Appendix 3 Investigating Variation in Moth Abundance and Community

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Appendix 3

Investigating variation in moth abundance and community composition with respect to artificial night light

Kate E. Plummer a, James D. Hale b, Jon P. Sadler b, Adam J. Bates b, Glyn Everett c, Dave Grundy d, Norman Lowe d, George Davis d, David Baker d, Malcolm Bridge d, Jon Clifton d, Roger Freestone d, David Gardner d, Chris Gibson d, Robin Hemming d, Stephen Howarth d, Steve Orridge d, Mark Shaw d, Tom Tams d, Heather Young d and Gavin M. Siriwardena a

a British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP24 2PU, UK; b School of Geography, c Earth and Environmental Sciences, The University of Birmingham, Birmingham, UK; School of Built and Natural Environment, University of Central Lancashire, Preston, Lancashire, PR1 2HE, UK; d The Garden Moth Scheme, Birmingham, UK

1. Introduction

As urban development expands globally, the distribution and intensity of artificial night lighting is rapidly increasing (Cinzano, Falchi & Elvidge 2001; Hölker et al. 2010). Considerable concern has been raised about the potential implications for ecological systems (e.g. Longcore & Rich 2004; Gaston et al. 2012; Gaston et al. 2013), and in particular, the effect this could have on nocturnal moth communities (e.g. Frank 1988; Eisenbeis 2006). Moths have an important functional role as herbivores, pollinators, detritivores and prey for a variety of mammalian and avian species. But in Britain, two-thirds of common and widespread of macro-moths species have declined in the last 40 years, and total abundance has reduced by an estimated 28% (Fox et al. 2013). It has been suggested that artificial night lights may be contributing to these declines (Conrad et al. 2006; Fox 2012; Fox et al. 2013); as they attract moths, causing direct fatalities as well as disrupting flight behaviour, life-history traits and predation rates (Frank 1988). However, difficulties in separating the effects of increased light pollution from the impacts of urbanisation more generally has meant that empirical evidence of the impact of artificial lighting on moths at a community-level is currently lacking.

There is much uncertainty about the attraction distance of moths to light sources. Estimates using light-traps vary from 3m (Baker & Sadovy 1978) to 500 – 700m (Bowden & Morris 1975; Bowden 1982), but are generally considered to be less than 200m for a range of species (e.g. Bowden 1982; Beck & Linsenmair 2006; Truxa & Fiedler 2012). However, much less is known about the distances over which insects are attracted to high-power artificial lighting sources. Eisenbeis (2006) estimated that the zone of attraction around a single street light would vary between ca. 50 – 600m, depending on background illumination. Flight-to-light behaviour may be stimulated not only in moths inhabiting the zone of attraction, but also in those which encounter the zone of attraction during foraging, dispersal and migration movements (Eisenbeis 2006). It is possible, therefore, that

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a single street light could influence moth communities over a considerable area, although the spatial scale over which moth communities are affected by artificial lighting is currently unknown.

In this study, we examine the impacts of variation in artificial night lighting on moth total abundance, species richness and diversity for the first time, using national-scale datasets. Using a range of spatial scales, consistent with different flight-to-light response estimates and moth mobility levels, we also investigate the scale at which moth assemblages respond most strongly to artificial light variation. It is recognised that a major constraint on quantifying impacts of artificial lighting is the high inter-correlation with land-use (Kuechly et al. 2012; Hale et al. 2013), making it difficult to separate the effects of lighting from preference for or avoidance of urban habitat more generally. We address this by controlling for the effects land cover within an information-theoretic model selection approach to distinguish variance in moth diversity metrics explained by artificial light radiance compared to that attributed to urban land cover.

Initial analyses for the study made use of lighting data from 2012 and the most recent moth data available at the beginning of the project, from 2010. Subsequently, moth data from 2012, potentially offering a better match to the lighting data, became available, and these data were analysed late in the project. The methods and results relating to analyses of the 2012 data are described below those for 2010 in each section below.

2. Methods

2.1 Moth sampling Moth abundance data were collected as part of the Garden Moth Scheme (GMS; Grundy 2013) during the 2010 flight season (2 March – 8 November 2010) within the UK and Ireland. GMS volunteer recorders run a single moth trap from a standard position within their garden during darkness hours for one night per week over the 36 week sampling period. Recorders used either a Skinner or Robinson moth trap, with one of six bulb types: 15W actinic, 20 – 40W actinic, 60W actinic, 80W mercury vapour (MV), 125W MV or 160W blended. The moth trap and bulb combination used was consistent within sites across the sampling period, and was accounted for in the analysis: see below. The abundances of 195 common and easily identifiable ‘core’ species of macro- and micro-moths were recorded (see Appendix, Table A1). The full dataset was standardised using data filtering protocols described by Bates et al (2013), which removed 101 sites with insufficient trapping frequencies, uncommon trap and bulb combinations and/or sites situated outside mainland Britain. The final dataset therefore included core species abundance data for 213 GMS sites (Figure 1a).

GMS data for 2012 were collected in the same way as for 2010, during the 2012 flight season (28 Feb – 5 November 2012). The same standardisation (following Bates et al. 2013) was applied, which removed 177 sites with insufficient trapping frequencies, uncommon trap and bulb combinations and/or sites situated outside mainland Britain. The final dataset therefore included core species abundance data for 193 GMS sites (Figure 1b). There was considerable turn-over of sites between the 2010 and 2012 field seasons. 79 new sites were added, with only 59% of sites having also been represented in the 2010 analyses.

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Figure 1| Distribution of Garden Moth Scheme (GMS) trapping locations within Britain in (a) 2010 (n = 213) and (b) 2012 (n = 193).

2.2 Moth species richness and diversity measures We used four different metrics to assess the impact of street lighting on moth communities: total abundance, observed species richness, estimated species richness and Fisher’s α diversity index (hereafter termed ‘moth diversity metrics’). Total abundance was the sum of all individuals recorded at each moth trap over the 36 week sampling period and ‘observed’ species richness was the sum of all species recorded at least once. Different moth species are known to differ in their susceptibility to light. Therefore raw species counts are likely to provide an unreliable measure of the true species richness (Gotelli & Colwell 2001). To account for this we used the first-order jackknife (Jack1) estimator to determine ‘estimated’ species richness (Burnham & Overton 1978; Burnham & Overton 1979), which is robust against non-random sampling and has been widely used for species richness estimation (Colwell et al. 1994; Boulinier et al. 1998; Kéry & Plattner 2007; Meyer et al. 2011). Fisher’s α diversity index measures the rate at which the number of species rises as more individuals are sampled (Magurran 2004). It has a low sensitivity to under-sampling and has commonly been applied to evaluate moth diversity (Kempton & Taylor 1974; Taylor, Kempton &

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Woiwod 1976; Thomas & Thomas 1994; Usher & Keiller 1998; Brehm, Colwell & Kluge 2007; Fuentes-Montemayor et al. 2012). Estimated species richness and diversity were calculated using the R package ‘vegan’ (Oksanen et al. 2013).

2.3 Spatial scale of moth response to environmental factors ArcGIS 10.0 (ESRI 2010) was used to create circular buffers around GMS gardens at five spatial scales; 200m, 700m, 1200m, 1700m and 2200m. These encompass the foraging distances of sedentary to highly mobile moth species (Merckx et al. 2009; Merckx et al. 2010), with the two smallest buffers also corresponding to published estimates of light attraction radii in moths (e.g. Bowden 1982; Eisenbeis 2006). Moth responses to both artificial night lighting and land cover parameters were tested at all spatial scales.

2.4 Artificial night light intensity data Night-time lighting information was extracted from radiance-calibrated Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery (see Lee et al. 2006), using the VIIRS ‘Nighttime lights – 2012’ dataset (NOAA 2013). This is the first global cloud-free composite of VIIRS night time lights, which has been created using images captured between 18-26 April and 11-23 October 2012. VIIRS radiance values provide a relative measure of solar/lunar reflection and both natural and anthropogenic night time light emissions, and at a higher resolution than other similar satellite imagery data currently available (Lee et al. 2006).

The thematic raster summary tool in ArcGIS 9.2 (Beyer 2004; ESRI 2006) was used to calculate mean radiance values (nano-Watts / (cm2 *steradians), resolution 750m) within the five spatial scale buffers around all 213 GMS gardens (Figure 2a). This process was repeated to generate minimum and maximum radiance estimates for the UK as a baseline for comparison. Approximately half of the UK land area had a radiance value of 0 nW/(cm2*sr) (i.e. minimum radiance) (Figure 2b). 200 – 2200m buffers positioned over Greater London were used to estimate maximum UK nightlight values, showing a decrease in average radiance from 119.88 – 67.00 nW/(cm2*sr) with increasing spatial scale (Figure 2c). For GMS sites, the maximum average radiance value was recorded at the 2200m scale, ranging from 0 – 30.80 nW/(cm2*sr) and averaging (±SD) 5.33 ± 6.50 nW/(cm2*sr). As such, the data used here to estimate impacts of night lighting on moth communities captures a high level of variation in night lighting across the UK.

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a b

Figure 2| Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery for the UK. (a) Example of night light distribution within 200m (average radiance = 9.96) and 2200m (average radiance = 7.78) buffers for a GMS garden on the outskirts of a brightly lit village. (b) Distribution of night lighting in the UK. (c) Night lighting in Greater London, from which maximum average radiance for the UK has been estimated, at the same spatial scales used for GMS sites. Bright blue = minimum radiance; Bright red = maximum radiance. IMAGES ARE COPYRIGHTED AND SHOULD NOT BE SHARED OR REPRODUCED WITHOUT PERMISSION.

2.5 Land cover data It was predicted that moth diversity would be strongly affected by the habitat-type and variability surrounding GMS gardens. To account for this in our assessment of night lighting impacts, we first needed to establish which land cover types where most influential in order that they could then be included as controls in further analyses. The proportional cover of nine broad habitat classes around GMS sites at each spatial scale was calculated using Land Cover Map 2000 (LCM2000). These were conifer woodland, broadleaf woodland, arable farmland, improved grassland, wetland, bare ground, urban and coastal. Pearson’s correlation coefficients were then compared to assess associations between land cover classes and moth diversity metrics (Figure 3). Four land cover variables – urban, improved grassland, arable farmland and broadleaf woodland – consistently explained the highest amount of variation across moth diversity metrics and spatial scales (Figure 3).

The proportion of surrounding arable farmland was most strongly associated with abundance and species richness measures (r= 0.206 – 0.338), with the strength of the relationship often greater at increasing spatial scales (Figure 3). The proportions of broadleaf woodland (r = 0.253 – 0.384) and improved grassland (r = 0.170 – 0.320) were positively associated with species richness and diversity measures, but were only associated with total abundance at smaller spatial scales. Increased urban land cover, however, was negatively associated with moth abundance, richness and diversity, particularly at the larger spatial scales (r =-0.505 – -0.383). Correlations with conifer woodland, semi-natural, wetland, bare ground and coastal habitats were all low (Figure 3). Since improved grassland was strongly negatively correlated with urban cover at all spatial scales (r = -0.520 – -

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0.647; p < 0.001), this was not selected for inclusion in further models so as to reduce collinearity among predictor variables. The remaining variables were weakly correlated amongst themselves across all scales. As such, proportional covers of broadleaf woodland, arable farmland and urban were selected as control parameters for further modelling.

Figure 3| Correlation coefficients (r) obtained from Pearson’s correlation analyses between the proportion of land covered by habitat variables and four moth community metrics (a – d) increasing spatial scales. Shading marks non-significant values, significance level at α= 0.05, although analyses do not account for non-independence of spatially aggregated data points and so these should be interpreted with caution.

2.6 Statistical analysis General linear mixed models (GLMMs) with a Gaussian error distribution were used to examine the independent effects of night lighting on total abundance, observed species richness, estimated species richness and Fisher’s α diversity of moths, as these models allow for random effects and spatial correlation to be fitted concurrently (Pinheiro et al. 2013). Trap-bulb combination was fitted as an 11-level random effect, since abundance and diversity of moths sampled in light traps are

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influenced by the type of trap and bulb employed (Truxa & Fiedler 2012; Bates et al. 2013). An exponential correlation structure using eastings and northings was included in each model (Dormann et al. 2007) to control for spatial autocorrelation of the response variables (Moran’s I test p ≤ 0.004). All models were checked for normality and homoscedasticity of residuals, with the total abundance response variable square root transformed to meet the model assumptions.

Using an information theoretic approach so that competing models could be directly compared (Burnham & Anderson 2002), the analysis was completed in two stages. Firstly, the spatial scale at which moth diversity metrics respond most strongly to land cover was determined. The four predefined land cover variables – broadleaf woodland, arable farmland and urban proportion covers – were included in GLMMs as fixed effects. Five separate models were fitted, with land cover variables included at a spatial scale of 200m, 700m, 1200m, 1700m or 2200m. The five models plus an intercept-only null model were ranked according to AICc, using the R package AICcmodavg (Mazerolle 2013).

Secondly, models controlling for land cover effects were re-fitted to examine to what extent and at what scale moth metrics were influenced by night lighting. For each moth diversity metric, a candidate set of 12 models were compared, using the ‘dredge’ function in R package MuMIn (Bartoń 2013). Models M1 – M5 included woodland, farmland and urban variables at the spatial scale determined in stage one of the analysis, plus average radiance fitted at one of each spatial scales from 200m – 2200m. A problem with detecting effects of lighting is that artificial night lighting is likely to be more intense where human habitation is denser. Therefore, in order to distinguish the effects of night lighting from those of urban land cover, models M6 – M10 were repeats of the first five models but with the urban cover explanatory variable excluded. Model M11 included all four land cover variables only, and Model M12 included farmland, grassland and woodland variables only. All predictor terms were standardised before model selection to a mean of 0 and SD of 0.5 to enable effect sizes to be compared directly (Gelman 2008).

At each stage of the analysis, completing models were compared using ΔAICc and Akaike weights (w), and evidence ratios were calculated using Akaike weights to evaluate the relative support for different models. Models with the lowest AICc value (i.e. top ranked models) were considered to be the most parsimonious, but models with ΔAICc ≤ 2.0 were also considered to have a similar level of support from the data. Model fit was evaluated using a pseudo-R2 value, following the coefficient of determination (R2) calculation for mixed effects models in Nagelkerke (1991).

Variance inflation factor (vif) values for land cover and lighting predictor terms in all GLMM candidate models were ≤ 4.80, indicating that collinearity was not of concern within the data. All statistical analyses were conducted in R version 3.0 (R Core Team 2013).

Analyses applied to the 2010 data were repeated for the 2012 dataset. In summary, GLMMs were fitted using a Gaussian error structure, with an exponential correlation structure to control for spatial autocorrelation (Moran’s I test p ≤ 0.030) and trap-bulb combination as the random effect. The total abundance response variable was square root transformed to meet the model assumptions. Broadleaf woodland, arable farmland and urban land classes were again found to be the land cover classes most strongly associated with moth metrics and did not result in collinearity when fitted together with lighting predictor terms (variance inflation factors ≤ 4.61), therefore

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allowing for the same two stage modelling protocol used for the 2010 data to be repeated with the new data.

3. Results

In the 2010 data set, a total of 383,065 individual moths were collected at the 213 GMS gardens included in the analyses. Total abundance per site averaged (±SD) 1798 ± 1102, with a range of 171 – 6129. Observed species richness averaged (±SD) 112 ±27, with a range of 45 – 187 species recorded per site of the total 195 species monitored.

In the 2012 data set, a total of 209,216 individual moths were collected at the 193 GMS gardens included in the analyses. Total abundance per site averaged (±SD) 1084 ± 665, with a range of 137 – 3640. Observed species richness averaged (±SD) 92 ±26, with a range of 17 – 166 species recorded per site of the total 195 species monitored.

3.1 Effects of land cover at different spatial scales Model comparisons found that, with the exception of total abundance, all moth diversity metrics were best explained by land cover data at the largest spatial scale (w ≥ 0.96, Table 1).

Moth total abundance was more strongly correlated with land cover data at large spatial scales (1200 – 2200m), compared to small spatial scales (200 – 700m). However, although the 1200m scale model was best supported by the data, all three large spatial scale models were all within 2 ∆AICc (Table 1). This suggests that, beyond 1200m, proportional land cover has a comparable influence on moth abundance.

Observed and estimated species richness, as well as Fisher’s α diversity, showed the same pattern in their relationship with land cover. The results of model selection indicate a significant improvement in the strength of the relationship as spatial scale increases between 200m – 2200m (Table 1). In all instances, the top model (2200m scale) is ≥ 24.6 times better supported by the data than the second best model (1700m scale) and ≥ 479.5 times more likely than 1200m, 700m and 200m scale models.

Therefore based on these results, farmland, urban and woodland proportion cover were fitted at the 1200m scale for total abundance, and the 2200m scale for richness and diversity measures when modelling impacts of night lighting.

In the analyses of 2012 data, the results were similar for estimated species richness and Fisher’s α diversity, but the best spatial scale for total abundance and observed species richness was 200m (Table 2). In all cases, there was a clear, single, best model on the basis of AICc values (Table 2), so the scale indicated was taken forward for the analyses of night lighting for 2012.

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Table 1 | Model selection table for GLMM analyses of land cover effects on moth diversity metrics at five spatial scales (2010 data). Null models in italics for comparison. Grey shading = models with ∆AICc ≤ 2.

Log- Akaike Pseudo- Rank Land cover spatial scale model k likelihood AICc ΔAICc weight R2

TOTAL ABUNDANCE 1 1200m 7 -802.6 1619.8 0.000 0.429 0.196 2 2200m 7 -803.4 1621.4 1.579 0.195 0.190 3 1700m 7 -803.5 1621.5 1.649 0.188 0.190 4 700m 7 -804.0 1622.5 2.691 0.112 0.186 5 200m 7 -804.4 1623.3 3.471 0.076 0.183 6 Intercept-only 4 -825.9 1660.0 40.196 0.000

OBSERVED SPECIES RICHNESS 1 2200m 7 -953.1 1920.8 0.000 0.959 0.345 2 1700m 7 -956.4 1927.3 6.414 0.039 0.324 3 1200m 7 -959.3 1933.2 12.394 0.002 0.305 4 700m 7 -965.2 1944.9 24.040 0.000 0.266 5 200m 7 -976.4 1967.4 46.598 0.000 0.184 6 Intercept-only 4 -998.1 2004.4 83.600 0.000

ESTIMATED SPECIES RICHNESS 1 2200m 7 -973.6 1961.7 0.000 0.978 0.345 2 1700m 7 -977.4 1969.3 7.596 0.022 0.321 3 1200m 7 -981.2 1976.9 15.175 0.001 0.297 4 700m 7 -987.8 1990.2 28.447 0.000 0.251 5 200m 7 -1000.9 2016.3 54.569 0.000 0.154 6 Intercept-only 4 -1018.7 2045.5 83.766 0.000

FISHER’S ALPHA DIVERSITY 1 2200m 7 -629.3 1273.0 0.000 0.971 0.311 2 1700m 7 -632.8 1280.1 7.099 0.028 0.287 3 1200m 7 -636.2 1287.0 13.950 0.001 0.264 4 700m 7 -641.7 1297.9 24.805 0.000 0.225 5 200m 7 -655.4 1325.3 52.265 0.000 0.118 6 Intercept-only 4 -668.8 1345.8 72.722 0.000

All analyses include proportional cover of arable farmland, urban and broadleaf woodland cover as fixed effects at the specified spatial scale.

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Table 2 | Model selection table for 2012 GLMM analyses of land cover effects on moth diversity metrics at five spatial scales. Null models in italics for comparison. Grey shading = models with ∆AICc ≤ 2.

Log- Akaike Pseudo- Rank Land cover spatial scale model k likelihood AICc ΔAICc weight R2

TOTAL ABUNDANCE 1 200m 7 -685.3 1385.2 0.000 0.996 0.105 2 700m 7 -691.1 1396.8 11.555 0.003 0.090 3 1200m 7 -692.6 1399.9 14.638 0.001 0.074 4 1700m 7 -694.3 1403.2 17.971 0.000 0.062 5 2200m 7 -695.6 1405.8 20.593 0.000 0.062 6 Intercept-only 4 -701.7 1411.7 26.499 0.000 0.105

OBSERVED SPECIES RICHNESS 1 200m 7 -879.1 1772.9 0.000 0.765 0.163 2 2200m 7 -881.2 1776.9 4.069 0.100 0.145 3 700m 7 -881.4 1777.3 4.465 0.082 0.143 4 1200m 7 -882.2 1779.1 6.222 0.034 0.135 5 1700m 7 -882.8 1780.2 7.366 0.019 0.130 6 Intercept-only 4 -896.3 1800.7 27.879 0.000

ESTIMATED SPECIES RICHNESS 1 2200m 7 -911.6 1837.7 0.000 0.665 0.155 2 1700m 7 -913.5 1841.6 3.850 0.097 0.138 3 700m 7 -913.6 1841.8 4.008 0.090 0.137 4 200m 7 -913.7 1842.0 4.288 0.078 0.136 5 1200m 7 -913.8 1842.2 4.480 0.071 0.135 6 Intercept-only 4 -927.8 1863.8 26.082 0.000

FISHER’S ALPHA DIVERSITY 1 2200m 7 -601.8 1218.3 0.000 0.694 0.183 2 1700m 7 -603.5 1221.5 3.258 0.136 0.169 3 700m 7 -603.7 1222.1 3.791 0.104 0.167 4 1200m 7 -604.2 1223.0 4.717 0.066 0.163 5 200m 7 -609.4 1233.5 15.191 0.000 0.116 6 Intercept-only 4 -621.3 1250.8 32.543 0.000

All analyses include proportional cover of arable farmland, urban and broadleaf woodland cover as fixed effects at the specified spatial scale.

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3.2 Effects of artificial night lighting on moths The model comparisons for night lighting analyses revealed some consistent patterns in total abundance, observed and estimated species richness, and Fisher’s α diversity responses to variation in night light intensity (Table 3).

Firstly, land cover only models (M11 and M12) had ∆AICc values >2 and received 7.6 – 2272.2 times less support from the data than top ranked models for all moth diversity metrics. Therefore, there is strong evidence that moth communities were better explained by models which included average radiance (at any spatial scale) in addition to land cover parameters, than those which included land cover parameters alone.

Secondly, estimates for the night light model parameters indicate that all moth diversity metrics were negatively associated with increasing average radiance levels (Table 3). However there was variation in the metric-specific responses to increases in the spatial scale of night light (see Figure 4). For total abundance, the relationship was strongest at the 2200m scale (Figure 4a) and decreased with decreasing spatial scale, but all 1200m – 2200m scale models were equally supported by the data (∆AICc < 2). For moth observed species richness the strength of the negative relationship with average radiance was greatest at the 1200m scale (Figure 4c). It did not vary consistently with increasing spatial scale, but average radiance at 700m – 2200m scales provided significantly better fits to the data than at the 200m scale (∆AICc > 2). The relationship for estimated species richness against average radiance showed a similar pattern, and was also strongest at the 1200m scale (Figure 4e). However, Fisher’s α diversity was most strongly correlated with average radiance at the 200m scale (Figure 4g) and the relationship showed a general decrease in strength with increasing spatial scale. Support for a relationship with average radiance at the 2200m scale was significantly poorer than for the lower spatial scales (∆AICc >2).

Thirdly, although night lighting effect sizes vary between the different moth diversity metrics, they were consistently several orders of magnitude larger than those for urban land cover (see Figure 4). For total abundance, for example, the standardised parameter estimate for average radiance (β = - 7.86) is 2.18 times larger than that for urban cover (β = -3.61), according to the highest ranked model including both lighting and urban cover variables (rank 2, M10). In addition, no top models included urban land cover, and models with urban land cover removed (M6 – M10) consistently ranked higher than those with it included (M1 – M5). This was most evident for estimated species richness, which did not include any models with urban land cover within 2 ∆AICc of the top model. These observations suggest that average radiance explains additional variance in moth diversity metrics over-and-above that explained by urban land cover alone (see Figure 4) and that it also plays a more informative role in predicting moth community variation.

Using the 2012 data, some of the results were consistent with 2010 and some were not. Specifically, for total abundance, urban land cover was consistently important, although lighting at all scales also received support, such that the results are equivocal as to whether lighting had significant explanatory power over and above that provided by urban cover (Table 4). All scales of lighting data received similar levels of support (Table 4).

For observed species richness, again urban cover appeared more important than it had been for the 2010 data, with models including and omitting urban cover having rather similar AICc values for each

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scale of lighting data, indicating equivocal evidence as to the additional explanatory power of lighting (Table 4). Further, while lighting at all scales except 200m received some support with the 2010 data (Table 3), the smaller scales were more important in 2012, including the 200m scale (Table 4).

Results from 2012 for estimated species richness and Fisher’s α diversity were more consistent with those from 2010, with generally stronger support for models excluding urban cover, although the strongest lighting effect on estimated species richness appeared to come at the 200m scale in 2012 (Table 4), whereas this scale was relatively unimportant for 2010 (Table 3).

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Table 3 | Model selection table for analyses of average radiance effects on moth diversity metrics in 2010 at five spatial scales. Grey shading = models with ∆AICc ≤ 2. Standardised parameter estimates *

Av rad Av rad Av rad Av rad Av rad Log- Akaike Pseudo- Rank Model 200 700 1200 1700 2200 Urban k likelihood AICc ΔAICc weight R2

TOTAL ABUNDANCE 1 M10 -10.37 7 -798.5 1611.6 0.000 0.212 0.227 2 M5 -7.86 -3.61 8 -797.6 1611.9 0.325 0.181 0.233 3 M9 -10.42 7 -798.7 1612.0 0.422 0.172 0.225 4 M4 -7.90 -3.53 8 -797.9 1612.5 0.913 0.135 0.231 5 M8 -10.37 7 -799.3 1613.1 1.495 0.101 0.221 6 M3 -7.66 -3.74 8 -798.4 1613.4 1.824 0.085 0.228 7 M2 -7.03 -4.30 8 -799.0 1614.7 3.068 0.046 0.223 8 M7 -10.13 7 -800.2 1615.0 3.354 0.040 0.214 9 M1 -5.95 -5.23 8 -800.0 1616.6 5.024 0.017 0.216 10 M6 -9.68 7 -801.8 1618.1 6.532 0.008 0.203 11 M11 -10.02 7 -802.6 1619.8 8.195 0.004 0.196 12 M12 6 -817.1 1646.6 35.021 0.000 0.079

OBSERVED SPECIES RICHNESS 1 M8 -24.21 7 -947.1 1908.8 0.000 0.241 0.381 2 M9 -24.11 7 -947.4 1909.4 0.592 0.179 0.379 3 M10 -23.87 7 -947.8 1910.2 1.360 0.122 0.377 4 M7 -23.70 7 -947.9 1910.4 1.591 0.109 0.376 5 M3 -21.21 -3.62 8 -946.9 1910.6 1.792 0.098 0.382 6 M4 -21.47 -3.13 8 -947.3 1911.3 2.499 0.069 0.380 7 M2 -19.17 -5.65 8 -947.4 1911.6 2.761 0.061 0.379 8 M5 -20.79 -3.67 8 -947.6 1912.0 3.173 0.049 0.378 9 M6 -23.15 7 -948.9 1912.4 3.580 0.040 0.370 10 M1 -17.48 -7.22 8 -948.1 1912.8 4.028 0.032 0.375 11 M11 -21.46 7 -953.1 1920.8 12.039 0.001 0.345 12 M12 6 -971.5 1955.4 46.553 0.000 0.221

ESTIMATED SPECIES RICHNESS 1 M8 -25.72 7 -965.9 1946.3 0.000 0.260 0.391 2 M9 -25.48 7 -966.4 1947.3 1.048 0.154 0.388 3 M7 -25.24 7 -966.5 1947.5 1.233 0.141 0.387 4 M10 -25.23 7 -966.7 1948.0 1.742 0.109 0.386 5 M3 -26.06 0.42 8 -965.9 1948.4 2.156 0.089 0.391 6 M6 -24.82 7 -967.1 1948.7 2.460 0.076 0.384 7 M4 -26.26 0.92 8 -966.4 1949.5 3.189 0.053 0.388 8 M2 -23.61 -2.03 8 -966.4 1949.6 3.285 0.050 0.388 9 M5 -25.54 0.37 8 -966.7 1950.2 3.899 0.037 0.386 10 M1 -22.03 -3.57 8 -966.9 1950.6 4.271 0.031 0.385 11 M11 -21.52 7 -973.6 1961.7 15.458 0.000 0.345 12 M12 6 -989.0 1990.4 44.121 0.000 0.243

* Av rad = average radiance; Urban = proportion urban land cover. All models control for broadleaf woodland, and arable farmland proportional land cover as fixed effects.

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Table 3 | Cont’d Standardised parameter estimates *

Av rad Av rad Av rad Av rad Av rad Log- Akaike Pseudo- Rank Model 200 700 1200 1700 2200 Urban k likelihood AICc ΔAICc weight R2

FISHER’S ALPHA DIVERSITY 1 M6 -3.76 7 -627.2 1269.0 0.000 0.189 0.324 2 M8 -3.80 7 -627.3 1269.1 0.078 0.182 0.323 3 M7 -3.75 7 -627.4 1269.4 0.353 0.158 0.323 4 M1 -2.70 -1.25 8 -626.8 1270.3 1.292 0.099 0.326 5 M2 -2.69 -1.24 8 -627.0 1270.7 1.640 0.083 0.325 6 M3 -2.82 -1.08 8 -627.0 1270.7 1.715 0.080 0.325 7 M9 -3.60 7 -628.1 1270.8 1.815 0.076 0.318 8 M4 -2.46 -1.35 8 -627.7 1272.1 3.038 0.041 0.321 9 M10 -3.48 7 -628.8 1272.2 3.172 0.039 0.313 10 M3 -2.09 -1.66 8 -628.1 1272.9 3.916 0.027 0.318 11 M11 -3.43 7 -629.3 1273.0 4.019 0.025 0.311 12 M12 6 -639.2 1290.7 21.684 0.000 0.243

* Av rad = average radiance; Urban = proportion urban land cover. All models control for broadleaf woodland, arable woodland and improved grassland proportional land cover as fixed effects.

Table 4 | Model selection table for analyses of average radiance effects on moth diversity metrics in 2012 at five spatial scales. Grey shading = models with ∆AICc ≤ 2. Standardised parameter estimates *

Av rad Av rad Av rad Av rad Av rad Log- Akaike Pseudo- Rank Model 200 700 1200 1700 2200 Urban k likelihood AICc ΔAICc weight R2

TOTAL ABUNDANCE 1 M11 -7.18 7 -685.3 1385.2 0.000 0.294 0.157 2 M2 -1.36 -6.31 8 -684.9 1386.6 1.364 0.149 0.160 3 M1 -1.32 -6.32 8 -684.9 1386.6 1.422 0.145 0.160 4 M3 -1.21 -6.42 8 -685.0 1386.7 1.528 0.137 0.160 5 M4 -1.19 -6.45 8 -685.0 1386.7 1.531 0.137 0.160 6 M5 -1.15 -6.49 8 -685.0 1386.8 1.549 0.136 0.160 7 M6 -4.37 7 -691.2 1397.0 11.751 0.001 0.104 8 M7 -4.36 7 -691.2 1397.0 11.790 0.001 0.104 9 M8 -4.19 7 -691.6 1397.8 12.586 0.001 0.100 10 M9 -4.09 7 -691.8 1398.2 13.005 0.000 0.098 11 M10 -3.98 7 -692.0 1398.7 13.486 0.000 0.096 12 M12 6 -696.7 1405.8 20.544 0.000 0.051 * Av rad = average radiance; Urban = proportion urban land cover. All models control for broadleaf woodland, arable woodland and improved grassland proportional land cover as fixed effects.

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Table 4 | Cont’d Standardised parameter estimates *

Av rad Av rad Av rad Av rad Av rad Log- Akaike Pseudo- Rank Model 200 700 1200 1700 2200 Urban k likelihood AICc ΔAICc weight R2 OBSERVED SPECIES RICHNESS 1 M1 -14.60 -7.10 8 -872.8 1762.4 0.000 0.215 0.216 2 M6 -18.03 7 -874.0 1762.6 0.117 0.202 0.206 3 M2 -13.98 -7.61 8 -873.3 1763.3 0.864 0.139 0.212 4 M7 -17.60 7 -874.6 1763.8 1.343 0.110 0.201 5 M3 -13.33 -8.18 8 -873.7 1764.2 1.736 0.090 0.209 6 M4 -12.86 -8.68 8 -873.9 1764.7 2.213 0.071 0.207 7 M8 -17.14 7 -875.2 1765.1 2.653 0.057 0.196 8 M5 -12.40 -9.16 8 -874.2 1765.1 2.664 0.057 0.205 9 M9 -16.77 7 -875.7 1766.0 3.584 0.036 0.192 10 M10 -16.40 7 -876.2 1767.0 4.518 0.022 0.188 11 M11 -16.59 7 -879.1 1772.9 10.425 0.001 0.163 12 M12 6 -887.3 1787.1 24.687 0.000 0.088

ESTIMATED SPECIES RICHNESS 1 M6 -19.48 7 -905.8 1826.3 0.000 0.320 0.204 2 M1 -24.79 6.76 8 -905.4 1827.5 1.249 0.172 0.207 3 M7 -18.91 7 -906.5 1827.6 1.345 0.164 0.198 4 M8 -18.38 7 -907.1 1828.9 2.586 0.088 0.193 5 M2 -24.33 6.74 8 -906.1 1829.0 2.673 0.084 0.202 6 M9 -17.86 7 -907.7 1829.9 3.648 0.052 0.188 7 M3 -24.42 7.27 8 -906.7 1830.2 3.887 0.046 0.197 8 M10 -17.35 7 -908.1 1830.8 4.529 0.033 0.185 9 M4 -24.04 7.31 8 -907.3 1831.3 5.018 0.026 0.192 10 M5 -22.34 5.92 8 -907.8 1832.5 6.181 0.015 0.187 11 M11 -13.19 7 -911.6 1837.7 11.459 0.001 0.155 12 M12 6 -916.0 1844.4 18.085 0.000 0.116

FISHER’S ALPHA DIVERSITY 1 M6 -3.75 7 -596.9 1208.4 0.000 0.336 0.224 2 M1 -4.65 1.14 8 -596.6 1210.0 1.533 0.156 0.226 3 M7 -3.58 7 -597.8 1210.1 1.683 0.145 0.217 4 M8 -3.51 7 -598.1 1210.9 2.451 0.099 0.214 5 M9 -3.42 7 -598.6 1211.7 3.305 0.064 0.210 6 M2 -4.40 1.01 8 -597.5 1211.8 3.398 0.061 0.219 7 M10 -3.34 7 -598.9 1212.4 3.949 0.047 0.208 8 M3 -4.49 1.17 8 -597.9 1212.5 4.074 0.044 0.216 9 M4 -4.43 1.19 8 -598.3 1213.4 4.963 0.028 0.212 10 M5 -4.16 0.97 8 -598.7 1214.2 5.782 0.019 0.209 11 M11 -2.56 7 -601.8 1218.3 9.838 0.002 0.183 12 M12 6 -605.9 1224.2 15.754 0.000 0.148

* Av rad = average radiance; Urban = proportion urban land cover. All models control for broadleaf woodland, arable woodland and improved grassland proportional land cover as fixed effects.

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Figure 4| The effects of night lighting (nW/(cm2*sr) ) and urban land cover (%) on total abundance (a – b), observed species richness (c –d), estimated species richness (e – f) and Fisher’s α diversity index (g – h). Night lighting plots are drawn using top models (Table 2; M10, M8, M8, M6 respectively);

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urban land cover plots are drawn with average radiance controlled used by using the highest ranking model which includes urban cover (Table 2; M5, M3, M3, M1 respectively). Lines ±95% confidence intervals (dotted) are predicted using model parameter estimates and standard errors averaged for all other variables, and plotted over observed data points. Histograms of the distribution of radiance and urban cover values are plotted to aid interpretation.

4. Discussion

2010 Moth data

The results presented here provide the first evidence of a broad scale impact of artificial night lighting on moth communities in the UK. We show that the total abundance, species richness and species diversity of moths is reduced with increasing average radiance levels. Furthermore, our findings indicate that artificial lighting is more influential than the amount of urban land cover in driving moth community changes.

Moth diversity metrics were influenced by land cover over a large spatial scale. The proportional cover of arable farmland, broadleaf woodland and urban within a 2200m radius of moth traps provided a significantly better fit to the species richness and diversity data than measures at smaller scales, whilst total abundance was most strongly influenced by land cover within a 1200m radius (Table 1). These findings suggest that moths are highly mobile and are consistent with previous studies showing moth abundance and species richness to be strongly correlated with the amount of woodland cover within 1 – 2.4 km (Ricketts et al. 2001; Summerville & Crist 2004; Fuentes- Montemayor et al. 2012). As such, the findings highlight the possibility that locations with high levels of artificial lighting could impact upon moth communities in areas more than 2km away.

The scale at which artificial night lighting had the greatest effect on moth communities varied according to the different moth diversity metrics. Correlations with average radiance were strongest at the 2200m scale for total abundance, at the 1200m scale for observed and estimated species richness most strongly correlated and at the 200m scale for species diversity. The reasons for these differences are not certain, but again the findings for abundance and richness do indicate that artificial night lighting can impact moth communities at large spatial scales. However, it must be noted that the resolution of the lighting data used (750m) means estimates at the two smaller spatial scales tested may be less informative than the larger scales, and the impacts of fine-scale variation in night lighting remain untested. Furthermore, further analysis is required to understand how these impacts vary between individual species and functional groupings: in reality, responses to lighting and habitat are likely to be species-specific, the effects on community indices being emergent combinations of these responses.

By using information-theoretic model selection to compare the effects of average radiance and urban land cover on moth communities, we have been able to distinguish the impacts of night lighting from the effects of urban development more generally. The findings indicate that most of the impact caused by urban land cover can be explained by artificial night lighting, since average radiance captures an equal or greater amount of variation in the moth diversity metrics than urban cover alone. The highest ranking models with both radiance and urban cover parameters fitted can

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be used to predict moth variation resulting from the two effects (Table 3). For example, a 50% increase in the amount of urban cover around a garden will result in 255.1 less individual moths captured, a reduction of 0 – 3.7 species observed according to observed and estimated richness, and a decrease in Fisher’s alpha diversity of 1.3. Whereas the equivalent percentage change in night light (+ 15 nW/(cm2*sr)) will lead to the trapping of 670.7 less individuals, 24.6 – 29.8 less species and a reduction in diversity of 3.2. These modelled predictions illustrate the relatively small addition urban habitat plays in estimating moth community declines compared to artificial night lighting.

There are, however, some limitations in drawing conclusions from the results presented here. Most notably, it is possible that the strength of the negative relationships between night light intensity and moth diversity metrics are an artefact of undersampling in brightly lit environments due to competing light pollution. However, Jack1 estimated species richness incorporates potential heterogeneity in species detectability in its calculation (Burnham & Overton 1978), whilst Fisher’s α diversity index has a low sensitivity to under-sampling (Magurran 2004). Therefore, these measures will effectively account for the possibility of moth trap inefficiency, and can be considered to elicit a more accurate response to environmental stimuli in the presence of light competition than raw abundance and species counts. Our findings indicate reductions in both of these measures in response to increased artificial light. Although using estimated measures to describe the moth community goes some way to reducing the possibility of a sampling artefact, it is impossible to quantify the extent to which the results are influenced by light competition. However, despite the limitations, these findings could represent an important new step in the understanding of recent declines in British moth populations.

2012 Moth data

The results using 2012 moth data showed several interesting differences from the 2010 results. These were particularly noteworthy in respect of total abundance and observed species richness, where the land cover and lighting responses were strongest at smaller spatial scales, and often at the smallest scale considered, 200m (Tables 2 and 4). In addition, urban land cover appeared to be a stronger determinant of variation in the 2012 data than it had in 2010, although lighting appeared still to be equally important. However, the results for estimated species richness and Fisher’s α diversity index were rather similar, so do not affect the conclusions reached above.

The differences between the 2010 and 2012 results for total abundance and observed species richness could indicate problems with the former due to the temporal mismatch between moth and lighting data, such that the lighting data were inaccurate for the gardens in the sample. However, the real situation is more complex, for several reasons. First, lighting will only have changed for a subset of the gardens covered in 2010, probably a minority, so the real effect of changes in lighting within the sample was probably limited. Second, there was considerable turnover in the GMS sample between 2010 and 2012, and the sample is unstructured; hence, the gardens considered in the two years could have been located in quite different average lighting and land cover contexts. Third, moth populations can be very variable from year to year, so it is quite possible that the moth community context was quite different between the two years, meaning that different numbers of species and individuals that are sensitive to lighting could have been present in the wider environment. Fourth, the patterns found in the land-cover-only analyses also differed between the

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two years, indicating that lighting was not the only influence on the patterns seen. All these issues mean that the 2012 results do not necessarily provide a more reliable indication of lighting effects on moth communities than the 2010 ones. It would therefore be wise to consider both sets of analyses together, while future work might profitably consider combining the 2010 and 2012 data in a single analysis (choosing 2012 data for gardens sampled in both years, or using all the data and a repeated measures design) to reveal the dominant patterns across the whole data set.

After considering the above, the main refinement of the messages from the 2010 analysis is that some of the patterns, notably those involving total abundance and observed species richness, as well, generally, as the relative importance of lighting versus urban land cover, are sensitive to the geographical composition of the sample and/or sampling year. It is unclear how inaccuracy in the lighting data for a limited proportion of the moth data in 2010 would produce the differences observed in the results with respect to scale or the importance of lighting versus urban cover, for example. Therefore, we conclude that the scale of responses to lighting is not as clear-cut as indicated by the 2010 results and that urban land cover is more important in some circumstances (relative to lighting) than they suggested.

5. Conclusion

Our findings suggest that moth abundance, species richness and diversity in urban areas have diminished as a consequence of artificial street lighting. Furthermore, the indication that moth communities were influenced by both land cover and light average radiance across large spatial scales suggests that the impacts of artificial night lighting could be far-reaching. However, the precise, quantitative patterns by which street lighting influences moths, interacting with gross habitat cover, remain uncertain and are probably rather variable in time and space. Further work is required to determine the mechanisms by which street lighting affects moth communities. Knowledge of the mechanisms would facilitate the design of mitigation measures that are integrated effectively with the human, social priorities associated with street lighting changes.

6. References

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Bowden, J. (1982) An analysis of factors affecting catches of insects in light-traps. Bulletin of Entomological Research, 72, 535-556. Bowden, J. & Morris, M.G. (1975) The influence of moonlight on catches of insects in light-traps in Africa. III. The effective radius of a mercury-vapour light-trap and the analysis of catches using effective radius. Bulletin of Entomological Research, 65, 303-348. Brehm, G., Colwell, R.K. & Kluge, J. (2007) The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Global Ecology and Biogeography, 16, 205-219. Burnham, K. & Anderson, D.J. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New Yolk. Burnham, K.P. & Overton, W.S. (1978) Estimation of the size of a closed population when capture probabilities vary among animals. Biometrika, 65, 625-633. Burnham, K.P. & Overton, W.S. (1979) Robust estimation of population size when capture probabilities vary among animals. Ecology, 60, 927-936. Cinzano, P., Falchi, F. & Elvidge, C.D. (2001) The first world atlas of the artificial night sky brightness. Monthly Notices of the Royal Astronomical Society, 328, 689-707. Colwell, R.K., Coddington, J.A., Colwell, R.K. & Coddington, J.A. (1994) Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 345, 101-118. Conrad, K.F., Warren, M.S., Fox, R., Parsons, M.S. & Woiwod, I.P. (2006) Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biological Conservation, 132, 279-291. Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., Ohlemüller, R., Peres-Neto, P.R., Reineking, B., Schröder, B., Schurr, F.M. & Wilson, R. (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609-628. Eisenbeis, G. (2006) Artificial night lighting and insects: attraction of insects to streetlamps in a rural setting in Germany. Ecological consequences of artificial night lighting (eds C. Rich & T. Longcore), pp. 281-304. Island Press, Washington. ESRI (2006) ArcView version 9.2. Redlands, CA, USA. ESRI (2010) ArcView version 10.0. Redlands, CA, USA. Fox, R. (2012) The decline of moths in Great Britain: a review of possible causes. Insect Conservation and Diversity, 6, 5-19. Fox, R., Parsons, M.S., Chapman, J.W., Woiwod, I.P., Warren, M.S. & Brooks, D.R. (2013) The state of Britain’s larger moths 2013. Butterfly Conservation and Rothamsted Research, Wareham, Dorset, UK. Frank, K.D. (1988) Impact of outdoor lighting on moths: an assessment. Journal of the Lepidopterists Society, 42, 63-93. Fuentes-Montemayor, E., Goulson, D., Cavin, L., Wallace, J.M. & Park, K.J. (2012) Factors influencing moth assemblages in woodland fragments on farmland: Implications for woodland management and creation schemes. Biological Conservation, 153, 265-275. Gaston, K.J., Bennie, J., Davies, T.W. & Hopkins, J. (2013) The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biological Reviews. Gaston, K.J., Davies, T.W., Bennie, J. & Hopkins, J. (2012) Reducing the ecological consequences of night-time light pollution: options and developments. Journal of Applied Ecology, 49, 1256- 1266. Gelman, A. (2008) Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine, 27, 2865-2873. Gotelli, N.J. & Colwell, R.K. (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379-391. Grundy, D. (2013) The Garden Moth Scheme. URL http://gms.staffs-ecology.org.uk.

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Hale, J.D., Davies, G., Fairbrass, A.J., Matthews, T.J., Rogers, C.D.F. & Sadler, J.P. (2013) Mapping Lightscapes: Spatial Patterning of Artificial Lighting in an Urban Landscape. PLoS One, 8, e61460. Hölker, F., Moss, T., Griefahn, B., Kloas, W., Voigt, C.C., Henckel, D., Hänel, A., Kappeler, P.M., Völker, S. & Schwope, A. (2010) The dark side of light: a transdisciplinary research agenda for light pollution policy. Ecology and Society, 15, 13. Kempton, R. & Taylor, L. (1974) Log-series and log-normal parameters as diversity discriminants for the Lepidoptera. Journal of Animal Ecology, 381-399. Kéry, M. & Plattner, M. (2007) Species richness estimation and determinants of species detectability in butterfly monitoring programmes. Ecological Entomology, 32, 53-61. Kuechly, H.U., Kyba, C.C.M., Ruhtz, T., Lindemann, C., Wolter, C., Fischer, J. & Hölker, F. (2012) Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany. Remote Sensing of Environment, 126, 39-50. Lee, T.E., Miller, S.D., Turk, F.J., Schueler, C., Julian, R., Deyo, S., Dills, P. & Wang, S. (2006) The NPOESS VIIRS Day/Night Visible Sensor. Bulletin of the American Meteorological Society, 87, 191-199. Longcore, T. & Rich, C. (2004) Ecological light pollution. Frontiers in Ecology and the Environment, 2, 191-198. Magurran, A.E. (2004) Measuring biological diversity. Blackwell Publishing, Oxford. Mazerolle, M.J. (2013) AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 1.30. URL http://CRAN.R-project.org/package=AICcmodavg. Merckx, T., Feber, R.E., Dulieu, R.L., Townsend, M.C., Parsons, M.S., Bourn, N.A.D., Riordan, P. & Macdonald, D.W. (2009) Effect of field margins on moths depends on species mobility: field- based evidence for landscape-scale conservation. Agriculture, Ecosystems & Environment, 129, 302-309. Merckx, T., Feber, R.E., Mclaughlan, C., Bourn, N.A.D., Parsons, M.S., Townsend, M.C., Riordan, P. & Macdonald, D.W. (2010) Shelter benefits less mobile moth species: the field-scale effect of hedgerow trees. Agriculture, Ecosystems & Environment, 138, 147-151. Meyer, C.F.J., Aguiar, L., Aguirre, L.F., Baumgarten, J., Clarke, F.M., Cosson, J.F., Villegas, S.E., Fahr, J., Faria, D. & Furey, N. (2011) Accounting for detectability improves estimates of species richness in tropical bat surveys. Journal of Applied Ecology, 48, 777-787. Nagelkerke, N.J.D. (1991) A note on a general definition of the coefficient of determination. Biometrika, 78, 691-692. NOAA (2013) VIIRS Nigthtime Lights - 2012. Earth Observation Group, NOAA National Geophysical Data Center. http://ngdc.noaa.gov/eog/viirs/download_viirs_ntl.html. Accessed 26 June 2013. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H. & Wagner, H. (2013) vegan: Community Ecology Package. R package version 2.0-7. http://CRAN.R-project.org/package=vegan. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R.C. (2013) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-109. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. Ricketts, T.H., Daily, G.C., Ehrlich, P.R. & Fay, J.P. (2001) Countryside biogeography of moths in a fragmented landscape: biodiversity in native and agricultural habitats. Conservation Biology, 15, 378-388. Summerville, K.S. & Crist, T.O. (2004) Contrasting effects of habitat quantity and quality on moth communities in fragmented landscapes. Ecography, 27, 3-12. Taylor, L., Kempton, R. & Woiwod, I. (1976) Diversity statistics and the log-series model. Journal of Animal Ecology, 255-272. Thomas, A. & Thomas, G. (1994) Sampling strategies for estimating moth species diversity using a light trap in a northeastern softwood forest. Journal of the Lepidopterists Society, 48, 85-105.

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Truxa, C. & Fiedler, K. (2012) Attraction to light- from how far do moths(Lepidoptera) return to weak artificial sources of light? European Journal of Entomology, 109, 77-84. Usher, M.B. & Keiller, S.W.J. (1998) The macrolepidoptera of farm woodlands: determinants of diversity and community structure. Biodiversity and Conservation, 7, 725-748.

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7. Appendix

Table A1 | List of 195 moth species in GMS dataset.

Scientific name Common English name Family

Hepialus humuli (L.) Ghost Moth Hepialidae Hepialus sylvina (L.) Orange Swift Hepialidae Hepialus lupulinus (L.) Common Swift Hepialidae Plutella xylostella (L.) Diamond-back Moth Yponomeutidae Hofmannophila pseudospretella (Stainton) Brown House-moth Oecophoridae Endrosis sarcitrella (L.) White-shouldered House-moth Oecophoridae Tachystola acroxantha (Meyrick) Ruddy Streak Oecophoridae Carcina quercana (Fab.) Oak Longhorned Oecophoridae Epiphyas postvittana (Walker) Light Brown Apple Moth Tortricidae Tortrix viridana (L.) Green Oak Tortrix Tortricidae Acleris variegana (Denis & Schiffermüller) Garden Rose Tortrix Tortricidae Alucita hexadactyla L. Twenty-plume Moth Alucitidae Chrysoteuchia culmella (L.) Garden Grass-veneer Crambidae Agriphila straminella (Denis & Schiffermüller) Agriphila straminella Crambidae Agriphila tristella (Denis & Schiffermüller) Agriphila tristella Crambidae Elophila nymphaeata (L.) Brown China-mark Crambidae Evergestis forficalis (L.) Garden Pebble Crambidae Eurrhypara hortulata (L.) Small Magpie Crambidae Phlyctaenia coronata (Hufnagel) Spotted Magpie Crambidae Udea olivalis (Denis & Schiffermüller) Udea olivalis Crambidae Udea ferrugalis (Hübner) Rusty Dot Pearl Crambidae Nomophila noctuella (Denis & Schiffermüller) Rush Veneer Crambidae Pleuroptya ruralis (Scopoli) Mother of Pearl Crambidae Hypsopygia costalis (Fab.) Gold Triangle Pyralidae Aphomia sociella (L.) Bee Moth Pyralidae Cilix glaucata (Scopoli) Chinese Character Drepanidae Thyatira batis (L.) Peach Blossom Thyatiridae Habrosyne pyritoides (Hufnagel) Buff Arches Thyatiridae Alsophila aescularia (Denis & Schiffermüller) March Moth Geometridae Hemithea aestivaria (Hübner) Common Emerald Geometridae Timandra comae (Schmidt) Blood-vein Geometridae Scopula imitaria (Hübner) Small Blood-vein Geometridae Idaea rusticata Lempke Least Carpet Geometridae Idaea biselata (Hufnagel) Small Fan-footed Wave Geometridae Idaea seriata (Schrank) Small Dusty Wave Geometridae Idaea dimidiata (Hufnagel) Single-dotted Wave Geometridae Idaea aversata (L.) Riband Wave Geometridae Xanthorhoe designata (Hufnagel) Flame Carpet Geometridae Xanthorhoe montanata (Denis & Schiffermüller) Silver-ground Carpet Geometridae Xanthorhoe fluctuata (L.) Garden Carpet Geometridae Scotopteryx chenopodiata (L.) Shaded Broad-bar Geometridae

19 March 2014 WC1057 Objective 3 Draft Report

Epirrhoe alternata (Müller) Common Carpet Geometridae Camptogramma bilineata (L.) Yellow Shell Geometridae Anticlea badiata (Denis & Schiffermüller) Shoulder-stripe Geometridae Anticlea derivata (Denis & Schiffermüller) The Streamer Geometridae Eulithis pyraliata (Denis & Schiffermüller) Barred Straw Geometridae Ecliptopera silaceata (Denis & Schiffermüller) Small Phoenix Geometridae Chloroclysta siterata (Hufnagel) Red-green Carpet Geometridae Chloroclysta truncata (Hufnagel) Common Marbled Carpet Geometridae Cidaria fulvata (Forster) Barred Yellow Geometridae Thera obeliscata (Hübner) Grey Pine Carpet Geometridae Thera britannica (Turner H J) Spruce Carpet Geometridae Colostygia pectinataria (Knoch) Green Carpet Geometridae Hydriomena furcata (Thunberg) July Highflyer Geometridae Epirrita dilutata (Denis & Schiffermüller), christyi November Moth agg. Geometridae (Allen), autumnata (Borkhausen) Perizoma affinitata (Stephens) The Rivulet Geometridae Perizoma alchemillata (L.) Small Rivulet Geometridae Eupithecia centaureata (Denis & Schiffermüller) Lime-speck Pug Geometridae Pasiphila rectangulata (L.) Green Pug Geometridae Gymnoscelis rufifasciata (Haworth) Double-striped Pug Geometridae Abraxas grossulariata (L.) The Magpie Geometridae Lomaspilis marginata (L.) Clouded Border Geometridae Macaria liturata (Clerck) Tawny-barred Angle Geometridae Petrophora chlorosata (Scopoli) Brown Silver-line Geometridae Opisthograptis luteolata (L.) Brimstone Moth Geometridae Ennomos alniaria (L.) Canary-shouldered Thorn Geometridae Selenia dentaria (Fab.) Early Thorn Geometridae Selenia tetralunaria (Hufnagel) Purple Thorn Geometridae Odontopera bidentata (Clerck) Scalloped Hazel Geometridae Crocallis elinguaria (L.) Scalloped Oak Geometridae Ourapteryx sambucaria (L.) Swallow-tailed Moth Geometridae Colotois pennaria (L.) Feathered Thorn Geometridae Lycia hirtaria (Clerck) Brindled Beauty Geometridae Biston strataria (Hufnagel) Oak Beauty Geometridae Biston betularia (L.) Peppered Moth Geometridae Agriopis marginaria (Fab.) Dotted Border Geometridae Peribatodes rhomboidaria (Denis & Schiffermüller) Willow Beauty Geometridae Alcis repandata (L.) Mottled Beauty Geometridae Cabera pusaria (L.) Common White Wave Geometridae Cabera exanthemata (Scopoli) Common Wave Geometridae Lomographa temerata (Denis & Schiffermüller) Clouded Silver Geometridae Campaea margaritata (L.) Light Emerald Geometridae Mimas tiliae (L.) Lime Hawk-moth Sphingidae Laothoe populi (L.) Poplar Hawk-moth Sphingidae Deilephila elpenor (L.) Elephant Hawk-moth Sphingidae Phalera bucephala (L.) Buff-tip Notodontidae Notodonta dromedarius (L.) Iron Prominent Notodontidae

19 March 2014 WC1057 Objective 3 Draft Report

Notodonta ziczac (L.) Pebble Prominent Notodontidae Pheosia gnoma (Fab.) Lesser Swallow Prominent Notodontidae Pheosia tremula (Clerck) Swallow Prominent Notodontidae Ptilodon capucina (L.) Coxcomb Prominent Notodontidae Pterostoma palpina (Clerck) Pale Prominent Notodontidae Drymonia ruficornis (Hufnagel) Lunar Marbled Brown Notodontidae Orgyia antiqua (L.) The Vapourer Lymantriidae Calliteara pudibunda (L.) Pale Tussock Lymantriidae Euproctis similis (Fuessly) Yellow-tail Lymantriidae Eilema griseola (Hübner) Dingy Footman Arctiidae Eilema lurideola (Zincken) Common Footman Arctiidae Arctia caja (L.) Garden Tiger Arctiidae Spilosoma lubricipeda (L.) White Ermine Arctiidae Spilosoma luteum (Hufnagel) Buff Ermine Arctiidae Diaphora mendica (Clerck) Muslin Moth Arctiidae Phragmatobia fuliginosa (L.) Ruby Tiger Arctiidae Nola cucullatella (L.) Short-cloaked Moth Nolidae Agrotis segetum (Denis & Schiffermüller) Turnip Moth Noctuidae Agrotis exclamationis (L.) Heart & Dart Noctuidae Agrotis ipsilon (Hufnagel) Dark Sword-grass Noctuidae Agrotis puta (Hübner) Shuttle-shaped Dart Noctuidae Axylia putris (L.) The Flame Noctuidae Ochropleura plecta (L.) Flame Shoulder Noctuidae Noctua pronuba (L.) Large Yellow Underwing Noctuidae Noctua comes Hübner Lesser Yellow Underwing Noctuidae Noctua fimbriata (Schreber) Broad-bordered Yellow Underwing Noctuidae Noctua janthe (Borkhausen) Lesser Broad-bordered Yellow Underwing Noctuidae Noctua interjecta Schawerda Least Yellow Underwing Noctuidae Lycophotia porphyrea (Denis & Schiffermüller) True Lover's Knot Noctuidae Diarsia mendica (Fab.) Ingrailed Clay Noctuidae Diarsia rubi (Vieweg) Small Square-spot Noctuidae Xestia c-nigrum (L.) Setaceous Hebrew Character Noctuidae Xestia triangulum (Hufnagel) Double Square-spot Noctuidae Xestia baja (Denis & Schiffermüller) Dotted Clay Noctuidae Xestia sexstrigata (Haworth) Six-striped Rustic Noctuidae Xestia xanthographa (Denis & Schiffermüller) Square-spot Rustic Noctuidae Naenia typica (L.) The Gothic Noctuidae Mamestra brassicae (L.) Cabbage Moth Noctuidae Melanchra persicariae (L.) Dot Moth Noctuidae Lacanobia thalassina (Hufnagel) Pale-shouldered Brocade Noctuidae Lacanobia oleracea (L.) Bright-line Brown-eye Noctuidae Melanchra pisi (L.) Broom Moth Noctuidae Orthosia cruda (Denis & Schiffermüller) Small Quaker Noctuidae Orthosia gracilis (Denis & Schiffermüller) Powdered Quaker Noctuidae Orthosia cerasi (Fab.) Common Quaker Noctuidae Orthosia incerta (Hufnagel) Clouded Drab Noctuidae

19 March 2014 WC1057 Objective 3 Draft Report

Orthosia munda (Denis & Schiffermüller) Twin-spotted Quaker Noctuidae Orthosia gothica (L.) Hebrew Character Noctuidae Mythimna conigera (Denis & Schiffermüller) Brown-line Bright-eye Noctuidae Mythimna ferrago (Fab.) The Clay Noctuidae Mythimna impura (Hübner) Smoky Wainscot Noctuidae Mythimna pallens (L.) Common Wainscot Noctuidae Mythimna comma (L.) Shoulder-striped Wainscot Noctuidae Aporophyla nigra (Haworth) Black Rustic Noctuidae Lithophane ornitopus (Dadd) Grey Shoulder-knot Noctuidae Lithophane leautieri Boursin Blair's Shoulder-knot Noctuidae Xylocampa areola (Esper) Early Grey Noctuidae Allophyes oxyacanthae (L.) Green-brindled Crescent Noctuidae Dryobotodes eremita (Fab.) Brindled Green Noctuidae Eupsilia transversa (Hufnagel) The Satellite Noctuidae Conistra vaccinii (L.) The Chestnut Noctuidae Conistra ligula (Esper) Dark Chestnut Noctuidae Agrochola circellaris (Hufnagel) The Brick Noctuidae Agrochola lota (Clerck) Red-line Quaker Noctuidae Agrochola macilenta (Hübner) Yellow-line Quaker Noctuidae Agrochola litura (L.) Brown-spot Pinion Noctuidae Agrochola lychnidis (Denis & Schiffermüller) Beaded Chestnut Noctuidae Atethmia centrago (Haworth) Centre-barred Sallow Noctuidae Omphaloscelis lunosa (Haworth) Lunar Underwing Noctuidae Xanthia aurago (Denis & Schiffermüller) Barred Sallow Noctuidae Xanthia togata (Esper) Pink-barred Sallow Noctuidae Xanthia icteritia (Hufnagel) The Sallow Noctuidae Acronicta aceris (L.) The Sycamore Noctuidae Acronicta leporina (L.) The Miller Noctuidae Acronicta psi (L.), tridens (Denis & Schiffermüller) Grey Dagger agg. Noctuidae Acronicta rumicis (L.) Knot Grass Noctuidae Cryphia domestica (Hufnagel) Marbled Beauty Noctuidae Amphipyra pyramidea (L.), berbera Fletcher Copper Underwing agg. Noctuidae Amphipyra tragopoginis (Clerck) Mouse Moth Noctuidae Rusina ferruginea (Esper) Brown Rustic Noctuidae Euplexia lucipara (L.) Small Angle Shades Noctuidae Phlogophora meticulosa (L.) Angle Shades Noctuidae Cosmia trapezina (L.) The Dun-bar Noctuidae Apamea monoglypha (Hufnagel) Dark Arches Noctuidae Apamea lithoxylaea (Denis & Schiffermüller) Light Arches Noctuidae Apamea crenata (Hufnagel) Clouded-bordered Brindle Noctuidae Apamea remissa (Hübner) Dusky Brocade Noctuidae Apamea sordens (Hufnagel) Rustic Shoulder-knot Noctuidae Oligia strigilis (L.), versicolor (Borkhausen), Marbled Minor agg. Noctuidae latruncula (Denis & Schiffermüller) Oligia fasciuncula (Haworth) Middle-barred Minor Noctuidae Mesapamea secalis (L.), didyma (Espa), remmi Common Rustic agg. Noctuidae Rezbanyai-Reser

19 March 2014 WC1057 Objective 3 Draft Report

Luperina testacea (Denis & Schiffermüller) Flounced Rustic Noctuidae Hydraecia micacea (Espa) Rosy Rustic Noctuidae Gortyna flavago (Denis & Schiffermüller) Frosted Orange Noctuidae Hoplodrina ambigua (Denis & Schiffermüller) Vine's Rustic Noctuidae Caradrina morpheus (Hufnagel) Mottled Rustic Noctuidae Paradrina clavipalpis (Scopoli) Pale Mottled Willow Noctuidae Diachrysia chrysitis (L.) Burnished Brass Noctuidae Polychrysia moneta (Fab.) Golden Plusia Noctuidae Autographa gamma (L.) Silver Y Noctuidae Autographa pulchrina (Haworth) Beautiful Golden Y Noctuidae Autographa jota (L.) Plain Golden Y Noctuidae Abrostola tripartita (Hufnagel) The Spectacle Noctuidae Scoliopteryx libatrix (L.) The Herald Noctuidae Rivula sericealis (Scopoli) Straw Dot Noctuidae Hypena proboscidalis (L.) The Snout Noctuidae Zanclognatha tarsipennalis (Treitschke) The Fan-foot Noctuidae Herminia grisealis (Denis & Schiffermüller) Small Fan-foot Noctuidae