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Author(s): Sara Fraixedas, Andreas Lindén, Magne Husby & Aleksi Lehikoinen Title: Declining peatland bird numbers are not consistent with the increasing Common Crane population Year: 2020 Version: Published version Copyright: The Author(s) 2020 Rights: CC BY 4.0 Rights url: http://creativecommons.org/licenses/by/4.0/ Please cite the original version: Fraixedas, S., Lindén, A., Husby, M. et al. Declining peatland bird numbers are not consistent with the increasing Common Crane population. J Ornithol (2020). https://doi.org/10.1007/s10336-020- 01777-6. Vol.:(0123456789) Journal of Ornithology https://doi.org/10.1007/s10336-020-01777-6 ORIGINAL ARTICLE Declining peatland bird numbers are not consistent with the increasing Common Crane population Sara Fraixedas1,2  · Andreas Lindén3,4 · Magne Husby5 · Aleksi Lehikoinen6 Received: 27 October 2019 / Revised: 5 March 2020 / Accepted: 26 March 2020 © The Author(s) 2020 Abstract The Common Crane (Grus grus) population has experienced an unprecedented increase across Europe during the last decades. Although cranes feed mostly on invertebrates, amphibians and berries during the breeding season, they can also eat eggs and young of other birds. Therefore, conservationists have raised concerns about the potential predatory effect of cranes on wetland avifauna, but the effects of crane predation on bird numbers have so far not been investigated. We here test the relationship between the crane and peatland bird population’ abundances in Finland for five common wader and passerine species, and a set of seven less common waders, using line-transect data spanning from 1987 to 2014. We found that the population densities of two small passerines (Meadow Pipit Anthus pratensis and Western Yellow Wagtail Motacilla flava) and one wader species (Wood Sandpiper Tringa glareola) were positively associated with crane numbers, probably related to a protective effect against nest predators. For the two other common species and the set of less common waders, we did not find any significant relationships with crane abundance. None of the species was influenced by the (lagged) effect of crane presence (i.e. years since crane was first observed). Peatland drainage was responsible for most species’ negative densities, indicating the need to protect and restore peatlands to mitigate the loss of peatland bird diversity in Finland. In addition, openness, wetness and area size were important peatland characteristics positively influencing most of the studied bird populations. The development in crane and other mire bird numbers in Europe should be monitored regularly to reveal any possible future predatory effects contributing to the shaping of the peatland bird community. Keywords Predation-prey interaction · Mire · Fennoscandia · Habitat degradation Zusammenfassung Bestandsrückgänge bei Moorvögeln stehen nicht im Einklang mit der wachsenden Kranichpopulation Während der letzten Jahrzehnte hat der Kranich (Grus grus) in Europa ein bisher nicht dagewesenes Populationswachstum erfahren. Obwohl sich Kraniche zur Brutzeit vorwiegend von Wirbellosen, Amphibien und Beeren ernähren, können sie auch die Eier und Küken anderer Vogelarten fressen. Daher haben Naturschützer Bedenken über einen möglichen Prädationseffekt der Kraniche auf die Vogelwelt in Feuchtgebieten geäußert; bislang wurden die Auswirkungen der Prädation durch Kraniche auf die Vogelbestände allerdings noch nicht erforscht. Hier untersuchten wir anhand von Linientransektdaten aus dem Zeitraum von 1987–2014 das Verhältnis zwischen den Populationsbeständen von Kranichen und anderen Moorvögeln – fünf Communicated by O. Krüger. * Sara Fraixedas sara.fraixedas@helsinki.fi 1 Helsinki Institute of Sustainability Science (HELSUS), Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland 2 Global Change and Conservation Lab, Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland 3 Novia University of Applied Sciences, 10600 Ekenäs, Finland 4 Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland 5 Section of Science, Nord University, 7600 Levanger, Norway 6 The Helsinki Lab of Ornithology (HelLO), Finnish Museum of Natural History, University of Helsinki, 00014 Helsinki, Finland Journal of Ornithology 1 3 häufige Limikolen- und Singvogelarten sowie einer Gruppe von sieben weniger häufigen Limikolenarten—in Finnland. Es ergab sich, dass die Populationsdichten zweier kleiner Singvogelarten (Wiesenpieper Anthus pratensis und Schafstelze Motacilla flava) sowie einer Limikolenart (Bruchwasserläufer Tringa glareola) in einem positiven Zusammenhang mit der Anzahl der Kraniche standen, was vermutlich auf eine Schutzwirkung vor Nesträubern zurückzuführen ist. Bei den beiden anderen häufigen Arten und der Gruppe weniger häufiger Limikolen ließ sich kein signifikanter Zusammenhang mit den Kranichbeständen feststellen. Keine der Arten wurde durch einen (zeitversetzten) Effekt der Anwesenheit der Kraniche (d. h. der Jahre seit dem ersten Auftreten der Kraniche) im Gebiet beeinflusst. Die Trockenlegung von Mooren zeichnete bei den meisten Arten verantwortlich für negative Dichtewerte, was die Notwendigkeit von Schutz- und Renaturierungsmaßnahmen für Moorgebiete unterstreicht, um dem Diversitätsverlust bei Moorvogelarten in Finnland entgegenzuwirken. Darüber hinaus waren Offenheit, Nässe und die Gebietsgröße der Moore wichtige Merkmale, die auf die meisten der untersuchten Vogelpopulationen eine positive Wirkung hatten. Die Bestandsentwicklungen bei Kranichen und anderen Moorvögeln in Europa sollten regelmäßig dokumentiert werden, um mögliche zukünftige Prädationseffekte aufzudecken, welche Einfluss auf die Zusammensetzung der Vogelgemeinschaften in Moorgebieten haben könnten. Introduction The Common Crane (Grus grus, hereafter crane) is a species of conservation importance that has been listed in Annex I of the Birds Directive since 1979 (Nilsson et al. 2019). During the last 40 years, the European population has expe- rienced a significant increase (Vegvari and Tar 2002; Mewes et al. 2010; Harris and Mirande 2013), recovering from a big decline of the breeding population that took place until the 1980s, largely associated with hunting, habitat loss and habitat degradation (European Union 2019; Nilsson et al. 2019). According to the last estimates, the population along the Western European flyway, one of the two major migra- tory routes used by European cranes (Prange 2005) and the Baltic–Hungarian route (used mostly by north-eastern European cranes; Prange 2005), consists of about 500,000 individuals: 350,000 account for the Western European fly- way and 150,000 for the Baltic–Hungarian route (Alonso et al. 2016; Prange 2016). The main reasons responsible for the population increase are the protection and manage- ment of many crucial staging, roosting and wintering sites under Natura 2000 (and other funding instruments from the European Union, such as LIFE programmes; e.g. Salvi et al. 1995), landscape-level changes (i.e. creation and restoration of large wetlands) and changed farming practices in the agri- cultural industry (increased food availability, e.g. due to the intensification of maize cultivation; Nilsson 2002; Mewes et al. 2010; Salvi 2010a, b; European Union 2019). The crane’s diet during the breeding season (including both breeding and non-breeding birds) mostly consists of invertebrates, amphibians and berries (Nowald 2001; Måns- son et al. 2013). Less commonly, cranes can also eat eggs and young of smaller birds, and exceptionally also e.g. adult warblers (Cramp and Simmons 1980). In the Norwegian literature, small birds are mentioned as part of cranes diet (Haftorn 1971), and cases have been documented of cranes stealing and eating the eggs of Common Quails Coturnix coturnix from an artificial nest with camera surveillance (Leistad 2011). Along these lines, concerns have been raised among conservationists about the impact of the increasing crane population on vulnerable birdlife reliant on wetlands due to predation on eggs and chicks (Harvey et al. 1968), and especially in the case of wader species in areas with high crane densities (Nilsson 2016). Despite the increase in crane numbers, evidence of the effects of crane predation on wetland biodiversity is lacking. In Finland, cranes are common breeders and passage migrants (Prange 2016). As in other parts of Europe, sur- veys in Finland have revealed that the national population has increased (Väisänen et al. 1998; Miikkulainen 2001; Leito et al. 2003), and that the breeding-season numbers have increased over six-fold since the early 1980s (Väisänen et al. 2018). The species currently inhabits most parts of Finland (Fig. 1), with an estimate of 37,000–51,000 breeding pairs (Lehikoinen et al. 2019). Breeding cranes were earlier found in rather low densities, generally in large peatlands, but an increasing number of cranes now breed in large reed beds around lakes and sea bays (Karlin 1995; Leito et al. 2005). Extensive drainage of peatlands has been done dur- ing the last decades, mostly for improving forestry (Turunen 2008). This has arguably resulted in certain changes in the distribution and habitat use of cranes (Leito et al. 2005). The total drained area is equivalent to 57,000 km2, which is about 60% of the original peatland area (Vasander 1996; Peltola 2004). Because of this practice, combined with large-scale extraction of peat for fuel, nowadays less than 25% of the original peatland area remains in southern and central Fin- land (Aapala et al. 1996; Auvinen et al. 2007). In a recent study carried out by Fraixedas et al. (2017), drainage of mires was found to negatively affect most of the studied species’ densities, since it removes the typi- cal peatland properties beneficial for peatland birds (e.g. openness, wetness, and low tree height). However, the impact of predation on peatland bird populations, such as the effect of cranes, was not examined. Assessing both biotic interactions and environmental factors may provide Journal of Ornithology 1 3 a more comprehensive understanding of the reasons why species populations decline and/or change their distribu- tions in the landscape, potentially leading to more ade- quate management solutions (Evans 2004; Andradas et al. 2019; Pass et al. 2019). As a follow-up to the study carried out by Fraixedas et al. (2017), this paper explores how the continuously increasing crane population affects wetland bird abundances based on 28 years of Finnish line-transect data, taking into account the species-specific habitat selec- tion and spatiotemporal trends of five common peatland birds and seven less common peatland wader species. We hypothesize that, if predation by cranes may reduce densities of smaller bird species in peatlands (Berg et al. 1992; Haddad et al. 2000), temporal trends will be more negative in sites after the crane has appeared for the first time, compared to those sites where a crane has not yet appeared. If this is the case, we predict that the species of concern may also avoid areas with many cranes, caus- ing an immediate negative relationship between crane and focal species abundances. Alternatively, bird abundances may be positively linked to crane numbers, if either or both species of concern prefer each other’s company, e.g. if smaller birds enjoy the protection provided by cranes against other predators. Positive or negative effects of this kind can also appear due to similar or different specific habitat selection if the model fails to account for them with the habitat variables included. Furthermore, if no connec- tion between changes in bird numbers is found, we expect the impact of crane predation to be so small that habitat quality is more relevant than predation. Fig. 1 Crane distribution in Finnish peatlands based on line-transect data divided into three different periods. The graph depicts only those sites when a crane was first observed: 24 sites (1987–1996), 21 sites (1997–2005) and 261 sites (2006–2014). Note that many new crane observations were detected during the last period, coinciding with the implemen- tation of a systematic sampling Journal of Ornithology 1 3 Materials and methods Study area and habitat data We used the same classification of peatland habitats as in Fraixedas et  al. (2017) and obtained the data from the Finnish line transect bird censuses. This monitoring scheme is coordinated by the Finnish Museum of Natu- ral History with data contributed by volunteers. Censuses were done between late May and early July (but mainly in June), when the vast majority of migratory birds have arrived at their breeding grounds (Koskimies and Väisänen 1991), therefore covering well the focal species of this study. Each route was surveyed only once per breeding season and most often by the same observer across years. Two distance belts are distinguished along the line tran- sect routes: the main belt (25 + 25 m wide) and the sup- plementary belt, including pairs observed > 25 m from the route and all individuals flying over (Koskimies and Väisänen 1991; Laaksonen and Lehikoinen 2013). Since 1986, habitat data have been recorded from the main belt and structured in twelve habitat categories (see Fraixedas et al. 2015 for additional information). In this study, only information from the habitat categories of forested and open peatland, and their corresponding sub-categories, was considered to extract our habitat-related variables (i.e. openness, wetness, area size, tree height, and two variables related to the drainage of mires). For more information on the selected habitat categories, see Fraixedas et al. (2017). Our dataset included censuses done in 1987–2014, and the observed cranes were presumed to include both breeding and non-breeding birds. There is no clear way to distin- guish between them with the line transect census method (i.e. crane numbers were counted during the whole census and we do not necessarily know where the birds breed). Both breeding and non-breeding birds can cause preda- tion, and we assumed that if there were a larger number of cranes observed, they could increase the predation risk in the area. Study species We chose the same study species as in Fraixedas et  al. (2017), with the exception of Rustic Bunting Emberiza rus- tica, since it has very different habitat requirements inside the range of peatland habitats compared to the other studied species. Rustic Buntings prefer forest peatlands and edges of natural peatlands (Väisänen and Rauhala 1983; Fraixe- das et al. 2017), while all other study species favour inte- rior peatland habitat, which is clearly the habitat of con- servation concern. Our five selected species were: Meadow Pipit Anthus pratensis, Common Reed Bunting Emberiza schoeniclus, Western Yellow Wagtail Motacilla flava, Common Snipe Gallinago gallinago and Wood Sandpiper Tringa glareola. We also analysed a set of seven less com- mon waders whose observations were summed: Broad-billed Sandpiper Calidris falcinellus, Ruff Calidris pugnax, Whim- brel Numenius phaeopus, Jack Snipe Lymnocryptes mini- mus, Red-necked Phalarope Phalaropus lobatus, European Golden Plover Pluvialis apricaria and Spotted Redshank Tringa erythropus. More details on why these species were chosen, and how we determine their general habitat selec- tion, are found in Fraixedas et al. (2017). Crane observations as explanatory variables We used the habitat data to extract information on crane abundance “crane.ab” by taking the observations from both the main and supplementary belts for each mire route (i.e. transect). The rationale behind using additional observations from the supplementary belt is that cranes observed outside the main belt may also have a predatory effect within the main belt, as they need large areas to search for food (Cramp and Simmons 1980). Crane observations were extracted in the same way as for the other peatland species. The main difference between crane observations and those calculated for the selected study species was that in the latter case, observations had been previously estimated only from the main belt for each mire route section (e.g. one observation of Wood Sandpiper found in the main belt of 450 m of open wet mires), and not for the whole mire route (see description of mire routes and “site” in Fraixedas et al. 2017). Therefore, the total amount of crane observations found in the main and supplementary belts of a particular mire route was the same for each mire route section. Based on the variable accounting for crane abundance, we created a second variable describ- ing the years elapsed from the first crane observation on a given transect “crane.lag”, being zero in the year when a crane was first observed (i.e. a lagged effect on the trend). In other words, the effect of crane presence defines a partial trend, describing how temporal trends differ in crane sites compared to sites without cranes, and the first presence of crane at a given line-transect represents the turning point of the overall temporal trend. We omitted cases where routes were counted only once. Given that all variation in crane abundance happened at the transect level, it was important that the random effect of transect was properly estimated, having several observations from all levels modelled as random effects. Statistical analyses We first analysed the temporal trend in crane abundance, using crane counts recorded per transect and year. For this, Journal of Ornithology 1 3 we applied a generalized linear mixed model (GLMM), with a Poisson error distribution and a logarithmic link- function. We placed “year” (continuous variable, centred to zero mean) in the fixed effects part to model the tem- poral trend, and included “year.f” (factor variable) and “transect” as random effects on the intercept. “Transect” accounts for variation in how good the line transect is for observing cranes on average, and “year.f” accounts for the common between-year variation (inducing spa- tial synchrony) not explained by the fitted trend. We first included also an observation-level random effect (OLRE) to model the extra-Poisson variation present in the data, but because the estimated standard deviation was zero, we refitted the model excluding the OLRE. To effectively model the trend in crane abundance (observed numbers per counting effort), we added the natural logarithm of the counted transect length as an offset variable in the model. The strength of temporal autocorrelation was tested by extracting the unexplained annual variation (i.e. the ran- dom effects of year), and arranging them in a time series to which the autocorrelation function “acf” was applied. Second, we used the models in Fraixedas et al. (2017), which explain species’ densities using peatland habitat char- acteristics (openness, wetness, area size, tree height, and two variables related to the drainage of mires) and spatiotempo- ral trends (latitude, longitude and year, modelling patterns in population densities not explained by habitat), and defined them as the null models. To understand whether crane pre- dation had any effect on species densities, we added to the null models the two crane variables described in the previous section (Table 1). We did this separately for each species and the set of less common waders. All models’ structures fol- lowed a separate generalized linear mixed model (GLMM) with a logarithmic link-function and Poisson error distribu- tion. There were differences between the covariates included in each species’ model (Table 1). Similarly, some species’ models included an OLRE accounting for the within-site extra-Poisson variation εis, whereas all models included the among-site random variation as (i.e. variation at the mire- route section level; Table 1). Therefore, the full model (the one including all habitat variables, the spatiotemporal trends and the two random effects) was altered in several cases. All models were fitted in R software version 3.6.1 (R Core Team 2018) using the package lme4 (Bates et al. 2015). Results Crane abundance showed a significant log-linear increase (b = 0.075, SE = 0.010, Z value = 7.33, p value < 0.0001; Fig. 2), which corresponds to an annual increase of 7.8%, or an eightfold increase during the 28-year study period. The estimated random-effect standard deviations of transect and year were 1.333 and 0.126, respectively. There was no Table 1 Description of the models for each study species and the less common wader set including the two crane variables defined in “Crane observations as explanatory variables” section: crane abundance “crane.ab” and years since first crane observation “crane.lag” The response variable is defined as the number of pairs observed in the main belt for the year i in site s. Note that for Common Reed Bunting and Common Snipe, the observation-level random effect (OLRE) of the site was not included in the model because the estimated standard devia- tion was zero according to Fraixedas et al. (2017). In other cases, the variable describing peatland extraction areas “peatext” was omitted from the null model since observations from that habitat were very scarce in our dataset, and therefore it was not possible to include this category in the analysis. Because an offset variable ln(lengthis) was introduced to scale the expected numbers to the km walked in a certain peatland habitat, we effectively modelled population densities (pairs/km). More information can be found in Table 2 Species Model structure Meadow pipit ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β5 × peatextis + β6 × wetnessis + β7 × areasizeis+ β8 × treeheightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as+ εis Common reed bunting ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β5 × peatextis + β6 × wetnessis + β7 × areasizeis+ β8 × treeheightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as Western yellow wagtail ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β5 × peatextis + β6 × wetnessis + β7 × areasizeis+ β8 × treeheightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as+ εis Common snipe ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β6 × wetnessis + β7 × areasizeis+ β8 × tree- heightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as Wood sandpiper ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β6 × wetnessis + β7 × areasizeis+ β8 × tree- heightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as+ εis Less common waders ln λis = α + β1 × crane.ab + β2 × crane.lag + β3 × open + β4 × ditchedis + β5 × peatextis + β6 × wetnessis + β7 × areasizeis+ β8 × treeheightis + β9 × lats + β10 × lons + β11 × yeari + β12 × lats × yeari + β13 × lons × yeari + offset(ln(lengthis)) + as+ εis Journal of Ornithology 1 3 relevant autocorrelation in the annual unexplained variation, the estimated correlation with lag 1 being < 0.1. In general terms, crane abundance had statistically sig- nificant positive effects in half of the cases, whereas we did not find any (lagged) effect of crane presence on the trend direction for any of the species. The non-significant effects related to crane occurrence implies that they did not provide additional information beyond the spatiotemporal trends and habitat-related variables included in the models (Table 1). The species with a significant positive immedi- ate effect of crane abundance were Meadow Pipit, West- ern Yellow Wagtail and Wood Sandpiper (Meadow Pipit: b = 0.101, SE = 0.035, Z value = 2.86, p value = 0.004; West- ern Yellow Wagtail: b = 0.084, SE = 0.031, Z value = 2.68, p value = 0.007; Wood Sandpiper: b = 0.066, SE = 0.033, Z value = 2.01, p value = 0.044; Table 2). Compared to the null models (in Fraixedas et al. 2017), we also detected a few differences in the habitat selection and spatiotemporal trends of some species after includ- ing the two crane variables. For Meadow Pipit, the posi- tive effect of area size in open peatlands, which was ear- lier found to be significant, was not detected in this case (Table 2). In addition, this species showed an almost sig- nificant latitudinal density shift northwards during the period 1987−2014 (b = 0.058, SE = 0.034, Z value = 1.68, p value = 0.093; Table 2). For Wood Sandpiper, the latitu- dinal shift northwards was now only a tendency (b = 0.074, SE = 0.038, Z value = 1.93, p value = 0.054; Table 2). For the set of less common waders, the positive effects of both wetness and area size were reinforced and now statistically significant (wetness: b = 0.426, SE = 0.201, Z value = 2.11, p value = 0.035; area size: b = 0.400, SE = 0.167, Z value = 2.39, p value = 0.017; Table 2), while they were pre- viously only tendencies. All other effects related to drain- age were maintained (see Fraixedas et al. 2017 to view the results of the aforementioned null models). Fig. 2 Estimated crane densities (pairs/km transect) in Finnish mires (main and supplementary belts) over the study period, with shaded areas representing 95% confidence intervals Table 2 Coefficients of the log-linear Poisson mixed models explain- ing variation in peatland bird densities for each study species and the set of less common waders Coefficients Estimate SE Z value p value Meadow pipit  α intercept − 3.575 0.189 − 18.87 < 0.0001  β1 crane.ab 0.101 0.035 2.86 0.004  β2 crane.lag − 0.048 0.036 − 1.34 0.180  β3 open 2.316 0.188 12.35 < 0.0001  β4 ditched − 0.853 0.272 − 3.13 0.002  β5 peatext 0.089 0.494 0.18 0.857  β6 wetness 0.144 0.103 1.41 0.159  β7 areasize 0.090 0.094 0.95 0.341  β8 treeheight − 2.229 0.623 − 3.58 < 0.001  β9 lat 0.140 0.034 4.11 < 0.0001  β10 lon − 0.197 0.068 − 2.91 0.004  β11 years − 0.474 0.100 − 4.72 < 0.0001  β12 lat × years 0.058 0.034 1.68 0.093  β13 lon × years 0.072 0.082 0.88 0.380  σs among-site SD 0.804 − − −  σe observation-level SD 0.336 − − − Common reed bunting  α intercept − 4.142 0.284 − 15.51 < 0.0001  β1 crane.ab 0.010 0.071 1.12 0.262  β2 crane.lag 0.035 0.054 0.83 0.408  β3 open 0.364 0.225 1.61 0.108  β4 ditched − 0.648 0.289 − 2.21 0.027  β5 peatext 0.822 0.882 0.93 0.355  β6 wetness 0.125 0.221 1.19 0.236  β7 areasize − 0.084 0.200 − 0.15 0.879  β8 treeheight − 1.536 0.704 − 2.32 0.021  β9 lat 0.263 0.066 3.81 < 0.001  β10 lon − 0.056 0.126 − 0.56 0.578  β11 years 0.083 0.214 1.13 0.260  β12 lat × years 0.007 0.084 0.29 0.775  β13 lon × years 0.046 0.173 0.32 0.751  σs among-site SD 1.700 − − −  σe observation-level SD − − − − Western yellow wagtail  α intercept − 2.660 0.136 − 19.49 < 0.0001  β1 crane.ab 0.084 0.031 2.68 0.007  β2 crane.lag 0.032 0.026 1.23 0.220  β3 open 1.412 0.143 9.90 < 0.0001  β4 ditched − 1.405 0.211 − 6.65 < 0.0001  β5 peatext − 0.879 0.781 − 1.13 0.260  β6 wetness − 0.056 0.108 − 0.52 0.606 β7 areasize 0.098 0.093 1.06 0.291  β8 treeheight − 2.292 0.428 − 5.36 < 0.0001  β9 lat 0.213 0.030 7.12 < 0.0001  β10 lon − 0.097 0.058 − 1.67 0.095  β11 years − 0.351 0.085 − 4.12 < 0.0001  β12 lat × years 0.071 0.032 2.20 0.028 Journal of Ornithology 1 3 Discussion In this study, we explored possible interspecific interac- tions based on the idea that both breeding and non-breeding cranes may act as predators on peatland bird species, there- fore having an impact on their populations. As an exten- sion of the work carried out by Fraixedas et al. (2017), we tested whether crane abundance and the (lagged) effect of crane presence on the trend direction could explain variation in densities of peatland bird species while also consider- ing habitat variables and spatiotemporal trends. The effect of cranes on wetland bird’s population dynamics is poorly known. Our work aimed to contribute with scientific knowl- edge about this subject. The three detected effects of cranes were in all cases positive and concerned immediate effects rather than changes in the direction of the temporal trend. The significant positive effects of crane abundance on Meadow Pipit, Western Yellow Wagtail and Wood Sandpi- per seem to reflect habitat selection or nesting place, rather than long-term population changes. These results could be related to a positive ecological interaction between cranes and passerines/waders, with the formation of a protective nesting association (Polak 2014). This kind of associations often involve an aggressive species (e.g. owl or raptor, and in our case cranes) and a less aggressive species, like is the case of a passerine or a wader, which builds its nest close by taking advantage of the defensive behaviour of the pro- tecting species (Polak 2014). This protective effect against nest predators (Tornberg et al. 2016) has been observed for instance between Northern Lapwings Vanellus vanel- lus and precisely Western Yellow Wagtail and Meadow Pipit in southwest Sweden, an association that the authors described as an anti-predator adaptation (Eriksson and Göt- mark 1982). The anti-predator adaptation was also pointed Table 2 (continued) Coefficients Estimate SE Z value p value  β13 lon × years 0.002 0.071 0.03 0.973  σs among-site SD 0.789 − − −  σe observation-level SD 0.556 − − − Common snipe  α intercept − 5.195 0.434 − 11.96 < 0.0001  β1 crane.ab 0.033 0.076 0.43 0.665  β2 crane.lag − 0.131 0.082 − 1.60 0.109  β3 open 1.823 0.294 6.20 < 0.0001  β4 ditched − 0.746 0.493 − 1.51 0.130  β5 peatext − − − −  β6 wetness 0.612 0.245 2.50 0.013  β7 areasize − 0.115 0.203 − 0.56 0.572  β8 treeheight − 0.002 0.953 − 0.00 0.999  β9 lat − 0.018 0.067 − 0.26 0.795  β10 lon − 0.061 0.153 − 0.40 0.691  β11 years 0.085 0.240 0.35 0.724  β12 lat × years 0.076 0.086 0.88 0.379  β13 lon × years 0.100 0.220 0.46 0.650  σs among-site SD 1.781 − − −  σe observation-level SD − − − − Wood sandpiper  α intercept − 2.833 0.139 − 20.31 < 0.0001  β1 crane.ab 0.066 0.033 2.01 0.044  β2 crane.lag − 0.006 0.030 − 0.19 0.852  β3 open 1.517 0.144 10.53 < 0.0001  β4 ditched − 1.056 0.230 − 4.58 < 0.0001  β5 peatext − − − −  β6 wetness 0.499 0.117 4.25 < 0.0001  β7 areasize 0.016 0.093 0.17 0.863  β8 treeheight − 1.218 0.465 − 2.62 0.009  β9 lat 0.206 0.032 6.46 < 0.0001  β10 lon 0.009 0.064 0.14 0.887  β11 years − 0.105 0.100 − 1.04 0.297  β12 lat × years 0.074 0.038 1.93 0.054  β13 lon × years − 0.046 0.086 − 0.54 0.590  σs among-site SD 0.657 − − −  σe observation-level SD 0.666 − − − Less common waders  α intercept − 5.149 0.333 − 15.46 < 0.0001  β1 crane.ab − 0.025 0.058 − 0.43 0.664  β2 crane.lag − 0.021 0.052 − 0.40 0.688  β3 open 2.508 0.293 8.57 < 0.0001  β4 ditched − 0.580 0.476 − 1.22 0.223  β5 peatext − 0.813 0.810 − 1.00 0.316  β6 wetness 0.426 0.201 2.11 0.035  β7 areasize 0.400 0.167 2.39 0.017  β8 treeheight − 2.336 1.059 − 2.21 0.027  β9 lat − 0.007 0.049 − 0.14 0.890  β10 lon − 0.115 0.106 − 1.09 0.278  β11 years 0.228 0.189 1.21 0.226 Table 2 (continued) Coefficients Estimate SE Z value p value  β12 lat × years − 0.087 0.064 − 1.36 0.176  β13 lon × years 0.060 0.159 0.38 0.704  σs among-site SD 0.414 − − −  σe observation-level SD 1.710 − − − The models include the effects of crane abundance (β1; “crane.ab”) and the (lagged) effect of crane presence on the trend direction (β2; “crane.lag”) (see “Crane observations as explanatory variables” sec- tion). Standard errors (SE), Z statistics and p values are presented. Statistically significant effects (p < 0.05) are in bold and tendencies (p < 0.1) are italicised. The estimated random-effect standard devia- tions (SD) of both “site” (random intercept of mire route section) and “observation ID” (within-site extra-Poisson variation) are also shown in the table. In two cases (Common Reed Bunting and Com- mon Snipe), the models are presented without the observation-level random effect (OLRE) since the site-level SD was estimated as zero (Fraixedas et al. 2017). The effects of “lat” and “lon” are per 100 km, and the effect of year is per decade Journal of Ornithology 1 3 out by Tryjanowski (2001), who found that the presence of Northern Raven Corvus corax had a positive impact on the breeding bird community of open farmland in western Poland, including the Western Yellow Wagtail. In Finland and Norway, the breeding Northern Goshawk Accipiter gen- tilis has a higher abundance of breeding open cup nesters close to its nests than further away, probably because the species poses little risk to small songbirds that benefit from protection against nest predators (Mönkkönen et al. 2007). It is worth highlighting the possibility that these results may simply reflect similar habitat selection in crane, Meadow Pipit, Western Yellow Wagtail and Wood Sandpiper on the focal spatial scale. However, such a habitat effect can only be caused by variation that cannot be explained with the habitat covariates included in the model. As for peatland habitat-related variables and spati- otemporal trends, Boström and Nilsson (1983) found that most wader densities were higher in larger and wetter open raised bogs, which corresponds with our results of the area having a positive effect on less common wader densities and wetness being an important factor for all our study wader species. Interestingly, the models including the crane variables reinforced in some cases the peatland habitat selection shown by birds (e.g. for the set of less common waders), strengthening the idea that openness, wetness, and area size (the latter particularly relevant in the case of less abundant waders) are common peatland char- acteristics benefitting species densities (Järvinen and Sam- malisto 1976; Väisänen et al. 1998). Conversely, tree height had a negative effect on almost all study species. All other effects related to drainage were maintained as in Fraixedas et al. (2017), and Common Snipe and the set of less com- mon waders were the only species not affected by ditching in forest mires. The new tendency detected for Meadow Pipit showing shifts in species distribution towards north- ern latitudes, a pattern followed by some of the study spe- cies (Western Yellow Wagtail and Wood Sandpiper), could possibly indicate the effects of climate change (Chen et al. 2011) and a higher level of habitat degradation in southern Finland (Virtanen et al. 2003). The density shift signals of climate change on peatland bird species are still relatively weak at present (Fraixedas et al. 2017), although climate change could incur in great degradation in the future (Virk- kala et al. 2008). In fact, the coupled effects of climate change and peatland habitat degradation could be arguably responsible for worsening the current population levels of peatland species, as has already been identified in other habitats (e.g. Burns et al. 2016). Given that the current panorama is being favourable to cranes, the population will likely continue to increase, and knowledge of the impact on wetland biodiversity and the ecological factors determining variation in predation rates may become relevant to species’ conservation. For example, non-breeding cranes congregating in large num- bers outside peatland habitats, such as other wetlands or agricultural areas, may locally contribute with predatory effects not covered by this study. To conclude, cranes seem to have positive effects on a subset of the peatland bird species populations investi- gated. Our results indicate that cranes may benefit some passerine and wader species, perhaps through protection against nest predators. Rather than pointing out strong or obvious effects of the crane, our results reinforce the need to improve the effectiveness of current management actions and strategies in mitigating loss of peatland bird diversity in Finland. Better conservation plans for peatland bird species are vital, especially taking into account that nest predation may increase with peat harvesting (Haddad et al. 2000; Fraixedas et al. 2017). In this sense, programs such as the Helmi Habitat Program launched by the Finn- ish Ministry of the Environment, which promotes peatland conservation and restoration among other issues, are key to enhance biodiversity and mitigate climate change (Min- istry of the Environment 2019). Acknowledgements Open access funding provided by University of Helsinki including Helsinki University Central Hospital. We thank all volunteers who contributed to data collection and the personnel at the Finnish Museum of Natural History (Luomus) who is in charge of maintaining the databases. Álvaro Fernández-Llamazares provided help with Fig. 1. The Finnish common bird monitoring has been sup- ported by the Finnish Ministry of the Environment. S. Fraixedas was financially supported by a post-doctoral fellowship from the Helsinki Institute of Sustainability Science (HELSUS), and A. Lehikoinen by the Academy of Finland (grant 275606). Author contributions All authors contributed to the study conception and design. Material preparation and data analysis were performed by Aleksi Lehikoinen, Andreas Lindén and Sara Fraixedas. The first draft of the manuscript was written by Sara Fraixedas and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The datasets generated during and/or analysed dur- ing the current study are available from the corresponding author on reasonable request. Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will Journal of Ornithology 1 3 need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. References Aapala K, Heikkilä R, Lindholm T (1996) Protecting the diversity of Finnish mires. 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