evidence for contrasting causes of population change in two closely related, sympatric breeding...

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Evidence for contrasting causes of population changein two closely related, sympatric breeding speciesthe Whinchat Saxicola rubetra and Stonechat Saxicolatorquata in BritainIan Hendersona, John Calladineb, Dario Massiminoa, Jennifer A. Taylorc & Simon Gillingsa

a British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2QA, UKb British Trust for Ornithology Scotland, Cottrell Building, University of Stirling, Stirling FK99LA, UKc Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UKPublished online: 08 Oct 2014.

To cite this article: Ian Henderson, John Calladine, Dario Massimino, Jennifer A. Taylor & Simon Gillings (2014): Evidence forcontrasting causes of population change in two closely related, sympatric breeding species the Whinchat Saxicola rubetra andStonechat Saxicola torquata in Britain, Bird Study

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Evidence for contrasting causes of population changein two closely related, sympatric breeding species theWhinchat Saxicola rubetra and Stonechat Saxicolatorquata in Britain

IAN HENDERSON1*, JOHN CALLADINE2, DARIO MASSIMINO1, JENNIFER A. TAYLOR3 andSIMON GILLINGS11British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2QA, UK; 2British Trust for OrnithologyScotland, Cottrell Building, University of Stirling, Stirling FK9 9LA, UK; 3Lancaster Environment Centre, LancasterUniversity, Lancaster LA1 4YQ, UK

Capsule The recent population decline of Whinchats has accelerated, including core breeding areas ofBritain. Contrasting patterns of change with Stonechat suggest a large-scale environmental driver isaffecting the entire Whinchat population.Aims To explore broad geographical and landscape related differences in long-term patterns of populationchange in the Whinchat and Stonechat across Britain to identify candidate mechanisms of change.Methods The study uses 40 years of large-scale, long-term data from a series of three atlas studies tocompare trends in range and abundance in Whinchats and Stonechats relative to landscape andweather variables.Results For Whinchats there has been a long-term and accelerating decline in abundance, that includesstronghold areas of Britain. The Stonechat population has undergone a net gain in abundance withregional and altitudinal variations. These two very different patterns of change suggest the relativeubiquity of decline in Whinchats has a common source affecting the whole population.Conclusions The scale and magnitude of decline in Whinchats should stimulate a revision of the speciesconservation status in Britain, with renewed focus on studying the species’ ecology across its breedingand winter range in order to determine the likely large-scale drivers of its decline.

The Whinchat Saxicola rubetra is one of several sub-

Saharan migrant species that breed in Europe that

have suffered major long-term declines in abundance

(Sanderson et al. 2006, Ockendon et al. 2012). The

Whinchat has undergone an estimated 71% decline in

abundance in Europe since the 1980 (EBCC 2012). In

Britain, the species has declined by 60% in abundance

since 1995 (Risely et al. 2013), though the true period

of decline spans several decades (Holloway 1996). The

cause of the decline in Whinchats in the last decade

or so is poorly understood with changes in either land-

use or climate in either Europe or in Africa being

implicated (Vickery et al. 2014).In Europe, the Whinchat is a species of open,

invertebrate-rich grasslands, sometimes in the presence

of light scrub or other perches such as Bracken Pteridium

aquilinum (Stillman & Brown 1994, Fuller 2012, Grant

& Pearce-Higgins 2012). In winter, in Africa, the

species also uses open grassy steppes, wetlands and

crops, such as maize (Hulme & Cresswell 2012). In

both seasons their habitat is vulnerable to agricultural

improvements, pesticide use and the loss of marginal

habitats. In addition, in Europe, the Whinchat occupies

a cool temperate range by occurring at higher densities

in moist alluvial grassland habitats, meadows or bogs or

lush upland landscapes (Hagemeier & Blair 1997,

Müller et al. 2005). In Africa, low rainfall may

contribute to higher Whinchat mortality (Dejaifve

1994), such that, in both seasons, Whinchats are

potentially vulnerable to a drying climate.

In Britain, the broad geographic and topographic

breeding range of Whinchats strongly overlaps with the

closely related but less migratory Stonechat Saxicolatorquata. The broad-scale habitats they occupy are*Correspondence author. Email: [email protected]

© 2014 British Trust for Ornithology

Bird Study (2014), 1–13http://dx.doi.org/10.1080/00063657.2014.962482

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mailto:[email protected]

frequently, superficially similar (Fuller & Glue 1977)

though Stonechats will use drier components, heathlands

and Mediterranean habitats (Hagemeier & Blair 1997).

Unlike the Whinchat, the overall population trend for

breeding Stonechats in Britain has shown a net increase

over the last two decades (Risely et al. 2013; www.BTO/

about-birds/birdtrends/2012). Their contrasting trends

suggest there has been an opposing demographic response

to common features of environmental change acting on

both species in summer, or they have been exposed to

different environmental drivers outside the breeding

season. For either species, ubiquitous population change

would imply that virtually the entire population has been

affected at once, whereas regionally contrasting trends

would imply context dependent exposure to

environmental change by different sub-populations.

Thus, a comparison of the direction and pattern of trends

in these two species had the potential to offer clues on

the drivers of change.

In Britain, two large-scale data sets are available for

analysing population change in birds. First, periodic

national atlas data are available from surveys carried out

at approximately 20 year intervals. To date, three ‘atlas’

surveys of breeding birds have been conducted since

1968 with the latest Bird Atlas 2007–11 (Balmer et al.2013) providing up-to-date data on range occupancy

and relative abundance change for a 40 year and 20

year period, respectively, at a 10 km scale of resolution.

Second, populations of common bird species are

monitored annually by the BTO/JNCC/RSPB Breeding

Bird Survey (BBS; Gregory et al. 2004), based on

approximately 2500 1 km squares (Risely et al. 2013).

Although this survey achieves light coverage in some

remote habitats in Britain (Cook et al. 2011), it

complements the geographic coverage of atlas studies

with a more rigid sampling protocol at a higher spatial

resolution (1 km scale). The BBS characteristics and

finer scale of survey allowed an analysis of change to be

measured in terms of elevation as well as latitude,

though without the more complete spatial coverage of

the atlas data. These two analyses complement each in

analysing different factors, but also provide a second

independent layer of evidence for assessing patterns of

change emanating from the two different survey methods.

In Britain, it is also possible to match national bird data

sets to landscape characteristics and weather patterns at

large spatial scales. Land-class data (Institute of

Terrestrial Ecology land classification; Bunce et al. 1998)and Land Cover Map data (Fuller et al. 2002) are

objective characterizations (generalizations) of the British

countryside. The data, along with weather data (Perry

et al. 2009), provide the environmental context against

which to analyse the population change characteristics

of these bird species. Our analysis is restricted to Britain

only, because comparable land classification data were

unavailable for Ireland as a geographic entity.

This paper set out to identify broad geographical,

altitudinal, weather and landscape related differences

in the 40 year and 20 year patterns of population

change observed in the Whinchat and Stonechat

across Britain. On national and regional level in the

last 20 years, the two species’ broadly sympatric

breeding ranges (Balmer et al. 2013) and habitat use

suggest that contrasting populations trends are not

readily explained by large-scale changes to habitat

extent per se. Even competition between the two

species has been raised as a potential source of change

(Phillips 1970) but a more likely explanation is that of

emerging climate niches that the birds may or may not

be able to exploit (Jiguet et al. 2009) or other aspect oftheir contrasting life-history traits. Thus, interactions

between warming weather patterns and habitat may

favour the resident/short-distance migrant Stonechat,

more so than in the Whinchat, due to the former’s

potential to exploit spring temperature amelioration, to

extend the breeding season forward. Interactions with

habitat might then be expected too, with geographic

variations in population trends relating to local climate

and landscape context. In contrast, as a late migrant to

Britain, Whinchats are less able to exploit variations

in spring temperatures and instead are exposed to

external drivers operating across the entire migrant

population until arriving Britain where habitats and

summer conditions may vary and change over the long

term. So it was important in this study to assess the

extent of population change in relation to regional,

landscape, latitude and elevation gradients, in order to

understand the context behind population change in

these two species. Key differences in the patterns of

change in population in these two species were used to

indicate plausible explanations for their contrasting

population trends, which for Whinchats, in particular,

may help direct conservation objectives.

METHODS

Periodic national atlas monitoring: survey scale2 km, analytical scale 10 km

Definitions and caveats

In Britain, three atlas surveys of breeding birds have been

conducted since 1968, referred to here as: the ‘first atlas

© 2014 British Trust for Ornithology, Bird Study, 1–13

2 I. Henderson et al.

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http://www.BTO/about-birds/birdtrends/2012

http://www.BTO/about-birds/birdtrends/2012

period’ – mid-point ‘1970’ (the 1968–1972 atlas;

Sharrock 1976), the ‘second atlas period’ – mid-point

‘1990’ (the 1988–1991 atlas; Gibbons et al. 1993) andthe ‘third atlas period’ – mid-point ‘2010’ (the BirdAtlas 2007–11; Balmer et al. 2013). The time between

the first and second atlas is referred to as the ‘early 20

year period’ (broadly, 1970–1990), while the period

between the second and third atlas is referred to as the

‘late 20 year period’ (broadly, 1990–2010). The survey

methods are covered fully in each publication and

summarized in Balmer et al. (2013). In brief, survey

volunteer observers chose one or more survey squares

(a 2 km × 2 km ‘tetrad’) from a list. There they

counted all birds seen or heard following standardized

survey guidelines both for recording birds and for

adding supporting information, such as breeding

evidence. During the second and third atlas studies,

observers followed self-determined transect routes,

known as a Timed Transect Visit (TTV). Each TTV

lasted for 1 hour and in some cases 2 hours. For the

first atlas, birds were recorded on a ‘roving’ basis, with

no set transect route, though the period of observation

was recorded. During all atlases, visited tetrads

received casual records made on an ad hoc basis

outside of any allocated route or recording period.

Casual records did not contribute to the relative

abundance values because recording effort could not be

taken into account (Balmer et al. 2013).In Britain, atlas survey coverage is attempted in at

least eight tetrads per visited 10 km square, and in

virtually every 10 km square available. The survey

coverage of 10 km squares during the first and the

third atlas periods was broadly comparable and

complete, but was lower during the second atlas period

in some remote locations (Balmer et al. 2013). Thus,changes in occupancy between the second and third

atlas periods may be, potentially, biased towards ‘gains’

in remote areas of northern Britain. Only records of

‘confirmed’ or ‘probable’ breeding status at either the

beginning or end of any 20 year period of comparison

were used in change statistics. However, the first and

third atlases also required that birds were observed in

suitable breeding habitat for breeding evidence to be

considered as valid. As this stipulation was not applied

to the second atlas, some apparent losses between the

second and third atlases may be spurious, though this

would mainly have affected transient individuals in

coastal locations.

In this study it was convenient to divide Britain into

four broad ‘regions’: Scotland, Wales, ‘Northern

England’ (extending north and west of a line drawn

between the Humber and the Severn estuaries) and

‘Southern England’ (land extending south and east of

the same line, and including south-east and south-west

England); see Fig. 1a.

Range occupancy and relative abundance

Tetrad-level data outlined above were used to establish

the presence or absence of birds within each 10 km

square (‘range occupancy’), where presence in two

consecutive atlas periods represented stable occupancy

or no change. The tetrad-level data were also used to

calculate a frequency index of ‘relative abundance’,

from the proportion of occupied to unoccupied tetradsper 10 km square, including squares of stable range

occupancy but of varying relative abundance. Change

in the relative abundance between atlas periods was

calculated only for 10 km squares with breeding

evidence recorded in at least one atlas period, so

eliminating very long-term, un-occupied squares from

the analysis. In places where a species was very

common, there could conceivably have been variation

in density (within tetrads) without the frequency index

dropping below 1, introducing some loss of sensitivity.

However, for the two subject species in this paper, this

scenario is considered a rare or unlikely scenario.

Change in range occupancy at the 10 km scale was

analysed by χ2 tests, assuming an equal likelihood of

gains to losses, and excluding range-stable squares (no

change). Change in relative abundance was analysed

by Generalized Linear Model logistic regression (SAS

Incorporated 2002) testing both Poisson (log function)

and binomial error structures (logit function; where 1

= positive change and 0 = negative change), to include

as much information from range-stable squares as

possible depending on the quality of the model fits.

Parameter estimates are output as likelihood ratios

(LR) with Type 3 probability values for partial effects

accounting for other variables in the model, such as

the environmental variables described below.

Superficially, the distribution of ‘gains’ in Whinchats

appears close to random and is consistent with there

being no clear regional or sub-regional patterns of

colonization occurring against the national trend.

Instead, only sporadic gains are apparent, perhaps as a

consequence of stochastic variation in the detection of

birds (following false absences during the early atlas

period) or transient occurrences of territory settlement,

or habitat change occurring at smaller spatial scales

than the resolution of the analysis. These scenarios are

more likely to occur within the current range of the


Populations trends for Whinchat & Stonechat 3

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Figure 1. A history of range occupancy of 10 km squares is shown for Whinchat (a) and Stonechat (b). The symbols represent: black triangles:large= loss since 1988–1991, small = old loss since 1968–1972; red triangles: large= gains since 1988–1991, small = gains since 1968–1972;pink shading= present in all atlases (maps reproduced from Balmer et al. 2013 with permission from the British Trust for Ornithology). The changein abundance per 10 km square between the 1988–1991 and 2007–2011 atlases is also shown for Whinchat (c) and Stonechat (d), respectively(black spots = gains, white spots = losses). Four analytical ‘regions’ were defined: Scotland, Wales, Northern and Southern England.



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species, but tend towards a random distribution of gains

as squares switch between active or not (where no strong

biological influence or systematic sampling bias was

introduced between atlas survey periods). By

comparison, redistributions of territories to patches of

newly available habitat (perhaps with social

attraction), would be expected to show stronger levels

of aggregation than random. Regional or sub-regional

clumping is expected where strong influences of land-

use change and/or climate change occur.

To examine whether or not the Whinchat gains were

statistically random in distribution, we compared mean

nearest neighbour distances (NND) between the

centres of 255 gain squares (within a 300 km buffer)

and 255 randomly distributed squares (within a 300

km buffer) selected from the entire breeding

distribution of the species in the last 20 years (all

gains, losses and stable occupancy squares, avoiding

(40+ year) long-term historical absence). The 300 km

buffer was imposed to effectively regionalize the

analysis – to look for local rather than gross patterns of

variation across Britain as a whole. NND mean and

variance was compared between gain squares and

between 20 re-iterations of randomly generated

squares. For the gain squares mean and confidence

intervals (CIs) were calculated by bootstrapping (999

iterations with replacement), and compared to the

distribution of random squares, via t- and F-tests(Sokal & Rohlf 1995), for signs of similarity and

aggregation (and normality). A similar analysis was run

for Stonechat, but this time with emphasis on the

distribution of losses rather than gains.

Associations with environmental variables

Gains relative to losses were analysed against time (early

and late 20 year periods), weather variables and two

landscape data sets: (a) Land Cover Map data

(LCM2000; Fuller et al. 2002) and (b) land-class data

(Bunce et al. 1998). LCM2000 satellite image variables

(Fuller et al. 2002) quantify proportional estimates of

land cover per 1 km square in Britain (here

summarized for 10 broad land-use categories being:

coastal (Coast), improved grassland (IG), semi-natural

grassland (SNG), Moorland/Heather/Bracken (MHB),

broad-leaved woodland (BLW), conifer woodland

(CON), arable, sea, inland water and unclassed). By

proportion, more than one land-use category may

occur in any given 1 km square and that proportion

may change between survey periods. The land-class

system places every 1 km square in Britain into just

one of 32–40 categories (32 this study) on a

descriptive basis of its landscape and topographical

character (Bunce et al. 1998). The land-class character

of a square is less prone to change over time than

LCM2000. In the absence of quantitative data on

land-use change between all three atlas periods, we

were limited to using landscape characteristics data

representing the mid-point for the late 20 year period

(LCM2000 in 1998).

Weather variables were included in the analyses as a

potential influence on habitat condition or

productivity (spring/summer variables), or for

Stonechat alone, survival (winter variable). Weather

variables were available as monthly values for every 5

km2 tile in the Britain (UK Meteorological Office).

For our purposes, variables ‘total rainfall’, ‘mean

temperature’ and ‘minimum temperature’ were first

averaged per month at the 10 km2 scale. They were

then averaged across months as mean summer rainfall

(MSR: May, June, July), mean summer temperature

(MST: May, June, July) and mean minimum

temperature for spring (MMS: March, April, May).

The difference between the proportional distribution

of all squares in which birds were recorded during the

late 20 year period (gains, losses and stable occupancy)

across the 32 land-classes and the actual proportional

availability of each land-class was calculated as the

response variable ‘Preferred’ (% occurrence−%

availability). ‘Difference’ was calculated as the

difference in the proportional distribution of gains to

losses (% gains−% loss) between land-class categories.

Significant differences in the distribution of response

variables between land-classes was analysed by

generalized linear models with a normal error structure.

This analysis was intended to identify broadly

favourable landscape characteristics between regions,

for species occurrence and species relative gains in

occurrence.

The relationship between change in bird occurrence

and LCM2000 variables (per 10 km square) was run in

a separate model to the land-class data because the two

are related. In this analysis, the bird data modelled

change as gains (score 1) or losses (score 0) using a

binomial error structure and logit link function. The

modelled structure analysed the probability that

change = species (Whinchat or Stonechat) + land

cover LCM200 variables (MHB + BLW+CON+ IG +

SNG+Coast) plus weather variables (MST +MSR +

MMS). Interaction terms were added to identify

species-specific effects of land cover or weather.

Limited screening of variable permutations below the



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full model structure above was carried out, manually.

Overall, this analysis was intended to identify

important broad habitats and weather conditions that

might help explain observed patterns of change across

Britain in this species.

Assessment of changes in breeding densities andrange from BBS monitoring (1 km scale)

The BTO/JNCC/RSPB BBS is an extensive volunteer

survey used to monitor breeding bird populations in

the UK every year since 1994. The BBS is undertaken

on a random sample of 1 km squares, but stratified

regionally to obtain representative coverage of habitats

(Risely et al. 2013). The survey is structured in that

each square is visited twice, once between April and

mid-May (early visit), and once between mid-May and

the end of June (late visit). Birds are recorded along

two 1 km line transects with a recommended period of

time spent on each transect and with sightings

classified into three distance bands (0–25 m, 25–100

m, 100 m+). Each transect is split into 200 m sections,

in which habitat is recorded using a hierarchical

coding system (Crick 1992). To account for

heterogeneity in detectability, we used a distance-

sampling approach and fitted half normal distributions

to the BBS count data (Buckland 2001, R

Development Core Team 2009, Thomas et al. 2010).The habitat of each 200 m section and the visit (early

or late visit during the breeding season) were included

as covariates to account for their potential effect on

detectability. From this model, we obtained an

estimate of the average detection probability (p) in

each surveyed square, which was used as an offset in

the following analysis. Models were weighted by the

inverse of the sampling effort within each region to

account for spatial variation in the coverage of BBS

squares across the country. Thus, squares from regions

with low survey coverage received a greater weight in

order to prevent the results being potentially biased by

well-surveyed regions. However, data from Northern

Ireland could not be included due to very low coverage

in the early years of the BBS.

Using the maximum number of individuals detected

(dependent variable) from either of the two annual visits

in each year (1994–2011), for each 1 km square, species

abundance was modelled using generalized additive

models as a smooth function of northing, easting,

elevation and year. We specified a logarithmic link

function and quasi-Poisson error structure (McCullagh &

Nelder 1989). The maximum degrees of freedom for the

four-dimensional smooth were set to 16, to avoid over-

fitting and keep the relationships unimodal.

Heterogeneity in detectability was accounted for by using

the log of the species-specific estimates of detection

probability (log p) as an offset (Renwick et al. 2011).Three reference values were calculated along the

latitudinal and elevation gradients of each species. The

three values were located: (1) at the point on each

gradient where population density peaked (PD), (2) at

the leading edge of each gradient (i.e. the northern

front for latitude or higher limit for elevation) and (3)

at the trailing edge of each gradient (i.e. the southern

tail – for latitude or lower limit for elevation). The

reference values for leading and trailing edges were

defined according to where the population density

equalled PD * exp(−0.5) (Heegaard 2002, Maggini

et al. 2011). We used the model to quantify shifts in

latitude or elevation for the three reference values,

between 1994 and 2011, with 95% CIs estimated by

bootstrapping (n = 200). The same model could also

estimate the change in population density for each

species over time, irrespective of any gradient shifts in

latitude or elevation.

RESULTS

National atlases

Occupancy of range

The number of 10 km squares in which Whinchats were

recorded with breeding evidence declined from 1484 to

696 between 1970 and 2010, a change of −53.1% over

40 years. There was a significantly higher proportion of

losses to gains during both the early and later 20 year

periods for Britain as a whole (x23 = 6.1, P < 0.05 and

x23 = 31.7, P < 0.01 respectively; Fig. 1a), which

increased from a 66% (n = 548) to 82.6% (n = 586)

between the early and later 20 year periods (x21 = 9.9,

P < 0.01; Fig. 2a). The results indicate an accelerated

loss of occupancy of 10 km squares across Britain since

1990 (also highly significant in all regions:

x23 = 105.4, 217.0, 26.7, 36.0 for Scotland, Northern

England, Southern England, Wales, respectively

(P < 0.01); Fig. 2a). Apparent stable range occupancy

of 10 km squares in Britain accounted for 38% of all

squares (gains 12.6% and losses 49.4%; Fig. 1a).

For Stonechat, the number of occupied 10 km squares

with breeding evidence increased from 1060 to 1634

between 1970 and 2010 (54%; Fig. 1b). The

proportion of gains to losses was 37% (i.e. <50% and a

net loss) during the early period, but increased to 89%



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during the late period, indicating an accelerated

gain in the occupancy of 10 km squares across

Britain since 1990 (also significant in all regions:

x23 = 290.6, 224.4, 85.2 and 63.19 for Scotland,

Northern England, Southern England and Wales,

respectively (P < 0.01); Fig. 2b). For Stonechat,

apparent stable range occupancy of 10 km squares in

Britain was mainly concentrated into milder north-

west Scotland and coastal areas elsewhere (Fig. 1b).

Relative abundance

Across theWhinchat’s breeding range, including squares of

stable range occupancy, the relative percentage of losses to

gains in abundance between 1990 and 2010 was 70%,

85%, 65% and 96% of squares, respectively, for Scotland,

Northern England, Wales and Southern England (a

mean of 79% losses for the whole of Britain, x21 = 5.7, P< 0.05; Fig. 1c). Within the range of the species the

gains squares (mean NND= 119.8; CI = 7.6) were

significantly closer than the ‘expected’ random

distribution (mean NND= 166.2 km, CI = 19.6; F-test:F255 = 2.2, P < 0.01) and likewise when compared to a

distribution of randomly selected losses squares (mean

NND= 167.2 km, CI = 26.2, n = 255 squares; F255 = 2.2,

P < 0.01). Thus, the apparent sporadic distribution of

gains (Fig. 1) masks, localized, small-scale aggregations

(re-distributions) of birds.

For Stonechat, the relative percentage of gains to

losses in abundance between 1990 and 2010 was 70%,

92% and 77% and 75% of squares, respectively, for

Scotland, North and West England, Wales and South

and East England; a mean of 78.4% gains across

Britain (x21 = 379.6, P < 0.0001; Fig. 1d). For

Stonechat, the mean NND for losses was significantly

closer than the mean NND for randomly placed

squares (151.0 km; CI = 6.1 and 170.3 km; CI = 17.8;

respectively; F356 = 3.8, P < 0.01), indicative of

stronger than ‘expected’ aggregation among losses.

Associations with environmental variables andinter-specifics

The distribution of abundance gains and losses by

landscape variables is summarized in Table 1. Note,

the landscape analyses were restricted to only those

land-classes present in each region and to squares that

were occupied during the second or third atlas periods

(i.e. gains and losses), in this way excluding 40 year,

long-term absences. Thus, for both the Whinchat and

Stonechat there were significant differences in the

proportional distribution of occupied squares between

land-classes (Table 1). Also, there were significant

differences in the proportional distribution of gains and

losses between land-classes. For Whinchats, ‘occupied’

squares were over-represented on hill slopes and

plateaux but under-represented in coastal habitats and

lowland plains, except for calcareous hills in the

southern and eastern England region. Whinchat gains

were associated with landscape characteristics

supporting agriculturally marginal habitats, but some

losses were associated with these habitats too (hillsides

and slopes) as well as lowland plains (Table 1).

Stonechat occupancy fitted a broader profile, including

hill slopes, ‘complex valleys’ and coastal habitats.

Gains were especially well represented in hilly areas

and losses with all landscape types but especially

lowland plains and coastal areas (significant negative

relationships in Table 1).

Figure 2. Change in the relative percentage of losses and gains of10 km square occupancy between second and third atlases(broadly, 1990–2010) for Whinchat (a) and Stonechat (b) for four‘regions’ of Britain.



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Late period abundance change among Whinchats was

not significantly associated with any land cover

LCM2000 variable, bar a negative association with IG

and CON (all tree-age categories combined; Table 2).

Although some young growth stage conifer plantations

will have supported Whinchats (Bibby et al. 1985,

Calladine & Bray 2012), appropriate data on

woodland age classes were not available and hence the

over-riding observation is for avoidance of forests. For

Stonechat, there was a negative association (more

Table 1. A regional summary of the distribution of Whinchat and Stonechat atlas change values by land-classcategory. ‘Preference’ is the proportional occurrence (gains + losses) across land-classes relative to theproportional availability of each land-class category. ‘Difference’ refers to the difference betweenthe proportions of gains to losses associated with each land-class category. The significance of theoverall model for each category of ‘Preference’ or ‘Difference’ is given in column 5 with probabilities presentedas **P<0.001, ***P<0.0001, meaning that significant differences were detected between land-class codes.Columns 2 and to 4 identify which land-class codes contributed to that overall result with a significant positive(+) or negative association (−) with the bird data, where P<0.05. For those in parenthesis, P<0.1. Asummary description is given to help interpret which land-class characteristics emerged as contributing factors.For official definitions of each land-class code, see Bunce et al. (1998).

Land-class code Summary description Effect Model: χ2

WhinchatScotland (df = 18)Preference (21), 22, 28 Slopes, plateaux + 47.1**

31 Rocky coasts (hills) −Difference 22 (25) (31) Slopes + 79.9**

27 (29) Lowland coasts −Northern England (df = 22)Preference 17, 18, 19 Slopes, plateaux + 577.8***

9, 10, 13, 16 Plains −Difference 17 (18) Slopes, plateaux + 185.5***

(10, 22) 25 Plains (ridges) −Wales (df = 16)Preference 17 (18) Hill slopes + 497.1***Difference 17 (18) Hill slopes + 237.2***

(1), 5, 6 Plains −Southern England (df = 17)Preference 2 (17) Calcareous low hills + 355.2***

3, 4, 11 Plains −Difference 2, Calcareous low hills, + 309.1***

1,6, 9, 10, (11), 17 Plains, slopes, hill sides −

StonechatScotland (df = 18)Preference 21, 29, 30 Slopes, coasts + 80.5***

22, 25 Mountains, plains −Difference 19, 22, 23, 25, 26 Uplands + 519.9***

27 Coastal −18, 24, 29, 30, 32

Northern England (df = 22)Preference 6, 8, 19, 20, 22 Complex valleys, slopes + 337.2***

9, 10 Plains −Difference 17, 18, 19, 22, 25 Upland slopes + 982.6***

1, 8, 9, 10 Plains −Wales (df = 16)Preference 6, 7 Complex valleys, coastal + 99.5***

17 Upland valleys −Difference 9, 17, 18 Upland slopes + 1694.3***

5, 6, 7, 8, 13 Coastal lowlands −Southern England (df = 17)Preference 1, 2, (5), 6 (7, 8, 9) Calcareous low hills + 491.2***

3, 11, 12 Plains −Difference 1, 17 Flood plains, upland slopes + 217.3***

(9, 11, 12) Plains −



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losses than gains) with MHB category, and a positive

association with SNG (implying some net colonization

of the grassier upland mosaics).

For Whinchats, there was no significant association

with weather variables but for Stonechat there was a

positive relationship with mean minimum spring

temperature (Table 2).

There was no clear evidence of Stonechats

contributing to the decline in Whinchats. Though the

population trends of the two species have diverged in

the last 20 years (Fig. 2), the proportion of losses to

gains in abundance for Whinchat was lower in squares

with Stonechat gains or presence than for squares with

Stonechat losses or absence (LR: x21 = 20.2, P < 0.001)

except in Wales where there was no significant

relationship (LR: x21 = −0.08, P = 0.77).

Assessment of changes in breeding density andrange from annual BBS monitoring (1 km scale)

The BBS modelling of the Whinchat’s distribution

(mean = 73 occupied squares per year, 137 individuals)

showed a strong and significant decline in density

between 1994 and 2011. At both the northern

(leading) and southern (trailing) edges of the

distribution, the abundance declined by −2.7 birds/km2

(P < 0.01; 95% CI = –5.2 to −1.5 for the southern and

−5.3 to −1.5 for the northern edge) and at the peak

centre of the distribution (centred on northern

England) by −4.5 birds/km2 (P < 0.01; 95% CI =−8.7to −2.5). No significant shift in latitude was detected

(Fig. 3a). A general gain in altitude was apparent but

was marginally non-significant (the optimal point

shifted by +92 m, P = 0.08, ns; 95% CI =−6 to 206 m,

and the upper edge centre point shifted by +92 m,

P = 0.09; 95% CI =−4 to 205 m; Fig. 3b). Although

the model fit was not very strong (deviance = 0.24) the

data imply a decline of abundance rather than a

decline in range, and an indication of a marginal shift

to higher altitudes.

For Stonechat, the model distribution was very

different from Whinchat. As the latitudinal gradient of

its density is rather flat, no latitudinal reference point

could be reliably located. Even the location of its peak

(or optimum) density was estimated with CIs that

spanned across the whole latitudinal range of Britain

(Fig. 3c). On the other hand, the Stonechat showed a

significant upward shift of its optimal altitudinal point

by 490 m, although uncertainty around this estimate

was also high (CI = 157–637 m; Fig. 3d). There was a

significant increase in density by 1.0 birds/km (P <0.01; 98% CI = 0.7–1.8) at the optimal altitudinal

point. Although the model fit was weak (deviance =

0.18) the data imply an expansion of range in the

north and into higher altitudes, upland landscapes for

Stonechat.

DISCUSSION

Over the 40 years since 1970, the pattern of change in

the status of breeding Whinchats has been

characterized by a strong and accelerating decline in

abundance throughout the species’ core breeding

range, including stronghold areas of northern and

western Britain in later years. This conclusion may be

in contrast to perceptions that the species has

maintained stability within habitats and landscapes

that support higher breeding densities (Gibbons et al.1993, Eaton et al. 2011). But with new atlas data

increasing the temporal and geographic scope of such

analyses, data reveal strong abundance declines rather

than range shift for this species. Only a patchy

distribution of abundance gains was evident, with no

clear regional geographic bias beyond the current

breeding range of the species and losses were also

associated with important hillside habitats too. This

pattern of gains reflects either sampling stochasticity or

small-scale redistributions of birds into newly created

habitats, principally on hillside slopes and possibly in

Table 2. An analysis of the association between Land Cover Map andweather variables and the change in abundance of Whinchats andStonechats in Britain between second and third atlas periods(broadly, 1990–2010). The statistics are Type 3, χ2 effect values andprobabilities P. Model fit: dev./df = 1.02.

Effect se χ2 P <

WhinchatIG −0.017 0.007 6.4 0.01SNG −0.009 0.007 1.8 0.18MHB +0.008 0.007 1.8 0.18CON −0.017 0.009 4.1 0.04Change in MST +1.04 0.75 1.98 0.17Change in summer rainfall −0.005 0.012 0.00 0.97Change in spring minimumtemperature

−1.37 0.841 2.6 0.10

StonechatIG +0.005 0.003 2.8 0.09SNG +0.032 0.002 1.5 0.21MHB −0.015 0.003 63.3 0.001CON −0.009 0.003 7.8 0.005Change in MST −2.8 0.27 112.1 0.001Change in summer rainfall −0.006 0.003 3.4 0.06Change in spring minimumtemperature

+2.69 0.32 78.8 0.001



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habitats such as new young-stage planation forest. This

pattern of change is primarily unrelated to regional

variations in breeding habitat or weather, that may be

evident for other long-distance migrant species, such as

the Willow Warbler Phylloscopus trochilus (cf. Morrison

et al. 2010, Balmer et al. 2013). For Whinchat, the

only strong habitat association with change was with

IG (an expected negative result for a highly sub-

optimal breeding habitat). However, a weak non-

significant, but nevertheless negative relationship with

SNG (potentially optimal breeding habitat; Grant &

Pearce-Higgins 2012) supports the broader view that

the species has declined even in ‘preferred’ habitats.

A negative relationship with conifer plantations

suffered from there being no distinction available

between plantations age classes. Whinchats will use

the grassy matrix of very young trees but are ultimately

displaced by ageing forest plantations (Grant &

Pearce-Higgins 2012).

For Stonechat, a complex change in distribution

follows an entirely different pattern to Whinchat. For

Stonechat, there have been both regional and

altitudinal differences in change involving a northward

and west-coastal expansion of range (increase in

density) and a detectable change in altitude. Unlike

the Willow Warbler, for example (Morrison et al.2010), there is no detectable shift of the southern

trailing edge of the species’ breeding distribution in

Britain which may be centred much further south in

mainland Europe. Still, regional differences in the

pattern of change combined with a net northward

population expansion over the last 40 years implies the

species may have benefited from long-term

ameliorations of winter or spring temperatures. This

conclusion was supported in the present study by a

correlation between atlas change in abundance and

spring temperatures. Unlike, the Whinchat, the

Stonechat’s life-history strategy as a short-distance

Figure 3. Density change between 1994 and 2011 for Whinchat and Stonechat. The dashed line shows the modelled 1994 distribution and thesolid line shows the modelled 2011 distribution, in relation to (a and c) northing and (b and d) elevation. The triangles represent the peak densitiesfor each species and the circles define the leading and trailing edge of each distribution. For the Whinchat, the relative position of the referencepoints between the two distributions indicates a decline in density but no significant shift in latitude or altitude (marginal change). For the Stonechat,densities imply a general increase, especially at higher latitudes and higher altitudes, where the altitudinal optimum has shifted to a much higherelevation.



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migrant and earlier breeding species allows it to benefit

from milder winters and springs by exploiting emerging

climate niches (Jiguet et al. 2009, Zollinger 2011). If

anything, the climate niche of Whinchat in hillside

grassland mosaics may be being squeezed (Calladine &

Bray 2012).

There was no evidence of competition between

Whinchats and Stonechat (Phillips 1970) driving the

population decline in Whinchats. Behavioural

dominance does not imply competitive exclusion and

there has been no convincing demonstration of

competitive exclusion operating in these species. These

species’ broadly, superficially similar breeding ranges mask

important fine-scale differences in habitat preference and

patterns of change undoubtedly reduce competition

(Stonechat: taller, drier scrub and ruderal vegetation;

Whinchat: more open, even humid grassland, Bracken

often with light scrub; Urquhart & Browley 2002).

Instead, the patterns of change in the two species appear

entirely independent of one another. They are more

consistent with their respective life-history traits than

competition or a common cause of change pertaining to

the breeding population (such as predation) as plausible

drivers of large-scale population change.

In Britain, these two species are broadly sympatric in

distribution and in habitat occupancy. Their contrasting

population trends and different patterns of change do

not suggest the influence of a large-scale (national or

regional) common driver of change in landscape

structure or suitability (such as forestry) on the breeding

grounds in the last 20 years, because this would have

affected both species. The comparison between the two

species also controls for any possibility of systematic

biases in atlas coverage contributing to the observed

patterns of change. Still, the ubiquity of decline in

Whinchats indicates a large-scale environmental driver

is operating across the majority of the population at

once, not least in core areas of the breeding range in

Britain, with formerly high densities. This pattern of

change is not consistent with a pattern of range

contraction into core habitats, and the sporadic gains

suggest that only localized re-distributions of the

population have occurred. Whinchats are a late

breeding species with a short breeding season, otherwise

exposed to environmental complexities in Africa and

during migration over long distances (Vickery et al.2014). Given the geographic extent of the decline,

which has included core breeding habitats and

landscapes (unlike Stonechat), reduced survival during

the winter or on migration tends to emerge as the

strongest recent candidate source of population

‘pressure’. This is not to say that change could not have

been exacerbated by changes in habitat suitability

during the breeding season given subtle differences in

habitat use between Whinchats and Stonechats that

could be contributing to their contrasting trends. For

example, Whinchats may be more vulnerable to

disruptions in peak summer abundances of biomass on

the breeding grounds than Stonechat due to a short

breeding season. The Stonechats’ longer breeding

season may buffer against pressure points caused by food

shortage or even predation. Also the Whinchats’ finer

grassland habitat use may be more vulnerable to grazing

intensification and grassland improvement measures

than the courser, drier, ruderal vegetation used by

Stonechats (Fuller & Gough 1999).

The Whinchats’ present relationship, in Britain, with

incidentally ‘protected’ grassland mosaics, among

Bracken, less accessible hillside scrub mosaics such as

ffridd in Wales (Fuller et al. 2006), or within young

plantations, emphasizes an increasing reliance upon

rare habitats, protected mainly by circumstances of

topography (difficult terrain), legislation (protected or

military sites or habitats) or fencing (forest

plantations). Also on mainland Europe, the species

persists where strict agri-environment schemes (Birrer

et al. 2007) or mowing controls (Müller et al. 2005,

Grüebler et al. 2008, Broyer et al. 2014) are imposed.

Such change may not have been detected in the

present study due to the bird (atlas) data not matching

directly to the historical landscape data. Moreover,

Whinchats can still occur at outstanding densities (60

pairs/ha) in lowland wet grasslands under ‘traditional’

low intensity management (M. Tome, pers. comm.).

Their persistence in such high quality habitats implies

that large scale, low input grassland habitats still offer

legitimate conditions for viable breeding populations,

with evidence of this effect on Salisbury Plain in

lowland England where a 400-pair, strong population

persists on a very large, extensively managed and

largely protected grassland (Henderson 2011). Yet,

with the species is now disappearing from previously

occupied and apparently suitable habitats in Britain

(Brown & Grice 2005), and in contrast to Stonechats,

the winter or stop-over ecology of Whinchats emerges

again as a convincing source of recent population

decline in this species. If this is true then we can

expect the current declining population trend to affect

even the strongest existing breeding populations and

especially in small and/or increasingly isolated locations.

Whinchats are exposed to potential migratory and

African obstacles (Vickery et al. 2014). In at least one



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context there is evidence from Nigeria of conditions

currently being favourable for this species (Hulme &

Cresswell 2012), shifting the focus for population

decline on to the stop-over ecology of this species.

Unfortunately, no ecological work has yet been done

on the British population of Whinchats in Africa that

probably lies mainly to the west of Nigeria. There is an

urgent need to identify the wintering range of the

species breeding in different regions of Europe to help

rule out or pinpoint key determinants of survival

operating on different sub-populations.

At the beginning of the 20th century breeding

Whinchats were common and widespread throughout

the British lowlands (Witherby et al. (1938–1941)

1943, Alexander & Lack 1944). An initial phase of

large-scale decline (Parslow 1973) was recognized by

the time of the first national atlas of birds in 1968–

1970 (Sharrock 1976) and was concurrent with large-

scale, post-war programmes of land conversion for

agriculture. Similar patterns of decline and range

change have been observed in other countries such as

Switzerland (Britschgi et al. 2006), Germany (Fischer

et al. 2013) and France (Archaux 2007, Broyer 2009).

Evidently, gross habitat loss has probably shaped the

broad distribution of this species in Britain, and both

habitat availability and predation pressure are known

to impart local and potentially exacerbating constraints

on colonization and productivity (Grant & Pearce-

Higgins 2012). A reversal of trend may be possible

locally by re-installing suitable habitat management

methods at a suitable, probably large, spatial scale. But

once again, the ubiquity of the decline in Whinchats

in the last 20 years suggests a common external driver

operating on virtually the entire population,

simultaneously. Whinchats have become all but

extinct as a breeding bird in most lowland regions

(Balmer et al. 2013) of southern Britain and on this

evidence will remain vulnerable to rapid decline

especially on remaining relatively isolated colonies. On

the strength of its decline, the species was moved from

the green to the amber list of conservation concern

(Eaton et al. 2011). However the scale and magnitude

of decline appears more acute than assumed, which

perhaps should stimulate a revision of the species

conservation status.

ACKNOWLEDGEMENTS

Thank you to all volunteers and organizers contributing to the

BTO/JNCC/RSPB Breeding Bird Survey and the Bird Atlas2007–11, which was a joint project between BTO,

BirdWatch Ireland and the Scottish Ornithologists’ Club.

Maps are reproduced with permission from the British Trust

for Ornithology.

FUNDING

The study was funded by the British Trust for Ornithology.

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(MS received 30 July 2014; revised MS accepted 2 September 2014)



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evidence for contrasting causes of population change in two closely related, sympatric breeding...

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