Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere.

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

Migration tracking reveals geographic variation in the vulnerability of a Nearctic-Neotropical migrant bird.

We compared the vulnerability of a Nearctic-Neotropical migrant (Swainson's Thrush, Catharus ustulatus) for three geographically-defined breeding populations in California by linking breeding and wintering regions, estimating migration distances, and quantifying relative forest loss. Using data from light-level geolocator and GPS tags, we found that breeding birds from the relatively robust coastal population in the San Francisco Bay area wintered predominantly in western Mexico (n = 18), whereas the far rarer breeding birds from two inland populations that occur near one another in the Sierra Nevada and southern Cascades mountain ranges migrated to farther wintering destinations, with birds from the Lassen region (n = 5) predominantly going to Central America and birds from the Tahoe region (n = 7) predominantly to South America. Landscape-level relative forest loss was greater in the breeding and wintering regions of the two Cascade-Sierra populations than those of coastal birds. Longer migration distances and greater exposure to recent forest loss suggest greater current vulnerability of Cascade-Sierra birds. Our results demonstrate that for some species, quantifying migration distances and destinations across relatively small distances among breeding populations (in this case, 140-250 km apart) can identify dramatically different vulnerabilities that need to be considered in conservation planning.

Light-level geolocator analyses: A user's guide.

Light-level geolocator tags use ambient light recordings to estimate the whereabouts of an individual over the time it carried the device. Over the past decade, these tags have emerged as an important tool and have been used extensively for tracking animal migrations, most commonly small birds. Analysing geolocator data can be daunting to new and experienced scientists alike. Over the past decades, several methods with fundamental differences in the analytical approach have been developed to cope with the various caveats and the often complicated data. Here, we explain the concepts behind the analyses of geolocator data and provide a practical guide for the common steps encompassing most analyses - annotation of twilights, calibration, estimating and refining locations, and extraction of movement patterns - describing good practices and common pitfalls for each step. We discuss criteria for deciding whether or not geolocators can answer proposed research questions, provide guidance in choosing an appropriate analysis method and introduce key features of the newest open-source analysis tools. We provide advice for how to interpret and report results, highlighting parameters that should be reported in publications and included in data archiving. Finally, we introduce a comprehensive supplementary online manual that applies the concepts to several datasets, demonstrates the use of open-source analysis tools with step-by-step instructions and code and details our recommendations for interpreting, reporting and archiving.

Carbon sequestration in riparian forests: A global synthesis and meta-analysis.

Restoration of deforested and degraded landscapes is a globally recognized strategy to sequester carbon, improve ecological integrity, conserve biodiversity, and provide additional benefits to human health and well-being. Investment in riparian forest restoration has received relatively little attention, in part due to their relatively small spatial extent. Yet, riparian forest restoration may be a particularly valuable strategy because riparian forests have the potential for rapid carbon sequestration, are hotspots of biodiversity, and provide numerous valuable ecosystem services. To inform this strategy, we conducted a global synthesis and meta-analysis to identify general patterns of carbon stock accumulation in riparian forests. We compiled riparian biomass and soil carbon stock data from 117 publications, reports, and unpublished data sets. We then modeled the change in carbon stock as a function of vegetation age, considering effects of climate and whether or not the riparian forest had been actively planted. On average, our models predicted that the establishment of riparian forest will more than triple the baseline, unforested soil carbon stock, and that riparian forests hold on average 68-158 Mg C/ha in biomass at maturity, with the highest values in relatively warm and wet climates. We also found that actively planting riparian forest substantially jump-starts the biomass carbon accumulation, with initial growth rates more than double those of naturally regenerating riparian forest. Our results demonstrate that carbon sequestration should be considered a strong co-benefit of riparian restoration, and that increasing the pace and scale of riparian forest restoration may be a valuable investment providing both immediate carbon sequestration value and long-term ecosystem service returns.

Inherent limits of light-level geolocation may lead to over-interpretation.

In their 2015 Current Biology paper, Streby et al.[1] reported that Golden-winged Warblers (Vermivora chrysoptera), which had just migrated to their breeding location in eastern Tennessee, performed a facultative and up to ">1,500 km roundtrip" to the Gulf of Mexico to avoid a severe tornadic storm. From light-level geolocator data, wherein geographical locations are estimated via the timing of sunrise and sunset, Streby et al.[1] concluded that the warblers had evacuated their breeding area approximately 24 hours before the storm and returned about five days later. The authors presented this finding as evidence that migratory birds avoid severe storms by temporarily moving long-distances. However, the tracking method employed by Streby et al.[1] is prone to considerable error and uncertainty. Here, we argue that this interpretation of the data oversteps the limits of the used tracking technique. By calculating the expected geographical error range for the tracked birds, we demonstrate that the hypothesized movements fell well within the geolocators' inherent error range for this species and that such deviations in latitude occur frequently even if individuals remain stationary.

Melting barriers to faunal exchange across ocean basins.

Accelerated loss of sea ice in the Arctic is opening routes connecting the Atlantic and Pacific Oceans for longer periods each year. These changes may increase the ease and frequency with which marine birds and mammals move between the Pacific and Atlantic Ocean basins. Indeed, recent observations of birds and mammals suggest these movements have intensified in recent decades. Reconnection of the Pacific and Atlantic Ocean basins will present both challenges to marine ecosystem conservation and an unprecedented opportunity to examine the ecological and evolutionary consequences of interoceanic faunal exchange in real time. To understand these changes and implement effective conservation of marine ecosystems, we need to further develop modeling efforts to predict the rate of dispersal and consequences of faunal exchange. These predictions can be tested by closely monitoring wildlife dispersal through the Arctic Ocean and using modern methods to explore the ecological and evolutionary consequences of these movements.

A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity.

BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. RESULTS: We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America. CONCLUSIONS: We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.

Current Status of Western Yellow-Billed Cuckoo along the Sacramento and Feather Rivers, California.

To evaluate the current status of the western population of the Yellow-billed Cuckoo (Coccyzus americanus) along the Sacramento and Feather rivers in California's Sacramento Valley, we conducted extensive call playback surveys in 2012 and 2013. We also quantified the amount and distribution of potential habitat. Our survey transects were randomly located and spatially balanced to sample representative areas of the potential habitat. We estimated that the total area of potential habitat was 8,134 ha along the Sacramento River and 2,052 ha along the Feather River, for a total of 10,186 ha. Large-scale restoration efforts have created potential habitat along both of these rivers. Despite this increase in the amount of habitat, the number of cuckoos we detected was extremely low. There were 8 detection occasions in 2012 and 10 occasions in 2013 on the Sacramento River, in both restored and remnant habitat. We had no detections on the Feather River in either year. We compared our results to 10 historic studies from as far back as 1972 and found that the Yellow-billed Cuckoo had unprecedentedly low numbers in 2010, 2012, and 2013. The current limiting factor for the Yellow-billed Cuckoo in the Sacramento Valley is likely not the amount of appropriate vegetation, as restoration has created more habitat over the last 30 years. Reasons for the cuckoo decline on the Sacramento and Feather rivers are unclear.

Projecting demographic responses to climate change: adult and juvenile survival respond differently to direct and indirect effects of weather in a passerine population.

Few studies have quantitatively projected changes in demography in response to climate change, yet doing so can provide important insights into the processes that may lead to population declines and changes in species distributions. Using a long-term mark-recapture data set, we examined the influence of multiple direct and indirect effects of weather on adult and juvenile survival for a population of Song Sparrows (Melospiza melodia) in California. We found evidence for a positive, direct effect of winter temperature on adult survival, and a positive, indirect effect of prior rainy season precipitation on juvenile survival, which was consistent with an effect of precipitation on food availability during the breeding season. We used these relationships, and climate projections of significantly warmer and slightly drier winter weather by the year 2100, to project a significant increase in mean adult survival (12-17%) and a slight decrease in mean juvenile survival (4-6%) under the B1 and A2 climate change scenarios. Together with results from previous studies on seasonal fecundity and postfledging survival in this population, we integrated these results in a population model and projected increases in the population growth rate under both climate change scenarios. Our results underscore the importance of considering multiple, direct, and indirect effects of weather throughout the annual cycle, as well as differences in the responses of each life stage to climate change. Projecting demographic responses to climate change can identify not only how populations will be affected by climate change but also indicate the demographic process(es) and specific mechanisms that may be responsible. This information can, in turn, inform climate change adaptation plans, help prioritize future research, and identify where limited conservation resources will be most effectively and efficiently spent.

Population dynamics of Hawaiian seabird colonies vulnerable to sea-level rise.

Globally, seabirds are vulnerable to anthropogenic threats both at sea and on land. Seabirds typically nest colonially and show strong fidelity to natal colonies, and such colonies on low-lying islands may be threatened by sea-level rise. We used French Frigate Shoals, the largest atoll in the Hawaiian Archipelago, as a case study to explore the population dynamics of seabird colonies and the potential effects sea-level rise may have on these rookeries. We compiled historic observations, a 30-year time series of seabird population abundance, lidar-derived elevations, and aerial imagery of all the islands of French Frigate Shoals. To estimate the population dynamics of 8 species of breeding seabirds on Tern Island from 1980 to 2009, we used a Gompertz model with a Bayesian approach to infer population growth rates, density dependence, process variation, and observation error. All species increased in abundance, in a pattern that provided evidence of density dependence. Great Frigatebirds (Fregata minor), Masked Boobies (Sula dactylatra), Red-tailed Tropicbirds (Phaethon rubricauda), Spectacled Terns (Onychoprion lunatus), and White Terns (Gygis alba) are likely at carrying capacity. Density dependence may exacerbate the effects of sea-level rise on seabirds because populations near carrying capacity on an island will be more negatively affected than populations with room for growth. We projected 12% of French Frigate Shoals will be inundated if sea level rises 1 m and 28% if sea level rises 2 m. Spectacled Terns and shrub-nesting species are especially vulnerable to sea-level rise, but seawalls and habitat restoration may mitigate the effects of sea-level rise. Losses of seabird nesting habitat may be substantial in the Hawaiian Islands by 2100 if sea levels rise 2 m. Restoration of higher-elevation seabird colonies represent a more enduring conservation solution for Pacific seabirds.

Establishing the breeding provenance of a temperate-wintering North American passerine, the Golden-crowned Sparrow, using light-level geolocation.

The migratory biology and connectivity of passerines remains poorly known, even for those that move primarily within the temperate zone. We used light-level geolocators to describe the migratory geography of a North American temperate migrant passerine. From February to March of 2010, we attached geolocator tags to 33 Golden-crowned Sparrows (Zonotrichia atricapilla) wintering on the central coast of California, USA, and recovered four tags the following winter (October to December 2010). We used a bayesian state-space model to estimate the most likely breeding locations. All four birds spent the breeding season on the coast of the Gulf of Alaska. These locations spanned approximately 1200 kilometers, and none of the individuals bred in the same location. Speed of migration was nearly twice as fast during spring than fall. The return rate of birds tagged the previous season (33%) was similar to that of control birds (39%), but comparing return rates was complicated because 7 of 11 returning birds had lost their tags. For birds that we recaptured before spring migration, we found no significant difference in mass change between tagged and control birds. Our results provide insight into the previously-unknown breeding provenance of a wintering population of Golden-crowned Sparrows and provide more evidence of the contributions that light-level geolocation can make to our understanding of the migratory geography of small passerines.

A climate change vulnerability assessment of California's at-risk birds.

Conservationists must develop new strategies and adapt existing tools to address the consequences of anthropogenic climate change. To support statewide climate change adaptation, we developed a framework for assessing climate change vulnerability of California's at-risk birds and integrating it into the existing California Bird Species of Special Concern list. We defined climate vulnerability as the amount of evidence that climate change will negatively impact a population. We quantified climate vulnerability by scoring sensitivity (intrinsic characteristics of an organism that make it vulnerable) and exposure (the magnitude of climate change expected) for each taxon. Using the combined sensitivity and exposure scores as an index, we ranked 358 avian taxa, and classified 128 as vulnerable to climate change. Birds associated with wetlands had the largest representation on the list relative to other habitat groups. Of the 29 state or federally listed taxa, 21 were also classified as climate vulnerable, further raising their conservation concern. Integrating climate vulnerability and California's Bird Species of Special Concern list resulted in the addition of five taxa and an increase in priority rank for ten. Our process illustrates a simple, immediate action that can be taken to inform climate change adaptation strategies for wildlife.

Riparian bird response to vegetation structure: a multiscale analysis using LiDAR measurements of canopy height.

The ability to measure vegetation structure at spatial scales that are biologically meaningful for wildlife is often limited because information about the spatial scale of habitat selection is lacking and there are logistical constraints to measuring vegetation structure at ever larger spatial scales. To address this challenge, we used LiDAR-derived measurements of vegetation canopy height to quantify habitat associations of riparian birds at the Cosumnes River Preserve in central California, USA. Our objectives were (1) to evaluate the utility of LiDAR (light detection and ranging) measurements for describing habitat associations of riparian passerine birds, and (2) to capitalize on the ease with which LiDAR measurements can be summarized at multiple spatial scales to evaluate the predictive performance of vegetation measurements across spatial scales from 0.2 to 50 ha. At each location where we conducted point-count surveys of the avian community, we summarized the mean and coefficient of variation of canopy height measured at five spatial scales (0.2, 0.8, 3.1, 12.6, and 50.2 ha). For each of these spatial scales, we used stepwise model selection to identify the best logistic-regression model describing patterns of occurrence for 16 species of passerine birds that were sufficiently abundant for analysis. We then used area-under-the-curve (AUC) values to identify models that performed well (AUC > 0.75) on a temporally independent data set. Of the 16 species, 10 species had logistic-regression models with AUC values > 0.75. For six of these species, AUC values were highest for the models with vegetation measurements at the 0.2-3 ha scale. For the other four species, AUC values were highest for the model with vegetation variables measured at the 50-ha scale. These results illustrate the utility of using LiDAR-derived measurements of vegetation to understand habitat associations of riparian birds and underscore the importance of using multiscale approaches to modeling wildlife habitat use.