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DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS CSG 15Research and Development

Final Project Report(Not to be used for LINK projects)

Two hard copies of this form should be returned to:Research Policy and International Division, Final Reports UnitDEFRA, Area 301Cromwell House, Dean Stanley Street, London, SW1P 3JH.

An electronic version should be e-mailed to [email protected]

Project title Development of a decision support system for ecologically sound rabbit management

DEFRA project code VC0232

Contractor organisation and location

Central Science LaboratorySand HuttonYO41 1LZ

Total DEFRA project costs £ 365,067

Project start date 01/04/02 Project end date 31/03/04

Executive summary (maximum 2 sides A4)

1. Rabbit damage to crops is a major economic problem for agriculture in the UK and rabbit numbers continue to increase as the effects of myxomatosis wane. Improvements in our ability to predict the effectiveness of different management methods under varying conditions are required to allow sound and flexible advice to be developed regarding optimal management strategies for particular agricultural contexts.

2. A substantial knowledge base has accrued through considerable Defra investment in rabbit research. This project seeks to integrate this knowledge into a decision support system for RDS wildlife advisors in their advisory and statutory work regarding rabbit damage in lowland agricultural landscapes, offering guidance on optimised management approaches.

3. Underpinning the system are ecologically sound models of rabbit population biology and rabbit damage to crops in terms of the relationships between rabbit numbers and yield loss.

4. In order to predict future losses, in a given problem setting, we need to be able to extrapolate from existing static models of the relationships between fixed numbers of rabbits and crop damage to more complex settings which are dynamic, in that rabbit numbers are allowed to fluctuate naturally and the animals have choices available to them in terms of foraging opportunities. The required research has been completed at CSL’s unique field research station in Hampshire where replicated studies can be conducted with complete control over agronomy and experimental design.

5. The dynamic damage studies suggest that we can allow for population changes by expressing damage as a linear function of cumulative rabbit grazing hours from crop emergence. This is how the decision support system now converts future predictions regarding rabbit numbers into predicted yield loss estimates. As a result of RDS user evaluation of the system, pro rata extrapolations from winter wheat to other crops are now included in the options available. These extrapolations need to be treated with care in the absence of scientifically rigorous crop damage studies for crops other than winter wheat, grass grown for silage and spring barley.

6. Previous results from our studies of enclosed populations suggest that juvenile recruitment can be impaired in low density populations, known in the ecological literature as an “Allee effect”. A possible mechanism is

CSG 15 (Rev. 6/02) 1

Projecttitle

Development of a decision support system for ecologically sound rabbit management

DEFRAproject code

VC0232

interaction between plant sward height and density dependent processes. Specifically, increased sward height could lead to reduced food quality, increased predation and increased thermoregulatory challenge to juveniles. Increased population density, above some critical threshold, could maintain low sward height and thus enhance juvenile survival. This possibility was examined in a study where both population density and sward height were manipulated, the latter by mowing.

7. There was no evidence that mowing led to enhanced juvenile recruitment. It was thus considered unnecessary to allow for the putative “Allee effect” in the rabbit population model underpinning the decision support system. However, given that future studies may uncover evidence for such processes, a critical threshold density function has been incorporated into the model which can be invoked by the user to reduce juvenile recruitment at an increasing rate the further the population size falls below the threshold.

8. The original rabbit population model, underpinning the decision support system, represented variation in productivity and mortality rates with respect to age, sex, season and density for closed populations only. Movements between populations, which may influence recovery rates following control operations, were thus not allowed for. This knowledge gap was addressed here by quantifying rabbit migration in the context of active control measures against populations in lowland agricultural landscapes.

9. This showed that the great majority of rabbits rarely disperse despite being given the opportunity of moving into depopulated areas. Specifically, only 8% of tagged animals moved into areas with artificially low population densities areas over a one-year period. The majority of dispersing individuals were males. The mean distance moved by male rabbits was 273m compared to 225m by females. Movements between populations are thus unlikely to have a major influence on population recovery rates following control operations. Nevertheless, migration has now been incorporated into the rabbit population model, using the rates recorded here, and users can specify the number of unmanaged adjacent populations present that might provide sources of immigration into the population that is the focus of concern and subject to control. The decision support system thus now functions for both closed and open rabbit populations

10. Fundamental to the development of the decision support system is the ability to relate the extent of problems, such as crop loss, to the size of rabbit populations. A simple and reliable method for assessing the size of the problem in any given lowland agricultural setting is thus required. Previous studies developed a validated method based on sight counts. Nevertheless, this technique requires a series of counts to be made at night along field margins and is therefore time consuming. This study thus sought to develop and validate a simple and robust daytime census method based on assessing rabbit signs during a single visit to a problem site. Overall, the study suggested that the measurement of activity signs during a single daytime visit does have the potential to either estimate rabbit numbers or at least offer an index of abundance, provided that different techniques are adopted for cereal and non-cereal fields. An approach based on systematic assessment of rabbit droppings is recommended for non-cereal sites and one based on assessment of rabbit scrapes for cereal sites. The specifications for the recommended census methods have been defined as part of the user information for the decision support system with instructions on how to use these census methods to initiate the system for a particular problem scenario.

11. The decision support system for ecologically sound rabbit management, named the Rabbit Management Adviser, or RabMan, now provides a reliable tool for predicting future numbers of rabbits in a given lowland agricultural setting, based on estimates derived from simple census methods; the potential costs of the damage those rabbits will cause in a specific agricultural setting; and cost-benefit analyses of management options. This has been subject to evaluation by users. The user requested revisions have been incorporated and a fully functional decision-support system made available to RDS wildlife advisors

12. These advisors are seen as the key focus for technology transfer of the extensive scientific knowledge base via the decision-support system. In the future, additional work may be required to cover new issues such as novel crops, management methods or agronomic practices. It is also likely that, as the system is put into practice, users will identify desirable additional functions or options. Once the system has been successfully established, in the challenging practical context of users who are already expert in this field, then there will be the potential for diversifying the target users to consultant agronomists, growers and farmers to maximise the technology transfer opportunities offered by this unique system.

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Projecttitle


DEFRAproject code

VC0232

Scientific report (maximum 20 sides A4)1. Introduction

The rabbit population in the UK continues to increase in size as it recovers from the waning effects of myxomatosis. In the mid-1980s economic losses to agriculture arising from rabbit damage were estimated at £100 million per annum and are now likely to be considerably higher. Under the 1954 Pest Act, Defra has enforcement responsibilities for the obligations of occupiers regarding rabbit control, and must therefore ensure that cost-effective, environmentally sensitive and humane management strategies are available. These strategies need to be informed by rabbit population biology, the costs of damage and the effectiveness and costs of different management methods. Considerable research has been carried out over the past twenty years on a variety of these issues so that we now have extensive knowledge of rabbit biology and management in lowland agricultural landscapes in the UK. This research has reflected five key areas. Firstly, developing an understanding of rabbit population biology and the ecological factors influencing rabbit abundance. Secondly, measuring the relationships between rabbit abundance and levels of damage to key agricultural crops. Thirdly, developing methods of measuring rabbit abundance. Fourthly, measuring the effectiveness of various management methods. Finally, developing new humane and environmentally sensitive approaches to reducing rabbit damage. Much of this work has been published in the peer reviewed scientific literature while the main means of technology transfer to date has been popular articles in the agricultural press and the production of advisory leaflets. Modern personal computer technology, however, offers the prospect of integrating this vast knowledge base into a scientifically robust rabbit management decision-support system. This system will integrate the extensive existing data set on rabbit biology, agricultural damage and management to provide guidance on costed, scientifically rigorous, coherent rabbit management strategies for a broad spectrum of the main problem settings in lowland agricultural landscapes. The model of rabbit population biology that underpins the system (Smith 1997), has been validated and, in general, makes reliable predictions for future rabbit numbers given different control scenarios (Smith 2001). However, there are four key gaps in our understanding of rabbit biology that need to be filled for a fully functional decision support system to be realised. Firstly, we need to be able to link our rabbit population model to one of the relationship between rabbit numbers and crop damage, in order to predict future losses in a given problem setting. Research thus needs to be completed that will enable extrapolation from our static models of the relationship between rabbit numbers and crop damage (Mckillop et al. 1996, Dendy et al. 2003, Dendy et al. in press) to more complex settings which are dynamic, in that rabbit numbers are allowed to fluctuate naturally and the animals have choices available to them in terms of foraging opportunities. Such dynamic systems are more representative of the real world. It is thus essential for the decision-support system to include such dynamic damage models if it is to be used with confidence under field conditions. Secondly, rabbit population biology sometimes appears to exhibit what is known as the ”Allee effect” (Allee 1931). Here at low densities, population growth is constrained which could explain why rabbit populations sometimes suddenly irrupt, perhaps having crossed some critical threshold. Identifying threshold levels, beneath which populations would be naturally held in check, could be vital to enhanced management strategies. Thirdly, the population model used to underpin the system represents a closed population reflecting variation in productivity and mortality rates with respect to age, season and density. Thus movements between populations, in the form of migration, are not considered. However, immigration may influence the rate of recovery of local populations that have been subject to control. Fourthly, in order to set the population model running for a particular problem setting, it is essential that a reliable estimate of initial rabbit population size be obtained. CSL has developed a validated night count census method to assess the actual number of rabbits utilising a particular tract of agricultural land (Poole et al. 2003). However, this technique requires a series of counts to be made at night and is therefore time consuming and impractical for everyday use at all sites where statutory concerns are raised. What is needed is a simple and robust daytime census method, which can be conducted quickly by advisors during a single site visit, so that a suite of census methods, offering varying degrees of resolution, appropriate for different levels of severity of problem, is available to enable full exploitation of the decision-support system as a tool to inform statutory and advisory services. This project seeks to address each of these gaps in our knowledge and complete the integration of information in the form of a fully functioning decision support system for RDS wildlife advisors.

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Projecttitle


DEFRAproject code

VC0232

2. Development of rabbit damage modelsA key concept in the development of the decision support system is linking predictions about future

rabbit numbers, provided by the rabbit population model for a particular setting, with predictions about the damage that these will cause to crops grown there. We thus need to develop models of the relationships between rabbit numbers and damage to particular crops. In the past yield loss due to rabbit grazing was estimated by protecting areas from grazing and comparing the yields of protected and unprotected areas within fields (e.g. Bell et al. 1999, Church et al. 1953, Crawley 1989, Gough 1955, Gough & Dunnet 1950). However, in the absence of information on the number of rabbits grazing fields in these studies it was not possible to relate yield loss to actual rabbit numbers. The unique facilities at the CSL field ecology research site in Hampshire offer complete control over both the agronomy and numbers of rabbits in large enclosures enabling, for the first time, losses to be related to rabbit numbers. Average yield losses to cereal were 1% rabbit -1 ha -1

for winter wheat (McKillop et al. 1996), 0.8% rabbit -1 ha -1 for grass grown for silage (Dendy et al. 2003) and 0.5% rabbit -1 ha –1 for spring barley (Dendy et al. in press). These damage models allowed us to paramaterise the decision support system but were based on static single-sex populations of fixed size feeding almost exclusively on the study crop. In the real world rabbit populations are dynamic in that they can change through both productivity and mortality. Furthermore, rabbits usually have alternative food sources available to them in field margins, hedgerows and woodland adjacent to the crop. We thus need to develop our static rabbit damage models so that they predict yield losses when rabbit numbers fluctuate naturally and choices are available in terms of foraging opportunities. The required studies were carried out on mixed-sex enclosed populations allowed to fluctuate naturally after establishment. The rabbits could choose to feed on the crop or on an adjacent grassed area where shelter was provided and they were encouraged to construct burrow systems.

2.1 MethodsIn each of three years different initial rabbit densities (10, 20, 40 or 60 rabbits) were established in four

2ha enclosures. Pasture was established in half of each enclosure and winter wheat drilled in the other (variety Mercia in 1999 & 2000, Herewood in 2002). Population numbers were monitored by regular live-capture mark and release sessions using session cages traps. A further two 1ha enclosures were used each year as controls with no rabbit grazing. Crop height was monitored throughout the growing season along with regular damage assessment. Crop samples were taken throughout the year and at harvest. Each sample was taken by placing a half metre stick at random following the direction of the drill lines and removing all the plants along its length for detailed inspection. Grain yield was measured each harvest and compared with the yields from the control enclosures. The final harvest of the winter wheat took place in August 2002 (Milestone 01/01). Detailed analyses of losses attributable to grazing have been performed and summaries are given below (Milestone 01/02). The autumn of 2001 was extremely wet and the trial had to be suspended for a year, as we were unable to plant a wheat crop. However as rabbits remained in the grassed areas of the trial enclosures it provided us with an opportunity to collect additional information relating to juvenile recruitment into populations. In all enclosures rabbits moved between the pasture and wheat areas via tunnels built into a dividing fence. In one of the enclosures these tunnels contained an antennae system designed to read unique passive integrated transponder (PIT) tag numbers carried by each adult rabbit on an ear tag. Data on the frequency and length of visit by individual rabbits to the part of the enclosure containing wheat were measured. The monitoring system ran constantly for five days each week. Bespoke computer programmes transformed the enormous quantities of raw data into formats more suitable for a comprehensive analysis. In order to quantify behaviour in the form of time budgets observations of the rabbits took place on three evenings each week between November 2002 and January 2003 (Milestone 02/01). The ranging and feeding behaviour of individual rabbits was observed through a night viewer and the details recorded on a tape recorder. The behaviour was broken down into seven categories and the amount of time spent carrying out each behaviour recorded.

2.2 ResultsTable 1 provides details of the rabbit densities used each year and the average total yields recorded at

harvest for the control plots. The higher yields in 2002/03 reflect the use of the more productive Herewood variety rather than Mercia. Table 1. The average yields (tonnes) in the enclosures in relation to initial rabbit numbers.

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Projecttitle


DEFRAproject code

VC0232

Year Control 10 rabbits 20 rabbits 40 rabbits 60 rabbits1999/00 3.78 3.34 3.342000/01 4.45 3.98 2.962002/03 7.2 6.4 4.38

Percentage yield loss was in general proportionate to initial rabbit density (Table 2), although yield loss was similar in 1999/00 for the 10 and 20 rabbits/ha enclosures. The results suggest that the rate of juvenile recruitment into rabbit populations initially increases with population density. Doubling the initial number of rabbits more than doubled the resulting loss, however the loss recorded in the final year, at the initial density of 60 rabbits/ha, was not proportionately higher. This partly reflected higher adult mortality at this density (see below). In addition, a high initial density does not lead proportionately to more juveniles being recruited.

Table 2. Average yield loss compared to control enclosures and mean number of juveniles recruited by the time of harvest.

High Density Year Initial density/ha% Yield lossJuvenile recruitment

1999/00 20 11 32000/01 40 34 502002/03 60 39 32

Low DensityYear Initial density/ha% Yield lossJuvenile recruitment

1999/00 10 11 02000/01 20 11 482002/03 20 11 36

The seasonal changes in population size in one of the high-density pens are shown in Figure 1. The initial population of 60 appears to suffer greatest adult mortality over-winter, allowing the population to stabilise as more food becomes available from the spring. Similar patterns were found in the low-density pens but with lower initial adult mortality.

In 2000/01 and 2002/03 fewer plants per half metre were found in the pens containing an initial density of 40 or 60 rabbits than in the controls. Plants from control pens were found to be taller than those taken from grazed ones. Plants from pens with the higher initial density were always found to be the smallest although this was not statistically significant at harvest time. Additional samples (from within a 0.25m quadrat) were taken just prior to harvest which were used to determine grain quantity and quality. No differences between any of the pens were found when the Harvest Index was measured (Table 3). This is a measure of the proportion of the sample weight that is grain. All the plants were found to fall within the required range (50% + 10%). However the plants from the pens containing the higher initial density for that year always produced the lowest numbers of ears and (except for 1999/00 when high density was only 20 rabbits) they also produced the lowest number of grains per ear. The thousand grain weights (TGW) taken from each of the samples were always highest in control pens and lowest in higher density pens in 2000/01 and 2002/03.

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Projecttitle


DEFRAproject code

VC0232

Figure 1. Changes in populations size with season in one high density pen (Montana 3, Year 1 2001/00, Year 2 2002/03).

Table 3. Variation in plant characteristics at harvest in relation to treatment type (+ SD).

Pen Density Number of ears

Number of grains

Number of grains/ear

Harvest Index

TGW (g)

40 or 60 50 + 9 1577 + 131 31 + 6 42 + 6.1 41 + 2.210 or 20 56 + 8 1999 + 216 35 + 1.4 46 + 4 45 + 3.4Control 57 + 8 2093 + 333 34 + 3 45 + 2.9 49 + 0.0

Analyses of the data derived from the automated activity recording system showed that individual rabbits spent most time in the wheat between November and May. Peak times were recorded during March, April and May, coinciding with crop growth after near dormancy during the winter. Less than 40 hours per rabbit were spent in June with a slight increase being seen in July (Figure 2). Overall males were more active than females, reflected in the higher number of visits by males than females at all times. However, there was no significant difference between the sexes in the length of time spent in the wheat. The total number of visits made to the wheat field increased rapidly after the crop was established in October, reflecting the very attractive qualities of wheat for rabbits at this young growth stage. Summary time budgets for the rabbits observed on the wheat crop between November and January are shown in Table 4. Approximately 34% of their time was spent feeding on the crop and there were no differences between months in the average amount of time spent grazing.

Table 4. Time budgets of rabbits observed on the wheat crop.

% Grazing

% Alert

% Grooming %

Running

% Social interaction

% Resting

% Moving

34.1 8.8 3.7 13.2 0.9 28.1 11.3

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Projecttitle


DEFRAproject code

VC0232

Figure 2. The number of hours spent each month by each rabbit in the winter wheat crop between 1999 and 2003.

2.3 DiscussionThe challenge for incorporating these data into the decision support system is to overlay the dynamic

processes revealed in this project on the simple static damage models developed previously. Fortunately, the dynamic damage studies suggest that, because of density-dependent variation in adult survival and juvenile recruitment, the effects of grazing on yield are essentially cumulative, at least until the crop grows away and becomes unpalatable. We thus simply allow for dynamic population changes by expressing damage as a linear function of cumulative rabbit grazing hours from crop emergence (Milestone 02/02). This is how the decision support system now converts future predictions regarding rabbit numbers into predicted yield loss. As a result of RDS user evaluation of the provisional decision support system pro rata extrapolations are made for crop types other than winter wheat based on estimates of relative damage from comparative static system studies. These extrapolations need to be treated with care in the absence of scientifically rigorous crop damage studies for crops other than winter wheat, grass grown for silage and spring barley. Another layer of complexity, in moving from our enclosures to the real world, is the area over which a given population causes damage. The typical topography of rabbit problems in lowland agricultural systems in the UK is for the animals to move across a linear interface from harbourage, in the form of woodland or hedgerow, into the field where they cause damage and it is this scenario that the system is designed to address. We have thus used existing data (Cowan et al. 1989) to model rabbit activity decreasing as a function of distance from the field margin, meaning that damage is unevenly distributed across the field with no animals generally risking being more than 50 metres away from the safety from predation afforded by the harbourage. We have assumed that this distribution is typical for rabbit populations operating across linear interfaces between harbourage and field and incorporated this into the decision support system as a generalised view of rabbit activity (Milestone 06/03).

CSG 15 (Rev. 6/02) 7

Projecttitle


DEFRAproject code

VC0232

3. Critical population threshold evaluationPrevious results from our studies of enclosed populations suggested that juvenile recruitment can be

impaired in low density populations. A possible mechanism is interaction between sward height and density dependent processes. Specifically, increased sward height could lead to reduced food quality, increased predation and increased thermoregulatory challenge to juveniles the latter caused by contact with long, wet vegetation. Increased population density, above some critical threshold, could maintain low sward height and thus enhance juvenile survival. This possibility was examined in a study where both population density and sward height were manipulated, the latter by mowing.

3.1 MethodsSix small pens (roughly 25 x 35 m2 each) were established with fences that allowed easy access for a

mower. Initially all the pens were mowed and two rabbit boxes placed in each pen. Rabbit populations were established using two treatment densities, high density with 12 rabbits (6 male, 6 female) and low density with 6 rabbits (3 male, 3 female). One pair of high density pens was established and two low density ones. The populations were introduced in pairs (i.e. same density, mowed - unmown) and the treatment randomly allocated to each pen. The designated pens were mowed at intervals throughout the season. The other pens received no management. Cage trapping took place in September to determine relative population sizes and juvenile recruitment. In the final year of the study 10 rabbits (5 males and 5 females) were introduced into pairs of 40m x 85m grass pens. One set of pens received no management throughout the trial whilst the height of the grass in the other set of pens was restricted by mowing at monthly intervals. The pens were monitored throughout the trial and any juveniles noted. Final population sizes in the pens were assessed by cage trapping. (Milestone 03/01)

3.2 ResultsThe results were inconclusive with breeding occurring in all the pens and there were no differences in

the numbers of juvenile recruited between treatments. There was no evidence that mowing led to enhanced juvenile recruitment.

3.3 DiscussionIn the absence of evidence supporting an inverse relationship between population density and juvenile

recruitment it was considered unnecessary to allow for the putative “Allee effect” in the rabbit population model underpinning the decision support system. However, given that future studies may uncover evidence for such processes, a critical threshold density function has been incorporated into the model which operates by reducing natality at an increasing rate the further the population size falls below the threshold (see also VC0227). There is a facility to allow the user, for research purposes, to choose whether to utilise this feature and to set and modify the thresholds as required (Milestone 06/02). This offers the prospect using the model to make predictions that could be tested by observations on free-living populations.

4. Measuring the effects of immigration on the rates of recovery of rabbit populations after controlThe original rabbit population model underpinning the decision support system represented only a

closed population reflecting variation in productivity and mortality rates in terms of age, season, density and broad habitat characteristics. Movements between populations, which may influence recovery rates following control operations, were thus not considered. While there are some data available on rates of long-term dispersal movements within and between rabbit populations, these are limited to confined populations (Webb et al. 1995, Kunkele & Holst 1996), free-living populations in Australia (Daly 1981, Parer 1982) or on uncultivated chalk downland (Cowan 1991). Current knowledge also relates only to populations unaffected by local perturbation caused by control. The aim of this study was to fill this knowledge gap by quantifying rabbit migration in the context of active control measures against populations in lowland agricultural landscapes. This would enable the development of a population biology model for such open populations subject to control measures. Incorporation of this information into the decision-support system will serve to enhance its reliability thus enabling the provision of informed and robust information to landowners and occupiers requesting advice.

CSG 15 (Rev. 6/02) 8

Projecttitle


DEFRAproject code

VC0232

4. 1 MethodsThe study was conducted during 2002 and 2003 on commercial farms in North Yorkshire. The main

objectives were to determine how many, how quickly and how far rabbits would move to colonise an area from which the resident population had been removed.

Two study sites, with well-established rabbit populations, were selected and a large discrete population identified within each. Over a one-year period, rabbits from these populations were removed, on a monthly basis, to represent control operations and also to create a void or population “sink” into which neighbouring rabbits could potentially move (Milestone 04/01). The areas surrounding these removal populations were surveyed for rabbit signs and well-used harbourage and the information used to select, at each site, distinct populations of rabbits nearby. The distances between the discrete removal populations and the adjacent populations ranged from 120-900m (Table 5). Individuals from these adjacent populations were live captured using cage-traps. Captured rabbits were sexed, individually marked with ear tags and released. A record was made of where each individual was caught. When possible, this process was repeated on a monthly basis. Any marked rabbits captured during the study were re-released and the distances travelled between their various capture points recorded. The dispersal rates to the removal populations from the adjacent populations were calculated. These rates were used to parameterise and validate the spatial scale dispersal model linked to the rabbit population model that underpins the decision support system (Milestone 04/02).

Table 5. Distances between the isolated removal populations and the adjacent populations at the two study sites.

Site Distances between removal populations and adjacent populations (m)Minimum Maximum

1 120 6002 150 900

4.2 ResultsIn total, 303 rabbits (105 males, 90 females; 108 unknown sex) were captured, tagged and released from

the populations adjacent to the removal populations (Table 6). Of these rabbits, 210 were trapped during only one of the monthly trapping sessions (136 at Site 1 and 74 at Site 2), 63 were trapped during two of the sessions (33 at Site 1 and 30 at Site 2) and 30 were trapped during three or more of the sessions (14 at Site 1 and 16 at Site 2).

Table 6. Numbers of rabbits ear tagged and released at the populations adjacent to the removal populations.

Site No. of rabbits marked and releasedMales Females Unknown Total

1 68 56 59 1832 37 34 49 120

Over the one-year period of the study at each site, only 24 tagged individuals (16 males; 7 females) dispersed to the population sites from where rabbits were regularly removed (total removed 322: 118 males; 123 females; 81 unknown). The dispersing males travelled a mean maximum distance of 273 m and the females 225 m. A breakdown of the trapping data from the test populations is presented in Table 7.

Table 7. Numbers of marked and unmarked rabbits captured at the removal populations and the mean maximum distances travelled

Site No. of unmarked rabbits No. of marked rabbits Mean maximum distances

CSG 15 (Rev. 6/02) 9

Projecttitle


DEFRAproject code

VC0232

captured and removed (m/f/u)

captured and released (m/f) travelled by marked rabbits (m/f))

1 242 (82/86/74) 17 (11/6) 268/243 m2 80 (36/37/7) 6 (5/1) 282/120 m

Marked rabbits did not appear to disperse to the removal population sites at any particular time of the year (Table 8).

Table 8. Timing of dispersal of tagged rabbits to the removal population sites from the adjacent populations (based on the first capture date at the removal population sites)

No. of marked rabbits dispersing to the removal population sites (males/females)Site 1 Site 2 Total

January 1 (0/1) 0 1 (0/1)February 2 (0/2) 1 (1/0) 3 (1/2)March 1 (1/0) 1 (1/0) 2 (2/0)April 1 (1/0) 0 1 (1/0)May 1 (1/0) 0 1 (1/0)June 3 (2/1) 0 3 (2/1)July 3 (2/1) 1 (0/1) 4 (2/2)August 0 1 (1/0) 1 (1/0)September 0 1 (1/0) 1 (1/0)October 2 (2/0) 0 2 (2/0)November 0 1 (1/0) 1 (1/0)December 3 (2/1) 0 3 (2/1)`

4.3 DiscussionDispersal, which can be described as the permanent movement of individuals from the natal site to an

alternative breeding site or one between breeding sites (Greenwood 1980), is a process that can directly influence population numbers (Parer 1982). Movements between populations can therefore have implications for the recovery rates of pest species following control operations and may ultimately determine their long-term effectiveness. For example, in Australia, Rowley (1968) noticed a rapid increase in rabbit numbers after poisoning if adjacent areas were not also cleared. In this context, key questions are how quickly do rabbits disperse, how many rabbits disperse and how far do they disperse? This study aimed to address these issues by removing high proportions of resident rabbits from areas with relatively high resource availability, thereby potentially creating voids into which neighbouring rabbits might move given that they may actively seek such conditions (Cowan 1991).

This study suggests that the vast majority of rabbits rarely disperse despite being given the opportunity of moving into a depopulated area. Of the 303 rabbits tagged, only 24 (8%) were observed to move into the artificially low population density areas over a one-year period. Furthermore, it is not known whether these animals, caught outside their normal range, were actually dispersing or whether they were making exploratory forays into neighbouring areas. Parer (1982) suggests that an animal can be considered to have dispersed if it is caught on at least two consecutive occasions at a foreign warren and providing that it is not caught again at its home warren. Only eight of the 24 rabbits (7 males and one female) observed to move in this study were re-trapped a second time at the removal sites. This figure suggests that the actual number of rabbits dispersing, in the long term, may have been even lower than the initial results imply.

Our results add support to a number of earlier studies suggesting that adult rabbits are relatively sedentary (Dunsmore 1974, Parer 1982, Cowan 1991). However, it is possible that the absolute proportion of animals dispersing from the study areas may have been higher than the rate recorded. This is because it is probable that (1) some animals dispersed outside of the trapped areas and (2) others dispersed but were not re-trapped.

CSG 15 (Rev. 6/02) 10

Projecttitle


DEFRAproject code

VC0232

The majority of individuals observed to move in this study were males, by a ratio of around 2.3:1 (16 males to 7 females). This is despite the sex ratio of the tagged rabbits being roughly equal (105 males, 90 females). Male–biased dispersal has been widely reported previously (Dunsmore 1974, Daly 1981, Parer 1982, Cowan 1991, Webb et al. 1995, Kunkele & Holst, 1996). It has been suggested that male-biased dispersal can be attributed to females investing more in reproduction than males and therefore being more dependent on predictable ecological resources which are more reliably accessed at the natal site (Kunkele & Holst 1996). In contrast, males compete directly for access to mates (Cowan1987 a, b) and male-biased dispersal would thus be predicted by Greenwood’s (1980) competition hypothesis (Cowan 1991).

The mean distance moved by the tagged male rabbits (273 m) is close to that recorded by Cowan (1991) for 45 male natal dispersal movements (264 m) in an area of chalk grassland. In the same study, females were observed to move an average of 149 m. This compares to 225 m in our study. The relative distances moved by males and females in our study are consistent with previous work which suggests that females tend to disperse over shorter distances (Cowan 1991) often into neighbouring territories (Kunkele & Holst 1996).

One aspect of dispersal reported previously, but not highlighted in our study, is a peak in juvenile dispersal in the autumn following birth (Cowan 1991). This is the period when juveniles, which disperse at a higher rate than adults, are most mobile (particularly the males), the peak age for dispersal being five months (Cowan 1991). In our study, the small number of rabbit movements away from their original capture site, appear to be more or less evenly distributed throughout the year. However, the precise dispersal dates for individual rabbits may have been masked. This is because rabbits were not necessarily trapped immediately after dispersal but may have been resident at the new site for some time prior to capture there.

Overall, these results suggest that rabbits are relatively sedentary and rarely disperse especially as adults. Male rabbits appear to be more mobile than females in respect of both the number that disperse and the distances that they travel. In practical terms, the results indicate that movements between populations are unlikely to dramatically influence recovery rates following control operations unless the social and ecological circumstances of an area are severely disrupted. The dispersal rates found here have been incorporated into the decision-support system. Furthermore, it will be possible for users to specify the number of unmanaged adjacent populations present that might provide sources of immigration into the population that is the focus of concern and subject to control. The decision support system thus now functions for both closed and open rabbit populations (Milestone 06/01).

5. Development of a method, for use during single day-time site visits, that estimates the likely extent of a given rabbit problem

Fundamental to the development of the decision support system is the ability to relate the extent of problems, such as crop loss, to the size of rabbit populations. A simple and reliable method for assessing the size of the problem in any given lowland agricultural setting is thus required. Most of the techniques devised to assess the numbers of individuals in free ranging mammal populations demand a great deal of time and effort and usually provide only an index of relative abundance rather than an estimate of actual numbers (Diaz 1998). More valuable would be a means to quickly and reliably estimating population size by calibrating indices of rabbit abundance. In the past, a number of indices have been used to assess the sizes of rabbit populations. Most commonly, rabbit numbers have been assessed by counting them, either at night using a spotlight or at dawn and dusk. This technique has been used extensively and recent work has overcome doubts associated with its use to provide a validated sight count method to assess the actual number of rabbits utilising particular tracts of lowland agricultural land (Poole et al., 2003). Unfortunately, this technique requires a series of counts to be made at night along field margins and is therefore time consuming. Indirect methods, whereby various signs of rabbit activity are used to indicate presence or estimate abundance, are particularly useful for nocturnal or elusive species such as the rabbit. The rabbit signs most commonly used to indicate presence or estimate abundance are droppings, burrows, scrapes, holes, runs and hair flecks (Taylor and Williams 1956, Myers et al. 1975, Parer & Wood 1986, Trout et al. 1986, Wood 1988, Velazquez 1994, Diaz 1998). Although activity sign based census methods have offered reliable estimates of abundance when validated in other species (Quy et al. 1993), when used with rabbits they have proven limited in terms of practicality and accuracy and no indirect census methods have been validated in terms of the actual numbers of rabbits present. The primary aim of this

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study was to therefore develop and validate a simple and robust daytime census method based on the presence or number of various activity signs.

5. 1 Methods The two-phase study was conducted between October and March of 2001-2004 on commercial farms in

North Yorkshire and Hampshire. The sites were selected to represent a range of habitat types applicable to the lowland agricultural contexts in which rabbit damage is a major concern. The first phase of the study aimed to: (1) determine how best to survey sites, (2) highlight surveying problems and (3) identify the activity signs most closely related to rabbit numbers (Milestone 05/01). The second phase aimed to utilise this information to quantify the strength of the relationships between rabbit numbers and activity signs and to ultimately determine the census survey techniques offering the best estimates of rabbit numbers (Milestone 05/02).Phase 1

Five test fields were selected and a survey conducted at each whereby the perimeter was divided into a series of continuous 10 x 5 m transects and the number of rabbit burrow entrances, scrapes, runs and hair flecks observed within each transect recorded. At the end of each transect, a 25 cm quadrat was placed on the ground and the number of rabbit droppings present recorded. During the survey period, spotlight count based estimates of the rabbit density (no./100 m) at each site were also made (Poole et al. 2003). A series of regression analyses were conducted to quantify the relationships between rabbit density and each of the rabbit activity signs. Phase 2Survey techniques

At each of 21 sites, a test field was selected and two main survey types, each consisting of nine different techniques, were conducted during the day. The first survey type focused on monitoring rabbit signs along the field margins between the crop and the harbourage. The second concentrated on the field interior and aimed to take account of rabbit activity within fields. Initially, the presence of droppings, scrapes, damage, burrow entrances, runs and hair flecks were recorded. However, it soon became apparent that the numbers of burrow entrances, runs and hair flecks were too small to be used reliably to predict the size of rabbit populations and efforts were therefore concentrated on scrapes, droppings and damage. Both the field perimeter and field interior surveys were conducted on the whole of the field where rabbits were active throughout, or on only the active area at sites where rabbit activity was restricted, in the main, to a smaller section of the field perimeter. Where only a proportion of the field was surveyed the census area extended 150 m beyond either end of the primary rabbit harbourage.Field perimeter surveys

Nine separate field perimeter census techniques were conducted during three census sweeps:a. Census sweep 1

The selected section of the test field perimeter was divided into a series of continuous transects, each 10 m in length. Each transect was walked and the number of rabbit scrapes observed within a 5 m strip (2.5 m either side of the transect line thereby sampling both the harbourage and the test field) recorded (survey technique 1). At the end of each transect a sampling point was established and a 25 cm quadrat placed on the ground (one of its edges against the field perimeter). Both the number of rabbit droppings (survey technique 2) and amount of damage (cereal fields only - survey technique 3) were recorded. Damage was recorded by randomly selecting two adjacent rows of plants within the quadrat and estimating both the percentage of leaves that were damaged and the average amount of damage to those leaves. Subsequently, each damage figure was given a score whereby; 0 = no damage, 1 = 1-33% damage, 2 = 34-66% damage, 3 = 67-99% damage and 4 = 100% damage. The presence/absence of scrapes (within each 10 m transect – survey technique 4) and droppings (within each quadrat – survey technique 5) was also recorded.b. Census sweep 2

A transect was walked along the selected section of the test field, 1 m from the field perimeter. Every 10 m a sampling point was established and the presence/absence of a number of rabbit signs, within a 2.5 m radius of the sampling point, recorded. The signs recorded were: scrapes (survey technique 6), droppings (survey technique 7), and damage- cereal fields only (survey technique 8). c. Census sweep 3

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A transect was walked along the selected section of the test field, 1 m from the field perimeter. The 25 cm wide transect was divided into 1 m sections and the presence/absence of rabbit droppings recorded for each section (survey technique 9). Field interior surveys

At each site, nine different census techniques were conducted during three census sweeps. All involved the walking of ten, 50 m, transects into the test field. The inter-transect distances were selected so as to be roughly equal while ensuring that no transect was closer than 50 m to any of the other field edges. Care was taken to cover the whole of the equivalent perimeter survey areas.a. Census sweep 1

Each of the ten transects was divided into five continuous 10 m sections. Each section was walked and a record made of the number of rabbit scrapes (survey technique 1) observed within a 5 m strip (2.5 m either side of the transect line). At the end of each 10 m section, a sampling point was established and a 25 cm quadrat placed on the ground. Both the number of rabbit droppings (survey technique 2) and amount of damage (cereal fields only – survey technique 3) were recorded. Damage was scored as in the perimeter survey. The presence/absence of scrapes (within each 10 m transect – survey technique 4) and droppings (within each quadrat – survey technique 5) was also recorded. b. Census sweep 2

Ten equally spaced transects were walked perpendicular to the field perimeter along the selected section of the test field. Every 10 m a sampling point was established and the presence/absence of scrapes (survey technique 6), droppings (survey technique 7) and damage (cereal fields only – survey technique 8) within a 2.5 m radius of the sampling point recorded.c. Census sweep 3

Ten equally spaced transects were walked, perpendicular to the field perimeter, along the selected section of the test field. The 25 cm wide transect was divided into 1 m sections and the presence/absence of rabbit droppings recorded for each section (survey technique 9).Estimation of rabbit numbers

During the survey period, night counts using a spotlight were also conducted at each site and the validated method of measuring rabbit numbers used to estimate the number of rabbits present (Poole et al., 2003). Six night counts (one hour after dark using a 1 million-candlepower spotlight) were conducted over a two-week period. Where possible counts were made from a pre-determined position from where the whole of the test area (the selected section of the test field perimeter extended 50m into the field) could be surveyed for the presence of rabbits. If the whole of the area could not be viewed using this technique a fixed transect was walked to cover it. Counts were conducted only when visibility was good and thus foggy and very wet nights were avoided. Extremely bright or windy nights were also avoided. Data analyses

Data were initially standardised to calculate either the mean number of each sign type present per 100 m (quantifiable data) or the mean percentage of positive hits for each sign type per 100 m (presence/absence data). The rabbit density (no./100 m of harbourage) calculated at each site was then used in a series of regression analyses to statistically determine which survey type and rabbit activity sign combination offered the best estimate of rabbit density. For a number of key relationships a statistical measure of correlation was also calculated.

Data from one of the 21 sites was excluded from the analysis because the landowner conducted a control operation during the spotlight counting period.5. 2 ResultsPhase 1

Of all the activity signs recorded, only the number of hair flecks appeared to be positively related to the estimated number of rabbits (r2=0.92). None of the other activity signs showed any significant association with rabbit abundance (droppings r2=0.25; holes r2=0.42; scrapes r2=0.18; runs r2=0.19) and their numbers actually appeared to decrease with increasing numbers of rabbits. These unexpected results were attributed to the survey method adopted picking up only a very small proportion of the activity signs actually present. This was unrepresentative of the real situation and the methodology was subsequently modified to address this concern.

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Phase 2Rabbit counts and population estimation

At each of the 20 sites, the mean spotlight count was used to estimate the actual number of rabbits present using a validated method of estimating the size of rabbit populations (Poole et al., 2003). This figure was converted into the mean number of rabbits per 100 m using the length of the selected section of the test field. A summary of the data is presented in Table 1.

Table 9. The field type, length, mean rabbit count, estimated population size and estimated number of rabbits/100 m for each of the 20 test sites

Site (field type) Field length (m)

Mean rabbit count (n=6, except where stated)

Estimated rabbit number

Rabbit density (no./100 m

1 (Cereal) 570 18.2 (n=5) 30.3 5.32 (Cereal) 640 17.2 28.6 4.53 (Cereal) 640 13.3 22.2 4.64 (Cereal) 700 23.5 39.2 5.65 (Cereal) 580 14.0 23.3 4.06 (Cereal) 450 8.5 14.2 3.17 (Cereal) 860 18.3 30.6 3.68 (Cereal) 630 8.3 13.9 2.29 (Cereal) 730 31.5 52.5 7.210 (Cereal) 560 10.0 16.7 3.011 (Cereal) 1180 26.3 43.9 3.712 (Cereal) 940 14.2 23.6 2.513 (non-cereal) 700 24.0 (n=2) 40.0 5.714 (non-cereal) 1100 43.8 (n=4) 73.0 6.615 (non-cereal) 640 8.5 14.2 2.216 (non-cereal) 760 14.7 24.4 3.217 (non-cereal) 540 17.0 28.3 5.218 (non-cereal) 450 31.9 53.3 11.819 (non-cereal) 600 24.2 40.3 6.720 (non-cereal) 730 8.2 13.7 1.9

Relationships between activity signs and rabbit densityA summary of the relationships between the estimated rabbit densities and the various survey types and

activity signs is presented in Table 10 (Milestone 05/03).Results from the second phase of the study, indicate that only perimeter based surveys have the potential

to accurately estimate rabbit numbers as no useful relationships were observed between rabbit density and any of the activity signs measured in the interior of fields (Table 10). By contrast, a number of the perimeter surveys demonstrated a strong relationship between rabbit numbers and, in particular, the number/presence of droppings (Milestone 05/04).

For non-cereal sites, both the presence and number of perimeter droppings appear to be closely related to rabbit density (Table 10). More specifically, the actual number of droppings in 25 cm quadrats – survey technique 2 (Pearson’s correlation coefficient: r=0.95, n=8, P<0.01 - Figure 3) and the proportion of 25 cm quadrats containing droppings – survey technique 5 (Pearson’s correlation coefficient: r=0.93, n=8, P<0.01 - Figure 4) both demonstrate a good linear relationship over the range of rabbit densities observed. A significant positive association also existed between rabbit density and the proportion of 1 m transects containing droppings – survey technique 9 (Pearson’s correlation coefficient: r=0.91, n=8, P<0.01 – Figure 5). The number/presence of rabbit scrapes was not a good indicator of rabbit numbers in non-cereal fields.

Cereal fields were more problematic and only weak relationships were identified during the study (Table 10). The strongest of these was the association between rabbit density and the percentage of 2.5 cm radius perimeter sampling points containing scrapes – survey type 6 (Pearson’s correlation coefficient: r=0.49, n=12, P>0.05 - Figure 6).

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The number/presence of rabbit droppings and damage were not good predictors of rabbit numbers in cereal fields (Table 10). No survey technique was able to accurately estimate rabbit numbers in both cereal and non-cereal fields.

Table 10. Summary details of the relationships between the estimated rabbit densities and the various survey types and activity signs (All n=20, cereal n=12, non-cereal n=8). Graphs have been included of the relationships highlighted in bold text.

Survey technique Field type Survey area (r2)Perimeter Interior

All 0.273 0.0751 (no. of scrapes / 10 m transect) Cereal 0.193 0.010

Non-cereal 0.480 0.150

All 0.512 0.0262 (no. of droppings / 25 cm quadrat) Cereal 0.014 0.028


All * *3 (level of damage in 25 cm quadrat) Cereal 0.076 0.333

Non-cereal * *

All 0.298 0.0094 (% of 10 m transects with scrapes) Cereal 0.203 0.001


All 0.296 0.0065 (% of 25 cm quadrats with droppings) Cereal 0.001 0.001


All 0.184 0.0156 (% of 2.5 m radius sampling points Cereal 0.243 0.173 with scrapes) Non-cereal 0.244 0.023

All 0.160 0.1407 (% of 2.5 m radius sampling points Cereal 0.057 0.173 with droppings Non-cereal 0.699 0.149

All * *8 (% of 2.5 m radius sampling points Cereal 0.157 0.144 with damage Non-cereal * *

All 0.254 0.0339 (% of 1 m transects with droppings) Cereal 0.138 0.114


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Figure 3. The relationship between the estimated rabbit density, in non-cereal fields, and the number of droppings in perimeter quadrats

Figure 4. The relationship between the estimated rabbit density, in non-cereal fields, and the percentage of perimeter quadrats containing droppings

5. 3 DiscussionIdeally, any daytime rabbit census method centred on the presence or actual numbers of certain activity

signs should be able to simply and reliably estimate the number of rabbits present in a given area. The survey technique adopted during the first phase of the study was therefore designed primarily to be quick and easy to perform. Unfortunately, as a consequence of this, the perimeter based technique only ‘picked up’ a very low proportion of the activity signs actually present and was not therefore representative of the real situation.

In order to address this concern, the survey methodology was subsequently modified and a number of additional survey techniques were introduced during the second phase of the study. These modifications aimed to increase the number of activity signs detected by increasing the survey area and concentrating the survey effort where the maximum numbers of signs were likely to be present. To this end, surveys that recorded only the presence/absence of signs, in a similar way to Trout et al. (1986), were incorporated to supplement those where the actual number of activity signs was determined. This enabled a larger area to be surveyed in a similar

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amount of time. A measure of rabbit damage was also included as was a series of surveys concentrating on the field interior rather than the perimeter. Where perimeter surveys were conducted the perimeter was defined as the maximum distance to which a farmer could cultivate thereby sampling both the test field and the harbourage. This was not the case in the first phase of the study during which only test field perimeters were surveyed.

Figure 5. The relationship between the estimated rabbit density, in non-cereal fields, and the percentage of perimeter transects containing droppings

Figure 6. The relationship between the estimated rabbit density, in non-cereal fields, and the percentage of perimeter sampling points containing scrapes.

Even given these changes to the survey methodologies during the second phase of the study, the numbers of burrow entrances, runs and hair flecks were considered too small to be used reliably to predict the size of rabbit populations. Previous work has suggested that the number of active burrow entrances can be directly related to rabbit abundance (Myers & Parker 1975, Parker et al. 1976, Parer & Wood 1986, Moller et al. 1996) but these studies differed from ours in that all the burrows within the survey area were counted rather than those along a fixed transect. This may explain, in part, why too few burrows were observed in our study to

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identify any relationship between the number of burrow entrances and rabbit density. Only Gibb & Ward (1970) and Trout et al. (1986) have previously attempted to use the presence of hair flecks and runs to estimate rabbit abundance. Neither, though, surveyed for these signs in isolation but rather included them in a raft of activity signs for which they searched.

Of the two main survey types conducted, those centred on the perimeter clearly offered the most reliable means by which to predict rabbit numbers. In contrast, the interior based surveys proved completely ineffective and there were no significant associations between rabbit density and any of the activity signs measured. Palomares (2001) similarly found that, during analysis, the replacement of pellet counts from scrubland with pellet counts from the edge of scrubland greatly improved the correlation with rabbit density. One explanation for these findings could be linked to the pattern of field use and, in particular, to the fact that rabbit activity declines with increasing distance from the field margin (Cowan et al. 1989). This would suggest that field interiors are used rather patchily by rabbits and that the distribution of activity signs is likely to be correspondingly erratic. Any survey attempting to monitor activity signs in this area is liable therefore to be unrepresentative of the whole rabbit population. Alternatively, because most rabbits rest in harbourage in woodland, hedgerows or embankments, and therefore have to cross the field margins to feed on adjacent crops, surveys of field perimeters for activity signs are more likely to yield results that reflect the activity of the total population. .

It is apparent that no single survey technique is capable of estimating the number of rabbits present on both cereal and non-cereal fields. It will be necessary therefore to adopt different techniques for each of these situations. For non-cereal fields, all of the perimeter dropping based surveys demonstrated a close relationship with the numbers of rabbits estimated to be present. The best of these were the number of droppings counted in quadrats (survey technique 2), and the percentage of quadrats containing droppings (survey technique 5). Either of these techniques could thus be used to simply and reliably estimate the number of rabbits using non-cereal fields. In addition, the proportion of 1 m transects containing droppings (survey technique 9) offers nearly as good a prediction of rabbit numbers and would be particularly easy to use. Unfortunately none of these techniques are useful measures of rabbit abundance on cereal fields.

A number of previous studies have demonstrated the successful use of droppings to estimate rabbit abundance (e.g. Taylor & Williams 1956, Gibb & Ward 1970, Wood 1988, Velazquez 1994, Diaz 1998). In the main, these studies differed from ours in that knowledge of both defecation and decay rates was used to produce their abundance estimates. By avoiding the complexity of this approach we enabled rabbit abundance, in non-cereal fields to be estimated after only a single visit saving considerable time and effort. Unfortunately, one drawback to this approach is that it may be difficult to derive comparable results over time or, as in our case, between sites of different habitat types e.g. cereal and non-cereal (Wilson & Delahay 2001).

It was much more difficult to identify a survey technique suitable for use on cereal fields. Indeed, none of the survey techniques was able to give statistically robust estimates of the numbers of rabbits present. The best indicator of abundance was the percentage of perimeter sampling points containing scrapes (Pearson’s correlation coefficient: r = 0.49). Although this technique will not be able to predict the precise numbers of rabbits present on cereal fields, it will to offer an index of abundance capable of ranking sites in terms of potential risk. In an earlier study, conducted in New Zealand, the number of scrapes was found to bear more relation to soil types and vegetation cover than to population density (Taylor & Williams 1956). Damage to cereal crops did not appear to be related to rabbit numbers, perhaps because it is difficult to quantify and compare damage levels at different times during the growing season.

It is not immediately apparent why it should be more difficult to identify a reliable census method appropriate for cereal fields as opposed to fields under other types of agricultural management. It may however be linked to the relative levels of disturbance associated with the two field types. In general, cereal fields are fairly dynamic in nature and are subject to considerable change throughout the growing season, even during the quieter winter months. By contrast, non-cereal field applications, such as set-aside and grassland, tend to be more stable in nature. Patterns of use, by rabbits, of the two field types could therefore be very different with a greater consistency of use associated with the non-cereal situations. In terms of activity signs, it is likely that they will be subject to greater essentially random disturbance at cereal sites which would undermine relationships with rabbit abundance. This may go some way in explaining the contrasting results obtained from cereal and non-cereal sites.

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Overall, the study suggests that the measurement of activity signs during a single daytime visit does have the potential to either estimate rabbit numbers or to provide an index of abundance, provided that different techniques are adopted for cereal and non-cereal fields. Of the two main survey types conducted, only those centred on the field perimeter are capable of reflecting the number of rabbits present. For non-cereal fields, three survey techniques were identified that could be used to reliably estimate rabbit numbers. The most accurate of these involves the counting of rabbit droppings in perimeter quadrats. A quicker, and only slightly less reliable estimate, can be obtained by recording the presence/absence of droppings in perimeter sampling points. For cereal fields, it was not possible to identify a census technique capable of accurately estimating actual rabbit numbers. However, it is possible using the best technique available (the presence/absence of scrapes in perimeter sampling points), to categorise cereal fields into those that are not at risk, those that may be at risk and those that are at high risk. Although not ideal, this information is also suitable for incorporation into the decision support system and could be used to identify sites that require further study, perhaps by means of more accurate spotlight counts. The specifications for the recommended survey methods have been defined as part of the user information for the decision support system (Milestone 06/05) with instructions on how to use these census methods to initiate the system for a particular problem scenario.

Implementation of these survey techniques into the decision support system will enable the scale of a given rabbit problem to be during a single daytime visit. This will allow sites to be categorised according to the potential level of damage they might suffer and therefore objectively separate those cases that warrant further investigation, perhaps in the form of spot-light counts, from those which justify no further action. Ideally, advisors engaged in statutory work would employ this suite of census techniques to measure the effectiveness of management action taken by occupiers.

6. Development of a fully functional and user tested rabbit management decision-support system The decision support system for ecologically sound rabbit management, named the Rabbit Management

Adviser, or RabMan, offers Defra advisors a reliable tool for predicting future numbers of rabbits in a given lowland agricultural setting, based on estimates derived from simple census methods; the potential costs of the damage those rabbits will cause in an agricultural setting; and cost-benefit analyses of management options. This has been subject to evaluation by users (Milestone 06/04). The user requested revisions have been incorporated and a fully functional decision-support system made available to RDS wildlife advisors (Milestone 06/06).

At the heart of the system lies the rabbit population model containing robust natality and mortality estimates for each age of rabbits, in steps of one month, for each calendar month. The model has shown a good relationship with reality in previous field validation trials. All forms of lethal control can be simulated using embedded data describing the age-bias and sex-bias for each method. In addition there are three features incorporated in the model which simulate other factors modifying the normal annual growth and decline of the population. The first allows for the effects of disease, myxomatosis and rabbit haemorrhagic disease, at different levels of intensity. The second simulates the re-colonisation of an area cleared of rabbits by immigration of animals from local unmanaged populations. The third can be used to allow for density-dependent effects reducing the birth rate in low-density populations (the “Allee effect”).

The starting point for estimating the likely level of damage that rabbits may cause to a crop is to build a scenario describing the problem. The system displays a form for entering this information. The first items are the size of the actual rabbit population and the month in which it was determined. Ideally this would be by spotlight count in the winter months, but provision has been made for the signs based approaches described above. The other items include the length of the affected field boundary, the crop and the period that it is vulnerable to damage by rabbits. When the scenario is completed, the model predicts the population size throughout the period of crop vulnerability. The damage area is calculated from the length of the affected field boundaries and the distance that rabbits are likely to forage into the field. The population density, derived from the population size and damage area, is fed into the damage estimate equation relating population density to percentage reduction in yield for a given crop. From these values and the value of a pristine crop, the likely financial loss attributable to the presence of rabbits is estimated. Saved scenarios can be loaded from saved files and when these are read in by the system, the values are automatically displayed in the scenario panel and the scroll bars are set accordingly.

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The scenario can also include a control strategy, consisting of the method of control and the month(s) in which control is practiced. The methods available include both lethal and non-lethal approaches e.g. rabbit fencing. If control is included in the scenario, the model predicts the size of the controlled population during the period of crop vulnerability and estimates the financial loss in the same way that it did in the uncontrolled situation. The two outcomes are compared and the savings in yield due to rabbit control are reported. The results can be displayed as text or graphically as histograms. The graphical display may be of crop value in the damage area, percentage yield, loss in value, rabbit numbers or rabbits culled (e.g. Figure 7).

The user may then modify the scenario to evaluate the available methods of rabbit control and determine their optimal timing for the specific situation. To assist in this there is an option that causes the system to simulate the chosen control method in each of ten months of the year and report the most effective in reducing crop damage. Each damage estimate is added to the graphical output to allow visual comparisons of outcomes. Clicking on a box below a bar on the histogram causes the respective input values to be displayed in the scenario panel. At the request of the RDS user evaluation the system has now been modified to allow damage estimates to be derived across four years instead of just one. If this is done, the system reports the damage for each of the four years for four different cases - no control, control in the first year only, control in alternate years, and control every year. This output can be viewed as text or histogram and also a population density graph is available.

The system can then assist in calculating the cost of control. If this is requested, the system displays a series of questions relating to the costs involved in the chosen method and offers default values as answers which can be accepted or changed. When all answers have been completed the system calculates the cost of control, compares it with the damage costs and displays a cost-benefit analysis for the year (e.g. Figure 8). If the four-year damage estimate had been made prior to the cost calculations, then cost-benefits are calculated for each of the four years and in total, for each of the control scenarios - control in the first year only, control in alternate years, and control every year.

In addition to the damage estimate and cost-benefit part of the system, the user may access the underlying population model directly. This is especially useful for assessing the likely consequences of different control scenarios prior to cost-benefit analysis. It affords greater freedom in the specification of control measures, even allowing several to occur at the same time if required.

The system also has an information centre containing text and images for up-to-date data on rabbits and their control in the UK. Short pieces of text are shown within a window in the system but larger articles are displayed by the system invoking Adobe Acrobat or Microsoft Word. Items are chosen by the user from drop-down contents lists.

Quality assurance is built into the system by the automatic production of audit trails which contain the user inputs and the steps in the calculations. These audit trails contain the date and time and the software revision number. Full checks have been made of the calculations contained in the system and a set of reference outputs is maintained.

In the future additional work may be required to allow the system to evolve and cover new issues such as novel crops, management methods or agronomic practices. It is also likely that, as the system is put into practice, users will identify desirable additional functions or options. The system has been designed and programmed in a modular fashion to allow efficient incorporation of such changes. The initial target for the system is the RDS wildlife advisor, given their statutory as well as advisory role in relation to the problems posed by rabbits in lowland agricultural landscapes. These advisors are thus seen in the immediate future as the vehicle for technology transfer of the extensive scientific knowledge base via the decision-support system. Once the system has been successfully established, in the challenging practical context of users who are already expert in this field, then there will be the potential for diversifying the target users to consultant agronomists, growers and farmers to maximise the technology transfer opportunities offered by this unique system.

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Figure 7. Graphical output show crop yield with and without application of a specific management approach.

Figure 8. Text output showing cost-benefit analysis of a specific management approach.

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7. References

Allee, W.C. (1931) Animal aggregations. A study in general sociology. University of Chicago Press, Chicago.Bell A C, Byrne P M, & Watson S. 1999 The effect of rabbit (Oryctolagus cuniculus) grazing damage on the growth, botanical composition and

yield of a rye grass re-seed. Annals of Applied Biology 135, 417-424.Church, B M, Jacob, F H & Thompson, H V (1953) Surveys of rabbit damage to wheat in England and Wales. Plant Pathology 2, 107-112.Cowan D.P. (1987a) Aspects of the social organisation of the European wild rabbit (Oryctolagus cuniculus). Ethology 75, 197-210.Cowan, D.P. (1987b). Group living in the European rabbit (Oryctolagus cuniculus): mutual benefit or resource localization. Journal of Animal

Ecology 56, 779-795.Cowan, D.P. (1991). The availability of burrows in relation to dispersal by the European wild rabbit. Symposia Zoological Society, London 63, 213-

230.Cowan D.P., Hardy A.R., Vaughan J.A. & Christer W.G. (1989) Rabbit ranging behaviour and its implications for the management of rabbit

populations. In Mammals as Pests (Ed. R.J. Putman), pp. 178-185. Chapman and Hall, London.Crawley, M J (1989) Rabbits as pests of winter wheat. In: Mammals as Pests, R J Putman (ed), Chapman and Hall, London, pp. 168-177.Daly, J.C. (1981). Effects of social organisation and environmental diversity on determining the genetic structure of a population of the wild rabbit,

Oryctolagus cuniculus. Evolution 35, 689-607.Dendy J., McKillop G., Fox S., Western G. & Langton S. (2003) The development of a model to assess the effects of rabbit grazing on grass. Annals

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