The Future of British Odonata

Determining temporal range dynamics from distribution patterns and dispersal

Alyson Pavitt

MSc Conservation Science Imperial College London, Silwood Park

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science and the Diploma of Imperial College London .

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Contents 1. Introduction ...... 1 1.1. Statement of need ...... 1 1.2. Range dynamics ...... 2 1.3. Distribution and dispersal ...... 3 1.4. Aims and objectives ...... 4 1.5. Hypotheses ...... 4 2. Background ...... 7 2.1. Analysis of range dynamics ...... 7 2.2. Odonates as a model taxon ...... 10 2.3. Spatial distribution patterns ...... 12 2.4. Use of traits in understanding species distribution ...... 15 3. Methods and materials ...... 20 3.1. Range dynamics ...... 20 3.2. Patterns of distribution ...... 21 3.3. Trait data ...... 22 3.4. Data analysis...... 23 3.5. Analysis of measurement methods ...... 24 4. Results ...... 25 4.1. Range dynamics ...... 25 4.2. Patterns of distribution ...... 26 4.3. Morphometric analysis ...... 27 4.4. Dispersal capacity ...... 32 4.5. Representativeness of museum specimens ...... 33 5. Discussion ...... 34 5.1. Spatial dynamics ...... 34 5.2. Patterns of Distribution ...... 36 5.3. Dispersal morphometrics ...... 37 5.4. Determining occupancy change ...... 38 5.5. Suborder variation ...... 42 5.6. Value of historic collections ...... 42 5.7. Recommendations for future research...... 43 6. Conclusions ...... 45 References...... 46 Appendices ...... 62

Acronyms

AOO Area of Occupancy BAP Biological Action Plan BDS British Dragonfly Society BRC Biological Records Centre EOO Extent of Occupancy IUCN International Union for Conservation of Nature NBN National Biological Network NHM Natural History Museum, London T1 Time period 1 (1970-1996) T2 Time period 2 (1997-2008) VCA Variance Component Analysis

Figures Page 4.1 The relationship between the fractal dimension of species distribution (D) and cell 27 occupancy. The change in shape between t1 and t2 is shown for each species, displayed with the linear regression model for mean species residual D against mean species cell occupancy (grey line). The dotted lines are species with a positive residual D change, these species became more aggregated from t1 to t2. The solid lines are species with a negative residual D change, and these are species which became sparser across time.

4.2 Linear regression models for the relationships between (a) dry wing length and the 28 logs of abdominal length, (b) thoracic volume and the logs of abdominal length 3, and (c) wing aspect ratio and the logs of abdominal length 2. Solid lines show the linear regression fitted straight to the data, whilst dashed lines show the linear regression once phylogenetic signal is controlled for.

4.3 Relationship between the log of wing aspect ratio and residuals of dry wing length 30 (controlling for phylogenetic signal) in anisopterans (filled points and solid regression line) and zygopterans (empty points and dashed regression line).

4.4 Difference between the two primary methods of measuring odonate wing length. 31 This is based on length measurements taken from the right forewing of Coenagrion puella (see Conrad et al . 2002; Hassall et al . 2008 for complete methodologies).

4.5 Distribution of occupied grid cells in the British ranges of (a) Orthopterum cancellatum 39 (a species increasing in occupancy) and (b) Calopteryx virgo (a species decreasing in occupancy),for t1 (left) and t2 (right) .

4.6 Distribution of occupied grid cells in the Britain range of Aeshna isosceles for t1 (left) 40 and t2 (right).

Tables Page 2.1 Comparison of the two main methods used to measure species range shift. 10

2.2 Variables responsible for determining species distribution patterns, grouped 12 according to their influences as defined by Guisan & Thuiller (2005).

2.3 Comparison of methods used to measure spatial distribution pattern. 13

2.4 Selection of species traits which have been found to correlate significantly with 16 changes to invertebrate ranges.

2.5 Morphological traits used as proxies for dispersal capacity in a range of species. 18

4.1 The number of species which showed cell occupancy or range shift changes. The 25 binomial sign test shows whether a significantly greater proportion of species showed a range expansion or northwards shift).

4.2 The number of species with more or less aggregated distributions (residual D), the 26 number of species changing over time (residual D change), and whether there was a significant proportion with more aggregated or increasingly aggregated ranges (binomial sign test).

4.3 The variance in dry wing length and aspect ratio accounted for by each of the nested 29 taxonomic scales (for n= 572 specimens).

4.4 The variance in dry wing length and aspect ratio accounted for by inter- (between 30 species and all higher taxonomic levels) and intraspecific (between and within sex) variation. The binomial sign test shows whether a significantly greater proportion of variation was accounted for interspecific variation.

4.5 The number of species with a positive change index (expansion in overall cell 32 occupancy from t1 to t2). The binomial proportion test shows whether there is a significant difference in the proportion of each sub-order showing a cell occupancy increase. Percentages given as a proportion of n.

4.6 Statistical outputs from Welch two sample t-tests investigating differences in wing 33 length between dry museum specimens and wet specimens (recently collected).

Abstract Temporal range dynamics (range shift and change index), dispersal morphometrics (wing length, wing aspect ratio, and thoracic volume), and distribution pattern (residual D) were investigated in British Odonata. Initial analyses discounted range shift from this study due to the absence of evidence that the data were showing directional shift rather than non-directional expansion. A significant proportion of change index (Telfer et al . 2002) was described by combining the three dispersal traits and residual D. Species with increasing range size were those with long, broad wings, large thoracic volumes and a more aggregated distribution. This morphology is found in the anisopteran (dragonflies) suborder, which showed a substantially greater increase in occupancy than the smaller and weaker zygopterans (damselflies). In a preliminary investigation into the extant representativeness of museum collections, there was found to be no differences in wing length with recently caught, wet specimens.

Key words: Dragonfly, damselfly, anisoptera, zygoptera, change index, range shift, fractal dimension

Acknowledgements I thank my supervisors Dr. Nick Isaac (CEH) and Dr. Simon Leather (Imperial College) for their comments, support and guidance; Steve Brooks (NHM) for all his help and advice throughout the project; Gary Powney for his assistance with statistical analyses; Dr. Christopher Hassall, Dr. Kelvin Conrad and Philip Bowles for their kindness in making their data available to me; and to everyone else who has supplied advice or comment on this work. I also extend thanks to the inexhaustible support, hugs and cake-eating of SW3, and to tea, without which none of this would have been possible.

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1. Introduction

1.1. Statement of need Under the combined force of multiple environmental and anthropogenic drivers, many species are undergoing extensive change to their global ranges. Past work has focussed on the impact of rising global temperatures (Houghton et al . 2002), but this is just one of the many pressures that species are under. The relative impacts of these drivers are often species specific, limiting the potential for proactive conservation without extensive, single-species research. Most species lack such detailed information, and there is neither the time nor the available resources to fill these knowledge gaps. In species with long historic records, however, it may be possible to study long term changes in light of more general species traits, with the potential to build a database of traits which are frequently associated with declining species ranges. Achieving this requires the in depth analysis of multiple and disparate taxa, with a heavy reliance on those for which substantial and long-running records already exist.

The odonates (order Odonata) are one such taxon, with records from amateur naturalists dating back centuries, although their value in this field has only recently been recognised (e.g. Hickling et al. 2005; Hassall et al. 2010). Through the study of their range dynamics and associated traits, this study will investigate the predictive potential of dispersal morphology and distribution pattern, testing the belief that key traits can indeed be used to predict species temporal changes. Findings of this study will also be important for national odonate conservation by providing additional means of determining species most at risk. Species with chronic declines, or increasing range fragmentation, may otherwise be overlooked by conservationists working with small snapshots of present distribution, particularly if they remain fairly common.

1.2. Range dynamics Focussing on changes to the odonates’ British distributions, this study will utilise two different range dynamic metrics: range shift and occupancy change (see Appendix I ). Range shift is characterised by a series of low latitude extinctions and high latitude colonisations (Malcolm et al . 2006; Thomas, 2010). Extensive research almost exclusively attributes this to shifting climate envelopes under climate change pressures (Elith & Leathwick, 2009). Past focus has primarily been on birds and butterflies (Parmesan et al . 1999; Warren et al . 2001; Poyry et al . 2009), but similar trends are increasingly being found across a wide variety of taxa (Hickling et al . 2006). Drivers of occupancy change, however, are far less certain. Although evidence suggests that there is an indirect link with climate (Robinet & Roques, 2010), this is only one of a suite of determinants. Other pressures include habitat loss and fragmentation, invasive species, and over-exploitation (Gaston, 1996; Korkeamaki & Suhonen, 2002; Hassall & Thompson, 2008). These are being counteracted in some instances by improved habitat quality, remediation and conservation work (Willis et al . 2009), resulting in complex effects and interactions.

Whilst regional scale studies are needed to fully understand range dynamics (Parmesan, 1996), national or local level research can still be hugely important, particularly in informing national conservation policy and management. Range shift poses the most problems for localised monitoring, because it will not incorporate both northern and southern range margins. Instead it can only be interpreted from the uni-directional, polewards expansion of the range margin under study.

1.2.1. Study taxa Macro-invertebrates are particularly useful for studying range dynamics because of their short generation times (Parmesan, 1999; Thomas et al . 2004). Many also have long running national data sets (e.g. in Britain) because their ease of collection and charisma made them appealing to amateur naturalists. This, coupled with its position at the dynamic margins of many of the species ranges (Wilson et al . 2004; Hassall et al . 2010), makes Britain an ideal site for entomological range dynamics

© Alyson Pavitt, 2011 [email protected] study. Until recently, research in this field centred on butterfly responses (e.g. Hill et al . 1999; Wilson et al . 2004; Willis et al . 2009), however odonates are now gaining recognition as an order of equal, if not greater importance. Not only do odonates encompass both aquatic and terrestrial habitats (Hickling et al . 2005), they are alos sufficiently small in number (~6,000 species) to allow a large, representative proportion to be studied in detail (Clausnitzer et al . 2009).

1.3. Distribution and dispersal Species range dynamics are heavily influenced by internal distribution patterns (see Appendix I ), and the ability of individuals to disperse between patches of suitable habitat (Hill et al . 1999). It is important to consider interactions between the two, as landscape may be more permeable to a sparsely distributed but mobile species, than to a more sedentary species with an aggregated distribution (Thomas et al . 1992; McCauley, 2006). This has implications for both the colonisation of habitat beyond current range boundaries, and the recolonisation of habitat following local population extinction.

1.3.1 Patterns of distribution Species are not evenly distributed across their range, but instead form unique patterns of occupancy and absence through a complex assortment of biotic and abiotic interactions (Wilcove et al . 1986; Fischer & Lindenmayer, 2007). Past work on butterflies suggests that a fixed snapshot of this spatial pattern can predict the species’ range dynamics across time, with more aggregated species showing range expansions (Wilson et al . 2004). By applying methods used in this study to a new taxon (Odonata), this project will investigate the value of distribution pattern as a predictive metric of range dynamics.

1.3.2. Dispersal capacity Because direct measures of dispersal distance are less accessible as predictors, and are unknown for a number of species, this study will take three accepted morphological proxies of dispersal instead: wing length, wing aspect ratio, and

© Alyson Pavitt, 2011 [email protected] thoracic volume (Malmqvist, 2000; Hughes et al . 2007; Meyer et al . 2008). The relationship between wing length/aspect ratio and flight capacity are well documented in the literature. Long wings and higher aspect ratios (narrow wings) have long been associated with economical flight and dispersal (Conrad et al . 2002; Rundle et al . 2007; Saino et al . 2010). Wing morphology, however, provides little information on the energetic requirements for flight, and thus its relationship with thoracic volume, heavily influenced by flight musculature (Pringle, 1949), is an informative one.

1.4. Aims and objectives The primary focus of this study is on identifying temporal changes in British odonates, and whether they show any general trends with dispersal morphology and distribution pattern. It also aims to investigate the value of museum collections as sources of this morphological trait data.

With these in mind the objectives of the project are to: 1.4.1 . Create an up-to-date picture of how species ranges are changing over time, both in latitude (range shift) and overall size (occupancy change). 1.4.2 . Investigate the potential for both static and dynamic measures of species distribution pattern to predict future range changes. 1.4.3 . Collate and generate dispersal trait data, and investigate their relationships with range dynamics. 1.4.3. (a) Investigate variation in different methods of measuring morphology. 1.4.3. (b) Investigate whether traits show the greatest level of variation within or between species. 1.4.4 . Investigate how representative museum specimens are of extant populations.

1.5. Hypotheses 1.5.1. Overall, significantly more species will show a polewards shift at their northern range margin. The range margins of all southern species will shift

© Alyson Pavitt, 2011 [email protected] northwards. Northern species will show no change at the polewards range margin due to geographic barriers (e.g. British coastline). 1.5.2 . Overall, significantly more species will increase their cell occupancy between the two time periods as conditions become more suitable under shifting climate envelopes. Northern species will be the only species to decrease in overall cell occupancy as fewer areas become suitable for them. 1.5.3 . There will be a significant positive correlation between northwards range shift and change index as species ranges move further into Britain. There will not be a corresponding movement at the eastern range margin, indicating a directional polewards shift rather than non-directional range expansion. 1.5.4 . Species with sparse distributions will decline in cell occupancy due to local extinctions which were not recolonised. More aggregated species will remain stable, or increase in cell occupancy. More aggregated species will also show a greater northwards range shift. 1.5.5 . Species becoming sparser will show a greater decline in cell occupancy as localised extinctions are not replaced. 1.5.6 . There will be a significant relationship between wing length, wing aspect ratio, and thorax volume, and together these will describe a significant proportion of variation in occupancy change and range shift. 1.5.7 . The combination of species morphology and distribution pattern will describe the most variance in occupancy change and range shift. 1.5.8 . Wing length will not vary between different measurement methods. 1.5.9 . The greatest variation in wing morphology will be seen above the level of the species, rather than being intraspecific. 1.5.10 . Wing morphology will not vary between museum specimens and wet specimens.

1.6 Scope This project is concerned with tracking temporal changes in the distribution of odonates within Britain, and identifying whether these changes can be pre-empted with an understanding of the species’ dispersal capacity and distribution pattern.

Two forms of temporal change will be measured; change in the number of grid cells occupied (occupancy change) and latitudinal movement at the margins of the range (range shift). Interspecific variation in three morphological dispersal traits (wing length, wing aspect ratio, and thoracic volume), and in distribution pattern (Wilson et al . 2004) will be analysed in relation to these range dynamics, both individually and together. The morphological data, collected from Natural History Museum (NHM) specimens, will also be compared to more recently collected wet measurements, in order to investigate the assumption that morphometric data from these old collections is representative of extant populations.

2. Background

2.1. Analysis of range dynamics Whilst much can be learnt about a species’ ecology from the size and distribution of its range (Gaston, 2003), this static measure is of limited use in understanding its long term viability. Species with small ranges (geographically rare), for example, may be stable and at optimal population size, whilst those with extensive ranges may be rapidly declining and in need of conservation attention (Gaston, 1994). It is therefore vital that not only the range size, but also temporal changes to that size, are understood. This ensures that limited conservation resources are targeted at the species with the most immediate risk.

2.1.1. Drivers Whilst many studies investigating species range dynamics have focussed on the escalating issue of climate driven changes (e.g. Parmesan, 1996; Parmesan & Yohe, 2003; Hickling et al. 2006; Termaat et al . 2010), it is important that this is not treated as a default driving force of all change. Even where species ranges are shifting in response to corresponding shifting climate envelopes (Elith & Leathwick, 2009), it is unlikely that they are responding in isolation from other drivers. Both range shift and occupancy change are ultimately determined by a unique combination of internal pressures, such as habitat transformation and altered interspecific interactions (Parmesan & Yohe, 2003; Hofmann & Mason, 2005). Some of these may directly or indirectly result from climate change, but this is not always the case. Some may be caused by population stochasticity generating “noisy biological data” (Parmesan & Yohe, 2003), or result from other anthropogenic factors such as pollution (Goffart, 2010) or land conversion. In such cases, range is limited by the suitability of the habitat, not by temperature tolerances, and must be interpreted as such. Indeed, Parmesan & Yohe (2003) claim the latter of these to have had more influence on 20 th century range shifts than any climate change effects.

2.1.2. Methods of analysis Metrics of range change have traditionally been based on the difference in species distribution between two separated time periods of fixed length (e.g. Hickling et al . 2006; Hassall et al. 2010). This convention has become less absolute in recent years, however, with unequal time periods being used to account for record effort (e.g. Termaat et al . 2010). Analyses tend to fall under one of two categories, either measurements of changing range size (“occupancy change”) or shifts to the overall species range.

Occupancy change Change in a species’ habitat occupancy can be measured using a number of different methods which are dictated by the form or quality of the input data: see Gaston & Fuller (2009) for a summary of these. By putting a value on the actual area of a range, area of occupancy (AOO) may provide the most information (e.g. Warren et al. 2001), but only tends to work on the small scale (Termaat et al . 2010). For large scale monitoring schemes such as species atlases and national databases, change needs to be calculated from coarser measurements of grid cell occupancy (Tefler et al . 2002; Hui, 2011). These rely on distribution maps built from binary records of species presence or absence within a regular system of grid cells, and can therefore only provide a measure of temporal change in the number of occupied cells at a given scale. Other methods that have been utilised include extent of occupancy (EOO) which maps the distance of the outer limits of the distribution (Gaston, 1996). Whilst this can be useful (e.g. Jetz et al . 2008), it is likely to be of limited value when tracking temporal changes, particularly in light of species conservation and management.

Whilst the value of range size and occupancy change cannot be overestimated (Gardenfors et al . 1999), a huge variation in scale (from globally ubiquitous species to single site endemics) means that no standard monitoring methodology has yet been implemented (Segurado & Araujo, 2004).

Range shift Because of its association with climate change, species range shift has been extensively studied, although only a few groups (primarily birds and butterflies) have been studied in great detail, making generalisations difficult. Recent analyses by Hickling et al . (2006), however, found the responses of such groups to be representative of trends seen across a range of less studied taxa. They analysed British distribution data for 16 taxonomic groups with ranges in the south of Britain. This included well studied species, such as birds (Aves) and butterflies (Rhopalocera), and understudied species like millipedes (Diplopoda). Across the taxa, 84% (of 329 species) were found to shift polewards at their range margin, averaging 31-60 km between the two time periods studied.

There are two main methods which can be utilised to measure range shift ( Table 2.1 ), the applicability of which vary depending upon the scale of the study and quality of the available data. Ideally range shift analyses should consider the entire range of a species, but in reality studies tend to focus on the more manageable local level, and usually only at one latitudinal range margin. Whilst tracking the mean range shift would be preferable, encompassing more individuals and taking into consideration latitudinal clines in morphology and behaviour (Hassall et al . 2008, 2009: Table 2.1 ), this is unlikely to detect smaller scale changes (Shoo et al. 2006). In light of this, and despite questions over the representativeness of the method, range shift continues to be studied predominantly at the range margins (e.g. Hickling et al . 2006; Hassall et al . 2010).

Table 2.1 : Comparison of the two main methods used to measure species range shift.

Marginal shift Central shift Shift is measured as change at (or Shift is measured as a change to the

close to) the margin of the range mean/ centre of the range (Mattila et (Hickling et al . 2005, 2006; Hassall et al . al . 2011).

Method 2010).

Detects small changes, making it Provides a picture of overall range

useful at the national and local level. change because it considers change to the spread of a large number of individuals under a range of different conditions (Archaux, 2004). Advantages

Cannot conclusively show range shift Produces smaller range shift values, is occurring from only a subsection of so is unlikely to detect any changes at

the range (i.e. from only one margin). the local level (Shoo et al . 2006). Dependent on a small number of grid cells, so is more affected by varying recorder effort (Shoo et al . 2006).

Disadvantages Based on extreme sightings which may fall outside the species’ normal range.

2.2. Odonates as a model taxon Globally there are around 6,000 known odonate species, 39 of which breed in Britain, with a further 12 as regular migrants (Corbet & Brooks, 2008). In recent years several migrants have established permanent colonies in Britain (Merritt et al . 1996; Parr, 2010), leading to disagreement over the exact number of “resident” species, though the commonly accepted migrants and residents are listed in Appendix II .

2.2.1. Life history Odonata is an ancient order of insects which has remained largely unchanged for over 300 million years. The extant species are classified into two sub-orders based on the morphology of their wings: the zygopterans (“similar wing”), small-bodied, slender “damselflies”, and anisopterans (“dissimilar wing”), the larger bodied “dragonflies”. Odonates are “morphologically generalised” (Corbet & Brooks, 2008) insects, with a larval form resembling the adult in all but the presence of fully developed wings. Almost all species have obligatory aquatic larvae before they develop into airborne imagos.

2.2.2. A model taxon for range dynamics research Odonates are one of the best studied insect taxa, with the British Dragonfly Society (BDS) database holding records dating back as far as 1807 (Hassall et al . 2007). Despite this, the impact of abiotic interactions on their spatial distribution is only just beginning to receive attention. Within Britain, work is currently underway to produce an up to date dragonfly atlas (due for publication by the BDS in 2013), and the past few years have seen several publications investigating the causes and impacts of range change in this taxa. Hassall et al . (2010) used range shift analyses to show a northward shift in all British odonate families, predicting changes to species distribution which could affect the reliability of odonate larvae as water quality indicators. Hickling et al . (2005, 2006) found the same trend in data from the Biological Records Centre (BRC) for both southerly and northerly species. The only species to be adversely affected were two of the four northern species ( Aeshna caerulea and Leucorrhinia dubia ), which showed overall range declines as their margins move further into Scotland (Hickling et al . 2005).

Despite being less studied than lepidopterans, odonates may have the advantage as a study group, in that there are sufficiently few species to allow most, if not all, to be studied individually. Numbering in the few thousand means there is scope for a more comprehensive species-level analysis, rather than relying on a small subset of species. This has allowed for the first global assessment of an insect order to be

© Alyson Pavitt, 2011 [email protected] conducted. Clausnitzer et al . (2009) studied and mapped the distributions of over 25% of all known odonates (1,500 species), to identify the order’s threat level. Such detailed analyses are not possible for groups like Lepidoptera where over 135,000 species have already been identified (Kristensen, 1999).

2.3. Spatial distribution patterns Species ranges are not homogenous across a landscape, but are aggregated in accordance with the suitability of the immediate environment and underlying habitat. This depends on a wide range of factors including inter-specific interactions such as predation, competition and parasitism (Williams et al . 2001; Begon et al . 2005; Martinez et al . 2008), as well as interactions with the abiotic environment (Guisan & Zimmermann, 2000; Huston, 2002; Guisan & Thuiller, 2005: Table 2.2 ). This distribution pattern is a key consideration when attempting to understand the determinants of species distribution and range changes.

Table 2.2: Variables responsible for determining species distribution patterns, grouped according to their influences as defined by Guisan & Thuiller (2005).

Influence Definition Examples Regulators “factors controlling species Temperature, soil eco-physiology” composition Disturbances “perturbations affecting Natural or anthropogenic environmental systems” habitat change Resources “components that can be Energy, water assimilated by organisms”

The exact determinants of a species geographic distribution has long been considered a key question in ecology (Levin, 1992), with little evidence that it will be resolved any time soon (Hartley et al . 2004; Gelfand et al . 2006). One of the central problems is that these variables work at different scales, and so are often not compatible. In a recent botanical study comparing patterns at different spatial scales,

Hartley et al . (2004) noted each scale to be driven by a different key variable. For example, the “intermediate scale”, used in this analysis to describe national (British) scale patterns, was determined predominantly by habitat distribution and persistence. Meanwhile, “fine scale” patterns were found to be more dependent on sampling consistency and intraspecific competition for space. As expected, determinants of distribution pattern correlated at similar scales, but become increasingly dissimilar as the difference between scales increased.

2.3.1. Methods of analysis It has been proposed that by understanding how distribution patterns correlate with range changes, predictions of future change could be made based on a present day snapshot of their distribution (Wilson et al . 2004; Hui, 2011). This could have huge implications for conservation, were specific patterns of distribution indicative of species or populations most vulnerable to decline. Numerous methods have been employed to provide a metric for spatial distribution pattern (Table 2.3 ), though many focus on the patterns of individuals within a population, rather than at the regional level (Davis et al . 2000).

Table 2.3 : Comparison of methods used to measure spatial distribution pattern.

Nearest neighbour Ripley’s K (or L) function Scaling patterns of occupancy (SPO) Individuals recorded as Point data, assuming all Binary point data on a grid point data within a points have been selected system based on population, assuming within a predefined study presence/absence data, randomly selected area. where distances between sample points and spatial sampling points are the

Data Data continuity. same (eg/ species atlas).

Table 2.3 cont : Comparison of methods used to measure spatial distribution pattern.

Nearest neighbour Ripley’s K (or L) function Scaling patterns of occupancy (SPO) Primarily distance Can describe patterns Can describe patterns at between individuals, between individuals at multiple scales, but works though recent multiple scales (e.g. particularly well at larger developments have Wiegand & Moloney, landscape scales (e.g.

Scale Scale shown it to work at larger 2004). Wilson et al . 2004) scales (Davis et al . 2000).

Compare the distance Sums records within a Compare summed between closest data given radial distance occupancy at two different, points with the expected around each data point nested scales (e.g. Wilson distance to (a) just the (Ripley, 1977). et al . 2004). nearest neighbour (Clark & Evans, 1954) or (b)

Methodology Methodology every other point (e.g. David et al . 2000).

Identifies whether a Populations with Relative to occupancy at population is clumped, aggregated distributions the fine (small) grid cell uniform or randomly will have more occupied scale, species with distributed. Populations cells in close proximity to aggregated distributions

will show uniform the central data point. show a lower occupancy at distributions when all Sparse or randomly the coarse (large) grid cell Results individuals are a similar distributed species will scale, than those which are distance apart (e.g. Smith have similar numbers of sparsely distributed. & Murphy, 1982). records at all distances from it (Dixon, 2002).

Whilst the nearest neighbour method and Ripley’s K function serve important roles in understanding the distribution patterns of individuals within a population, this cannot readily be scaled up for landscape level studies of range dynamics. In order to generate a meaningful measure of species distribution pattern, extensive data sets across large spatial scales are needed (e.g. species atlas and national surveys). Data is usually presented in the form of binary values of grid cell occupancy (presence/ absence), rather than treating each individual sighting as its own data point. The limited amount of data available at this scale means that these finer analyses are not applicable, instead, coarser methods like SPO need to be utilised (e.g. Wilson et al . 2004).

2.4. Use of traits in understanding species distribution It is possible to consider each species as a system comprising of a unique set of trait variables (see Table 2.4). In understanding the relationships between these traits and species distribution, it may be possible to predict how a species (or species with a similar set of traits) will respond to change. In an ideal situation, each species would be studied in comprehensive detail, however limited resources make this impossible. If traits are found to significantly correlate with distribution change, as is suggested by numerous studies (Dukes & Mooney, 1999; Hill et al . 1999; Holt, 2003; Rundle et al . 2007; Poyry et al . 2009; Termaat et al . 2010), then they have the potential to predict under-studied species most at risk. This will allow for better management and the focussing of limited conservation resources. They may also be used to roughly predict future responses to climate change events, enabling a proactive conservation management approach.

Table 2.4: Selection of species traits which have been found to correlate significantly with changes to invertebrate ranges.

Trait Reference Body size Body size positively correlated with Angelibert & dispersal and range shift in odonates. Giani (2003)

Wing aspect Aspect ratio was positively correlated Hassall et al . ratio with dispersal capacity in odonates. (2008)

Wing: body Wing: body length ratio was positively Rundle et al . length ratio correlated with range size in odonates. (2007)

Wing length/ Wing length/ span were positively Rundle et al . span correlated with dispersal, range expansion (2007); Poyry et Morphological traits Morphological and range size in butterflies and odonates. al . (2009)

Wing loading Wing loading negatively correlated with Hassall et al . dispersal capacity in odonates. (2008)

Agricultural Butterflies found to tolerate agricultural Poyry et al . tolerance land showed greater range size and (2009) expansion.

Breeding habitat Butterflies breeding in the forest edge Poyry et al . showed a greater northwards expansion (2009) than those in open grasslands.

Ecological traits Ecological Diet breadth Food plant number was positively Slove & Janz correlated with range size in butterflies. (2011)

Tab le 2.4 cont : Selection of species traits which have been found to correlate significantly with changes to invertebrate ranges.

Trait Reference Distribution Distribution size was positively correlated Poyry et al . size with range expansion in butterflies. (2009)

Growth rate Growth rate was positively correlated with Bale et al . (2002) range expansion in butterflies.

Habitat Beetle species in coniferous forests had Bense (1995): preference larger ranges than those in broadleaf. Clausnitzer et al . Species of odonate in grasslands and lotic (2009) waterways had much smaller ranges, and

range shifts than in other habitats.

Habitat There was a positive correlation between the Hill et al. (1999) tolerance number of habitats occupied and range shift

Ecological traits Ecological in odonates.

Larval host Species of butterfly found on woody plants Poyry et al . plant showed a greater northwards expansion (2009) than those feeding on herbaceous plants or grasses.

Mobility Dispersal capacity was positively correlated Hill et al . (1999) with range shift in odonates.

Threat status Non-threatened species of butterfly showed Mattila et al . (red list) poleward shifting ranges, whilst threatened (2011) (red list) species remained stationary.

2.4.1. Dispersal ability It is important to consider the capacity for individuals to disperse through the landscape, particularly in light of growing threats such as climate change. Actual measures of dispersal distance are rare however, so morphological traits tend to be relied upon as proxies (e.g. Table 2.5 ).

Table 2.5 : Morphological traits used as proxies for dispersal capacity in a range of species.

Morphology Dispersal capacity Examples Aspect ratio (wing shape) Higher aspect ratio (narrow Odonates (Conrad et wings) is associated with al. 2002); Birds (Saino migratory species with long et al . 2010) dispersal distances.

Body size (proxies inc. Larger species have longer Butterflies (Mattila et abdomen length and wing (odonates & multiple species) al . 2011); Multiple length; Cordero, 1994) or shorter (butterflies) species (Jenkins et al . dispersal distances. 2007); Odonates (Allen et al . 2010)

Wing length Long wings are associated Birds (Rayner, 1988); with migratory species with Odonates (Conrad et long dispersal distances. al . 2002)

2.4.2. Problems with the use of traits With a few exceptions (e.g. Hassall & Thompson, 2008; Hassall et al . 2008, 2009) most invertebrate studies have focussed on butterflies. This large bias means that many conclusions drawn are largely based on the work from one or two taxa, and thus will be hugely influenced by taxon-specific characteristics. This emphasises a need for further research in a more representative range of taxa since many traits, particularly those focussed on the morphology of the species, are likely to be fairly taxon specific. For example, wing aspect ratio (the length/ width of the wing) has been found to

© Alyson Pavitt, 2011 [email protected] correlate positively with dispersal capacity in odonates (Hassall et al . 2008), but negatively in some butterfly studies (Hill et al . 1999). Body size is another such example. Recent research on one of the smaller British odonates, Ischnura pumilio , suggests a positive relationship between species size, dispersal distance and range shift (Allen et al . 2010). Work on butterflies suggests that the opposite is true, with larger species being more prone to range contractions (Mattila et al . 2011).

Due to the heritable nature of species traits, their use can be statistically invalidated if species relatedness isn’t considered and controlled for. One of the core assumptions for statistical analysis is that data points are independent from one another; comparing species which share a common ancestor breaches this assumption. Furthermore, some species will be monophyletic to the exclusion of others. This problem has been dealt with in a number of ways, the commonest of which is through the use of independent contrast scores (Rundle et al . 2009), generated from comparative analyses such as Comparative Analysis by Independent Contrast (CAIC) (Purvis & Rambaut, 1995). This allows taxa to be compared independent of their phylogenetic relatedness.

Although frequently used in the literature, direct body measurements should be treated with caution because of the huge number of other variables which confound them. Because the body size of individual insects is so heavily influenced by variables such as temperature and diet (Ikeya et al . 2002; Nijhout et al . 2006; Parker & Johnston, 2006), even minor variations in conditions may have a noticeable impact. Individual physiological plasticity is not the only issue; whilst wings remain largely unchanged, body size will vary substantially between “wet” and dry specimens because of the water content of the body. Indeed, it has been estimated that insects can lose up to 20% of their length during post-senescence drying (Schoener & Janzer, 1968). Subsequently, the conditions and duration of storage will have a significant bearing on measurements obtained. Body mass measurements are also problematic, particularly in species which eat as adults, as they will be highly dependent on the food availability and time from emergence (Johansson, 2003).

3. Methods and materials

3.1. Range dynamics 3.1.1. Data sources Data on species’ distributions within Britain were obtained from National Biological Network (NBN) Ordnance survey maps of cell occupancy (for n=37 species), primarily from records held with the BDS. Cell occupancy was grouped into two time periods of approximately equal record size: 1 Jan. 1970- 31 Dec. 1996 (t1: n= 309,830 records), and 1 Jan. 1997- 31 Dec. 2008 (t2: n= 312,571 records). Cell occupancy values were taken for 10 x 10 km grid cells at t1 (t1_10) and t2 (t2_10), and for 100 x 100 km grid cells at t1 (t1_100) and t2 (t2_100) (see Appendix I ).

3.1.2. Change index Occupancy change was measured using the “change index” method (see Appendix I) developed by Telfer et al. (2002). T1 and t2 (at 10 km scale) were converted to proportions of the total survey area, logit transformed, and a linear regression model was fitted. Species change index was taken from standardised residuals of this model (see Telfer et al . 2002 and Telfer, 2003 for complete methodology). This method assumes that recorders report all species within each grid cell equally, and controls for the effects of variation in geographic coverage and recorder effort by restricting analyses to grid cells recorded in both time periods.

3.1.3. Range shift Using records of cell occupancy, range shift was calculated to investigate the directionality of species range movements in response to climate change. Range shift was quantified as the difference in the northern range margin between t1 and t2 on the 10 km scale. For each time period, the mean northing was taken from the species’ top ten occupied grid cells within Britain, and the distance (in km) between them was used to generate overall range shift (Warren et al . 2001; Hickling et al . 2005, 2006). This is the most frequently used range shift metric (Thomas & Lennon, 1999; Warren et al . 2001; Hickling et al . 2005, 2006; Hassall et al . 2010) because it only

© Alyson Pavitt, 2011 [email protected] requires 20 grid cells to be occupied at each time period (i.e. is not hugely data hungry). As with the change index method, it also accounts for variation in geography and recorder effort by using only grid cells recorded in both time periods.

3.2. Patterns of distribution Work on butterflies suggests that a species distribution pattern can be indicative of its temporal range dynamics (Wilson et al . 2004). This could have important implications for conservation if a stationary snapshot can be used to interpret species trends without several decades’ worth of distribution data.

A metric for distribution pattern (fractal dimension) was calculated using methods derived from Wilson et al . (2004). “Fractal dimension” (D) was defined as “a descriptive measurement of spatial aggregation over a narrow range of scales” (Wilson et al . 2004), rather than being a measurement of the true mathematical fractal dimension at infinite scales. For each species the number of occupied cells at t1_10 and t1_100 were individually summed and converted to AOO. The log10 of cell side length (a measure of scale) was plotted against the log of AOO, and D was calculated from the resulting slope using the formula:

D= 2-(y2-y1)/(x2-x1)

where x is the log10 of the cell side and y is the log of AOO. D2 was calculated in the same manner using the number of occupied cells at t2_10 and t2_100.

These were standardised for cell occupancy by taking residuals from D and D2 regressed against the logs of t1_10 and t2_10 respectively. Positive values were more aggregated than expected for the relative occupancy, whilst negative values were sparser. Temporal change in these residuals was calculated using the formula:

Residual D change= (residual D2- residual D)/log(t2_10/t1_10)

3.3. Trait data 3.3.1. Data sources Trait data was obtained from several sources (listed in Appendix I ). Abdominal lengths were obtained from a meta-analysis of the field guide literature (Bowles, 2010). Measurements of wing length (using the computer package ImageJ; Rasband, 1997-2007) were obtained for Calopteryx splendens (n=129), Coenagrion puella (n=193), Erythromma najas (n=123), and Pyrrhosoma nymphula (n=444) (Hassall, pers.comm .); for complete collection methods refer to Hassall et al . (2008, 2009). Additional wing lengths (using calipers) were obtained for C. puella (n=770) (Conrad, pers.comm ) to enable a comparison of measurement methods; see Conrad et al . (2002) for complete methods.

3.3.2. Morphometrics In addition to those collected from the literature, morphological measurements were taken from dry specimens held in NHM collections. This enabled conclusions to be drawn on the representativeness of museum specimens to extant populations.

The dry length and width (at the widest point) of each wing, and the three dimensions of the thorax, were taken from specimens (for n=37 species: ♀=8, ♂=8 for each species) using digital callipers (0.1 mm precision). Specimens which had incomplete labels, damaged wings or thorax, or were recorded as “bred” were omitted. One specimen for each species was re-measured to estimate measurement error. Given wing symmetry and the significant correlation between fore- and hindwings in NHM specimens ( Appendix III ), the left hindwing will be used across this study as a proxy for all wings. Hindwings are preferable as their shape shows greater intraspecific variation, and is thus more likely to inform species ecology.

3.3.3. Controlling for allometry Direct body measurements are unlikely to be independent of overall body size. To identify these allometric relationships, the three dispersal morphometrics were regressed against a specific abdomen length measure: dry wing length against

© Alyson Pavitt, 2011 [email protected] abdomen length, aspect ratio against abdomen length2 and thoracic volume against abdomen length 3. Where strong correlations were identified, regression residuals were used in the analyses.

3.3.4. Sample size A power analysis was used to determine the minimum number of specimens which needed to be measured in order to generate a representative sample size. The analysis was based on four species obtained from previous research (see section 3.3.1 Hassall pers. comm .), and used the formula:

n= (z 2 x σ 2) / k 2

where z is the error, taken at 1.96 at 95% confidence interval; σ is the standard deviation; and k is the margin of error, defined as the difference between the actual mean and the estimated mean.

k= µ x p

where µ is the mean of the sample data; and p is precision, the accepted variance of the data around the mean (+/- 5% in this study) (Hayek & Buzas, 1997).

3.4. Data analysis 3.4.1. Controlling for phylogenetic signal Many statistical analyses assume independence between data points, however this assumption doesn’t hold true in across-species analyses because of varying degrees of phylogenetic relatedness. Given that some species will be monophyletic to the exclusion of others, this also means that shared trait properties may only exist because of a close shared ancestry.

To ensure data point independence, this study controlled for phylogenetic signal using the package “caper” in R 2.11 (Orme et al. 2011). Unless otherwise stated,

© Alyson Pavitt, 2011 [email protected] phylogenetic signal was controlled for in all analyses. A phylogenetic tree (Appendix IV) was compiled for this analysis in FigTree v1.3.1 (Rambaut & Drummond, 2009) from several sources (Artiss et al . 2001; Von Ellenrieder, 2002; Saux et al . 2003; Fleck et al . 2008; Pilgrim & Von Dohlen, 2008; Dumont et al . 2010).

3.4.2. Variance component analysis (VCA) Variance in residual dry wing length and aspect ratio were analysed across a number of nested taxonomic scales using VCA. The levels used in increasing order were: sex, species, genus, family and suborder. This was carried out in R 2.11 (R Development Core Team, 2011) using the code:

NHM.varcomp<-varcomp(lme(value~1,random=~1| suborder/family/genus/species/Sex),1) where “ value ” is trait of interest; in this case, residual dry wing length and wing aspect ratio. This required the installation of “nlme” and “ape” libraries (Paradis et al . 2004; R Development Team, 2011). These analyses did not control for phylogenetic signal.

3.5. Analysis of measurement methods Comparisons were drawn between two different measurement methods for Coenagrion puella right forewing length using a Welch two sample t-test. The two data sets were collected using the computer software package ImageJ (Hassall et al . 2008) and calipers to 0.1 mm precision (Conrad et al . 2002).

A subset of NHM specimens (♂=3, ♀=3) was selected at random from the four species noted in Section 3.1.1 . Measurements were made to the first cross vein of the left fore- and hindwings, allowing direct comparison with the wet specimen data from Hassall et al . (2008, 2009). Differences in length between the two data sets were investigated using Welch two sample t-tests, enabling conclusions to be drawn on the representativeness of dried museum specimens.

4. Results

4.1. Range dynamics This study found there to be an even split between the number of species showing a decline in grid cell occupancy (n=19) and those showing an increase (n=18). Following the removal of Aeshna isosceles and Coenagrion hastulatum which had insufficient cell occupancies to comply with the minimum requirements of the range shift metric (>20 grid cells: Hickling et al . 2006), there was also no significant difference in the number of species moving north or south (Table 4.1 ). Even after removing northern and widespread species which may be restricted by geographic barriers, there was still not a significant proportion of species expanding their ranges, or moving polewards at their northern extent ( Table 4.1 ).

Table 4.1: The number of species which showed cell occupancy or range shift changes. The binomial sign test shows whether a significantly greater proportion of species showed a range expansion or northwards shift.

All Southern Northern Widespread n species 37 22 4 11 Increase 18 (49%) 14 (64%) 1 (25%) 3 (27%) Change Decrease 19 (51%) 8 (36%) 3 (75%) 8 (73%) index Sign test: p-value 1 0.29 0.63 0.23 n species 35 21 3 11 Northwards 18 (52%) 9 (43%) 1 (33%) 8 (73%) Range Southwards 15 (43%) 12 (57%) 2 (67%) 1 (9%) shift Sign test: p-value 1 0.66 1 0.23

Although northwards and eastwards range shift did not strongly correlation (f=0.46, df=33, p=0.64), a significant proportion of species showed corresponding outwards (north and east shift) or inwards (south and west shift) movements at the north and east range margins (binomial sign test: p=0.029). Furthermore, change index shows a

© Alyson Pavitt, 2011 [email protected] strong positive correlate with both northwards and eastwards range shift (f=7.90, df=33, p=0.0016 and f=5.58, df=33, p=0.0082 respectively). This was sufficient to question the degree to which the range “shift” metric was actually recording range shift, and not just non-directional, marginal range expansion. In light of this doubt, range shift was discounted from subsequent analyses and this study instead focused on change index as the primary measurement of range dynamics.

4.2. Patterns of distribution Controlling for cell occupancy at t1, a significant proportion of species were found to be sparser than expected ( Table 4.2 ). When considered across time, however, species were equally likely to become more aggregated as they were to become sparser.

Table 4.2 : The number of species with more or less aggregated distribution (residual D), the number of species changing over time (residual D change), and whether there was a significant proportion with more aggregated or increasingly aggregated ranges (binomial sign test). All (More) (More) Sign test: species Aggregated sparse p value Residual D 37 12 (32%) 25 (68%) 0.047 Residual D change 37 18 (49%) 19 (51%) 1

It was hypothesised that species with sparser distributions (negative residual D) would decline in cell occupancy over time (negative change index), but this was not found to be the case (f=0.77, df=35, p=0.47). Neither was there a correlation between residual D change and change index (f=0.11, df=35, p=0.90), however a significant proportion of the species did show a positive relationship between the two ( Figure 4.1; binomial sign test: p<0.001).

1.6

1.4

1.2

1.0 D

0.8

0.6

0.4

2 3 4 5 6 7 8

log(cell occupancy)

Figure 4.1 : The relationship between the fractal dimension of species distribution (D) and cell occupancy. The residual D change between t1 and t2 is shown for each species, displayed with the linear regression model for mean species D against mean species cell occupancy (fine black line). The dotted lines are species with a positive residual D change, these species became more aggregated from t1 to t2. The solid lines are species with a negative residual D change; these became sparser across time.

4.3. Morphometric analysis 4.3.1. Data independence Linear regressions in the presence and absence of a phylogenetic signal control are shown for the three dispersal traits and their respect abdomen length metrics in Figure 4.2 . The significant correlations for dry wing length and thoracic volume against their respective abdomen metrics justify the use of residuals (f=95.74, df=35, p<0.001 and f=41.46, df=35, p<0.001 respectively; Appendix V). Wing aspect ratio showed no correlation with abdomen length 2 once phylogenetic signal was

© Alyson Pavitt, 2011 [email protected] controlled for (f=0.018, df=35, p=0.99; Appendix V). This indicates that aspect ratio does not show allometric scaling, and thus does not need to be transformed further.

(a) (b) 4.0 8

3.8 7

3.6 6 3.4 5 3.2

log(dry wing length (mm)) length wing log(dry 3.0 log(thoracic volume (mm3)) volume log(thoracic

2.8 3

3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 9 10 11 12 13 log(abdomen length (mm)) log(abdomen length^3 (mm3)) (c) 1.8

1.6

1.4

1.2

1.0 log(wing aspect ratio(mm2)) aspect log(wing

0.8

6.0 6.5 7.0 7.5 8.0 8.5 log(abdomen length^2 (mm2)) Figure 4.2 : Linear regression models for the relationships between (a) dry wing length and the logs of abdominal length, (b) thoracic volume and the logs of abdominal length 3, and (c) wing aspect ratio and the logs of abdominal length 2. Solid lines show the linear regression fitted straight to the data, whilst dashed lines show the linear regression once phylogenetic signal is controlled for .

4.3.2. Body morphometrics Residuals of dry wing length and thoracic volume were shown to be significantly correlated (f=137.30, df=35, p<0.001); species with relatively longer wings also had relatively larger thoraces. Considered together, these two traits also showed a

© Alyson Pavitt, 2011 [email protected] significant negative relationship with wing aspect ratio (f=6.28, df=33, p<0.001). Controlling for both phylogenetic and allometric signals, this study found species with longer wings and larger thoraces to have significantly broader (lower aspect ratio) wings.

4.3.3. Level of morphometric variance Whilst this study incorporates a control for phylogenetic signal, it is important to ensure that the assumption of negligible intra-specific trait variation holds true in order to justify across-species comparisons. VCAs for residual dry wing length and wing aspect ratio both show most variation at the level of the suborder (Table 4.3 ).

Table 4.3: The variance in dry wing length and aspect ratio accounted for by each of the nested taxonomic scales (for n= 572 specimens).

% variance accounted for by taxonomic scale

Taxonomic level Residual dry wing length Wing aspect ratio Suborder 36.88 65.60 Family 33.61 24.94 Genus 13.53 3.28 Species 6.42 1.10 Sex 4.53 1.60 Within 5.03 3.46

Overall, significantly more variation is accounted for at, or above, the level of the species (see Table 4.4 : sign test), with only 9.56% (residual dry wing length) and 5.06% (aspect ratio) accounted for at an intraspecific scale.

Table 4.4 : The variance in dry wing length and aspect ratio accounted for by inter- (between species and all higher taxonomic levels) and intraspecific (between and within sex) variation. The binomial sign test shows whether a significantly greater proportion of variation was accounted for interspecific variation .

% variance accounted for by taxonomic scale Level of variation Residual dry wing length Wing aspect ratio Interspecific 90.44 94.92 Intraspecific 9.56 5.06 Sign test: p-value <0.001 <0.001

Differences between suborders account for the majority of the variation seen in wing aspect ratio (65.6%), with anisopteran wings significantly broader (lower aspect ratio) than those in zygopterans (t test: t=-9.15, df=16, p<0.001). For example, regressions of aspect ratio against residual dry wing length show very different trends between the suborders ( Figure 4.3 : zygoptera: f=15.22, df=13, p<0.001; anisoptera: f=1.45, df=20, p=0.26).

1.7

1.6

1.5

1.4

1.3

1.2 log(wing aspect ratio) 1.1

1.0 -0.2 0.0 0.2 0.4 0.6 residual dry wing length

Figure 4.3 : Rela tionship between the log of wing aspect ratio and residuals of dry wing length (controlling for phylogenetic signal) in anisopterans (filled points and solid regression line) and zygopterans (empty points and dashed regression line).

4.3.4. Measurement errors NHM measurements were accurate to an average of 0.12 mm. Measurements taken from the thorax were less accurate than those from the wings; with mean accuracies of 0.20 mm and 0.086 mm respectively. Thoracic depth showed the greatest variation, with a mean difference of 0.28 mm across all species.

When incorporating data from numerous sources, the method of collection can also have implications for the accuracy and comparability of the data. Within this study, absolute wing length was shown to vary significantly between the two predominant methods of measuring odonate wings (Figure 4.4 : t test: t= 46.38, df= 355, p<0.001).

18 right forewing length (mm) 16 Calipers ImageJ measurement method

Figure 4.4 : Difference between the two primary methods of measuring odonate wing length. This is based on length measurements taken from the right forewing of Coenagrion puella (see Conrad et al. 2002; Hassall et al. 2008 for complete methodologies).

4.4. Dispersal capacity Dispersal traits and their interactions were not sufficient to explain a significant amount of the variation seen in change index (f=0.93, df=10, p=0.53). The inclusion of residual D, however, significantly increased the explanatory power of the model (f=3.244, df=21, p=0.0064). Following model simplification, the minimum adequate model included all dispersal traits and residual D, as well as the interactions between residual dry wing length, residual thoracic volume and residual D (f=6.30, df=28, p<0.001). This described 54% of the variation seen in change index.

This study found species with longer, broader wings, larger thoraces, and relatively aggregated distributions to be showing the greatest range expansions. This morphology is most commonly seen in anisopterans, which have significantly longer wings (t test: t=-4.048, df=26, p<0.001), larger thorax volumes (t test: t=-2.16, df=21, p=0.043), and broader wings (t test: t=-9.15, df=16, p<0.001) than the zygopterans. The positive link between anisopteran-like morphology and range expansion is further reflected in the substantially higher proportion of anisopterans expanding their ranges ( Table 4.5 : 64% compared to 27% of anisopterans).

Table 4.5 : The number of species with a positive change index (expansion in overall cell occupancy from t1 to t2). The binomial proportion test shows whether there is a significant difference in the proportion of each sub-order showing a cell occupancy increase. Percentages are given as a proportion of n.

Species n species Expanding All 37 18 (49%) Anisoptera 22 14 (64%) Zygoptera 15 4 (27%) Prop.test: p-value 0.061

4.5. Representativeness of museum specimens There was no significant difference in wing length between museum specimens (n=6 for the species in Section 3.1.1 ) and measurements from wet field specimens ( Table 4.6; Appendix VI).

Table 4.6 : Statistical outputs from Welch two sample t-tests investigating differences in wing length between dry museum specimens (NHM) and wet specimens (recently collected).

Forewing Hindwing

Species T df P t df p Calopteryx splendens -0.94 5 0.39 -0.89 5 0.41 Coenagrion puella -1.93 5 0.11 -0.11 5 0.92 Erythromma najas 0.86 5 0.43 -1.47 5 0.2 Pyrrhosoma nymphula 0.041 5 0.97 1.25 5 0.26

5. Discussion

5.1. Spatial dynamics Past work has shown most British odonate species to be responding positively to climate change, exhibiting widespread range expansions and poleward shifts (e.g. Hickling et al. 2005, 2006; Hassall et al . 2010). This study was unable to substantiate either of these trends ( Table 4.1 ). Furthermore, findings cast doubt on whether the range shift metrics can be considered exclusively representative of shifting species range, independent of general occupancy change.

Disparity between these and previous findings may arise from differences in the time periods covered. Whilst this study encompasses all years from 1970-2008 (622,401 records), past studies such as Hickling et al . (2005, 2006) have used two short time periods of equal length set several years apart. The use of two unequally sized, adjacent time periods is not extensive in the literature, despite having some advantages over the conventional method. A longer first time period can account for lower rates of recording in earlier years (Hill et al . 2002; Wilson et al. 2004; Matilla et al. 2008; Termaat et al . 2010), whilst consecutive time periods ensures that all data is utilised (Lenoir et al . 2008; Termaat et al . 2010).

5.1.1. Range shift Range shift was measured in accordance with methods from previous odonate range studies (Hickling et al . 2005). Whilst working with extreme records of presence (ten north-most occupied cells) risks generating trends that do not represent the range as a whole, this method was less data “hungry” than viable alternatives. Hassall & Thompson (2010) proposed basing shift on changes at the 95 th quantile to account for extreme sightings, but this requires species to occupy over 45 grid cells at each time period. Under this proposed method, around a quarter of species (9/37) would have been excluded, as opposed to the two which this study removed.

When studies focus on a subsection of the range margin, shift can only be inferred from the latitudinal dynamics of this area. Most studies, however, fail to consider corresponding longitudinal dynamics, which may lead to premature conclusions that latitudinal movements reflect directional range shift. Whilst there was no correlation in the size of northwards and eastwards range shift, a significant proportion of species were found to move outwards (north and east) or inwards (south and west) at both north and east range margins (p=0.029), and both directions showed strong positive correlations with change index. Whilst it may be that species are just exhibiting corresponding latitudinal and longitudinal shifts, the significant number of species showing this trend suggests that the two are related, even though the distances moved are not proportional. This lack of correlation between north and east range shift could be explained by factors such as unequal dispersal barriers (e.g. British coastline). Apparent non-directionality in range margin movement was sufficient to cast doubt on the representativeness of “range shift” as an unambiguous measure of shift in a species range. It was for this reason that the study focussed predominantly on changes in range occupancy.

5.1.2. Change index Changes in occupancy depend on a species-specific combination of factors, of which climate change is just one. This makes any conclusions about the relative impacts of different drivers, including climate change, difficult to interpret. For example, population contractions or declines may result from habitat destruction or fragmentation, as is likely to be the case for the declining Coenagrion mercuriale which has seen significant habitat loss in recent years (JNCC, 2007). Meanwhile, the migration of species into previously occupied habitats could result from improving water quality (Gibbins & Moxton, 1998; Environment Agency, 2008), an increase in habitat (e.g. garden ponds and gravel pits: Mill et al . 2010), or the adoption of aquatic pesticide buffer zones around waterways. The introduction of targeted habitat restoration schemes, such as that for Aeshna isosceles , also corresponds with an increase in cell occupancy.

Whilst the drivers themselves may be unknown, what can be drawn from range change metrics is an indication of how species are responding to the combined pressures they are under. Although this limits potential for proactive conservation, understanding how species are changing is still important to ongoing management. Indeed, changes to a species’ overall occupancy is considered so indicative of species threat and stability that declining AOO is one of the criteria used by the International Union for Conservation of Nature (IUCN) in their species Red List (Gardenfors et al . 1999; Daguet et al . 2008).

5.2. Patterns of distribution

Significantly more species (68%) were sparser than expected for a given cell occupancy. It can be speculated that species freshwater habitat preferences may account for some of this, with substantially more species found in discrete lentic (“still”) systems than lotic (“flowing”) systems (Aguilar et al . 1986; Brooks, 2005). Rivers and streams (lotic) provide a network of permanent or semi-permanent dispersal corridors linking neighbouring grid cells, whilst lentic waterways (e.g. lakes and ponds) are often more geographically isolated and ephemeral (Angelibert & Giani, 2003; Hof et al . 2006).

Lotic waterways function as the predominant dispersal pathway for lotic species (Ward & Mill, 2007; Chaput-Bardy et al . 2008), mostly through imago dispersal, but this is also likely to be enhanced by some nymphal drift (Beukema, 2002; Chaput- Bardy et al . 2008). Lentic species, however, must rely wholly on terrestrial imago dispersal, and are therefore more likely to establish local metapopulations around key source sites. Whilst these lentic species are considered better dispersers on the whole (Corbet, 1999; Clausnitzer et al. 2009), the isolation and unpredictability of potentially suitable habitat may be such, that there is limited capacity for the establishment of new sustainable populations far from a dispersal source. In support of this, of the six obligatory lotic species recognised in this study ( Calopteryx splendens, Calopteryx virgo, Cordulegaster boltonii, Gomphus vulgatissimus, Platycnemis

pennipes and Coenagrion mercuriale ) (Bowles, 2010), the only one to be more sparsely distributed than expected was C. mercuriale . This is unsurprising given its rarity and designation as a UK BAP (Biological Action Plan) species threatened by “isolation and habitat scarcity” (JNCC, 2007; BDS, 2011). Whilst no conclusions can be drawn without further investigation, this does suggest that when cell occupancy is controlled for, lotic species are more likely to display aggregated distributions than lentic species.

5.3. Dispersal morphometrics With direct measures of dispersal distance lacking for most species, morphological traits act as indirect indicators. Because past studies are often relied upon as source of this data, it is vital that the exact methodologies and their implications are understood. As shown here with wing length, different measurement methods can result in significantly different values for seemingly identical traits. Wing lengths obtained using the computer software ImageJ (Rasband, 1997-2007) were significantly smaller than those obtained from hand measuring with calipers ( Figure 4.4 ). Because ImageJ works with scanned images of the wing, the very base is omitted from analysis and is therefore not comparable to other methods which measure wing length to the base.

Residuals were taken for both dry wing length and thoracic volume to control for body size, as both strongly correlated with metrics of abdomen length (body size proxy). Whilst generating thoracic volume residuals from abdomen length 3 may rely on the use of an artificial metric, cubing the length values did ensure that allometric scaling was corrected for in the right dimension. In general, measurements of abdomen length are prone to inaccuracies, particularly in species with telescopic abdomens like the odonates (Nijhout et al . 2006), however it was used in this study because it was the most readily available proxy for body size (Serrano-Meneses et al. 2007). The value of these wing metrics in across-species comparisons was validated using VCAs. In trait analyses, within-species variation is often assumed to be absent but this is rarely the case (Ives et al . 2007). VCAs can serve an important function in

© Alyson Pavitt, 2011 [email protected] identifying the taxonomic or population level where most variation occurs. In this study there was negligible intraspecific variation for both wing measurements, with most variance found above the species level ( Table 4.4 ). This increases the certainty that it is interspecific differences, and not overlapping intraspecific variation, which result in the trends observed in this study.

5.4. Determining occupancy change 5.4.1. Distribution pattern change Although there was no significant correlation between the sizes of change index and residual D change, a significant number of species (>86%) showed a positive relationship between the two (Figure 4.1 ). This may arise from species declining due to internal localised extinctions (Thomas, 2000; Tscharntke et al. 2002; e.g. Figure 4.5(b) ), and increasing due to step-wise expansion from previously occupied cells (e.g. Figure 4.5(a) ). For example, improvements in river management and water quality have seen populations of species such as Libellula fulva and Platycnemis pennipes expanding their ranges along the lengths of riverine systems (Corbet & Brooks, 2008).

(a) © Alyson Pavitt, 2011 [email protected]

(b)

Figure 4.5 : Distribution of occupied grid cells in the British ranges of (a) Orthopterum cancellatum (a species increasing in occupancy) and (b) Calopteryx virgo (a species decreasing in occupancy),for t1 (left) and t2 (right).

Of the five species showing the reverse trend, becoming sparser with increasing cell occupancy, the most notable was Aeshna isosceles . This rare species (Endangered on the IUCN British Red List; Daguet et al. 2008) is highly sensitive to pollution, with a British range confined to Norfolk and north-east Suffolk, where it breeds exclusively in ditches supporting the aquatic plant Stratiotes aloides (water soldier) (Corbet & Brooks, 2008). Because of the small number of occupied cells, single sightings far beyond the species’ acknowledged range will disproportionately influence the results. Three notably extreme records, each based on a single sighting, account for over 11% of all occupied grid cells at t2 ( Figure 4.6 ). Such unexpected sightings may be explained by vagrant individuals transported by extreme weather, coupled with an increase in recorder effort and species awareness since its appearance in the 1995 UK Biodiversity Strategy.

Figure 4. 6: Distribu tion of occupied grid cells in the Britain range of Aeshna isosceles for t1 (left) and t2 (right).

5.4.2. Distribution and dispersal Neither distribution pattern (residual D), nor dispersal morphometrics (residual dry wing length, wing aspect ratio, and residual thoracic volume) were sufficient in describing change index when considered alone. The descriptive power of the model, however, was significantly enhanced when the two were considered together (accounting for 54% of the variation seen in change index). This reinforces the importance of both dispersal capacity and underlying population distribution in determining a species’ ability to colonise new areas.

Species with the greatest range expansion were dominated by those with long, broad wings and large thoracic volumes, in keeping with previously published findings on invertebrate dispersal (e.g. Malmqvist, 2000; Rundle et al . 2007). Long, broad wings maximise surface area for efficient flight, whilst a large thoracic volume (indirect measure of muscle mass; Schilder & Marden, 2004) is indicative of the greater power needed to generate and sustain the wing beat frequency of these larger wings

The assumption that sparse distributions will always be declining towards extinction (Wilson et al. 2004) does not hold true, because it fails to consider both variation in the dispersal ability of individuals, and in the inherent sparsity of the habitat. As a sole measure of species threat this metric makes the erroneous assumption that all odonate species start with a relatively aggregated distribution. For example, lentic habitats, as previously discussed in Section 4.2 , are likely to be more distributed across the landscape than lotic habitats.

When considered with weak dispersal capacity (small, narrow wings and small thoracic volume) however, those species with sparser distributions were indeed those with declining range occupancies. New areas are easier to colonise if there is a concentrated source of potential colonists in close proximity, particularly in species with the greatest capacity for dispersal. Conversely, the landscape is less permeable to species with limited dispersal ranges and which are already sparsely distributed (either through underlying habitat distribution, or through local extinctions which

© Alyson Pavitt, 2011 [email protected] have not been recolonised). Amongst these declining species are the four British Coenagrion damselflies ( C. hastulatum, C. mercuriale, C. puella and C. pulchellum ). This genus has been in decline here since the 1950’s, with two species becoming nationally extinct between 1953-1983 (Freeland & Conrad, 2002). They have similar morphologies, with small, delicate thoraces and relatively short, narrow wings; all are sparsely distributed and all, with the exception of C. mercuriale , favour lentic habitats. Their limited capacity for recolonisation, and the inherent isolation of their primary habitats, makes this genus particularly vulnerable to habitat destruction and fragmentation processes (Daguet et al . 2008; Lowe et al. 2008; BDS, 2011).

5.5. Suborder variation In light of the morphological associations with occupancy change, the significant difference in dispersal morphometrics between suborders could have crucial implications for their respective abilities to respond to changing conditions. The phenotype found in British anisopterans (significantly longer, broader wings and larger thoracic volume) was that associated with range expansion, suggesting that they are responding far better to changes in the past four decades than zygopterans. This corresponds with extensive evidence that anisopterans perform longer migrations (Ruppell, 1989), are better colonists (comprising over 75% of recent British colonists; Appendix II ), and exhibit faster flight across greater distances than the more manoeuvrable but weaker zygopterans (Wakeling & Ellington, 1997).

5.6. Value of historic collections In order for analyses to have any explanatory powers, a sufficient number of specimens are needed to represent the population or species under study. When species are rare, cryptic or difficult to find, collecting enough data points may be difficult. Under these circumstances, natural history collections can provide an invaluable source of data. These specimens will have been dried and are often decades, if not centuries old, calling into question their value in representing extant populations (Doucet & Hill, 2009). Although only preliminary, findings from this study show the wing morphology of odonate NHM specimens to be

© Alyson Pavitt, 2011 [email protected] indistinguishable from those of recently caught, wet specimens. These results do, of course, need to be treated with caution, and no claims can be made regarding their representativeness of other morphological traits or taxonomic groups. Odonate wings have little water content and so are unlikely to shrink or warp to any significant degree when they dry. The same cannot be said for the body, particularly the abdomen, where results are likely to be more variable.

5.7. Recommendations for future research Whilst this study investigated general trends in odonate distribution, and looked at the value of distribution pattern (residual D) and dispersal morphology in describing these changes, no attempt was made to explore potential drivers of this change. Future research needs to focus on pulling apart the relative impacts of climate and habitat change in order to guide pre-emptive conservation action. Climate change investigations need to move on from focussing on gradual temperature change to encompass other variables. Maximum temperature thresholds, the frequency of extreme weather (such as droughts and floods), and changing humidity will all effect species distribution and survival potential.

Although this study does not dispute the validity of range shift metrics, it does question the readiness to attribute all polewards range movement to climate change induced range shift. There is the risk that polewards shift may be concluded prematurely, particularly when there is no corresponding investigation into longitudinal movements, or the possibility that trends may result from non- directional occupancy change. Further investigation is needed in this area to ensure that no rash conclusions are drawn which may overlook vital factors or driving forces of change.

Despite being one of the best studied taxa in Britain, there remain many gaps in our knowledge of simple odonate life history. Although it has fallen from conservation favour in recent years, there is a pressing need for more targeted natural history research, not only from data collected by amateur naturalists and enthusiasts, but

© Alyson Pavitt, 2011 [email protected] also from controlled scientific study and experimentation. For example, there is limited information on many species’ dispersal capacity (this study is only able to take morphology as a proxy). For a number of species, such information is only speculative, or based on a small number of observations.

Further research is also needed into the value of museum specimens, an area only briefly touched upon in the scope of this project. Whilst preliminary findings do suggest that historic collections are representative of extant species morphometrics, it is pertinent to note that this was only carried out on a subsample of specimens for four species stored at NHM. It would be important to extend this to other species and other collections, ensuring a spatially and temporally representative sample set. It is known that some physical traits shrink when specimens are dried, however there has been little research into different drying or storage methods, and whether interspecific variation remains representative after drying.

6. Conclusions Models are increasingly being used to predict large scale changes in species distribution, particularly in light of climate change. Because there is insufficient data on many taxa, the few that have been extensively studied, and which have long- standing historic records, are being investigated for generally applicable trends. Using a well studied group of British odonates, this study shows that the combination of dispersal morphology (dry wing length and aspect ratio, and thoracic volume) and distribution pattern can be used as an indicator of changes in range occupancy over time.

Species showing the biggest range expansions were predominantly those with long, broad wings and large thoracic volumes, particularly when showing aggregated distributions. This was linked to suborder differences, with most anisopterans (generally large winged and large bodied) exhibiting range expansions, whilst most zygopterans (smaller, weaker dispersers) showed a decline. Given the importance of obtaining reliable morphometrics, this study also investigated the value of museum specimens as sources of morphometric data. Measurements obtained from NHM were not found to differ from the variation seen in extant populations of the species, drawing the, albeit preliminary, conclusion that these collections remain representative of extant populations.

Whilst there were not a significant number of species with decreasing cell occupancies, neither was there the overwhelming number of increasing species that past studies have found (e.g. Hickling et al. 2005). This study therefore, urges caution in prematurely writing off the British odonates as “safe” under predicted environmental and climatic changes. It also highlights the risk of automatically concluding a climate change induced range shift from the dynamics of a single range margin.

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Appendices

Appendix I : Variables used in the analysis of species range dynamics .

Name Definition Source #of species Cell occupancy The number of grid cells a species was Source data 37 recorded in during the time periods t1 from NBN (1970-1996) or t2 (1997-2008).

Change index Metric for the relative change in the cell Source data 37 occupancy between t1 and t2 (see from NBN Methods ; Telfer et al . 2002).

D/ D2 Fractal dimension of species Source data 37 distribution, calculated from 10 km and from NBN 100 km grid cell occupancy at t1 (D) and

t2 (D2) (see Methods ; Wilson et al . 2004).

Distribution The level of population aggregation 37 pattern within a species’ range, measured using

Range variables Range residual D.

Occupancy Change in cell occupancy from t1 to t2, 37 change measured using change index.

Range shift Directional movement at the northern or Source data 37 eastern margins of a species’ British from NBN range, based on the 10 north- or east- most occupied grid cells (see Methods ; Hickling et al . 2006).

Appendix I cont : Variables used in the analysis of species range dynamics .

Name Definition Source #of species Residual D/ The residuals of D and D2 regressed Source data 37 Residual D2 against cell occupancy at t1 and t2 from NBN

respectively (10 km cells) (see Methods ; Wilson et al . 2004).

Residual D Change between residual D and Source data 37

Range variables Range change residual D2 (see Methods ). from NBN

Abdominal Midpoint between the minimum and Bowles, 37 length maximum abdominal lengths, with 2010 measurements taken from base to tip.

Dry wing Mean length of the left hindwing from NHM 37 length the tip to the base of the wing plug. Measured using 0.1 mm digital calipers.

Residual Residuals of thoracic volume (length x Source data 37 thoracic width x depth) regressed against the from NHM volume abdominal length 3.

Trait variables Trait Residual dry Residuals of dry wing length regressed Source data 37 wing length against abdominal length. from NHM

Wing aspect Shape of the left hindwing, expressed as Source data 37 ratio the ratio between dry wing length and from NHM width (taken at the widest point of the wing).

Appendix I cont : Variables used in the analysis of species range dynamics .

Name Definition Source #of species Wing length Length of the left hindwing (unless Hassall et al . 4 (ImageJ) otherwise specified) from tip to the first 2008,2009

cross vein from the base. Measured using the computer software package ImageJ (Rasband, 1997-2007).

Trait variables Trait Wing length Length of the forewing from tip to the Conrad et 1 (Calipers) base. Measured using 0.1 mm digital al . 2002 calipers

Appendix II: List of all species commonly found in Britain (from Corbet & Brooks, 2008) and freshwater habitat preferences.

Species name Described Aeshna caerulea Azure hawker Strom, 1783 Aeshna cyanea Southern hawker Muller, 1764 Aeshna grandis Brown hawker Linnaeus 1758 Aeshna isosceles Norfolk hawker Muller, 1767 Aeshna juncea Common hawker Linnaeus, 1758 Aeshna mixta Migrant hawker Latreille, 1805 An ax imperator Emperor dragonfly Leach, 1815 Brachytron pratense Hairy hawker Muller, 1764 Calopteryx splendens Banded demoiselle Harris, 1782 Calopteryx virgo Beautiful demoiselle Linnaeus, 1758 Ceriagrion tenellum Small red damselfly De Villers, 1789

Coenagrion hastulatum Northern dragonfly Charpentier, 1825 Coenagrion mercuriale Southern damselfly Charpentier, 1840 Coenagrion puella Azure damselfly Linnaeus, 1758 Coenagrion pulchellum Variable damselfly Vander Linden, 1825 Cordulegaster b oltonii Golden-ringed dragonfly Donovan, 1807

Residents Cordulia aenea Downy emerald Linnaeus, 1758 Enallagma cyathigerum Common blue damselfly Charpentier, 1840 Erythromma najas Red-eyed damselfly Hansemann, 1823 Erythromma viridulum* Small red-eyed damselfly Charpentier, 1840 Gomphus vulgatissimus Common club-tail Linnaeus, 1758 Ischnura elegans Blue tailed damselfly Vander Linden, ,1823 Ishnura pumilio Scarce blue-tailed Charpentier, 1825 damselfly Lestes dryas Scarce emerald damselfly Kirby, 1890 Lestes sponsa Emerald damselfly Hansemann, 1823 Leucorrhinia dubia White-faced darter Vander Linden, 1825

Appendix II cont : List of all species commonly found in Britain (from Corbet & Brooks, 2008) and freshwater habitat preferences.

Species name Described Libellula depressa Broad-bodied chaser Linnaeus, 1758 Libellula fulva Scarce chaser Muller, 1764 Libellula quadrimaculata Four-spotted chaser Linnaeus, 1758

Orthetrum cancellatum Black tailed skimmer Linnaeus, 1758 Orthetrum coerulescens Keeled skimmer Fabricius, 1798 Platycnemis pennipes White- legged damselfly Pallas, 1771 Pyrrhosoma nymphula Large red damselfly Sulzer, 1776 Somatochlora arctica Northern emerald Zetterstedt, 1840 Somatochlora metallica Brilliant emerald Vander Linden, 1825 Residents Sympetrum danae Black darter Sulzer, 1776 Sympatrum sanguineum Ruddy darter Muller, 1764 Sympetrum striolatum Common darter Charpentier, 1840

Anax junius Green darner Drury, 1770 Anax parthenope Lesser emperor Selys, 1839 Anax ephippiger Vagrant emperor Burmeister, 1839

Coenagrion lunulatum Irish damselfly Charpentier, 1840 Crocothemis erythraea Scarlet darter Brulle, 1832 Lestes barbarous Southern emerald Fabricius, 1798 Lestes viridis Willow emerald damselfly Vander Linden, 1825 Pantala flavescens Wandering glider Fabricus, 1798 Linnaeus, 1758

Migrants Sympatrum flaveolum Yellow-winged darter Sympetrum fonscolombii Red-veined darter Selys, 1840 Sympetrum meridionale Southern darter Selys, 1841 Sympetru m vulgatum Vagrant darter Linnaeus, 1758

*although this species is now a confirmed British resident, it is too recent a colonist to be included in these analyses.

Appendix III : Linear regression models showing highly significant positive correlations between mean wing lengths and aspect ratios (length/width) for each of the four wings. Data was taken from specimens held in NHM collections .

f d.f p left vs right hindwing 4.36 4 35 <0.001 Wing left vs right forewing 9.43 3 35 <0.001 length left forewing vs hindwing 1.10 4 35 <0.001 right forewing vs hindwing 8.74 3 35 <0.001 left vs right hindwing 6.057 3 35 <0.001 Wing left vs right forewing 57 35 <0.001 aspect left forewing vs hindwing 1.009 2 35 <0.001 ratio right forewing vs hindwing 41.8 35 <0.001

Appendix IV: Phylogenetic tree for the resident British odonates (n=37 species), synthesised from published morphological (Von Ellenrieder, 2002) and molecular (Artiss et al. 2001; Saux et al. 2003; Fleck et al. 2008; Pilgrim & Von Dohlen, 2008; Dumont et al. 2010) phylogenetic analyses.

Appendix V : Linear models for dry wing length, thoracic volume and wing aspect ratio regressed against abdomen lengths. The abdomen length metrics used were abdomen length (mm), abdomen length 3 (mm 3) and abdomen length 2 (mm 2) respectively. Model outputs are given for models with and without phylogenetic signal controlled for .

With phylogenetic Without phylogenetic control control Body metric F Df p F df P Dry wing length 95.74 35 <0.001 107.40 35 <0.001 Thoracic volume 41.46 35 <0.001 81.14 35 <0.001 Wing aspect ratio 0.0044 35 0.99 6.19 35 0.018

34 20 24 30 32 23 19 30 22 25 28 18 21 26 17 20 20 24 19 16 22 18 left forewing length(mm) left forewing 20 15 17 15

32 19 22 28 30 21 18 26 28 20 24 17 26 19 22 24 16 18 20 22 17 18 15 20 16 16 left hindwing length(mm) left hindwing 18 14 15 14 museum wet museum wet museum wet museum wet Calopteryx splendens Coenagrion puella Erythromma najas Pyrrhosoma nymphula

Appendix VI: Differences in left wing lengths between dry museum specimens (collected in the time period 1905-1953) and “wet” (recently caught) specimens (collected in 1999; see Hassall et al. (2008,2009)).