Research Paper

INTRODUCTION

Individuals in some species of long-distance migrant shorebirds are well-known to spend the (boreal) summer on low-latitude non-breeding areas rather than migrating to the Arctic to breed (termed “over-summering”). Most explanations invoke the idea that over-summering occurs when migration and/or breeding is unlikely to succeed (Summers et al. 1995). Factors such as parasite infection (McNeil et al. 1994, Wille and Klaassen 2022), poor condition, sterility, injury or illness (Wetmore 1927), sexual immaturity (Eisenmann 1951, Soto- Montoya et al. 2009, Ayala-Perez et al. 2021), inexperience or low foraging prowess (Puttick 1979, Hockey et al. 1998), low-quality plumage (Johnson and Johnson 1983), large carry-over effects (Piersma 2019), and predation danger (Ydenberg et al. 2022) have all been suggested as factors possibly lowering the expectation of breeding success.

Fernández et al. (2004) and O’Hara et al. (2005) were the first to make explicit the idea that shorebird over-summering might provide a fitness benefit offsetting the cost of foregoing a breeding opportunity. Over-summering birds have higher survival than migrants (Tavera et al. 2020), may be able to avoid carry-over effects contingent on migration (e.g., by allowing molt at a more advantageous time; O’Hara et al. 2002), and might be able to explore the non-breeding region to develop good local knowledge (Battley et al. 2020). For over-summering to be adaptive, such benefits must exceed the costs by a margin great enough to compensate in fitness terms for the foregone breeding season. Ydenberg et al. (2022) demonstrate that the magnitude of the survival advantage differs between Semipalmated (Calidris pusilla) and Western Sandpipers (C. mauri) over-summering at Paracas in Perú, as well as between age groups within each species, in a manner consistent with the strikingly different over-summering patterns of these groups. They point out that two large, independent studies both find that the survival of migrant Semipalmated Sandpipers over the 6-month breeding and migration season (April–September) is lower by a remarkable 0.15–0.17 than that of the closely-related Western Sandpiper. They suggest that the low survival of the former is attributable to the introductions after 1975 of Peregrines (Falco peregrinus) to the large and important Atlantic coast staging areas used by migrating shorebirds. Scores of Peregrine pairs now breed in the Bay of Fundy (Hope et al. 2020), and along the mid-Atlantic coastal plain (Virginia, Delaware, Maryland, New Jersey; Watts et al. 2015), regions historically free of breeding Peregrines. This rising migratory danger has made over-summering increasingly advantageous.

Semipalmated and Western Sandpipers are well-documented to have made safety-enhancing adjustments to migratory behavior (Ydenberg et al. 2004, Hope et al. 2020), and even wing morphology (Ydenberg et al. 2023) as falcon numbers rose after the mid-1970s (Ydenberg et al. 2017). Over-summering can also be considered a form of anti-predator behavior that avoids all migratory exposure to predators. In a large data set collected in 2011–2017 at Paracas, Perú, Tavera et al. (2020) estimated over-summering by 19% of adult and 28% of yearling Semipalmated Sandpipers. Unfortunately, there is little historical information with which to evaluate the hypothesis that historical levels were lower. In the most detailed account, McNeil (1970, see his Fig. 24) reports low numbers (~10%) of this species present in summer in Venezuela. Two accounts from Brazil report levels of 0% (Barbieri 2007: years 2003–2004) and 8% (Fedrizzi et al. 2004: years 1990–1997). (In each case the value reported is the number over-summering as a percentage of that present in winter. No age-specific details are given.) In Suriname, Haverschmidt (1955) reports that “many” remain throughout the northern summer, and Spaans (1984) reports “mass over-summering” by yearlings, but neither gives any details on ages or numbers. Rice et al. (2007) reports that no Semipalmated Sandpipers over-summer in Puerto Rico. Low levels of over-summering are reported for small (< 50 g) shorebird species in surveys made during the 1970s and 1980s in South Africa (0.6–5.2%; Summers et al. 1995). These data suggest that over-summering was undertaken historically by a small fraction of individuals in small-bodied shorebird species. The reported rates are all lower than that currently measured for Semipalmated Sandpipers at Paracas.

Shorebirds over-summering in the southern hemisphere non-breeding regions do so unseen by studies in the northern hemisphere (which constitute the large majority), with the consequence that its conservation significance may be overlooked (Martínez-Curci et al. 2020, Navedo and Ruiz 2020, Tavera et al. 2020). For migrant species, events at one location and life history stage are inextricably linked to others (Sheehy et al. 2010), and intermittent breeding should be explicitly considered in demographic models, especially in declining populations (Lee et al. 2017, Öst et al. 2018). Nor has over-summering been the subject of much theoretical analysis (Shaw and Levin 2011, 2013). Here we develop a matrix population model to investigate the effect of over-summering on shorebird demography. Our intent is to investigate the degree to which the hypothesized recent increase in over-summering might be able to have contributed to the large decline (~80%) measured in Semipalmated Sandpipers since 1980 (Morrison et al. 2012).

METHODS

Model description

The model developed here simulates a population of Semipalmated Sandpipers, based on parameters measured at Paracas, Perú (Tavera 2020, Tavera et al. 2020), a non-breeding location. Several thousand Semipalmated Sandpipers are present at Paracas, including birds from eastern, central, and western breeding populations (Tavera et al. 2016). Our approach was inspired by Hitchcock and Gratto-Trevor’s (1997) stage-structured model of a breeding population of Semipalmated Sandpipers at La Pérouse Bay in Arctic Canada, which accurately simulated the decline censused at that location from 1983 to 1993.

The simulated population is enumerated annually on 1 October, at the start of the annual cycle. This runs October to September, is divided into “winter” (alternatively “non-breeding”; October–March), and “summer” (alternatively “breeding”; April–September) seasons. Individuals are classified as “juveniles” during the summer of their birth, and are censused as such at the start of the subsequent annual cycle (at ~4 months of age). The juvenile classification extends through the non-breeding (October–March) season, until they by definition become “yearlings” on April 1 (at ~10 months of age). The yearling designation distinguishes them from the new generation of juveniles that will be born during the coming summer. In much shorebird literature these age classes are together termed juveniles or immatures, but are distinguished here because they face rather different circumstances. Yearlings by definition become “adults” at the start of their second non-breeding season (i.e., at ~16 months of age) and are censused as such. Adults that are older were born three or more breeding seasons earlier, and in each intervening summer either migrated to the Arctic, or over-summered on the non-breeding area.

The numbers of juveniles, over-summering adults, and migrating adults censused at the start of annual cycle t are represented by J(t), A_o(t), and A_m(t), respectively. The sub-scripts “o” and “m” refer to “over-summering” and “migration” during the preceding summer. The population’s demography is modeled with the matrix equation

(1)

Where

(2)

The derivation of matrix M is detailed in Appendix 1. Matrix equation (1) incorporates winter and summer survival probabilities, modified by the probabilities of over-summering or migrating, and reproduction by adults and yearlings. With terms in matrix M numbered as follows for ease of reference,

(3)

Equation (2) can be understood with the aid of the transition diagram (Fig. 1). Eggs are hatched in June on Arctic breeding grounds. (Note that the annual cycle begins 1 October, so the juveniles in the census of year t were born in year t - 1.) The parameter S_J represents the proportion of eggs hatched that survive the hatchling period and southward migration, to be censused as J(t) juveniles in October. Juveniles survive the subsequent 6-month non-breeding period (October–March) with probability S_JN by definition becoming yearlings at the transition to the summer season on 1 April.

Yearlings decide at this point whether they will migrate or over-summer. A proportion over-summers (P_Yo), surviving until the start of the following non-breeding season with probability S_Yo. These by definition become adults on 1 October and are tallied as part of the cohort A_o(t + 1) (term 1 in eqn 3). The remaining portion (1 – P_Yo) migrate and survive the migratory round-trip with probability S_Ym. (Survival in each direction is assumed to be √S_Ym, the square root of round-trip survival.) Each surviving northward migrant produces F_Y (female) hatchlings (term 3 in eqn 3). Yearlings that survive the entire migration graduate to adulthood at the end of the breeding season and are tallied as part of the adult cohort A_m(t + 1), at the start of the next annual cycle (term 2 in eqn 3).

Yearlings and adults that over-summered in annual cycle t survive from October until April of annual cycle t + 1 with probability S_AN. A portion of these (P_Ao) over-summer in annual cycle t + 1, survive the (boreal) summer with probability S_Ao, and are tallied as part of the adult cohort A_o (term 4) in October. Those that did not over-summer (part 1 – P_Ao) migrate, survive the north- and southward migrations and breeding period in the Arctic with probability S_Am, and are tallied as part of the adult cohort A_m in October (term 5). Survival of the northward migration is √S_Am, and surviving adults each produce F_A female hatchlings. These migrate to Paracas and are censused in October as juveniles, having survived the hatchling period and the southward migration from the Arctic with probability S_J (term 6).

Yearlings and adults that migrated and bred in annual cycle t survive the non-breeding period of annual cycle t + 1 with probability S_AN. A portion (P_Ao) over-summer, survive the summer period with probability S_Ao, and join the adult cohort A_o (term 7) in October. Those that did not over-summer (portion 1 – P_Ao) migrate, survive the round-trip migration and breeding period in the Arctic with probability S_Am, and join A_m in October (term 8). Adult migrants survive the northward trip to the Arctic with probability √S_Am, each producing F_A female offspring who are censused in October as juveniles, having survived the hatchling period and the southward migration from the Arctic with probability (S_J) (term 9).

Model analysis

Survival parameters were estimated in a population of Semipalmated Sandpipers at Paracas, Perú. Monthly visits of about a week were made in 2011 to 2015 to Paracas, during which 1963 Semipalmated Sandpipers were captured and marked, and 3229 re-sightings made. The analysis of these data is described in Tavera et al. (2020), and parameter value estimates are summarized in Table 1. The exception is the parameter S_J (survival from hatch through the initial southward migration), the value of which is unknown. Shorebird clutch size and hatching success have been measured in many field studies, but hatchlings are precocial and leave the nest soon after hatch, moving to feeding areas on the tundra. This makes it difficult to measure their progression through the post-hatching and migratory periods that precede their arrival at non-breeding areas. This entire process is compressed for model purposes into a single parameter, S_J, used in our analyses as a tuning parameter (see below).

Population trajectories were simulated using matrix equation 1. For numerical calculations each model run was initiated with 500 individuals in each age/migration class and run for 200 generations. A stable age distribution was quickly attained (< 10 generations). Parameters were numerically evaluated over generations 101–200. The population growth factor and age distribution calculated numerically closely match the eigenvector and eigenvalues derived analytically using the characteristic equation reported in Appendix 2.

To evaluate the impact of over-summering on population growth we used four “baseline” cases (Table 2) with assumed historical over-summering rates ranging from low (Baseline Case 1: adult 1%, yearling 5%) to high (Baseline Case 4: adult 10%, yearling 20%). For each case we adjusted (“tuned”) S_J to set λ equal to 1.000, in effect assuming that the historical rate of over-summering supported a stable population. In subsequent simulations, the assumed historical over-summering rates were raised to that measured at Paracas (yearlings 0.28, adults 0.19), the value of λ re-evaluated, and the change (%) in population size that would be observed over 20, 30, 40, and 50 years calculated. We also report the number of years required to reduce the population size by 50%.

It is plausible that the value of the parameter S_J (the probability that a chick hatched in the Arctic survives until arrival on non-breeding areas after southward migration) declined after Peregrines were introduced to staging sites. We modelled a decline in the value of S_J by reducing it in 0.01 increments, using Baseline Case 4 (adult/yearling over-summering 0.10/0.20), as well as the currently-measured over-summering rates. At each value of S_J we calculated the consequent value of λ, the change (%) in population size that would be observed over 20, 30, 40 and 50 years, and the number of years required for population size to decline by 50%.

To assess the effects of stochasticity, we drew for each parameter a value from a uniform distribution (range ±5%, ±10%, ±20%, or ±50%) centered on the value reported in Table 1. We entered these into eqn 1, ran the simulation for 100 years, and determined the population growth factor. We report the mean and distribution of population growth factors of 1000 such simulations.

RESULTS

The estimated value, 95% confidence interval, elasticity, and sensitivity of each parameter are reported in Table 1. As expected, the population growth factor (λ) is positively related to all survival parameters, and negatively related to the rate of over-summering. Also as expected, λ is more sensitive to adult than to yearling parameter values, and more sensitive to the survival of migrants than of over-summering birds. The population growth factor λ is most sensitive to changes in adult non-breeding survival.

To assess the impact of an increase in over-summering rate the value of survival from hatch until arrival at non-breeding was tuned to set λ to 1.000 in each of the four baseline historical cases. The tuning value varies with the over-summering rate, ranging from 0.4743 in the lowest (Baseline Case 1) to 0.5017 in the highest (Baseline Case 4; see Table 2). When the level of over-summering is raised from the presumed historical level to that currently measured at Paracas (adults 19%; yearlings 28%), the population growth factor λ falls below 1.000. The impact is surprisingly large. Even for Baseline Case 4 (the highest historical level of over-summering), the population size over 20–50 years would be reduced by 29.3–58.0%, and only 41 years would be required to reduce the population size by half. The impact is even greater for Baseline Case 1 (the lowest historical level of over-summering): the population size over 20–50 years would be reduced by 49.5–81.8%, and only 21 years would be required to reduce population size by half. Baseline Cases 2 and 3 lie in between these values.

The sensitivity of the population growth factor to the value of S_J is 0.313 (Table 1). With the over-summering rate set to that in Baseline Case 4 (adult/yearling over-summering 0.10/0.20) the value of S_J required to tune λ to 1.000 is 0.5017 (Table 3). As S_J is lowered in 0.01 increments from 0.5100 to 0.4700 (i.e., beginning just above 0.5017), λ declines from 1.005 to 0.979. At the lowest level of S_J the population size falls by 34.3–65.0% over 10–50 years. The effect is stronger if based on the over-summering rate currently measured at Paracas (adult/yearling over-summering 0.19/0.28; Table 3). The value of S_J tuning λ to 1.000 is 0.5307 (which would make population growth strongly positive in any of the other scenarios modeled here). As S_J is dropped from 0.5400 to 0.4700 (0.01 increments) λ declines from 1.0055 to 0.9639, and the population declines over 10–50 years ranges from 52.1% to 84.1%. In the deterministic simulations (Table 2), the time required to halve the population size when the level of over-summering is raised from the presumed historical level to that currently measured at Paracas ranges from 21 to 41 years (Baseline Cases 1–4, respectively).

The effect of adding stochasticity is summarized in Table 4. With the value of S_J set to that tuning λ to 1.000, and other parameter values randomly drawn from uniform distributions (±5%, ±10%, ±20%, ±50%) about the measured value reported in Table 1, the median durations for population size to decrease by half are 127.9 y (±5%), 87.5 y (±10%), 46.5 y (±20%), and 11.4 y (±50%). These differ from each other because of the unequal effects of variation above and below the median (known as Jensen’s inequality). The 50% reduction time shortens with higher stochasticity because of the higher incidence of simulated populations going to extinction within the 100 year run. In all cases the resultant rate of growth is below 1.0.

DISCUSSION

The model presented here calculates the demographic impact of over-summering on the growth potential of a shorebird population. The basic finding that population growth rate is lowered by raising the level of over-summering was expected, but the magnitude of this effect is startling. If, for example, the over-summering rate of Semipalmated Sandpipers recently measured at Paracas (Tavera et al. 2020; yearlings 28%, adults 19%) had risen from a historical rate of 10% for yearlings and 5% for adults, the population growth factor (λ) would have fallen to 0.973. Assuming that the population had been stable at the historical level, it would with the higher over-summering rate be expected to have fallen by 42.5% over 20 years, and by 66.9% over 40 years (Table 2). The impact is even larger if the historical over-summering rate was lower.

We are unable to calculate the current value of λ with complete confidence because we lack an independent estimate of the value of S_J (juvenile survival from hatch to arrival on the non-breeding area). Using the current over-summering rates for yearlings and adults, the tuning value of S_J required to keep the model population size stable is 0.5307. This value would in every other scenario considered here result in strong positive population growth.

Most of the evidence indicates that Semipalmated Sandpipers have undergone a strong decline in numbers since 1980. For example, aerial counts made along the coast of French Guyana (the center of the wintering range) during the non-breeding season in 1980 were repeated in 2008–2010 by the same observer using the same methods (Morrison et al. 2012). These data show a large decline (~80%) in the wintering population. Breeding populations censused in the Arctic (Hitchcock and Gratto-Trevor 1997), trends estimated at migratory staging sites (Bart et al. 2007), and large-scale indices compiled from surveys at many staging sites (Smith et al. 2023) all conclude that the population of Semipalmated Sandpipers has fallen by a large fraction.

The population growth factor estimated by Weiser et al. (2020) based on western Arctic breeding populations of Semipalmated Sandpipers does not differ significantly from 1.0. This could be because their estimates have great uncertainty, or because (in contrast to eastern Arctic breeding populations), those in the western Arctic are not declining (Hicklin and Chardine 2012). Comparing Weiser et al.’s (2020) simulations (see their Fig. 3E) with the values of λ simulated here (Table 4) suggests that the former have greater uncertainty. Their values of λ range from about 1.75 to 1.1, while the distribution at the greatest level of uncertainty (±50%) reported in Table 4 has a lower bound of 0.8993 and an upper bound of 0.9429 (median 0.9229). However the methods used to make these estimates differ, and a direct comparison requires caution.

Two conditions are required for the over-summering hypothesis developed here to be able to explain (a portion of) the population decline. First, the current over-summering rate must exceed the historical level. This is consistent with all the historical evidence available, which indicates that older estimates of the over-summering rate are more similar to Baseline Case 1 or 2 rather than to Case 3 or 4. The data are scanty, but the quantitative papers all report over-summering rates of 10% or less.

A second condition is that compensatory demographic feedbacks are absent, or limited in their impact. In principle any of the parameters listed in Table 1 could have changed in a compensatory fashion. For example, adult non-breeding season survival S_AN (sensitivity of S_AN in Table 1 is 0.769) could have risen because of density-dependence as population numbers fell. Though the model developed here does not incorporate any of these possibilities, any compensatory changes that may have occurred are incorporated into the parameter estimates in Table 1, which are based on data collected in 2011–2017 (Tavera et al. 2020). If such changes occurred they may have lessened the rate of population decline, but they have not been powerful enough to offset it.

There are sound theoretical reasons to expect the level of over-summering by Semipalmated Sandpipers to have increased. The analyses of Shaw and Levin (2011, 2013) on delayed maturity and intermittent breeding in migrant species associate skipped breeding with low migratory survival. An “environmentally cued condition-based threshold” decision model (Tomkins and Hazel 2007) of shorebird over-summering (Ydenberg, in press) reaches the same conclusion. Two recent and independent estimates both show that the migratory survival of Semipalmated Sandpipers is lower by a large margin than of the closely-related and ecologically similar Western Sandpiper (summarized in Ydenberg et al. 2022).

Over-summering can be considered a form of anti-predator behavior, increasing survival at the expense of reproduction. In an illustrative example, DeWitt et al. (2019) found that porcupines Erethizon dorsatum had slower growth, delayed maturity, and reduced fecundity in regions of Wisconsin where populations of their specialist predator, fishers Pekania pennanti, had recovered. They hypothesize that as fishers increased in abundance porcupines became more cautious, which increased their survival but lowered the rate of population growth. Analogously, over-summering by Semipalmated Sandpipers improves individual survival but delays maturity and lowers breeding frequency, the joint impact of which lowers the growth rate of the population. Creel et al. (2019) term such population consequences of defensive actions “risk effects.”

Creel et al. (2019) outline how the total impact of predators on a prey population is the sum of direct predation mortality and risk effects. In other systems (reviewed by Preisser et al. 2005), the impact of risk effects on prey populations is several times that of direct predation mortality, suggesting that most of the demographic impact of Peregrines on sandpipers can be expected to arise from risk effects. Direct killing by Peregrines is assumed by the hypothesis outlined in Ydenberg et al. (2022) to contribute to the low migratory survival of Semipalmated Sandpipers, and the model developed here estimates the magnitude of the resultant risk effect. Direct killing on staging sites is described by Dekker et al. (2011), but its extent has not been quantified. Descriptions by Page and Whitacre (1975) and Dekker et al. (2011, 2012) of falcons hunting groups of shorebirds over extended periods indicate that this could be substantial.

The recovery of Peregrine populations in the Americas is ongoing (e.g. Ydenberg et al. 2017) and could be considered a hemispheric analog of the reintroduction of wolves into Yellowstone National Park (see Creel et al. 2019). The extensive popular and scientific literature on wolf re-introduction emphasizes its powerful direct and indirect ecological effects, and active debate and investigation of these effects continues. Peregrine recovery has received far less attention. Most accounts are popular, (justly) depicting this as a heroic rescue from near-extinction. Little scientific attention has been given to its basic ecology (though see Watts et al. 2015), and the possibility of powerful ecological effects such as those we raise here has hardly been considered. Closer scrutiny of the influence of this apex predator is warranted.

LITERATURE CITED

Ayala-Perez, V. O., R. Carmona, N. Arce, and Y. V. Albores-Barajas. 2021. Over-summering shorebirds in Guerrero Negro, Baja California Sur, Mexico and the particular case of the Marbled Godwit. Wader Study 128:109-116. https://doi.org/10.18194/ws.00229

Barbieri, E. 2007. Seasonal abundance of shorebirds at Aracaju, Sergipe, Brazil. Wader Study Group Bulletin 113:40-46.

Bart, J., S. Brown, B. Harrington, and R. I. G. Morrison. 2007. Survey trends of North American shorebirds: population declines or shifting distributions? Journal of Avian Biology 38:73-82. https://doi.org/10.1111/j.0908-8857.2007.03698.x

Battley, P. F., J. R. Conklin, A. M. Parody-Merino, P. A. Langlands, I. Southey, T. Burns, D. S. Melville, R. Schuckard, A. C. Riegen, and M. A. Potter. 2020. Interacting roles of breeding geography and early-life settlement in Godwit migration timing. Frontiers in Ecology and Evolution 8:52. https://doi.org/10.3389/fevo.2020.00052

Creel, S., M. Becker, E. Dröge, J. M’soka, W. Matandiko, E. Rosenblatt, T. Mweetwa, H. Mwape, M. Vinks, B. Goodheart, J. Merkle, T. Mukula, D. Smit, C. Sanguinetti, C. Dart, D. Christianson, and P. Schuette. 2019. What explains variation in the strength of behavioral responses to predation risk? A standardized test with large carnivore and ungulate guilds in three ecosystems. Biological Conservation 232:164-172. https://doi.org/10.1016/j.biocon.2019.02.012

Dekker, D., I. Dekker, D. Christie, and R. Ydenberg. 2011. Do staging Semipalmated Sandpipers spend the high-tide period in flight over the ocean to avoid falcon attacks along shore? Waterbirds 34:195-201. https://doi.org/10.1675/063.034.0208

Dekker, D., M. Out, M. Tabak, and R. Ydenberg. 2012. The effect of kleptoparasitic Bald Eagles and Gyrfalcons on the kill rate of Peregrine Falcons hunting Dunlins wintering in British Columbia. Condor 114:290-294. https://doi.org/10.1525/cond.2012.110110

DeWitt, P. D., D. R. Visscher, M. S. Schuler, and R. P. Thiel. 2019. Predation risks suppress lifetime fitness in a wild mammal. Oikos 128:790-797. https://doi.org/10.1111/oik.05935

Eisenmann, E. 1951. Northern birds summering in Panama. Wilson Bulletin 63:181-185.

Fedrizzi, C. E., S. Júnior, and M. Larrazábal. 2004. Body mass and acquisition of breeding plumage of wintering Calidris pusilla (Linnaeus) (Aves, Scolopacidae) in the coast of Pernambuco, north-eastern Brazil. Revista Brasileira de Zoologia 21(Suppl 2):249-252. https://doi.org/10.1590/S0101-81752004000200013

Fernández, G., P. D. O’Hara, and D. B. Lank. 2004. Tropical and subtropical Western Sandpipers (Calidris mauri) differ in life history strategies. Ornitología Neotropical 15:385-394.

Gratto, C. L., F. Cooke, and R. I. G. Morrison. 1983. Nesting success of yearling and older breeders in the Semipalmated Sandpiper, Calidris pusilla. Canadian Journal of Zoology 61:1133-1137. https://doi.org/10.1139/z83-149

Haverschmidt, F. 1955. North American shore birds in Surinam. Condor 57:366-368. https://doi.org/10.2307/1364795

Hicklin, P. W., and J. W. Chardine. 2012. The morphometrics of migrant Semipalmated Sandpipers in the bay of Fundy: evidence for declines in the eastern breeding population. Waterbirds 35:74-82. https://doi.org/10.1675/063.035.0108

Hitchcock, C. L., and C. Gratto-Trevor. 1997. Diagnosing a shorebird local population declines with a stage-structured population model. Ecology 78:522-534. https://doi.org/10.1890/0012-9658(1997)078[0522:DASLPD]2.0.CO;2

Hockey, P. A. R., J. K. Turpie, and C. R. Velasquez. 1998. What selective pressures have driven the evolution of deferred northward migration by juvenile waders? Journal of Avian Biology 29:325-300. https://doi.org/10.2307/3677117

Hope, D. D., D. B. Lank, P. A. Smith, J. Paquet, and R. C. Ydenberg. 2020. Migrant Semipalmated Sandpipers (Calidris pusilla) have over four decades steadily shifted towards safer stopover locations. Frontiers in Ecology and Evolution 8:3. https://doi.org/10.3389/fevo.2020.00003

Johnson, O. W., and P. M. Johnson. 1983. Plumage-molt-age relationships in “over-summering” and migratory Lesser Golden Plovers. Condor 85:406-419. https://doi.org/10.2307/1367979

Lee, A. M., J. M. Reid, and S. R. Beissinger. 2017. Modelling effects of nonbreeders on population growth estimates. Journal of Animal Ecology 86:75-87. https://doi.org/10.1111/1365-2656.12592

Martínez-Curci, N. S., J. P. Isacch, V. I. D’Amico, P. Rojas, and G. J. Castresana. 2020. To migrate or not: drivers of over-summering in a long-distance migratory shorebird. Journal of Avian Biology 2020:e02401. https://doi.org/10.1111/jav.02401

McNeil, R. 1970. Hivernage et estivage d’oiseaux aquatiques Nord-Américains dans le nord-est du Vénézuela (mue, accumulation de graisse, capacité de vol et route de migration). L'Oiseau et la Revue française d'ornithologie 40:185-302.

McNeil, R., M. T. Díaz, and A. Villeneuve. 1994. The mystery of shorebird over-summering: a new hypothesis. Ardea 82:143-152.

Morrison, R. I. G., D. S. Mizrahi, R. K. Ross, O. H. Ottema, N. de Pracontal, and A. Narine. 2012. Dramatic declines of Semipalmated Sandpipers on their major wintering areas in the Guianas, northern South America. Waterbirds 35:120-134. https://doi.org/10.1675/063.035.0112

Navedo, J. G., and J. Ruiz. 2020. Over-summering in the southern hemisphere by long-distance migratory shorebirds calls for reappraisal of wetland conservation policies. Global Ecology and Conservation 23:e01189. https://doi.org/10.1016/j.gecco.2020.e01189

O’Hara, P. D., G. Fernández, F. Becerril, H. De La Cueva, and D. B. Lank. 2005. Life history varies with migratory distance in western sandpipers Calidris mauri. Journal of Avian Biology 36:191-202. https://doi.org/10.1111/j.0908-8857.2005.03368.x

O’Hara, P. D., D. B. Lank, and F. S. Delgado. 2002. Is the timing of moult altered by migration? Evidence from a comparison of age and residency classes of Western Sandpipers (Calidris mauri) in Panamá. Ardea 90:61-70.

Öst, M., A. Lindén, P. Karell, S. Ramula and M. Kilpi. 2018. To breed or not to breed: drivers of intermittent breeding in a seabird under increasing predation risk and male bias. Oecologia 188:129-138. https://doi.org/10.1007/s00442-018-4176-5

Page, G., and D. F. Whitacre. 1975. Raptor predation on wintering shorebirds. Condor 77:73-83. https://doi.org/10.2307/1366760

Piersma, T. 2019. Ornithology from the flatlands. Ardea 107:115-117. https://doi.org/10.5253/arde.v107i2.a9

Preisser, E. L., D. I. Bolnick, and M. F. Benard. 2005. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86:501-509. https://doi.org/10.1890/04-0719

Puttick, G. M. 1979. Foraging behaviour and activity budgets of Curlew Sandpipers. Ardea 67:111-122.

Rice, S. M., J. A. Collazo, M. W. Alldredge, B. A. Harrington, and A. R. Lewis. 2007. Local annual survival and seasonal residency rates of Semipalmated Sandpipers (Calidris pusilla) in Puerto Rico. Auk 124:1397-1406. https://doi.org/10.1093/auk/124.4.1397

Shaw, A. K., and S. A. Levin. 2011. To breed or not to breed: a model of partial migration. Oikos 120:1871-1879. https://doi.org/10.1111/j.1600-0706.2011.19443.x

Shaw, A. K. and S. A. Levin. 2013. The evolution of intermittent breeding. Journal of Mathematical Biology 66:685-703. https://doi.org/10.1007/s00285-012-0603-0

Sheehy, J., C. M. Taylor, K. S. McCann, and D. R. Norris. 2010. Optimal conservation planning for migratory animals: integrating demographic information across seasons. Conservation Letters 3:192-202. https://doi.org/10.1111/j.1755-263X.2010.00100.x

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:duad003. https://doi.org/10.1093/ornithapp/duad003

Soto-Montoya, E., R. Carmona, M. Gomez, V. Ayala-Perez, N. Arce, and G. D. Danemann. 2009. Oversummering and migrant red knots at Golfo de Santa Clara, Gulf of California, Mexico. Wader Study Group Bulletin 116:191-194.

Spaans, A. L. 1984. Waterbird studies in coastal Suriname: a contribution to wetland conservation in northeastern South America. Annual Report 1983. Netherlands Research Institute for Nature Management Annual Report 1983:63-76.

Summers, R. W., L. G. Underhill, and R. P. Prys-Jones. 1995. Why do young waders in southern Africa delay their first return migration to the breeding grounds? Ardea 83:351-357.

Tavera, E. A. 2020. Survivorship and life history strategies in relation to migration distance in Western and Semipalmated Sandpipers in Perú. Dissertation. Simon Fraser University, Burnaby, British Columbia, Canada.

Tavera, E. A., D. B. Lank, and P. M. González. 2016. Effects of migration distance on life history strategies of Western and Semipalmated Sandpipers in Perú. Journal of Field Ornithology 87(3):293-308. https://doi.org/10.1111/jofo.12164

Tavera, E. A., G. E. Stauffer, D. B. Lank, and R. C. Ydenberg. 2020. Over-summering juvenile and adult Semipalmated Sandpipers in Perú gain enough survival to compensate for foregone breeding opportunity. Movement Ecology 8:42. https://doi.org/10.1186/s40462-020-00226-6

Tomkins, J. L., and W. Hazel. 2007. The status of the conditional evolutionarily stable strategy. Trends in Ecology and Evolution 22:522-528. https://doi.org/10.1016/j.tree.2007.09.002

Watts, B. D., K. E. Clark, C. A. Koppie, G. D. Therres, M. A. Byrd, and K. A. Bennett 2015. Establishment and growth of the Peregrine Falcon breeding population within the mid-Atlantic coastal plain. Journal of Raptor Research 49:359-366. https://doi.org/10.3356/rapt-49-04-359-366.1

Weiser, E. L., R. B. Lanctot, S. C. Brown, H. R. Gates, J. Bêty, M. L. Boldenow, R. W. Brook, G. S. Brown, W. B. English, S. A. Flemming, S. E. Franks, H. G. Gilchrist, M.-A. Giroux, A. Johnson, S. Kendall, L. V. Kennedy, L. Koloski, E. Kwon, J.-F. Lamarre, D. B. Lank, C. J. Latty, N. Lecomte, J. R. Liebezeit, R. L. McGuire, L. McKinnon, E. Nol, D. Payer, J. Perz, J. Rausch, M. Robards, S. T. Saalfeld, N. R. Senner, P. A. Smith, M. Soloviev, D. Solovyeva, D. H. Ward, P. F. Woodard, and B. K. Sandercock. 2020. Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain. Condor 122:duaa026. https://doi.org/10.1093/condor/duaa026

Wetmore, A. 1927. Our migrant shorebirds in southern South America. U.S. Department of Agricultural Technology Bulletin 26:1-24.

Wille, M., and M. Klaassen. 2022. Should I stay, should I go, or something in between? The potential for parasite-mediated and age-related differential migration strategies. Evolutionary Ecology 37:189-202. https://doi.org/10.1007/s10682-022-10190-9

Ydenberg, R. C. In press. Partial migration, over-summering, and intermittent breeding by shorebirds. American Naturalist.

Ydenberg, R. C., J. Barrett, D. B. Lank, C. Xu, and M. Faber. 2017. The redistribution of non-breeding Dunlins in response to the post-DDT recovery of falcons. Oecologia 183:1101-1110. https://doi.org/10.1007/s00442-017-3835-2

Ydenberg, R. C., R. W. Butler, D. B. Lank, B. D. Smith, and J. Ireland. 2004. Western Sandpipers have altered migration tactics as Peregrine Falcon populations have recovered. Proceedings of the Royal Society B: Biological Sciences 271:1263-1269. https://doi.org/10.1098/rspb.2004.2713

Ydenberg, R. C., G. Fernández, G., E. Ortiz Lopez, and D. B. Lank. 2023. Avian wings can lengthen rather than shorten in response to increased migratory predation danger. Ecology and Evolution 13:e10325. https://doi.org/10.1002/ece3.10325

Ydenberg, R. C., E. A. Tavera, and D. B. Lank. 2022. Danger, risk and anti-predator behavior in the life history of long-distance migratory sandpipers. Journal of Avian Biology 2022(6):e03002. https://doi.org/10.1111/JAV.03002/v2/response1