Trait correlates of distribution trends in the Odonata of Britain and Ireland: Southern species
benefit from climate warming
Gary D. Powney1*, Steve Cham2, Dave Smallshire3 & Nick J.B. Isaac1
NERC Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Wallingford, Oxfordshire, OX10
British Dragonfly Society, 24 Bedford Avenue, Silsoe, Bedfordshire, MK45 4ER, UK
British Dragonfly Society - Dragonfly Conservation Group, 8 Twindle Beer, Chudleigh, Newton Abbot,
TQ13 0JP, UK
* Corresponding author: Gary D. Powney, NERC Centre for Ecology & Hydrology, Maclean Building, Benson
Lane, Wallingford, Oxfordshire, OX10 8BB, UK, Tel: +44 (0)1491 838800, [email protected]
A major challenge in ecology is understanding what enables certain species to persist, while others
decline, in response to environmental change. Trait-based comparative analyses are useful in this
regard as they can help identify the key drivers of decline, and highlight traits that promote
resistance to change. Despite their popularity trait-based comparative analyses tend to focus on
explaining variation in range shift and extinction risk, seldom being applied to actual measures of
species decline. Furthermore they have tended to be taxonomically restricted to birds, mammals,
plants and butterflies. Here we utilise a novel approach to estimate trends for the Odonata in
Britain and Ireland, and examine trait correlates of these trends using a recently available trait
dataset. We found the dragonfly fauna in Britain and Ireland has undergone considerable change
between 1980 and 2012, with 33 and 39% of species showing significant declines and increases
respectively. Distribution type was the key trait associated with these trends, where southern
species showed significantly higher trends than widespread and northern species. We believe this
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reflects the impact of climate change as the increased ambient temperature in Britain and Ireland
better suits species that are adapted to warmer conditions. We conclude that northern species are
particularly vulnerable to climate change due to the combined pressures of a decline in climate
suitability, and competition from species that were previously limited by lower thermal tolerance.
Defaunation, the loss of species and populations (Dirzo et al., 2014), is occurring at an alarming rate
with recent estimates suggesting that the current extinction rate is 1000 times that of the historical
natural background rate (De Vos et al., 2014). These declines are driven by environmental change,
particularly habitat loss and climate change, and can be measured in a number of ways, e.g. changes
in distribution and abundance (Thomas et al., 2004; Biesmeijer et al., 2006; Butchart et al., 2010;
Chen et al., 2011). Variation in species responses to environmental change has been found across
broad taxonomic groups (Hickling et al., 2006; Angert et al., 2011) but also within taxonomic groups,
i.e. between species within an order (Hickling et al., 2005). A major challenge in conservation
ecology is to gain a better understanding of this interspecific variation in response to environmental
change, i.e. what enables certain species to persist while others decline?
Species traits play an important role in determining species’ ability to resist environmental change.
For example, several studies have shown that ecological generalists out-perform specialists (Walker
& Preston, 2006; Ozinga et al., 2012; Newbold et al., 2013). Such comparative trait-based analyses
are popular, as the models help to identify the main drivers of change and allow the prediction of
future biodiversity changes based on environmental forecasts (Fisher & Owens, 2004; Cardillo et al.,
2006). Previous comparative trait analyses have tended to focus on explaining variation in range
shift (Angert et al., 2011; Mattila et al., 2011; Grewe et al., 2012) and extinction risk (Purvis et al.,
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2000; Koh et al., 2004; Cardillo et al., 2008; Cooper et al., 2008; Fritz et al., 2009). Despite its
popularity, the comparative trait-based approach has seldom been applied to direct measures of
species’ changing status (i.e. rates of decline or increase). Currently data on such measures of
decline are rare, particularly at large (e.g. national) scales and across multiple species. With the
increase in public participation in biological recording, the availability of large-scale distribution
datasets has increased (Silvertown, 2009). Such data tend to be collected without systematic
protocols and thus contain many forms of sampling bias and noise, making it hard to detect genuine
signals of change (Tingley & Beissinger, 2009; Hassall & Thompson, 2010; Isaac et al., 2014b).
However, recent advances in analytical approaches have improved our ability to estimate reliable
trends from these unstructured biological records (Isaac et al., 2014b). In this study we utilise these
novel approaches to estimate trends for the Odonata in Britain and Ireland, and use species traits to
test hypotheses for the interspecific variation in trends.
We chose to examine Odonata for a number of reasons. Firstly, previous trait-based comparative
analyses have tended to focus on birds, mammals, plants and butterflies. Despite being highly
species rich and their crucial role across ecosystems, the non-butterfly invertebrate fauna are
comparatively poorly studied (IUCN, 2001; Dirzo et al., 2014). Secondly, Odonata are thought to be
excellent bioindicators as they are sensitive to degradation of water ecosystems (Samways &
Steytler, 1996; Sahlén & Ekestubbe, 2001; Lee Foote & Rice Hornung, 2005). Thirdly, they provide a
valuable ecosystem service as they feed on many insect pests (Brooks & Lewington, 2007). Finally,
the publication of a new atlas (Cham et al., 2014) and trait datasets (Powney et al., 2014) for British
Odonata together constitute some of the best quality data of any non-butterfly invertebrate group.
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Previous research based on Odonata occurrence data has focussed on the impact of climate change
on phenology and distribution. For example Hassall et al., (2007) discovered that emergence from
overwintering had significantly advanced over the past 50 years, while Hickling et al., (2005) showed
that the upper latitudinal margin shifted north between 1960 and 1995. Outside Britain, Bush et al.,
(2014) used species distribution models (SDMs) to predict which Australian odonates were under
threat from climate change.
Several studies have utilised traits to explain variation in several aspects of Odonata ecology, but
typically focus on explaining variation in species response to climate change. In terms of
phenological advancement, Hassall et al., (2007) noted that spring species and those without egg
diapause exhibited increased phenological shifts. Angert et al (2011) examined trait correlates of
range shift across multiple taxonomic groups, finding that exophytic Odonata species in Britain
shifted further north, on average, than endophytic species. These insights, combined with extensive
knowledge about their natural history (Brooks & Lewington, 2007), form the basis of seven
competing hypotheses (outlined below) that aim at explain the interspecific variation in the
distribution trends among British Odonata.
All traits included in the analysis have been shown to affect species’ ability to respond to
environmental change. Habitat breadth is frequently related to species trends, where habitat
generalists outperform specialists due to their greater ability to adapt to novel environmental
conditions (Fisher & Owens, 2004; Menéndez et al., 2006; Botts et al., 2012). Ball-Damerow et al.,
(2014) found evidence of the widespread expansion of habitat generalists which has led to biotic
homogenization in the dragonfly fauna of California and Nevada over the last century. We test the
hypothesis that Odonata in Britain and Ireland follow the patterns outlined above, with generalists
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out-performing specialists. Dispersal ability affects species’ ability to respond to environmental
pressures, with higher dispersal ability linked to an enhanced ability to respond (Thomas et al., 2001;
Pöyry et al., 2011; Grewe et al., 2012). Using SDMs, Hof et al., (2012) found lentic (i.e. pond and lake
dwelling) species had a greater ability to track changes in their climatic niche. This was linked to
greater dispersal ability, which is essential given the ephemeral nature of their breeding sites (Hof et
al., 2006). We predict lentic species will have higher (more positive) trend estimates than lotic
species as their increased dispersal ability enables them to persist during times of environmental
change through the efficient relocation to newly suitable areas. Geographic range size and body size
are both frequently used as surrogates for a whole host of traits associated with ecological
specialism and competitive ability (Gittleman, 1985; Gaston, 2003; Angert et al., 2011). We predict
that widespread species and the larger, therefore more competitive species, are likely to show
positive trends. Climate warming has increased the suitability of the landscape to those species that
were previously limited by their lower thermal tolerance threshold (Devictor et al., 2008;
Dingemanse & Kalkman, 2008; Bellard et al., 2012), therefore we predict that southerly distributed
species will show the highest trend estimates. An additional aspect of climate change that has been
linked with trends in Odonata is the increase in flood events in Britain. Species which overwinter as
larvae are particularly vulnerable to flooding as they can be swept away from ideal habitat areas to
unsuitable regions in which they cannot persist (Cham et al., 2014). Alternatively, floods may aid the
dispersal of such species that overwinter as larvae and therefore we may expect to see positive
trends for such species. Finally we test the hypothesis that flight period will be positively related
with species’ trend. Grewe et al., (2012) argued that species with longer flight periods have
increased dispersal ability, and therefore have a greater capacity to adapt in response to
Materials & Methods
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Trends were estimated from Odonata distribution records in Britain and Ireland collected by the
Dragonfly Recording Network and coordinated by the British Dragonfly Society. Our analyses are
based on 895,022 records of 38 native species collected between 1980 and 2012 where the
recording date is known and the location was recorded to 1 km2 precision or better. As these
occurrence records were collected without a specific sampling design they contain a variety of bias
which inhibit their use in estimating reliable trends. For example, the number of records collected
each year has increased dramatically over time (Cham et al., 2014), such that simply counting the
number of occupied sites would produce biased trend estimates (Prendergast et al., 1993; Isaac et
al., 2014b). To account for these biases we estimated species trends using a method known
elsewhere as the ‘well-sampled sites’ (Isaac et al., 2014a), which aims to remove the noise and bases
the statistical inference on a ‘well-sampled’ subset of the data. We first arranged the records into
239,392 visits, which are defined as unique combinations of date and 1 km2 grid cell (site). For each
visit, each of the 38 species was coded as either recorded (1) or not-recorded (0). We then removed
all visits where less than three species were recorded, since these short lists probably represent
incomplete sampling (van Strien et al., 2010). We then selected sites with at least three years of
data, ensuring we retained only the ‘well-sampled’ sites (Figure 1). Our final dataset contains
357,654 records from 67,382 visits to 5,352 sites (30,481 site-year combinations). Different
thresholds for defining the well-sampled set (two species recorded and two years of data) produced
qualitatively identical results (not shown). For each species, we estimated a linear trend in the
probability of being recorded on an average site visit. This was achieved using binomial generalised
linear mixed-effects models (GLMMs), implemented by the R package lme4 (Bates et al., 2011), with
the log odds of being recorded modelled as a linear function of a fixed effect for year, and a random
intercept for site. We used the slope estimate for the fixed effect of year as our trend measure.
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We included data on seven traits extracted from Powney et al. (2014) (Table 1). Two traits were
based on characteristics of a species’ distribution pattern, the first, species status, was measured as
an ordinal variable based on distribution size, moving from very rare through to very widespread.
Secondly, distribution type was a categorical variable that defined a species broad climatic
restriction. Species were classified into one of four levels, northern, southern, oceanic or
widespread based on their distribution pattern. We included a single morphological trait, thorax
length (mm), which was taken as the mean of multiple measurements from museum specimens.
Flight period duration was measured as the number of months during which adults are typically
recorded in flight. We included two habitat based traits, habitat breadth measured the number of
broad habitats a species can utilise, while breeding habitat classified species based on breeding
habitat preference, lentic, lotic or both. Finally, we classified species based on their overwintering
stage, either eggs, larvae or both. Overwintering stage, breeding habitat and distribution type were
coded as continuous variables: Overwintering stage (eggs = -1, both = 0, larvae = 1), breeding habitat
(lentic = -1, both = 0, lotic = 1), distribution type (very rare = -1.5, rare = -1, scarce = -0.5, local = 0.5,
widespread = 1, very widespread = 1.5). All continuous traits were centred on zero prior to the
analysis and ordinal variables were treated as continuous. Following the various exclusion criteria
and the coverage of trait data, the final dataset used in this study covered 36 species.
We used the pgls function from the R package caper (Orme, 2012) to run phylogenetically informed
linear models to examine trait-trend relationships while accounting for phylogenetic non-
independence (Freckleton et al., 2002). In all phylogenetically informed models, the level of
phylogenetic correction (Pagel’s λ) was estimated via maximum likelihood (Pagel, 1999; Freckleton
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et al., 2002). Due to data limitations we used a phylogeny based on taxonomy for the analyses. The
phylogeny was built using the as.phylo function from the R package ape (Paradis et al., 2005) with
nodes based on Suborder, Family, Genus and Species, and all branch lengths were set to one.
The trend measures extracted from each species model formed the response variable for the trait-
trend analysis. While this year slope estimate is a useful measure of the direction and intensity of
the temporal trend in occupancy in an average site, it does not account for uncertainty in its
estimation. We therefore repeated all trait analyses using the year slope estimate weighted by the
inverse of its standard error and also the z-score of the fixed effect of year as the response variables.
These additional analyses enabled us to examine how robust our results were in relation to
uncertainty in our trend estimates.
To determine the main trait correlates of our species trends we utilised a multi-model inference
approach. We applied the dredge function of the R package MuMIn (Barton, 2013) to fit models for
all possible combinations of explanatory trait variables and then ranked them based on AICc. We
then extracted the model averaged coefficient for each trait that was present in at least one
candidate model from the subset of top models. In addition, we identified the importance of each
trait based on its frequency in the subset of top models. The importance scores and the model
averaged coefficients were used to determine the main traits for explaining species trends. The set
of candidate models was defined as models with ΔAIC ≤ 2. Burnham et al. (2010) suggest there is
often support for models where ΔAIC is < 7, however, we chose to use ΔAIC < 2 as the null model
was the 3rd best model with a ΔAIC of 1.12 and therefore increasing the ΔAIC threshold was simply
adding noise (models with little evidence to support them) to the key result.
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A multi-model inference approach while accounting for phylogeny is not straight-forward. In our
PGLS models, λ was estimated independently for each model and can therefore be different
between the candidate models. Using AIC to compare between these models could be misleading as
we could not disentangle the influence of a difference in the evolutionary model (λ) from the
influence caused by changing which traits were included in the model on AIC scores. However, all of
the models in the top subset had an estimated λ value of 0, implying that species trend in the UK is
not phylogenetically-patterned. Therefore ΔAIC was measuring the effect of the trait differences
rather than any potential difference in the evolutionary model in this case. All analyses were carried
out using R 3.0.2 (R Core Team, 2013).
We found significant trends for 72% of the species in this study: of these, 12 were decreasing and 14
species were increasing. Species included that showed the greatest declines included: Ischnura
pumilio, Leucorrhinia dubia and Sympetrum danae, while Libellula fulva, Erythromma najas and
Brachytron pratense showed the greatest increases. Using the fitted values from the species trend
models we estimated the change in probability of observation over a ten year period for each
species. Each species was then categorised using these ten year changes (Figure 2). This figure
illustrates the substantial variation in the trend estimates between species, and again highlights the
large proportion declining species which is a cause for concern.
Six models containing various combinations of three traits (distribution type, flight period and thorax
length) formed the top subset of models for explaining Odonata distribution trends (Table 2). Of
these three traits, distribution type was the most important (importance score = 0.6), and was
present in three of the top models. The model averaged coefficients for distribution type reveal that
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southern species tend to have increased relative to the other categories and northern species have
declined on average, with the other two categories (oceanic and widespread) intermediate (Figure
3). Notable exceptions to this trend include the strong declines in Ischnura pumilio and Gomphus
vulgatissimus both of which were classified as southern species. Flight period was present in two of
the top models and had an importance score of 0.34. The coefficient was negative, suggesting that
species with longer flight periods had a lower trend estimates (i.e. they declined relative to species
with short flight periods). Thorax length was also present in two of the top models but had the
lowest importance score (0.24) of all traits present in the top model subset. The model averaged
slope for the relationship between thorax length and trend estimate was marginally positive, which
suggests that larger species were faring better than smaller species. We note that the 95%
confidence intervals of both flight period and thorax length spanned zero, and that the null model
was the third best model based on AICc. The top two models explained a modest 13 and 16% of the
variation in species trend.
In general, the key trait-trend relationships and importance scores were robust across the different
response variables and modelling approaches (Appendix 1 & 2). Distribution type was the most
important trait for four of the five response/modelling approach combinations, while flight period
and thorax length were consistently important (Appendix 3). The model averaged coefficients for
these three traits were similar across approaches. Other traits including habitat breadth,
overwintering stage, breeding habitat and status, were retained in the top model subset for some of
the other approaches. However, the model averaged 95% confidence intervals spanned zero in the
vast majority of cases for these additional traits (Appendix 2).
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We found that the dragonfly fauna in Britain and Ireland has undergone considerable change during
recent decades, with high levels of inter-specific variation in occurrence trends (Figure 2). We found
twelve species (33%) had significant negative trends, while 14 species (39%) showed significant
increases. Although more species increasing than decreasing is good news for conservation, this
could be interpreted as a signal of biotic homogenization, i.e. the fauna becomes dominated by a
small number of species, and local and regional difference between communities are eroded (Keith
et al., 2009).
We found distribution type was the key correlate of Odonata trends, with southern species tending
to have higher trend estimates than the all other distribution types (Figure 3). This result is in line
with our hypothesis that increased temperatures has increased the climate suitability of Britain and
Ireland for southerly distributed species. A variety of studies have provided evidence of this
relationship, i.e. Devictor et al. (2008) found bird communities in France between 1989 and 2006
were increasingly dominated by species that prefer warmer conditions, while Lima et al. (2007)
found evidence of northward range expansions in warm-water adapted Portuguese algae. Hickling
et al. (2005) used distribution type to explain variation in range shift and expansion in British
Odonata, finding that southern species showed greater poleward shifts and expansions compared to
northern species. By contrast, Angert et al. (2011) found no correlation between range shift and
position of the northern range limit (which is related to our measure of distribution type). Despite
the wealth of evidence that points to climate change as the likely driver of increases in southern
species, we cannot ignore the role of improved water quality and standing water availability in
southern Britain (Hickling et al., 2005; Cham et al., 2014). Not all southern species showed positive
trend (notably Ischnura pumilio and Gomphus vulgatissimus), this limited expansion in response to
climate warming is likely due to availability of suitable habitat.
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The lower trend estimates for northern species is likely to be the result of the combined pressures of
a decline in climate suitability and competition from species that were previously limited by lower
thermal tolerance (Myers et al., 2009; Thomas, 2010). Evidence of the loss of northern species has
been seen in a variety of taxonomic groups across a variety of geographic regions (Hill et al., 2002;
Devictor et al., 2008; Myers et al., 2009; Foufopoulos et al., 2011), and with the persistent and
increasing threat of anthropogenically induced climate change, northern species and those reliant
upon them are likely to become increasingly threatened.
We found no evidence for six other hypotheses about the drivers of species trends. Flight period and
thorax length appeared marginally important but evidence for this was weak as the 95% CI of these
traits spanned zero. Additionally when these two traits were modelled against species trend
individually they performed no better than the null model. Body size and flight period were used as
surrogates for competitive ability and dispersal ability: it is plausible that more direct measures of
these traits do predict the species in decline. The reliability of the trait-trend results depend on the
accuracy of the underlying trait data. We note that within a given species, traits can vary spatially
(i.e. habitat specificity can vary across a species range – Oliver et al., 2009), however here we use a
single value per trait per species. This is a common approach within the comparative analysis
literature but is a potential source of noise in the results. One problem with “well-sampled sites”
approach is that it amplifies the spatial gradient in recording intensity, such that trends for northern
species are estimated from a relatively small number of sites. This has implications for the precision
of trend estimates for northern vs southern species, which is accounted-for in the weighted trait
models (Appendix 1b). Basing the trend on a small number of sites is unlikely, on its own, to bias the
estimate (Isaac et al., 2014b), although we don’t know the degree to which trends on these sites
(and others considered well-sampled) reflect changes in the wider countryside. We found no
evidence of phylogenetic signal in our models, although our phylogeny was based on taxonomy.
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Using a phylogeny constructed from sequence data would be more rigorous, but such genetic data
are currently limited.
In conclusion, we found variation in species distribution trends was best explained by distribution
type, with southern species showing significantly higher trends than widespread and northern
species. We believe this reflects the impact of climate change as the increased ambient
temperature in Britain and Ireland better suits species that are adapted to warmer conditions. The
lower trend estimates for northern species is a cause of conservation concern as this result
combined with evidence in previous studies shows that northern species are shifting to higher
latitudes and altitudes, are declining in range size and abundance, and are therefore particularly
vulnerable to the ever increasing threat of climate change.
We are indebted the British Dragonfly Society and its vast collection of volunteer recorders, without
them this project would not be possible. We thank Oliver Pescott, Colin Harrower, Tom August and
Louise Barwell for their advice on the data analysis.
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Table 1. An overview of the Odonata traits included in the comparative analysis.
Species categorised on distribution size: very widespread, widespread, local, scarce,
rare, very rare.
Broad climatic categorisation of species: widespread, southern, northern or oceanic.
Mean thorax length based on 10 museum specimens (mm).
The duration of the flight period in months.
A count of the number of habitat types utilised by the species.
Species were classified on their preferred breeding habitat, either lentic, lotic or both.
Species categorised as overwintering as larvae, eggs, or both.
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Table 2 Parameter estimates for the subset of best models. For the categorical variable (distribution
type) ● denotes that it was present in the selected model, while the slope is displayed for the
continuous traits present in the selected model. The final column expresses the importance value
for each trait included in the subset of best models.
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Figure 1 The distribution and density of monads from which the trend estimates were derived. The
shading represents the number of unique monads within the hectad that were included in the
analysis, the “well-sampled sites”.
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Figure 2 The proportion of species in each trend category. Using the fitted values from the species
models, trends were estimated as the percentage change in probability of observation over a ten
year period. Shades of red symbolises declines while shades of green are used for increases, the
intensity of colour reflects the strength of the trend.
-80 to -50
-49.9 to -30
-29.9 to -10
-9.9 to 9.9
10 to 29.9
30 to 49.9
50 to 79.9
Proportion of species
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Figure 3 The model averaged coefficients for traits that were retained in the subset of best models.
The reference distribution type was “southern”, which has a parameter estimate set to 0.
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