Avian Conservation and Management

INTRODUCTION

Population estimates are fundamental for assessing species status, identifying priority conservation areas, and informing management actions. When collected over appropriate spatial and temporal scales, such estimates also provide the basis for evaluating population trends and extinction risk (Rosenberg et al. 2019). However, for many species, and particularly for those occupying remote or under-surveyed habitats, population size remains poorly quantified, limiting the effectiveness of conservation planning.

Migratory shorebirds exemplify this challenge. Despite extensive monitoring across portions of their annual cycle, many species show sustained regional and flyway-level declines (Smith et al. 2023), while remaining poorly quantified in key regions. Large-scale monitoring initiatives such as the Neotropical Wetland Census (NWC), the International Shorebird Survey (ISS), and the Migratory Shorebird Project (MSP) have substantially improved our understanding of these trends (e.g., Reiter et al. 2025). Nevertheless, significant knowledge gaps persist, particularly in habitats that are difficult to access or that have historically received less attention (Reiter et al. 2025). Rocky shorelines represent one such habitat where standardized monitoring remains limited, despite its importance for several species (Senner and McCaffery 2020).

In the Americas, the Surfbird (Calidris virgata) is a Nearctic-Neotropical migratory shorebird that breeds in the mountains of Alaska and in a small adjacent area of northwestern Canada, and that is strongly associated with rocky intertidal habitats during the non-breeding season, which occur along the Pacific coast of the Americas from Alaska to southern Chile (Senner and McCaffery 2020). Current global assessments list the Surfbird as Least Concern (BirdLife International 2024), yet recent trend analyses suggest an overall declining trajectory (Fink et al. 2023). Although these rocky shorelines are spatially restricted and often support predictable aggregations (conditions that facilitate targeted population assessments), few quantitative data are available for the species. To date, there is very limited information available on population size, and the most commonly cited estimate is approximately 70,000 individuals, a figure that has remained effectively unchanged for two decades because of the lack of systematic surveys across the species’ range (Morrison et al. 2001, Morrison et al. 2006, Andres et al. 2012). Consequently, both population size and spatial distribution remain poorly understood, particularly toward the southern extent of the non-breeding range.

In South America, the population has been estimated through citizen-science surveys undertaken in 2014 that were analyzed and published as atlases for Peru and Chile (Senner and Angulo-Pratolongo 2014, García-Walther et al. 2017). However, these surveys were conducted primarily at sandy beach sites in both countries. Consequently, the population estimation for Peru was 20 birds (Senner and Angulo-Pratolongo 2014), whereas for Chile, a population size of 58,400 birds was estimated (García-Walther et al. 2017). Because rocky intertidal habitat, the species’ principal non-breeding habitat, was under-represented in these surveys, uncertainty remains regarding population size and spatial distribution, and an estimate targeting rocky shores is needed to complement these earlier assessments.

Addressing these gaps is essential for improving global population assessments and identifying key regions for conservation. Chile, with its extensive and heterogeneous rocky coastline, represents a potentially important but under-quantified component of the species’ non-breeding range. However, no estimate on the basis of surveys targeting rocky intertidal habitat currently exists for the country.

We provide the first estimate of the non-breeding Surfbird population along the rocky coast of continental Chile (north of 43° S) using coordinated surveys of rocky shoreline habitats and a Bayesian hierarchical modeling framework. By integrating structured citizen-science data with models that account for sparse detections and spatial variation, we aim to (1) estimate population size at national and regional scales, (2) describe spatial patterns in abundance, and (3) evaluate the contribution of Chile’s rocky coastline to the Surfbird’s global population. More broadly, this study demonstrates how combining citizen-science networks with hierarchical models can help address persistent data gaps for sparsely distributed species across large spatial scales.

METHODS

Study area

We surveyed rocky shoreline habitat between the Arica y Parinacota region (18° S) in northern Chile and the Los Lagos region (43° S) in southern Chile during February 2025. The study area included rocky platforms, and pebble and gravel beaches, which together constitute the non-breeding habitat of the Surfbird. We restricted surveys to the coast north of 43° S because Surfbird records south of this latitude are very scarce, shorebird records in the Patagonian fjords are sparse in general, and this remote coastline is logistically difficult to access; the same latitudinal limit was applied by García-Walther et al. (2017).

Fieldwork

We classified the coastline into rocky shores and sandy beaches, and then generated a set of 2808 candidate 500 m transects randomly placed in rocky coastal areas and distributed so as to represent rocky habitat within each region. Because the rocky coastline is not continuous, we drew random points from these candidate transects to define a sampling frame of 1279 points. From this frame, we prioritized re-surveying transects that had been censused during a pilot survey in the austral summer of 2024, and added further transects so as to cover at least 1% of each region’s rocky shoreline. Observers validated habitat classifications during fieldwork, confirming that the mapped transects matched on-site conditions.

Survey methods

Only shorebirds actively using the area (feeding, resting, or present within transect boundaries) were counted; birds flying over or outside of the transect were excluded. Although all shorebird species encountered were recorded, here we focus on the Surfbird.

Surveys were conducted by a network of trained participants, including seven team members of the Red de Observadores de Aves y Vida Silvestre de Chile (ROC) and 31 volunteers with previous experience in shorebird surveys, distributed along the Chilean coast. All observers followed a standardized monitoring protocol and surveyed pre-assigned transects.

Surveys were carried out between 9 and 23 February 2025 along 500 m transects one to three hours before or after low tide, when birds are most likely to be actively using the intertidal zone. For each survey, observers recorded start and end coordinates, start time, duration, number of observers, and distance walked.

All checklists were submitted to an eBird account specific to this project, including transects with zero detections. Of the transects visited in the field, those that proved inaccessible (e.g., because of terrain or restricted access) were documented separately and excluded from analysis to avoid their misinterpretation as absences.

Tidal data

Considering that surveys might be biased by the tides, tide height was included as a potential covariate in models, because shorebird detectability and habitat availability may vary with tidal stage (Granadeiro et al. 2006). We obtained predicted tide heights for the nearest tidal station to each survey location from the Chilean navy’s hydrographic service (SHOA). Tide height at the time of each survey was interpolated from the predicted high and low tide times and heights using linear interpolation.

Statistical analysis

We analyzed Surfbird counts using a Bayesian zero-inflated negative binomial (ZINB) model implemented in R (R Core Team 2025) with the package brms (Bürkner 2017), which provides an interface to Stan for Bayesian inference (Carpenter et al. 2017). The ZINB distribution was chosen to account for the high proportion of zero counts (86% of checklists), which is characteristic of sparsely distributed species, as well as for overdispersion in positive counts. The model structure was: Count ~ log(Duration) + (1|Region), where log(Duration) is the natural logarithm of survey duration in minutes, reflecting the expected diminishing returns in detection probability with increasing survey effort, and Region is a random intercept allowing abundance to vary among the 13 administrative regions of Chile. We evaluated alternative models, including additional covariates (tide height, transect length, number of observers, latitude), but these did not improve model fit (ΔLOO < 2) and were not retained.

We specified weakly informative priors: Normal(0, 2) for the intercept, Normal(0, 0.5) for the duration coefficient, and Exponential(2) for the regional random effect standard deviation. The exponential prior on the random effect variance provides regularization that shrinks regional estimates toward the global mean, which is appropriate when data are sparse in some regions (Gelman and Hill 2006). Models were fit using four chains of 10,000 iterations each (5000 warmup), with adapt_delta = 0.99 to reduce divergent transitions.

Population extrapolation

To estimate the total Surfbird abundance along Chile’s rocky coast, we generated a prediction grid consisting of points spaced at 500 m intervals along all mapped rocky shoreline segments. For coastline segments shorter than 500 m, a single point was placed at the midpoint. This yielded 5706 prediction points across all regions. For each prediction point, we predicted the expected Surfbird count under standardized survey conditions (30 min survey duration, i.e., log[30] = 3.40), approximately the mean survey duration in our data, so that predicted counts are directly comparable across points. Regional population estimates were obtained by summing predicted counts across all prediction points within each region.

Uncertainty in population estimates was quantified by extracting 8000 posterior draws of the expected count at each prediction point from the fitted model, then summing across points for each posterior draw to obtain a posterior distribution of regional and national totals. We report posterior means and 95% credible intervals (CrI) from the 2.5th and 97.5th percentiles of these distributions.

RESULTS

Survey effort

We surveyed 147 transects totaling 84.5 km of rocky coastline across 13 regions, representing 3.0% of Chile’s 2853 km mapped as rocky coast north of 43° S (Table 1). Survey coverage varied regionally, ranging from 1.2% in Atacama to 54.9% in Arica y Parinacota. A subset of these transects had also been surveyed during a pilot census in the austral summer of 2024, allowing for a qualitative comparison between years.

Surfbird distribution and abundance

Surfbirds were recorded at 21 transects in eight of the 13 regions surveyed, with a total count of 1218 individuals (Fig. 1). The highest counts were in Tarapacá (472 individuals across 10 transects, range 3–180 per transect) and Arica y Parinacota (323 individuals at two transects, counts of 59 and 264 individuals). Single transects in Antofagasta, Atacama, Valparaíso, and Los Ríos yielded counts of 7–65 individuals. In the southern portion of the study area, Surfbirds were recorded at three transects in Biobío (132 individuals total) and two transects in Araucanía (170 individuals total, counts of 24 and 146 individuals per transect). The highest observed densities occurred in Araucanía (83.7 birds/km) and Arica y Parinacota (43.6 birds/km), whereas several central and southern regions (Coquimbo, O’Higgins, Maule, Ñuble, and Los Lagos) had no detections despite survey effort. Observed density across all surveyed transects averaged 14.4 birds/km.

Model results

The Bayesian ZINB model converged with all R-hat values equal to 1.00 and adequate effective sample sizes (Bulk ESS > 1300 for all parameters), although five divergent transitions occurred after warmup. The estimated zero-inflation probability was 0.82 (95% CrI: 0.64–0.90), indicating that approximately 82% of zero counts represent structural zeros (sites where Surfbirds are absent) rather than sampling zeros (sites where Surfbirds were present but undetected). Log-transformed survey duration had a positive effect on counts (β = 0.44, 95% CrI: −0.19 to 1.06), suggesting longer surveys detected more birds when present, although the credible interval overlapped zero. The regional random effect standard deviation was 0.78 (95% CrI: 0.02–3.25), indicating substantial but highly uncertain variation among regions. The negative binomial shape parameter was 1.23 (95% CrI: 0.25–2.52), confirming overdispersion in the count data.

Population estimates

The model estimated a total Surfbird non-breeding population of 45,000 individuals (posterior median; 95% CrI: 20,000–107,000) along Chile’s rocky coastline (Table 1, Fig. 1B). The posterior mean was 51,500 individuals. Regional estimates were associated with substantial uncertainty, with wide credible intervals particularly for regions with extensive coastline but few or no detections.

The regions with the highest estimated populations were Atacama (10,843; 95% CrI: 2448–31,870), Coquimbo (8218; 95% CrI: 6–24,526), and Antofagasta (7754; 95% CrI: 584–20,349). These high estimates reflect the extensive rocky coastline in these regions (489–556 km) rather than high observed densities; indeed, Coquimbo had zero detections. In contrast, Arica had the highest observed density (43.6 birds/km), but a relatively modest population estimate (521; 95% CrI: 143–1944) because of its short rocky coastline (13.5 km).

Model-predicted densities were relatively uniform across regions (range: 14–39 birds/km of coastline) because of the regularizing effect of the hierarchical prior, which shrinks regional estimates toward the global mean when local data are sparse. Regions with no detections (Coquimbo, Los Lagos, Maule, O’Higgins, Ñuble) received non-zero population estimates on the basis of the global mean and their coastline extent, although these estimates had the highest uncertainty as indicated by credible intervals approaching or including zero (Fig. 1B).

DISCUSSION

Our results provide the first estimate of the non-breeding population of the Surfbird in Chile on the basis of surveys targeting its rocky intertidal habitat, and indicate that the country supports a substantial portion of the species’ global population. With a median estimate of approximately 45,000 individuals concentrated mainly in northern Chile, including Arica y Parinacota, Antofagasta, and Tarapacá, Chile alone may harbor a large fraction of the global population of ~70,000 individuals (Morrison et al. 2001, Andres et al. 2012). Notably, our estimate is of the same order as the ~58,400 individuals reported by the Atlas de las Aves Playeras de Chile (García-Walther et al. 2017), which was also concentrated in the north, despite the two studies using different sampling designs (beach-focused versus rocky habitat-targeted) and being conducted 11 years apart (February 2014 versus February 2025). This convergence strengthens confidence that Chile supports a major component of the species’ non-breeding population, and provides no indication of a steep change between the two periods. Our results corroborate earlier work; together, these independent estimates suggest that the global total of ~70,000, which is largely unchanged for two decades, may be too low, or otherwise in need of revision.

Chile as a key non-breeding stronghold

Both estimates suggest that Chile is a major non-breeding stronghold for the Surfbird within the Pacific Americas flyway. The commonly cited global total of ~70,000 individuals is derived largely from counts made on the species’ breeding grounds and migratory staging areas in Alaska (Morrison et al. 2001, Senner and McCaffery 2020), rather than from surveys of its non-breeding range. That two independent, non-breeding-season estimates for Chile alone now approach or exceed this figure suggest that the global total may need to be revised upward.

Although higher densities were observed in northern Chile, model-based estimates indicate that suitable habitat along much of the coastline may support non-trivial numbers of birds, including regions with few or no detections. This pattern may reflect both underlying spatial variation in habitat suitability and the challenges of detecting sparsely distributed species across large spatial extents. We also caution that, because surveys targeted rocky intertidal habitat, birds using adjacent sandy or mixed substrates would not have been counted; our estimate may therefore be conservative for the small proportion of the population that occurs away from rocky shores.

These findings highlight the need for coordinated range-wide monitoring of migratory shorebirds. Integrating standardized surveys across regions and habitats will be essential to refine global population estimates, evaluate population trends, and support robust conservation assessments.

Strengths, limitations, and inference from sparse data

Our study demonstrates the potential of combining structured citizen-science surveys with hierarchical modeling to estimate population size for sparsely distributed species across large spatial scales. Despite a high proportion of zero counts, zero-inflated Bayesian models are well suited to such data structures and allow for robust inference under conditions of patchy occurrence and overdispersion (Kéry and Royle 2016).

Uncertainty in our estimates remains substantial, and arises primarily from sparse detections and uneven spatial coverage. With detections at only 14% of transects, regional estimates rely partly on model-based extrapolation, resulting in partial pooling toward the global mean. Consequently, predicted densities are more homogeneous across regions than may be ecologically realistic. This is most evident in regions with extensive coastline but no detections, such as Coquimbo, where the model nonetheless returns a substantial estimate; such values should be regarded as habitat-based expectations rather than empirically confirmed abundances, and may also indicate that not all mapped rocky shoreline is of equal value to Surfbirds.

Our extrapolation assumes that surveyed transects are representative of unsurveyed coastline within each region. Although only a small proportion of the total coastline was surveyed, transects were distributed across the full latitudinal extent of the study area (18°–43° S), capturing broad environmental variation. Nevertheless, increasing spatial coverage remains a key priority for improving the precision of future estimates.

We did not explicitly model detection probability, which may influence absolute abundance estimates. However, surveys were standardized in timing, protocol, and observer experience to reduce variability, and the modeling framework accounts for overdispersion and excess zeros. Importantly, our primary inference concerns the magnitude and spatial distribution of population size at large scales, rather than precise local densities. Future work incorporating repeated surveys or distance sampling would allow explicit separation of detection and ecological processes (Royle 2004, Kéry and Royle 2016).

Although our analysis is based on a single season, a subset of transects was also surveyed during a 2024 pilot census, and counts were broadly consistent between years; combined with the agreement between our estimate and that of García-Walther et al. (2017) over an 11-year interval, this provides no indication of large inter-annual swings, although dedicated repeat surveys would be required to quantify temporal variation.

Human activity may also influence Surfbird distribution through effects on prey availability. In particular, harvesting of intertidal kelps (Lessonia spp.) can alter benthic communities associated with holdfasts, which support diverse invertebrate assemblages (Cancino and Santelices 1984, Bularz et al. 2022). Field observations during our surveys indicated extensive harvesting in some regions, suggesting that habitat condition may influence local carrying capacity. Incorporating habitat quality metrics into future surveys could improve model predictions and help identify areas where anthropogenic impacts limit abundance.

Conservation implications and future directions

Our findings carry several conservation and monitoring implications. They highlight Chile as a potentially critical region for the conservation of the Surfbird, and underscore the importance of rocky intertidal habitats, which remain underrepresented in many monitoring programs. Identifying and formally recognizing key sites along the Chilean coast, through national protection measures or international initiatives, could play an important role in safeguarding a substantial portion of the species’ population.

Building on the present survey, we recommend that this census be repeated at regular intervals (e.g., every three to five years, or annually at a core set of sites) to establish a baseline for detecting trends. Future surveys would benefit from increased coverage in under-sampled regions, finer stratification of rocky-shore habitat according to its suitability for Surfbirds, a strategy for accessing or accounting for transects that prove inaccessible, and the systematic recording of disturbances and human activities, such as kelp harvesting, so that habitat condition can be tracked over time. The methods applied here are well suited to such a recurring, flyway-aligned monitoring scheme.

At a broader scale, our results emphasize the need for coordinated, flyway-level monitoring efforts that integrate data across countries and habitats. Establishing collaborative networks spanning the Pacific coast of the Americas would facilitate standardized surveys, improve data comparability, and support more robust global assessments.

Ultimately, refining the global population estimate for the Surfbird will require integrating standardized counts across the full extent of its range and annual cycle because the species is distributed across multiple regions and no single localized survey can capture the global total on its own. Such efforts will be essential not only for reassessing the species’ conservation status, but also for understanding the ecological processes shaping its distribution in a rapidly changing coastal environment.

LITERATURE CITED

Andres, B. A., P. A. Smith, R. I. G. Morrison, C. L. Gratto-Trevor, S. C. Brown, and C. A. Friis. 2012. Population estimates of North American shorebirds, 2012. Wader Study Group Bulletin 119(3):178-194.

BirdLife International 2024. Surfbird Calidris virgata. BirdLife International, Cambridge, U.K. https://dx.doi.org/10.2305/IUCN.UK.2024-2.RLTS.T22693356A154445909.en

Bularz, B., M. Fernández, M. D. Subida, E. A. Wieters, and A. Pérez-Matus. 2022. Effects of harvesting on subtidal kelp forests (Lessonia trabeculata) in central Chile. Ecosphere 13:e3958. https://doi.org/10.1002/ecs2.3958

Bürkner, P.-C. 2017. brms: an R package for Bayesian multilevel models using Stan. Journal of Statistical Software 80:1-28.

Cancino, J., and B. Santelices. 1984. Ecological importance of holdfasts of Lessonia nigrescens Bory (Phaeophyta) in Central Chile. Revista Chilena de Historia Natural 57:23-33.

Carpenter, B., A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell. 2017. Stan: a probabilistic programming language. Journal of Statistical Software 76:1-32.

Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, S. Ligocki, O. Robinson, W. Hochachka, L. Jaromczyk, C. Crowley, K. Dunham, et al. 2023. eBird status and trends, data version: 2022. Cornell Lab of Ornithology, Ithaca, New York, USA. https://science.ebird.org/status-and-trends/data-access/ebird-status-and-trends-data-version-2022

García-Walther, J., N. Senner, H. Norambuena, and F. Schmitt. 2017. Atlas de las Aves Playeras de Chile: sitios importantes para su conservación. Red de Observadores de Aves y Vida Silvestre de Chile, Universidad Santo Tomás, Bogota, Columbia.

Gelman, A., and J. Hill. 2006. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, U.K. https://doi.org/10.1017/CBO9780511790942

Granadeiro, J. P., M. P. Dias, R. C. Martins, and J. M. Palmeirim. 2006. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29(3):293-300. https://doi.org/10.1016/j.actao.2005.11.008

Kéry, M., and J. A. Royle. 2016. Applied hierarchical modeling in ecology: analysis of distribution, abundance and species richness in R and BUGS. Volume 1. Academic Press, London, U.K. https://doi.org/10.1016/B978-0-12-801378-6.00001-1

Morrison, R. I. G., R. E. Gill Jr., B. A. Harrington, S. Skagen, G. W. Page, C. L. Gratto-Trevor, and S. M. Haig. 2001. Estimates of shorebird populations in North America. Occasional paper number 104. Canadian Wildlife Service, Ottawa, Ontario, Canada.

Morrison, R. I. G., B. J. McCaffery, R. E. Gill Jr., S. K. Skagen, S. L. Jones, G. W. Page, C. L. Gratto-Trevor, and B. A. Andres. 2006. Population estimates of North American shorebirds, 2006. Wader Study Group Bulletin 111:67-85.

R Core Team. 2025. R: a language and environment for statistical computing (Version 4.5.1). R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org

Reiter, M. E., C. M. Hickey, D. Eusse-González, E. Palacios, B. A. Barbaree, D. W. Bradley, F. Angulo, J. van Dort, G. Fernández, R. Clay, et al. 2025. Trends in nonbreeding shorebirds along the Pacific Americas Flyway. Ornithological Applications 128(1)1-13. https://doi.org/10.1093/ornithapp/duaf076

Rosenberg, K. V., A. M. Dokter, P. J. Blancher, J. R. Sauer, A. C. Smith, P. A. Smith, J. C. Stanton, A. Panjabi, L. Helft, M. Parr, et al. 2019. Decline of the North American avifauna. Science 366(6461):120-124. https://doi.org/10.1126/science.aaw1313

Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60(1):108-115. https://doi.org/10.1111/j.0006-341X.2004.00142.x

Senner, N., and F. Angulo-Pratolongo. 2014. Atlas de las aves playeras del Perú: sitios importantes para su conservación. Centro de Ornitología y Biodiversidad (CORBIDI), Lima, Peru.

Senner, S. E., and B. J. McCaffery. 2020. Surfbird (Calidris virgata). Version 1.0. in A. F. Poole and F. B. Gill, editors. Birds of the World. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.surfbi.01

Smith, P. A., A. C. Smith, B. Andres, C. M. Francis, B. Harrington, C. Friis, R. I. G. Morrison, J. Paquet, B. Winn, and S. Brown. 2023. Accelerating declines of North America’s shorebirds signal the need for urgent conservation action. Ornithological Applications 125(2):duad003. https://doi.org/10.1093/ornithapp/duad003