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Habitat preferences and causes of population decline for Barn Owls Tyto alba: A multi-scale approach

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Aims: Habitat preferences of Barn Owls was studied in two areas of Spain undergoing large-scale habitat alteration. Location: Alicante (dry cultivations) and Valencia (irrigated cultivations) in eastern Spain. Methods: Habitat composition around occupied and unoccupied territories in dry cultures and irrigated cultures was compared (n = 71, 1989-2000). This study also described differences in habitat composition between occupied and deserted territories after major habitat alterations started in 1996. Generalized Linear Models were used to examine patterns of habitat preference at three different spatial scales: nest site, home range and landscape. Results: The study population declined by 69% in both study areas. At the nest site scale, Barn Owls preferred undisturbed areas with high availability of cavities, mainly in man-made structures. At the home range and landscape scales, Barn Owls occupied undisturbed areas with a high availability of cavities and high percentages of edges and ditches. Territory desertion was prompted by the modernisation or disappearance of man-made structures, depletion of edges and ditches, expansion of the road network and persecution. Accordingly, the spatial distribution of territories in irrigated cultures changed from uniform to random after habitat alteration. The availability of cavities alone does not account for all of the explained deviance, i.e., Barn Owls occupy structurally complex landscapes. Conclusions: Compensation measures for habitat loss such as nest-box programs, usually proposed within the framework of environmental impact assessment, are discouraged unless habitat restoration and effective control of persecution are promoted first.
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Ardeola 51(2), 2004, 303-317
HABITAT PREFERENCES AND CAUSES OF POPULATION
DECLINE FOR BARN OWLS TYTO ALBA:
A MULTI-SCALE APPROACH
Jose Antonio MARTÍNEZ*1& Íñigo ZUBEROGOITIA**
SUMMARY.—Habitat preferences and causes of population decline for Barn owls Tyto alba: a multi-sca-
le approach.
Aims: Habitat preferences of Barn Owls was studied in two areas of Spain undergoing large-scale habitat al-
teration.
Location: Alicante (dry cultivations) and Valencia (irrigated cultivations) in eastern Spain.
Methods: Habitat composition around occupied and unoccupied territories in dry cultures and irrigated cul-
tures was compared (n = 71, 1989-2000). This study also described differences in habitat composition between
occupied and deserted territories after major habitat alterations started in 1996. Generalized Linear Models
were used to examine patterns of habitat preference at three different spatial scales: nest site, home range and
landscape.
Results: The study population declined by 69% in both study areas. At the nest site scale, Barn Owls prefe-
rred undisturbed areas with high availability of cavities, mainly in man-made structures. At the home range
and landscape scales, Barn Owls occupied undisturbed areas with a high availability of cavities and high per-
centages of edges and ditches. Territory desertion was prompted by the modernisation or disappearance of
man-made structures, depletion of edges and ditches, expansion of the road network and persecution. Ac-
cordingly, the spatial distribution of territories in irrigated cultures changed from uniform to random after ha-
bitat alteration. The availability of cavities alone does not account for all of the explained deviance, i.e., Barn
Owls occupy structurally complex landscapes.
Conclusions: Compensation measures for habitat loss such as nest-box programs, usually proposed within the
framework of environmental impact assessment, are discouraged unless habitat restoration and effective
control of persecution are promoted first.
Key words: Agriculture abandonment, environmental impact assessment, strategic environmental as-
sessment, persecution, nest-boxes, Barn Owl, Tyto alba.
RESUMEN.—Preferencias de hábitat y causas de declive poblacional para la Lechuza Común Tyto alba:
una aproximación a varias escalas.
Objetivos: Estudiar las preferencias de hábitat de la Lechuza Común en dos zonas de sometidas a grandes
transformaciones del suelo.
Localidad: Alicante (cultivos de secano) y Valencia (cultivos de regadío) en el este de España.
Métodos: Se comparó la composición del hábitat alrededor de territorios ocupados y no ocupados en cultivos
de secano y de regadío (n = 71, 1989-2000). También se describió las diferencias en la composición del há-
bitat entre los territorios ocupados y los abandonados tras el pico en la tasa de alteración del paisaje de 1996.
Hemos utilizado Modelos Generales Linearizados para examinar los patrones de preferencias a tres escalas es-
paciales: lugar de nidificación, área de campeo y paisaje.
Resultados: La población total de estudio decreció en un 69%. A la primera escala, las Lechuzas prefirieron
áreas poco alteradas con gran disponibilidad de cavidades, principalmente en edificios. A la segunda y tercera
escalas, prefirieron áreas poco alteradas con gran disponibilidad de cavidades, altos porcentajes de ecotonos
y acequias con cañaverales desarrollados. El abandono de territorios está favorecido por la modernización o
la desaparición de edificios con cavidades, acequias y ecotonos así como por la expansión de la red de ca-
rreteras y la caza ilegal. En virtud de estos factores, la distribución espacial de los territorios en el regadío ha
cambiado de uniforme a aleatoria.
Conclusiones: La restauración del hábitat y el control efectivo de la caza ilegal son las medidas prioritarias a
tomar para la conservación de esta especie en el área de estudio, desaconsejándose los programas de coloca-
ción de cajas anidaderas que no incluyan mejora o preservación de hábitat. La colocación de dichas cajas, su-
*C/ Juan de la Cierva 43, El Campello, E-03560, Alicante, Spain. E-mail: qvcocotiers@hotmail.com
** Lab. Zoología, Dpto. Zoología. Facultad de Ciencias. Universidad del País Vasco. Aptdo 644.
E-48080, Bilbao, Spain. E-mail: inigo.zuberogoitia@wanadoo.es
1 Corresponding author: E-mail address: qvcocotiers@hotmail.com
INTRODUCTION
The landscape in the Mediterranean basin
has been subjected to a relentless process of
alteration for centuries (Pain & Pienkowski,
1997). The rate of habitat alteration has steeply
increased over the last fifty years so that little
remains of the original or agricultural landsca-
pe, especially in coastal areas (Agencia del Me-
dio Ambiente, 1997). In particular, the rate of
habitat loss to large housing developments and
extended road networks in the east coast of
Spain peaked in 1996 (Simarro, 2002). Habitat
loss occurred mostly at the expense of dry and
irrigated cultures, which are otherwise being
replaced by large fruit/vegetable greenhouses
favoured by the reform of the Common Agri-
cultural Policy (CAP) (Pain & Pienkowski,
1997). Contrary to other areas across Europe
(Penteriani et al., 2002), abandonment of agri-
cultural lands has not lead to a substantial ex-
pansion of the surface of forest mainly because
of the rapid changes on the suitability for buil-
ding of the abandoned lands and subsequent
urbanisation (Simarro, 2002).
Interestingly, human-maintained semi-natu-
ral systems have the potential to host a higher
richness and diversity of species than extensive
cultures or large forests as long as traditional
agricultural practices are put into practice (Rico
1997; Sánchez-Zapata & Calvo, 1999; Zubero-
goitia, 2000, Zuberogoitia, 2002). Indeed, the
bulk of the raptor and owl population in Ali-
cante (east of Spain) inhabits the agro-pastoral
complex (Rico et al., 2001; Martínez et al.,
2003). Paradoxically, birds of prey are protec-
ted but the conservation status of their preferred
nesting and foraging habitats is low because
they are located out of the network of Natural
Parks. Therefore, the question is raised as to
how the shrinking availability of the agro-pas-
toral complex would affect the probability of
having occupied territories of listed species.
An approach combining long temporal data
series with multi-scale descriptions of the habi-
tat preferences of the target species can produ-
ce meaningful results with regard to the res-
ponse of animals to habitat loss (Penteriani et
al., 2001; Marchesi et al., 2002; Martínez et
al., 2003). The multi-scale approach to the
study of habitat preferences is mostly based on
the conceptual framework proposed by Johnson
(1980), whose basic assumption is that animals
are capable of making decisions regarding re-
sources at consecutively smaller scales. Accor-
dingly, general habitat selection can be consi-
dered as a hierarchical process regarding, for
example, a suitable patch for breeding at a
small scale and apt areas for foraging at a bro-
ader scale (Martínez et al., 2003). The multi-
scale approach may be especially appropriate to
identify key factors involved in habitat prefe-
rence of owls because they have large home
ranges consisting of different patches for bree-
ding and foraging (Mikkola, 1983).
The Barn Owl Tyto alba is one of the ar-
chetypal inhabitants of the agri-pastoral mo-
saics of Spain (Zuberogoitia, 2002). In com-
mon with other countries across Europe, a
negative trend in population size has been re-
ported in Spain, the ultimate causes of which
are still poorly documented (Tucker & Heath,
1994; Martínez & Zuberogoitia, 2003a). Ho-
wever, several studies have pointed out the
owl’s sensitivity to small-scale habitat changes
(Taylor, 1994; Ramsden, 1998; Zuberogoitia,
2002), which suggests the importance of moni-
toring how changes in the traditional land uses
affect the abundance of Barn Owls. Further-
more, recently released fauna catalogues such
as the Decreto 32/2004 DOGV, 27 February
(Comunidad Valenciana, East of Spain) do not
list the Barn Owl as protected, an assessment
based on the subjective criteria of the board
because no large-scale survey has ever been
performed in the area. On these grounds, we
aim to: (1) catalogue relevant environmental
characteristics affecting habitat preferences of
Barn Owls at three different spatial scales (nest
areas, home ranges and landscape) and (2) as-
ses how habitat loss may be influencing the
sustainability of a Barn Owl population.
304 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
gerida tanto en los estudios de impacto ambiental como dentro de evaluaciones estratégicas ambientales de
planes de desarrollo en la zona de estudio, se considera una medida compensatoria errónea, pues los modelos
muestran que las Lechuzas Comunes ocupan paisajes estructuralmente más complejos que los determinados
por la simple disponibilidad de cavidades para anidar.
Palabras clave: Abandono de la agricultura, estudios de impacto ambiental, evaluaciones estratégicas am-
bientales, caza ilegal, cajas anidaderas, Lechuza Común, Tyto alba.
MATERIAL AND METHODS
Study area
The study was carried out between 1989 and
2000 in two study areas: (1) dry cultures, in the
province of Alicante, and (2) irrigated cultures, in
the province of Valencia (Fig. 1). In dry cultures
the climate varies from semi-arid meso-medite-
rranean to sub-humid Mediterranean. Average
annual rainfall is about 400 mm, and annual ave-
rage temperature is about 19°C. The landscapes
is dominated by dry cultivated fields, mainly Ca-
rob Ceratonia siliqua, Almond Prunus amygda-
lus, Olive trees Olea europaea, vineyards, bar-
ley, sunflowers, wheat, scrubland and pine
forests (Pinus halepensis and Pinus pinea) and
medium-sized cities. Altitude varies between 0
and 1500 m a.s.l. In the irrigated cultures area,
the main land uses are citrus groves interspersed
with vegetable smallholdings. Average annual
rainfall is about 500 mm, and annual average
temperature is about 18°C. Altitude varies bet-
ween 0 and 300 m a.s.l. See Martínez (1998)
and Martínez & López (1999) for further details.
Survey methods
In order to locate Barn Owls, nests, moulted
feathers, droppings and food remains were loo-
ked for in man-made structures, ephemeral ri-
vers and trees (Shawyer, 1987; Taylor, 1994;
Ramsden, 1998; Martínez & López, 1999; Zu-
berogoitia & Campos, 1998; Toms et al.,
2001). As a complementary method, recordings
of the male’s main territorial call were broad-
cast (Zuberogoitia & Campos, 1998). All te-
rritories (and random sites, see below) were vi-
sited four times each year to ascertain their
occupancy according to the presence or absen-
ce of nests, pellets, droppings, moulted feat-
hers, sightings or responses to playback (Tay-
lor, 1994).
Selection of scales
Following the rationale of Johnson (1980),
Martínez et al. (2003) and Martínez & Zubero-
goitia (2004), three different spatial scales were
used to study habitat preferences of Barn Owls:
a) Nest site scale. Core areas during the bre-
eding season can be encircled by a circle 1 km
radius from the nests (Taylor, 1994) including
nests, diurnal or nocturnal roosts and hunting
grounds (Taylor, 1994). Hence, an average core
area was assumed to be a circular 317 ha plot
around the nests (1 km radius) (Taylor, 1994).
b) Home range scale. Barn Owl home ranges
vary in length from 2 to 5 km (Taylor, 1994).
From these studies, an average territory was
HABITAT LOSS AND BARN OWL POPULATION DECLINE 305
Ardeola 51(2), 2004, 303-317
FIG.1.—Percentage of deviance explained at three spatial scales by the Generalized Linear Models for the
probability of presence of Barn Owls comparing occupied vs. non occupied territories in dry areas.
[Porcentajes de devianza explicados a tres escalas espaciales por los Modelos Generales Linearizados
para la probabilidad de presencia de Lechuzas Comunes comparando territorios ocupados y no ocupados en
áreas de cultivos de secano.]
Unexplained
bosque]
assumed to be a circular area of 2826 ha around
centres of activity (3 km radius).
c) Landscape scale. Since landscape ecology
addresses the relationships between animal dis-
tribution and mosaics of ecosystems (Forman
& Gordon, 1986) tests were made for a possi-
ble response of Barn Owls to habitat composi-
tion at a broad landscape level. Thus, an area of
100 km2around nests (5.6 km radius) was cho-
sen because in Alicante and Valencia it is likely
to find substantial changes in landscape com-
position within this radius (Agencia del Medi
Ambient, 1997). Furthermore, other studies
have reported responses of birds of prey and
owls to this landscape scale in the study areas
(Rico et al., 2001; Martínez et al., 2003; Martí-
nez & Zuberogoitia, 2004).
Selection of variables
From 1:2000 aerial photographs and 1:2000
maps, an a priori set of environmental varia-
bles related to topography were selected (Burn-
ham & Anderson, 2002) (1), human disturban-
ce (3), land use (6), edges (3), linear structures
(1) and nesting or roosting requirements (1)
(see Appendix 1). Two studies have singled
out illegal hunting as one of the most important
proximate causes of mortality of raptors and
owls in the study areas (Martínez et al., 1996;
Martínez et al., 2001). From these studies and
from unpublished data maps were developed
which depicted the location of every raptor or
owl casualty attributed to persecution. Then,
the variable «persecution» was constructed as
the presence (1) or absence (0) of records of
persecution in the three circular sampling areas
around centres of activity. The aim of the study
was to test if territory abandonment is more li-
kely to occur in areas where persecution takes
place. During the course of routine visits the
number and location of potential roosting or
breeding sites was plotted on aerial photo-
graphs or detailed maps.
Centres of activity are defined as nests or as
the most frequently used roost judging from
the amount of signs of activity (Ramsden,
306 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
APPENDIX 1
Variables used to characterize centres of activity at the nest, home range and landscape scales.
[Variables utilizadas para caracterizar el centro de actividad a escala del nido, territorio y paisaje.]
Physiography
RELIEF, number of 100 m contours cut by four lines starting from the centre of the area in directions N, S, E
and W.
Human disturbance
PAVED ROADS, metres around the centre of activity.
HOUSING DEVELOPMENTS, %
PERSECUTION, presence (1) or absence (0) of records of persecution in the circular sampling area.
Land use (%)
CAROB, OLIVE, ALMOND CULTURES (TREE CULTURES)
CEREAL CULTIVATIONS
CITRUS GROVES
VEGETABLE SMALLHOLDINGS
EPHEMERAL RIVERS
FOREST
Edges (m)
CERAL-FOREST
CITRUS GROVES-SET ASIDE CULTIVATIONS
CITRUS GROVES-VEGETABLE SMALLHOLDINGS
Linear structures (m)
DITCHES
Nesting/roosting requirements
NUMBER OF BUILDINGS WITH CAVITIES
1998). Habitat composition around 71 active
Barn Owl territories (31 in dry cultures; 40 in
irrigated cultures) was compared with 82 sites
chosen randomly (41 in dry cultures, 41 in irri-
gated cultures). Random sites were located at a
minimum distance of 5 km from each other or
from occupied sites. All non-occupied random
sites remained empty throughout the study pe-
riod. These analyses were referred to the year
2000, using cartography for that year.
Major habitat alteration started in 1996 in
both study areas involved: (1) renovation and
corresponding inaccessibility of man-made
structures (e.g., screening of belfries, obstruc-
tion of niches of cemeteries), (2) abandonment
of traditional agricultural practices and corres-
ponding decay of man-made structures used
for breeding (e.g., old warehouses, dovecots),
misuse or decay of irrigation ditches and di-
sappearance of edges and (3) construction of
large housing developments and expansion of
the road network. Forty-nine territories became
vacant between 1996 and 2000 (23 in dry cul-
tures; 26 in irrigated cultures). Thus, habitat
composition around abandoned centres of acti-
vity was compared with habitat composition
around these occupied before (1995) and after
(2000) large-scale habitat loss started. Fittingly,
the cartography used corresponds to the same
years.
Analytical procedures
Generalised Linear Models (GLMs, McCu-
llagh & Nelder, 1989) were used to obtain the
mathematical descriptions of habitat preferen-
ces. GLMs allow for the use of appropriate
error formulations from the exponential family
distributions, hence avoiding some of the limi-
tations of the conventional regression models.
Generalised Linear Models consists of a linear
predictor, an error function and a link function.
The linear predictor (LP) is defined as: LP =
a+ bx1 + cx2 + … where ais the intercept, b,
c… are the parameter estimates to be obtained
from the observed data, and x1, x2,… are the
explanatory variables. The error and link func-
tions depend on the nature of the data. The pre-
sence of owls follows a binomial distribution
(binary response variable: presence = 1, absen-
ce = 0). Therefore, a logit link was used (Bus-
tamante, 1997; Martínez et al, 2003a). Six se-
parate GLMs were conducted for the environ-
mental description of data. Each variable was
tested for significance in turn, and only those
variables that contributed to the largest signifi-
cant change in deviance were retained. Only
variables significant at the 1% level were in-
cluded in the models (Nicholls, 1989). The fi-
nal models were selected by likelihood ratio
tests for type I analysis (SAS Institute, 1996).
Recommendations have been recently made
that ecologists reduce their reliance on predic-
tion success as a performance measure in pre-
sence-absence modelling when independent
sets of data are not available to validate habitat
models (Manel et al., 2001). In these circums-
tances, Kappa statistics (Titus et al., 1984) are
recommended to test whether model discrimi-
nation significantly improves chance classifi-
cations. Kappa is a robust statistic that is spe-
cially adequate as a measure of the proportion
of all possible cases of presence or absence that
are predicted by a model after accounting for
chance effects, offering a meaningful numerical
variable for intercomparison between models
or between different statistical algorithms (Ma-
nel et al., 2001). The output variables (i.e., the
predicted values) in each case have a value bet-
ween 0 and 1, and presence for all samples was
accepted at a threshold probability of 0.5. For
Kappa, values of prediction success of 0-40%
are considered to indicate slight to fair model
performance, values of 40-60% moderate, 60-
80% substantial and 80-100% almost perfect
(Landis & Koch, 1977; Manel et al., 2001).
The breeding ecology of the Barn Owl in
irrigated cultures has been described in full el-
sewhere (Martínez, 1998; Martínez & López,
1999). Here, tests were made for a relationship
between the total length edges and ditches and
productivity (number of young per successful
pair), because Taylor (1994) has pointed out
the importance of edges in explaining the bre-
eding outcome for Barn Owls. Regularity of
dispersion of centres of activity in irrigated
cultures was assessed by means of the G-sta-
tistic (Brown, 1975), calculated as the ratio of
the geometric mean to arithmetic mean of the
squared nearest neighbour distances. The G-
statistic ranges from 0 to 1, with values over
0.65 indicating regularity of spacing. It is pos-
sible that 1-2 territories went unnoticed in dry
cultures, thus precluding calculation of the G-
statistic.
HABITAT LOSS AND BARN OWL POPULATION DECLINE 307
Ardeola 51(2), 2004, 303-317
RESULTS
Overall, a 69% population decline was found
in both study areas. Two sets of models of ha-
bitat preferences were produced. Firstly, occu-
pied versus non occupied territories in dry and
irrigated cultures were compared. The first set
of models showed that occupied territories were
characterized by a greater availability of cavi-
ties, linear structures and edges and a less de-
veloped road network than in non occupied te-
rritories (Tables 1 and 2). Secondly, occupied
versus deserted territories in dry and irrigated
cultures were compared. The second set of mo-
dels showed that deserted territories were more
likely to be found in areas were the road net-
work has been extended, housing developments
have been constructed, roosting or nesting pla-
ces become scarce, edges between traditional
land uses have been depleted and persecution
of birds of prey and owls takes place (Tables 3
and 4). The values of prediction success are
acceptable, ranging between 72-97% (Tables
1 to 4). The percentage of deviance explained
at each spatial scale by the GLMs is shown in
the figures 2 to 5. It was a combination of va-
riables that accounted for the explained de-
viance of the models, rather than a predomi-
nant variable (Fig. 2 to 5).
The spacing of centres of activity in irrigated
cultures was uniform before 1996 (G = 0.87).
However, the value of the G-statistic (0.25)
shows random dispersion of centres of activity
after 1996. A significant relationship was found
between the total length of edges and ditches
and the productivity before 1996 at the home
range scale (r2= 0.88, df = 34 , P = 0.001).
DISCUSSION
Barn Owls in dry cultures breed and roost
mainly in rural houses, dovecots and abando-
ned rabbit (Oryctolagus cuniculus) furrows in
the banks of ephemeral rivers, whereas in irri-
gated cultures they use niches in cemeteries,
belfries, rural houses and old warehouses (Mar-
tínez, 1998; Martínez & López, 1999). Unlike
other European populations (Shawyer, 1987;
Taylor, 1994), trees are very seldom used for
breeding, although mature carobs, olives, dense
pines and cypresses are frequently used as diur-
nal or nocturnal roosts. This probably reflects
the increasing scarcity of old trees resulting
from the conversion of cultures into urbanised
areas. Moreover, the abandonment of traditio-
nal agricultural practices has prompted the di-
sappearance of old trees through over-matu-
rity, disease or decay (Simarro, 2002). In
irrigated cultures, orange and lemon trees are
trimmed so that no cavities are available. Po-
llard trees, which occurred in the margins of
citric cultivations at the beginning of the study
period, have been completely depleted. Occu-
pied territories occurred in less disturbed areas
(as measured by the length of roads) than ran-
dom sites in both study areas.
Variables reflecting the feeding habits of the
owls enter the models at the home range scale
(Tables 1 and 2). This probably reflects the in-
creased density of prey in edges between habi-
tats (McCollin, 1998). Indeed, Rico (1997)
recommends a network of cereal patches in-
terspersed in Mediterranean forest and scru-
bland in order to increase the length of frontiers
between habitats and therefore small mammal
density in Alicante. Herbaceous edges in both
study areas are mowed or burnt periodically to
prevent overgrowth that would cause labour
difficulties, obstruct ditches or else «attract
rats» (Simarro, 2002), which probably enhan-
ces detection of prey by owls (Ille & Grinschgl,
2000). Barn Owls also prefer areas with high
availability of ditches. This traditional irrigation
system is frequently used by rats (Rattus spp),
which are the staple prey of Barn Owls in the
study areas (Martínez & López, 1999). Howe-
ver abundant ditches are in dry cultures, they
are mostly in a state of decay because, firstly,
of the abandonment of agricultural practices
and, secondly, because drip irrigation slowly
takes over as a more efficient irrigation sys-
tem, for instance in the fruit/vegetable green-
houses (Simarro, 2002). Furthermore, old acti-
ve ditches usually become overgrown with
small reed beds where small mammals are
abundant. All the same, reeds have been al-
most completely depleted (Simarro, 2002).
Again, occupied territories occurred in undis-
turbed areas and with a high availability of ca-
vities.
At the landscape level, the amount of man-
made structures with cavities entered the model
with positive value, and the increasing length of
paved roads emerges as a negative factor af-
fecting the probability of settlement of Barn
308 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
HABITAT LOSS AND BARN OWL POPULATION DECLINE 309
Ardeola 51(2), 2004, 303-317
TABLE 1
(A) Generalized Linear Models for the probability of presence of Barn Owls (comparing occupied vs. non oc-
cupied territories) in dry areas. (B) Explanatory power of the models (percentage of territories correctly clas-
sified, percentage of improvement over a classification by chance and test of significance). Level of signifi-
cance: ***<0.001.
[(A) Modelos Generales Linearizados para la probabilidad de presencie de Lechuzas Comunes (comparan-
do territorios ocupados con no ocupados) en cultivos de secano (B) Poder explicativo de los modelos (por-
centaje de territorios clasificados correctamente, porcentaje de mejora respecto a una clasificación al azar
y prueba de significación. Nivel de significación: ***<0,001.]
(A) Nest scale Home range-scale Landscape scale
[A escala del nido] [A escala del territorio] [A escala del paisaje]
Factor bSE (b)
χ
2bSE (b)
χ
2bSE (b)
χ
2
Intercept 20.331 0.672 –14.7 1.004 –1.22 0.123
[Punto de corte]
Buildings with cavities 0.06 0.01 48.52*** 0.106 0.011 57.999*** 0.048 0.016 25.008***
[Edificios con cavidades]
Roads –0.043 0.001 27.532*** –0.03 0.014 38.891*** –0.06 0.002 21.562***
[Carreteras]
Cereal-forest edges 0.266 0.007 58.011*** 0.043 0.011 9.905***
[Ecotono cereal-bosque]
Ditches 0.15 0.005 50.987***
[Acequias]
Residual deviance 39.08 10.05 39.87
[Devianza residual]
Variance explained by 60.1 92.3 58.3
model [Varianza explicada
por el modelo]
(B) % of correct Classification better Kappa Test (Z)
classification than chance (%)
Nest scale
[A escala del nido]
Occupied territories 87 77 8.1***
[Territorios ocupados]
non occupied territories 88
[Territorios no ocupados]
Home range scale
[A escala del territorio]
Occupied territories 97 97 9.1***
non occupied territories 98
Landscape scale
[A escala del paisaje]
occupied territories 89 78 7.7***
non occupied territories 85
310 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
TABLE 2
(A) Generalized Linear Models for the probability of presence of Barn Owls (comparing occupied vs. non oc-
cupied territories) in irrigated areas. (B) Explanatory power of the models (percentage of territories co-
rrectly classified, percentage of improvement over a classification by chance and test of significance). (Level
of significance ***< 0.001).
[(A) Modelos Generales Linearizados para la probabilidad de presencie de Lechuzas Comunes (comparan-
do territorios ocupados con no ocupados) en cultivos de regadío (B) Poder explicativo de los modelos
(porcentaje de territorios clasificados correctamente, porcentaje de mejora respecto a una clasificación al
azar y prueba de significación. Nivel de significación: ***<0,001.]
(A) Nest scale Home range-scale Landscape scale
[A escala del nido] [A escala del territorio] [A escala del paisaje]
Factor bSE (b)
χ
2bSE (b)
χ
2bSE (b)
χ
2
Intercept 16.109 0.073 18.47 0.802 –1.52 0.09
[Punto de corte]
Buildings with cavities 0.019 0.001 59.622*** 0.077 0.04 52.06*** 0.066 0.04 40.121***
[Edificios con cavidades]
Roads –0.044 0.002 42.321*** -0.043 0.002 66.176*** –0.09 0.001 38.099***
[Carreteras]
Smallholdings-citric edges 0.031 0.001 66.93*** 0.051 0.001 69.072*** 0.099 0.001 40.541***
[Ecotono minifundios-
cítricos]
Ditches 0.033 0.02 39.799***
[Acequias]
Residual deviance 52.1 24.1 35.3
[Devianza residual]
Variance explained by 49.5 79.1 65.2
model [Varianza explicada
por el modelo]
(B) % of correct Classification better Kappa Test (Z)
classification than chance (%)
Nest scale
[A escala del nido]
Occupied territories 77 80 8.01***
[Territorios ocupados]
non occupied territories 73
[Territorios no ocupados]
Home range scale
[A escala del territorio]
Occupied territories 93 92 10.22***
non occupied territories 89
Landscape scale
[A escala del paisaje]
occupied territories 83 72 6.36***
non occupied territories 71
HABITAT LOSS AND BARN OWL POPULATION DECLINE 311
Ardeola 51(2), 2004, 303-317
TABLE 3
(A) Generalized Linear Models for the probability of presence of Barn Owls (comparing occupied vs. deser-
ted territories) in dry areas. (B) Explanatory power of the models (percentage of territories correctly classified,
percentage of improvement over a classification by chance and test of significance). (Level of significance:
***< 0.001).
[(A) Modelos Generales Linearizados para la probabilidad de presencie de Lechuzas Comunes (comparan-
do territorios ocupados con abandonados) en cultivos de secano (B) Poder explicativo de los modelos (por-
centaje de territorios clasificados correctamente, porcentaje de mejora respecto a una clasificación al azar
y prueba de significación. Nivel de significación: ***<0,001]
(A) Nest scale Home range-scale Landscape scale
[A escala del nido] [A escala del territorio] [A escala del paisaje]
Factor bSE (b)
χ
2bSE (b)
χ
2bSE (b)
χ
2
Intercept 25.14 0.661 –9.58 0.157 2.42 1.15
[Punto de corte]
Buildings with cavities 0.092 0.033 64.63*** 0.072 0.003 41.013*** 0.088 0.004 31.32***
[Edificios con cavidades]
Roads –0.041 0.000 28.144*** –0.081 0.05 23.33*** –0.063 0.002 50.006***
[Carreteras]
Housing developments –0.063 0.000 49.555*** –0.079 0.004 49.02*** –0.052 0.001 66.446***
[Urbanizaciones]
Cereal-forest edges 0.098 0.002 62.871***
[Ecotono cereal-bosque]
Persecutio –0.069 0.023 22.505***
[Caza ilegal]
Residual deviance 11.4 10.7 26.7
[Devianza residual]
Variance explained by 85.3 89.9 73.4
model [Varianza explicada
por el modelo]
(B) % of correct Classification better Kappa Test (Z)
classification than chance (%)
Nest scale
[A escala del nido]
Occupied territories 86 89 6.78***
[Territorios ocupados]
non occupied territories 93
[Territorios no ocupados]
Home range scale
[A escala del territorio]
Occupied territories 90 94 9.04***
non occupied territories 96
Landscape scale
[A escala del paisaje]
occupied territories 86 73 8.44***
non occupied territories 83
312 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
TABLE 4
(A) Generalized Linear Models for the probability of presence of Barn Owls (comparing occupied vs. deser-
ted territories) in irrigated areas. (B) Explanatory power of the models (percentage of territories correctly clas-
sified, percentage of improvement over a classification by chance and test of significance).(Level of signi-
ficance: ***< 0.001).
[(A) Modelos Generales Linearizados para la probabilidad de presencie de Lechuzas Comunes (comparan-
do territorios ocupados con abandonados) en cultivos de regadío (B) Poder explicativo de los modelos (por-
centaje de territorios clasificados correctamente, porcentaje de mejora respecto a una clasificación al azar
y prueba de significación. Nivel de significación: ***<0,001.]
(A) Nest scale Home range-scale Landscape scale
[A escala del nido] [A escala del territorio] [A escala del paisaje]
Factor bSE (b)
χ
2bSE (b)
χ
2bSE (b)
χ
2
Intercept –5.688 0.722 29.71 0.367 2.05 0.209
[Punto de corte]
Buildings with cavities –0.044 0.001 43.644*** –0.068 0.006 45.715*** –0.072 0.06 44.135***
[Edificios con cavidades]
Roads –0.087 0.001 30.691*** –0.047 0.003 42.719*** –0.099 0.009 46.336***
[Carreteras]
Housing developments –0.093 0.002 39.164*** –0.098 0.004 39.064*** –0.064 0.02 63.254***
[Urbanizaciones]
Smallholdings-citric edges 0.009 0.000 56.892*** 0.053 0.004 79.038***
[Ecotono minifundios-
cítricos]
Persecutio –0.093 0.027 29.111***
[Caza ilegal]
Residual deviance 19.9 6.2 21.9
[Devianza residual]
Variance explained by 80.2 94 78.2
model [Varianza explicada
por el modelo]
(B) % of correct Classification better Kappa Test (Z)
classification than chance (%)
Nest scale
[A escala del nido]
Occupied territories 86 89 6.51***
[Territorios ocupados]
non occupied territories 80
[Territorios no ocupados]
Home range scale
[A escala del territorio]
Occupied territories 98 98 8.02***
non occupied territories 97
Landscape scale
[A escala del paisaje]
occupied territories 77 77 6.25***
non occupied territories 79
HABITAT LOSS AND BARN OWL POPULATION DECLINE 313
Ardeola 51(2), 2004, 303-317
FIG.2.—Percentage of deviance explained at three spatial scales by the Generalized Linear Models for the
probability of presence of Barn Owls comparing occupied vs. non occupied territories in irrigated areas.
[Porcentajes de devianza explicados a tres escalas espaciales por los Modelos Generales Linearizados
para la probabilidad de presencia de Lechuzas Comunes comparando territorios ocupados y no ocupados en
áreas de cultivos de regadío.]
FIG. 3.—Percentage of deviance explained at three spatial scales by the Generalized Linear Models for the
probability of presence of Barn Owls comparing occupied vs. deserted territories in dry areas.
[Porcentajes de devianza explicados a tres escalas espaciales por los Modelos Generales Linearizados
para la probabilidad de presencia de Lechuzas Comunes comparando territorios ocupados y abandonados en
áreas de cultivos de secano]
Owls. Yet again, a set of variables describing
the length of edges between traditional agri-
cultural land uses enter the models with positi-
ve value.
The spatial dispersion of centres of activity
in irrigated cultures was uniform before major
habitat changes started, but the pattern was at
random after 1996. The models suggest that
this change is due mainly to the desertion of
territories through habitat change or to the de-
ath of individuals in the expanded road net-
work. Indeed, the decline of the Barn Owl po-
pulation reported in this study is the result of a
complex combination of factors that emerge
selectively at different spatial scales (Fig. 2
and 5). Nonetheless, the variable «number of
man-made structures with cavities» entered all
the models. The Barn Owl is sensitive to
small-scale habitat change, as shown by the
finding that the loss of a single occupied site
can lead to the desertion of other nearby sites
(the «knock-on effect»; Ramsden, 1998). Furt-
hermore, the disappearance of the preferred
roost can lead to territory desertion even if the
nest is left untouched (Martínez, 1998). Ac-
cordingly, the increasing loss of traditional
nesting or roosting sites may have prompted
territory desertion in our study areas. In an
non-exclusive way, it could be suggested that
owls died in the extended road network, as
suggested by the fact that the variable «roads»
enters every single model with negative value
(Tables 1 to 4) (see Van der Hut et al., 1992;
Taylor, 1994).
The loss of foraging areas (edges, ditches)
could have a major bearing in explaining the
absence of multiple broods and the reduction in
productivity recorded after 1996 in irrigated
cultures (Martínez, 1998; Martínez & López,
1999; see also Van der Hut et al., 1992; Taylor,
1994; De Jong, 1995; Alasdair et al., 2000). In
the absence of habitat change, the Barn Owl
population in this area has been found to be
self-maintaining, as shown by the finding that
the observed productivity before 1996 is no-
tably higher than the critical productivity (Mar-
tínez, 1998). However, the critical productivity
is not reached after habitat loss started and no
imports take over vacancies (Martínez, 1998;
Martínez & López, 1999). Bruijn (1994) has
also documented variations in the ability to
compensate mortality through reproduction bet-
ween two Barn Owl populations.
Interestingly enough, the likelihood of ha-
ving a deserted territory increases when records
of persecution are available within a 5.6 km
radius around the centres of activity in both
areas. This is amenable to the finding that wild-
314 MARTÍNEZ, J. A. & ZUBEROGOITIA, Í.
Ardeola 51(2), 2004, 303-317
FIG. 4.—Percentage of deviance explained at three spatial scales by the Generalized Linear Models for the
probability of presence of Barn Owls comparing occupied vs. deserted territories in irrigated areas.
[Porcentajes de devianza explicados a tres escalas espaciales por los Modelos Generales Linearizados
para la probabilidad de presencia de Lechuzas Comunes comparando territorios ocupados y abandonados en
áreas de cultivos de regadío]
life protection laws have not been effectively
put into practice over the last decade in the
East of Spain (Martínez & López, 1995; Martí-
nez et al., 1996; Martínez et al., 2001). A prio-
ri, the variable «persecution» could have been
regarded as a bad predictor, especially at the
high-resolution spatial scales (i.e., nest-site sca-
le) because there are few data on raptor and
owl casualties available in relation to the area
surveyed. The fact that it enters the occupation
versus desertion models at the low-resolution
scale stresses the importance of illegal hunting
in Spain.
Subsequent to the success of various Euro-
pean population reinforcement programs (Van
der Hut et al., 1992; Bruijn, 1994; Taylor,
1994), the erection of nest-boxes in alternative
sites is a commonly proposed measure within
the framework of environmental impact stu-
dies in order to compensate for habitat loss for
Barn Owls in Spain. The models show that the
loss of cavities accounts for between 10 to 39%
of the explained deviance (Fig. 4 and 5), the
rest being accounted for by habitat loss and
persecution. Territory occupancy by Barn Owls
is the result of a complex combination of fea-
tures, which suggests that erecting nests-boxes
would achieve limited success in our study are-
as unless managers promote policies encoura-
ging the preservation or restoration of the agri-
pastoral network in designed core areas as well
as the effective control of persecution. The im-
plementation of such policies would be also
beneficial for the guild of Strigiformes and Fal-
coniformes in the East of Spain (Rico et al.,
2001; Sánchez-Zapata & Calvo, 1999; Martí-
nez et al., 2003; Martínez & Zuberogoitia,
2004), where negative population trends have
been already reported for owls (Martínez &
Zuberogoitia, 2003b, 2004; Alonso et al.,
2003).
In conclusion, it is suggested that: (1) the
current distribution and abundance of Barn
Owls in the study areas are artefacts of anthro-
pogenic land transformation, (2) population
reinforcement schemes would benefit from mo-
delling pre and post-transformation habitat af-
finities of owls, (3) environmental studies pro-
tecting only small areas around occupied Barn
Owl nests may not be sufficiently meeting the
habitat requirements of this species, as shown
by the relatively high percentages of deviance
explained by the GLMs at broad spatial scales
(Tables 1 and 2), (4) cataloguing of owls would
achieve optimal efficiency if based on scientific
evidence and (5) should the population decline
caused by habitat loss persist in the study areas,
a review for possible re-cataloguing of the Barn
Owl will be needed in brief.
ACKNOWLEDGEMENTS.—We are most indebted to
Fernando Falcó, Luis Rico, HELIACA, Roque Be-
lenguer, Alejandro Izquierdo, Juanjo Izquierdo, Fran,
G.E.R.-Valencia, RONCADELL, Javier Simarro,
Mavi Corell, Javier Martínez-Valle, Domingo, Ma-
nuel Carrascosa, Angel, Paco Segarra, Luis Fidel,
Alfonso Lario, the C.E.P.M.N., and the S.E.R.
Thanks are due to AMBARTEC S.L. for their logis-
tic support. A previous study with David Serrano
provided us with useful ideas for this study. Vincen-
zo Penteriani and the Editorial Board of Ardeola
made valuable comments on the manuscript. Javier
Seoane greatly enhanced our understanding of the
statistics in this paper.
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Jose Antonio Martínez is a freelance ecologists that
started his own long-term project on habitat selection
for the guild of raptors and owls in the East of Spain
in 1989. Iñigo Zuberogoitia carries out research on
the community of raptors, owls and carnivores in
the North of Spain (since 1989) as a member of the
E. M. Icarus s.l. environmental consulting. They joi-
ned efforts in 1996 to produce a join scheme aimed
at evaluating the effects of habitat loss on animal
communities, among others.
[Recibido: 11-12-03]
[Aceptado: 02-06-04]
HABITAT LOSS AND BARN OWL POPULATION DECLINE 317
Ardeola 51(2), 2004, 303-317
... Movement areas corresponding to the area of circles with radii of four and 5.6 km were taken into account by Martínez and Zuberogoitia. 33 In the Balearic Islands, it has been found that Common Barn-owls also fly over the 4.5 km wide strait for hunting to catch prey on the neighboring island. 34 According to a study performed in Canada, owls move 8-10 km from their regular roost site for one night only. ...
... In our study area, the hunting area with a radius of 1 km around the resting area lacked the typical wetland and forest habitats, so the small mammals that preferred these habitats were probably preyed on by the owls at a greater distance. Our results confirm that circles with a radius of 1.5 km or less may only be suitable for characterizing the environment of the breeding site, 33 however, many researchers use it to characterize the hunting area of Common Barn-owls. 20,21,23,[25][26][27] It should not be overlooked that the range of motion of the Common Barn-owls is relatively small, 3,19,46 and cannot be described by a regular circle, but it follows irregularly shaped habitats most suitable for hunting. ...
... Our finding supports the results that the hunting area of Common Barn-owls corresponded to a circle with a radius of 3 km or more. 10,18,32,33 Based on radio-telemetry studies, individual ranges of movement can be very different and the size of their hunting area may be determined by the size of pasture and cereal crop areas. 48 The Common Barn-owl hunts mainly in such open areas, as the abundance and availability of prey are optimal there. ...
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The relative abundance of small mammal species detected from Common Barn-owl pellets reflects the landscape structure and habitat pattern of the owl’s hunting area, but it is also affected by the size of the collected pellet sample and the size of the supposed hunting area. The questions arise: how many pellets should be collected and analyzed as well as how large hunting area should be taken into consideration in order to reach the best correspondence between the owl’s prey composition and the distribution of habitats preferred by small mammals preyed in supposed hunting areas? For this study, we collected 1045 Common Barn-owl pellets in a village in southern Hungary. All detected small mammal species were classified into functional groups (guilds) preferring urban, open, forest and wetland habitats. The proportion of functional groups was compared to the proportion of these habitats around the pellet collection site within circles of one, two, and three kmradius. Saturation curves showed that at least 300 pellets or ca. 600 mammalian remains are required for the detection of the 19 small mammal species. The share of small mammals detected in the prey and their functional groups according to their habitat preference showed an increasing consistency with the distribution of real habitats in the potential hunting area of a radius of 3 km around the owl’s breeding or resting place.
... Monitoring is also important because owl populations greatly fluctuate and they are drastically decimated by road traffic accidents, use of rodenticides, the decline of available breeding sites and cold winters (e.g. Bunn et al. 1982, Martinez & Zuberogoitia 2004, Tóth et al. 2009). These negative effects can be somewhat mitigated by opening the attic spaces as potential sites for breeding and roosting as well as by providing nest boxes (Taylor 1994, Klein et al. 2007, Tórizs 2011. ...
... According to a survey conducted in the eastern part of Baranja between 2006 and 2009, there were 9 to 10 pairs of common barn-owls but only half of them were nesting, mainly due to nest boxes placed between 2006 and 2007 (Tórizs 2011). This example confirms that the decrease in the number of owls is primarily influenced by the lack of appropriate breeding sites (Martinez & Zuberogoitia 2004). However, the placement of breeding boxes is not always a solution, because without regular maintaining, these artificial breeding sites may become an ecological trap (Klein et al. 2007). ...
... However, the placement of breeding boxes is not always a solution, because without regular maintaining, these artificial breeding sites may become an ecological trap (Klein et al. 2007). With the increase in road traffic intensity, the mortality of birds could also increase (Mikuska 1990, Martinez & Zuberogoitia 2004. ...
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Changes in land use affect the life of small mammals and the owls that prey on them. The aim of the present work was to follow changes in the composition of the common barn-owls' prey and the landscape structure of their hunting areas. In 2007 and 2016, we collected owl pellets in the whole area of Baranja region but samples from both periods originated only from 10 sites. The diversity of small mammals in the prey was similar in both years, while common vole (Microtus arvalis) dominated with over 60%. In pellets collected in 2016, we found significantly more bicolored shrews (Crocidura leucodon), lesser white-toothed shrews (Crocidura suaveolens) and yellow-necked mice (Apodemus flavicollis), while the proportions of harvest mouse (Micromys minutus) and eastern house mouse (Mus musculus) were significantly lower than in the previous sampling. The landscape structure of hunting areas has hardly changed over the decade; therefore, the temporal variations in the owls' diet composition may have been caused by changes in farming practices such as the frequent mowing of meadows, the regulation of the use of pesticides, the depopulation of villages and also the effects of extreme weather.
... Using this technique, a large amount of data is quickly available, although some limitation of this approach has been highlighted (Yom-Tov & Wool, 1997;Andrade et al., 2016). For example, since some owl species are stenoecious, inhabiting only specific habitats (as in the case of barn owl Tyto alba foraging mainly in open-mosaic landscapes; Martínez & Zuberogoitia, 2004), information about small mammal assemblages may be obtained only for these contexts. Moreover, this type of sampling is highly opportunistic since it was indirectly obtained from the predators, therefore not following a standardized sampling protocol). ...
Article
In order to investigate diversity patterns and similarities in the small mammal communities of an agroforestry landscape in western central Italy (Maremma of Lazio), we analyzed, in a multivariate setting (Cluster analysis, DCA-Detrended Correspondence Analysis), the prey content of barn owl Tyto alba pellets collected along one year in five sampling sites. Small mammal communities were composed by guilds typical of habitats included in agroforestry landscapes (croplands and mosaics, forests and ecotones, wet habitats and synanthropic ones). Since landscape matrices were characterized almost everywhere by croplands, typical agro-ecosystem species (Apodemus cfr. sylvaticus, Microtus savii, Mus domesticus and Soricidae) dominated in the majority of the collecting sites. The statistical analyses show how small changes in land use and cover can explain the faunal differences between sites, with the occasional presence of Arvicola italicus in wet habitats, and of Muscardinus avellanarius and Sorex samniticus in sites dominated by forest or agroforestry ecotones. Communities recorded in sites characterized by wet and forest habitats showed a higher distance from the others, dominated by croplands. Communities occurring in landscapes with the lowest habitat diversity showed also the lowest species diversity.
... For instance, raptor richness tends to be negatively affected by urban development [12]. Indeed, urbanization is leading to systematic raptor population declines due to the reduction in the area of suitable habitat available [13,14]. Nevertheless, some studies have revealed that this group of birds has a certain capacity to colonize urban areas [15,16]. ...
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Natural habitats are being altered and destroyed worldwide due to urbanization, leading to a decrease in species abundance and richness. Nevertheless, some species, including tawny owls, have successfully colonized this novel habitat. Consequences at the population level have not been described; thus, our main objective was to describe the effects that urban structure have on the tawny owl population at local and landscape levels. Data were obtained from 527 survey points over 7 months in a large-scale owl survey in the Basque Country (northern Spain) in 2018. At the local scale, the interaction between forest and urban cover affected tawny owl abundance, the optimum being in medium forested areas. The interaction between urban cover and clumpiness index (urban patch distribution) showed a generally negative effect. At the landscape scale, its abundance decreased in complex-shaped urban patches and when distance between them was greater. In conclusion, at the local scale, when a minimal forest structure is present in urbanized areas, the species can exploit it. At the landscape scale, it prefers smaller urban towns to cities. Thinking ahead, the current tendency toward “green capitals” should benefit tawny owl populations.
... Sousa et al. (2010) showed that roads appear to cause an avoidance effect on the establishment of Barn Owl home ranges in Portugal, and that core areas of Barn Owl home ranges typically did not include highways. In Spain, the expansion of the road network and subsequent mortality on roads has been implicated as one of the main factors causing local Barn Owl population declines (Martínez and Zuberogoitia 2004). ...
Technical Report
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The development and expansion of road infrastructures represents one of the most widespread forms of modification of the natural landscape over the past century. Roads have altered ecosystems and directly and indirectly effect a wide range of wildlife species. One of the most obvious effects of roads on wildlife is mortality caused by vehicle collisions. Barn Owls Tyto alba, due to their low flight and hunting behaviour, are particularly susceptible. Vehicle collisions are a major cause of death for Barn Owls and have been implicated as a contributing factor in the decline of their populations in Europe. The extent of road mortality and the route and landscape characteristics which influence risk of collision have been reported for specific road systems; however, the implementation of effective and evaluated mitigation solutions to minimise negative effects of roads on Barn Owl populations remains a significant challenge. In addition to knowledge on the nature and effects of road mortality, an understanding of the individual behaviour response and interactions of Barn Owls to road networks is necessary to identify potential for evidence-based mitigation solutions. In this context, to determine Barn Owl interactions with roads in relation to mortality patterns, we investigated: (i) the extent of road mortality and factors which influence Barn Owl vehicle collisions, (ii) the suitability of roadside verges for foraging Barn Owls, (iii) the spatial distribution, occupancy and breeding performance of Barn Owls in relation to road networks, and (iv) the foraging behaviour and movement patterns of individuals in relation to major roads.
... The importance of uncultivated land on nest box occupancy and persistence suggests that farm conservation should prioritize the habitats that most benefit barn owls, especially grasslands. Previous studies have also found that barn owls tend to choose breeding sites away from roads, likely to avoid disturbance (Martinez and Zuberogoitia 2004, Frey et al. 2011, Charter et al. 2012. Future work should model changing landscape composition, as well as the effects of habitat loss and restoration on occupancy rates. ...
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Abstract Landscape composition can strongly affect the delivery of ecosystem services in agroecosystems. Conserving uncultivated habitats can support ecosystem services, but in Mediterranean biomes, these lands can also increase the area susceptible to wildfires. In the world‐renowned wine‐producing region of Napa Valley, California, wine grape growers install nest boxes to attract American barn owls (Tyto furcata), which may reduce rodent crop damage. Annual monitoring of 273 nest boxes began in 2015, and devastating wildfires burned approximately 60,000 ha in the region in 2017, including homes and businesses, as well as some vineyards and uncultivated land. The goal of this study was to determine whether changes in nest box occupancy were attributed to wildfires, nest box design, land cover type, or some combination of these variables. Occupancy surveys before and after these wildfires revealed changes in habitat selection at the nest scale. Occupancy increased during the study, reaching its highest point after the fires. Owls were found breeding in recently burned areas that were previously unoccupied and modeling results showed that nest box occupancy had a positive relationship with burned areas, particularly with edges of the fire perimeter. Barn owls also consistently showed a strong preference for taller nest boxes that were surrounded by more grassland than other land cover types and a moderate selection for wooden over plastic boxes. These results illustrate an incentive for the conservation of uncultivated habitat, particularly grassland, in vineyard ecosystems, and they provide an example of a mobile pest predator’s response to wildfire disturbance. In this case, results suggest an agroecosystem service made resilient to wildfire by the owls’ selection of burned and uncultivated habitats.
... Given the European decline of the Barn Owl due to pesticides and reduction of potential shelter sites [31,32], keeping the ecological knowledge of this species updated is required to better address effective conservation plans [12]. In this work, we estimated frequencies and volumes of each prey in the diet of the Barn Owl in Elba island after over 30 years since its previous assessment [30]. ...
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The Barn Owl Tyto alba is commonly reported as a non-selective predator of small mammals, and its diet has been thoroughly analyzed also to assess the small mammal assembly composition in many study areas. The aim of this work was to analyze the diet of the Barn Owl in the Elba island through the analysis of 161 pellets collected in September 2020. Undigested fragments were isolated and compared with reference collections. We confirmed that the Barn Owl is a typical predator of field mice (62% of relative frequency), with synanthropic murid rodents as the second category of prey. The frequency of consumption of shrews increased by 9% with respect to the previous work, suggesting that the natural environment of Elba island is still in a good health status. Moreover, fragments of two newborn hares were detected, increasing the knowledge on the local trophic spectrum of the Barn Owl. Finally, the skull of a Geoffroy’s Myotis Myotis emarginatus confirmed the presence of this species in Elba island after over 60 years from the first unconfirmed record. Repeated studies conducted in the same study site may provide useful information on prey population trends and local environmental status.
... Studies show that species richness in restored forests was comparable to intact forests [56]. Forest management or rehabilitation in conjunction with Environmental Pedagogy in a National Reserve high availability of manmade habitat boxes leads to success [57]. Nest boxes can bring back species that were once threatened or endangered [58][59][60]. ...
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The Pinelands National Reserve is one of the most integrated regional planning regimes for conservation in the world. Environmental protection is overlapped by the State Pinelands Area, the Pinelands National Reserve, and the New Jersey Pinelands Biosphere Reserve (United Nations Educational, Scientific and Cultural Organization). Stockton University, a 4-year state university with an 800-hectare campus operates within this mix of preservation and working landscape. In the Environmental Studies program, faculty engage students in the outdoor classroom to study the complexities of balancing development and conservation. This case highlights the creation of the first National Reserve and a University within the protected area and focuses on students analyzing species and habitat to encourage native cavity nesting animals to return and breed in the Pinelands. Readers will be able to navigate the complexities and opportunities of working in a protected area and apply these lessons in the classroom. With this case study, instructors, researchers, and students will be able to apply the symbiotic relationship between protected region and university to other areas of the world.
... The barn owl is an avian predator specialising on small mammals in open farmlands and it is known to use buildings, especially churches, for nesting (Barn Owl Trust, 2012). Its population has declined dramatically in many European countries during the last decades (Toms et al., 2001;Martinez and Zuberogoitia, 2004;Poprach, 2017). The occurrence of barn owls was surveyed in nearly 2800 churches in Poland and we linked it to two categories of variables: ordinary climate and land-use (including infrastructure) variables and socio-economic statistics at the commune level. ...
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Variation of habitats and resources important for farmland birds seems to be only partly captured by ordinary statistics on land-use and agricultural production. For instance, densities of rodents being prey for owls and raptors or structures of rural architecture providing nesting sites for many species are central for bird diversity but are not reported in any official statistics. Thus, modelling species distributions, population abundance and trends of farmland birds may miss important predictive habitat elements. Here, we involve local socio-economy factors as a source of additional information on rural habitat to test whether it improves predictions of barn owl occurrence in 2768 churches across Poland. Barn owls occurred in 778 churches and seemed to prefer old churches made of brick located in regions with a milder climate, higher share of arable land and pastures, low road density and low levels of light pollution. Including data on local unemployment, the proportion of elder citizens, commune income per citizen, the share of citizens with high education and share of farmers among working population improved the model substantially and some of these variables predicted barn owl occurrence better than several land-use and climate data. Barn owls were more likely to occur in areas with high unemployment, a higher proportion of older citizens in a local population and higher share of farmers among working population. Importantly, the socio-economy variables were correlated with the barn owl occurrence despite all climatic, infrastructure and land-use data were present in the model. We conclude that the socio-economy of local societies may add important but overlooked information that links to spatial variation in farmland biodiversity.
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Agriculture intensification drives changes in bird populations but also in the space use by farmland species. Agriculture in Eastern Europe still follows an extensive farming model, but due to policy shifts aimed at rural restructuring and implementation of government subsidies for farmers, it is being rapidly intensified. Here, we aimed to document the ranging behaviour and habitat use of a declining farmland bird of prey—Montagu’s Harrier—and to compare it to findings from Western Europe. In 2011–2018, 50 individuals were followed with GPS loggers in Eastern Poland to study species spatial ecology. We found home ranges (kernel 90%) to be considerably large: 67.3 (± 42.3) km ² in case of males, but only 4.9 (± 6.1) km ² in females. Home ranges overlapped by 40%, on average, with other males in colonies and by 61%, on average, between consecutive breeding seasons of a particular male. The average daily distance travelled by males and females reached, respectively, 94.5 and 45.3 km, covering a daily home range of 32.3 and 3.1 km ² . Individuals foraged up to 35 km from nests (3.5 km on average). Daily distance travelled and daily home ranges varied across the breeding season, in case of females being shortest in July, but sharply increasing in August. Also, individuals with breeding success had higher daily distance travelled but smaller daily home ranges. Average harriers’ distance to nest was generally increasing over the season, but was also changing over time of day: birds were closest to nest during night time, but at the end of the season, males roosted up to 16 km from the nest. While foraging males slightly preferred grasslands, higher elevation and smaller land-use patches, they avoided slopes and proximity of roads. We conclude that the surprisingly large home ranges of breeding harriers may suggest reduced prey availability or high fragmentation of hunting areas, both driving birds to utilise large areas and potentially contributing to population decline.
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I controlled a total of 212 Barn Owl Tyto alba pairs in six years (1993-1998). Significant differences were found in the proportion of pairs that bred among the six years. These differences were correlated with the overall rainfall in February and March, that was one of the factors limiting breeding of Barn Owls. Moreover, there was a significant positive correlation between the precipitation in February and March and the mean egg-laying dates. Also, there were significant differences in the mean egg-laying dates between years. On the other hand, the breeding success per successful pair (3.8 eggs and 2.16 young on average) was one of the smallest reported for the species. Both, the low breeding success and the scarcity of second clutches could be related to a stable Barn Owl population that does not seem to experience population fluctuations.
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We analysed the breeding density of a Mediterranean Eagle Owl Bubo bubo population and the characteristics of the landscape surrounding the nest, in an attempt to identify the determinants of habitat preferences within a radius of 1000 m around each nest. A total of 59 nest site were identified (15.3 nest sites 100 km2; mean nearest neighbour distance 1770 m). Eleven variables were correlated with the presence of an Eagle Owl nest: three variables describing the patch composition of the landscape, three variables of landscape heterogeneity, and five variables for minimum distance from landscape components. The comparison between the landscape features surrounding the nest sites and the control plots (defining the whole landscape structure) showed a significant difference. Openlands and landscape heterogeneity around the nest are a key determinant for the settlement of the Eagle Owl.
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Modern ecological research often involves the comparison of the usage of habitat types or food items to the availability of those resources to the animal. Widely used methods of determining preference from measurements of usage and availability depend critically on the array of components that the researcher, often with a degree of arbitrariness, deems available to the animal. This paper proposes a new method, based on ranks of components by usage and by availability. A virtue of the rank procedure is that it provides comparable results whether a questionable component is included or excluded from consideration. Statistical tests of significance are given for the method. The paper also offers a hierarchical ordering of selection processes. This hierarchy resolves certain inconsistencies among studies of selection and is compatible with the analytic technique offered in this paper.
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• 1. Models for predicting the distribution of organisms from environmental data are widespread in ecology and conservation biology. Their performance is invariably evaluated from the percentage success at predicting occurrence at test locations. • 2. Using logistic regression with real data from 34 families of aquatic invertebrates in 180 Himalayan streams, we illustrate how this widespread measure of predictive accuracy is affected systematically by the prevalence (i.e. the frequency of occurrence) of the target organism. Many evaluations of presence–absence models by ecologists are inherently misleading. • 3. With the same invertebrate models, we examined alternative performance measures used in remote sensing and medical diagnostics. We particularly explored receiver-operating characteristic (ROC) plots, from which were derived (i) the area under each curve (AUC), considered an effective indicator of model performance independent of the threshold probability at which the presence of the target organism is accepted, and (ii) optimized probability thresholds that maximize the percentage of true absences and presences that are correctly identified. We also evaluated Cohen's kappa, a measure of the proportion of all possible cases of presence or absence that are predicted correctly after accounting for chance effects. • 4. AUC measures from ROC plots were independent of prevalence, but highly significantly correlated with the much more easily computed kappa. Moreover, when applied in predictive mode to test data, models with thresholds optimized by ROC erroneously overestimated true occurrence among scarcer organisms, often those of greatest conservation interest. We advocate caution in using ROC methods to optimize thresholds required for real prediction. • 5. Our strongest recommendation is that ecologists reduce their reliance on prediction success as a performance measure in presence–absence modelling. Cohen's kappa provides a simple, effective, standardized and appropriate statistic for evaluating or comparing presence–absence models, even those based on different statistical algorithms. None of the performance measures we examined tests the statistical significance of predictive accuracy, and we identify this as a priority area for research and development.
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We studied nesting and hunting habitat preferences of Bonelli’s Eagles in the province of Alicante. In the period between 1989 and 2000 we located all nests of the species in the study area, and their positions were translated to 1:25000 topographic maps. Since ecological processes can be scale-dependent, we measured a series of variables (Table 1) at two different scales: in a circle of 2.8 km of radius centred on the most frequently used nest and in a second circle of 5.6 km of radius aimed at representing the Eagles’ home ranges. Due to the lack of information about the Eagle’s home range size, both scales were arbitrarily set. We compared land uses, relief and degree of human disturbance between occupied circles and non-occupied circles centred on suitable cliffs higher than 10 m selected at random. For analytical purposes, we used logistic regression models. Relief was the best predictor of nest locations at the 2.8 km-scale. At the 5.6 km-scale, we found that the surface of scrubland increased the probability of finding nests, while the surface of irrigated cultivation decreased that probability. Both models reduced low percentages of the variance of the null model, suggesting that the response of Bonelli’s Eagles to the variables was weak, that the scales, arbitrarily set, may not be biologically significant for the species, or both. We suggest that heavy and sustained persecution by man is a confounding factor when addressing the habitat preferences of the Bonelli’s Eagle in Alicante. The pattern of habitat preferences described here may not correspond with the distribution of resources that this large predator may be able to use in the absence of human persecution. Therefore, the process of habitat selection cannot be inferred from the current pattern of habitat preferences. We encourage radio-tracking studies to improve conservation strategies.
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A method for evaluating the classification table from a discriminant analysis is described. The statistic, kappa, is useful to ecologists in that it removes the effects of chance. It is useful even with equal group sample sizes although the need for a chance-corrected measure of prediction becomes greater with more dissimilar group sample sizes. Examples are presented.
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