Modeled distribution shifts of North American birds over four decades based on suitable climate alone do not predict observed shifts.

As climate change alters the global environment, it is critical to understand the relationship between shifting climate suitability and species distributions. Key questions include whether observed changes in population abundance are aligned with the velocity and direction of shifts predicted by climate suitability models and if the responses are consistent among species with similar ecological traits. We examined the direction and velocity of the observed abundance-based distribution centroids compared with the model-predicted bioclimatic distribution centroids of 250 bird species across the United States from 1969 to 2011. We hypothesized that there is a significant positive correlation in both direction and velocity between the observed and the modeled shifts. We then tested five additional hypotheses that predicted differential shifting velocity based on ecological adaptability and climate change exposure. Contrary to our hypotheses, we found large differences between the observed and modeled shifts among all studied bird species and within specific ecological guilds. However, temperate migrants and habitat generalist species tended to have higher velocity of observed shifts than other species. Neotropical migratory and wetland birds also had significantly different observed velocities than their counterparts, which may be due to their climate change exposure. The velocity based on modeled bioclimatic suitability did not exhibit significant differences among most guilds. Boreal forest birds were the only guild with significantly faster modeled-shifts than the other groups, suggesting an elevated conservation risk for high latitude and altitude species. The highly idiosyncratic species responses to climate and the mismatch between shifts in modeled and observed distribution centroids highlight the challenge of predicting species distribution change based solely on climate suitability and the importance of non-climatic factors traits in shaping species distributions.

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.

Decline of the North American avifauna.

Species extinctions have defined the global biodiversity crisis, but extinction begins with loss in abundance of individuals that can result in compositional and functional changes of ecosystems. Using multiple and independent monitoring networks, we report population losses across much of the North American avifauna over 48 years, including once-common species and from most biomes. Integration of range-wide population trajectories and size estimates indicates a net loss approaching 3 billion birds, or 29% of 1970 abundance. A continent-wide weather radar network also reveals a similarly steep decline in biomass passage of migrating birds over a recent 10-year period. This loss of bird abundance signals an urgent need to address threats to avert future avifaunal collapse and associated loss of ecosystem integrity, function, and services.

On the robustness of N-mixture models.

N-mixture models provide an appealing alternative to mark-recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.

On the reliability of N-mixture models for count data.

N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the "constant p" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.

Multidirectional abundance shifts among North American birds and the relative influence of multifaceted climate factors.

Shifts in species distributions are major fingerprint of climate change. Examining changes in species abundance structures at a continental scale enables robust evaluation of climate change influences, but few studies have conducted these evaluations due to limited data and methodological constraints. In this study, we estimate temporal changes in abundance from North American Breeding Bird Survey data at the scale of physiographic strata to examine the relative influence of different components of climatic factors and evaluate the hypothesis that shifting species distributions are multidirectional in resident bird species in North America. We quantify the direction and velocity of the abundance shifts of 57 permanent resident birds over 44 years using a centroid analysis. For species with significant abundance shifts in the centroid analysis, we conduct a more intensive correlative analysis to identify climate components most strongly associated with composite change of abundance within strata. Our analysis focus on two contrasts: the relative importance of climate extremes vs. averages, and of temperature vs. precipitation in strength of association with abundance change. Our study shows that 36 species had significant abundance shifts over the study period. The average velocity of the centroid is 5.89 km·yr-1 . The shifted distance on average covers 259 km, 9% of range extent. Our results strongly suggest that the climate change fingerprint in studied avian distributions is multidirectional. Among 6 directions with significant abundance shifts, the northwestward shift was observed in the largest number of species (n = 13). The temperature/average climate model consistently has greater predictive ability than the precipitation/extreme climate model in explaining strata-level abundance change. Our study shows heterogeneous avian responses to recent environmental changes. It highlights needs for more species-specific approaches to examine contributing factors to recent distributional changes and for comprehensive conservation planning for climate change adaptation.

An integrated population model for bird monitoring in North America.

Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad-scale avian monitoring data sets: count data from the North American Breeding Bird Survey (BBS) and capture-recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first-order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log-linear function of the annual index of population abundance, observation effects (observer identity and first survey year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack-Jolly-Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992-2008) of two bird species, Gray Catbird (Dumetella carolinensis) and Wood Thrush (Hylocichla mustelina) in the New England/Mid-Atlantic coastal Bird Conservation Region of the United States. The Gray Catbird population was relatively stable (trend +0.4% per yr), while the Wood Thrush population nearly halved (trend -4.5% per yr) over the 17-yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand-alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for Wood Thrush was stronger than for Gray Catbird. The IPM's unified modeling framework facilitates integration of these important data sets.

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey.

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Consistent response of bird populations to climate change on two continents.

Global climate change is a major threat to biodiversity. Large-scale analyses have generally focused on the impacts of climate change on the geographic ranges of species and on phenology, the timing of ecological phenomena. We used long-term monitoring of the abundance of breeding birds across Europe and the United States to produce, for both regions, composite population indices for two groups of species: those for which climate suitability has been either improving or declining since 1980. The ratio of these composite indices, the climate impact indicator (CII), reflects the divergent fates of species favored or disadvantaged by climate change. The trend in CII is positive and similar in the two regions. On both continents, interspecific and spatial variation in population abundance trends are well predicted by climate suitability trends.

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage.

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Global biodiversity: indicators of recent declines.

In 2002, world leaders committed, through the Convention on Biological Diversity, to achieve a significant reduction in the rate of biodiversity loss by 2010. We compiled 31 indicators to report on progress toward this target. Most indicators of the state of biodiversity (covering species' population trends, extinction risk, habitat extent and condition, and community composition) showed declines, with no significant recent reductions in rate, whereas indicators of pressures on biodiversity (including resource consumption, invasive alien species, nitrogen pollution, overexploitation, and climate change impacts) showed increases. Despite some local successes and increasing responses (including extent and biodiversity coverage of protected areas, sustainable forest management, policy responses to invasive alien species, and biodiversity-related aid), the rate of biodiversity loss does not appear to be slowing.

Modeling the effects of environmental disturbance on wildlife communities: avian responses to prescribed fire.

Prescribed fire is a management tool used to reduce fuel loads on public lands in forested areas in the western United States. Identifying the impacts of prescribed fire on bird communities in ponderosa pine (Pinus ponderosa) forests is necessary for providing land management agencies with information regarding the effects of fuel reduction on sensitive, threatened, and migratory bird species. Recent developments in occupancy modeling have established a framework for quantifying the impacts of management practices on wildlife community dynamics. We describe a Bayesian hierarchical model of multi-species occupancy accounting for detection probability, and we demonstrate the model's usefulness for identifying effects of habitat disturbances on wildlife communities. Advantages to using the model include the ability to estimate the effects of environmental impacts on rare or elusive species, the intuitive nature of the modeling, the incorporation of detection probability, the estimation of parameter uncertainty, the flexibility of the model to suit a variety of experimental designs, and the composite estimate of the response that applies to the collection of observed species as opposed to merely a small subset of common species. Our modeling of the impacts of prescribed fire on avian communities in a ponderosa pine forest in Washington indicate that prescribed fire treatments result in increased occupancy rates for several bark-insectivore, cavity-nesting species including a management species of interest, Black-backed Woodpeckers (Picoides arcticus). Three aerial insectivore species, and the ground insectivore, American Robin (Turdus migratorius), also responded positively to prescribed fire, whereas three foliage insectivores and two seed specialists, Clark's Nutcracker (Nucifraga columbiana) and the Pine Siskin (Carduelis pinus), declined following treatments. Land management agencies interested in determining the effects of habitat manipulations on wildlife communities can use these methods to provide guidance for future management activities.

Microarray analysis of gene expression changes in feeding female and male lone star ticks, Amblyomma americanum (L).

A collection of EST clones from female tick Amblyomma americanum salivary glands was hybridized to RNA from different feeding stages of female tick salivary glands and from unfed or feeding adult male ticks. In the female ticks, the expression patterns changed dramatically upon starting feeding, then changed again towards the end of feeding. On beginning feeding, genes possibly involved in survival on the host increased in expression as did many housekeeping genes. As feeding progressed, some of the survival genes were downregulated, while others were upregulated. When the tick went into the rapid feeding phase, many of the survival genes were downregulated, while a number of transport-associated genes and genes possibly involved in organ degeneration increased. In the males, the presence of females during feeding made a small difference, but feeding made a larger difference. Males showed clear differences from females in expression, as well. Protein synthesis genes were expressed more in all male groups than in the partially fed females, while the putative secreted genes involved in avoiding host defenses were expressed less.

Seasonal components of avian population change: joint analysis of two large-scale monitoring programs.

We present a combined analysis of data from two large-scale surveys of bird populations. The North American Breeding Bird Survey is conducted each summer; the Christmas Bird Count is conducted in early winter. The temporal staggering of these surveys allows investigation of seasonal components of population change, which we illustrate with an examination of the effects of severe winters on the Carolina Wren (Thryothorus ludovicianus). Our analysis uses a hierarchical log-linear model with controls for survey-specific sampling covariates. Temporal change in population size is modeled seasonally, with covariates for winter severity. Overall, the winter-spring seasons are associated with 82% of the total population variation for Carolina Wrens, and an additional day of snow cover during winter-spring is associated with an incremental decline of 1.1% of the population.

Gene expression in male tick salivary glands is affected by feeding in the presence of females.

In this study, we have compared gene expression in the salivary glands of male Dermacentor andersoni ticks fed in the presence or absence of females to that in unfed males. We have established that the patterns of expression are different using arbitrarily primed polymerase chain reaction following reverse transcription of RNA (RAP-PCR) and differential display. Our results indicate a significant difference in salivary gland gene expression between fed and unfed males and also between males fed in the presence of females versus males fed in the absence of females. This difference in expression was partially confirmed by hybridization of probes made from mRNA from the three groups of ticks against the cloned gene fragments. Gene fragments for septin, utrophin, eukaryotic chain release factor 3, and elongation factor 2 were identified in this study, though the majority of fragments were not similar to anything in the databases.

RNAi-mediated gene silencing to assess the role of synaptobrevin and cystatin in tick blood feeding.

In addition to being the conduit for pathogens into hosts, tick saliva contains a broad array of secretory products that facilitate prolonged tick attachment and blood feeding. Proteins found in tick saliva modulate host hemostasis and immune responses. However, it is not clear whether ticks manipulate the immune responses of their hosts by disrupting the antigen-processing pathways of the hosts. Protein secretion into tick saliva from the salivary glands is due to exocytosis of vesicular membrane-bound granular material regulated by SNARE complex proteins. Proteins associated with vesicles (v-SNAREs) are essential components of the exocytotic process. In this study, we assessed the functional significance of synaptobrevin, a SNARE protein, and cystatin, a cysteine protease inhibitor to blood feeding success, in the lone star tick, Amblyomma americanum, using in vivo RNA interference. In separate experiments, tick salivary cystatin and synpatobrevin genes were silenced by injecting adult ticks with 500 ng of dsRNA complementing each gene sequence. Silencing was demonstrated by reduced transcript in midguts and salivary glands. Additionally, disrupting expression of cystatin and synaptobrevin by RNAi reduced the ability of ticks to feed successfully, as demonstrated by feeding inhibition and reduced engorgement weights. Moreover, normal ticks exposed to a rabbit previously exposed to cystatin-silenced ticks exhibited significant resistance to tick feeding. Based on these findings, ticks appear to skillfully evade the host immune system by secreting cystatin, which disrupts normal antigen processing in antigen-presenting cells of hosts.

Application of RNA interference in tick salivary gland research.

Ticks are obligate ectoparasites that feed on a variety of hosts including mammals, birds and reptiles. Prolonged attachment on the host and an ability to transmit a wide variety of pathogens are the special features of tick feeding. Salivary glands are the major route for secretion of excess fluid, several proteins, and factors that counteract the host immune response and hence play a significant role in the success of tick feeding. RNA interference (RNAi) enables scientists to silence genes encoding proteins in an absolutely sequence specific manner at the mRNA level. This technique has already been successfully employed in analyzing roles of proteins of important functions or in assigning roles to several proteins of unknown functions in a variety of animals. In this review, we outline the process of RNAi and the applicability of RNAi in tick salivary gland research.

Amblyomma americanum salivary gland homolog of nSec1 is essential for saliva protein secretion.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor proteins assemble in tight core complexes which promote fusion of carrier vesicles with target compartments. Members of this class of proteins are expressed in all eukaryotic cells and distributed in distinct subcellular compartments. All vesicle transport mechanisms known to date have an essential requirement for a member of the Sec1 protein family, including the nSec1 in regulated exocytosis. A homolog of nSec1 was cloned and sequenced from the salivary glands of partially fed female ticks. Double-stranded RNA was used to specifically reduce the amount of nSec1 mRNA and protein in female adult tick salivary glands. This reduction was accompanied by a decrease in anticoagulant protein release by the glands and by abnormalities in feeding by dsRNA treated ticks. We report the efficacy of double-stranded RNA-mediated interference in "knocking down" nSec1 both in vivo and in vitro in tick salivary glands and the applicability of this technique for studying the mechanism of exocytosis in tick salivary glands.

The relationship between species detection probability and local extinction probability.

In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow detection probabilities that are <1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species detection probability covaries negatively with local probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction probability performed fairly well and provided no basis for preferring one to the other.