Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.     

N-mixture models for estimating population size from spatially replicated counts

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (N-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, N, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for N. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on N that is exploited by the N-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the N-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.     

Comparative Analysis of Mourning Dove Population Change in North America

ABSTRACT Mourning doves (Zenaida macroura) are surveyed in North America with a Call-Count Survey (CCS) and the North American Breeding Bird Survey (BBS). Analyses in recent years have identified inconsistencies in results between surveys, and a need exists to analyze the surveys using modern methods and examine possible causes of differences in survey results. Call-Count Survey observers collect separate information on number of doves heard and number of doves seen during counting, whereas BBS observers record one index containing all doves observed. We used hierarchical log-linear models to estimate trend and annual indices of abundance for 1966–2007 from BBS data, CCS-heard data, and CCS-seen data. Trend estimates from analyses provided inconsistent results for several states and for eastern and central dove-management units. We examined differential effects of change in land use and noise-related disturbance on the CCS indices. Changes in noise-related disturbance along CCS routes had a larger influence on the heard index than on the seen index, but association analyses among states of changes in temperature and of amounts of developed land suggest that CCS indices are differentially influenced by changes in these environmental features. Our hierarchical model should be used to estimate population change from dove surveys, because it provides an efficient framework for estimating population trends from dove indices while controlling for environmental features that differentially influence the indices.     

Combining Breeding Bird Survey and Christmas Bird Count Data to Evaluate Seasonal Components of Population Change in Northern Bobwhite

Abstract Annual surveys of wildlife populations provide information about annual rates of change in populations but provide no information about when such changes occur. However, by combining data from 2 annual surveys, conducted in different parts of the year, seasonal components of population change can be estimated. We describe a hierarchical model for simultaneous analysis of 2 continent-scale monitoring programs. The Christmas Bird Count is an early winter survey, whereas the North American Breeding Bird Survey is conducted in June. Combining information from these surveys permits estimation of seasonal population variance components and improves estimation of long-term population trends. The composite analysis also controls for survey-specific sampling effects. We applied the model to estimation of population change in northern bobwhites (Colinus virginianus). Over the interval 1969–2004, bobwhite populations declined, with trend estimate of −3.56% per year (95% CI = [−3.80%, −3.32%]) in the surveyed portion of their range. Our analysis of seasonal population variance components indicated that northern bobwhite populations changed more in the winter and spring portion of the year than in the summer and fall portion of the year. (JOURNAL OF WILDLIFE MANAGEMENT 72(1):44–51; 2008)     

Flexible risk metrics for identifying and monitoring conservation-priority species

Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available.     

Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red‐eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated‐detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red‐eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.     

Reclamation and habitat‐disturbance effects on landbird abundance and productivity indices in the oil sands region of northeastern Alberta,Canada

The pace and scale of reclamation in Alberta's oil sands region are increasing, and techniques to measure and validate the ecological function of developing habitats are needed. In Alberta, achievement of equivalent land capability to that present before disturbance is a regulatory requirement of reclamation certification. We compared landbird abundance and productivity indices from mist‐netting data collected in 2011–2013 using the Monitoring Avian Productivity and Survivorship (MAPS) protocol with local habitat covariates at 35 monitoring stations in natural, reclaimed, and disturbed habitats. Principal component analysis of habitat covariates explained 83% of the variation in 20 habitat‐structure variables. We found significant relationships between habitat covariates and captures of adult birds, young birds, and/or the probability of capturing a young bird (productivity) for 12 landbird species; in some cases, capture responses contrasted with productivity responses to habitat variables. Responses to reclamation age were as expected, given habitat preferences of our target species. Positive responses to reclamation age from obligate forest‐dwelling species take more years to become evident than those for species preferring successional‐stage habitats, while one species that prefers open, grassland habitats appeared to decline with reclamation age, presumably due to habitat succession. Application of the MAPS protocol as a tool to evaluate and track the performance of reclaimed and disturbed habitats is demonstrated, with landbird abundance and productivity indices in natural habitats being useful indicators of equivalent land capability.     

Filling in the GAPS: evaluating completeness and coverage of open‐access biodiversity databases in the United States

Primary biodiversity data constitute observations of particular species at given points in time and space. Open‐access electronic databases provide unprecedented access to these data, but their usefulness in characterizing species distributions and patterns in biodiversity depend on how complete species inventories are at a given survey location and how uniformly distributed survey locations are along dimensions of time, space, and environment. Our aim was to compare completeness and coverage among three open‐access databases representing ten taxonomic groups (amphibians, birds, freshwater bivalves, crayfish, freshwater fish, fungi, insects, mammals, plants, and reptiles) in the contiguous United States. We compiled occurrence records from the Global Biodiversity Information Facility (GBIF), the North American Breeding Bird Survey (BBS), and federally administered fish surveys (FFS). We aggregated occurrence records by 0.1° × 0.1° grid cells and computed three completeness metrics to classify each grid cell as well‐surveyed or not. Next, we compared frequency distributions of surveyed grid cells to background environmental conditions in a GIS and performed Kolmogorov–Smirnov tests to quantify coverage through time, along two spatial gradients, and along eight environmental gradients. The three databases contributed >13.6 million reliable occurrence records distributed among >190,000 grid cells. The percent of well‐surveyed grid cells was substantially lower for GBIF (5.2%) than for systematic surveys (BBS and FFS; 82.5%). Still, the large number of GBIF occurrence records produced at least 250 well‐surveyed grid cells for six of nine taxonomic groups. Coverages of systematic surveys were less biased across spatial and environmental dimensions but were more biased in temporal coverage compared to GBIF data. GBIF coverages also varied among taxonomic groups, consistent with commonly recognized geographic, environmental, and institutional sampling biases. This comprehensive assessment of biodiversity data across the contiguous United States provides a prioritization scheme to fill in the gaps by contributing existing occurrence records to the public domain and planning future surveys.