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Habitat preferences of two sparrow species are modified by abundances of

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DOI: 10.1093/cz/zow069

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 Current Zoology, 2016, 62(4), 357–368
doi: 10.1093/cz/zow069
Article

Article

Habitat preferences of two sparrow species

are modified by abundances of other birds in
an urban environment
a,
Piotr SKORKA
 *, Katarzyna SIERPOWSKAb, Andzelika
_ HAIDTc, Łukasz MYCZKOb,
Anna EKNER-GRZYBd, Zuzanna M. ROSINe, Zbigniew KWIECINSKI b,f
,
b b g
Joanna SUCHODOLSKA , Viktoria TAKACS , Łukasz JANKOWIAK ,
Oskar WASIELEWSKIb, Agnieszka GRACLIKb, Agata J. KRAWCZYKh,
Adam KASPRZAKb, Przemysław SZWAJKOWSKIi,j, Przemysław WYLEGAŁAk,
Anna W. MALECHAb, Tadeusz MIZERAb, and Piotr TRYJANOWSKIb
a
Institute of Nature Conservation, Polish Academy of Sciences, Al. Mickiewicza 33, 31-120 Krakow, bInstitute of
Zoology, Poznan University of Life Sciences, Wojska Polskiego 71 C, 60-625 Poznan, cDepartment of Forest
Ecology, Forest Research Institute, Braci Lesnej 3, Sȩkocin Stary, 05-090 Raszyn, dDepartment of Behavioural
Ecology, eDepartment of Cell Biology, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614
Poznan, fZoological Garden, Browarna 25, 61-063 Poznan, gDepartment of Vertebrate Zoology and Anthropology,
University of Szczecin, Wa˛ska 13, 71-412 Szczecin, hDepartment of Systematic Zoology, Faculty of Biology, Adam
Mickiewicz University, Umultowska 89, 61-614 Poznan, iDivision of Rural Tourism, Poznan University of Life
Sciences, Witosa 45/114b, 61-693 Poznan, jDepartment of Animal Physiology and Biochemistry, Poznan University
of Life Sciences, Wołynska 35, 60-637 Poznan, and kPolish Society for Nature Conservation Salamandra, Stolarska
7/3, 60-788 Poznan, Poland
*Address correspondence to Piotr Skorka. E-mail: pskokra@iop.krakow.pl.
Received on 11 August 2015; accepted on 23 December 2015

Abstract
Every species has certain habitat requirements, which may be altered by interactions with other
co-occurring species. These interactions are mostly ignored in predictive models trying to identify
key habitat variables correlated with species population abundance/occurrence. We investigated
how the structure of the urban landscape, food resources, potential competitors, predators, and
interaction between these factors influence the abundance of house sparrow Passer domesticus
and the tree sparrow P. montanus in sixty 25 ha plots distributed randomly across residential areas
of the city of Poznan (Poland). The abundance of the house sparrow was positively correlated with
the abundance of pigeons but negatively correlated with human-related food resources. There
were significant interaction terms between abundances of other urban species and habitat vari-
ables in statistical models. For example, the abundance of house sparrow was negatively corre-
lated with the abundance of corvids and tree sparrows but only when food resources were low.
The abundance of tree sparrows positively correlated with density of streets and the distance from
the city center. The abundance of this species positively correlated with the abundance of corvids
when food resources were low but negatively correlated at low covers of green area. Our study in-
dicates that associations between food resources, habitat covers, and the relative abundance of
two sparrow species are altered by the abundance of other urban species. Competition, niche sep-
aration and social facilitation may be responsible for these interactive effects. Thus, biotic

C The Author (2016). Published by Oxford University Press.

V 357
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358 Current Zoology, 2016, Vol. 62, No. 4

interactions should be included not only as an additive effect but also as an interaction term be-
tween abundance and habitat variables in statistical models predicting species abundance and
occurrence.

Key words: landscape ecology, public information, spatial autocorrelation, urban ecosystems.

Species distribution models combine observations of species pres- have shown that competition for these resources may be strong, but
ence or abundance with environmental data in order to develop pre- it remains unclear how this affects the population densities of urban
dictive estimates about species distribution and abundance (Kosicki birds (Sol et al. 1998; Shochat 2004; Wysocki and Walasz 2004;
and Chylarecki 2013). The main explanatory variables are habitat Shochat et al. 2010). Niche theory predicts that inter-specific com-
features, land covers, or landscape characteristics (Baker et al. 2014; petition may be strong when species are ecologically similar and
Kosicki et al. 2015). However, there are several aspects that can af- have similar habitat requirements (Giller 1984; Robertson 1995). In
fect the predictive performance species distribution models. For ex- such a scenario, competition should lead to a negative relationship
ample, species traits and the presence of associated or avoided between the population densities of two species and to their spatial
species may cause differential responses to the processes that control segregation (Bengtsson 1989). Body size is regarded as a good indi-
their distribution (Campomizzi et al. 2008; Kissling et al. 2011; cator of competitive abilities (Alatalo and Moreno 1987; Jonart
Morelli and Tryjanowski 2015 ). Indeed, recent works showed that et al. 2007), thus in a community of species utilizing similar re-
the inclusion of biotic interactions (abundance of other species) in sources the successful species (the most abundant one) are those
models increase their predictive performance (Campomizzi et al. characterized by larger body size. Therefore, the population density
2008; Rödder and Lötters 2010, Morelli and Tryjanowski 2015). of a larger species may negatively affect the density of smaller ones.
However, most of these models assume additive effects of all studied We choose the house sparrow Passer domesticus and the tree
variables and thus do not consider the interaction between variables. sparrow P. montanus, very similar species in terms of body size and
This may lead to flaws in models. To explain this, let us consider a diet and common species, as a demonstration case to investigate
hypothetical situation in which two species compete for a key re- these issues. There is a potential competitive relationship between
source. The resource positively affects the population densities of the house sparrow and tree sparrow (Summers-Smith 1994;
two species. When resources are limited the stronger competitor Veps€ al€
ainen et al. 2005), but there have been no studies on the ef-
should negatively affect the population size of weaker competitor. fects of possible interactions between the two species on their rela-
However, when resources are abundant, the population sizes of tive abundances (Summers-Smith 1994; Veps€ al€
ainen et al. 2005).
both weak and strong competitors may be positively correlated. In Both species are directly associated with a human altered environ-
other words, it is not enough to include the abundance of competi- ment (Luniak 1983; Anderson 1984, 2006). In Europe, these species
tors, predators or social facilitators as a substitute for biotic inter- inhabit villages, towns and cities, and they build nests in holes in
action in predictive models. These biotic interactions will change buildings and nest boxes (Møller et al. 2012). However, the house
with different values of environmental variables (generally called re- sparrow is considered a typical town dweller, while the tree sparrow
sources). In statistical meaning they should be modeled as inter- is believed to rely more on natural resources, for example it often
action terms between the abundance of species that are indicators of breeds in tree holes (Pinowski 1966, 1967; Shaw et al. 2008;
biotic relations with environmental variables. This approach is un- Kuczy nski and Chylarecki 2012). In recent years, the population
fortunately very rare (Heikkinen et al. 2007). size of house sparrows in Poland and other European countries has
Most models predicting species occurrence or abundance that in- decreased both in towns and farmlands, but the population size of
clude biotic interaction were built for agricultural ecosystems tree sparrow is stable or even increasing (Chamberlain et al. 2007;
(Morelli and Tryjanowski 2015), wetlands (Baker et al. 2014), or Kuczy nski and Chylarecki 2012). Despite the often high abundance
forests (Heikkinen et al. 2007). Analyses that include biotic inter- of both species, there are still relatively few studies on their spatial
actions are very rare for urban areas (Przybylska et al. 2012). Towns ecology. Existing data are old, mainly from times when both species
and cities are nowadays the most rapidly developing areas in the were seen as farmland pests (Pinowski and Kendeigh 1977; Cordero
world, and they have a profound effect on wildlife (Tomiałojc 1976; 1993). Since both species have a similar body size and utilize similar
Luniak 1983; Marzluff et al. 2001, Lin et al. 2008; Evans et al. resources (food, nesting habitat, and sites), one may expect a nega-
2010). In urban landscapes, the presence and density of animal tive relationship between their relative abundances. Thus, the effect
populations is limited by the availability of suitable habitats, human of resources on their population abundance may depend on the
disturbance, collisions with vehicles, and behavioural shyness abundance of a counterpart species, which, in statistical formula,
(Gorski and Antczak 1999; Fern andez-Juricic and Jokim€ aki 2001; implies a significant interaction between the effect of the environ-
Randler 2003; Chace and Walsh 2006; Ditchkoff et al. 2006; Wang mental variable and abundance of the potential competitor. Adding
et al. 2009; Møller 2010). However, little is known about how dif- to this, the abundance of pigeons (e.g., Columba livia) and corvids
ferent urban colonizers respond to the structural complexity of an (e.g., Pica pica) often varies across towns in Europe (Fontana et al.
urban landscape and to resources and population densities of other 2011) and these species have a diet and habitat overlapping with
species (Marzluff et al. 2001; Sk orka et al. 2006; Devictor et al. both sparrow species (Holland et al., 2006). Pigeons and corvids are
2007). An urban environment often offers a release from predators much larger species thus, they are potentially stronger competitors
and provides abundant resources, such as man-made food and nest to sparrows and their population abundances should negatively af-
sites (Jokim€aki and Suhonen 1998; Marzluff et al. 2001; Jokim€aki fect the sparrow population size (Summers-Smith 2003). Pigeons
and Kaisanlathi-Jokim€ aki 2003; Fuller et al. 2008; Robb et al. and corvids may prevent access to food (sparrows usually wait at
2008). Theoretical models (Anderies et al. 2007) and empirical data the edge of a foraging flock) and also access to water left in paddles
  rka et al.  Sparrows in an urban environment
Sko 359

and containers (authors’ unpublished observations). Additionally,

corvids are known as predators of nests, fledglings, and even adult
sparrows (Pinowski 1966; Pinowski et al. 1994).
The aim of this article is to investigate the effects of habitat struc-
tural complexity, number of potential food resources, and the abun-
dance of different species within an urban environment on the
abundance and distribution of house- and tree sparrows.
Specifically, we tested following predictions
The abundance of both sparrow species is positively related to
shelter, the abundance of human-related food resources and plot
covers of tall buildings and lawns which are breeding and foraging
sites, respectively.
The abundances of both species should be negatively correlated
with road densities because collisions with cars are one of the most
important mortality factor in sparrows (Erritzoe et al. 2003).
If biotic interactions (e.g., competition) between two species
exist then it is expected that the effect of environmental variables on
the abundance of one species should depend on the relative abun-
dance of other sparrow species. Also, abundances of both species Figure 1. The interpolated abundance (number of individuals per 25 ha plot)
should be negatively correlated; however, the negative correlation of house and tree sparrows in residential areas within the administrative
should be the strongest at low level of resources (breeding habitat, boundaries of Poznan  . The interpolation was performed with an inverse dis-
food). tance method in Quantum GIS software. The black diamond indicates the his-
If there are biotic interactions between sparrows and corvids as torical centre of the city.

well as pigeons, then the effect of environmental variables on the

abundances of sparrows should vary depending on the abundance of rain and heavy wind). Observers walked in plots (one observer per plot)
corvids and pigeons. Also, abundances of both sparrows should be to cover the entire area visually and aurally. All birds seen within plots
negatively correlated with the abundance of these larger potential were counted. We counted all seen birds because these sparrow species
competitors. However, again, the negative correlation should be are social and it appeared difficult to assess their number based on hear-
strongest at a low level of resources. ing only. When a sparrow was seen or heard, we scanned by binoculars
all buildings (including roofs and windowsills), trees and shrubs to
count the number of individuals. We also visited all sites where birds
Materials and Methods foraged. Observers did not count birds that were suspected to be al-
ready noted. Because many sparrows breed in inaccessible lofts we were
Study area unable to establish the exact number of breeding pairs and our analysis
The study was conducted in Pozna n (52 17’34’’–52 30’27’’N, was based on the mean number of individuals noted during three sur-
16 44’08’’–17 04’28’’E), in western Poland (Figure 1) in 2010. veys. Mean number was strongly positively correlated with maximal
Poznan is one of the largest Polish cities with 556 thousand inhabit- numbers (house sparrow: r ¼ 0.981, P < 0.001, tree sparrow r ¼ 0.956,
ants and covers an area of 261.3 km2 (population density 2,123 peo- P < 0.001) and minimal numbers (house sparrow: r ¼ 0.939, P < 0.001,
ple per km2). The altitude ranges from 60 m to 157 m a.s.l. The tree sparrow r ¼ 0.916, P < 0.001).
climate of Poznan is a humid continental climate with relatively cold
winters and fairly hot summers (mean temperature in the coldest
month, December, is –0.2  C and in the hottest, June, 17.4  C). Explanatory variables
Annual rainfall is about 500 mm (Anonymous 2003). The following explanatory variables potentially affecting the relative
abundance of sparrows were measured in each plot (see Table 1 for
an overview of the variables including mean values and standard
Bird counts errors).
To estimate the relative abundances of both sparrows, we selected sixty
0.5 km  0.5 km (25 ha) plots within the residential areas of the city. Abundance of pigeons. We recorded three species: the feral pigeon
The house sparrow does not occur in habitats other than human settle- Columba livia (average abundance 6 SE was 13.6 6 1.74 individuals
ments. The tree sparrow also occurs mostly in residential areas. As we per 10 ha), the common wood pigeon Columba palumbus (0.8 indi-
aimed to gain information about the interactions between the two spe- vidual 6 0.10 per 10 ha) and the collared dove Sterptopelia decaocto
cies, the inclusion of other habitats (e.g., arable fields) would have pro- (1.2 6 0.15 individuals per 10 ha) in the same plots where sparrows
duced meaningless results. Residential areas are also the dominating were counted. The mean total number of all individuals of these spe-
land cover in the city of Poznan. Plots were chosen by a random selec- cies recorded in a plot was later used in analyses as the proxy of their
tion of geographical coordinates of points that were upper-left corners abundance. We decided to pool abundances of these species because
of the square plots. The mean distance between the borders of the near- their diet overlaps with the diet of sparrows (Holland et al. 2006)
est plots was 1050 m (minimal was 122 m, maximal was 2745 m). The and all are larger and thus potentially stronger competitors than
selection was performed with Quantum 1.7 GIS software. Sparrows sparrows. Analysis with the most abundant species only—the feral
were surveyed in June. This period covers the peak of reproduction of pigeon—produced similar results (Tables S1–S2 in Supplementary
these species (Pinowski 1965, 1968). Three counts were done in each material).
plot at approximately 10-day intervals. Each visit to each plot lasted The abundance of corvids. Two species were recorded, the mag-
1 h. Counts were made during favorable weather conditions (without pie Pica pica (1.4 6 0.24 individuals per 10 ha) and the hooded crow
360 Current Zoology, 2016, Vol. 62, No. 4


Table 1. Mean values (6 SE) of variables investigated in the studied plots (n ¼ 60) in residential areas of Poznan

Variable code Explanation Mean SE Min–Max

HouseSpar Population abundance (individuals per 10 ha) 14.5 1.3 0–62.7

TreeSpar Population abundance (individuals per 10 ha 1.8 0.4 0–17.6
Corvids Population abundance (individuals per 10 ha) 1.6 0.2 0–10.9
of corvids (Pica pica and Corvus cornix)
Pigeons Population abundance (individuals per 10 ha) 15.7 1.7 0.5–54.0
of all pigeon species (Streptopelia decaocto,
Columba palumbus and Columba livia domestica)
FoodRes Sum of all food resources (waste bins, 32.9 4.9 0–197
restaurants, groceries, feeding sites
StreetDen Density of streets (m per10 ha) 1346 45 515–1970
HedgDen Density of hedgerows (m per 10 ha) 173 15 0–485
GreenArea Percentage of the plot covered by green area (parks, lawns, fallows) 22.9 2.1 0–63
HighBuild Percentage of the plot covered by tall buildings 28.6 3.4 0–89
CityCentr Distance of the plot from the city centre (km) 4.5 0.3 0.1–9.3

Corvus cornix (0.1 6 05 individuals per 10 ha) during sparrow association (decreasing abundances towards the city center) with the
counts. These two species are commonly seen foraging with spar- tree sparrow, which is a less urbanized species.
rows and are also predators both of adult sparrows and their Variables 1–3 were recorded directly in the plots. Variables 4–8
broods. were determined from aerial photos supported by field data and cal-
The availability of human-related food resources. We counted all culated in Quantum 1.7 GIS software. Our dependent variables
sites where birds fed (based on direct observations of people feeding were the relative abundance of house sparrows and tree sparrows
birds and left food remains), the number of litter-bins (of any type) calculated as the mean number of individuals per 25 ha plot from
and the number of grocery stores and fast-food restaurants. The the three surveys.
number of food resources was a sum of these elements (Table 1).
When litter-bins occurred in groups (e.g., in refuse heaps) each
litter-bin was treated as a separate unit. We originally intended to Statistical analysis
use each category as a separate variable but their numbers were The first analytical goal was to estimate the detection probability of
highly positively correlated (all r > 0.700). both species within plots using the approach proposed by
The density of streets (metres per 10 ha, Table 1). Traffic may in- MacKenzie et al. (2002). The detection probability was modeled
fluence mortality of sparrows through collisions with vehicles using a generalized linear model with a logit-link function in
(Erritzoe et al. 2003). Presence 4.0 software (Hines 2006). We modeled two scenarios: a
The density of hedgerows (m per 10 ha, Table 1). A hedgerow detection probability, which was the same among surveys p(.), and a
was defined as a line of closely spaced shrubs below 4 m high. We survey-specific detection probability of individuals p(t). However,
expected a positive association between densities of sparrows and the estimated proportion of plots occupied did not differ substan-
hedgerows because the latter may be a shelter for both species. tially from our naive estimates of occupied plots without correction
The percentage cover of green space (Table 1). Green space was for detectability. Also, detection probabilities were high (see results).
defined as all the parks, squares, lawns, and fallows within residen- Therefore, it was not necessary to consider imperfect detectability in
tial areas. As they provide foraging habitat with natural food re- our statistical analyses (Cozzi et al. 2008).
sources for sparrows, we expected positive correlation between We used Moran’s I correlograms (Legendre 1993) to describe the
green cover and the abundance of both sparrows. spatial aggregation in densities and occupancy of both species. The
The percentage cover of tall buildings (of over four floors) in the spatial autocorrelation value at a given distance class indicates how
plot (Table 1). We expected a positive association between this vari- predictable (positively or negatively) population density or occu-
able and the abundance of sparrows because more people live in tall pancy is at a given point of the sampling framework.
buildings, therefore more additional nesting sites and food (e.g., on Autocorrelation using Moran’s index typically varies between 1
windowsills or just thrown out the window) for both sparrow spe- and 1, with non-significant values close to zero. To test the signifi-
cies is expected in such areas. cance of the autocorrelation, we estimated P-values based on 500
We also noted the percentage cover of low-rise buildings up Monte Carlo simulations. As we found statistically significant auto-
to four floors (e.g., family houses), but since this variable was correlation (see results), we used inverse distance weighted interpol-
highly negatively correlated with the cover of tall buildings ation (Shepard 1968) implemented in QGIS 1.7 to visualize
(r ¼ 0.795, P < 0.001), only the latter was used in analyses. It abundances of both sparrows in Pozna n city. This interpolation
was a dominating type of residential area, specific for the urban method assumes that plots that are close to one another are more
environment. alike than those that are farther apart. This interpolation method
Distance of the plot to the city centre (taken as the historical cen- uses the abundance recorded in the surrounding plots and those
tral square in the Old City district; Table 1, Figure 1). This variable abundances recorded closest to the prediction location have more in-
represents the urbanization gradient, and we expected a negative as- fluence on the predicted value than do those farther away.
sociation of this the variables with the abundances of house sparrow We used model selection procedures based on information the-
(increasing abundances towards the city centre) and a positive ory (Burnham and Anderson 2002) to identify factors affecting the
  rka et al.  Sparrows in an urban environment
Sko 361

relative abundance of both species in Pozna n. For each sparrow spe-

cies, we built a generalized linear model with a negative binomial
error variance and log-link function for a dependent variable. As ex-
planatory independent variables we used variables 1–8 presented
above. Moreover, we included interaction terms:
abundance of other sparrow species  density of food resources,
abundance of other sparrow species  density of hedgerows,
abundance of other sparrow species  cover of green spaces,
abundance of other sparrow species  cover of high buildings,
abundance of corvids  density of food resources,
abundance of corvids  density of hedgerows,
abundance of corvids  cover of green spaces,
abundance of corvids  cover of high buildings,
abundance of pigeons  density of food resources,
abundance of pigeons  density of hedgerows,
abundance of pigoens  cover of green spaces, and
abundance of pigeons  cover of high buildings.
Akaike information criterion corrected for a small sample size
(AICc) was used to identify the most parsimonious model from each
candidate set. To take spatial autocorrelation into account, we
applied the approach proposed by Diniz-Filho et al. (2008). We
fixed a spatial variable which was obligatorily present in all models
to eliminate spatial autocorrelation in the residuals. The fixed spa-
tial variable was defined as an autoregressive term given by qWY
where W was the connectivity matrix, Y the response variable and q
the autoregressive coefficient (Dormann et al. 2007). Finally, we
ranked all possible model combinations according to their DAICc Figure 2. Spatial autocorrelogram (Moran’s I) for (a) abundance of house
values and used models with the lowest AICc together with associ- sparrows and (b) abundance of tree sparrows in 60 plots in Pozna
n. Red cir-
ated weight values (the probability that a given model is the best) as cles indicate spatial autocorrelations significant at a < 0.05.

those best describing the data. We considered models with DAICc

lower than 2 as equally well supported (Burnham and Anderson constant detection probability had slightly better support
2002). We used model averaging for estimates of function slopes of (AICc ¼ 196.56, weight ¼ 0.55) than the model with survey-specific
parameters of interest (Burnham and Anderson 2002). For averag- detection probability (AICc ¼ 198.00, weight ¼ 0.45). The relative
ing, we used models with weights which had DAICc values lower abundance of house sparrows was higher than that of tree sparrows
than four (Burnham and Anderson 2002). (t ¼ 12.851, df ¼ 110, P < 0.001, Table 1). We found statistically
We used square root transformation (for human-related food re- significant positive spatial autocorrelation of both house sparrow
sources, hedgerow density, cover of green area, cover of tall build- and tree sparrow population abundance at a distance up to 2 km
ings and abundances of two sparrows, corvids, and pigeons) to (Figure 2).
reduce the effects of outlier observations (Quinn and Keough 2002). Model selection based on Akaike’s criterion showed that six
Moreover, in all regression models, variables were standardized models were equally good and explained on average 35% of the
(with mean ¼ 0 and standard deviation ¼ 1) to allow a direct com- variation in the relative abundance of house sparrows (Table 2).
parison of beta (slope) estimates. There was no collinearity between Examination of slope estimates and their standard errors showed
the explanatory variables (r < 0.5 for every pair; Mertler and that the abundance of house sparrow was positively correlated with
Vannatta 2002). pigeon population abundance, but it was negatively related to an-
Moran’s statistics and spatial autocovariates were computed in thropogenic food resources (Table 3). However, there were signifi-
SAM 4.0 statistical software (Rangel et al. 2010). The model selec- cant interaction terms among the best models (Tables 2 and 3).
tion procedure was performed in the MuMIn library (Barto n 2014 ) Corvids and tree sparrows negatively correlated with abundances of
in R (R Core Developmental Team 2015). All estimates of statistical house sparrows but only when food resources were low (Tables 2
parameters (means, betas) are quoted with standard errors (SE) and and 3, Figure 3). Also, the abundance of tree sparrows negatively
95% confidence intervals (CI). We considered that the slopes of the correlated with that of house sparrows when cover of tall buildings
regression function to be significant if their 95% CI did not overlap was high (Tables 2 and 3, Figure 3). Contrarily, the relative abun-
with zero. dance of pigeons positively correlated with that of house sparrows
in plots with high and moderate cover of tall buildings but not with
the cover of low buildings (Tables 2 and 3, Figure 3).
Results Model selection based on Akaike’s criterion showed that 10
The detection probability of house sparrows in a plot was near per- models were equally good and explained 23% of the variation in
fect (0.99 6 0.01), and the model with a constant survey-specific de- the relative abundance of tree sparrows on average (Table 2).
tection probability had almost the same support (AICc ¼ 35.28, Examination of function slopes and their standard errors re-
weight ¼ 0.51) as the model with a constant detection probability vealed that the abundance of tree sparrow positively correlated
(AICc ¼ 35.97, weight ¼ 0.49). The detection probability of tree with distance from the city centre and the density of streets; how-
sparrows in a plot was also high (0.80 6 0.04), and the model with ever, there were statistically significant interaction terms in the
 362


Table 2. Best models explaining the relative abundance of house sparrows and tree sparrows in 60 plots in Poznan

Model R2 logLik AICc delta w

House Sparrow
Corvids þ HighBuild þ FoodRes þ Pigeons þ TreeSpar þ Corvids  FoodRes þ HighBuild  Pigeons þ 0.38 242.7 516.03 0 0.13
HighBuild  TreeSpa þ FoodRes  TreeSpar
Corvids þ HighBuild þ FoodRes þ Pigeons þ TreeSpar þ Corvids  FoodRes þ HighBuild  TreeSpa þ 0.36 244.72 516.95 0.92 0.08
FoodRes  TreeSpar
Corvids þ HighBuild þ FoodRes þ Pigeons þ TreeSpar þ Corvids  FoodRes þ HighBuild  Pigeons þ 0.37 241.56 517.02 0.99 0.08
HighBuild  TreeSpa þ FoodRes  Pigeons þ FoodRes  TreeSpar
Corvids þ FoodRes þ Pigeons þ Corvids  FoodRes 0.30 250.62 517.4 1.36 0.06
Corvids þ HighBuild þ FoodRes þ Pigeons þ TreeSpar þ Corvids  HighBuild þ Corvids  FoodRes þ 0.34 241.74 517.4 1.37 0.06
HighBuild  Pigeons þ HighBuild  TreeSpa þ FoodRes  TreeSpar
Corvids þ HighBuild þ FoodRes þ Pigeons þ StreetDen þ TreeSpar þ Corvids  FoodRes þ 0.35 241.84 517.59 1.56 0.06
HighBuild  Pigeons þ HighBuild  TreeSpa þ FoodRes  TreeSpar
TreeSparrow
Corvids þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ Corvids  FoodRes þ HedgDen  Pigeons 0.23 136.23 303.1 0 0.07
Corvids þ GreenArea þ CityCentr þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ Corvids  GreenAreaþ 0.24 133.01 303.35 0.25 0.06
Corvids  FoodRes þ HedgDen  Pigeons
Corvids þ GreenArea þ CityCentr þ FoodRes þ HedgDen þ Pigeons þ StreetDen þ Corvids  GreenArea þ 0.23 134.79 303.49 0.38 0.06
Corvids  FoodRes þ HedgDen  Pigeons
Corvids þGreenArea þ CityCentr þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ Corvids  0.25 131.4 303.72 0.61 0.05
GreenArea þ Corvids  FoodRes þ FoodRes  HouseSpar þ HedgDen  Pigeons
Corvids þ GreenArea þ CityCentr þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ 0.21 134.97 303.85 0.75 0.05
Corvids  FoodRes þ HedgDen  Pigeons
Corvids þ CityCentr þ FoodRes þ HedgDen þ Pigeons þ Street þ Corvids  FoodRes þ HedgDen  Pigeons 0.22 138.2 303.91 0.8 0.05
Corvids þ CityCentr þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDenþ Corvids  FoodRes þ 0.23 135.13 304.18 1.07 0.04
FoodRes  HouseSpar þ HedgDen  Pigeons
Corvids þ HighBuild þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ Corvids  FoodRes þ 0.21 136.89 304.42 1.31 0.04
HedgDen  Pigeons
Corvids þ GreenArea þ CityCentr þ FoodRes þ HedgDen þ Pigeons þ StreetDen þ Corvids  FoodRes þ 0.22 136.89 304.42 1.32 0.04
HedgDen  Pigeons
Corvids þ GreenArea þ CityCentr þ FoodRes þ HedgDen þ HouseSpar þ Pigeons þ StreetDen þ 0.23 133.79 304.91 1.8 0.03
Corvids  FoodRes þ FoodRes  HouseSpar þ HedgDen  Pigeons

For explanation of the variable codes: see Table 1.

Current Zoology, 2016, Vol. 62, No. 4
  rka et al.  Sparrows in an urban environment
Sko 363

Table 3. Factors affecting the abundance of house sparrow and only the abundance of other species (predators, competitors) as the

tree sparrow in Poznan additive effect (modelled as a covariate) together with habitat vari-
ables (e.g., Przybylska et al. 2012; Baker et al. 2014; Morelli and
Effect Estimate SE Z P
Tryjanowski 2015) but also include interaction terms among these
House Sparrow types of variables. Inspection of the function slopes of interaction
Intercept 3.369 0.093 36.323 <0.001 terms indicated that function slopes of biotic variables (species
Corvids 0.057 0.079 0.714 0.475 abundances) often had opposite signs across levels of environmental
FoodRes 0.313 0.106 2.949 0.003 variables.
HighBuild 0.124 0.104 1.196 0.232
Pigeons 0.338 0.090 3.74 0.000
TreeSpar 0.131 0.077 1.699 0.089
SpatialAutocovariate 0.351 0.070 5.024 <0.001 Biotic interactions versus environmental variables
StreetDen 0.085 0.075 1.145 0.252 A statistically significant effect was found for the number of human-
FoodRes  Corvids 0.304 0.087 3.499 0.000 related food resources that was negatively correlated with the abun-
HighBuil  Pigeons 0.201 0.098 2.045 0.041 dance of house sparrows. This result contradicts our expectations.
HighBuil  TreeSpar 0.238 0.084 2.819 0.005
Usually, the availability of such food attracts a number of species,
TreeSpar  FoodRes 0.321 0.111 2.882 0.004
which congregate in such places (Belant 1997 ; Jerzak 2001 Chace
FoodRes  Pigeons 0.129 0.077 1.687 0.092
and Walsh 2006; Sk orka et al. 2009; Maciusik et al. 2010). The
HighBuild  Corvids 0.124 0.102 1.211 0.226
Tree Sparrow negative effect of human-related food resources indicates that during
Intercept 1.029 0.172 5.971 <0.001 the breeding period sparrows may use more natural food sources
Corvids 0.288 0.213 1.354 0.176 (e.g., invertebrates, weeds), whose importance for nestlings is
CityCentr 0.650 0.259 2.51 0.012 known (Pinowski and Kendeigh 1977). It is also possible that
FoodRes 0.298 0.267 1.117 0.264 human-related food resources were correlated with a confounding
GreenArea 0.288 0.178 1.622 0.105 variable that was not included in this study. For example, anthropo-
HedgDen 0.411 0.218 1.884 0.060 genic food resources may attract some potential predators of spar-
HighBuild 0.330 0.326 1.014 0.311
rows such as feral cats. They may hunt sparrows and also affect
HouseSpar 0.331 0.249 1.331 0.183
their abundance by the non-lethal effect of fear (Lima 1998; Turner
Pigeons 0.374 0.229 1.631 0.103
and Bateson 2000; Krauze-Gryz et al. 2013).
SpatialAutocovariate 0.039 0.153 0.255 0.799
StreetDen 0.787 0.200 3.939 <0.001 Moreover, it must be noted that the association between the
Corvids  FoodRes 0.935 0.273 3.425 0.001 abundance of the two sparrow species and food resources was
HedgDen  Pigeons 0.552 0.198 2.785 0.005 altered by the abundance of corvids. The abundance of corvids nega-
Corvids  GreenArea 0.307 0.153 2.009 0.045 tively correlated with abundances of house sparrows but this rela-
FoodRes  HouseSpar 0.399 0.222 1.796 0.072 tionship was positive if the level of food resources was higher. In our
opinion, this may be evidence for competition between these species.
Averaged parameters are presented. Statistical significance of estimates in the
Corvids are known to forage intensively on human-related resources
last two columns. Statistically significant relationships are emboldened. For
(Kristan et al. 2004; Lenda et al. 2012). When resources are rare
further explanations see Tables 1 and 2.
corvids may outcompete house sparrows or hunt their chicks or
adults (Pinowski 1966; Pinowski et al. 1994). However, when
best models (Tables 2 and 3). These interactions indicated that human-related resources are abundant a positive correlation be-
the abundance of corvids positively correlated with abundances tween corvids and house sparrows may emerge. Corvids are known
of tree sparrows when food resources were low (Tables 2 and 3, for aggressive mobbing behaviour towards aerial and ground preda-
Figure 4). However, corvids negatively correlated with tree spar- tors such as Eurasian sparrowhawks Accipiter nisus and feral cats.
rows when the cover of green areas was low but positively corre- These predators may negatively affect sparrow populations (Biadu n
lated with tree sparrows if the cover was moderate or high 2006; Bell et al. 2010), and mobbing and vigilance behaviors are the
(Tables 2 and 3, Figure 4). The abundance of pigeons positively most efficient when the abundance of birds is high (Krams et al.
correlated with the abundance of tree sparrows when hedgerow 2009). As opposed to house sparrows, the association between
density was low but negatively correlated with the abundances of abundances of corvids and tree sparrows was positive when food re-
tree sparrows when the hedgerows grew more densely in plots sources were low. It is possible that, if human-related food sources
(Tables 2 and 3, Figure 4). are low, corvids may seek natural food and tree sparrows may use
this public information (Danchin et al. 2004), which, in turn may in-
crease their abundance.
Direction of the relationship between tree sparrows and corvid
Discussion abundance varied also depending on the cover of green areas. We
We showed that relative abundances of both sparrows were corre- found that the abundance of tree sparrows was negatively correlated
lated with several environmental variables. However, the effect of with that of corvids when the cover of green areas was low. This
these environmental variables was altered by variables describing bi- supports the above explanations, that tree sparrows depend on more
otic relations as indicated by statistically significant interaction natural habitats in an urban environment. Tree sparrows may find
terms in the best models. The biological meaning of these statistical natural food resources (e.g., weed seeds) in green areas (Pinowski
interaction terms is that abundances of different species may affect and Kendeigh 1977), and people often visit such places and fre-
others, but the strength and direction of these relationships changes quently feed birds, mostly corvids (Kristan et al. 2004). This, of
with varying values of environmental variables. These findings indi- course, is a tentative explanation that should be studied in detailed
cate that species distribution/abundance models should include not observational and experimental study.
364 Current Zoology, 2016, Vol. 62, No. 4

Figure 3. The effect of the abundance of other species on the abundance (number of individuals in 25 ha plot) of house sparrows modified by environmental vari-
ables: (A) the interaction between the abundance of pigeons and the cover of tall buildings, (B) the interaction between the abundance of corvids and food re-
sources, (C) the interaction between the abundance of tree sparrow and the cover of tall buildings, (D) the interaction the between abundance of tree sparrows
and food resources. The environmental (continuous) variables were divided into categorical variables with the levels: low (blue circles and fitted line), medium
(green circles and fitted line) and high (red circles and fitted line) . Each level contained 20 cases.

Contrary to expectations, the abundance of house sparrows was or negative associations between pigeons and house sparrow and
positively correlated with the abundance of pigeons indicating some tree sparrow, respectively.
kind of social facilitation between these birds. It is an interesting re- The association between pigeons and tree sparrows also de-
sult because pigeons are much larger species than house sparrows pended on the density of hedgerows. If the density of hedgerows was
and often forage in flocks which allow them to monopolize food re- low, the abundance of tree sparrows increased with the abundance
sources. Body size is also one of the major indicators of competitive of pigeons, but it decreased if hedgerows became more densely dis-
ability in animals (Alatalo and Moreno 1987; Jonart et al. 2007). tributed. Hedgerows may provide food resources for many species,
However, smaller species, if abundant enough, are able to resist including sparrows, and shelter from predators in an urban environ-
larger species or avoid competition (Quintana and Yorio 1998). ment (Deckers et al. 2004; Biadun 2006). However, pigeons also fre-
Moreover, smaller species may be more efficient foragers (scramble quently seek food along hedgerows (Przybylska et al. 2012). Thus,
competition, Lima et al. 1999). It is possible that house sparrows at a higher density of hedgerows, pigeons probably use them as a
may directly benefit from the presence of pigeon flocks as social in- foraging site and perhaps negatively affect tree sparrows. This
formation on scattered food resources. We also observed sparrows should be investigated in more detail in further study.
foraging on the ground within flocks of feral pigeons and collared
doves and, although some aggressive encounters were noted, the
food items taken by the sparrows were smaller than those eaten by Possible interactions between house sparrow and tree
the pigeons (authors’ unpublished data). It is also likely that smaller sparrows
sparrows may benefit from the social behaviour of pigeons in terms The abundance of house sparrows was higher than the abundance of
of the improved vigilance of predators (Lima 1995). However, the tree sparrows what may reflect the different time of colonization of
above explanations seem to be relevant only when the cover of tall the urban environment in Europe by these species (Møller et al.
buildings is high. Tall buildings are a usual breeding habitat for feral 2012). The abundances of house sparrows and tree sparrows were
pigeons—the most abundant species included in the variable “pi- also spatially structured with significant positive spatial autocorrel-
geons” in this study (Mizera 1988; Jokim€ aki and Suhonen 1998; ation recorded in our study area. They were positively autocorre-
Buijs and van Wijnen 2001; Przybylska et al. 2012). When the cover lated up to a distance of 1.5–2 km. This distance is short and
of tall buildings was low, the estimate of function slope indicated no corresponds to low dispersal abilities observed in these species
  rka et al.  Sparrows in an urban environment
Sko 365

attributes and biotic interactions play a more important role in the

colonization of urban environments by this species.
In statistical models for the abundance of house sparrows, the
abundance of tree sparrows was one of the predictors that interacted
with the number of food resources as well as the cover of tall build-
ing. When human-related food resources were low, the abundance
of tree sparrows was negatively associated with the abundance of
house sparrows. This was not the case at a higher level of food sup-
ply. These results may be an indication of competition for food be-
tween the species; however it must be underlined that human-
related food resources generally negatively affected the abundances
of house sparrow. The abundance of tree sparrows also negatively
correlated with the abundance of house sparrows when the cover of
tall buildings was high. We speculate that, if competition for breed-
ing sites plays a role then the negative association between tree spar-
rows and house sparrow should be the highest at a low cover of high
buildings. It is also possible that a high cover of tall buildings at-
tracts a large number of pigeons that may actually indirectly in-
crease competition for breeding sites between the two sparrow
species. Interestingly, no statistical model explaining the abundance
of tree sparrows contained significant effects of house sparrow
abundance. Assuming competitive interactions between these two
sparrow species this indicates that the tree sparrow is possibly a
stronger competitor than the house sparrow despite its generally
smaller body size (Cordero 1993). Therefore, the colonization of an
urban environment by tree sparrows might be another factor respon-
sible for the population decrease of house sparrows in towns and cit-
ies of Europe (Pinowski and Kendeigh 1977).

Study limitations
Our data on the abundance of house and tree sparrows have limita-
tions which should be taken into account when interpreting the re-
sults and generalizing to other areas and species. First, our study is
correlative. Abiotic and biotic interactions are very complex and our
data might not allow for an explicit delineation of mechanisms of
the interactions between species and environmental variables (Wisz
et al. 2012). Thus, our finding should be treated as a starting point
to better understand the underlying mechanisms shaping the
observed patterns of sparrow abundance.
Several associations between the abundance of sparrows and
environmental variables seem to be difficult to explain, for exa-
mple, interaction terms between abundance of pigeons and hedge-
row density, or interaction between the pigeons’ abundance and
the cover of tall buildings. Difficulties in explaining these statistica-
lly significant interactions suggest that our biological interpreta-
tion should be treated with caution. Given the complexity of biotic
interactions we must also accept the possibility that some hypothe-
ses stated by us to explain our results may be biologically
irrelevant.
Results of our study might be also affected by the manner in
Figure 4. The effect of the abundance of other species on the abundance which variables were defined (Gregory et al. 2004). However, our
(number of individuals per 25 ha) of tree sparrows modified by environmental
sample size, despite being large, could not allow us to accomplish
variables: (A) the interaction between the abundance of corvids and food re-
complex species-specific analyses including models with many inter-
sources, (B) the interaction between the abundance of pigeons and the dens-
ity of hedgerows, (C) the interaction between the abundance of corvids and action terms. Analyses performed with only the most abundant pi-
the cover of green areas. For further explanations: see Figure 3. geon (feral pigeon) produced similar results as in the case of analysis
with pooled abundances of pigeons and doves. We believe this latter
variable is good as it includes the total potential impact of large-
(Pinowski 1965, 1967). The spatial autocorrelation was also a stat- bodied species with a diet highly overlapping that of both sparrow
istically significant predictor of house sparrows after accounting for species. Such multiple competitors are common in bird assemblages
other effects. In models for tree sparrow abundance, however, the (Triplet et al. 1999). Similar criticism applies to the definition of
spatial autocorrelation was non-significant indicating that physical human-related food resources, which do not include natural food
366 Current Zoology, 2016, Vol. 62, No. 4

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Conspecific attraction is a missing component in wildlife habitat modeling.
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