RSPB/NE Countdown 2010: Bringing Reedbeds to Life Project Wildlife surveys CHAPTER 6: Light trap surveys for C J Hardman With helpful comments on draft by Mark Parsons ( Conservation)

Contents Summary ...... 1 METHODS ...... 2 Light trap survey field methods ...... 2 Analysis methods ...... 5 RESULTS ...... 10 What were found on the three reedbed reserves? ...... 10 Species composition ...... 13 What habitat variables were reedbed specialist moths associated with? ...... 14 What habitat were internal reed-feeding moths associated with? ...... 19 What habitat were wetland specialist moths associated with? ...... 24 How did the number of reedbed and wetland specialist compare between the wettest and driest areas? ...... 29 How did the number of reedbed and wetland specialist Lepidoptera compare between old and new areas? ...... 30 What reedbed habitat conditions were associated with maximum number of Lepidoptera species at the survey sites? ...... 35 What habitat variables were associated with moths with a conservation status? ...... 46 Composite habitat variables ...... 49 References ...... 50

Summary Twelve light traps were set at each of three key reedbed sites (Hickling Broad in Norfolk, Ham Wall in Somerset and Stodmarsh in Kent). Each trap was set three times, at intervals of three weeks between June and August 2010. Over all surveys at all sites 5 524 individuals and 202 species were trapped. Of the 202 species trapped, 16 species were classified as reedbed specialists and 47 as wetland specialists, according to expert opinion based on agreed definitions. Two Endangered (RDB 1) species were trapped ( anachoreta and ) three Vulnerable (RDB 2 & pRDB 2) species ( genitalana, divisella and

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Phragmataecia castaneae), three Rare (RDB 3 & pRDB 3) species (Chortodes brevilinea, and Yponomeuta rorrella) and 12 Nationally Scarce species. 20 UK BAP moth species were trapped: one being a reedbed specialist: Chortodes brevilinea (Fenn’s Wainscot) and 19 being widespread but rapidly declining species. In total, 135 moth species were caught at Hickling Broad, 78 at Ham Wall and 128 at Stodmarsh. Hickling Broad not only had higher overall species diversity, but also had a higher number of species of conservation interest and reedbed specialists. Stodmarsh had high numbers of wetland specialist moths. Ham Wall was lower than the other two sites for all measures of moth diversity. Habitat variables measured around the traps were tested to see which best explained variation in number of species caught in traps. Because of the differences in moth diversity between sites and differences in geography between sites, general conclusions are sometimes constrained by site differences overriding trends in other habitat variables. These habitat associations should be interpreted with caution due to the site differences and the small sample size (12 traps at each site). However they provide a good starting point indicating which potential relationships would be interesting to investigate further. A study with more traps at sites in a similar geographic area (e.g. only the Broads) would be an interesting next step. Traps that had a high overall diversity of moth species tended to be in places where the litter was not fully submerged in the four months before trapping and where there was a high diversity of plants. Moths with a conservation score tended to be trapped at points with high plant species richness and litter that was not fully submerged before surveys. • Higher numbers of wetland specialists were trapped at points with fully submerged litter, more standing water, deeper litter and greater stem densities. Higher numbers of reedbed specialists were trapped at points with deeper litter, standing water, further from scrub, with high stem densities. Higher numbers of internal reed feeders were trapped at points with deeper litter, standing water and thicker reed. Areas of reedbed at Hickling and Stodmarsh that were restored in 1998 had similar numbers of reedbed and wetland specialist moth species to much older areas of the sites. Small Dotted Footman, Pelosia obtuse, (Endangered), Reed Leopard, castaneae, (Vulnerable) and Fenn’s Wainscot, Chortodes brevilinea, (UK BAP Priority Species, Rare) were all trapped in the Hundred Acre reedbed (12 years old) at Hickling Broad along with 8 other Rare or Nationally Scarce moth species.

METHODS Light trap survey field methods A subset of water trap points were surveyed for moths in 2010 using light traps. 12 points out of the 21 water trap points at each site were randomly selected and the final selection was adjusted to ensure all hydrological categories had been covered. Since hydrological data had already been collected in 2009 at the sampling points, these data were used to ensure a mix of hydrological strata were sampled. Surveys were spread over three sessions, detailed below. Session 1: 21-25 June (Stodmarsh); 28 June – 2 July (Hickling); 5-9 July (Ham Wall) Session 2: 12-16 July (Stodmarsh); 19-23 July (Hickling); 26-30 July (Ham Wall) 2

Session 3: 2-6 August (Stodmarsh); 9 – 13 August (Hickling); 16-20 August (Ham Wall) Four traps were set up each night before dusk and checked at 6 am the following morning. The groups of four were changed each week with a mixture of hydrological strata being surveyed each week. Trapping was only done on suitable nights without high wind or heavy rain as these conditions can affect catches.

Heath type 15 W actinic traps were used, supplied by Anglia Lepidopterist Supplies. They were fitted with rain-guards and used 12 Amp hour batteries, attached to the poles to stay dry. One large egg box tray (roughly 30cm square) ripped into six pieces lined the base of the trap. Traps were fitted firmly to platforms on top of wooden poles as in the water trap survey and the height of the pole was recorded in order to account for this in analysis. Traps were turned on by a light sensor and this mechanism was tested during every set up. The reed in 1 m radius around the trap was flattened at each location to standardise immediate habitat influence.

Figure 6.1: Moth trap in situ (Chris Nall) The following morning, moths were identified in situ by ecological contractors (Hickling Broad: Jon Clifton and Jim Wheeler, Stodmarsh: Sean Clancy, Ham Wall: James McGill). Only moths found inside the trap were recorded. All species were identified and the number of individuals of each species was counted. Each morning a basic habitat survey was carried out. This involved recording live and dead reed height, whether there was standing water in the vicinity and the standing water level in the core. Weather reports were completed after trap set up and before and after trap checking each morning. Donna Harris, Chloe Hardman, Chris Nall and Stephen Gregory carried out surveys with the ecological contractors. Surveys were designed by Donna Harris with advice from Mark Parsons (Butterfly Conservation).

Further habitat surveys During the first trapping session, litter was measured 0.5m into reeds from the core edge, in each of NE, SE, SW, NW directions. In the same directions, 1 m into reeds, standing water level (surface to boot sole) was measured. Plant species were recorded in four 50 x 50cm quadrats 2m from the core edge. On the second trapping session, at 1m into reeds from core edge, in each of N, E, S, W directions, standing water level (surface to boot sole) was measured. Then, also at 1m into reeds, using a 50 x 50cm quadrat, number of live and dead stems (above chest height) and the number of panicles was measured. Four randomly selected stem base diameters from within the quadrat were measured using callipers. On the third trapping session, at 1.5m from core edge, in each of N, E, S,

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W directions water height (surface to boot sole) was measured. All survey equipment was collected up in this session. Locations of the survey points

Figure 6.2: Map of moth trap locations at Ham Wall

Figure 6.3: Map of moth trap locations at Hickling Broad

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Figure 6.4: Map of moth trap locations at Stodmarsh

Analysis methods Invertebrate species data The three trapping sessions were combined to produce a list of species and individuals caught at each trapping point over all three trap nights. All invertebrate identifications were checked for taxonomic consistency using MapMate. Taxa were included as ‘species’ if they were identified to species level, or if they were the only member of a higher taxa, e.g. the only record of a particular . Lists of reedbed and wetland specialists were compiled by Mark Parsons (Butterfly Conservation), Tony Prichard (Suffolk Moths), and the ecological consultants who carried out the surveys. The following definitions of reedbed and wetland specialists were used: Reedbed specialist: a species that is dependent on reed, reared from reed, or only found in reedbed habitats Wetland specialist: a species that is generally found in wetlands

All reedbed specialists were also classified as wetland specialists. For lists of the species that met these definitions, see appendix. ISIS (the Invertebrate Species-habitat Information System) was also used to classify species as wetland or reedbed specialists. ISIS classifies habitats as broad assemblage types (BATs) and specific assemblage types (SATs). BATs are “a comprehensive series of assemblage types that are characterised by more widespread species” whereas SATs are “characterised by ecologically restricted species and are generally only expressed in lists from sites with conservation value”. The freshwater wetland BATs were: W1 - flowing water, W2 - mineral marsh and open water and W3 - permanent wet mire. For this project, wetland specialists within reedbeds were best represented by the BAT categories W2 and W3. Reedbed specialists were represented by the SAT category W314 (reedfen and pools). However, when compared to the lists compiled by moth experts, these lists

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were not considered as comprehensive so were not used. Some moth families had been omitted and the reedbed specialist category also includes fen species which was less appropriate for our purposes.

Raw species richness was calculated as the number of species in each trap. All species in traps were identified except one micro moth at Stodmarsh. The total abundance of all invertebrates in each trap was also calculated. The abundance of individuals in each trap will vary depending on how effective the trap was at catching invertebrates. For example, moth light traps surrounded by tall reed will attract moths from a smaller radius than light traps in an open area that are visible from further away. A more effective trap is likely to catch more individuals and hence more species, regardless of whether the habitat is better quality. Two methods were used to try to account for this in analysis.

Firstly the pole height at each point was measured and included as a variable in models to see how far it explained variation in the number of species caught and whether taller poles caught more moth species. Secondly, abundance of individuals trapped was controlled for to just look at the variation in number of species in traps. A variable called “bootstrapped number of species” was calculated. This is similar to rarefaction, which is a necessary ecological technique when comparing species richness of samples of different sizes. R code was written to take a random sample of 42 individuals from each trap and calculate how many species were in that sample (this was the maximum sample size that could be taken based on the number of individuals in the traps). The sampling was repeated 100 times (with replacement) and the average number of species was calculated. Bootstrapped number of species was used rather than a species diversity index, because it was considered more transparent and suitable for the purposes of this study.

A conservation score for each trap was calculated based on the following criteria: Score of 10 for: Vulnerable, Endangered, Rare Score of 5 for: Nationally Scarce, UK BAP These scores were assigned to numbers of species in the traps and not multiplied up by numbers of individuals in the traps.

For each trap, the following response variables were available: raw number of species, bootstrapped number of species, abundance of individuals, number of wetland specialists, number of reedbed specialists, number of internal reed feeding species and conservation scores. All of these were analysed except abundance of individuals which was considered less informative.

Habitat data From the habitat variables recorded in the field, the following set of explanatory variables was derived. Averages were calculated for habitat variables that were measured at more than one point around the sampling location.

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Table 6.1: Explanatory variables used in models relating habitat variables to moth species diversity Variable Type of variable Units Description Litter saturation 2009 Explanatory categorical Data from May-August 2009 for 2 categories; dry and wet litter saturation 2009 and April – (wet.dry.09) June 2010 for litter saturation Litter saturation 2010 2010. Dry if predicted water (wet.dry.10) level never exceeds litter depth. Wet if predicted water level exceeds litter depth at any point in these periods. Litter depth 2009 Explanatory continuous cm Average of 4 litter depth measurements in 1 m radius of Litter depth 2010 trap point. Total stem density Explanatory continuous Stems Average of 4 counts of number per m2 of stems in 50 x 50 cm quadrat, (stems) in 1 m radius of trap point, multiplied by four to get stems per m2. Dead stems percentage Explanatory continuous % Percentage of stems that were dead. Pole height Explanatory continuous m Height of pole on which Heath trap was mounted. Mean reed height Explanatory continuous m Height of live reed, average of 4 measures in 1m radius of trap point. Stem diameter Explanatory continuous mm Stem diameter at base, average of 4 measures in 1m radius of trap point. Distance to scrub Explanatory continuous m Distance to nearest patch of scrub/trees calculated from aerial photos by CDMU using an add on tool in MapInfo 6.5. Plant species richness Explanatory continuous Total number of plants (excluding australis) found over 4 quadrats in 1 m radius of trap point. Standing water level Explanatory continuous cm Average standing water level from four points in the summer 2010 untrampled reed 1m from the pole.

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Water level data Data on the temporal variation in water levels was available for each point. This was based on topography measurements and seasonal readings of gauge boards. Gauge boards were read periodically throughout the project by survey staff, site staff and volunteers. At Hickling Broad, data on the broad-side sampling points was available from the Environment Agency tidal site (NGR: TG4107822508) which records daily mean water levels. In October 2009, a Topcon device (GRS 1 TOPCON – uses OSTN(O2) co-ordinate system) was hired to make precise altitude readings. It was used to get the elevation AOD at the water trap, pitfall trap and gauge board locations. The levels of a number of gauge boards at Stodmarsh were corrected based on this data (gauge boards 1, 4, 6c, 6a).

Water levels were recorded periodically, by hydrological unit, at each site and converted to AOD water levels in order to predict water levels for each of our survey points throughout the year, for any dates on which gauge boards were read by either site staff or us. Predicted water level was calculated as:

Predicted water level = Gauge board water level at AOD – altitude at survey point AOD

The accuracy of these predictions was checked through a comparison with actual water levels. During the various wildlife surveys, water levels were measured using a ruler at the actual survey points from the same “ground” baseline (i.e. the base of the surveyors wader when surveyor in normal standing position) as the altitude AOD was taken using the Topcon. Such data are available from a few occasions – October (water trap and pitfall points, when ground elevations were measured, November (bittern locations, when ground elevations were measured) and January (water levels only during water vole surveys).

Only water measurements from the untrampled reed were used in water level prediction calculations. Error of prediction was calculated as the difference between predicted and actual water level. Where error was larger than could be realistically explained by variation in microtopography, factors such as hydrological blockages may be present, so the predicted values were not used. Average error across all sites was 0.0639 m (for error calculations see water trap survey chapter 4, appendix table 4C).

Data was only ever used for the time period before sampling took place. Since moth surveys were carried out in 2010 (the second year of surveying for this project), data from both 2009 and 2010 was available. The data from 2009 was from May-August, since this was the time period over which gauge boards had been read most regularly to allow water levels to be predicted. In 2010, the most similar period we had data for was April-July. For each of these periods, the litter level was compared to the predicted water level to put the point into a category: wet or dry. Points where the predicted water level exceeded the litter level at any point in the period specified were classed as wet. It is important to note that these litter saturation categories only reflect one season of the year and water levels are likely to vary throughout the rest of the year, which was not able to be captured here due to lack of available data. Also the litter saturation categories varied between 2009 and 2010, showing that data from one year is only a snapshot.

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Multivariate analysis Before analysis began, scatter plots of response variables against explanatory variables and explanatory against explanatory were created. Data exploration was carried out in the way recommend by Zuur et al (2010), checking for outliers and normality.

The R function DREDGE from the MuMIn package was used to examine all possible models and examine the model with the lowest AICc. We used glm with poisson errors here as glm.nb did not seem compatible with this function. We stated that the data contained NAs. Then we took the best model and ran it as a prediction. By testing the linear regression between actual and predicted values we can check how good the fit of the model is. The best model explained only 53 % of the variance. An algorithmic modelling technique, called Random forest, was tried in comparison. This technique produced a model that explained 93% of the variance, so seems to be a powerful way of analysing our dataset. Random forest is a machine learning algorithm that builds an ensemble of regression trees (a forest) (Breiman et al 1984). Random forest models are suited to this type of exploratory analysis where we do not have specific hypotheses but want to explore the relative importance of all variables and their potential interactions (Hochachka 2007). This technique can deal with a large number of predictor variables. It automatically includes all interactions and it doesn’t matter if the variables are not normally distributed. The technique copes well with missing values, outliers and irrelevant predictor variables.

500 regression trees were constructed, using a random subset of four variables at each split, with replacement sampling. Random forest (Breiman 2001) uses a subset of 63% the data to build the model then the remaining 37% to test the model (out of bag data). At each node, it tries four variables and chooses the one which best splits the data into two. Then for these two groups it repeats the process and so on until the data points are predicted by this series of rules. An example of such a tree is given in figure 6.5.

diameter < 0.35875 |

litsat2009:ab plantrich < 4.5

maxreedheight < 1.495 155.1 livestems < 43.75 115.4 71.8 118.8

238.6 183.8

Figure 6.5: example of one regression tree within a “random forest”

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To find out the importance of each variable, the function permutes the values (rearranges the data for example by replacing the 3rd row with the 5th). Then it checks if the mean squared error increases or not. If it does increase this shows that the variable is important. A few variables actually reduce the mean squared error when permuted so this shows they are not important at all (negative values). Each random forest model was run 10 times, and the % Increase in Mean Squared Error was calculated for each habitat variable. This process was carried out for each of the response variables for each of the three datasets. For habitat variables which were of high importance, the direction of the relationship was investigated further using partial plot and plots of the raw data. For the moth dataset the separate site results cannot stand alone as they are based on a sample size of twelve.

A number of habitat variables were associated with each other, due to the nature of reedbed successional habitat. This does not affect the predictive performance of random forest models but can affect the variable importance. If variables are correlated the model can use either variable at splits in the regression tree, so the variable importance of both can be reduced. To test if this was the case for the correlated habitat variables models were run separately with each variable alone, and then together, to check whether the variable importance scores of the correlated variables were always higher alone than together. For variables where this was the case, either an average was taken (e.g. for live height and dead height) or one variable was excluded.

In addition, a principle component analysis was carried out for each of the three invertebrate datasets. This creates composite habitat variables that best explains variation in habitat between the invertebrate survey points. The correlation between each of the top four principle components and bootstrapped species richness was tested to see which composite environmental gradients best explained variation in species diversity. These results were compared to the results of random forest analyses and consistent messages were drawn out.

RESULTS What moth species were found on the three reedbed reserves? Over all surveys at all sites 5 524 individuals and 202 species were trapped. In total, 135 moth species were caught at Hickling Broad, 78 at Ham Wall and 128 at Stodmarsh. Lepidoptera of conservation importance There were two Endangered (RDB 1), three Vulnerable (RDB 2 & pRDB 2), three Rare (RDB 3 & pRDB 3) and 13 Nationally Scarce moth species, as listed below.

Table 6.2: Lepidoptera species of conservation concern found in surveys Number of records Species latin Species Higher Conservation HB HW SM Total name common taxon status name Small dotted Arctiidae Pelosia obtusa footman Lepidoptera Endangered 17 0 0 17

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Spilosoma Water Nationally Arctiidae urticae Ermine Lepidoptera Scarce 9 0 1 10 Pelosia Dotted Arctiidae muscerda Footman Lepidoptera Rare 4 0 0 4 Reed Phragmataecia Leopard castaneae Moth Lepidoptera Vulnerable 58 0 0 58 Monochroa A micro divisella moth Lepidoptera Vulnerable 7 0 0 7 Brachmia A micro Nationally Gelechiidae inornatella moth Lepidoptera Scarce 0 0 2 2 Monochroa A micro Nationally Gelechiidae palustrella moth Lepidoptera Scarce 0 1 2 3 Flame Nationally flammea Wainscot Lepidoptera Scarce 15 0 0 15 Webb's Nationally Noctuidae sparganii Wainscot Lepidoptera Scarce 2 0 0 2 Macrochilo Dotted Nationally Noctuidae cribrumalis Fan-foot Lepidoptera Scarce 1 0 38 39 Reed Nationally Noctuidae albovenosa Dagger Lepidoptera Scarce 58 0 24 82 Chortodes Fenn’s UK BAP brevilinea Wainscot Priority Noctuidae Lepidoptera Species, Rare 5 0 0 5 Scarce Clostera Chocolate anachoreta Tip moth Lepidoptera Endangered 0 0 1 1 A micro Nationally Pyralidae moth Lepidoptera Scarce 3 0 2 5 A micro Nationally Pyralidae paludella moth Lepidoptera Scarce 2 86 14 102 A micro Nationally Pyralidae gigantella moth Lepidoptera Scarce 65 0 59 124 Cnephasia A micro genitalana moth Lepidoptera Vulnerable 1 0 0 1 Phalonidia A micro Nationally Tortricidae manniana moth Lepidoptera Scarce 3 0 1 4

Since Yponomeuta rorrella and Itame brunneata are immigrant species, they were not included in the conservation score analysis. However they do have conservation statuses which are listed below.

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Table 6.3: Numbers of immigrant species with conservation statuses trapped. Number of records Family Species latin Species Higher Conservation HB HW SM Total name common taxon status name Nationally Scarce, Immigrant, Itame Rannoch resident only Geometridae brunneata Looper Lepidoptera in Scotland 0 0 1 1 Yponomeuta Rare (possibly Yponomeutidae rorrella Ermine Lepidoptera an immigrant) 0 0 1 1

UK BAP species All the UK BAP species recorded are designated as Species “of principal importance for the purpose of conserving biodiversity” covered under section 41 (England) and 42 (Wales) of the NERC Act (2006), except Fenn’s Wainscot which is only on the English list. Of the 20 UK BAP moth species encountered, one is a reedbed specialist, Fenn’s Wainscot (Chortodes brevilinea), which was given its status due to substantial threat to its highly specialised habitat. Fenn’s Wainscot is also a Red Data Book species, designated as Rare (RDB 3). The other 19 species are widespread but rapidly declining (see table 6.4). These species have declined markedly over the last 35 years (with declines ranging between 71% to 95%, see appendix). None of these species are reedbed specialists and only two were wetland specialists (Celaena leucostigma and ).

Table 6.4: Number of UK BAP widespread but rapidly declining species trapped at each site

Family Species latin name Species Higher taxon HB HW SM Total common name Noctuidae Acronicta rumicis Knot Grass Lepidoptera 1 0 0 1 Noctuidae oculea Ear Moth Lepidoptera 1 0 0 1 Amphipyra Mouse Noctuidae tragopoginis Moth Lepidoptera 4 0 0 4 Apamea remissa Dusky Noctuidae Brocade Lepidoptera 9 0 0 9 Arctia caja Garden Arctiidae Tiger Lepidoptera 148 66 14 228

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Brachylomia Minor viminalis Shoulder- Noctuidae Knot Lepidoptera 1 0 0 1 Caradrina morpheus Mottled Noctuidae Rustic Lepidoptera 0 0 2 2 Celaena leucostigma Crescent Noctuidae Lepidoptera 62 26 23 111 Noctuidae Hoplodrina blanda Rustic Lepidoptera 0 0 6 6 Hydraecia micacea Rosy Noctuidae Rustic Lepidoptera 0 2 3 5 Malacosoma Lackey Lasiocampidae neustria Lepidoptera 0 2 0 2 Melanchra pisi Broom Noctuidae Moth Lepidoptera 2 0 0 2 Mythimna comma Shoulder- striped Noctuidae Wainscot Lepidoptera 2 0 1 3 Orthonama vittata Oblique Geometridae Carpet Lepidoptera 31 16 0 47 Pelurga comitata Dark Geometridae Spinach Lepidoptera 0 1 0 1 Spilosoma White Arctiidae lubricipeda Ermine Lepidoptera 0 0 1 1 Spilosoma luteum Buff Arctiidae Ermine Lepidoptera 6 8 8 22 Geometridae Timandra comae Blood-vein Lepidoptera 0 3 0 3 Watsonalla binaria Oak Hook- Drepanidae Tip Lepidoptera 4 0 3 7

Species composition There were more macro moths than micro moths at all sites. Hickling Broad had the highest number of macro moth individuals, whereas Stodmarsh had the highest number of micro moth species. In terms of numbers of individuals, Ham Wall was not far behind Stodmarsh, however in terms of numbers of species, Ham Wall was lower than the other two sites. To investigate how far the differences in numbers of species between sites are due to geography, the NBN species database (www.nbn.org.uk) was used to look up the species ranges. There were 50 more species caught at Stodmarsh compared to Ham Wall and 57 more species caught at Hickling Broad compared to Ham Wall. 20 of the 187 species found at Hickling Broad and Stodmarsh combined, would not be expected to occur in the SW. Therefore the lower species diversity at Ham Wall can be partly attributed to geographical location but not entirely.

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Table 6.5: Number of individuals of macro and micro moths at each site Totals over 12 traps set 3 Ham Wall Hickling Broad Stodmarsh times (36 trap nights)

Macro Number of individuals 956 1894 948 Number of species 59 91 79 Micro Number of individuals 514 328 625 Number of species 19 44 49 Total Number of individuals 1470 2222 1573 Number of species 78 135 128

What habitat variables were reedbed specialist moths associated with? Random Forest models were used to find out which habitat variables measured around the moth trap points best explained the number of reedbed specialist moth species trapped at each point (not abundances as considered too unreliable). This was based on lists of reedbed specialists defined by moth experts using agreed definitions. Many of the trends were affected by Ham Wall having lower species diversity and different habitat variables to the other sites. Therefore Hickling Broad and Stodmarsh were also analysed alone for comparison.

Figure 6.6: Relative importance of habitat variables in describing variation in number of reedbed specialist species per trap. These models explained 92 % of the variance. Site Ham Wall had fewer reedbed specialist moths on average per trap than the other two sites (Hickling Broad and Stodmarsh had 12 reedbed specialist moth species trapped in total across all traps whereas Ham Wall had 7). Six of the sixteen reedbed specialist moths identified across all three sites would not be expected to occur at Ham Wall due to geographical ranges not extending to this region Chortodes brevilinea, Mythimna flammea, Pelosia obtuse, , Schoenobius

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gigantella, (www.nbn.org.uk). Therefore considering the age of the site and its geographic location, the number of reedbed specialist moths at Ham Wall is encouraging.

Figure 6.7: Relationship between site and number of reedbed specialists in moth traps

Mean reed height Since site was such an important factor, ideally we would have analysed each site separately, however there was not enough data to do this (n=12 traps at each site, less than 5 unique values for number of reedbed specialists at Hickling Broad). Instead, the scatter plots have been coloured to indicate which site the points are from. The negative relationship between reed height and number of reedbed specialist moth species appears to be a product of Ham Wall having taller reed and a lower number of reedbed specialist moth species from the scatter plot.

Partial Plot Raw Data

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Figure 6.8: Relationship between mean reed height around each trap and the number of reedbed 15 specialist moths caught in the trap

Scrub distance This relationship was not clear since each site showed a different trend. When Hickling Broad and Stodmarsh were analysed without Ham Wall, points further from scrub were associated with more reedbed specialist moths.

Partial Plot Raw Data

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Distance to Scrub Distance to Scrub Figure 6.9: Relationship between scrub distance and number of reedbed specialist species

Litter depth 2009 Trap points with deeper litter (measured the year before) were associated with a greater number of reedbed specialist moths. Litter depth measured in 2010 was less important, but also showed a positive relationship with the number of reedbed specialist moths. Four of the 16 reedbed specialist moths overwinter as either pupae or larvae in reed litter (Pelosia obtuse, Mythimna obsolete, and Simyra albovenosa). At least 2 of these litter-dwelling species were found in all traps. If we assume these litter-dwellers pupated near where they were trapped, deeper litter in 2009 could have promoted a higher larval/pupal survival rate because it is less likely to get inundated with water. This was true when Ham Wall was excluded from analysis.

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Partial Plot Raw Data

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Litter Depth 2010 Litter Depth 2010 Figure 6.10: Relationship between litter depth 2009 and number of reedbed specialist moths

Stems The random forest model indicates that points with over 280 stems per square metre were associated with more reedbed specialist moths. However this appears to be confounded by Ham Wall having low stem densities and a low number of reedbed specialist moths. When Ham Wall points were removed from analysis this variable was no longer important.

Partial Plot Raw Data

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Figure 6.11: Relationship between stem density and number of reedbed specialist moths

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Litter saturation 2009 Points where the litter had not totally flooded in May-August 2009 were associated with higher numbers of reedbed specialist moths. However litter saturation 2010 was not so important. Perhaps some of these reedbed specialists did pupate in the litter in 2009, hence not being flooded offered a survival advantage.

Figure 6.12: Relationship between litter saturation 2009 and reedbed specialist moths

Standing water level Points with lower levels of standing water (0-10 cm) were associated with the highest numbers of reedbed specialist moths trapped. We would expect reedbed specialists to be able to survive in shallow standing water, because generally they are internal feeders often pupating in situ.

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Partial Plot Raw Data

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Figure 6.13: Relationship between standing water during surveys and number of reedbed specialist moths

What habitat were internal reed-feeding moths associated with? A subset of reedbed specialist moths, those that feed internally on (see appendix), were analysed separately. The number of these internal reed-feeding moths was calculated for each trap and the analysis below shows how these numbers related to habitat variables. In the other models, stem diameter was not included together with mean reed height because the two variables are correlated. Just mean reed height was used and inferences about diameters were made in interpretations. However for internal feeders, diameter was used in the models instead of mean reed height because it is more directly related to the feeding and life history of these moths.

Points that trapped high numbers of internal reed feeding moths tended to: Be at Hickling Broad or Stodmarsh, not at Ham Wall Have thicker stem diameters if at Hickling Broad or Stodmarsh Have deeper litter Have litter that was not fully saturated between May and August 2009 When Hickling Broad and Ham Wall were analysed alone, internal reed feeders were trapped more at points with more standing water, taller reed and shallower litter and thicker stem diameters and deeper litter 2009

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Figure 6.14: Importance of habitat variables in explaining variation in number of internal reed- feeding moths in traps. These models explained 90 % of the variance.

Site The most important factor in explaining variation in number of internal reed-feeding moths in traps was site. Hickling Broad had the highest number of internal reed-feeding moths of the three sites. This is not surprising as some of these internal feeding species have distributions restricted to the Norfolk Broads. Three of the ten internal reed feeding species identified across all surveys would not be expected to occur at Ham Wall (Chortodes brevilinea, Phragmataecia castaneae, ).

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Figure 6.15: Relationship between number of internal reed-feeding moths and site

Diameter Ham Wall has much thicker reed stem diameters (and taller reed) than the other two sites and also the lowest number of internal reed feeding moths. This explains why the thickest diameters were associated with lower numbers of internal feeders. At Hickling Broad and Stodmarsh alone, scatter plots showed points with thicker stems (and hence taller reed) were associated with greater numbers of internal reed feeding specialists.

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Figure 6.16: Relationship between reed stem diameters and number of internal reed feeding moths

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Scrub distance The relationship between internal feeders and scrub was unclear. Although this variable can reflect successional stage, it is not directly relevant to these moths in question that feed on reed.

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Figure 6.17: Relationship between distance to scrub and number of internal reed feeding moths

Litter depth 2009 Litter depth was also important in explaining variation in the number of internal reed-feeding moths. Over all sites, a tendency for points with deeper litter to be associated with a greater number of internal reed-feeding moths was seen. Looking at a scatter plot with each site coloured differently, this trend only appears evident at Stodmarsh on an individual site level. In our dataset it is unclear what type of reedbed deep litter is associated with. Both wet and dry points had deep litter recorded. Further investigation of litter depths along hydrological gradients would be interesting.

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Figure 6.18: Relationship between internal reed feeding moths and litter4 depth 2009 0 10 20 30 40 Litter saturation 2009 standing.water.summer.2010 Litter saturation in 2009 and 2010 was of low importance in explaining variation in internal reed- feeding moths. Points where the litter did not entirely flood between May and August 2009 were associated with higher numbers of internal reed feeding moths. Dry here means points where the litter was either totally dry or partly wet but the water level did not exceed the litter level.

Figure 6.19: Relationship between litter saturation 2009 and number of internal reed specialist moth species 23

Standing water This variable was of low importance, however points with standing water did trap internal feeders. We would expect internal feeders to be able to survive shallow inundation more than litter feeders.

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In summary: Points at Stodmarsh and Hickling had greater numbers of wetland specialists than Ham Wall Points with deeper litter were associated with more wetland specialist moth species (when measured both in 2009 and 2010) (true for Hickling and Stodmarsh alone) Points with more standing water were associated with more wetland specialist moth species (true for Hickling and Stodmarsh alone) Both wet and dry points in 2009 had high numbers of wetland specialists but wetter points in 2010 had higher numbers (true for Hickling and Stodmarsh alone) Points with greater stem densities were associated with more wetland specialist moth species Points with shorter pole heights were associated with more wetland specialist moth species

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Figure 6.21: Importance of habitat variables in explaining variation in number of wetland specialist moth species in traps. These models explained 93 % of the variance. Litter depth 2009 Points with deeper litter in 2009 were associated with greater numbers of wetland specialist moth species. (This was also true for litter measured in 2010, but not to the same extent). In our dataset wet points tended to have deep litter measured, whereas dry points had both deep and shallow litter. Therefore the points here with deep litter may have also been wet points. This fits in with the relationship between standing water level and number of wetland specialist moth species.

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Site In contrast to number of reedbed specialist moth species and internal feeding species, site was not the most important factor in explaining variation in number of wetland specialist moths. Stodmarsh had the greatest number of wetland specialist moth species, followed by Hickling Broad and then Ham Wall. Ham Wall is the wettest site, but has a lower overall moth diversity than the other two sites. At these survey points in 2010, Stodmarsh had on average more standing water than Hickling Broad which fits in with Stodmarsh points trapping more wetland specialist species than Hickling points.

Figure 6.23: Relationship between number of wetland specialist species and site Litter saturation 2009 When points were classed as having totally saturated (wet) litter or partly saturated or dry (dry) litter in the year before surveys, dry points were associated with a greater number of wetland specialist species. It is not clear why dry points were associated with more wetland specialists in 2009 but wet points were associated with more wetland specialist moth species in 2010. The great range of moth life cycles included in analysis makes it difficult to explain.

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Figure 6.24: Relationship between litter saturation 2009 and number of wetland specialist moths

Stems Points with stem densities over 300 stems per square metre tended to be associated with greater numbers of wetland specialist moth species.

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Pole height Points with shorter poles were associated with greater numbers of wetland specialist moth species. At least this shows tall pole height did not have an overwhelming influence on the effectiveness of

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the trap. Perhaps shorter traps tended to trap species within the reeds rather than species from the wider habitat around.

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Figure 6.26: Relationship between pole height and number of wetland specialist moth species

Standing Water Points with higher levels of standing water during surveys tended to trap greater numbers of wetland specialists. This fits with expectations since in wetter areas, wetland specialists would be expected to outcompete species without adaptations to living near water.

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Figure 6.27: Relationship between standing water and number of wetland specialist moths 28

Litter saturation 2010 There were more wetland specialist moths trapped at points where the litter had been fully saturated between April and July 2010 (the months preceding surveys). This ties in with the standing water finding above.

Figure 6.28: Relationship between number of wetland specialist moths and litter saturation category 2010

How did the number of reedbed and wetland specialist Lepidoptera compare between the wettest and driest areas? Since there is much debate over the relationship between wetness of reedbed and moth diversity, further analyses were attempted to investigate this. However the analysis options were found to be limited by the data and no further analysis better than the random forest models was satisfactory. Firstly an attempt to separate out the points on litter saturation categories was made. However, points where the water level fully flooded the litter were much more prevalent than points where the litter was dry or partially saturated (Wet=24 points, Partial=5 points, Dry=6 points). Another way to summarise the hydrological data was to classify the points according to whether they had a dry/wet summer and/or dry/wet winter (using data from Sept 09 to Aug 10). However, these categories were confounded by site, since not all categories were found at all sites. The only site with all categories was Stodmarsh and there were not enough data points in each category for analysis. Since different months of data were missing from different sites the analysis options are limited. The original hydrological categorisations assigned to sampling points at the design stage could not be used as they were not found to reflect actual hydrology and they were not consistent in definition between sites. In order to investigate the diversity of reedbed/wetland specialist moths along hydrological gradients in reedbeds, further trapping is needed, along defined hydrological gradients, on one (or more) sites, preferably over multiple years. Future studies on the importance of dry reedbed for certain species such as Fenn’s Wainscot and Small Dotted Footman are worth further investigation. 29

How did the number of reedbed and wetland specialist Lepidoptera compare between old and new areas? Stodmarsh has a newer area of reedbed (Grove Ferry) that was restored from grazing marsh with a few reed lined ditches to full reedbed in 1998. Hickling Broad has an area of reedbed (Hundred Acre) which was restored between 1997 and 1999. These areas were restored with funding from the EU LIFE project with a view to providing more habitat for bitterns. The moth catches in the restored areas were compared with the rest of each site which is known to be much older. T-tests were used to compare differences in number of reedbed specialists and wetland specialist moth species between old and new areas within Stodmarsh and Hickling Broad.

Comparing Grove Ferry reedbeds with those at the rest of Stodmarsh, there were no significant differences in the number of reedbed specialist moths or wetland specialist moths in terms of numbers of species and numbers of individuals (see table). There were no significant differences in the number of reedbed and wetland specialist species between Hundred Acre and Hickling Broad. However for reedbed specialist individuals, there were more at Hundred Acre than at Hickling Broad (see table). The ability to detect effects was limited by the small sample size at each site (12 traps per site). However the initial results are encouraging in showing that moths have colonised the reedbed after substantial restoration work took place. Comparisons of species lists and numbers of individuals of species of conservation concern are used to investigate further.

Table 6.6: Differences in reedbed and wetland specialist moths at Grove Ferry and rest of Stodmarsh tested with 2 sample T – tests with unequal variances. (5 traps in new reedbed at Grove Ferry, 7 in older reedbed at Stodmarsh) Mean Mean Degrees of T value P value number per number per freedom trap at trap at Grove Ferry Stodmarsh Reedbed 7.4 7.7 8 -0.34 0.744 specialist species Wetland 17.2 16.1 9 0.37 0.719 specialist species Reedbed 39.8 54.3 9 -1.06 0.315 specialist individuals Wetland 84.8 84.9 9 <0.001 0.998 specialist individuals

Table 6.7: Differences in reedbed and wetland specialists between Hundred Acre and rest of Hickling Broad tested with 2 sample T – tests with unequal variances. Hickling Broad: 5 traps in old reedbed around the Broad, 7 in new Hundred Acre reedbed.

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Mean Mean Degrees of T value P value number per number per freedom trap at trap at Hundred Hickling Acre Broad Reedbed 9.14 8.4 9 -1.03 0.329 specialist species Wetland 16.4 16.0 5 -0.24 0.823 specialist species Reedbed 58 42.2 9 -2.73 0.023 * specialist individuals Wetland 86.6 68.6 7 -1.82 0.112 specialist individuals

Comparison of specialist moth species lists at Stodmarsh in old and new reedbeds When all traps in the area were considered together, Stodmarsh traps only had two more reedbed specialist species and four more wetland specialist species than Grove Ferry. It was surprising that Archanara dissolute and Archanara geminipuncta were absent from Grove Ferry. They are both internal feeders, but differences in reed diameters did not explain these results. Further trapping would reveal if these species had colonised Grove Ferry.

Table 6.8: Reedbed specialist moths found at new reedbed (Grove Ferry) and older reedbed (Stodmarsh). Species found at both are highlighted with an asterisk, species with conservation status are in bold. Stodmarsh Grove Ferry Family Species Family Species Noctuidae Archanara dissoluta 6 Gelechiidae Brachmia inornatella 2 Noctuidae Archanara geminipuncta 3 Noctuidae phragmitidis * 11

Noctuidae * 23 Noctuidae maritimus * 32 Noctuidae * 50 Noctuidae * 19 Noctuidae Mythimna obsoleta * 44 Noctuidae Mythimna straminea * 39 Noctuidae Mythimna straminea * 184 Noctuidae Simyra albovenosa * 3 Noctuidae Simyra albovenosa * 21 Pyralidae phragmitella * 23 Pyralidae * 27 Pyralidae forficella * 30 Pyralidae Donacaula forficella * 2 Pyralidae Schoenobius gigantella * 40 Pyralidae Donacaula mucronellus 1 Pyralidae Schoenobius gigantella * 19

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Table 6.9: Wetland specialist moths found at new reedbed (Grove Ferry) and older reedbed (Stodmarsh). Species found at both are highlighted with an asterisk, species with a conservation status are in bold. Stodmarsh Grove Ferry Family Species No. Family Species No. of of inds inds Arctiidae 1 Arctiidae * 39 Arctiidae Thumatha senex * 11 phragmitella * 2 Cosmopterigidae * 5 Gelechiidae Brachmia inornatella 2 Geometridae Pterapherapteryx sexalata 4 Glyphipterigidae Orthotelia sparganella 5 Noctuidae Apamea ophiogramma * 3 Noctuidae Apamea ophiogramma * 1 Noctuidae Apamea unanimis 1 Noctuidae Arenostola phragmitidis * 11 Noctuidae Archanara dissoluta 6 Noctuidae Celaena leucostigma * 13 Noctuidae Archanara geminipuncta 3 Noctuidae Chilodes maritimus * 32 Noctuidae Arenostola phragmitidis * 23 Noctuidae Macrochilo cribrumalis * 28 Noctuidae Celaena leucostigma * 10 Noctuidae Mythimna obsoleta * 19 Noctuidae Chilodes maritimus * 50 Noctuidae Mythimna straminea * 39 Noctuidae Macrochilo cribrumalis * 10 Noctuidae typhae * 1 Noctuidae Mythimna obsoleta * 44 Noctuidae Parastichtis ypsillon * 1 Noctuidae Mythimna straminea * 184 Noctuidae Schrankia costaestrigalis * 1 Noctuidae Nonagria typhae 2 Noctuidae Simyra albovenosa * 3 Noctuidae Parastichtis ypsillon * 1 Pyralidae Acentria ephemerella * 22 Noctuidae Schrankia costaestrigalis * 2 Pyralidae * 11 Noctuidae Simyra albovenosa * 21 Pyralidae Cataclysta lemnata * 39 Pyralidae Acentria ephemerella * 79 Pyralidae Chilo phragmitella * 23 Pyralidae Calamotropha paludella * 3 Pyralidae Donacaula forficella * 30 Pyralidae Cataclysta lemnata * 43 Pyralidae Elophila nymphaeata * 13 Pyralidae Chilo phragmitella * 27 Pyralidae Parapoynx stratiotata * 29 Pyralidae Donacaula forficella * 2 Pyralidae Phlyctaenia perlucidalis * 4 Pyralidae Donacaula mucronellus 1 Pyralidae Schoenobius gigantella * 40 Pyralidae Elophila nymphaeata * 19 Tortricidae Bactra furfurana * 6 Pyralidae Nascia cilialis 2 Tortricidae Phalonidia manniana 1 Pyralidae Parapoynx stratiotata * 10 Pyralidae Phlyctaenia perlucidalis * 2 Pyralidae Schoenobius gigantella * 19 Tortricidae Bactra furfurana * 3

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Overall, the species diversity of specialist moths in newer reedbeds was not as far behind that of older reedbeds at Stodmarsh as we would have expected, in terms of reedbed and wetland specialists and moths with a conservation status.

Comparison of specialist moth species lists at Hickling Broad in old and new reedbeds Table 6.10: Reedbed specialists in old and new reedbed at Hickling Broad. Species found in both areas are highlighted with an asterisk. Species with a conservation status are highlighted in bold. Broad Hundred Acre No. of No. of Family Species inds Family Species inds Arctiidae Pelosia obtusa * 11 Arctiidae Pelosia obtusa * 6 Cossidae Phragmataecia castaneae * 42 Cossidae Phragmataecia castaneae * 16 Noctuidae Archanara dissoluta * 2 Noctuidae Archanara dissoluta * 21 Noctuidae Arenostola phragmitidis * 30 Noctuidae Arenostola phragmitidis * 42 Noctuidae Chilodes maritimus * 5 Noctuidae Chilodes maritimus * 25 Noctuidae Chortodes brevilinea * 3 Noctuidae Chortodes brevilinea * 2 Noctuidae Mythimna flammea * 6 Noctuidae Mythimna flammea * 9 Noctuidae Mythimna straminea * 53 Noctuidae Mythimna straminea * 98 Noctuidae Simyra albovenosa * 10 Noctuidae Simyra albovenosa * 48 Pyralidae Chilo phragmitella * 23 Pyralidae Chilo phragmitella * 54 Pyralidae Schoenobius gigantella * 14 Pyralidae Donacaula forficella 1 Pyralidae Schoenobius gigantella 51

The only difference in reedbed specialists trapped in the two areas at Hickling was that Hundred Acre had one more species (Donacaula forficella) which was not captured in these surveys around the Broad.

Six Small Dotted Footman individuals were trapped in Hundred Acre reedbed and eleven in the reed around the Broad (Traps: HB1, HB12, HB13, HB16, HB17, HB19, HB5, HB7, HB8). 16 Reed Leopard were trapped in Hundred Acre reedbed and 42 in the reed surrounding the Broad (Traps: HB1, HB11, HB12, HB13, HB16, HB17, HB19, HB5, HB7, HB8, HB9). Note: actinic traps were used to try to reduce the area over which moths could have flown in from, however we can still not be entirely sure that these moths were feeding/living in Hundred Acre just from evidence that adults were trapped there. Larval searches will be needed to confirm this and the GPS coordinates of trapping locations here could be used as a starting point. Also, note that there has been reed on Hundred Acre for 40 years (before that it was arable land). However this evidence does suggest that the bittern-focused management and cutting regimes on Hundred Acre have not been detrimental for these moths. Further work would be needed to confirm this, by looking at changes over time in the moths of Hundred Acre.

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Table 6.11: Wetland specialists in old and new reedbed at Hickling Broad

Broad Hundred Acre Family Species Family Species Arctiidae Pelosia muscerda * 2 Arctiidae Pelosia muscerda * 2 Arctiidae Pelosia obtusa * 11 Arctiidae Pelosia obtusa * 6 Arctiidae Spilosoma urticae * 3 Arctiidae Spilosoma urticae * 6 Arctiidae Thumatha senex * 1 Arctiidae Thumatha senex * 2 Coleophoridae Coleophora follicularis 1 Cossidae Phragmataecia castaneae * 16 Cosmopterigidae Limnaecia phragmitella 2 Geometridae Orthonama vittata * 2 Cossidae Phragmataecia castaneae * 42 Glyphipterigidae Orthotelia sparganella 1 Gelechiidae Monochroa divisella 7 Noctuidae Archanara dissoluta * 21 Geometridae Orthonama vittata * 29 Noctuidae Archanara sparganii * 1 Geometridae Pterapherapteryx sexalata 3 Noctuidae Arenostola phragmitidis * 42 Noctuidae Archanara dissoluta * 2 Noctuidae Celaena leucostigma * 44 Noctuidae Archanara sparganii * 1 Noctuidae Chilodes maritimus * 25 Noctuidae Arenostola phragmitidis * 30 Noctuidae Chortodes brevilinea * 2 Noctuidae Celaena leucostigma * 18 Noctuidae Mythimna flammea * 9 Noctuidae Chilodes maritimus * 5 Noctuidae * 32 Noctuidae Chortodes brevilinea * 3 Noctuidae Mythimna straminea * 98 Noctuidae Macrochilo cribrumalis 1 Noctuidae Nonagria typhae * 5 Noctuidae Mythimna flammea * 6 Noctuidae Schrankia costaestrigalis 1 Noctuidae Mythimna pudorina * 11 Noctuidae Simyra albovenosa * 48 Noctuidae Mythimna straminea * 53 Pyralidae Acentria ephemerella 1 Noctuidae Nonagria typhae * 5 Pyralidae Calamotropha paludella * 1 Noctuidae Plusia festucae 1 Pyralidae Cataclysta lemnata * 16 Noctuidae Simyra albovenosa * 10 Pyralidae Chilo phragmitella * 54 Adaina microdactyla 1 Pyralidae Donacaula forficella 1 Pyralidae Calamotropha paludella * 1 Pyralidae Eudonia pallida * 10

Pyralidae Cataclysta lemnata * 2 Pyralidae Nascia cilialis * 1 Pyralidae Chilo phragmitella * 23 Pyralidae Parapoynx stratiotata 7 Pyralidae Elophila nymphaeata 1 Pyralidae Phlyctaenia perlucidalis 2 Pyralidae Eudonia pallida * 3 Pyralidae Schoenobius gigantella * 51 Pyralidae Nascia cilialis * 2

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Pyralidae Schoenobius gigantella * 14 Tortricidae Phalonidia manniana 3

There were 29 wetland specialist moth species found in the Hundred Acre reedbed and 32 in the reedbed around the Broad. The two areas had 23 wetland specialist species in common. Hundred Acre had higher abundances of wetland specialists than the reedbed around the Broad. What reedbed habitat conditions were associated with maximum number of Lepidoptera species at the survey sites? Number of species Firstly raw number of species per trap was analysed in relation to the different habitat variables. Then this analysis was repeated on bootstrapped number of species (examining average diversity within a fixed number of individuals in each trap).

Figure 6.29: Relative importance of habitat variables in explaining variation in number of moth species per trap. These models explained 94 % of the variance.

Site Differences between sites were more important than habitat variation within sites in explaining moth species diversity in traps. Hickling Broad and Stodmarsh had higher moth species diversity per trap than Ham Wall. Since site differences were more important than other habitat variables, and sites cannot be analysed separately since there are only 12 traps per site, these results may be confounded by differences in habitat variables between sites.

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Figure 6.30: Relationship between number of species in moth traps and site

Litter saturation 2009 Points where the litter did not flood entirely in the summer season before surveys tended to trap more moth species than points where the litter did flood.

Figure 6.31: Relationship between number of species in moth traps and litter saturation 2009

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Litter saturation 2010 Points where the litter did not flood entirely in the season of surveys tended to trap more moth species than points where the litter did flood.

Figure 6.32: Relationship between number of moth species in traps and litter saturation 2010

Mean reed height Traps with shorter reed tended to trap more moth species. However this appears to be a product of Ham Wall trap locations having short reed and low numbers of moths trapped.

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Figure 6.33: Relationship between number of species in moth traps and mean reed height 37

Litter depth 2010 Traps with deeper litter tended to trap more moth species.

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Pole height Surprisingly points with shorter poles tended to trap more moth species, which was the opposite of expectations. We expected traps mounted on taller poles to draw species in from a larger area. By including this variable in models along with reed height, we have taken into account the interaction between pole height and reed height.

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Plant species richness Points with greater plant species richness were associated with greater numbers of moth species.

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Figure 6.36: Relationship between number of species in moth traps and plant species richness

Bootstrapped number of species Important habitat variables in explaining variation in moth species diversity Points closer to scrub tended to trap a greater diversity of moth species. Points with short reed (and this was correlated with thinner reed) tended to trap a higher diversity of moths. This was not necessarily because these points were more exposed because pole height was included in the model. Points where the litter had not fully saturated in the four months preceding the survey were associated with a higher overall diversity of moths. However points with standing water during surveys trapped only a slightly lower species diversity of moths than points without standing water. Points with high plant species richness tended to be associated with a high diversity of moth species. Ham Wall had a lower overall moth diversity than the other two sites. Points with deeper litter tended to trap a higher diversity of moth species but this may be a product of the sites sampled.

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Figure 6.29: The relative importance of habitat variables in explaining variation in bootstrapped number of moth species. These models explained on average 92% of the variance.

Distance to Scrub Points that were closer to scrub tended to trap a more diverse array of moth species. This would be expected since more plant species will support a greater range of species, such as those associated with carr or other plants linked with a later stage in plant succession. However traps along a gradient of increasing distance from scrub would be good to confirm this trend.

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Distance to Scrub Distance to Scrub Figure 6.37: Relationship between distance to scrub and bootstrapped number of moth species

Mean reed height Points with shorter reed tended to have a more diverse array of species caught in traps. There is the possibility that points where reed was taller than the trap were not able to attract moths over such a large area as points where reed was below the trap. This factor probably had some effect. However when pole height was included in models it was much less important than mean reed height in explaining bootstrapped number of species. Also by bootstrapping, we are measuring the diversity of moths within a set number of individuals, so this reduces the effect of traps that trap more individuals trapping more species.

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Pole height Pole height Figure 6.38: Relationship between pole height and bootstrapped number of species 41

If pole height had a major effect, we would expect it to have been an important factor in the model and for taller poles to be linked to higher moth species richness. In fact, shorter poles were linked with higher moth species richness and this was true even when the outlier of the extremely tall pole with low moth species diversity was removed.

Litter saturation 2010 Litter saturation in 2010 was much more important than in 2009. Points where the litter was never fully saturated were associated with higher moth species diversity. When all species are considered there is a greater range of life cycles, so perhaps this is why both litter saturation in 2009 and 2010 are important in explaining overall diversity.

Figure 6.39: Relationship between litter saturation and bootstrapped number of moth species.

Standing water Points with lower levels of standing water during surveys tended to trap a more diverse array of moth species. However points with higher levels of standing water trapped almost as many moth species. Perhaps this reflects wet and dry areas supporting a different suite of species.

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18.6 Bootstrapped No. SpeciesNo. Bootstrapped

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Standing Water Standing Water Figure 6.40: Relationship between standing water around the trap during surveys and bootstrapped number of moth species trapped

Site

Figure 6.41: Relationship between site and bootstrapped number of species

Hickling Broad had the greatest number of moth species and the greatest abundance of individuals. This could be because Hickling Broad is the largest site. When abundance was accounted for by bootstrapping, Stodmarsh had equal bootstrapped number of species per trap to Hickling Broad. Stodmarsh had the greatest variance of number of species between traps. Ham Wall had the lowest 43

abundance and diversity of moths, but abundance was not far behind Stodmarsh. The lower diversity at Ham Wall is likely to be a combination of its newer age and western location. 20 of the 187 species found at Hickling Broad and Stodmarsh combined would not be expected to occur in the SW which accounts for the lower species diversity at Ham Wall to some extent.

Table 6.12: Number of species and abundance of moths at each site Hickling Broad Ham Wall Stodmarsh

(n=12) (n=12) (n=12) Number of traps 135 78 128 Total number of moth species Total abundance of moth 2222 1470 1573 individuals Average of number of moth 42.08 27.25 37.17 species Variance of number of moth 105.90 71.84 141.06 species Average of abundance of moth 185.17 122.50 131.08 individuals

Litter saturation 2009 This variable was included because it may have affected moths that pupated in the litter the previous year. Again points where the litter did not totally flood were associated with a higher overall moth species diversity.

Figure 6.42: Relationship between litter saturation 2009 and bootstrapped number of moth species. 44

Plant richness Points with higher plant diversity had a higher diversity of moths trapped at them, as expected.

Partial Plot Raw Data

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Plant richness Plant Richness Figure 6.43: Relationship between plant species richness and bootstrapped number of moth species

Litter depth 2010 Points with deeper litter were associated with greater overall moth diversity. However this may be a product of the sites surveyed. Further surveys would reveal more about this trend.

Partial Plot Raw Data

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0 10 20 30 40 Bootstrapped No. SpeciesNo. Bootstrapped

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Litter Depth 2010 Litter Depth 2010 Figure 6.44: Relationship between litter depth 2010 and bootstrapped number of species

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What habitat variables were associated with moths with a conservation status? This analysis relates the conservation score of each moth trap to the habitat variables measured in the vicinity.

Figure: Relative importance of habitat variables in explaining variation in conservation scores between traps. These models explained 95% of the variance.

Site

Hickling Broad had a higher number of species of high conservation importance than the other two sites.

Figure: Relationship between conservation score in moth traps and site 46

Mean reed height

Traps with tall reed were associated with lower numbers of moths of conservation status. However from the scatter plot this appears to be a product of the sites surveyed. Ham Wall had taller reed and fewer moths with a conservation status that the other sites.

Partial Plot Raw Data

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80

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38 20

1.5 2.0 2.5 1.5 2.0 2.5

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Figure: Relationship between conservation score in moth traps and mean reed height

Plant species richness

Points with higher plant species richness tended to trap more moths with a conservation status. This reflects the range of food plants within the UK BAP widespread but declining species.

Partial Plot Raw Data 11

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standing.water.summer.2010

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Plant richness Plant richness

Figure: Relationship between conservation score in moth traps and plant species richness 47

Litter saturation 2009

Points where the litter was not fully saturated in the summer season 2009 tended to trap more species of conservation status. This may be because some moths pupated in this litter the year before, or it may reflect more general habitat preferences for areas that tend to have dry periods.

Figure: Relationship between conservation score in moth traps and litter saturation 2009

Litter saturation 2010

Points where the litter was not fully saturated in the summer season 2010 tended to trap more species of conservation status.

48 Figure: Relationship between conservation score in moth traps and litter saturation 2010

Standing water 2010

The highest conservation scores in moth traps were at dry points. However points with standing water did also trap species with conservation statuses.

Partial Plot Raw Data

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Standing Water Standing Water

Figure: Relationship between conservation score in moth traps and standing water 2010

Composite habitat variables A number of the measured habitat variables are associated with each other, due to the nature of reedbed successional habitat. Although this inter-correlation was controlled for in random forest models, a separate principle component analysis (PCA) was carried out to see if similar results were found when composite habitat variables were used.

Summary of tests correlating principle components for each survey dataset with bootstrapped number of species in moth traps The first four principle components explained 81% of the data. Principle component 1 was most strongly negatively correlated with bootstrapped number of species in moth traps. PC1 represents a gradient of increasing reed height, increasing stem diameter and decreasing stem density. Shorter, thinner reed at higher stem densities was correlated with higher bootstrapped species number of moth species in this dataset. Random forest analysis also found shorter, thinner reed to be associated with greater moth species diversity. Plant species richness was important in random forest models, which is less evident here with only a weak correlation between PC2 and bootstrapped number of species. This may be because wetter points tended to have high stem density and high plant species richness. Stem density was a large contributor to PC1 so may have already explained some of the variation associated with plant diversity, leading to a reduced effect of plant diversity.

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Table 6.13: Principle components (rotated varimax) for the moth dataset habitat variables PC1 PC2 PC3 PC4 Standing Water 0.140 -0.501 0.490 -0.200 Litter depth -0.369 -0.290 0.443 -0.333 Reed height 0.512 -0.336 -0.141 -0.207 Diameter 0.557 -0.163 0.043 0.059 Plant species richness -0.004 0.490 0.022 -0.750 Stem density -0.405 -0.358 -0.141 -0.132 Dead stems (%) 0.036 -0.277 -0.642 -0.430 Distance to scrub 0.326 0.285 0.333 -0.193 Standard Deviation 1.581 1.350 1.178 0.878 Proportion of variance 0.312 0.228 0.174 0.096 Cumulative proportion of 0.312 0.540 0.714 0.810 variance

Table 6.14: Results of Pearson’s correlation test between the first four principle components and bootstrapped number of species in moth traps Component Pearsons df t p PC1 -0.541 34 -3.75 0.000657 *** PC2 0.24 34 1.439 0.1594 PC3 -0.068 34 -0.397 0.6937 PC4 -0.139 34 -0.820 0.418

These Pearson’s tests show what composite habitat variables correlate most strongly with bootstrapped number of species. In other words, the environmental gradients that are most strongly associated with differences in diversity of moths. The results validate the outcomes of the random forest models, since many of the same trends were seen. It is interesting to note that the principle components varied between the three invertebrate datasets, showing that the key environmental gradient results are very dependent on where the sampling points were placed.

References Breiman, L. (2001). Random forests. Machine Learning J. 45 5- 32.

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Hochachka WM, Caruana R, Fink D, Kelling S, Munson A, Riedewald M, Sorokina D (2007) Data mining for discovery of pattern and process in ecological systems. e Journal of Wildlife Management, 71: 2427–2437

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UK BAP (2007) UK Priority Species data collation Chortodes brevilinea version 2 updated on 15/12/2010. Available at http://www.jncc.gov.uk/_speciespages/2169.pdf. [Accessed 26.01.11]. Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1: 3–14.

Appendix Table 6A: List of reedbed specialist moths trapped in these surveys with internal feeders highlighted in bold (as defined by experts) Family Species Notes Arctiidae Pelosia obtusa External feeder, probably on algae on reed litter. Cossidae Phragmataecia castaneae Internal feeder, pupates in stem, Common reed probably an internal feeder, pupating in the stem, Gelechiidae Brachmia inornatella Common reed Noctuidae Archanara dissoluta Internal feeder, pupates in stem, Common reed Noctuidae Archanara geminipuncta Internal feeder, pupates in stem, Common reed Noctuidae Arenostola phragmitidis Internal feeder, pupates in stem, Common reed Internal feeder, probably pupates in stem, Noctuidae Chilodes maritimus Common reed Internal feeder, pupates in plant debris, Common Noctuidae Chortodes brevilinea reed External feeder. Hides in broken stem by day, Noctuidae Mythimna flammea pupates in stem, Common reed External feeder. Hides in dead or fallen stems, Noctuidae Mythimna obsoleta pupating in situ, Common reed External feeder. Hides by day in old stems or leaf litter. Probably pupates on the ground. Common Noctuidae Mythimna straminea reed and Reed canary-grass External feeder. Pupates on or near the ground. Noctuidae Simyra albovenosa Mainly on Common reed. Internal feeder, pupates in stem, Common Reed Pyralidae Chilo phragmitella or Reed Sweet-grass External feeder, pupating in a spinning. Common Pyralidae Donacaula forficella Reed, Reed Sweet-grass, bur-reed and sedges. Internal feeder, pupates in stem, Common Reed, Reed Sweet-grass, Greater Pond Sedge and other Pyralidae Donacaula mucronellus sedges

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Internal feeder, pupates in stem, Common Reed Pyralidae Schoenobius gigantella and Reed Sweet-grass.

Table 6B: List of wetland specialist moths trapped in these surveys (wetland specialists as defined by experts) Family Species Notes Arctiidae Pelosia obtusa Reedbed specialist External feeder, pupates in plant debris, Water Arctiidae Spilosoma urticae mint, Water dock etc. External feeder. Prob. mosses and lichens, Arctiidae Thumatha senex pupating on ground External feeder, pupating in a case often on or near the foodplant. Hemp agrimony, common Coleophoridae Coleophora follicularis fleabane. In the flower-spike, pupating therein. Bulrush or Cosmopterigidae Limnaecia phragmitella Lesser bulrush Cossidae Phragmataecia castaneae Reedbed specialist Gelechiidae Brachmia inornatella Reedbed specialist Gelechiidae Monochroa divisella Not known, poss. on Iris External feeder, prob. pupating on the ground, Geometridae Orthonama vittata Marsh bedstraw, Heath bedstraw Carr, external feeder, prob. pupating in plant Geometridae Pterapherapteryx sexalata debris, sallows etc. Internal feeder, pupating in the stem. Burr-reed, Glyphipterigidae Orthotelia sparganella Iris spp. or Reed sweet-grass. Internal feeder, pupates on the ground or in leaf Noctuidae Apamea ophiogramma litter. Reed Canary-grass, Reed sweet-grass Noctuidae Archanara dissoluta Reedbed specialist Noctuidae Archanara geminipuncta Reedbed specialist Internal feeder, pupating in the stem. Bulrush, Noctuidae Archanara sparganii Lesser bulrush, Yellow Iris etc. Noctuidae Arenostola phragmitidis Reedbed specialist Internal feeder, pupating in leaf litter. Yellow iris Noctuidae Celaena leucostigma and Great fen-sedge Noctuidae Chilodes maritimus Reedbed specialist Noctuidae Chortodes brevilinea Reedbed specialist Internal feeder, pupating in the stem. Jointed Noctuidae rufa rush, , Sharp-flowered rush, Soft-rush Noctuidae Macrochilo cribrumalis External feeder, pupating in plant debris. Poss. 52

on Wood sedge, Hairy wood-rush etc. Noctuidae Mythimna flammea Reedbed specialist Noctuidae Mythimna obsoleta Reedbed specialist External feeder, pupates close to the ground. Noctuidae Mythimna pudorina Broad-leaved grasses Noctuidae Mythimna straminea Reedbed specialist Internal feeder, pupating in the stem. Bulrush Noctuidae Nonagria typhae and occ. Lesser bulrush

External feeder, pupating between leaves of Noctuidae Plusia festucae rushes etc. Tufted sedge, Glaucous sedge etc. Noctuidae Schrankia costaestrigalis unknown in the wild Noctuidae Simyra albovenosa Reedbed specialist Internal feeder, pupating in the stem. Hemp Pterophoridae Adaina microdactyla agrimony. Aquatic, pupating under the water. Canadian Waterweed, pondweeds, stoneworts, filamentous algae and possibly other water- Pyralidae Acentria ephemerella plants. Internal feeder, pupating in the stem. Bulrush Pyralidae Calamotropha paludella and occ. Lesser bulrush Aquatic, pupating just below water surface. Pyralidae Cataclysta lemnata Duckweed, including Greater Duckweed. Pyralidae Chilo phragmitella Reedbed specialist Pyralidae Donacaula forficella Reedbed specialist Pyralidae Donacaula mucronellus Reedbed specialist Aquatic, pupating just below water surface. Pyralidae Elophila nymphaeata Polyphagous on a range of water plants In a spinning, probably pupating therein. Poss. Pyralidae Eudonia pallida on range of mosses and lichens on the ground External feeder, prob. pupating amongst sedge litter. Great Fen-sedge, Greater-pond Sedge and Pyralidae Nascia cilialis other sedge species Aquatic, pupating near water surface. Pondweeds, Canadian Waterweed, Hornwort Pyralidae Parapoynx stratiotata and other water-plants. External feeder, prob. pupating near the ground. Creeping Thistle, Marsh Thistle and Pyralidae Phlyctaenia perlucidalis probably other thistle species Pyralidae Schoenobius gigantella Reedbed specialist

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Internal feeder, pupating in the stem. Common Tortricidae Bactra furfurana club-rush and Sharp-flowered rush Internal feeder, pupating in the stem. Water Tortricidae Phalonidia manniana mint and Gipsywort Carr, pupation site not known. Prob. on algae and lichens on bushes etc. This was on ISIS list, Arctiidae Pelosia muscerda but not the Experts list! External feeder. Pupates in leaf litter or in a dead stem. Grasses inc. Reed canary-grass. This Noctuidae Apamea unanimis was on ISIS list, but not the Experts list! Carr. External feeder, pupates under bark or on Noctuidae Parastichtis ypsillon the ground, sallow, willow, poplars.

Fenn’s Wainscot, Chortodes brevilinea Table 6C: Records of Fenn’s Wainscot at Hickling Broad Trap number Grid Reference Date Number of individuals Trap 17 TG4364021000 21/07/2010 2 Trap 11 TG4367121320 09/08/2010 1 Trap 8 TG4334021209 09/08/2010 1 Trap 1 TG4277120996 20/07/2010 1

Points 8 and 11 were in the newer reedbed “100 Acre” whereas points 17 and 1 were in the older reedbed fringing the broad.

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Figure 6A: Hydrology of the points where Fenn’s Wainscott was recorded.

Table 6D: The habitat variables measured at moth trapping points Trap number Fenn’s points All Hickling Points Habitat Variable HB1 HB8 HB11 HB17 max mean min max mean min Live Reed Height 0.57 0.45 0.3 0.17 0.57 0.37 0.17 0.67 0.34 0 Dead Stems % 0.75 0.51 0.44 0.51 0.75 0.55 0.44 0.75 0.4 0 Dead Reed Height 0.8 0.64 0.67 0.34 0.8 0.61 0.34 0.89 0.62 0.15 Maximum Reed Height 0.8 0.64 0.67 0.34 0.8 0.61 0.34 0.89 0.58 0.14 Diameter 3.57 3.11 2.52 1.91 3.57 2.78 1.91 3.57 2.88 1.91 Standing Water 0 8.67 5.67 0 8.67 3.58 0 9.5 3.38 0 Plant Richness 12 0 4 6 12 5.5 0 18 6.58 0 Litter Depth 2010 19.5 12.5 15.25 2.25 19.5 12.38 2.25 38.25 17.96 2.25

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Litter Depth 2009 23.5 44.75 47 15.75 47 32.75 15.75 51.5 32.83 6.5 Live Stem Density 54 256 127 102 256 134.76 54 388 154.08 54 Dead Stem Density 206 307 107 121 307 185.24 107 307 120.08 0 Total Stem Density 260 563 234 223 563 320 223 563 274.16 79 Litter Saturation 2009 W W W W * * * * * * Litter Saturation 2010 P W W P * * * * * * Scrub distance(m) 47 47 126 86 126 76.5 47 218 93.5 25

Table 6E: Recorded declines in the UK BAP widespread but rapidly declining moth species trapped during reedbed surveys Species Common name Reason for BAP status Acronicta rumicis Knot Grass Declined by 80% over the last 35 years Amphipoea oculea Ear Moth Declined by 71% over the last 35 years. Amphipyra tragopoginis Mouse Moth Declined by 73% over the last 35 years Apamea remissa Dusky Brocade Declined by 76% over the last 35 years Arctia caja Garden Tiger Declined by 89% over the last 35 years. Brachylomia viminalis Minor Shoulder-Knot Declined by 73% over the last 35 years. Caradrina morpheus Mottled Rustic Declined by 73% over the last 35 years Celaena leucostigma Crescent Declined by 82% over the last 35 years Hoplodrina blanda Rustic Declined by 75% over the last 35 years Hydraecia micacea Rosy Rustic Declined by 86% over the last 35 years. Malacosoma neustria Lackey Declined by 90% over the last 35 years. Melanchra pisi Broom Moth Declined by 77% over the last 35 years Mythimna comma Shoulder-striped Wainscot Declined by 72% over the last 35 years. Orthonama vittata Oblique Carpet Declined by 83% over the last 35 years Pelurga comitata Dark Spinach Declined by 95% over the last 35 years. Spilosoma lubricepeda White Ermine Declined by 77% over the last 35 years Spilosoma luteum Declined by 73% over the last 35 years Timandra comae Blood-vein Declined by 79% over the last 35 years

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Watsonalla binaria Oak Hook-Tip Declined by 81% over the last 35 years

Table 6F: GPS coordinates of moth trap locations Site Trap code Easting Northing HB HB1 642771 320996 HB HB11 643671 321320 HB HB12 643628 321411 HB HB13 643117 320887 HB HB16 643420 320870 HB HB17 643640 321000 HB HB19 643572 320772 HB HB3 642978 321354 HB HB5 643270 321420 HB HB7 643143 321107 HB HB8 643340 321209 HB HB9 643441 321093 HW HW1 346500 140150 HW HW10 346424 139867 HW HW12 345978 140463 HW HW13 346310 140120 HW HW14 346378 139815 HW HW20 346620 140420 HW HW21 346827 140256 HW HW3 345739 140545 HW HW4 345995 140182 HW HW7 346034 139844 HW HW8 346546 140362 HW HW9 346546 140175 SM SM1 621570 161560 SM SM11 622820 161820 SM SM12 622770 161790 SM SM13 623184 162533 SM SM16 623400 162505 SM SM17 623472 162790 SM SM19 623719 162684 57

SM SM2 621590 161180 SM SM21 623949 162720 SM SM6 622230 161170 SM SM8 622160 161890

58