аЯрЁБс>ўџ ZіјўџџџШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюя№ёђѓєѕњ}ѓ\фMЮЦT§LѕCь{фpыf к [!м!]"л"\#н#^$п$`%с%b&у&d'х'f(ч(h)щ)j*ы*l+ў+t,џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџьЅС јП kbjbjUqUq -77UQТџџџџџџlІІІІ$> &a&a&aаіeфкgм > м,ВТuдvԘw˜w˜whŒhŒhŒQ+S+S+S+S+S+S+$Ž. Ў0Dw+hŒъ‰zdŒhŒhŒw+™ІІ˜w˜wR|,$™™™hŒІ<˜w^†˜wQ+™hŒQ+™X™hŸњЯlт|ф6б˜wЖu P .а.VХ> шV&av‘&…а*бФY ,<м,Џаођ0œ–tђ0б™> > ІІІІй BTO Research Report No. 367 The production of population trends for UK mammals using BBS mammal data, 1995-2002 Stuart Newson & David Noble March 2005 A report by the British Trust for Ornithology under contract to the Joint Nature Conservation Committee (Contract No. F90-01-427) BBS is funded by a partnership of the British Trust for Ornithology, the Joint Nature Conservation Committee (on behalf of English Nature, Scottish Natural Heritage and the Countryside Council for Wales, and also on behalf of the Environment and Heritage Service in Northern Ireland) and the Royal Society for the Protection of Birds. Mammal monitoring within the BBS is part of a wider suite of schemes looking at the changing fortunes of our mammal populations. These schemes are coordinated through the Tracking Mammals Partnership. Љ British Trust for Ornithology British Trust for Ornithology The Nunnery, Thetford, Norfolk, IP24 2PU Registered Charity No. 216652 S.E. Newson & D.G. Noble The production of population trends for UK mammals using BBS mammal data, 1995-2002 BTO Research Report No. 367 A report by the British Trust for Ornithology under contract to the Joint Nature Conservation Committee (Contract No. F90-01-427) Published in March 2005 by the British Trust for Ornithology The Nunnery, Thetford, Norfolk IP24 2PU, UK Copyright Љ British Trust for Ornithology ISBN 1-904870-12-0 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form, or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publishers. CONTENTS Page No LIST OF TABLES AND FIGURES 3 EXECUTIVE SUMMARY 5 1. INTRODUCTION 7 2. METHODS 9 2.1 Survey methods 9 2.2 Temporal trends in abundance 9 2.3 Temporal trends in presence 10 2.4 Mapping the spatial distribution of British mammals 10 3. RESULTS 15 3.1 Temporal changes in abundance 15 3.2 Temporal changes in presence 26 3.3 Interpolated maps of abundance 28 4. DISCUSSION 31 4.1 UK populations trends from sightings 31 4.2 Factors affecting population change 31 4.3 Regional trends 32 4.4 Analyses by habitat 32 4.5 Population trends from presence/absence data 32 ACKNOWLEDGEMENTS 35 REFERENCES 37 APPENDICES 39 LIST OF TABLES AND FIGURES Page No TABLES Table 2.1 Definition of seven aggregate habitat classes and associated subclasses 13 Table 3.1 Number of BBS squares across the UK recording individual mammal species by recording category. 16 Table 3.3.1 Comparison of model fit and error associated with the prediction of Brown Hare abundance across the UK from BBS sightings data for 1995 and 2002 And CEH lancover data aggregated into seven habitat categories 29 FIGURES Figure 2.1 The location of 1 km BBS squares surveyed for mammals (1995-2002) 12 Figure 2.2 English Governement Office Regions and Country boundaries 12 Figure 2.3 The six Environmental Zones of Great Britain used in analyses of landscape types 13 Figures 3.1.1 to 3.1.9 Species accounts 17-25 Figure 3.2.1 Summary of the change in presence on BBS squares of six mammal species 26 Figure 3.3.1 Interpolated abundance of Brown Hare from BBS mammal data 30 EXECUTIVE SUMMARY The BTO/RSPB/JNCC Breeding Bird Survey (BBS) was expanded in 1995 to record mammals as well as birds. This was the first multi-species, annual mammal survey to be carried out in the UK. It focuses on large-sized easily identifiable species, although observers record any mammal species seen or known to be present. In this report we build upon the findings and recommendations of Newson & Noble (2003) by using BBS mammal data for 1995-2002 to generate estimates of population change. Annual indices of relative abundance are produced at a national scale for nine mammal species for 1995-2002 - Brown Hare, Mountain/Irish Hare, Rabbit, Grey Squirrel, Red Fox, Red Deer, Fallow Deer, Roe Deer and Reeves’s Muntjac. Comparing the abundance of these species in 2002 relative to 1995, Grey Squirrel, Roe Deer and Reeves’s Muntjac were significantly higher in 2002, whilst Rabbit, Mountain Hare, Red Fox, Red Deer and Fallow Deer were significantly lower. Several of these species show significant fluctuations in abundance between years, whereas Roe Deer and Reeves’s Muntjac have increased progressively during this time. Newson & Noble (2003) examined the potential for producing regional indices of relative abundance for three broad regions of Britain, the north, the south-east and the south-west. These regions do not correspond with any political jurisdictions and for this reason, we explore here the production of population trends for the nine English Government Office Regions (GOR) and the four countries that constitute the UK. Indices of relative abundance could be produced for five mammal species (Brown Hare, Rabbit, Grey Squirrel, Red Fox and Roe Deer) for two or more regions. Additionally, data were sufficient to produce trends for Red Deer in Scotland and for Fallow Deer and Reeves’s Muntjac in England. It is recommended that in the future population trends be produced at the GOR and country level where data permit. Population trends are produced for government Environmental Zones for the most commonly sighted species. Environmental Zones are categories of landscapes found in the UK from the lowlands of the south and east, to the uplands and mountains of the north and west. The resolution of these analyses is at the 1 km square level, and hence this approach is comparable with other mammal surveys associated with the Tracking Mammals Partnership, such as the BTO/MS Winter Mammal Monitoring (Noble et al. 2002). There are six mammal species (Badger, Mole, Hedgehog, Brown Rat, Stoat and Weasel) for which there were insufficient count data to produce indices of abundance, but for which observers collected a large amount of information on presence/absence from field signs, dead animals or local knowledge. These data were used to examine their change in presence/absence on BBS squares over time. As discussed in Newson & Noble (2003), interpreting the data from the first few years may be difficult because they may reflect increasing awareness by the observer of the presence of a particular species. With existing data, it is not possible to assess the significance of this potential bias. However, since 2002 observers have recorded the criteria that they used for reporting presence (live animals, field signs, dead animals, local knowledge of presence from that season or live animals seen on additional visits), which should aid interpretation in the future. We present information on the change in presence on BBS squares of these six species from 1996 to 2002 and discuss reasons why caution is needed in interpreting these trends. We explore the potential of geostatistics for examining finer scale spatial patterns in relative abundance than is possible through the production of regional indices or visually through the production of distribution maps of species presence. Geostatistical methods are based on statistical models that model autocorrelation (statistical relationship among measured points). Using Brown Hare as an example and the geostatistical method of co-kriging, we explore the extent to which CEH landcover data improves the model fit and hence prediction of relative abundance for 1995 and 2002. In this example it was found that including arable habitat as a predictor greatly improved the model fit, which was improved slightly further by including moorland and heath in the model. In a similar way, statistically valid maps of this type could be used to produce maps of presence/absence using indicator co-kriging. Analyses of this type are at present time-consuming for the analyst as well as computationally, so it is not suggested that interpolated maps of this type are produced routinely. However, the results for this species are encouraging and demonstrate the potential of this methodology for the future. Data for a large proportion of mammal species recorded by the BBS are insufficient to calculate robust indices of relative abundance or occurrence. However, these data still provide important information on the distribution of species, which in many cases are not properly monitored by any existing scheme. For most of these species, it would not be useful to produce annual maps of distribution, but distribution maps of species presence over intervals of perhaps five or ten-year blocks might be considered as more data are collected. There is also the potential for combining these data with those from other surveys and perhaps with incidental records through the National Biodiversity Network to provide a better understanding of species distribution and perhaps if temporal data were available, identify changes in distribution over time. Using the geostatistical methods trialed here, one could predict species presence at unsurveyed/unrecorded sites and by controlling for survey/recorder coverage using declustering there is potential for producing unbiased maps of species distribution. 1. INTRODUCTION Whilst data on national distribution and abundance are available for most British mammal species (e.g. Brown Hare Lepus europaeus: Hutchings & Harris, 1996; Badger Meles meles: Wilson et al., 1997; Otter Lutra lutra: Strachan & Jefferies, 1996; Hazel Dormouse Muscardinus avellanarius: Bright et al. 1996; Yellow-necked Mouse Apodemus flavicollis: Marsh 1999; Water Vole Arvicola terrestris: Strachan et al. 2000; Pine Marten Martes martes: Strachan et al. 1996; Polecat Mustela putorius: Birks & Kitchener 1999), reliable information on population change is sparse. Few surveys have been carried out in a standardized manner to allow comparisons to be made between surveys, and surveys are often not repeated frequently enough to separate the underlying population change from natural between-year variation. This lack in reliable monitoring data is highlighted in a review of population estimates and conservation status of British mammals (Harris et al. 1995) and more recently by Macdonald & Tattersall (2001). Annual monitoring data of this type are important for a number of reasons, including the setting of conservation priorities, the management of pest species and sustainable use of game species and for examining the effect of change in land-use, habitat or climate (Battersby & Greenwood 2004). In response to the scarcity of reliable mammal monitoring data, in 1995 the British Trust for Ornithology (BTO), with the agreement from its partners, the Royal Society for the Protection of Birds (RSPB) and the Joint Nature Conservation Committee (JNCC), expanded the scope of the national bird-monitoring scheme, the Breeding Bird Survey (BBS) to also collect information on British mammals. BBS observers, who are almost all volunteers, were asked to provide information on any mammals detected or known to be present whilst carrying out bird surveys on randomly allocated 1-km squares or during any other visits to these sites. This is the first multi-species, annual mammal survey to be carried out in the UK and although the focus was on medium to large sized easily identifiable species, observers have the opportunity to record any mammal species. In this report we update and develop preliminary analyses of BBS mammal data (Newson & Noble 2003) to produce population trends (trends in relative abundance) from count data for the most commonly sighted species of British mammal (Brown Hare, Mountain Hare, Rabbit, Red Fox, Grey Squirrel, Roe Deer, Red Deer, Fallow Deer and Reeves’s Muntjac) using data from the first eight years of the survey, 1995-2002. Where data are sufficient, we present trends at a regional level (nine English Government Office regions and four countries of the UK) and for different landscape types (six Environmental Zones within Great Britain). Northern Ireland has its own set of Environmental Zones that have been devised on a different basis to those used for Great Britain. Because the number of sites surveyed in Northern Ireland is small, we do not consider it worth examining the production of separate trends for this region. There are several species for which there are seldom sufficient count data to produce reliable indices of abundance. However, a large amount of indirect information on their occurrence from field signs, dead animals or local knowledge is collected and with which it may be possible to examine the change in presence over time. In this report we examine the change in presence on BBS squares for six species (Badger, Mole, Hedgehog, Brown Rat, Stoat and Weasel). A distribution map is produced for each of the fifteen species for which we examine the change in abundance or presence on BBS squares from information that demonstrates the presence of that species in one or more years of the survey. We discuss the utility of maps of this type for highlighting the strongholds of particular species, and trial an alternative approach for interpolating maps of relative abundance using geostatistical methods. 2. METHODS 2.1 Survey methods The BBS uses a stratified random sampling design, with 1 km squares from the National Grid assigned randomly within BTO regions (Noble et al. 2004). The survey is coordinated at BTO headquarters through a network of volunteer Regional Organisers, who are responsible for the volunteer observers in their region. All recording forms, including the mammal data are returned to the BTO after the field season for input and analyses over the winter. Mammal recording is carried out during the course of the bird surveys. In total BBS fieldwork involves three visits to each survey square per year. On the first visit, a transect route through the allocated 1 km square is determined comprising two roughly parallel lines, ideally 500 m apart and 250 m from the edge of the square and divided into ten equal sections of 200 m in length. Habitat is recorded for each transect section according to an established system, common to a range of BTO schemes (Crick 1992), although these data are not examined here. All mammals detected from the transect lines during the two bird counts are counted and recorded. The first BBS visit is made between April and mid-May and the second at least four weeks later between mid-May and the end of June. BBS visits are timed to start at between 0600 and 0700 hours and to last less than two hours. Visits during heavy rain, strong winds or poor visibility are discouraged. Unlike the BBS bird data, data for mammals are recorded within a single distance category. In order to collect information on widespread but seldom seen species such as Mole and Badger, observers are asked to record the presence of mammal species on the basis of counts of live and dead animals, counts made on any additional visits to the square, from field signs (e.g. tracks, droppings, molehills) or known to be present that season from local knowledge (e.g. from a gamekeeper or landowner). Prior to 2002, observers did not record the method or methods by which the species was known to be present, while since 2002 observers have recorded this information. The location of BBS squares recording mammals during the period 1995-2002 is shown in Figure 2.1. 2.2 Temporal trends in abundance For the species for which counts are made, the maximum number of each species of mammal sighted over the two visits (early and late) was determined for each 1 km square in each year from 1995 to 2002. Survey work was severely affected by foot-and-mouth restrictions in 2001, resulting in a heavy bias towards particular areas of the country. For this reason, we exclude survey data for 2001 from all analyses. Using these data, log-linear Poisson regression was used to model site counts, with site and year effects (ter Braak et al., 1994) for the UK, where the year effect is an index of the change in numbers relative to 1995, the first year of the survey. This year, (1995) is set to an arbitrary index value of 1 from which all other years are measured. Counts of animals can violate the assumption of a Poisson distribution, so corrections for over-dispersion are made using the dscale option in SAS (SAS 1996). As with many long-term surveys these data include many missing values, where a particular site was not surveyed in a particular year. The model is estimated using the observed counts to predict the missing counts and calculate the indices from a full data set, including the observed and predicted counts. The model requires that two points in the time series are available to estimate parameters, so squares counted in one year only are excluded from the analysis. If the data contain too many missing values, the model parameters cannot be estimated. Because the stratified random sampling design results in unequal representation of regions across the UK, annual counts are weighted by the inverse of the proportion of each region that is surveyed in that year. Only results for species occurring on a mean of 40 or more squares in two or more years over the seven years for which survey data are available are presented, because of the low precision associated with small sample sizes (Joys et al. 2003). The significance of the trends were examined by making a comparison between the first and last years of the survey. Because non-overlapping of 95% confidence intervals provides a crude means of assessing significance at the 5% level or more, separate formal analyses to examine differences between indices were not performed. To examine whether the UK trends are representative within different regions and landscape types, annual indices were produced in the same way as above, where data allowed, for the nine English Government Office Regions and for England, Scotland, Wales and Northern Ireland and for six Environmental Zones of Great Britain, shown graphically in Figures 2.2 & 2.3. The six Environmental Zones produced from the CS2000 field survey, are based on combinations of CEH land classes which cover the range of environmental conditions that we find in Great Britain, from the lowlands of the south and east, through to the uplands and mountains of the north and west (Bunce et al. 1996). Northern Ireland has its own set of Environmental Zones that have been devised on a different basis to those used for Great Britain. Because the number of sites surveyed in Northern Ireland is small, we do not consider it worth examining the production of separate trends for this region. 2.3 Temporal trends in presence For six species that are not counted in sufficient numbers for trend analysis, but which leave obvious field signs or which are known to be present within a BBS square, we examined the change in presence/absence on surveyed squares. Species presence is defined here as information demonstrating that the species is present on a BBS square in a particular year. This may include counts of live animals as used in the above analyses, dead animals, field signs (e.g. tracks, scats, mole-hills), local knowledge of presence for that year from a gamekeeper or landowner or live animals seen on additional visits to the square during that season. In response to recommendations made in preliminary analyses of BBS mammal data (see Newson & Noble 2003), a change in the survey form in 2002 asked observers to indicate the primary method or methods by which the species was recorded as being present. Preliminary examination of the data suggest that of those species that cannot be monitored through counts of live animals, it may be possible to monitor changes in presence of Badger, Brown Rat, Mole, Hedgehog, Stoat and Weasel. To examine whether there has been a significant change in the presence of these species on BBS squares, we modelled presence/absence as a function of site and year using logistic regression. The year effect here is the relative odds ratio, which is the odds of being present on a particular BBS square in a particular year relative to the odds of being present on that square in the first year in the time series. In these analyses we treat 1996 as if this were the first year in the series, because most species of interest appeared for the first time on the survey form in this year. To illustrate the concept of the odds ratio, if in the first year, the probability of being present is 0.2, the probability of being absent is 0.8. The odds of being present would therefore be 0.2/0.8 = 0.25. If, five years later, the probability of being present was 0.8 and the probability of being absent was 0.2, the odds of being present would be 4, and the odds ratio relative to the first year would be 4/0.25 = 16. Unlike the analyses of count data, the change in odds ratio described above is not intuitive. For this reason, we present simple figures showing the percentage change in the presence of these species on BBS squares, although use logistic regression to test the significance of this change. 2.4 Mapping the spatial distribution of British mammals Distribution maps that demonstrate the presence of that species on BBS squares could be produced for all species recorded on BBS squares. Whilst maps of this type provide useful information on the distribution of species, and are likely to highlight the strongholds of particular species, these may be biased towards areas of higher observer density if, as in the case of the BBS the survey is not strictly random (the BBS is stratified by region). Using sightings data for Brown Hare for 1995 and 2002 as an example, we trial here an alternative approach to interpolate statistically valid maps of relative abundance using geostatistical methods, using the Geostatistical Analyst extension of ArcGIS (Johnston et al. 2001). Advances in the application of geostatistics over the past ten years have improved the estimation and precision of predicting occurrence or relative abundance at non-surveyed sites and so allow the potential for producing reliable maps over the area of interest. Geostatistical methods are based on statistical models that model autocorrelation (statistical relationship among measured points). Not only do these techniques have the capability of producing a prediction surface, but they can also provide some measure of the accuracy of the predictions. A number of geostatistical interpolation techniques have been developed, of which kriging is the most applicable to this work. Kriging weights the surrounding measured values to derive a prediction for unsurveyed locations. In these, the weights are based on the distance between measured sites and the prediction location, but also on the overall spatial arrangement in the weights (the spatial autocorrelation). For a full discussion of geostatistics and geostatistical methods see Chiles & Delfiner (1999). Because mammal species show some form of habitat preference, we feel that it is important to examine the extent to which habitat may improve our predictions. For this we use Centre for Ecology and Hydrology (CEH) 2000 land cover data for simple co-kriging. CEH land cover data provides information on the proportions of each square that are of each of 27 habitat classes. In these trial analyses, we use data classified into seven aggregate classes as defined in Table 2.1. Information for sea and estuary, coastal and inland water and unclassified habitat are not used in the analyses here. In these trials we use each habitat in turn as a predictor of relative abundance. Once the best predictor habitat has been determined, a second habitat variable can be added to the model to examine whether this improves the reliability of predictions further. For the predictions to be unbiased (centered on the measurement values), the prediction errors should be close to zero. This depends on the scale of the data, which we standardize by dividing the prediction error by their prediction standard errors to give standardized mean prediction errors, which should also be close to zero. The predictions should also be as close as possible to the measurement values. To examine this we compute the root-mean-square prediction errors (the square root of the average of the squared distances between the predictions and their true values), for which the smaller the value the closer the model predicts the measured values. Because the BBS employs a stratified sampling design that results in unequal representation of coverage in different areas of the UK, we need to control for this in the analyses. For this we use the method of declustering, which preferentially weights the count data, with counts in densely sampled areas receiving less weight and counts in sparsely sampled areas receiving greater weight (see Isaaks & Srivastava 1989 for a further discussion of this method). This effectively decides how much the data at each site contributes to the calculation of autocorrelation functions across the entire data set. In Geostatistical Analyst there is a choice of two declustering methods that can be used: cell declustering, which arranges rectangular cells over BBS squares in a grid and weight attached to each BBS square is inversely proportional to the number of BBS squares in its cell; or polygonal declustering, which weights each BBS square in proportion to the areas that it represents. We choose the first method in preference to the second, because with the second, it is likely to be difficult to define weights towards the coastline of Britain. It should be noted that although several geostatistical methods require that the data be normally distributed, prediction maps do not require this assumption to be met. BBS count data is unlikely to ever be normally distributed because there are a substantial proportion of zero counts.  Figure 2.1 The location of 1 km BBS squares surveyed for mammals (1995-2002).  Figure 2.2 English Government Office Regions and Country boundaries used in the regional analyses.  Figure 2.3 The six Environmental Zones of Great Britain used in the analyses of landscape types. Table 2.1 Definition of seven aggregate habitat classes and associated subclasses. Aggregate class definition Subclass definition Mountain, heath, bog Bog (deep peat), open and dense dwarf shrub heath, montane habitats, inland bare ground Broad-leaved / mixed woodlandBroad-leaved / mixed woodlandConiferous woodlandConiferous woodlandImproved grasslandImproved grasslandSemi-natural grasslandNeutral grass, set-aside grass, bracken, calcareous grass, acid grassland, fen, marsh and swampArable and horticultureArable cereals, arable horticulture and arable non-rotationalBuilt up areas and gardensSuburban / rural development, continuous urban  3. RESULTS During 2002 mammal data were collected from a total of 1814 1 km BBS squares. The number (and percentage) of squares with counts for each species in each recording category (e.g. sightings of live animals, field signs) is shown in Table 3.1. This highlights those species for which data are sufficient to produce trends from sightings data and the additional species that are not counted in sufficient number for trend analyses, but which leave obvious field signs or which are known to be present within a BBS square and for which we examine the change in presence on BBS squares. 2002 was the first year in which observers were asked to record the method by which they report species presence. Prior to this, we have information on number of squares reporting sightings of each species, whilst the category presence is a combination of counts of live animals, dead animals, field signs (e.g. tracks, scats, mole-hills), local knowledge of presence for that year from a gamekeeper or landowner and live animals seen on additional visits to the square during that season. To examine 2002 in relation to other years, we present the number (and percentage) of BBS squares reporting sightings and presence of all species in Appendices 1a and 1b. When interpreting these tables, it is important to highlight a number of changes to the BBS mammal survey form, which have influenced the apparent abundance (and presence) on BBS squares of some mammal species. Whilst observers have always been asked to record all mammal species sighted or known to be present, the survey form lists a number of the most regularly recorded species with space for recording count and presence information. Following the first year of the survey, a number of species were added to the 1996 list, including Hedgehog, Brown Rat, Badger, Mole, Stoat and Weasel. Additionally in 2000, Feral Cat and Sika Deer were added to the standard list of species and Common Shrew removed because of the difficulty in validating sightings of this species. In most of these cases, the addition of a species to the standard list resulted in an apparent increase in the number and proportion of squares reporting these species, and the removal of Common Shrew in 2000, a fall in the apparent abundance. The only species from this list that appeared little affected by these survey changes include Stoat, Weasel and Sika Deer. Another change to the survey form in 2000 was intended to improve the clarity but it also may have increased the scope for observers to record presence as well as counts and species presence on the survey form. Prior to this, the relatively high proportion of squares reporting sightings of Mole may reflect known presence from molehills rather than sightings of live animals. A number of the problems discussed above have no particular consequence, because data are not sufficient to produce trends in abundance or presence/absence. For example, we do not produce trends for Feral Cat, small mammals (e.g. Common Shrew) and Sika Deer. We also do not use sightings data to produce trends in abundance for Mole and other species rarely sighted. Species for which changes in survey form could potentially have an important influence are those for which trends in presence/absence are produced including Hedgehog, Badger, Brown Rat, Mole, Stoat and Weasel. 3.1 Temporal changes in abundance In the following section (Figures 3.1.1-3.1.9), we pool the results of analyses of sightings data and distribution information described in the method section above to present a species by species account of what the BBS tells us about population change for these species for 1995-2002. Table 3.1 Number of BBS squares across the UK recording individual mammal species by recording category. The figure as a percentage of total BBS squares recording mammal in 2002 is shown in brackets.  Recording categories*Species 1 2 3  4 5 Trends in abundance Rabbit1129 (62.24)32 (1.77)158 (8.72)49 (2.71)77 (4.25) Brown Hare537 (29.61)6 (0.34)6 (0.34)43 (2.38)52 (2.87) Mountain/Irish Hare39 (2.15)07 (0.39)7 (0.39)2 (0.12) Grey Squirrel527 (29.06)8 (0.45)48 (2.65)74 (4.08)78 (4.3) Red Fox230 (12.68)18 (1)232 (12.79)136 (7.5)67 (3.7) Red Deer43 (2.38)022 (1.22)13 (0.72)7 (0.39) Fallow Deer51 (2.82)022 (1.22)20 (1.11)8 (0.45) Roe Deer300 (16.54)4 (0.23)56 (3.09)45 (2.49)42 (2.32) Reeves’s Muntjac57 (3.15)1 (0.06)20 (1.11)26 (1.44)17 (0.94)Trends in presence/absence Hedgehog15 (0.83)54 (2.98)33 (1.82)77 (4.25)33 (1.82) Mole00610 (33.63)00 Brown Rat23 (1.27)13 (0.72)58 (3.2)80 (4.42)27 (1.49) Stoat15 (0.83)1 (0.06)4 (0.23)74 (4.08)22 (1.22) Weasel10 (0.56)02 (0.12)59 (3.26)21 (1.16) Badger8 (0.45)25 (1.38)204 (11.25)78 (4.3)15 (0.83)Not possible to monitor Common Shrew11 (0.61)4 (0.23)2 (0.12)2 (0.12)1 (0.06) Pygmy Shrew1 (0.06)1 (0.06)01 (0.06)1 (0.06) Water Shrew01 (0.06)1 (0.06)1 (0.06)0 Lesser W-T Shrew0001 (0.06)0 Greater Horseshoe Bat0001 (0.06)0 Pipistrelle Bat0002 (0.12)2 (0.12) Red Squirrel12 (0.67)05 (0.28)8 (0.45)6 (0.34) Bank Vole1 (0.06)0 2 (0.12)02 (0.12) Field Vole3 (0.17)1 (0.06)7 (0.39)01 (0.06) Orkney Vole1 (0.06)01 (0.06)00 Water Vole4 (0.23)1 (0.06)4 (0.23)3 (0.17)3 (0.17) Wood Mouse1 (0.06)01 (0.06)4 (0.23)3 (0.17) Harvest Mouse001 (0.06)1 (0.06)0 House Mouse0001 (0.06)3 (0.17) Pine Marten002 (0.12)1 (0.06)0 Feral Ferret1 (0.06)001 (0.06)0 American Mink002 (0.12)17 (0.94)8 (0.45) Otter3 (0.17)1 (0.06)6 (0.34)6 (0.34)2 (0.12) Feral/Domestic Cat252 (13.9)05 (0.28)60 (3.31)63 (3.48) Wild Boar0001 (0.06)0 Minke Whale1 (0.06)0000 Common Seal2 (0.12)0000 Sika Deer9 (0.5)01 (0.06)7 (0.39)1 (0.06) Chinese Water Deer3 (0.17)0001 (0.06) Feral Goat3 (0.17)000 Red-necked Wallaby1 (0.06)0000 Recording categories* Category 1 = live animals sighted Category 2 = Dead animals Category 3 = field signs (e.g. tracks, scats, mole-hills) Category 4 = local knowledge of presence for that year from a gamekeeper or landowner Category 5 = live animals sighted on additional visits to the square during that season Figure 3.1.1 RABBIT Oryctolagus cuniculus Summary Significant continuous decline in the UK from 1997 to 2002 Largest decline in Scotland and to lesser extent England, in which East and West Midlands have shown the greatest detectable declines  a) Mean number of squares with Rabbit counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM1090 -23 *COUNTRIES England873-17* Scotland104-40* Wales759ENGLISH REGIONS North West England90-30* Yorkshire & The Humber764 East Midlands71-57* East of England16329* West Midlands93-41* South East England208-24* South West England1391ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)479-16* (Zone 2) Westerly lowlands (England/Wales)367-14* (Zone 3) Uplands (England/Wales)105-12 (Zone 4) Lowlands (Scotland)60-41* b) Change in relative abundance from counts in the UK from 1995 –2002 (see Appendix 1 for raw data).).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002. Figure 3.1.2 BROWN HARE Lepus europaeus Summary No significant change in abundance overall in the UK between 1995 and 2002. However, regional differences suggest that abundance has fallen in Scotland and South East England and in the Uplands of England/Wales, whilst abundance has increased in South West England and in the Westerly lowlands of England/Wales.  a) Mean number of squares with Brown Hare counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM546 -5COUNTRIES England4676 Scotland56-43*ENGLISH REGIONS North West England54-19 Yorkshire & The Humber460 East Midlands6039 East of England13016 South East England72-25* South West England5127*ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)2924 (Zone 2) Westerly lowlands (England/Wales)14516* (Zone 3) Uplands (England/Wales) 53 -20* b) Change in relative abundance from counts in the UK 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002. Figure 3.1.3 MOUNTAIN HARE (IRISH HARE) Lepus timidus Summary Significant decline in abundance in the UK between 1995 and 2002. However, large fluctuation in abundance between years suggests that this may not be an underlying trend.  a) Mean number of squares with Mountain Hare counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM48 -14 * b) Change in relative abundance from counts in the UK from 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.4 GREY SQUIRREL Sciurus carolinensis Summary Significant increase in abundance overall in the UK between 1995 and 2002, with a large peak in 1996, perhaps related to high productivity in this year. The largest increase has occurred in Wales and to a lesser extent England in which South West England has increased significantly. Due to the westerly nature of these increases, the abundance has increased significantly in the Westerly lowlands of England/Wales.  a) Mean number of squares with Grey Squirrel counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM485 28 *COUNTRIES England43517* Wales3977*ENGLISH REGIONS East of England773 West Midlands607 South East England128-4 South West England6681*ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)24310 (Zone 2) Westerly lowlands (England/Wales)19742* b) Change in relative abundance from counts in the UK from 1995 –2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.5 RED FOX Vulpes vulpes Summary Significant decline in abundance overall in the UK between 1995 and 2002, relating to a decline in 2002, rather than an underlying trend over the entire period. Significant increase in the Westerly lowlands of England/Wales.  a) Mean number of squares with Red Fox counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM242 -19 *COUNTRIES England193-12ENGLISH REGIONS South East England53-20 South West England42-18ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)10510 (Zone 2) Westerly lowlands (England/Wales)8442* b) Change in relative abundance from counts in the UK from 1995–2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.6 RED DEER Cervus elaphus Summary Significant decline in abundance between 1995 and 2002. This does not relate to an underlying decline in this species, but instead relates to a steep decline in 1996, due to a small number of sites not recording large herds in this year and in subsequent years. Because there are relatively few sites in the model to start with, a small number of sites not recording large herds in subsequent years, can have a large influence on the apparent relative abundance of this species. The majority of BBS squares reporting Red Deer are in Scotland.  a) Mean number of squares with Red Deer counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM56 -58 *COUNTRIES Scotland44-58* b) Change in relative abundance from counts in the UK from 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.7 FALLOW DEER Dama dama Summary Significant decline in abundance between 1995 and 2002. This does not relate to an underlying decline in this species, but instead relates to a steep decline in 1996, due to a small number of sites not recording large herds in this year and in subsequent years. Because there are relatively few sites in the model to start with, a small number of sites not recording large herds in subsequent years, can have a large influence on the apparent relative abundance of this species. The majority of BBS squares reporting Fallow Deer are in England.  a) Mean number of squares with Fallow Deer counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM41 -55 *COUNTRIES England40-62* b) Change in relative abundance from counts in the UK from 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.8 ROE DEER Capreolus capreolus Summary Significant continuous increase in the UK from 1995 to 2002 Large increase in England in the South East and South West and in Scotland.  a) Mean number of squares with Roe Deer counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM246 56 *COUNTRIES England17766* Scotland6845*ENGLISH REGIONS South East England59110* South West England63110*ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)10168* (Zone 2) Westerly lowlands (England/Wales)6580* b) Change in relative abundance from counts in the UK from 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  Figure 3.1.9 REEVES’S MUNTJAC Muntiacus reevesi Summary Significant continuous increase in the UK from 1995 to 2002. Large increase within its stronghold of England.  a) Mean number of squares with Reeves’s Muntjac counts (1995-2002). See Appendices 2a-c for raw data. Mean squaresPercent change P d" 0.05 UNITED KINGDOM47 46 *COUNTRIES England4631*ENVIRONMENTAL ZONES (Zone 1) Easterly lowlands (England/Wales)413 b) Change in relative abundance from counts in the UK from 1995-2002 (see Appendix 1 for raw data).  EMBED Excel.Chart.8 \s  c) Distribution from recorded presence in one or more year, 1995-2002.  3.2 Temporal changes in presence The number of BBS squares reporting the presence of mammals from counts of live animals, dead animals, field signs (e.g. tracks, scats, mole-hills), local knowledge of presence for that year from a gamekeeper or landowner or live animals seen on additional visits to the square during that season for all species recorded in 1995-2002 are shown in Appendix 2a. This shows that 52 species were recorded on BBS squares during this period. For the six species for which we examine the change in presence on BBS squares (Badger, Brown Rat, Mole, Hedgehog, Stoat and Weasel), the apparent presence on BBS squares increased significantly for Badger, Brown Rat, Mole and Hedgehog from 1996-2002, whilst there was no significant change in the presence of Stoat and Weasel on BBS squares during this period. The significance of the change in presence over time is examined using logistic regression, the results of which are shown in Appendix 3. However, because the change in odds ratio is difficult visually interpret, we present below simple figures showing the percentage change in the presence of these species on BBS squares. This information is summarised in Figure 3.2.1. (See section 4.5 for a discussion of the reliability of these trends). Figure 3.2.1 Summary of the change in presence on BBS squares of six mammals species. Summary Apparent increase in presence of Mole, Hedgehog, Badger and Brown rat on BBS squares (P d" 0.05) between 1995 and 2002. No significant change in the presence of Stoats and Weasels on BBS squares (P > 0.05).  Key Black = present: White = absent (species not recorded) a) Mole  EMBED Excel.Chart.8 \s  b) Hedgehog  EMBED Excel.Chart.8 \s   c) Brown Rat  EMBED Excel.Chart.8 \s  d) Badger  EMBED Excel.Chart.8 \s  e) Stoat  EMBED Excel.Chart.8 \s     f) Weasel  EMBED Excel.Chart.8 \s  3.3 Interpolated maps of abundance Comparing the root-mean-square prediction errors (measures how close the model predicts measured values) and standardized mean prediction errors (the extent to which the predictions are centered on the measurement values) between models in Table 3.3.1, it is clear that the addition of habitat as the predictor can improve the resulting predictions of relative abundance across the UK. In both 1995 and 2002, arable habitat was the single best predictive habitat variable for Brown Hare. Adding the next best predictors (moorland, heath & bog and broadleaved woodland) to arable in the model, resulted for moorland, heath & bog resulted in predictions which were more closely centered on the measurements, with little change in how close the predictions were to the measured value, so this model is preferred to using arable only in the model. The addition of broadleaved woodland and arable in the model resulted in predictions which were the closest of all to the measured values, but the predictions were less centered on the measurements than when including arable only and in the arable and moorland, heath & bog model. For this reason we chose to use predictions from the arable and moorland, heath & bog model for interpolating a map of Brown Hare relative abundance for both 1995 and 2002. A co-kriging model using Brown Hare sightings data and CEH landcover data for arable and moorland, heath & bog to interpolate Brown Hare abundance across the UK in 1995 and 2002 is shown in (Figure 3.3.1). In addition, we present a single map showing the change in abundance between 1995 and 2002 (Figure 3.1.2). For interpretative purposes, it is important to note that this change map is more likely to highlight change in areas where the species is most abundant in the first year, because a halving of the population in an area where initial abundance is large shows a greater change in relative abundance in this area, than a halving in an area starting at a lower population base. For this reason, the usefulness of a change map of this type is uncertain and needs further discussion. Table 3.3.1 Comparison of model fit and error associated with the prediction of Brown Hare abundance across the UK from BBS sightings data for 1995 and 2002 and CEH landcover data aggregated into seven habitat categories. For the predictions to be unbiased (centered on the measurement values), the prediction errors should be close to zero. This depends on the scale of the data, which we standardize by dividing the prediction error by their prediction standard errors to give standardized mean prediction errors, which should also be close to zero. The predictions should also be as close as possible to the measurement values. To examine this we compute the root-mean-square prediction errors (the square root of the average of the squared distances between the predictions and their true values), for which the smaller the value the closer the model predicts the measured values. The best models are highlighted in bold. Model: Brown Hare 1995Root-mean-square prediction errors Standardized mean prediction errors Model: Brown Hare 2002Root-mean-square prediction errorsStandardized mean prediction errorsNo habitat: Simple kriging2.396-0.1604No habitat: Simple kriging2.403-0.1697Moorland, heath & bog2.405-0.1573Moorland, heath & bog2.423-0.1631Broadleaved woodland2.433-0.1522Broadleaved woodland2.461-0.1570Coniferous woodland2.398-0.1613Coniferous woodland2.415-0.1677Improved grassland2.392-0.1604Improved grassland2.406-0.1753Semi-natural grassland2.402-0.1628Semi-natural grassland2.413-0.1678Arable2.364-0.1041Arable2.395-0.1170Human2.562-0.1887Human2.562-0.1969Arable + Broadleaved woodland2.335-0.1043Arable + Broadleaved woodland2.361-0.1261Arable + Moorland, heath & bog2.369-0.1032Arable + Moorland, heath & bog2.399-0.1147 a) 1995 b) 2002 Figure 3.3.1 Interpolated abundance of Brown Hare from BBS mammal data. Relative abundance increases from green to dark red (light to dark grey if in monochrome).  Figure 3.1.2. Change in the relative abundance of Brown Hare from 1995-2002. Declines are shown in orange (small to large decline – yellow to dark red) and increases shown in green (small to large increase – light to dark green). 4. DISCUSSION 4.1 UK population trends from sightings This report highlights the importance of the BBS for annual monitoring of a number of terrestrial mammals in the UK. Data were sufficient to produce population trends based on count data at a UK level for nine species of mammal (Brown Hare, Mountain/Irish Hare, Grey Squirrel, Red Fox, Red Deer, Fallow Deer, Roe Deer, Reeves’s Muntjac and Rabbit). Whilst annual indices of this type are important for identifying annual variation in abundance at various scales, comparing abundance between the first and last years in the series could be misleading if the species fluctuates widely in abundance between years. Fitting linear trends as in Newson & Noble (2003) can be used to examine the significance of the underlying trend, although, as the time series becomes more extensive, the potential of generalized additive models (GAMs) for reducing noise resulting from annual fluctuations in abundance should be considered. Unlike conventional generalised linear models (GLMs), which allow change in mean abundance over time to follow a linear form or sequence of unrelated estimates, GAMs allow mean abundance to follow any smooth function, the formulation of which is described in detail by Hastie & Tibshirani (1990). Whilst the analyses here covered a relatively short time period (1995-2002), it is already apparent that there have been a number of important changes within these populations during this time. Comparing abundance of the above species at a UK level in 2002 relative to 1995, Grey Squirrel, Roe Deer and Reeves’s Muntjac were significantly higher in 2002, whilst Rabbit, Mountain Hare, Red Fox, Red Deer and Fallow Deer were significantly lower in this year. Several of these species show significant fluctuations in abundance between years, although Roe Deer and Reeves’s Muntjac increased continually over this period. For the nine core species for which we produce trends in abundance from count data, additional information from squares reporting presence only from field signs and other information would add very little information if trends in presence/absence were to be produced for these species (see Table 2). An exception may perhaps be field signs of Red Fox, which are easy to identify and based on data for 2002 would add about an additional 232 squares to the 230 squares reporting sightings if trends in presence/absence were produced for this species. 4.2 Factors affecting population change Grey Squirrel showed a particularly large fluctuation in abundance in 1996. It is encouraging to observe that trends for Grey Squirrel based on independent game bag data for this species show a similar peak in this year (Whitlock et al. 2003). Examining the proportion of BBS squares reporting the presence of Grey Squirrels in this year (see Appendix 1b) there is no evidence of an increase in the distribution of this species, so this fluctuation perhaps reflects high productivity in 1996. In a similar way there is no evidence from presence data for a contraction in the range of Rabbits from 1997, although there is an observed decline in relative abundance on recording squares from 1997 onwards, which is also seen in independent analyses of game bag data for this species (Whitlock et al. 2003). For Roe Deer and Reeves’s Muntjac there is an increase in relative abundance and an increase in the proportion of BBS squares reporting these species. This suggests that the increase in relative abundance may have occurred through expansion of its existing range during the survey period. The decrease in the proportion of squares reporting the presence of Red Deer, which are mainly in Scotland, could reflect contraction in the range of this species, although examination of the raw count data suggests that the drop in abundance in 1996 is mainly the result of a small number of sites reporting large herds in 1995 but not in following years. Because there are relatively few sites in the model to start with, a small number of sites not recording large herds after 1995, can have a large influence on the apparent, but not necessarily real abundance of this species. 4.3 Regional trends Where data were sufficient, annual indices were produced at a Government Office Region level and for Environmental Zones, which cover a range of environmental conditions that we find in the UK from the lowlands of the south and east, through to the uplands and mountains of the north and west. In preliminary analyses of BBS mammal data (Newson & Noble 2003), trends were produced for three arbitrary broad regions of Britain the north, the southeast and the southwest of Britain. These regions have little political meaning and for this reason, we examine here the production of population trends for English Government Office Regions (GOR) and countries of the UK. Trends in relative abundance could be produced for five mammal species (Brown Hare, Rabbit, Grey Squirrel, Red Fox and Roe Deer) for two or more regions and for Red Deer in Scotland and Fallow Deer and Reeves’s Muntjac in England. This is the first time that trends in relative abundance have been produced from BBS mammal data at the GOR and country level and for government Environmental Zones. 4.4 Analyses by habitat Whilst habitat information is recorded for each 10 x 200 m transect section surveyed, counts of mammals are made at the 1-km square level. For this reason, preliminary analyses by Newson & Noble (2003) produced habitat-specific trends for species based on the predominant habitat within a 1 km square (i.e. 50% of more of a squares belong to one primary habitat class). Obviously the dominant habitat may not necessarily be the habitat in which the mammal was recorded. Whilst this is not ideal it was believed to be the most appropriate approach to the problem. Although producing trends by Environmental Zone does not improve the level of resolution by which the trends are produced, this approach is comparable with other mammal surveys, such as the BTO/Mammal Society Winter Mammal Monitoring Survey (Noble et al. 2002), and will therefore be of utility. Although we do not make comparisons between the BBS, the Winter Mammal Monitoring Survey and other independent surveys in this report, would be a useful comparison. 4.5 Population trends from presence/absence data In this report we examine the change in presence, using evidence of species presence from field signs, dead animals, local knowledge of presence, counts of live animals made during the survey or any additional visits, for six species, which are rarely seen. Change in the populations of these species should be interpreted with caution for a number of reasons. The first is related to the criteria for recording presence, data for which is currently available only for 2002 (although these data were also collected in 2003 and 2004). For example, the presence of moles is exclusively recorded from field signs (mole-hills), whilst a large proportion of hedgehogs are reported from dead animals. In fact, hedgehog is the only species for which dead animals are likely to contribute significantly to analyses of presence/absence. The majority of Badger records are based on field signs, and to a lesser extent local knowledge. It should be noted that field signs here include both setts and latrines, and there is no way of distinguishing between these in the current data. The reliability of monitoring the presence of a species where a large proportion of the information is obtained through word of mouth (local information gained from landowner or gamekeeper) is difficult to assess without more supplementary information, but it is probably poor. For example the high similarity in UK trends of Stoat and Weasel, which are both gleaned mainly from local knowledge, should perhaps be treated with caution. Other species for which local knowledge contributes a significant proportion of the recorded presence includes Brown Rat and Hedgehog and to a lesser extent Red Fox. Now that the criteria for presence are recorded, further analyses could examine the influence of excluding records based solely on local knowledge on the resulting trends. The second important point to make is that there have been a number of changes to the survey form that may affect the apparent presence of species on BBS squares during the survey period. In 1996, a number of species were added to the species list, including Badger, Hedgehog, Brown Rat, Mole, Stoat and Weasel. For this reason, data for 1995 may not be comparable with 1996 and for this reason, as we have done here, trends should be calculated from 1996 (see section 3.2). Furthermore additional changes to the form were made in 2000. The most important change in 2000 was to clarify what the category of presence should include, making it clearer in the instructions that this should include the recording of dead animals, information from personal communication with landowners/gamekeepers and stating specifically on the survey form examples of signs including mole-hills and Badger latrines. These changes may have an effect of increasing the number of records of species presence in these categories. Additionally Sika Deer, Mink and Feral/Domestic Cat were added to the survey form in 2000, although all but Feral Cat are unlikely to be recorded in sufficient squares for trends of presence to be produced. Lastly, following recommendations, the survey form was changed again in 2002 to ask observers to specify the criteria for recording presence, i.e. whether presence was from live animals, dead animals, field signs, local knowledge of presence for that year from a gamekeeper or landowner or live animals seen on additional visits to the square during that season. The change to the survey form in 2002 was intended to provide more detail and should in principle have little influence on rate of recording of presence, but it is not to possible to confirm this from the data collected. We perhaps have three distinct time series of data. The first year (1995) is excluded from all analyses of presence/absence because there may be a year effect resulting from observers acquainting themselves with mammal recording and the absence of Badger, Mole, Hedgehog and Brown Rat from the form in 1995. The second series covers the period 1996-99, during which there were no obvious changes to the survey form that would result in a change in apparent presence, although increasing observer awareness of the presence of a species in a square (e.g. after a badger sett is found) could result in an apparent increase in the presence of these species during this period. The data for 2000 are likely to be comparable with data in 2002, 2003 and 2004, although the data form was changed in 2002 to record the criteria for recording presence (e.g. counts of live animals, dead animals etc.), although this should not change the incidence of reported presence on BBS squares. Data for 2001 are excluded because coverage in this year was severely biased by the influence of foot-and-mouth disease. With further years of data, it is hoped that it will be possible to be more confident in our estimates of change in populations of these species. It may be sensible in the future to continue to exclude all data for 1995 as we have done here, because of the potential year effect and exclusion of a number of key species and to join trends for the periods 1996-99 to the index for 2000 onwards without including the change from 1999 to 2000. This also shows that unless it is absolutely essential to do so, there should be no further changes to the survey form. 4.6 Monitoring distribution Whilst the above analyses cover a range of mammal species recorded on BBS squares, data for a large proportion of mammal species recorded by the BBS are still insufficient to calculate robust indices of relative abundance or occurrence. However, these data still provide important information on the distribution of species, which in many cases are not properly monitored by any existing scheme. Distribution maps of species presence combined over intervals of perhaps five or ten-year blocks, as more data are collected, might be considered. Trials in this report using geostatistical methods show that this method has greater potential for improving our understanding of finer scale spatial patterns in relative abundance or distribution, than is possible through the production of regional indices or visually through the production of distribution maps of species presence. Whilst we examine here the potential of this methodology using an example species, the Brown Hare, it does not seem unreasonable to expect that statistically valid maps of this type could be produced in a similar way for the nine species for which we produce UK trends. It may also be possible to produce maps of species presence for species that are rarely seen, such as Badger, Mole, Hedgehog, Brown Rat, Stoat and Weasel and to make comparisons where more than one indicator of presence is recorded. An example would be to compare predicted presence for Red Fox from sightings and field signs. Results from the Brown Hare trial, demonstrate the importance of habitat requirements for this species, and how information of this type at a 1 km scale, such as the CEH land cover data used here can improve our predictions. Although considerably time consuming for the analyst, predictions may be improved if models are produced and compared for each of the 27 separate landcover classes, rather than for the aggregated classes used here. 5. CONCLUSIONS Although the majority of British mammal species are recorded on too few squares to be monitored effectively by this survey, this report demonstrates that we can monitor a core group of common medium to large-sized mammals using sightings data. Data on the presence/absence of an additional group of species provides potential for increasing the number of monitorable species further, but changes in recording protocols limit the conclusions from the first years of the BBS. Nevertheless, now that observers record the criteria that they use for reporting presence (e.g. live animals, field signs, dead animals, local knowledge of presence from that season or live animals seen on additional visits), the potential for reliability monitoring and interpreting change in the presence of these species improves greatly. For the remaining species reported on BBS squares, they are reported on too few squares to do little more than map presence. In isolation these data are of little importance, apart from perhaps identifying the strongholds of particular species. However, it is important to highlight the potential for combining these data with those from other surveys and perhaps with incidental records through the National Biodiversity Network to provide a better understanding of their distribution. ACKNOWLEDGMENTS This work is funded by the Joint Nature Conservation Committee (JNCC) under the supervision of Dr Jessa Battersby. We are grateful to Mike Raven for managing the BBS mammal database, Steve Freeman for statistical advice and John Marchant for commenting on this draft. The BBS is supported by a partnership between the BTO, the Royal Society for the Protection of Birds (RSPB) and JNCC - the on behalf of the Countryside Council for Wales, English Nature, Scottish Natural Heritage and the Department of Environment for Northern Ireland. REFERENCES Battersby, J.E. & Greenwood, J.J.D. 2004. Monitoring terrestrial mammals in the UK: past, present and future, using lessons from the bird world. Mammal Review, 34, 3-29. Birks, J.D.S. & Kitchener, A.C. eds. 1999. The Distribution and Status of the Polecat Mustela putorius in Britain in the 1990s. The Vincent Wildlife Trust. Bunce, R G H, Barr, C J, Clarke, R T, Howard, D.C & Lowe, A M J. 1996 The ITE Merlewood Land Classification. Journal of Biogeography, 23, 625-634. Bright, P.W., Morris, P.A. & Mitchell-Jones, A.J. 1996. A new survey of the Dormouse Muscardinus avellanarius in Britain, 1993-4. Mammal Review, 26, 189-195. Chiles, J. & Delfiner, P. 1999. Geostatistics. Modeling Spatial Uncertainty. John Wiley & Sons, New York. Crick, H.Q.P. 1992. A bird-habitat coding system for use in Britain and Ireland incorporating aspects of land-management and human activity. Bird Study 39, 1-12. Harris, S, Morris, P, Wray, S & Yalden, D. 1995. A review of British mammals: population estimates and conservation status of British mammals other than cetaceans, Joint Nature Conservation Committee, Peterborough. Hastie, T.J. & Tshibirani, R.J. (1990). Generalized Additive Models. Chapman and Hall. London Hutchings, M R W. & Harris, S. 1996. Current status of the brown hare in Britain. Report of the Joint Nature Conservation Committee, Peterborough. Isaaks, E.H. & Srivastava, R.M. 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York. Johnston, K., Ver Hoef, J.M., Krivoruchko, K. & Lucas, N. 2001. Using ArcGIS Geostatistical Analyst. ESRI. Joys, A.C., Noble, D.G. & Baillie, S.R. 2003. Evaluation of species coverage and precision using the BBS indexing method. BTO Research Report No. 317. Macdonald, D W. & Tattersall, F. 2003. The State of Britain’s Mammals 2003. Mammals Trust UK and WildCRU, London. Marsh, A.C.W. 1999. Factors determining the range and abundance of the yellow-necked mouse Apodemus flavicollis in Great Britain. Unpublished PhD Thesis, University of Bristol. Newson, S.E. & Noble, D.G. 2003. Preliminary analyses of Breeding Bird Survey (BBS) mammal data. BTO Research Report No. 321. Noble, D.G., Newson, S.E., Baillie, S.R., Raven, M.J. & Gregory, R.D. 2004. Recent changes in UK bird populations measured by the Breeding Bird Survey. Bird Study, in press. SAS. Institute Inc. 1996. SAS/Stat Software: Changes and Enhancements through Release 6.11. SAS Institute, Inc., Cary, North Carolina. Strachan, R. & Jefferies, D.J. 1996. Otter Survey of England 1991-1994. A report on the decline and recovery of the otter in England and on its distribution, status and conservation in 1991-1994. The Vincent Wildlife Trust, England. Strachan, C., Strachan, R. & Jefferies, D.J. 2000. Preliminary report on the changes in the Water Vole population of Britain as shown by the national surveys of 1989-90 and 1996-98. The Vincent Wildlife Trust, London. ter Braak, C.J.K., van Strien, A.J., Meijer, R. & Verstrael, T.J. 1994. Analysis of monitoring data with many missing values: Which method? Bird Numbers 1992. Proceedings of the 12th International Conference of IBCC and EOAC Noordwijkerhout, The Netherlands. Statistics Netherlands, Voorburg/Heerlen & SOVON, Beek-Ubbergen. Wilson, G., Harris, S. & McLaren, G. 1997. Changes in the British Badger Population 1988-1997. PTES, London. Whitlock, R.E., Aebischer, N.J. & Reynolds, J.C. 2003. The National Gamebag Census as a Tool for Monitoring Mammal Abundance in the UK. A report to JNCC. Appendix 1a The number of BBS squares recording counts of mammals on BBS squares (percentage of total BBS squares surveyed in shown in parentheses). We excluded data here and in the analyses for 2001 due to the bias in survey coverage caused by the outbreak of foot-and-mouth disease. Species occurring on a mean of 40 or more squares over the seven years and for which we produce annual trends in relative abundance are highlighted in bold.  Year Species 1995 1996 1997 1998 1999 2000 2002  Hedgehog8 (0.6)27 (1.7)43 (2.3)29 (1.5)35 (1.7)29 (1.5)14 (0.8)Mole18 (1.4)76 (4.7)56 (3)30 (1.5)45 (2.2)6 (0.3)0Common Shrew19 (1.4)52 (3.2)47 (2.5)74 (3.8)68 (3.3)4 (0.2)11 (0.6)Pygmy Shrew0001 (0.1)02 (0.1)1 (0.1)Water Shrew001 (0.1)2 (0.1)000Natterer's Bat01 (0.1)00000Pipistrelle Bat001 (0.1)02 (0.1)00Rabbit827 (62)980 (60.6)1163 (61.8)1177 (60.1)1194 (58.8)1169 (61.5)1117 (61.6)Brown Hare428 (32.1)512 (31.7)599 (31.8)577 (29.4)599 (29.5)574 (30.2)536 (29.5)Mountain Hare28 (2.1)48 (3)60 (3.2)60 (3.1)57 (2.8)44 (2.3)39 (2.1)Red Squirrel7 (0.5)18 (1.1)21 (1.1)16 (0.8)16 (0.8)14 (0.7)12 (0.7)Grey Squirrel301 (22.6)501 (31)500 (26.6)517 (26.4)509 (25.1)542 (28.5)523 (28.8)Bank Vole3 (0.2)7 (0.4)5 (0.3)4 (0.2)3 (0.1)2 (0.1)1 (0.1)Field Vole2 (0.2)6 (0.4)5 (0.3)9 (0.5)7 (0.3)2 (0.1)3 (0.2)Orkney Vole0 (0)0 (0)0 (0)0 (0)1 (0)1 (0.1)1 (0.1)Water Vole4 (0.3)7 (0.4)8 (0.4)7 (0.4)19 (0.9)11 (0.6)4 (0.2)Wood Mouse2 (0.2)9 (0.6)2 (0.1)3 (0.2)3 (0.1)4 (0.2)1 (0.1)Harvest Mouse01 (0.1)00000House Mouse000001 (0.1)0Brown Rat13 (1)23 (1.4)17 (0.9)16 (0.8)24 (1.2)30 (1.6)23 (1.3)Red Fox180 (13.5)256 (15.8)255 (13.5)240 (12.2)286 (14.1)245 (12.9)230 (12.7)Pine Marten2 (0.2)2 (0.1)01 (0.1)1 (0.1)3 (0.2)0Stoat26 (2)28 (1.7)33 (1.8)31 (1.6)37 (1.8)28 (1.5)15 (0.8)Weasel9 (0.7)14 (0.9)22 (1.2)22 (1.1)20 (1)15 (0.8)10 (0.6)Polecat01 (0.1)3 (0.2)1 (0.1)2 (0.1)00Ferret0000001 (0.1)American Mink3 (0.2)1 (0.1)3 (0.2)2 (0.1)1 (0.1)6 (0.3)0Badger5 (0.4)21 (1.3)14 (0.7)14 (0.7)13 (0.6)5 (0.3)8 (0.4)Otter1 (0.1)3 (0.2)3 (0.2)3 (0.2)1 (0.1)4 (0.2)3 (0.2)Feral/Domestic Cat2 (0.2)1 (0.1)2 (0.1)3 (0.2)4 (0.2)194 (10.2)250 (13.8)Park Cattle1 (0.1)000000Minke Whale0000001 (0.1)Harbour Porpoise1 (0.1)000000Common Seal2 (0.2) 1 (0.1)1 (0.1)2 (0.1)2 (0.2)2 (0.2)Common Seal1 (0.1)0 ()1 (0.1)1 (0.1)2 (0.1)1 (0.1)2 (0.1)Grey Seal02 (0.1)2 (0.1)4 (0.2)2 (0.1)1 (0.1)3 (0.2)Red Deer51 (3.8)76 (4.7)56 (3)65 (3.3)55 (2.7)45 (2.4)43 (2.4)Sika Deer4 (0.3)4 (0.2)3 (0.2)5 (0.3)4 (0.2)8 (0.4)9 (0.5)Fallow Deer30 (2.3)34 (2.1)40 (2.1)45 (2.3)36 (1.8)51 (2.7)51 (2.8)Roe Deer182 (13.7)214 (13.2)228 (12.1)249 (12.7)277 (13.6)270 (14.2)300 (16.5)Reeves’s Muntjac40 (3)35 (2.2)40 (2.1)47 (2.4)58 (2.9)49 (2.6)57 (3.1)Chinese Water Deer1 (0.1)1 (0.1)003 (0.1)2 (0.1)3 (0.2)Feral Goat4 (0.3)2 (0.1)1 (0.1)3 (0.2)3 (0.1)3 (0.2)3 (0.2) Appendix 1b The number of BBS squares recording the presence of mammals on BBS squares from counts of live animals, as used in the above analyses, dead animals, field signs (e.g. tracks, scats, mole-hills), local knowledge of presence for that year from a gamekeeper or landowner or live animals seen on additional visits to the square during that season (percentage of total BBS squares surveyed in shown in parentheses). We excluded data here and in the analyses for 2001 due to the bias in survey coverage caused by the outbreak of foot-and-mouth disease. Species for which analyses to examine the change in species presence on BBS squares is carried out are highlighted in bold.  Year Species 1995 1996 1997 1998 1999 2000 2002  Hedgehog25 (1.9)138 (8.6)162 (8.7)233 (11.9)244 (12.1)281 (14.8)197 (10.9)Mole95 (7.2)284 (17.6)292 (15.6)389 (19.9)510 (25.2)587 (30.9)610 (33.7)Common Shrew27 (2.1)100 (6.2)89 (4.8)157 (8.1)171 (8.5)16 (0.9)19 (1.1)Pygmy Shrew1 (0.1)2 (0.2)2 (0.2)4 (0.3)3 (0.2)3 (0.2)4 (0.3)Water Shrew001 (0.1)2 (0.2)01 (0.1)2 (0.2)Lesser white-toothed Shrew01 (0.1)02 (0.2) 001 (0.1)Greater Horseshoe Bat0000001 (0.1)Natterer's Bat02 (0.2)00000Noctule Bat2 (0.2)1 (0.1)1 (0.1)2 (0.2)1 (0.1)1 (0.1)0Leisler's Bat00001 (0.1)00Pipistrelle Bat4 (0.4)5 (0.4)6 (0.4)4 (0.3)10 (0.5)4 (0.3)4 (0.3)Long-eared Bat01 (0.1)01 (0.1)1 (0.1)00Rabbit962 (72.2)1120 (69.4)1304 (69.3)1366 (69.7)1438 (70.9)1351 (71.1)1294 (71.4)Brown Hare493 (37)583 (36.1)651 (34.6)642 (32.8)679 (33.5)646 (34)605 (33.4)Mountain Hare40 (3.1)65 (4.1)71 (3.8)76 (3.9)66 (3.3)51 (2.7)53 (3)Red Squirrel15 (1.2)30 (1.9)32 (1.7)35 (1.8)29 (1.5)28 (1.5)27 (1.5)Grey Squirrel398 (29.9)571 (35.4)607 (32.3)669 (34.2)719 (35.5)742 (39.1)676 (37.3)Bank Vole3 (0.3)15 (1)10 (0.6)8 (0.5)5 (0.3)4 (0.3)5 (0.3)Field Vole15 (1.2)25 (1.6)14 (0.8)16 (0.9)16 (0.8)11 (0.6)12 (0.7)Orkney Vole2 (0.2)1 (0.1)03 (0.2)3 (0.2)2 (0.2)2 (0.2)Water Vole5 (0.4)8 (0.5)12 (0.7)14 (0.8)24 (1.2)18 (1)13 (0.8)Wood Mouse9 (0.7)15 (1)6 (0.4)6 (0.4)12 (0.6)11 (0.6)8 (0.5)Yellow-necked Mouse001 (0.1)001 (0.1)0Harvest Mouse01 (0.1)1 (0.1)01 (0.1)01 (0.1)House Mouse02 (0.2)1 (0.1)2 (0.2)2 (0.1)4 (0.3)3 (0.2)Brown Rat23 (1.8)78 (4.9)64 (3.4)129 (6.6)154 (7.6)196 (10.4)187 (10.4)Common Dormouse1 (0.1)1 (0.1)2 (0.2)1 (0.1)1 (0.1)00 Red Fox423 (31.8)527 (32.7)476 (25.3)592 (30.3)686 (33.8)701 (36.9)632 (34.9)Pine Marten4 (0.4)9 (0.6)3 (0.2)2 (0.2)2 (0.1)5 (0.3)2 (0.2)Stoat37 (2.8)86 (5.4)85 (4.6)123 (6.3)162 (8)159 (8.4)111 (6.2)Weasel19 (1.5)69 (4.3)70 (3.8)104 (5.4)125 (6.2)122 (6.5)88 (4.9)Polecat01 (0.1)3 (0.2)3 (0.2)6 (0.3)4 (0.3)0Ferret000001 (0.1)2 (0.2)American Mink7 (0.6)8 (0.5)7 (0.4)10 (0.6)9 (0.5)28 (1.5)25 (1.4)Badger82 (6.2)152 (9.5)156 (8.3)235 (12)273 (13.5)287 (15.1)305 (16.9)Otter6 (0.5)13 (0.9)12 (0.7)14 (0.8)8 (0.4)18 (1)16 (0.9)Wild Cat1 (0.1)000000Feral/Domestic Cat3 (0.3)2 (0.2)2 (0.2)3 (0.2)4 (0.2)350 (18.5)365 (20.2)Park Cattle (Chillingham Cattle)1 (0.1)000000Wild Boar0000001 (0.1)Minke Whale0000001 (0.1)Harbour Porpoise1 (0.1)000000Common Seal2 (0.2) 1 (0.1)1 (0.1)2 (0.1)2 (0.2)2 (0.2)Grey Seal1 (0.1)2 (0.2)2 (0.2)4 (0.3)2 (0.1)1 (0.1)4 (0.3)Red Deer84 (6.4)100 (6.2)98 (5.3)108 (5.6)93 (4.6)71 (3.8)75 (4.2)Sika Deer5 (0.4)5 (0.4)3 (0.2)8 (0.5)4 (0.2)11 (0.6)17 (1)Fallow Deer47 (3.6)57 (3.6)57 (3.1)86 (4.4)78 (3.9)89 (4.7)90 (5)Roe Deer245 (18.4)296 (18.4)301 (16)356 (18.2)394 (19.4)385 (20.3)408 (22.5)Reeves’s Muntjac60 (4.6)67 (4.2)74 (4)100 (5.2)103 (5.1)122 (6.5)110 (6.1)Chinese Water Deer1 (0.1)2 (0.2)1 (0.1)03 (0.2)2 (0.2)3 (0.2)Feral Goat5 (0.4)3 (0.2)1 (0.1)3 (0.2)3 (0.2)2 (0.2)3 (0.2)Red-necked Wallaby0000001 (0.1) Appendix 2a UK temporal trends in relative abundance for nine mammal species for the period 1995-2002. 95% confidence intervals are shown in brackets. Indices are measured relative to the year 1995, which is set to one. Although we exclude data for 2001 from the analyses due to foot-and-mouth disease, we interpolate an index here for 2001. An asterisk denotes a significant difference between the first and last years of the survey at the 5% level or more. A visual representation of temporal trends in abundance for the UK are shown in Figure 2.3.   Year Species n 1995 1996 1997 1998 1999 2000 2001 2002  Brown Hare54611.06 (0.99-1.13)0.97 (0.91-1.04)0.98 (0.92-1.05)0.90 (0.84-0.96)0.96 (0.89-1.03)0.95 (0.83-1.09)0.95 (0.89-1.02)Mountain Hare*4811.46 (1.37-1.57)2.04 (1.91-2.17)1.42 (1.33-1.53)1.17 (1.08-1.26)1.13 (1.05-1.22)1.00 (0.90-1.10)0.86 (0.80-0.93)Rabbit*109011.08 (1.02-1.14)1.26 (1.19-1.34)1.01 (0.95-1.08)0.84 (0.79-0.90)0.94 (0.89-1.01)0.86 (0.79-0.93)0.77 (0.72-0.82)Grey Squirrel*48512.12 (1.96-2.29)1.28 (1.18-1.39)1.11 (1.02-1.22)0.94 (0.86-1.03)1.23 (1.13-1.34)1.25 (1.19-1.32)1.28 (1.17-1.39)Red fox*24211.30 (1.17-1.45)0.96 (0.86-1.08)0.93 (0.83-1.05)0.96 (0.85-1.07)1.06 (0.94-1.19)0.94 (0.83-1.05)0.81 (0.72-0.92)Red Deer*5610.61 (0.57-0.66)0.63 (0.59-0.68)0.66 (0.62-0.72)0.35 (0.32-0.39)0.44 (0.40-0.49)0.43 (0.32-0.55)0.42 (0.39-0.46)Roe Deer*24611.09 (1.00-1.19)1.06 (0.97-1.15)1.19 (1.10-1.30)1.19 (1.09-1.29)1.32 (1.21-1.44)1.44 (1.40-1.48)1.56 (1.43-1.69)Fallow Deer*4110.47 (0.43-0.52)0.46 (0.41-0.51)0.36 (0.33-0.40)0.24 (0.22-0.27)0.58 (0.53-0.63)0.52 (0.42-0.63)0.45 (0.41-0.50)Reeves’s Muntjac* 47 1 1.22 (1.10-1.34) 1.13 (1.02-1.26) 1.17 (1.05-1.31) 1.19 (1.07-1.32) 1.32 (1.19-1.46) 1.39 (1.35-1.43) 1.46 (1.32-1.62)  Appendix 2b Regional temporal trends in relative abundance for eight mammal species for the period 1995-2002. 95% confidence intervals are shown in brackets. Indices are measured relative to the year 1995, which is set to one. Although we exclude data for 2001 from the analyses due to foot-and-mouth disease, we interpolate an index here for 2001. An asterisk denotes a significant difference between the first and last years of the survey at the 5% level or more.   Year Species n 1995 1996 1997 1998 1999 2000 2001 2002  Brown Hare North West England5411.22 (1.00-1.48)0.98 (0.79-1.21)1.04 (0.83-1.28)0.76 (0.6-0.97)1.01 (0.8-1.27)0.91 (0.72-1.15)0.81 (0.64-1.04) Yorkshire & The Humber4611.35 (1.03-1.78)1.15 (0.86-1.54)1.02 (0.76-1.37)0.88 (0.65-1.20)0.86 (0.63-1.17)0.93 (0.66-1.29)1.00 (0.73-1.37) East Midlands6011.19 (0.93-1.53)0.85 (0.65-1.11)0.84 (0.64-1.10)1.15 (0.90-1.47)1.15 (0.90-1.47)1.27 (1.03-1.59)1.39 (1.08-1.79) East of England13011.20 (1.03-1.40)1.08 (0.92-1.26)1.08 (0.92-1.27)1.13 (0.97-1.33)1.12 (0.95-1.31)1.14 (0.97-1.34)1.16 (0.98-1.37) South East England*7210.97 (0.84-1.13)0.91 (0.79-1.06)0.87 (0.75-1.02)0.84 (0.72-0.98)0.76 (0.65-0.89)0.76 (0.64-0.90)0.75 (0.64-0.88) South West England*5111.66 (1.34-2.04)1.18 (0.95-1.46)1.42 (1.15-1.76)0.90 (0.72-1.13)0.97 (0.77-1.23)1.12 (0.88-1.43)1.27 (1.01-1.61) England46711.18 (1.10-1.27)1.02 (0.94-1.10)0.98 (0.91-1.06)0.93 (0.86-1.01)1.00 (0.92-1.08)1.03 (0.95-1.11)1.06 (0.98-1.15) Scotland*5610.67 (0.55-0.82)0.87 (0.72-1.06)0.92 (0.77-1.11)0.71 (0.58-0.86)0.70 (0.57-0.85)0.64 (0.50-0.80)0.57 (0.46-0.71)Rabbit North West England*9011.16 (0.94-1.43)1.07 (0.87-1.33)0.80 (0.63-1.01)0.53 (0.40-0.70)0.87 (0.68-1.12)0.79 (0.61-1.01)0.70 (0.54-0.91) Yorkshire & The Humber7611.28 (1.02-1.62)1.37 (1.09-1.71)1.00 (0.79-1.27)1.02 (0.80-1.30)1.17 (0.92-1.48)1.11 (0.87-1.41)1.04 (0.82-1.32) East Midlands*7110.56 (0.45-0.69)0.70 (0.55-0.89)0.56 (0.44-0.73)0.37 (0.28-0.49)0.55 (0.43-0.71)0.49 (0.37-0.65)0.43 (0.31-0.59) East of England*16311.68 (1.44-1.95)1.59 (1.37-1.86)1.16 (0.98-1.37)1.12 (0.94-1.32)1.11 (0.94-1.32)1.20 (1.00-1.43)1.29 (1.09-1.52) West Midlands*9310.58 (0.47-0.71)0.75 (0.61-0.91)0.71 (0.58-0.86)0.69 (0.56-0.84)0.62 (0.50-0.76)0.61 (0.48-0.75)0.59 (0.47-0.73) South East England*20811.11 (0.98-1.26)1.16 (1.02-1.31)0.95 (0.83-1.09)0.91 (0.79-1.04)0.81 (0.70-0.93)0.79 (0.67-0.92)0.76 (0.66-0.88) South West England13910.91 (0.76-1.10)1.58 (1.32-1.90)1.13 (0.93-1.37)1.38 (1.14-1.66)1.56 (1.30-1.88)1.29 (1.22-1.36)1.01 (0.81-1.26) England*87311.07 (1.00-1.14)1.16 (1.09-1.25)0.92 (0.85-0.98)0.87 (0.81-0.93)0.92 (0.85-0.98)0.88 (0.80-1.14)0.83 (0.77-0.90) Scotland*10411.07 (0.91-1.26)1.49 (1.27-1.75)1.15 (0.97-1.35)0.79 (0.66-0.95)0.98 (0.82-1.17)0.79 (0.63-0.98)0.60 (0.49-0.74) Wales7511.07 (0.87-1.31)0.78 (0.61-0.99)0.82 (0.65-1.05)0.82 (0.65-1.05)0.81 (0.62-1.06)0.95 (0.76-1.19)1.09 (0.85-1.38)Grey Squirrel East of England7712.38 (1.94-2.91)1.35 (1.08-1.69)1.18 (0.94-1.49)0.98 (0.77-1.25)1.11 (0.88-1.4)1.07 (0.80-1.41)1.03 (0.80-1.32) West Midlands6011.70 (1.36-2.13)1.01 (0.79-1.30)0.77 (0.59-1.01)0.78 (0.59-1.02)0.99 (0.76-1.28)1.03 (0.80-1.33)1.07 (0.82-1.38) South East England12811.90 (1.61-2.25)1.12 (0.93-1.34)1.09 (0.91-1.32)0.80 (0.66-0.98)1.21 (1.00-1.45)1.09 (0.89-1.32)0.96 (0.79-1.16) South West England* 6612.01 (1.60-2.53)1.57 (1.23-1.99)0.98 (0.75-1.28)1.13 (0.87-1.47)1.40 (1.08-1.82)1.61 (1.28-2.02)1.81 (1.41-2.32) England* 43512.01 (1.85-2.19)1.25 (1.14-1.37)1.05 (0.95-1.15)0.88 (0.80-0.98)1.17 (1.06-1.28)1.17 (1.06-1.29)1.17 (1.06-1.29) Wales*3912.88 (2.11-3.94)1.55 (1.09-2.21)1.69 (1.19-2.39)1.28 (0.89-1.84)1.45 (1.00-2.11)1.61 (1.09-2.34)1.77 (1.24-2.51)Red Fox South East England5311.19 (0.91-1.55)1.16 (0.89-1.53)1.24 (0.94-1.63)1.14 (0.87-1.49)1.55 (1.20-2.01)1.18 (0.90-1.54)0.80 (0.59-1.08) South West England4211.27 (0.96-1.68)0.76 (0.55-1.04)0.85 (0.63-1.16)0.89 (0.66-1.21)0.75 (0.54-1.04)0.79 (0.55-1.11)0.82 (0.59-1.15) England19311.34 (1.19-1.50)1.06 (0.93-1.20)0.94 (0.83-1.08)0.88 (0.77-1.01)0.91 (0.80-1.04)0.90 (0.78-1.04)0.88 (0.77-1.01)Red Deer Scotland*4410.61 (0.50-0.75)0.63 (0.52-0.77)0.63 (0.52-0.78)0.36 (0.28-0.45)0.43 (0.33-0.56)0.43 (0.33-0.55)0.42 (0.33-0.54)Roe Deer South East England*5911.45 (1.15-1.81)0.93 (0.73-1.19)1.15 (0.90-1.46)1.31 (1.04-1.66)1.36 (1.08-1.72)1.73 (1.45-2.09)2.10 (1.69-2.60) South West England*6311.17 (0.92-1.48)1.09 (0.86-1.37)0.92 (0.72-1.16)0.84 (0.66-1.06)1.59 (1.26-2.01)1.85 (1.58-2.18)2.10 (1.68-2.63) England*17711.01 (0.91-1.12)0.97 (0.88-1.07)0.92 (0.83-1.02)0.99 (0.90-1.10)1.16 (1.05-1.28)1.41 (1.30-1.53)1.66 (1.51-1.82) Scotland*6811.18 (0.94-1.48)1.17 (0.93-1.47)1.50 (1.20-1.87)1.42 (1.13-1.77)1.51 (1.20-1.89)1.48 (1.20-1.83)1.45 (1.15-1.83)Fallow Deer England*4010.36 (0.33-0.41)0.39 (0.35-0.44)0.32 (0.29-0.36)0.22 (0.19-0.24)0.51 (0.46-0.56)0.45 (0.41-0.49)0.38 (0.34-0.42)Reeves’s Muntjac England*4611.21 (1.08-1.36)1.12 (0.99-1.27)1.16 (1.02-1.31)1.18 (1.04-1.32)1.32 (1.17-1.48)1.31 (1.17-1.46)1.31 (1.16-1.48) Appendix 2c Temporal trends in relative abundance for 9 mammal species for the period 1995-2002 within the six environmental zones in Great Britain. The six Environmental Zones are based on combinations of CEH land classes which cover the range of environmental conditions that we find in Great Britain, from the lowlands of the south and east, through to the uplands and mountains of the north and west (Bunce et al. 1996). 95% confidence intervals are shown in brackets. Indices are measured relative to the year 1995, which is set to one. Although we exclude data for 2001 from the analyses due to foot-and-mouth disease, we interpolate an index here for 2001. An asterisk denotes a significant difference between the first and last years of the survey at the 5% level or more.  Year Species n 1995 1996 1997 1998 1999 2000 2001 2002  Brown Hare Zone 129211.14 (1.03-1.26)1.05 (0.94-1.16)0.92 (0.83-1.03)0.93 (0.84-1.04)0.96 (0.86-1.07)1.00 (0.90-1.11)1.04 (0.94-1.16) Zone 2*14511.37 (1.22-1.54)0.98 (0.86-1.12)1.18 (1.04-1.34)1.04 (0.91-1.18)1.17 (1.03-1.33)1.17 (1.03-1.32)1.16 (1.02-1.31) Zone 3*5310.86 (0.71-1.04)0.65 (0.53-0.8)0.83 (0.68-1.01)0.68 (0.56-0.83)0.98 (0.80-1.20)0.89 (0.74-1.07)0.80 (0.65-0.99)Rabbit Zone 1*47911.00 (0.92-1.09)1.13 (1.03-1.23)0.89 (0.8-0.98)0.85 (0.77-0.94)0.90 (0.82-0.99)0.87 (0.78-0.96)0.84 (0.76-0.93) Zone 2*36711.19 (1.08-1.32)1.22 (1.10-1.35)0.98 (0.88-1.10)0.91 (0.81-1.02)0.97 (0.87-1.09)0.92 (0.81-1.04)0.86 (0.76-0.97) Zone 310510.95 (0.78-1.16)0.98 (0.80-1.19)0.89 (0.73-1.09)0.85 (0.69-1.04)0.86 (0.70-1.07)0.87 (0.70-1.08)0.88 (0.72-1.09) Zone 4*6011.04 (0.83-1.31)1.34 (1.07-1.68)0.65 (0.50-0.83)0.68 (0.53-0.87)0.68 (0.52-0.88)0.64 (0.46-0.87)0.59 (0.44-0.79)Grey Squirrel Zone 124311.89 (1.68-2.12)1.19 (1.05-1.35)1.03 (0.90-1.18)0.83 (0.73-0.95)1.27 (1.12-1.45)1.19 (1.04-1.36)1.10 (0.96-1.25) Zone 2*19712.09 (1.84-2.38)1.35 (1.17-1.55)1.17 (1.01-1.36)1.01 (0.87-1.18)1.18 (1.01-1.37)1.30 (1.12-1.51)1.42 (1.22-1.64)Red Fox Zone 110511.47 (1.25-1.74)1.17 (0.98-1.40)1.21 (1.01-1.45)0.98 (0.81-1.17)1.19 (0.99-1.43)1.08 (0.89-1.31)0.97 (0.80-1.18) Zone 2*8411.14 (0.96-1.35)0.76 (0.63-0.93)0.73 (0.60-0.90)0.88 (0.73-1.07)0.75 (0.61-0.92)0.75 (0.60-0.93)0.75 (0.61-0.92)Roe Deer Zone 1*10111.01 (0.88-1.16)1.07 (0.93-1.22)1.03 (0.9-1.18)1.06 (0.93-1.22)1.06 (0.92-1.22)1.37 (1.22-1.54)1.68 (1.47-1.91) Zone 2*6511.01 (0.86-1.18)0.82 (0.71-0.96)0.68 (0.58-0.80)0.86 (0.73-1.00)1.35 (1.16-1.58)1.58 (1.42-1.76)1.80 (1.55-2.08)Reeves’s Muntjac Zone 14110.82 (0.70-0.96)0.87 (0.75-1.02)0.94 (0.80-1.11)0.93 (0.80-1.09)1.10 (0.94-1.28)1.07 (0.93-1.23)1.03 (0.88-1.21)  Appendix 3 Change in the presence of six mammal species for the period 1995-2002 (for Stoat and Weasel) and 1996-2002 (for Mole, Hedgehog, Badger, Brown Rat, Stoat and Weasel). 95% confidence intervals are shown in brackets. Indices are measured relative to the year 1995, which is set to one. Although we exclude data for 2001 from the analyses due to foot-and-mouth disease, we interpolate an index here for 2001. An asterisk denotes a significant difference between the first and last years of the survey at the 5% level or more.  Year Species n 1995 1996 1997 1998 1999 2000 2001 2002  Mole*441-10.58 (0.57-0.60)1.21 (1.18-1.23)2.52 (2.46-2.57)5.84 (5.70-5.98)5.48 (5.42-5.53)5.12 (5.00-5.25)Hedgehog*208-10.86 (0.84-0.89)1.90 (1.85-1.96)1.67 (1.62-1.72)3.63 (3.52-3.74)2.78 (2.72-2.83)1.94 (1.88-2.00)Badger*229-10.67 (0.65-0.69)1.72 (1.67-1.78)2.07 (2.00-2.13)3.66 (3.55-3.78)4.05 (3.99-4.11)4.44 (4.31-4.59)Brown Rat*133-10.28 (0.27-0.30)1.02 (0.98-1.05)1.60 (1.55-1.67)3.69 (3.56-3.83)3.40 (3.34-3.45)3.10 (2.99-3.22)Stoat*10814.46 (4.27-4.65)2.61 (2.49-2.73)6.20 (5.94-6.48)9.09 (8.72-9.49)10.62 (10.17-11.09)8.10 (7.80-8.39)5.58 (5.34-5.83)Weasel*8518.15 (7.74-8.59)5.15 (4.87-5.44)9.74 (9.23-10.29)11.36 (10.76-11.99)17.27 (16.34-18.24)12.94 (12.36-13.52)8.62 (8.16-9.10) PAGE  BTO Research Report No 367 March 2005 PAGE 1 BTO Research Report No 367 March 2005 PAGE 26 BTO Research Report No 367 March 2005 BTO Research Report No 367 September 2004 PAGE 30 BTO Research Report No 367 September 2004 PAGE 28 BTO Research Report No 367 March 2005 PAGE 31 BTO Research Report No 367 March 2005 PAGE 31 BTO Research Report No 367 September 2004 PAGE 34 BTO Research Report No 367 March 2005 PAGE 38 BTO Research Report No 367 March 2005 PAGE 48 BTO Research Report No. 367 March 2005 PAGE 48 BTO Research Report No 367 43 March 2005 BTO Research Report No 367 42 March 2005 BTO Research Report No 367 41 March 2005 BTO Research Report No 367 40 March 2005 BTO Research Report No 367 39 March 2005 BTO Research Report No 367 44 March 2005 BTO Research Report No 367 45 March 2005 BTO Research Report No 367 46 March 2005 BTO Research Report No 367 47 March 2005 BTO Research Report No 367 29 March 2005 %&|}~‚ЂГ5679RSUuw•ОмнїљLNimm — œ Ё Ў Џ Б Г  4 @ A B x Х Ю 3?,s}МЦ4MYІВђѕёёыётррмиеиебЭЪемШПШмемЛмиЭимЕАЕЭЕеЕеЕеЕеЕеЕеЕеЕеЕеЕеmH sH  5CJ\CJ\56CJ\]\CJ5\5CJCJ5CJCJ\5OJPJQJ^J 6CJ ]6]jUmHnHuD$%&Ka|}~€‚žŸ ЁЂЃЎ§§§§§ћћііёііііьрььёёёёёёёё 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$$Ifa$Ю€з€с€э€і€ііііі?xЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџFџџџџџџџџyџџџџџџџџџџџџтџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$љ№№№№№ $$Ifa$$If4>GPYHB9999 $$Ifa$$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџFџџџџџџџџyџџџџџџџџџџџџтџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laіGOPXYabcvw€ˆ‹“”œžБДМНХЦЮбвъёљќ§‚!‚)‚,‚-‚D‚K‚S‚T‚\‚]‚^‚r‚s‚|‚‚‡‚ˆ‚‚‘‚™‚š‚›‚Ќ‚­‚Е‚Ж‚И‚Й‚С‚Ф‚Ь‚Э‚Ю‚с‚т‚ъ‚ы‚ѓ‚є‚ќ‚џ‚ƒƒ ƒƒƒ&ƒ)ƒ1ƒ6ƒ7ƒJƒKƒSƒTƒ\ƒ]ƒeƒfƒnƒoƒwƒxƒћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћєћє CJPJaJCJaJ`Ybcw€‰‹і?ь9ііі$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$‹”žВДНіі?а9іі$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$НЦЯбвыэііі?Ќ9і$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$эяёњќ§‚іііі?Р9$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$‚‚‚!‚*‚,‚-‚ііііі?ФЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$-‚E‚G‚I‚K‚T‚]‚љ№№№№№ $$Ifa$$If]‚^‚s‚}‚‚ˆ‚‘‚HєB9999 $$Ifa$$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі‘‚š‚›‚­‚Ж‚Й‚Т‚і?Ь9ііі$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$Т‚Ф‚Э‚Ю‚т‚ы‚є‚іі?ь9іі$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$є‚§‚џ‚ƒ ƒƒ'ƒііі?И9і$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$'ƒ)ƒ2ƒ4ƒ6ƒ7ƒKƒіііі?9$IfЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$KƒTƒ]ƒfƒoƒxƒyƒііііі?ьЗ$$If–lжжˆ”џt К3tŒ$р џџџџџџџџџџџџFџџџџџџџџџџџџyџџџџџџџџџџџџџџџџтџџџџџџџџџџџџџџџџ_џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџж0џџџџџџіј$6жџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџџџџжџџџџџџџџџџџџџџџџџџџџџ4ж laі $$Ifa$xƒyƒ–ƒ˜ƒГƒДƒЪƒЯƒзƒиƒрƒуƒфƒјƒџƒ„„„„„&„+„3„4„<„?„@„U„V„^„c„k„n„o„…„Š„’„“„œ„„Ѕ„І„Ї„Е„Ж„О„П„Ч„Ш„а„б„й„к„т„у„ф„џ„… … ………… 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