Received: 15 February 2022 | Revised: 15 August 2022 | Accepted: 19 August 2022 DOI: 10.1002/ece3.9292 RESEARCH ARTICLE The abundance of small mammals is positively linked to survival from nest depredation but negatively linked to local recruitment of a ground nesting precocial bird Veli-Matti Pakanen | Risto Tornberg | Eveliina Airaksinen | Nelli Rönkä | Kari Koivula Ecology and Genetics Research Unit, University of Oulu, Oulu, Finland Correspondence Veli-Matti Pakanen, Ecology and Genetics Research Unit, University of Oulu, PO Box 3000, 90014 Oulu, Finland. Email: veli-matti.pakanen@oulu.fi Funding information Biotieteiden ja Ympäristön Tutkimuksen Toimikunta, Grant/Award Number: 128384 and 278759; Emil Aaltosen Säätiö; Finnish Foundation for Nature Conservation; Koneen Säätiö; Oulun Yliopiston Tukisäätiö; Suomen Kulttuurirahasto; Suomen Ympäristökeskus; Tauno Tönning Foundation; the Faculty of Science, University of Oulu, Finland; Ympäristöministeriö Abstract Generalist predators using small mammals as their primary prey are suggested to shift hunting alternative prey such as bird nests, when small mammals are in short supply (the alternative prey hypothesis, APH). Nest survival and survival of young individuals should be positively linked to small mammal abundance and negatively linked to predator abundance, but little information exists from survival of chicks, especially until recruitment. We test these predictions of the APH using 13 years (2002–2014) of life history data from a ground nesting shorebird breeding on coastal meadows. We use small mammal abundance in the previous autumn as a proxy for spring predator abundance, mainly of mammalian predators. We examine whether small mammal abundance in the spring and previous autumn explain annual variation in nest survival from depredation and local recruitment of the southern dunlin Calidris alpina schinzii. As predicted by the APH, survival from nest predation was positively linked to spring small mammal abundance and negatively linked to autumn small mammal abundance. Importantly, local recruitment showed opposite responses. This counterintuitive result may be explained by density-dependent survival. When nest depredation rates are low, predators may show stronger numerical and functional responses to high shorebird chick abundance on coastal meadows, whereas in years of high nest depredation, few hatching chicks lure fewer predators. The opposite effects on nest and local recruitment demonstrate the diverse mechanisms by which population size variation in primary prey can affect dynamics of alternative prey populations. KEYWORDS alternative prey, local recruitment, nest success, voles, wader TA X O N O M Y C L A S S I F I C AT I O N Demography; Life history ecology; Population ecology; Zoology This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Ecology and Evolution. 2022;12:e9292. https://doi.org/10.1002/ece3.9292 www.ecolevol.org | 1 of 11 2 of 11 | 1 I NTRO D U C TI O N | PAKANEN et al. mammal abundance is high (Figure 1). Such relationships have been found in multiple avian studies (Bêty et al., 2001, 2002; Marcström Generalist predators, which prey on small mammals (e.g., voles) et al., 1988; McKinnon et al., 2014; Schmidt & Ostfeld, 2008; Wegge as their primary prey, are suggested to shift to hunting alter- & Storaas, 1990) but some studies find other mechanisms acting via, native prey such as bird nests, hares or roe deer fawns, when e.g., weather or incidental depredation with or without an aggre- small mammals are in short supply (“Alternative prey hypothesis” gation of predators (numerical response) to be more important for APH, Angelstam et al., 1984; Barraquand et al., 2015; Dell'Arte explaining variation in nest depredation (Grendelmeier et al., 2018; et al., 2007; Kjellander & Nordström, 2003; Korpimäki et al., 1991; Ludwig et al., 2020; Machín et al., 2019; Pöysä et al., 2016; Schmidt Reif et al., 2001). Thus, variation in abundance of the main prey et al., 2008; Weiser et al., 2018). of generalist predators should cause variation in depredation pres- Depredation rates are partly dependent on predator abundance sure of alternative prey species and have consequences for alterna- (Weiser et al., 2018; Zanette & Jenkins, 2000). Hence, the relation- tive prey populations (Angelstam et al., 1984). This prediction has ship between depredation rates of alternative prey and small mam- been confirmed by a vast number of empirical studies (Angelstam mal abundance may not be clear when the abundance of predators et al., 1984; Bowler et al., 2020; Breisjøberget et al., 2018; Kjellander varies in time. Predators show a strong numerical response to small & Nordström, 2003; Lehikoinen et al., 2016; Tornberg et al., 2012). mammal abundance, and autumnal small mammal abundance affects Importantly, not all empirical studies have found support for the the number of predators present in the next breeding season by in- APH (e.g., Ludwig et al., 2020; Reiter & Andersen, 2011) indicating creasing winter survival of predators and by affecting investment on potentially more complex predator prey dynamics and the need for reproduction (Brommer et al., 2002; Korpimäki et al., 1991, 2020; more research. Korpimäki & Norrdahl, 1991; Masoero et al., 2020). Therefore, small A change in population size of alternative prey results mainly mammal abundance in the autumn can be a good proxy of predator from lowered reproduction when predators switch to foraging abundance in the next breeding season. If the small mammal popula- on egg and juvenile stages of the alternative prey (Breisjøberget tion has crashed during the previous autumn or winter, predator num- et al., 2018; Kjellander & Nordström, 2003). Among avian alternative bers may have also crashed due to low survival of young and adults prey populations, this is expected to impact especially ground nest- leading to lower nest depredation rates (Figure 1). Alternatively, if ing birds, such as grouse, shorebirds and waterfowl, whose nests are the small mammal numbers have remained high, the predator popu- vulnerable to predators and who are not the focus of depredation lation may be large and thereby nest depredation rates remain high when small mammal populations are high (Brook et al., 2005; Iles (Figure 1). Thus, examination of the alternative prey hypothesis also et al., 2013; McKinnon et al., 2014; Valkama et al., 2005). Indeed, warrants the consideration of predator abundance or lagged effects positive correlations between the number of juvenile shorebirds at of rodent numbers from the previous year. nonbreeding sites and the rodent abundance at their arctic breeding Here, we test predictions of the alternative prey hypothesis on sites during the breeding season when the chicks hatched provide ev- a small ground nesting shorebird, the southern dunlin (Calidris al- idence for prey switching acting on ground nesting birds (Blomqvist pina schinzii; hereafter dunlin) which breeds on coastal meadows in et al., 2002; Summers et al., 1998). This may result from increased Bothnian Bay, Finland. As in many ground-nesting bird populations, depredation of eggs and/or young. However, evidence from survival the most common cause of nest failure is nest depredation (Pakanen of young (during pre-fledging and post-fledging phases) is scarce, et al., 2011). There is substantial annual variation in predation pres- especially until local recruitment, and often indirectly measured via sure (Pakanen et al., 2011), which could be linked to the abundance brood size (Breisjøberget et al., 2018; Ludwig et al., 2020; Marcström of small mammals as the most commonly seen nest predators include et al., 1988; Schmidt et al., 2008) and results from nest survival both mammalian predators (e.g., red foxes Vulpes vulpes, Kaasiku studies are not consistent. APH predicts low nest survival when et al., 2022; own observations and raccoon dogs Nyctereutes procy- small mammal abundance is low, and high nest survival when small onoides; Dahl & Åhlén, 2019; own observations) and bird predators F I G U R E 1 Hypothetical effects of previous autumn and spring small mammal abundances on bird nest survival. Breeding season predator abundance results from a numerical response to previous autumn small mammal abundance. | PAKANEN et al. 3 of 11 (e.g., marsh harriers Circus aeruginosus; Opermanis, 2004; own ob- returned to breed in the population were caught and ringed with servations) that are known to consume small mammals. Small mam- a combination of color rings and they were subsequently resighted mal populations in Finland also show annual fluctuation (Korpimäki using these combinations (Pakanen et al., 2016). These data allowed et al., 2005; Sundell et al., 2004), and this variation can potentially exert us to estimate local recruitment, i.e., survival of chicks from hatching varying predation pressure towards alternative prey. Importantly, until age of one year (see below). As the breeding sites of dunlin are small mammals are extremely rare on these coastal meadows during coastal pastures that are short-vegetated patches among unsuitable the breeding season (own observations). This is likely due to the fact habitat (e.g., reed beds and forest), we were able to include all of that coastal meadow habitats in Bothnian Bay are very low and easily their breeding sites in our sampling. Furthermore, the population develop an ice cap due to recurrent winter flooding making these is separated by 400 km to the next dunlin population, and it is habitats inhospitable until the summer when new vegetation starts genetically differentiated from the rest of the dunlin populations to grow. Furthermore, dunlins arrive to the breeding sites in late April breeding in the Baltic region (Rönkä et al., 2021). Thus, natal dispersal or early May and start to breed as early as possible after the ice and movements beyond the scale of the study area should be extremely snow melt, and their nests usually hatch when the meadow vegeta- rare, which allowed us to reliably estimate survival of chicks from tion starts to grow in June (Pakanen et al., 2016, 2018). Small mam- hatching until age one (Pakanen et al., 2016, 2017). mals nevertheless live in coastal forests and agricultural field areas that border the meadows, and hence this primary prey source exists within ca. 200–1000 m from the dunlin nest sites (own observa- 2.2 | Small mammal trapping tions). The low abundance or near absence of the primary prey species (small mammals) on these coastal meadow breeding sites of the We monitored variation in small mammal abundance in Sanginjoki (N dunlin makes our study population an insightful system to test the 65° 0′; E 25° 46′), which is situated roughly 10 km east from the city alternative prey hypothesis because in most systems where the APH of Oulu. The trapping area is ca. 30–50 km from the study meadows, has been previously tested, the primary prey and alternative prey co- but vole abundances can be safely assumed to reflect abundances in occur in the same areas (e.g., McKinnon et al., 2014). This means that study area because of much larger scale geographical synchrony in those predators that use small mammals as their primary prey must their population dynamics (e.g. Sundell et al., 2004). We trapped small specifically travel to the coastal meadows to prey on shorebirds, in- mammals using the small quadrat method (Myllymäki et al., 1971). stead of finding shorebirds while searching for small mammals. In this Each small quadrat was 15 m × 15 m in size. We placed three baited study, we use 13 years (2002–2014) of data on breeding dunlins and (rye bread) snaptraps roughly 1–2 meters from each corner of the small mammal abundance to test whether variation in the number small quadrat. Thus, each small quadrat had 12 snaptraps. We of primary prey (small mammals) for general predators in the spring monitored 10 small quadrats annually during the spring (mid-May to and previous autumn explain temporal variation in nest depredation mid-June; spring small mammal abundance) and autumn (late August rates and local recruitment (survival from hatching until age of one to September; autumn small mammal abundance). Traps were years) of the dunlin. monitored for 2–3 nights, and the traps were checked nightly. All small mammal species (Microtus, Myodes, and Sorex) were recorded. 2 | M E TH O DS These species fluctuate synchronously (Korpimäki et al., 2005). The number of trap nights was on average 251 (SD 75) per season. It varied based on the number of days the traps were monitored. We 2.1 | Study population and data collection calculated an annual index of small mammal abundance separately for spring and autumn as the number of trapped small mammals We studied a dunlin population breeding on coastal meadows at per 100 trap nights. The small quadrats were placed in abandoned Bothnian Bay, Finland (64° 50′ N, 25° 00′ E). We have collected field habitat (3), pine forests (1), spruce forests (3), deciduous forest individual based data from this population since 2002 (Pakanen (1), and young planted forest (2), which represent common habitats et al., 2016). Each year, we started field work when the dunlins where small mammals live in this region. started to arrive and display at the breeding sites, from late April or early May, and continued until July. Field work included searching for all territories and nests and following nest fates until failure or 2.3 | Data analysis hatching (Pakanen et al., 2011). We determined the cause of nest failure from depredation, flooding, trampling, or other causes We analyzed daily nest survival of dunlin from depredation (Pakanen et al., 2014). We estimated hatching dates on the basis with program MARK version 9.0 (Dinsmore et al., 2002; White & of egg laying phase or incubation phase by floating the already Burnham, 1999) using data from 2002 until 2014. The data included incubated eggs (Liebezeit et al., 2007). After the eggs hatched, we 441 nests and 5005 nest days. Depredated nests were considered ringed all chicks with metal rings (Pakanen et al., 2016; Figure 2). The as failed, whereas nests that were destroyed by other causes than rings allowed us to identify young individuals when they recruited depredation were considered to have survived until the estimated back to the breeding population as adults. Those offspring that later time of failure (mid-point of last two observations). 4 of 11 | PAKANEN et al. We used program MARK to analyze local recruitment (survival For both daily nest survival from depredation and local recruit- from hatching until age one) using an age-dependent version of the ment, we first fit time-dependent (categorical year effect) models CJS model (Cormack-Jolly-Seber model; Lebreton et al., 1992). Here, to examine temporal variation in survival and to allow estimation of we used birds that were ringed as hatchlings from 2002 until 2014 the percentage of temporal variation explained by covariates (see but included their encounter histories until 2017 to control for re- below). We compared the time-dependent model with a constant cruitment of individuals born in the latter years of the study. These (intercept only) model to check for annual variation in survival. After data included 873 chicks. In the CJS model, survival probability (Φ) is this, we fitted models where survival was a function of linear or qua- corrected for the probability of recapture (p). We started with a gen- dratic effects of (1) spring small mammal abundance and/or (2) pre- eral model [Φ(age1t/age2c) p(age1t/age2c)]. This model included the vious autumn small mammal abundance. Here, spring abundance effect age (age: juveniles age1 vs. adults age2). Survival and recap- is the number of small mammals trapped per 100 nights during ture probabilities were constant in time for adults (age2c) but time- the same spring as the nests were followed and juveniles hatched dependent for juveniles (age1t; a categorical variable). This model fit (year t). If the generalist predators switch to hunting shorebird eggs the data (bootstrapping goodness of fit; p = .16, ĉ = 1.11). We were and chicks when the small mammal abundance is low, we can ex- not able to reliably estimate survival in 2004 because only 9 chicks pect a positive association between small mammal abundances and hatched. Hence, we separated the year 2004 with a separate param- survival. Previous autumn small mammal abundance is the number eter and fitted the covariates to years (2002–2003 and 2005–2014). of small mammals trapped per 100 nights during the autumn of the previous year (i.e., year t − 1). If the autumn small mammal abundance affects how well the predators and their offspring survive from autumn to the next breeding season, it should be linked to the amount of predators in the next breeding season, and we can expect that there is a negative association between previous autumn small mammal abundance and survival. Local recruitment includes both pre-fledging survival (period before chicks are capable of flying) and post-fledging survival (period after starting to fly) until age of one year. In shorebirds such as the dunlins, local recruitment reflects mostly pre-fledging survival rather than survival during the postfledging period (Pakanen et al., 2021). This means that we can expect that the small mammal abundances at the breeding sites affect local recruitment. We compared the covariate models with the intercept model to calculate support in explaining variation in daily nest survival. Furthermore, we used the analysis of deviance (ANODEV) to cal- F I G U R E 2 Adult dunlin (Calidris alpina schinzii) brooding chicks. Photo by Kari Koivula culate the percentage of annual temporal variation explained by the covariate(s) as follows: TA B L E 1 Models explaining variation in daily dunlin nest survival from depredation during 2002 to 2014 # Model AICc ΔAICc w k Deviance % A1 Year 795.58 0.00 0.920 13 769.51 A2 Spring + Spring2 + Autumn + Autumn2 801.26 5.68 0.054 5 791.25 A3 Spring + Autumn + Autumn2 802.84 7.26 0.024 4 794.83 47 A4 Spring + Spring2 + Autumn 809.75 14.16 0.001 4 801.74 33 A5 Spring + Autumn 810.47 14.88 0.001 3 804.46 27 A6 Spring*Autumn 812.45 16.87 0.000 4 804.45 28 A7 Spring 815.87 20.28 0.000 2 811.86 12 A8 Autumn 816.53 20.95 0.000 2 812.53 11 55 A9 Spring + Spring2 816.80 21.21 0.000 3 810.79 14 A10 Autumn + Autumn2 818.22 22.64 0.000 3 812.22 11 A11 Intercept 819.71 24.13 0.000 1 817.71 Note: Spring = spring small mammal index individuals/100 trap nights; autumn = previous autumn small mammal index individuals/100 trap nights, 2 = quadratic effect; intercept = constant model; year = annual variation; k = number of parameters; w = Akaike weight; AICc = Akaike's information criterion corrected for small sample size; ΔAICc = difference in AICc to best model, % percent of temporal variation explained by the covariate model. | PAKANEN et al. Dev(c) − Dev(cov) Dev(c) − Dev(t) 5 of 11 variables and explained 55% of annual variation (Table 1). While the best covariate model (A2) was 18.5 AICc units more supported than the intercept model, the time-dependent model remained the most where Dev(c) is the deviance from the constant model, Dev(cov) is devi- supported model suggesting other sources of variation on annual ance from the covariate model and Dev(t) is the deviance from the time- values (Table 1). dependent model (Grosbois et al., 2008, see, e.g., Oro et al., 2021). We used the Akaike's information criterion corrected for small There was no support for strong annual variation in local recruitment (model B5 vs. model B10, Table 3). Mean survival from sample size AICc when comparing support for nest survival models hatching until age one was 0.236 (SE 0.025). Annual estimates from and the Quasi-AICc when comparing support for models explain- the time-dependent model varied between 0.169 and 0.472 but the ing local recruitment (Burnham & Anderson, 2002). We considered confidence intervals of annual estimates were wide (Figure 5). The models to have equal support when their difference in (Q)AICc was best model explaining this variation in survival suggested a quadratic less than 2 units and considered model selection uncertainty by effect of small mammal abundance in the previous autumn and a using model averaging to calculate survival estimates (Burnham & linear effect of spring small mammal abundance being 4.06 QAICc Anderson, 2002). However, if models within two AICc units from units better than the intercept model (Table 3). This model predicted the most supported model included were more complex versions of a negative effect of spring small mammal abundance and posi- a model with lower AICc (i.e., more parameters), we omitted them tive effect of previous autumn small mammal abundance (Table 4; from the model averaging (Richards et al., 2011). Figure 6). The second-best model included a quadratic effect of spring small mammal abundance, but inclusion of this parameter did not increase model support. Support for linear effects of previous 3 | R E S U LT S autumn and spring small mammal abundance were low (models B7 and B8 in Table 3). Annual variation in daily nest survival from depredation was strong (Table 1, models A1 vs. A11, ΔAICc = 24.13) varying between 0.927 and 0.992, which translate to 13–81% survival probability over 4 | DISCUSSION the 26-day incubation (Figure 3). Overall mean daily survival from depredation was 0.975 (SE ±0.005). The best model explaining We tested predictions of the alternative prey hypothesis on both temporal variation in daily nest survival included quadratic effects nest depredation and local recruitment by combining 13 years of life of both spring and autumn small mammal abundance, although history data from a small ground nesting bird, and trapping data from the quadratic effect of spring small mammal abundance was weak small mammals, the primary prey of mesopredators. Results from (Table 2). Nest survival was positively linked to spring small mammal nest depredation supported the alternative prey hypothesis but re- abundance and negatively linked to small mammal abundance in sults from local recruitment suggested an opposite pattern. Hence, the previous autumn (Figure 4). Including autumn and spring small our detailed analysis of reproduction from egg laying until local re- mammal abundance together clearly increased support for both cruitment highlights the diverse mechanisms by which population F I G U R E 3 Annual variation in daily survival of dunlin nests from depredation (with 95% CI) during 2002–2014 (estimates from model A1 in Table 1) and variation in spring and previous autumn small mammal abundance. 6 of 11 | PAKANEN et al. TA B L E 2 Regression coefficients of the best covariate model (model A2) explaining temporal variation in daily nest survival from depredation. Parameter Coefficient SE CI− shift in predation pressure towards the coastal meadows from the surrounding areas. Generalist mammalian predators may be the key factor in causing this variation by consuming nests themselves but CI+ also by facilitating access of avian predators to the breeding sites. Intercept 4.3830 0.3637 3.6702 5.0958 When the nests of larger shorebird species (lapwing Vanellus vanel- Spring 0.2653 0.0795 0.1095 0.4211 lus, Eurasian curlew Numenius arquata and black-tailed godwits Spring2 −0.0109 0.0057 −0.0220 0.0003 Limosa limosa) survive, these species provide shelter for smaller Autumn −0.1808 0.0490 −0.2768 −0.0849 shorebird species by deterring avian predators such as corvids and 0.0031 0.0010 0.0012 0.0050 birds of prey (e.g., Elliot, 1985). However, in low small mammal years, Autumn2 Note: Spring = spring small mammal index individuals/100 trap nights; autumn = previous autumn small mammal index individuals/100 trap nights; 2 = quadratic effect. stronger movement of generalist mammalian predators such as red foxes and raccoon dogs to the coastal meadows in search of food, likely leads into depredation of nests of the larger wader species (Seymour et al., 2003). Consequently, avian nest predators such as marsh harriers and corvids will likely have better access to coastal meadows and further worsen depredation rates in years of low small mammal abundance. Nest survival was also negatively linked to small mammal abundance in the previous autumn which we used as a proxy for predator abundance during the spring breeding season. This is in line with depredation risk being dependent on the ratio between small mammals and predators (Tornberg et al., 2011). Our results suggest that predators, which forage also on shorebird nests, show a numerical response to small mammal abundance in the previous autumn, and the peaks in small mammals likely inflict lagged longterm consequences to nest success (Bêty et al., 2002). Small mammal abundance during previous autumn received more support in explaining nest depredation than spring small mammal abundance. In addition, the inclusion of autumn small mammal abundance was important for finding the impact of spring mammal abundance F I G U R E 4 Daily nest survival of dunlin nests (with 95% CI) in relation to small mammal abundance in the previous autumn (x-axis) when small mammal abundance during the breeding season (spring) is low (dashed line and dark green CI) or high (solid line and light green CI). Estimates were derived by model averaging models B2 and B3 in Table 1. on nest survival. These results, therefore, warrant further studies where predator abundance is considered when testing the alternative prey hypothesis (see e.g., McGuire et al., 2020; Weiser et al., 2018). Our best models explained 55% of annual variation in nest survival. The unexplained part of temporal variation in the depredation size variation in primary prey can affect dynamics of alternative prey of nests can be linked to multiple processes. For example, annual populations. variation in the number of breeding larger wader species that deter In support of the alternative prey hypothesis, we found that avian predators from the breeding sites (e.g., lapwings, see above) spring small mammal abundance was negatively linked to nest depre- may cause annual variation in nest depredation of smaller species dation of shorebirds. Interestingly, our results describe a new aspect such as the dunlin. Furthermore, predators that specialize in small on how switching of prey may occur in space. In most studies, the mammals can show varying behavior, and their population sizes can primary prey (small mammals) and the alternative prey occupy the be affected by conditions during the annual cycle such as spread of same areas, and predators switch to depredating bird nests when diseases, competition or predation that that may not linked to small small mammal are low in abundance (Bêty et al., 2001; Marcström mammal abundance. Finally, depredation by predator species that et al., 1988; McKinnon et al., 2014). However, small mammals are do not specialize in small mammals when they are abundant (e.g., extremely rare at the coastal meadows during the dunlin breeding common gulls Larus canus) can create further variation. season (see Section ‘1’). Nevertheless, we found that nest depreda- Intriguingly, we found that small mammal abundance in the spring tion of dunlins increased when small mammals were regionally low and autumn caused opposite effects on local recruitment compared in abundance. This suggests that predators shift foraging habitats on with nest depredation. Survival of chicks from hatching until age one was the basis of prey availability (e.g., Gese et al., 1996). In Finland, small lower when spring small mammal abundance was high and small mam- mammals are mainly depredated by mammalian predators, owls, mal abundance in the previous autumn was low. Our result is similar to and hawks (e.g., Korpimäki et al., 1991; Sundell et al., 2004). When Ludwig et al. (2020), who reported increased depredation of red grouse small mammals are low in numbers, the lack of food may cause a (Lagopus lagopus scotica) chicks in years with high vole abundance. Such | PAKANEN et al. 7 of 11 TA B L E 3 Models explaining variation in local recruitment of dunlin from 2002 to 2014 # Model QAICc ΔQAICc w k QDeviance % B1 Spring + Autumn + Autumn2 2008.22 0.00 0.387 8 1992.11 50 B2 Spring + Spring2 + Autumn + Autumn2 2008.75 0.53 0.297 9 1990.61 57 B3 Autumn + Autumn2 2010.59 2.37 0.118 7 1996.50 B4 Spring + Spring2 2011.83 3.61 0.064 7 1997.75 28 B5 Intercept 2012.28 4.06 0.051 5 2002.24 22 B6 Spring + Spring2 + Autumn 2013.82 5.60 0.023 8 1997.72 22 B7 Autumn 2013.92 5.70 0.022 6 2001.86 2 B8 Spring 2014.20 5.98 0.019 6 2002.14 0 B9 Spring + Autumn 2015.92 7.70 0.008 7 2001.84 2 B10 Year 2016.45 8.24 0.006 17 1981.99 B11 Spring*Autumn 2017.59 9.37 0.004 8 2001.48 4 Note: Spring = spring small mammal index individuals/100 trap nights; autumn = previous autumn small mammal index individuals/100 trap nights, 2 = quadratic effect; intercept = constant model; year = annual variation; k = number of parameters; w = Akaike weight; QAICc = quasi-Akaike's information criterion corrected for small sample size; ΔQAICc = difference in QAICc to best model, % percent of temporal variation explained by the covariate model. The survival models include an age effect (two classes) and a separate parameter for 2004. Recapture probability model structure includes the intercept and age (two classes), i.e., p(age). F I G U R E 5 Annual variation in local recruitment of dunlin chicks (with 95% CI) during 2002–2014 (estimates from model B10 in Table 3) and variation in spring and autumn small mammal abundance. TA B L E 4 Regression coefficients of the best model (model B1) explaining temporal variation in local recruitment. Parameter Intercept Coefficient CI− SE CI+ 1.522 0.116 1.295 1.749 −3.742 0.417 −4.560 −2.923 −13.926 1113.011 −2195.428 2167.575 −0.082 0.039 −0.159 −0.005 Autumn 0.196 0.062 0.073 0.318 Autumn2 −0.004 0.001 −0.007 −0.001 Age Year 2004 Spring Note: Spring = spring small mammal index individuals/100 trap nights; autumn = previous autumn small mammal index individuals/100 trap nights, 2 = quadratic effect. a pattern could be explained by the apparent competition hypothesis, our case this is unlikely because small mammals are very rare at coastal i.e., incidental depredation of chicks by predators that were after small meadows (see above). Instead, we hypothesize that depredation of juve- mammals (Grendelmeier et al., 2018; Mckinnon et al., 2013). However, in niles could be density dependent (Gunnarson et al., 2006). In years when 8 of 11 | PAKANEN et al. mammal abundance. Hence, these predators have the potential to exert constantly high predation pressure. In this risky environment, peak years in small mammal abundance are extremely valuable for the ground nesting bird populations as they provide temporary relief from nest depredation. AU T H O R C O N T R I B U T I O N S Veli-Matti Pakanen: Conceptualization (lead); data curation (equal); formal analysis (lead); funding acquisition (equal); investigation (equal); methodology (lead); project administration (equal); resources (equal); supervision (equal); writing – original draft (lead); writing – review and editing (equal). Risto Tornberg: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); resources (equal); supervision (equal); writing – F I G U R E 6 Local recruitment of dunlin chicks (with 95% CI) in relation to small mammal abundance in the previous autumn (x-axis) when small mammal abundance during the breeding season (spring) is low (dashed line and dark green CI) or high (solid line and light green CI). review and editing (equal). Eveliina Airaksinen: Conceptualization (equal); formal analysis (equal); methodology (equal); resources (equal); writing – review and editing (equal). Nelli Rönkä: Data curation (equal); methodology (equal); resources (equal); writing – review and editing (equal). Kari Koivula: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition nest survival of shorebirds is low, there are very few shorebird broods, (equal); investigation (equal); methodology (equal); project admin- and predators will not have such a strong response to them. However, in istration (equal); resources (equal); supervision (equal); writing – re- years when nest success is high and dunlin and other shorebird chicks view and editing (equal). are more abundant, predators may show both a numerical response via aggregation to the breeding sites from other sites and a functional AC K N OW L E D G M E N T S response to an increasing food source (Gilg et al., 2006). Assuming the We thank all the people that helped in the field, especially Aappo same amount of initiated nests, there would be a six-fold difference in Luukkonen, Aija Lehikoinen, and Robert L. Thomson. We thank the number of chicks present when nest survival was at the maximum three anonymous referees for constructive comments on the we measured (81%) versus the minimum (13%). Generalist predators will manuscript. Our long-term research and present work were use the most profitable prey and can quickly learn to use an abundant funded by the Academy of Finland (128384, KK and 278759, VMP), food source (Panzacchi et al., 2008). Furthermore, juvenile shorebirds Ministry of the Environment (KK, VMP). The Finnish Foundation are often depredated by opportunistic avian predators (marsh harriers, for Nature Conservation (VMP), the Faculty of Science, University corvids, common gulls and arctic skuas Stercorarius parasiticus). The na- of Oulu, Finland (NR), Finnish Cultural Foundation (VMP), the ture of the breeding sites, i.e., distinct patches that are surrounded to a Finnish Environment Institute (KK), the Emil Aaltonen Foundation large degree by forest and reedbeds, may facilitate this pattern, espe- (NR and VMP), the Kone Foundation (VMP), the University of Oulu cially when broods of most shorebird species aggregate to the shoreline. Scholarship Foundation (NR), and the Tauno Tönning Foundation We show that variation in nest success of a ground nesting (VMP). All applicable institutional and/or national guidelines for the shorebird is linked to the abundance of small mammals. This link may care and use of animals were followed. The work complies with the be formed by generalist predators switching to alternative prey, such current laws of Finland. Fieldwork in Finland was completed with as shorebird nests, when small mammals are low in abundance. If permission from the North-Ostrobothnian regional environment this is the case, the observed pattern has important conservation center (PPO-2004-L-289-254, PPO-2006-L-206-254). implications as many ground nesting bird species, including a number of shorebirds, suffer from increased nest depredation and are C O N FL I C T O F I N T E R E S T declining in numbers (e.g., Kaasiku et al., 2022; Kubelka et al., 2018; The authors declare that they have no conflict of interest. McMahon et al., 2020; Rönkä et al., 2006). This results in part from an increase in the number of generalist predators, especially alien DATA AVA I L A B I L I T Y S TAT E M E N T species that potentially cause more severe effects on avian prey Data are available from the Dryad Digital Repository: <https:// populations than native predators (Dahl & Åhlén, 2019; Krüger doi:10.5061/dryad.tx95x6b1q>. et al., 2018; Nordström et al., 2003; Salo et al., 2007). Importantly, these generalist species are opportunistic, and consequently, their ORCID populations do not decline strongly following the crash of small Veli-Matti Pakanen https://orcid.org/0000-0003-4838-9927 PAKANEN et al. REFERENCES Angelstam, P., Lindström, E., & Widén, P. (1984). Role of predation in short-term population fluctuations of some birds and mammals in Fennoscandia. Oecologia, 62, 199–208. https://doi.org/10.1007/ BF00379014 Barraquand, F., New, L. F., Redpath, S., & Matthiopoulos, J. (2015). Indirect effects of primary prey population dynamics on alternative prey. Theoretical Population Biology, 103, 44–59. https://doi. org/10.1016/j.tpb.2015.04.002 Bêty, J., Gauthier, G., Giroux, J.-F., & Korpimäki, E. (2001). Are goose nesting success and lemming cycles linked? Interplay between nest density and predators. Oikos, 93, 388–400. https://doi. org/10.1034/j.1600- 0706.2001.930304.x Bêty, J., Gauthier, G., Korpimaki, E., & Giroux, J. F. (2002). Shared predators and indirect trophic interactions: Lemming cycles and arcticnesting geese. Journal of Animal Ecology, 71, 88–98. https://doi. org/10.1046/j.0021-8790.2001.00581.x Blomqvist, S., Holmgren, N., Åkesson, S., Hedenström, A., & Pettersson, J. (2002). Indirect effects of lemming cycles on sandpiper dynamics: 50 years of counts from southern Sweden. Oecologia, 133, 146– 158. https://doi.org/10.1007/s00442- 002-1017-2 Bowler, D. E., Kvasnes, M. A. J., Pedersen, H. C., Sandercock, B. K., & Nilsen, E. B. (2020). Impacts of predator-mediated interactions along a climatic gradient on the population dynamics of an alpine bird. Proceedings of the Royal Society B: Biological Sciences, 287, 20202653. doi:10.1098/rspb.2020.2653 Breisjøberget, J. I., Odden, M., Wegge, P., Zimmermann, B., & Andreassen, H. (2018). The alternative prey hypothesis revisited: Still valid for willow ptarmigan population dynamics. PLoS One, 13(6), e0197289. https://doi.org/10.1371/journal.pone.0197289 Brommer, J. E., Pietiäinen, H., & Kolunen, H. (2002). Reproduction and survival in a variable environment: Ural owls (Strix Uralensis) and the three-year vole cycle. Auk, 119, 544–550. https://doi.org/10.1093/ auk/119.2.544 Brook, R. W., Duncan, D. C., Hines, J. E., Carrière, S., & Clark, R. G. (2005). Effects of small mammal cycles on productivity of boreal ducks. Wildlife Biology, 11, 3–11. https://doi.org/10.2981/09096396(2005)11[3:EOSMCO]2.0.CO;2 Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. Springer-Verlag. Dahl, F., & Åhlén, P. A. (2019). Nest predation by raccoon dog Nyctereutes procyonoides in the archipelago of northern Sweden. Biological Invasions, 21, 743–755. https://doi.org/10.1007/s1053 0- 018-1855- 4 Dell'Arte, G. L., Laaksonen, T., Norrdahl, K., & Korpimäki, E. (2007). Variation in the diet composition of a generalist predator, the red fox, in relation to season and density of main prey. Acta Oecologica, 31, 276–281. https://doi.org/10.1016/j.actao.2006.12.007 Dinsmore, S. J., White, G. C., & Knopf, F. L. (2002). Advanced techniques for modeling avian nest survival. Ecology, 83, 3476–3488. https://doi.org/10.1890/0012- 9658(2002)083[3476:ATFMA N]2.0.CO;2 Elliot, R. D. (1985). The exclusion of avian predators from aggregations of nesting lapwings (Vanellus vanellus). Animal Behaviour, 33, 308–314. https://doi.org/10.1016/S0003-3472(85)80144-5 Gese, E. M., Ruff, R. L., & Crabtree, R. L. (1996). Intrinsic and extrinsic factors influencing coyote predation of small mammals in Yellowstone National Park. Canadian Journal of Zoology, 74, 784–797. https://doi. org/10.1139/z96- 090 Gilg, O., Sittler, B., Sabard, B., Hurstel, A., Sane, R., Delattre, P., & Hanski, I. (2006). Functional and numerical responses of four lemming predators in high arctic Greenland. Oikos, 113, 193–216. https:// doi.org/10.1111/j.2006.0030-1299.14125.x Grendelmeier, A., Arlettaz, R., & Pasinelli, G. (2018). Numerical response of mammalian carnivores to rodents affects bird reproduction in | 9 of 11 temperate forests: A case of apparent competition? Ecology and Evolution, 8, 11596–11608. https://doi.org/10.1002/ece3.4608 Grosbois, V., Gimenez, O., Gaillard, J.-M., Pradel, R., Barbraud, C., Clobert, J., Møller, A. P., & Weimerskirch, H. (2008). Assessing the impact of climate variation on survival in vertebrate populations. Biological Reviews, 83, 357–399. https://doi. org/10.1111/j.1469-185X.2008.00047.x Gunnarson, G., Elmberg, J., Sjöberg, K., Pöysä, H., & Nummi, P. (2006). Experimental evidence for density-dependent survival in mallard (Anas platyrhynchos) ducklings. Oecologia, 149, 203–213. https:// doi.org/10.1007/s00442- 006- 0446-8 Iles, D. T., Rockwell, R. F., Matulonis, P., Robertson, G. J., Abraham, K. F., Davies, J. C., & Koons, D. N. (2013). Predators, alternative prey and climate influence annual breeding success of a long-lived sea duck. The Journal of Animal Ecology, 82, 683–693. https://doi. org/10.1111/1365-2656.12038 Kaasiku, T., Rannap, R., & Männil, P. (2022). Predation-mediated edge effects reduce survival of wader nests at a wet grassland-forest edge. Animal Conservation. https://doi.org/10.1111/acv.12774 Kjellander, P., & Nordström, J. (2003). Cyclic voles, prey switching in red fox, and roe deer dynamics – A test of the alternative prey hypothesis. Oikos, 101, 338–344. https://doi. org/10.1034/j.1600- 0706.2003.11986.x Korpimäki, E., Hongisto, K., Masoero, G., & Laaksonen, T. (2020). The difference between generalist and specialist: The effects of wide fluctuations in main food abundance on numbers and reproduction of two co-existing predators. Journal of Avian Biology, 51, e02508. https://doi.org/10.1111/jav.02508 Korpimäki, E., & Norrdahl, K. (1991). Numerical and functional responses of kestrels, short-eared owls and long-eared owls to vole densities. Ecology, 72, 814–826. https://doi.org/10.2307/1940584 Korpimäki, E., Norrdahl, K., Huitu, O., & Klemola, T. (2005). Predator– induced synchrony in population oscillations of coexisting small mammal species. Proceedings of the Royal Society B, 272, 193–202. https://doi.org/10.1098/rspb.2004.2860 Korpimäki, E., Norrdahl, K., & Rinta-Jaskari, T. (1991). Responses of stoats and least weasels to fluctuating food abundances: Is the low phase of the vole cycle due to mustelid predation? Oecologia, 88, 552–561. https://doi.org/10.1007/BF00317719 Krüger, H., Väänänen, V. M., Holopainen, S., & Nummi, P. (2018). The new faces of nest predation in agricultural landscapes—A wildlife camera survey with artificial nests. European Journal of Wildlife Research, 64, 76. https://doi.org/10.1007/s1034 4- 018-1233-7 Kubelka, V., Šálek, M., Tomkovich, P., Végvári, Z., Freckleton, R. P., & Székely, T. (2018). Global pattern of nest predation is disrupted by climate change in shorebirds. Science, 362, 680–683. https://doi. org/10.1126/science.aat8695 Lebreton, J.-D., Burnham, K. P., Clobert, J., & Anderson, D. R. (1992). Modelling survival and testing biological hypothesis using marked animals: A unified approach with case studies. Ecological Monographs, 62, 67–118. https://doi.org/10.2307/2937171 Lehikoinen, A., Fraixedas, S., Burgas Riera, D., Eriksson, H., Henttonen, H., Laakkonen, H., Lehikoinen, P., Lehtomäki, J., Leppänen, J., Mäkeläinen, S., Niemimaa, J., Pihlajaniemi, M., Santaharju, J., & Välimäki, K. (2016). The impact of weather and the phase of the rodent cycle on breeding populations of waterbirds in Finnish Lapland. Ornis Fennica, 93, 31–46. Liebezeit, J. R., Smith, P. A., Lanctot, R. B., Schekkerman, H., Tulp, I., Kendall, S. J., Tracy, D., Rodrigues, R. J., Meltofte, H., Robinson, J. A. R., Gratto-Trevor, C., McCaffery, B. J., Morse, J., & Zack, S. W. (2007). Assessing the development of shorebird eggs using the flotation method: Species-specific and generalized regression models. Condor, 109, 32–47. https://doi.org/10.1093/condor/109.1.32 Ludwig, S. C., Roos, S., & Baines, D. (2020). Fluctuations in field vole abundance indirectly influence red grouse productivity via a 10 of 11 | shared predator guild. Wildlife Biology, 2020(2), 1–10. https://doi. org/10.2981/wlb.00642 Machín, P., Fernández-Elipe, J., Hungar, J., Angerbjörn, A., Klaassen, R. H. G., & Aguirre, J. I. (2019). The role of ecological and environmental conditions on the nesting success of waders in sub-Arctic Sweden. Polar Biology, 42, 1571–1579. https://doi.org/10.1007/s0030 0019- 02544-x Marcström, V., Kenward, R., & Engren, E. (1988). The impact of predation on boreal tetraonids during vole cycles: An experimental study. Journal of Animal Ecology, 57, 859–872. https://doi. org/10.2307/5097 Masoero, G., Laaksonen, T., Morosinotto, C., & Korpimäki, E. (2020). Age and sex differences in numerical responses, dietary shifts, and total responses of a generalist predator to population dynamics of main prey. Oecologia, 192, 699–711. https://doi.org/10.1007/s00442020- 04607-x McGuire, R. L., Lanctot, R. B., Saalfeld, S. T., Ruthrauff, D. R., & Liebezeit, J. R. (2020). Shorebird reproductive response to exceptionally early and late springs varies across sites in arctic Alaska. Frontiers in Ecology and Evolution, 8, 577652. https://doi.org/10.3389/ fevo.2020.577652 McKinnon, L., Berteaux, D., & Bêty, J. (2014). Predator-mediated interactions between lemmings and shorebirds: A test of the alternative prey hypothesis. Auk, 131, 619–628. https://doi.org/10.1642/ AUK-13-154.1 Mckinnon, L., Berteaux, D., Gauthier, G., & Bêty, J. (2013). Predatormediated interactions between preferred, alternative and incidental prey in the arctic tundra. Oikos, 122, 1042–1048. https://doi. org/10.1111/j.1600- 0706.2012.20708.x McMahon, B. J., Doyle, S., Gray, A., Kelly, S. B. A., & Redpath, S. M. (2020). European bird declines: Do we need to rethink approaches to the management of abundant generalist predators? Journal of Applied Ecology, 57, 1885–1890. https://doi.org/10.1111/1365-2664.13695 Myllymäki, A., Paasikallio, A., Pankakoski, A., & Kanervo, V. (1971). Removal experiments on small quadrats as a means of rapid assessment of the abundance of small mammals. Annales Zoologici Fennici, 8, 177–185. Nordström, M., Högmander, J., Nummelin, J., Laine, J., Laanetu, N., & Korpimäki, E. (2003). Effects of feral mink removal on seabirds, waders and passerines on small islands in the Baltic Sea. Biological Conservation, 109, 359–368. https://doi.org/10.1016/S0006 -3207(02)00162-3 Opermanis, O. (2004). Appearance and vulnerability of artificial duck nests to avian predators. Journal of Avian Biology, 35, 410–415. Oro, D., Sanz-Aguilar, A., Carbonell, F., Grajera, J., & Torre, I. (2021). Multi-species prey dynamics influence local survival in resident and wintering generalist predators. Oecologia, 197, 437–446. https:// doi.org/10.1007/s00442- 021- 05042-2 Pakanen, V.-M., Aikio, S., Luukkonen, A., & Koivula, K. (2016). Grazed wet meadows are sink habitats for the southern dunlin (Calidris alpina schinzii) due to nest trampling by cattle. Ecology and Evolution, 6, 7176–7187. https://doi.org/10.1002/ece3.2369 Pakanen, V. M., Hagstedt, R., Pauliny, A., & Blomqvist, D. (2021). Survival during the pre-fledging period rather than during post-fledging drives variation in local recruitment of an endangered migratory shorebird, the southern dunlin Calidris alpina schinzii. Journal of Ornithology, 162, 119–124. https://doi.org/10.1007/s10336- 02001814- 4 Pakanen, V. M., Koivula, K., Flodin, L.-Å., Grissot, A., Hagstedt, R., Larsson, M., Pauliny, A., Rönkä, N., & Blomqvist, D. (2017). Between-patch natal dispersal propensity declines with increasing natal patch size and distance to other patches in two endangered southern dunlin Calidris alpina schinzii metapopulations. Ibis, 159, 611–622. https:// doi.org/10.1111/ibi.12463 Pakanen, V.-M., Luukkonen, A., & Koivula, K. (2011). Nest predation and trampling as management risks in grazed coastal PAKANEN et al. meadows. Biodiversity and Conservation, 20, 2057–2073. https:// doi.org/10.1007/s10531- 011- 0075-3 Pakanen, V.-M., Jaakkonen, T., Saarinen, J., Rönkä, N., Thomson, R. L., & Koivula, K. (2018). Migration strategies of the Baltic Dunlin: Rapid jump migration in the autumn but slower skipping type spring migration. Journal of Avian Biology, 49, e01513. https://doi. org/10.1111/jav.01513 Pakanen, V. M., Rönkä, N., Thomson, R., & Koivula, K. (2014). Informed renesting decisions: The effect of nest predation risk. Oecologia, 174, 1159–1167. https://doi.org/10.1007/s00442- 013-2847-9 Panzacchi, M., Linnell, J. D. C., Odden, J., Odden, M., & Andersen, R. (2008). When generalist becomes specialist: Patterns of red fox predation on roe deer fawns under contrasting conditions. Canadian Journal of Zoology, 86, 116–126. https://doi.org/10.1139/Z07-120 Pöysä, H., Jalava, K., & Paasivaara, A. (2016). Generalist predator, cyclic voles and cavity nests: Testing the alternative prey hypothesis. Oecologia, 182, 1083–1093. https://doi.org/10.1007/s0044 2- 016-3728-9 Reif, V., Tornberg, R., Jungell, S., & Korpimäki, E. (2001). Diet variation of common buzzards in Finland supports the alternative prey hypothesis. Ecography, 24, 267–274. https://doi. org/10.1034/j.1600- 0587.2001.240304.x Reiter, M., & Andersen, D. (2011). Arctic foxes, lemmings, and Canada goose nest survival at Cape Churchill, Manitoba. Wilson Journal of Ornithology, 123, 266–276. http://www.jstor.org/stable/23033394 Richards, S. A., Whittingham, M. J., & Stephens, P. A. (2011). Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behavioral Ecology and Sociobiology, 65, 77– 89. https://doi.org/10.1007/s00265- 010-1035-8 Rönkä, A., Koivula, K., Ojanen, M., Pakanen, V.-M., Pohjoismäki, M., Rannikko, K., & Rauhala, P. (2006). Increased nest predation in a declining and threatened Temminck's stint (Calidris temminckii) population. Ibis, 148, 55–65. https://doi. org/10.1111/j.1474-919X.2006.00481.x Rönkä, R., Pakanen, V. M., Pauliny, A., Thomson, R. L., Nuotio, L. M., Pehlak, H., Thorup, O., Lehikoinen, P., Rönkä, A., Blomqvist, D., Koivula, K., & Kvist, L. (2021). Genetic differentiation in an endangered and strongly philopatric, migrant shorebird. BMC Ecol Evo, 21, 125. https://doi.org/10.1186/s12862- 021- 01855- 0 Salo, P., Korpimäki, E., Banks, P. B., Nordström, M., & Dickman, C. R. (2007). Alien predators are more dangerous than native predators to prey populations. Proceedings of the Royal Society B: Biological Sciences, 274, 1237–1243. https://doi.org/10.1098/rspb.2006.0444 Schmidt, K. A., & Ostfeld, R. S. (2008). Numerical and behavioral effects within a pulse-driven system: Consequences for shared prey. Ecology, 89, 635–646. https://doi.org/10.1890/07- 0199.1 Schmidt, K. A., Rush, S. A., & Ostfeld, R. S. (2008). Wood thrush nest success and post-fledging survival across a temporal pulse of small mammal abundance in an oak forest. Journal of Animal Ecology, 77, 830–837. https://doi.org/10.1111/j.1365-2656.2008.01378.x Seymour, A. S., Harris, S., Ralston, C., & White, P. C. (2003). Factors influencing the nesting success of lapwings Vanellus vanellus and behaviour of red fox Vulpes vulpes in lapwing nesting sites. Bird Study, 50, 39–46. https://doi.org/10.1080/00063650309461288 Summers, R. W., Underhill, L. G., & Syroechkovski, E. E., Jr. (1998). The breeding productivity of dark-bellied Brent geese and curlew sandpipers in relation to changes in the numbers of arctic foxes and lemmings on the Taimyr peninsula, Siberia. Ecography, 21, 573–580. https://doi.org/10.1111/j.1600- 0587.1998.tb00549.x Sundell, J., Huitu, O., Henttonen, H., Kaikusalo, A., Korpimäki, E., Pietiäinen, H., Saurola, P., & Hanski, I. (2004). Large-scale spatial dynamics of vole populations in Finland revealed by the breeding success of vole-eating avian predators. The Journal of Animal Ecology, 73, 167–178. https://doi.org/10.1111/j.1365-2656.2004.00795.x Tornberg, R., Helle, P., & Korpimäki, E. (2011). Vulnerability of black grouse hens to goshawk predation: Result of food supply or | PAKANEN et al. predation facilitation? Oecologia, 166, 577–584. https://doi. org/10.1007/s00442- 010-1884-x Tornberg, R., Reif, V., & Korpimäki, E. (2012). What explains forest grouse mortality: Predation impacts of raptors, vole abundance or weather conditions? International Journal of Ecology, 375260, 1–10. https:// doi.org/10.1155/2012/375260 Valkama, J., Korpimäki, E., Arroyo, B., Beja, P., Bretagnolle, V., Bro, E., Kenward, R., Mañosa, S., Redpath, S. M., Thirgood, S., & Viñuela, J. (2005). Birds of prey as limiting factors of gamebird populations in Europe: A review. Biological Reviews, 80, 171–203. https://doi. org/10.1017/S146479310 400658X Wegge, P., & Storaas, T. (1990). Nest loss in capercaillie and black grouse in relation to the small rodent cycle in Southeast Norway. Oecologia, 82, 527–530. https://doi.org/10.1007/BF00319796 Weiser, E. L., Brown, S. C., Lanctot, R. B., Gates, H. R., Abraham, K. F., Bentzen, R. L., Bêty, J., Boldenow, M. L., Brook, R. W., Donnelly, T. F., English, W. B., Flemming, S. A., Franks, S. E., Gilchrist, H. G., Giroux, M.-A., Johnson, A., Kendall, S., Kennedy, L. V., Koloski, L., … Sandercock, B. K. (2018). Effects of environmental conditions on 11 of 11 reproductive effort and nest success of Arctic-breeding shorebirds. Ibis, 160, 608–623. https://doi.org/10.1111/ibi.12571 White, G. C., & Burnham, K. P. (1999). Program MARK: Survival estimation from populations of marked animals. Bird Study, 46, 120–139. https://doi.org/10.1080/00063659909477239 Zanette, L., & Jenkins, B. (2000). Nesting success and nest predators in forest fragments: A study using real and artificial nests. Auk, 117, 445–454. https://doi.org/10.1093/auk/117.2.445 How to cite this article: Pakanen, V.-M., Tornberg, R., Airaksinen, E., Rönkä, N., & Koivula, K. (2022). The abundance of small mammals is positively linked to survival from nest depredation but negatively linked to local recruitment of a ground nesting precocial bird. Ecology and Evolution, 12, e9292. https://doi.org/10.1002/ece3.9292