however, the efficacy of this approach has been questioned. We examined whether protecting lands positively influences bird species in the U.S., and thus overall biodiversity. We used the North American Breeding Bird Survey and Protected Areas Database of the U.S. to assess effects of protected and multiple-use lands on the prevalence and long-term population trends of imperiled and non-imperiled bird species. We evaluated whether both presence and proportional area of protected and multiple-use lands surrounding survey routes affected prevalence and population trends for imperiled and non-imperiled species. Regarding presence of these lands surrounding these survey routes, our results suggest that imperiled and non-imperiled species are using the combination of protected and multiple-use lands more than undesignated lands. We found no difference between protected and multiple-use lands. Mean population trends were negative for imperiled species in all land categories and did not differ between the land categories. Regarding proportion of protected lands surrounding the survey routes, we found that neither the prevalence nor population trends of imperiled or non-imperiled species was positively associated with any land category. We conclude that, although many species (in both groups) tend to be using these protected and multiple-use lands more frequently than undesignated lands, this protection does not appear to improve population trends. Our results may be influenced by external pressures (e.g., habitat fragmentation), the size of protected lands, the high mobility of birds that allows them to use a combination of all land categories, and management strategies that result in similar habitat between protected and multiple-use lands, or our approach to detect limited relationships. Overall, our results suggest that the combination of protected and multiple-use lands is insufficient, alone, to prevent declines in avian biodiversity at a national scale.

What are the competitive effects of invasive species? Forty years of the Eurasian collared-dove in North America

Eurasian collared-doves (Streptopelia decaocto; hereafter ‘collared-doves’) have spread throughout North America since they first colonized Florida in the early 1980s. Here I test for adverse effects of this introduced species on four confamilial potential competitor dove and pigeon species using data from the breeding season (North American Breeding Bird Survey; BBS) and the winter (Audubon Christmas Bird Count; CBC). Within sites of both sets of surveys, correlations between populations of collared-doves and all four potential competitor species have generally been either nonsignificant or positive, indicating a lack of adverse competitive effects due to collared-doves. Similarly, there were no significant differences in population trends of any of the four species in sites where collared-doves were present compared to those where they were not, and there have been no significant declines in population trends of the four species driven by differences in collared-dove abundance in areas where the latter were present. Overall, analyses revealed no negative effects of collared-doves on populations of these potential competitors. Evidence thus far supports a ‘passenger’ rather than a ‘driver’ role for collared-doves in North America, although future monitoring of potential competitor species is warranted, especially if collared-dove populations continue to increase.

Model selection for the North American Breeding Bird Survey.

The North American Breeding Bird Survey (BBS) provides data that can be used in complex, multiscale analyses of population change, while controlling for scale-specific nuisance factors. Many alternative models can be fit to the data, but most model selection procedures are not appropriate for hierarchical models. Leave-one-out cross-validation (LOOCV), in which relative model fit is assessed by omitting an observation and assessing the prediction of a model fit using the remainder of the data, provides a reasonable approach for assessing models, but is time consuming and not feasible to apply for all observations in large data sets. We report the first large-scale formal model selection for BBS data, applying LOOCV to stratified random samples of observations from BBS data. Our results are for 548 species of North American birds, comparing the fit of four alternative models that differ in year effect structures and in descriptions of extra-Poisson overdispersion. We use a hierarchical model among species to evaluate posterior probabilities that models are best for individual species. Models in which differences in year effects are conditionally independent (D models) were generally favored over models in which year effects are modeled by a slope parameter and a random year effect (S models), and models in which extra-Poisson overdispersion effects are independent and t-distributed (H models) tended to be favored over models where overdispersion was independent and normally distributed. Our conclusions lead us to recommend a change from the conventional S model to D and H models for the vast majority of species (544/548). Comparison of estimated population trends based on the favored model relative to the S model currently used for BBS summaries indicates no consistent differences in estimated trends. Of the 18 species that showed large differences in estimated trends between models, estimated trends from the default S model were more extreme, reflecting the influence of the slope parameter in that model for species that are undergoing large population changes. WAIC, a computationally simpler alternative to LOOCV, does not appear to be a reliable alternative to LOOCV.

Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the United States.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.

Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada

Abstract Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America.

Associations of breeding-bird abundance with climate vary among species and trait-based groups in southern California.

The responses of individuals and populations to climate change vary as functions of physiology, ecology, and plasticity. We investigated whether annual variation in seasonal temperature and precipitation was associated with relative abundances of breeding bird species at local and regional levels in southern California, USA, from 1968–2013. We tested our hypotheses that abundances were correlated positively with precipitation and negatively with temperature in this semiarid to arid region. We also examined whether responses to climate varied among groups of species with similar land-cover associations, nesting locations, and migratory patterns. We investigated relations between seasonal climate variables and the relative abundances of 41 species as estimated by the North American Breeding Bird Survey. Associations with climate variables varied among species. Results of models of species associated with arid scrublands or that nest on the ground strongly supported our hypotheses, whereas those of species associated with coniferous forests or that nest in cavities did not. Associations between climate variables and the abundances of other trait-based groups were diverse. Our results suggest that species in arid areas may be negatively affected by increased temperature and aridity, but species in nearby areas that are cooler and less arid may respond positively to those fluctuations in climate. Relations with climate variables can differ among similar species, and such knowledge may inform projections of future abundance trajectories and geographic ranges.

Differential winter and breeding range shifts: Implications for avian migration distances

AbstractAimFor many migratory avian species, winter and breeding habitats occur at geographically distinct locations. Disparate magnitudes and direction of shifts in wintering and breeding locations could lead to altered migration distances. We investigated how shifts in the centre of abundance (COA) of winter and breeding ranges have changed for 77 species of short‐distance migratory birds. We addressed whether species tracked their historical average temperature and precipitation conditions at their winter and breeding COA, using data from 1990 to 2015.LocationNorth America.MethodsWe calculated the COA for winter and breeding ranges from the National Audubon Society's Christmas Bird Count and the North American Breeding Bird Survey. We regressed the annual change in distance (km) between the two annual COAs of each species as a proxy for change in migration distance. We constructed a series of generalized linear mixed models (GLMMs) to evaluate changes in average temperature and precipitation at the wintering and breeding COAs.ResultsWinter shifts in COA were predominantly northward. For most species, average temperature and precipitation that species experienced had not changed. Breeding shifts in COA varied in direction. For breeding season COAs, average temperature warmed, but average precipitation had not changed. Thirty‐one species significantly decreased their migration distances, mainly driven by northward shifts in the winter range. Ten species increased their migration distances.Main conclusionsWinter and breeding range shifts in COA have not occurred at the same magnitude and direction and have therefore impacted migration distance. Our results suggest that wintering and breeding range shifts occur independently and under different climate pressures.

Modeling avian full annual cycle distribution and population trends with citizen science data.

Information on species’ distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range‐wide extents for two main reasons. First, landscape‐scale processes that affect populations vary throughout the year and across species’ ranges, requiring high‐resolution, year‐round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year‐round, range‐wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long‐distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra‐annual estimates of the range, intra‐annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non‐breeding seasons. The range‐wide population trajectories for Wood Thrush show a close correspondence between breeding and non‐breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range‐wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non‐breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra‐ and interannual distributional dynamics for broadly distributed, highly mobile species.