1 SC 11419 ADVISORY COMMITTEE ON PESTICIDES ENVIRONMENTAL PANEL MEETING DATE: 2 MARCH 2006 BIRD AND MAMMAL RISK ASSESSMENT: REFINING THE PROPORTION OF DIET OBTAINED IN THE TREATED CROP AREA (PT) THROUGH THE USE OF RADIO TRACKING DATA Authors: Mrs Elizabeth Finch & Mr Mark Payne Appendices provided by Dr Joe Crocker AIM OF PAPER On behalf of PSD, the UK’s Central Science Laboratory (CSL) has conducted radio tracking studies on some commonly occurring species of birds in arable and orchard crops. This paper demonstrates how, by making certain assumptions, the proportion of diet obtained in treated areas (PT) by birds can be estimated from the recorded radio tracked ‘active time’ data and discusses the most appropriate percentile value for regulatory use in the current deterministic non-target bird risk assessment for pesticides. It is also proposed to include the derived PT values in WEBFRAM models where the whole distribution will be used to determine the risk in a probabilistic manner. It should be noted that the focus of attention in this paper is the estimation of PT for populations of possible key or focal species of birds which are found to use the crop situations under assessment and which therefore have the potential to forage in these areas. Pesticide risk assessments that use these PT estimates will therefore relate to the risk to the locally occurring population rather than the general farmland population which may include unexposed individuals that do not occur in the treated areas. This is considered to be in line with the assessment approach described in the Guidance Document on Risk Assessment for Birds and Mammals (SANCO/4145/2000). It is proposed that the values obtained for PT from this radio tracking data will be made available to applicants, via the PSD website, as part of the Checklist for Bird and Mammals risk assessments (SC11418), for use in the refinement of bird risk assessments. Other key species for which radio tracking is on-going will be added once these studies have been completed. INTRODUCTION The bird and mammal risk assessment is based on a deterministic approach, whereby the appropriate toxicity end point is divided by the estimated theoretical exposure (ETE) to produce a toxicity:exposure ratio (TER). If the TER exceeds the relevant trigger value then it is considered that the risk is acceptable. If the TER is
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SC 11419 ADVISORY COMMITTEE ON PESTICIDES
ENVIRONMENTAL PANEL
MEETING DATE: 2 MARCH 2006
BIRD AND MAMMAL RISK ASSESSMENT: REFINING THE PROPORTION OF DIET OBTAINED IN THE TREATED CROP AREA (PT) THROUGH THE USE OF
RADIO TRACKING DATA
Authors: Mrs Elizabeth Finch & Mr Mark Payne Appendices provided by Dr Joe Crocker AIM OF PAPER On behalf of PSD, the UK’s Central Science Laboratory (CSL) has conducted radio tracking studies on some commonly occurring species of birds in arable and orchard crops. This paper demonstrates how, by making certain assumptions, the proportion of diet obtained in treated areas (PT) by birds can be estimated from the recorded radio tracked ‘active time’ data and discusses the most appropriate percentile value for regulatory use in the current deterministic non-target bird risk assessment for pesticides. It is also proposed to include the derived PT values in WEBFRAM models where the whole distribution will be used to determine the risk in a probabilistic manner. It should be noted that the focus of attention in this paper is the estimation of PT for populations of possible key or focal species of birds which are found to use the crop situations under assessment and which therefore have the potential to forage in these areas. Pesticide risk assessments that use these PT estimates will therefore relate to the risk to the locally occurring population rather than the general farmland population which may include unexposed individuals that do not occur in the treated areas. This is considered to be in line with the assessment approach described in the Guidance Document on Risk Assessment for Birds and Mammals (SANCO/4145/2000). It is proposed that the values obtained for PT from this radio tracking data will be made available to applicants, via the PSD website, as part of the Checklist for Bird and Mammals risk assessments (SC11418), for use in the refinement of bird risk assessments. Other key species for which radio tracking is on-going will be added once these studies have been completed. INTRODUCTION The bird and mammal risk assessment is based on a deterministic approach, whereby the appropriate toxicity end point is divided by the estimated theoretical exposure (ETE) to produce a toxicity:exposure ratio (TER). If the TER exceeds the relevant trigger value then it is considered that the risk is acceptable. If the TER is
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below this value, then refinement of the risk assessment is required (see SANCO/4145/2000 for details). The ETE is calculated via the following equation:
ETE = (FIR/bw) x C x AV x PT x PD (mg/kg bw/d)
Where: FIR = food intake rate of indicator species (g fresh weight/d) bw = body weight (g) C = concentration of compound in fresh diet (mg/kg) AV = Avoidance factor (1=no avoidance, 0=complete avoidance) PT = Fraction of diet obtained in treated area (1=all of diet, 0=none of diet) PD = Fraction of food type in diet (1=all diet of one type, 0=none of diet) At the first tier assessment, a worse case approach is taken where AV, PT and PD are set at 1, i.e. an animal does not avoid any contaminated food, it obtains all of its diet from the treated area, and all of its diet is made up of one type of contaminated food. This may significantly over-estimate the exposure. In refining the risk assessment it is possible to include more realistic values for AV, PT and PD, provided that appropriate data are available with which to do so. The following paper concerns itself with the refinement of PT (fraction of diet obtained within the treated area). SANCO/4145/2000 (Section 5.6) states that PT can be estimated from the time spent active in a treated area, assuming that the active time spent in a habitat is a reliable indicator of the measure of food obtained there. It is considered that one of the most appropriate means of obtaining these data is through radio tracking, with such studies also ideally including visual observations of tracked birds / mammals to help back up and provide further behavioural details in relation to measured ‘active’ and ‘non-active’ time. CSL have conducted several radio tracking programmes in which the activity of commonly occurring bird and small mammal species were tracked in arable and orchard landscapes (DEFRA project codes PN0915 & PN0903). This document is only concerned with the data collected on birds as it was considered that further work was required on the wood mouse to take account of the possible presence of resident populations occurring within crops. The following summarises the radio tracking methodology used by CSL and describes how these data have been interpreted with regards to the estimation of PT, including any assumptions made. SUMMARY OF RADIO TRACKING STUDY METHODOLOGY Before radio tracking can be carried out it is important to identify those species which are relevant to the particular crop/situation in question. These are known as the ‘focal’ or key species. In determining which bird species should be chosen for radio tracking in the arable environment, the prevalence and frequency of species within this setting were first established by carrying out censuses in different crops and in the different seasons (Crocker & Irving 1999). From these censuses it was
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considered that the most relevant UK arable crop small bird species were the yellowhammer, skylark and linnet. In fruit orchards, results of mist netting conducted by CSL indicated blackbird, chaffinch, blue tit and robin were the most frequently occurring and abundant species. These species were therefore tracked in the orchard study. The table below gives details of the focal species observed in the separate arable and orchard (apple) radio tracking studies, including details of their dietary requirements and the crop habitats for which PT was assessed. Table 1: Details of the focal species of birds for which PT was assessed in the
arable and orchard radio tracking studies Relevant Crops/situations
Focal species Dietary details*
Skylark Omnivorous, including insects, vegetation and seeds
Linnet Granivorous including crop and/or weed seed.
Oilseed rape
Blackbird Omnivorous including insects, earthworms and seeds
Blackbird Omnivorous including insects, earthworms and seeds
Blue tit Insectivorous Chaffinch Omnivorous – insects in summer;
predominantly seed at other times.
Orchards
Robin Insectivorous *From Buxton et al (1998). For birds, radio-tags were mounted on the base of the tail feathers. Tags were chosen to suit the size of the species being tracked, with tag weight being no more than 10% of the typical species body weight in the non-orchard studies and 5% in the orchard studies. Data were collected in orchards from April to September and in non-orchard situations in both summer and winter months. The aim of the work was to capture a representative day in the life of each tagged individual. The day was divided into 2 hour slots, with the aim being to obtain at least
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one hour’s contact with a tagged individual in each slot. The size of the tags meant that the battery life was limited to a few days, so once an individual was tagged it was followed intensively. During a typical monitoring session a single individual was tracked continuously. The tracker kept close enough to the individual to be sure of its whereabouts and, if possible, to see it, but not so close as to disturb it. In the orchard environment this resulted in an observation distance of 20-50 metres. At frequent intervals, notes were made of the individual’s location, the habitat occupied and its activity. Weather and temperature were also recorded. Although for much of the time it was difficult to see what tracked animals were doing, it was usually possible to determine from small fluctuations in the signal and shifts in location whether a tracked animal was active or not. For the purposes of the studies, ‘active’ was taken to mean ‘potentially foraging’. This category included all instances of recorded foraging and excluded all instances where the animal was known to be performing some other activity (e.g. singing, nest building) or where it was considered to be inactive. DATA ANALYSIS Please refer to SC11411 for details of the raw data used in the following data analysis. In order to derive estimates of the proportion of total diet obtained in each scenario in which bird species were tracked, the following assumptions and criteria were applied:
i) The ‘active time’ data recorded for each scenario are taken to represent the time spent foraging in these situations. The observational information obtained whilst radio tracking generally supports this relationship, with foraging being the predominant ‘active’ behaviour. Also all instances where the animal was known to be performing some other activity (e.g. singing, nest building) were excluded from the ‘active time’ data.
ii) The derived data for time spent foraging (based on ‘active time’ data) is
considered to provide a measure of the relative amount of diet consumed in each of the recorded scenarios.
iii) Inactive time data are excluded from the data set used to calculate PT,
since by definition individuals cannot forage while they are inactive.
iv) By comparing for each radio tracked individual, the ‘active time’ in a particular scenario with the total recorded ‘active time’, the proportion of total ‘active time’ spent in that situation is obtained, this being equivalent (by extrapolation) to the proportion of diet consumed (i.e. PT).
v) As is the nature of ecological data collection it was not possible to fill every
2 hour time slot for all radio tracked individuals. In addition, it normally took 2 to 3 days to collect a full day’s contact data. As a result, it was necessary to determine a minimum time needed to estimate PT. From assessing the relationship between PT and the total amount of monitored contact time it was concluded that after an hour or two PT starts to stabilise
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in the majority of cases. In plotting this relationship it appears that after about 150 minutes contact time, PT becomes relatively stable for most individuals (see Appendix 1 for details). As a result, the analyses that follows excludes individuals for which there was less than 150 minutes contact time.
vi) Data on individual tracked birds are only included in the PT estimations
below where they were active for 30 minutes or more in the particular situation under assessment, i.e. they were considered to be potential ‘consumers’. The full analysis for all individuals, regardless of their use, is included in Appendix 4 for information only.
vii) There was found to be very little difference in the distribution of PT
between data collected on a single day for an individual and data collected over several days. As a result, it was considered that as the multiple days data gave a fuller coverage of bird habitat use throughout the day this should be used in preference to data collected for only part of a single day (See Appendix 2 for details).
viii) In relation to bird feeding preferences in orchards, PT estimates were
made for each bird species over the ‘summer’ radio tracked period of April to September. For the non-orchard situations in which bird species were tracked throughout the year, separate PT estimates were made for the summer and winter period (with the precise months included in each varying slightly according to the scenario being assessed).
ix) The 90th and 95th percentiles for the data sets were estimated by assuming
that the data is a random sample from a parent distribution with known mathematical properties. As the data are limited between 0 and 1, the normal distribution does not provide a good fit for these proportional data. A more appropriate distribution is the Beta which is bounded between 0 and 1 and is flexible enough to represent the different shapes seen in the radio tracking data, including unimodal (a peak anywhere between 0 and 1) and bimodal (peaks at 0 and 1).
Two methods were tried for fitting Beta distributions to the radio tracking data: maximum likelihood and method of moments. In both cases, 90% confidence intervals were estimated for the distribution, to take account of sampling uncertainty due to the varying size of the datasets (see Appendix 3 for details of methods used). Visual examination of graphs of the distributions by fitted maximum likelihood showed that for many datasets the fit was poor, with many data points falling outside the fitted confidence interval. Graphs of distributions fitted by the method of moments showed a satisfactory fit for every dataset (see Appendix 3 for further details and graphs). Simulation studies by Frey et al (1999) also showed that the method of moments performs better than maximum likelihood for Beta distributions. The method of moments was therefore used to generate the results shown below.
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Median estimates for the 90th and 95th percentiles, together with their 90% confidence intervals, were generated for each dataset. Results for ‘consumers only’ (those birds which visited the relevant crop during the tracking period) are tabulated below (see Appendix 4 for the analysis of the whole data set).
RESULTS The following tables show the 90th and 95th percentiles for the proportion of active time spent by potential ‘consumers’ within the crops identified (see point vi of Data Analysis), accompanied by the relevant confidence limits for 5% (lower bound) and 95% (upper bound). It is important to note that the confidence limits relate only to sampling uncertainty (i.e. the effect of limited sample size on estimation of the distribution). Please refer to the Data Analysis section above for details of how these figures were derived. Cereals Table 2: Calculated 90th and 95th percentiles for PT values in cereal crops Season Species No. of
individuals 90th percentile (CL)
95th percentile (CL)
Skylark 26 0.97 (0.86 – 1.00)
0.99 (0.94 – 1.00)
Summer (Apr-Aug)
Yellowhammer 17 0.87 (0.67 – 0.99)
0.95 (0.79 – 1.00)
Skylark 10 0.94 (0.59 – 1.00)
0.98 (0.72 – 1.00)
Winter (Sep-Mar)
Yellowhammer 10 0.14 (0.09 – 0.23)
0.18 (0.11 – 0.30)
The figures in bold highlight those scenarios where the upper confidence limit is 1.00, which is the maximum value for PT. CL – upper and lower 95% confidence limits for each percentile value. Skylark and yellowhammer forage intensively in cereal crops during the summer. During the winter, only the skylark makes extensive use of cereal crops. As a result, these are considered to be the appropriate species for refinement of the risk assessment. Beet/Potatoes Table 3: Calculated 90th and 95th percentiles for PT values in beet/potato crops Season Species No. of
individuals 90th percentile (CL)
95th percentile (CL)
Skylark# 18 0.88 (0.68 – 0.99)
0.95 (0.80 – 1.00)
Yellowhammer 13 0.94 (0.76 – 1.00)
0.98 (0.85 – 1.00)
Summer (Apr-Nov)
Linnet* 11 0.59 (0.38 – 0.84)
0.69 (0.47 – 0.92)
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#Skylark data are very limited for potatoes and given the lack of crop palatability to herbivorous birds an insectivorous bird such as the yellowhammer is considered a more appropriate focal species for risk assessment purposes. *Linnet is totally granivorous and therefore it is only appropriate to consider this species when a risk to seed-eating birds has been identified. CL – upper and lower 95% confidence limits for each percentile value. The figures in bold highlight those scenarios where the upper confidence limit is 1.00, which is the maximum value for PT. The selection of focal species for use in the regulatory refined risk assessment will depend on the feeding group of birds identified at potential risk in the first tier risk assessment. The skylark would be a suitable herbivorous species and the yellowhammer a suitable insectivorous species. Oilseed rape Table 4: Calculated 90th and 95th percentiles for PT values in oilseed rape Season Species No. of
individuals 90th percentile (CL)
95th percentile (CL)
Skylark 7 0.57 (0.42 – 0.80)
0.64 (0.47 – 0.87)
Yellowhammer (Apr-Aug)
7 0.86 (0.65 – 1.00)
0.92 (0.73 – 1.00)
Linnet* 6 0.99 (0.61 – 1.00)
1.00 (0.73 – 1.00)
Summer (Apr-Jul)
Blackbird** (May-Jul)
9 0.98 (0.84 – 1.00)
0.99 (0.90 – 1.00)
Skylark 4 0.98 (0.38 – 1.00)
1.00 (0.51 – 1.00)
Winter (Aug-Mar)
Yellowhammer (Sep-Mar)
4 0.61 (0.15 – 1.00)
0.79 (0.22 – 1.00)
The figures in bold highlight those scenarios where the upper confidence limit is 1.00, which is the maximum value for PT. *Linnet is completely granivorous and therefore should only be considered where a risk to seed-eating birds has been identified at tier 1. **Blackbirds were observed by research workers at CSL to be frequently associated with maturing oilseed rape crops and for this reason a limited radio tracking study was conducted including a total of ten tracked individuals all caught and tagged close to or within oilseed rape fields. PT values derived from the radio tracked active time data collected between the months of May to July are presented in the table above. CL – upper and lower 95% confidence limits for each percentile value. During the summer months, blackbirds, linnet and yellowhammer intensively foraged in oilseed rape. However, during the winter, more use of this crop was made by skylark. As stated above, the selection of focal species for use in the regulatory refined risk assessment will depend on the feeding group of birds identified at potential risk in the first tier risk assessment.
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Orchards Table 5: Calculated 90th and 95th percentiles for PT values in orchards Season Species No. of
individuals 90th percentile (CL)
95th percentile (CL)
Blackbird 28 0.75 (0.60 – 0.89)
0.84 (0.71 – 0.95)
Blue tit 16 0.58 (0.42 – 0.79)
0.68 (0.51 - 0.88)
Chaffinch 29 0.76 (0.63 – 0.88)
0.85 (0.73 – 0.95)
Summer (Apr-Sep)
Robin 24 0.56 (0.44 – 0.70)
0.66 (0.53 – 0.80)
CL – upper and lower 95% confidence limits for each percentile value. N.B. These data differ from those in SC11411 as the previous analysis was conducted using not only individuals using the orchard but also those using the perimeter. In this subsequent paper it is considered that only those individuals using the orchard are relevant. In the orchard scenario it was observed that the blackbird and chaffinch were more active, compared with the blue tit and robin which used the orchards for a similar amount of time. However, these species use orchards differently, e.g. if there is a risk to earthworm-eating birds then it is appropriate to use blackbird as the focal species, but if the risk is to insectivorous species then the chaffinch, blue tit and robin would be more appropriate. DISCUSSION/CONCLUSIONS Using the radio tracking data the proportion of diet likely to be obtained in treated crops (PT) has been estimated for several commonly occurring bird species. It is considered that radio tracking accompanied by observational data is considered the most appropriate methodology for refining PT at this stage. However, it may be in the future that there are other techniques available which are just as valid. Whatever methodology is used for the refinement of PT, the quality of the data must be comparable with that collected as part of the studies detailed in this paper. Choice of percentile value for PT The selection of the most appropriate percentile value for PT for use in the regulatory risk assessment is considered to be a risk manager’s decision according to what level of protection needs to be afforded. At present there is no agreed value for the required or desired level of protection. It is proposed that a 90th percentile PT value is used for regulatory purposes. There are several approaches which can be taken in dealing with the chosen end point. The following outlines these: Approach 1: Decide on a percentile and use the mean value for PT Although this approach is very simple as you can simply pick the PT values from the tables presented above, there is no consideration of any of the uncertainties surrounding PT.
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Approach 2: Decide on a percentile and use the 95% confidence limit (upper confidence bound) Again, this approach is very simple and the values are provided in the tables presented above. As the upper confidence bound has been chosen, uncertainty regarding PT in terms of sample size has been accounted for. It does not, however, take into account any other uncertainties which may attribute to PT. Approach 3: Decide on a percentile and consider all of the uncertainty factors Table 1 in Appendix 3 gives details of all the types of uncertainty which may affect PT. From this table it can be seen that the uncertainties have not been quantified, and to do so would be very subjective, difficult and time consuming, but their combined impact on PT has been qualitatively assessed. Of the three approaches presented above, it is felt that Approach 1 is not appropriate as it does not consider any of the uncertainties regarding PT. Approach 3 does address all of the uncertainties, however, due to the state of knowledge it can only at present be considered qualitatively, and therefore is not a viable choice. As a result, it is proposed to use Approach 2 as this deals with some of the uncertainty regarding PT in terms of sample size. As stated above, as there is no agreed value it is proposed to select a 90th percentile. If Approach 2 is applied, then this will mean that the risk to 90% of the population will be assessed with 95% confidence. Using this approach, a summary of the values which are considered appropriate for risk assessment purposes are detailed in Table 6 below. It should be noted that this paper only considers PT and does not discuss the use of different percentiles with regards to refining other factors within the equation surrounding the calculation of ETE. Ideally we should refine all parameters equally whilst maintaining the same level of protection and this will be considered further as part of the WEBFRAM 3 project. However, in selecting the 95% confidence limit of the 90th percentile it is felt that this should provide an appropriate level of protection, as long as other factors are refined appropriately to maintain this level of protection. Risk assessment implications of derived PT values The results presented show that in some scenarios, e.g. cereals and oilseed rape, the radio tracking data has identified very little difference between the PT default value of 1 and the more realistic values calculated, implying that although this is a significant refinement of the risk assessment there is little scope for reducing the estimated theoretical exposure value. This also implies that in these scenarios the results confirm the appropriateness of using 1 in the tier 1 risk assessment. Use of the derived end points From the tables above it is anticipated that Notifiers/applicants will be able to identify for the assessed crop scenarios the appropriate focal species based on which ‘feeding guilds’ (or generic ‘indicator species’) failed the first tier risk assessment under SANCO/4145/2000. Therefore, for example, if there is an issue with
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insectivorous birds in beet/potatoes at tier 1, then an appropriate focal species to consider in a refined risk assessment would be yellowhammer and if a risk to granivorous species has also been identified, linnet as well. The following table summarises the focal species for each scenario and also the derived 90th percentile upper confidence limit (95%) PT values rounded to two decimal points: Table 6: Summary of the crop scenarios, focal species and the respective 90th percentile upper confidence limit (95%) PT values Scenario Species Most relevant feeding
Robin Insectivorous 0.70 #Skylark data are very limited for potatoes and given the lack of crop palatability to herbivorous birds an insectivorous bird such as the yellowhammer is considered a more appropriate focal species for risk assessment purposes. It should be noted that the selection of focal species for use in the regulatory refined risk assessment will depend on the feeding group of birds identified at potential risk in the first tier risk assessment. For example, in beet/potatoes and oilseed rape the linnet is only an appropriate species where a risk to granivorous birds has been identified at tier 1. Applicability to acute, short-term and long-term risk assessments It is considered that the PT values quoted are appropriate for acute, short-term and long-term risk assessments. Although these values are based on a theoretical ‘day in the life of’ a bird, the data were collected over several days and a mean for the 90th percentile was calculated based on the distribution of the available data. In addition, if individuals were observed for a longer period of time it is likely that the PT values could be lower due to a greater diversity of suitable alternative food sources being available. As a result, using these data for the acute, short-term and long-term assessments is deemed to be precautionary and appropriate given the available information.
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Suitability of extrapolations to other scenarios One further point, which would be of value to applicants, is the ability to make use of suitable extrapolations of PT values to other crops. Of course, any extrapolation needs to be appropriately justified. At the moment, it is considered that extrapolations to other cereal crops from wheat and barley would be appropriate, as would extrapolating between different orchard crops. In determining whether further extrapolations are appropriate it is necessary to consider whether a crop is being used in the same way, e.g. potato leaves are not palatable to birds and therefore only insectivorous species are required to be assessed, however, this is not the case for the majority of leafy crops in which herbivorous species would also be exposed. As a result, if an extrapolation is proposed, it must be fully justified and may require some data to confirm that such an extrapolation is acceptable. It should be noted that it is hoped that some further research work will be undertaken by CSL with regards to extrapolations from the major crop groups to minor crops in the future. Further work and the appropriateness of adding other data sets CSL are currently collecting data on herbivorous birds (greylag geese, wood pigeon) and mammals (brown hare) and it is proposed that these data are dealt with in the same way as described above, although these data will be sufficiently different from those collected for small birds and therefore may require different statistical methodologies to be employed. In addition, it is proposed that further work is done on the wood mouse, ensuring that where resident populations exist these are radio tracked. When these studies are complete and the data analysed, they will be presented to the Panel for consideration. With regards to adding data from other studies to this extensive and comprehensive data set, it is acknowledged that this is a possibility. However, care will be needed to ensure that any additional data stands up to rigorous scientific scrutiny with data being derived from similar well conducted and analysed studies. Acknowledgements The authors would like to acknowledge the help and co-operation of CSL colleagues Dr Phil Prosser, Dr Joe Crocker, Willem Roelefs and Dr Andy Hart in preparing this document. References Anon (2006): SC11418 - Checklist of issues to note when carrying out a bird and mammal risk assessment according to SANCO 4145/2002/EEC – Version 2. Buxton JM, Crocker DR & Pascual JA (1998): 1998 Update - Contract PN0919 Milestone Report Birds and Farming: Information for Risk Assessment
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Crocker DR & Irving PV (1999): Project PN0915 Improving estimates of Wildlife Exposure to Pesticides in Arable Crops: Milestone report 02/01 Variation of bird numbers on arable crops. Frey, HC & Burmaster, DE (1999): Methods for characterizing variability and uncertainty: comparison of bootstrap simulation and likelihood-based approaches. Risk Analysis 19 (1) 109-130. Gurney JE, Perrett J, Crocker DR & Pascual JA (1998): 1998 Update - Contract PN0910 /PN0919 Milestone Report Mammals and Farming: Information for Risk Assessment Pascual J, Crocker J & Hart A (1998): Project PN0919 Improving estimates of the exposure of non-target wildlife to Pesticides in Arable Crops – A review of existing data Payne M & Finch E (2006): SC11411 – Bird and Mammal Risk Assessment: Refining the proportion of diet obtained in the treated crop area (PT) through the use of radio tracking data. SANCO/4145/2000 (2002): Guidance Document on Risk Assessment for Birds and Mammals Under Council Directive 91/414/EEC.
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Appendix 1: Justification for a minimum tracking time for the estimation of PT (details provided by CSL)
We wish to estimate the exposure to pesticides for different wildlife species in typical UK agricultural habitats. Bird species are relatively mobile and may visit several different habitats in a single day. Therefore we used radio tracking to follow birds in the field and estimate what proportion of time they spent in treated (agricultural crops) compared with untreated (hedgerow, set-aside etc) habitats. Our aim was to capture a day in the life of individual bird and to show how proportion of time spent in a pesticide treated area (PT) varies between individuals Because it was rarely possible to keep in constant contact with an active individual we divided the day into 2-hour slots and attempted to follow a given bird for 1 hour out of each 2 hour slot. We stopped tracking when we had data for all slots. Depending on the time of year, this might amount to 4 to 8 hours of contact time. However, there are two important shortcomings of the data we collected and they have contrasting consequences for our estimate of PT.
1. Incomplete data: we didn’t succeed in filling every slot for every bird Small birds could be fitted only with small radio-tags with limited range and lifetime, and birds often moved out of range so that that they were “lost” to the tracker for several hours at a time. This tends to exaggerate the extremes of the distribution of PT. The degree of exaggeration will depend on the particular movement habits of the individual but in the extreme case where we have say only 1 minute of contact time it is clear, since the individual cannot be in two places at once, that it will either be in treated habitat or it will not, and PT must be either 1 or 0. With more contact time it is more likely that that more habitats will be visited and PT will less extreme.
Ideally we would include only birds where we had a full day’s contact data. Unfortunately our monitoring schedule precluded this: no birds were monitored all day in a single day. Therefore we need to decide what is the minimum amount of contact time that we will accept as a reasonable estimate of PT.
2. Extended timescale: it usually took 2 or 3 days to collect a full day’s
contact data. For example, a bird might be caught and tagged in the early afternoon, tracked for 2 hours in the late afternoon/evening, and followed again the next day until early afternoon. Extending the monitoring period across several days will tend to underestimate extreme values of PT. Again, it depends how often a bird
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moves between habitats, but it seems likely that a bird is more likely to be seen in different habitats if it has been watched for 1 hour on each of 5 days, than if it were observed for 5 hours continuously on a single day. A bird may be quite likely to spend all day in one field but less likely to spend several days in the same field. Therefore we need to decide whether PT collected over several days needs to be adjusted to more closely reflect PT for a single day. Minimum time necessary to estimate PT Birds do not move instantly between habitats, but rather stay for some time in one place before moving to another. An estimate of PT based on a very short contact time is likely to be close to 1 or 0. As contact time increases, then for birds that explore both cropped and non-cropped habitat, PT will tend to swing between these extremes. The pattern of these swings will depend on the length of typical residence time in relation to contact time. As contact time further increases, PT (calculated as the cumulated active time spent within a crop as a proportion of total active contact time) will inevitably become more stable. Our focus for risk assessment purposes is the habitat preference shown in a single day. The length of contact time required for fluctuations in habitat preference to settle down gives us a criterion for rejecting - because PT is too unstable - birds with less than this amount of contact time. In Figures 1 and 2 the relationship between PT (proportion active time spent in crop) against the total amount of contact time monitored through radio tracking has been plotted for all individual birds followed in orchard and arable habitat. Although not very easy to take in, these plots support the argument above that: where contact time is very short PT will be close to 0 or 1 and may fluctuate rapidly. After an hour or two of monitoring PT appears to stabilise for most individuals under most habitats. Ideally, we would fit separate equations to each individual plot and estimate the length of contact time by which PT had reached a fixed proportion (e.g. 90%) of its asymptotic value. We might then take the average of these values to establish an objective criterion for the minimum quantity of monitoring that would give a reasonable estimate of PT. Unfortunately, from my initial investigations, it is not very clear what general form the equations should take (e.g. logistic, logarithmic) and how suitable is this approach where individuals have different amounts of contact time? At this stage it may be as well to judge by eye the point at which PT becomes stable. Acknowledging a degree of subjectivity I would estimate that after about 150 minutes contact time, PT appears to be relatively stable for most individuals. Therefore, the analyses that follow will exclude birds for which we had less than 150 minutes contact data.1
1 We additionally rejected data for 2 birds for which we had successfully collected contact time > 150 minutes but which had been inactive for nearly all that time. All birds included in the analyses had contact time > 150 minutes and active time recorded of > 30minutes.
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Figure 1. PT in orchard centre as a function of time in radio contact.
Figure 2 PT in arable crops as a function of time in radio contact.
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0.9
1
0 100 200 300 400 500
Skylark - Contact time (min)
PT -
Prop
n ac
tive
time
in c
rop
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
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1
0 100 200 300 400 500
Yellowhammer - Contact time (min)
PT -
Prop
n ac
tive
time
in c
rop
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 100 200 300 400 500
Linnet - Contact time (min)
PT -
Prop
n ac
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time
in c
rop
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0.1
0.2
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0.6
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0.8
0.9
1
0 100 200 300 400 500
Blackbird - Contact time (min)
PT
- Pro
pn a
ctiv
e tim
e in
cro
p
17
Appendix 2: Justification for averaging PT over several days (details provided by CSL) If birds change their habitat preferences from day to day, then averaging PT across several days is likely to indicate less extreme preferences than would restricting analyses to data collected on a single day. We looked for any such effect by comparing the distribution of PT (collected for birds with a minimum contact time of 150 minutes (see Appendix 1) based on observation periods collected in the course of a single day, with PT collected from monitoring across several days. Figure 3 to 10 show the distribution of PT for species of birds in orchard and arable scenarios in which they may be exposed to pesticides. Figures on the left show distributions for the full dataset collected over several days. Figures on the right show the distribution of PT collected for the same individuals for the single day with greatest contact time (where minimum contact time >150 min). If the extended timescale was affecting PT we would expect to see fewer birds with PTs close to 0 or 1 in the figures on the left with multiple days data. We looked for statistical evidence of the effect using the Moses test of extreme reactions (Siegel, 1956). This is a non-parametric test designed to highlight experimental conditions that may affect some subjects in one way and others in the opposite way. In our case we are using it to test whether restricting data to a single day causes some birds to show a PT closer to 0 and other closer to 1. Data were also separately analysed for 2 ensembles of interest to the risk assessor. Figures at the top of each page represent the full population of all birds caught in the orchard or arable landscape with >150 min data. Figures at the bottom show the sub-population of birds that additionally spent some active time in the orchard or arable crop. If the target of the risk assessment is to ensure that a pesticide has no unacceptable effects on the population of birds that live in and around orchard or arable crops then the appropriate ensemble is the full population of birds for which we have sufficient data. If the focus of interest is in ensuring the safety of any bird that visits a pesticide treated area then the appropriate ensemble should be ‘consumers-only’ excluding those birds that spent no active time in orchard or crop. The latter ensemble will generally lead to more conservative assessments since it assumes that all birds will be exposed to pesticide to some extent. For the “consumers only” ensemble, statistical tests revealed no significant differences between the multiple day and single day scenarios. For the “all birds” scenarios the Moses test showed a significant difference between multiple and single day scenarios for both yellowhammer and skylark in arable crops. But this result is compromised by the large number of ties (where PT= 0) between the groups which may invalidate the result. In general there appears to be relatively little difference in the distribution of PT between data collected over several days and data collected in a single
18
day. Because the extended timescale over which observations were collected tends to moderate extreme preferences, this to some extent will counterbalance the tendency for shortfalls in the data (see above) to exaggerate extreme preferences. Therefore since the multiple-days data gives a fuller coverage of a bird habitat use throughout the day we will use them in preference to data collected for only part of a single day.
19
Figure 3. Distribution, for Blackbirds in Orchards, of PT (%active time in centre). Data are presented where individuals have radio contact time >150 minutes. “All birds caught in orchards” – includes the full data set of birds that were caught in orchards. “Orchard consumers only” is a subset of the data that excludes those birds that were never seen active amongst orchard trees. “Multiple days set” includes PT values calculated from radio tracking data that may have taken several days to collect. “Single day with most contact time”- where data was collected over more than 1 day, PT is calculated for the day that radio contact time was greatest (Birds are rejected where maximum PT < 150 minutes)
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
5
10
15
20
25
Freq
uenc
y
Mean = 21.0658Std. Dev. = 31.20379N = 37
Group: Max in one day, Species: BLABI
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
5
10
15
20
25
Freq
uenc
y
Mean = 33.3006Std. Dev. = 26.80275N = 28
Species: BLABI, Group: All data
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
5
10
15
20
25
Freq
uenc
y
Mean = 41.0229Std. Dev. = 32.91156N = 19
Species: BLABI, Group: Max in one day
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
5
10
15
20
25Fr
eque
ncy
Mean = 23.3104Std. Dev. = 27.13286N = 40
Group: All data, Species: BLABI
All birds caught in orchards
Orchard consumers only
Multiple days Single day with most contact time
20
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 21.2884Std. Dev. = 21.65214N = 20
Group: All data, Species: BLUTI
Figure 4. Distribution, for Blue tits in Orchards, of PT (%active time in centre). Data are presented where individuals have radio contact time >150 minutes. “All birds caught in orchards” – includes the full data set of birds that were caught in orchards. “Orchard consumers only” is a subset of the data that excludes those birds that were never seen active amongst orchard trees. “Multiple days set” includes PT values calculated from radio tracking data that may have taken several days to collect. “Single day with most contact time”- where data was collected over more than 1 day, PT is calculated for the day that radio contact time was greatest (Birds are rejected where maximum PT < 150 minutes)
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 24.3838Std. Dev. = 23.26622N = 20
Group: Max in one day, Species: BLUTI
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 26.6105Std. Dev. = 21.04203N = 16
Species: BLUTI, Group: All data
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 30.4797Std. Dev. = 22.07892N = 16
Species: BLUTI, Group: Max in one day
All birds caught in orchards
Orchard consumers only
Multiple days Single day with most contact time
21
Figure 5 Distribution, for Chaffinches in Orchards, of PT (%active time in centre). Data are presented where individuals have radio contact time >150 minutes. “All birds caught in orchards” – includes the full data set of birds that were caught in orchards. “Orchard consumers only” is a subset of the data that excludes those birds that were never seen active amongst orchard trees. “Multiple days set” includes PT values calculated from radio tracking data that may have taken several days to collect. “Single day with most contact time”- where data was collected over more than 1 day, PT is calculated for the day that radio contact time was greatest (Birds are rejected where maximum PT < 150 minutes)
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 31.8123Std. Dev. = 27.34907N = 33
Group: All data, Species: CHAFF
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 33.9096Std. Dev. = 23.41082N = 24
Group: Max in one day, Species: CHAFF
All birds caught in orchards
Multiple days
Orchard consumers only
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 29.0654Std. Dev. = 24.75653N = 28
Group: Max in one day, Species: CHAFF
Single day with most contact time
0.00 20.00 40.00 60.00 80.00 100.00
% active time in centre
0
2
4
6
8
10
Freq
uenc
y
Mean = 36.0915Std. Dev. = 26.36499N = 29
Group: All data, Species: CHAFF
22
Figure 6. Distribution, for Robins in Orchards, of PT (%active time in centre). Data are presented where individuals have radio contact time >150 minutes. “All birds caught in orchards” – includes the full data set of birds that were caught in orchards. “Orchard consumers only” is a subset of the data that excludes those birds that were never seen active amongst orchard trees. “Multiple days set” includes PT values calculated from radio tracking data that may have taken several days to collect. “Single day with most contact time”- where data was collected over more than 1 day, PT is calculated for the day that radio contact time was greatest (Birds are rejected where maximum PT < 150minutes)
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
12
Freq
uenc
y
Mean = 20.3882Std. Dev. = 20.68095N = 29
Group: All data, Species: ROBIN
0 20 40 60 80 100
% active time in centre
0
1
2
3
4
5
6
7
Freq
uenc
y
Mean = 24.6358Std. Dev. = 20.28519N = 24
Group: All data, Species: ROBIN
All birds caught in orchards
Orchard consumers only
0 20 40 60 80 100
% active time in centre
0
1
2
3
4
5
6
7
Freq
uenc
y
Mean = 28.0262Std. Dev. = 18.56594N = 20
Group: Max in one day, Species: ROBIN
0 20 40 60 80 100
% active time in centre
0
2
4
6
8
10
12
Freq
uenc
y
Mean = 20.7601Std. Dev. = 20.21231N = 27
Group: Max in one day, Species: ROBIN
Single day with most contact time
Multiple days
23
0 20 40 60 80 100
% active time in crop
0
1
2
3
4
5
6
Freq
uenc
y
Mean = 76.6197Std. Dev. = 24.08202N = 10
Species: BLABI, Group: All days
Figure 7 Distribution, for Blackbirds in arable crops, of PT (%active time in crop). Data are presented where individuals have radio contact time >150 minutes.
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
1
2
3
4
5
6
Freq
uenc
y
Mean = 57.2794Std. Dev. = 45.14096N = 11
Species: BLABI, Group: Max day
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
1
2
3
4
5
6
Freq
uenc
y
Mean = 69.6543Std. Dev. = 32.49059N = 11
Species: BLABI, Group: All days
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
1
2
3
4
5
6
Freq
uenc
y
Mean = 70.0082Std. Dev. = 39.30174N = 9
Species: BLABI, Group: Max day
All birds
Single day with most contact time
Multiple days
Crop consumers only
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
2
4
6
8
10
Freq
uenc
y
Mean = 35.0349Std. Dev. = 31.47413N = 23
Species: LINNE, Group: All days
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
2
4
6
8
10
Freq
uenc
yMean = 29.8765Std. Dev. = 33.18998N = 20
Species: LINNE, Group: Max day
All birds
Single day with most contact time
Multiple days
Crop consumersonly
24
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
2
4
6
8
10
Freq
uenc
y
Mean = 42.4106Std. Dev. = 29.67439N = 19
Species: LINNE, Group: All days Figure 8. Distribution, for Linnets in arable crops, of PT (%active time in crop). Data are presented where individuals have radio contact time >150 minutes.
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
2
4
6
8
10
Freq
uenc
y
Mean = 39.8354Std. Dev. = 32.70999N = 15
Species: LINNE, Group: Max day
25
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
5
10
15
20
25
30
Freq
uenc
y
Mean = 51.9275Std. Dev. = 34.03246N = 48
Species: SKYLA, Group: All days
Figure 9. Distribution, for Skylarks in arable crops, of PT (%active time in crop). Data are presented where individuals have radio contact time >150 minutes
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
Freq
uenc
y
Mean = 36.6547Std. Dev. = 37.15709N = 68
Species: SKYLA, Group: All days
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
Freq
uenc
y
Mean = 34.438Std. Dev. = 40.13643N = 67
Species: SKYLA, Group: Max day
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
5
10
15
20
25
30
Freq
uenc
y
Mean = 59.1628Std. Dev. = 36.00912N = 39
Species: SKYLA, Group: Max day
All birds
Single day with most contact time Multiple days
Crop consumers only
26
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
40
50
Freq
uenc
y
Mean = 23.2405Std. Dev. = 32.8481N = 72
Species: YELHA, Group: All days
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
40
50
Freq
uenc
y
Mean = 41.833Std. Dev. = 34.15565N = 40
Species: YELHA, Group: All days
Figure 10. Distribution, for Yellowhammers in arable crops, of PT (%active time in crop). Data are presented where individuals have radio contact time >150 minutes
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
40
50
Freq
uenc
y
Mean = 49.8437Std. Dev. = 36.21777N = 34
Species: YELHA, Group: Max day
0.00 20.00 40.00 60.00 80.00 100.00
% active time in crop
0
10
20
30
40
50
Freq
uenc
y
Mean = 26.0721Std. Dev. = 36.1361N = 65
Species: YELHA, Group: Max day
All birds
Single day with most contact time
Multiple days
Crop consumers only
27
Appendix 3: Estimation of the 90th and 95th percentile PTs (details provided by CSL) For any radio tracked individual we can ask how much it used a particular crop at a particular season and hence calculate PT. For our focal species we can then plot a distribution of PTs. We assume that those individuals that spend a greater proportion of time in the crop are at greater risk from pesticide exposure. The most likely exposure for any unknown individual is given by the mean of the distribution. But for the purpose of risk assessment it is common practice to choose a value for exposure that is conservative, one that protects the majority of the population, for example the 90th or 95th percentile. A simple way of estimating the 95th percentile is to rank the individuals in increasing order of PT and to choose the value of PT corresponding to the 90th percentile individual, say. (Where there is no precise identity between an individual and the percentile of interest, we can interpolate between values of neighbouring individuals in the sequence.). A problem with this approach is that when the sample size if small (which it is for many of our scenarios) the value of the 90th percentile may be very variable between samples (more variable than the mean would be). A better estimate of the 90th percentile may be obtained by assuming that our data is a random sample from a parent distribution with known mathematical properties. For many real-world measurements, statisticians assume that a sample comes from a normal distribution with parameters µ and σ estimated by the mean and standard deviation of the sample. However, the normal distribution (with infinite upper and lower bounds) does not often provide a good fit for proportional data (limited between 0 and 1). There are three distributions that may be easily applied to data bounded between 0 and 1: Uniform (min=0, max=1), Triangular (min=0, min<most-likely<max, max=1) and the Beta (α, β). The Uniform distribution is in fact a special case of the Beta where α= β=1. The Triangular distribution tends to be used when we have no actual data but expect that values in the middle will be more common than values at the extremes. We can see from our radio tracking data (Figure 3 to 10) that PT often peaks at 0 or 1 and that sometimes there are two peaks. Therefore we have used the Beta distribution as the most appropriate for describing PT. A further advantage of the Beta is that it is very flexible and can take a wide variety of shapes, including bimodal distributions (Figure 11).
28
Figure 11. Examples of Beta distributions with different alpha and Beta parameter values Goodness of fit. How well does the Beta distribution fit PT? The Beta distribution has 2 parameters (α and β). We have explored 2 alternative ways of fitting these parameters for our datasets:
• The method of moments • Maximum likelihood
For many distributions, maximum likelihood is the preferred solution. Figure 12 shows the beta distribution which, according to maximum likelihood method, best fits the distribution of PTs seen in radio tracking on skylarks visiting cereal fields in summer. The α and β parameters are both <1 giving a characteristic U-shaped distribution, reflecting the fact that Skylarks appear to be divided into 2 groups, one spending very little time in the cereal crop, and the other spending nearly all their time there. Goodness of fit tests suggest that it is a reasonable description of the empirical data. Figure 1313 shows an example where the fit is less good. This dataset is for 72 yellowhammers and includes 45 birds which were successfully radio tracked but which never visited a cereal crop. The distribution is therefore strongly weighted toward the 0 PT values. Maximum likelihood has fitted another U shaped Beta distribution which may be reasonable for the zero values but is not a very accurate description of the distribution of PT near 1. Goodness-of-fit tests all indicate significant differences between the empirical data and the theoretical distribution.
Beta(2, 2)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 0.2 0.4 0.6 0.8 1
Beta(0.5, 0.5)
0
1
2
3
4
5
6
7
8
0 0.2 0.4 0.6 0.8 1
Beta(10, 10)
0
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4
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Beta(1, 0.5)
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0 0.2 0.4 0.6 0.8 1
29
Beta(0.30994, 0.24889)X <= 0.999
90.0%X <= 0.000
0.0%
00 0.2 0.4 0.6 0.8 1
PT
Chi square Anderson-Darling Kolmogorov-SmirnovTest Value 4.769 2.002 0.2296 P Value 0.3118 0.05 <= p <= 0.1 0.0553 Figure 12. Plot of best-fitting Beta distribution (red line) against histogram of raw PT data (blue bars) for 26 skylarks in cereals from April-August excluding birds that never visited the crop. Table shows results of goodness of fit tests.
Chi square Anderson-Darling Kolmogorov-SmirnovTest Value 240.2 9.608 0.3995 P Value 0 < 0.001 0 Figure 13. Plot of best-fitting Beta distribution (red line) against histogram of raw PT for cereal crops (blue bars) for 72 yellowhammers, including all birds caught on arable land. Table shows results of goodness of fit tests.
30
The poor fit of the maximum likelihood solution is more clearly visible when the fitted distribution and data are plotted as cumulative distributions. Figure 14 shows the same dataset and fitted distribution as Figure 12, but clearly suggests a systematic lack of fit with a number of points outside the 95% confidence interval at intermediate values of PT. Investigation revealed that this appears to be caused by the Maximum Likelihood method giving very high weight to data points very close to zero or one. In Figure 14, high weight given to the top two points “pulls” the fitted distribution to the right.
0 0.2 0.4 0.6 0.8 1 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulat
ive
Pro
babi
lity
PT
Cereals Skylark - Consumers Summer
Data95% Conf. IntervalMedian
Figure 14. Cumulative plot of raw PT data and Beta distribution fitted by Maximum Likelihood for 26 skylarks in cereals from April-August excluding birds that never visited the crop. Dotted lines show 95% confidence interval for the fitted distribution. This is the same dataset as shown in Figure 12, but the cumulative plot suggests a systematic lack of fit which was not obvious in Figure 12. Cumulative graphs were plotted for all the datasets, and several of the “consumers only” datasets showed a deviation of the type shown in Figure 14. Some “all birds” datasets showed the reverse pattern, with multiple data points at zero pulling the fitted distribution to the left (e.g. Figure 15). These results suggest that the Maximum Likelihood method performs poorly for at least some of our datasets, especially those with data points at (or very close to) zero or one. We therefore examined the alternative approach, method of moments, to fit beta distributions to our data.
31
0 0.2 0.4 0.6 0.8 1 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulat
ive
Pro
babi
lity
PT
Orchard Chaff - All
Data95% Conf. IntervalMedian
Figure 15. Cumulative plot of raw PT data and Beta distribution fitted by Maximum Likelihood for 33 chaffinches in orchards (including birds that never visited the orchard). Dotted lines show 95% confidence interval for the fitted distribution. The cumulative plot suggests a systematic lack of fit at lower values of PT. The method of moments estimates the parameters of the Beta distribution (α and β) from the mean and variance of the raw data, whereas the Maximum Likelihood method uses all the individual data points. The method of moments is therefore less subject than Maximum Likelihood to strong influence from extreme values at 0 and 1. As a result, it provides a much more reasonable fit to distributions with data at 0 or 1, and in fact visual inspection suggests a very reasonable fit to every one of our datasets, regardless of sample size. For example, Figures 16 and 17 shows distributions fitted by the method of moments to the same datasets as Figures 14 and 15: in both cases, the method of moments provides a superior fit. All the points fall between the confidence limits, even though they are narrower (90% intervals are shown in Figures 16 and 17, 95% intervals in Figures 14 and 15). Our results are consistent with simulation studies by Frey et al. (1999) who also showed that the method of moments performs better than maximum likelihood for Beta distributions. In the light of these findings we decided to use method of moments for fitting Beta distributions to all the datasets considered in this paper. Appendix 4 includes graphs of the distributions fitted by method of moments to all the datasets.
32
0 0.2 0.4 0.6 0.8 1 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulat
ive
Pro
babi
lity
PT
Cereals Skylark - Consumers Summer
Data90% Conf. IntervalMedian
Figure 16. Cumulative plot of raw PT data and Beta distribution fitted by method of moments for 26 skylarks in cereals from April-August excluding birds that never visited the crop. Dotted lines show 90% confidence interval for the fitted distribution. This is the same dataset as shown in Figures 12 and 14, but the method of moments provides a better fitting distribution.
0 0.2 0.4 0.6 0.8 1 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulat
ive
Pro
babi
lity
PT
Orchard Chaff - All
Data90% Conf. IntervalMedian
Figure 17. Cumulative plot of raw PT data and Beta distribution fitted by method of moments for 33 chaffinches in orchards (including birds that never visited the orchard). Dotted lines show 90% confidence interval for the fitted distribution. This is the same dataset as shown in Figure 15, but the method of moments provides a better fitting distribution. Quantifying sampling uncertainty: confidence limits for the distribution of PT Because the number of birds radio tracked is always limited, estimation of the fitted distribution is affected by sampling uncertainty, which is represented by the confidence intervals shown in Figures 14-17. Confidence intervals for the Maximum Likelihood distributions were obtained by Bayesian simulations
33
using the software package WinBugs. When the Beta distribution was fitted by method of moments, the confidence intervals were obtained by parametric bootstrapping. Briefly, 1000 samples of size equal to the raw dataset were sampled at random from a Beta distribution with the α and β parameters fitted by the method of moments; each sample was used to fit a new Beta distribution by the method of moments; and the 1000 distributions were used to derive a 90% confidence interval for each percentile of PT. Note that: • The confidence intervals for the method of moments distributions
represent only sampling uncertainty: other types of uncertainty affecting PT are discussed below.
• 90% confidence intervals were chosen because this means the upper bound can be interpreted as a one-sided 95% confidence limit for PT. However, although 95% confidence bounds are conventionally used in many types of research, they are not necessarily the appropriate choice for risk management purposes. Other bounds (e.g. 90%, 99%) could be provided if risk managers required a lower or higher level of certainty.
Evaluation of other uncertainties affecting the estimation of PT Sampling uncertainty due to the limited numbers of birds studied is quantified by the confidence intervals provided with the results. Other uncertainties affecting the estimation of PT are summarised in Table 1, together with a qualitative assessment of their direction and magnitude. Although there are a large number of uncertainties, most of them are thought to be unbiased (about equally likely to cause under- or over-estimation), and most of them are expected to operate independently so they will tend to average out, to some extent. Overall, taking all the identified uncertainties into account, our view is that the higher percentiles for PT could be either under- or over-estimated, and that the total uncertainty might be approximately double that shown by the 90% confidence intervals given with the results.
If desired, it would be possible to investigate the potential combined effect of the various uncertainties quantitatively using Monte Carlo simulations: however, as the uncertainties could only be estimated subjectively, the results would be only indicative (a form of sensitivity analysis). Extrapolation to other species and crops If it were desired to extrapolate to other species or crops, additional uncertainty would be introduced. Briefly: • Extrapolation to other species: The radio tracking studies were focussed
on species known to be abundant and frequent in crop, so are expected to over-estimate most (but not all) other species.
• Extrapolation to other crops: PT could be significantly higher or lower, depending on attractiveness of crop to species concerned. This would need to be assessed case-by-case.
34
Table 1. Qualitative evaluation of the main sources of uncertainty affecting the estimation of high percentiles of the between-bird distribution of PT. Key to direction and magnitude: +, ++, +++ = uncertainty thought to be causing small, medium or large over-estimation of PT; -, - -, - - - = uncertainty thought to be causing small, medium and large under-estimation of PT. “Medium” is used where the uncertainty is thought to be of approximately similar magnitude to that shown by the 90% confidence intervals provided for sampling uncertainty. Source of uncertainty Direction &
magnitude of uncertainty
Potential bias in the sample of birds monitored. If the probability of capture in a habitat is positively correlated with the time an individual spends in that habitat, as seems likely, then the tracked birds will be biased towards those that spend more time in the crop. More likely for orchards and oilseed rape due to differences in location of trapping sites.
++ orchard and oilseed rape,
+/- other crops
Uncertainty in the determination of bird locations while tracking. +/- Uncertainty in determining active/inactive time while tracking. +/- Uncertainty due to possibility that birds are disturbed by presence of observer (though care was taken to minimise this). This would most likely cause under-estimation of PT.
+/- -
Uncertainty about relation between active time and foraging time, and between foraging time and the proportion of diet consumed in that habitat (which determines exposure).
++/- -
Bias and imprecision due to limited duration of tracking per bird (this was limited by excluding birds with < 150 minutes).
+/-
Extrapolation from study sites to other locations. ++/- - Uncertainty if data are used to estimate PT for longer periods. High percentiles are more likely to be over-estimated than under-estimated, as day-to-day differences in PT will tend to average out over time.
++/- (for longer term
assessments)
Deviations from fitted distributions. Graphical inspection indicates reasonable fit for all datasets, so deviations from Beta are thought to add little to the sampling uncertainty shown by the confidence intervals.
+/-
Under-estimation of variance in small samples. This might cause under- or over-estimation of high percentiles and confidence intervals. However, this is thought to be of minor practical significance here as the upper confidence intervals for small samples are already close to 1 (see Appendix 4).
+/-
Sampling uncertainty for sample α and β may over- or under-estimate sampling uncertainty for true α and β, especially with smaller samples. Effect should average out over many datasets but will be large for some individual datasets.
+/-
Simulation uncertainty: repeat runs of the estimation process change tabulated percentiles and confidence bounds by amounts in the order of 0.01-0.02 (could be reduced by more iterations).
+/-
Qualitative evaluation of overall impact of unquantified uncertainties: total uncertainty might be approximately double that shown by the 90% confidence intervals given with the results.
++/- -
35
Appendix 4. Results for all datasets (details provided by CSL) Orchard (April-September)
All birds Species PT 90th percentile 95th percentile
Table 1. Orchard. Estimation of 90th and 95th percentile PT values. For each percentile, the median estimate is shown together with the lower and upper bounds of the 90% confidence interval. Estimates are made separately for the population comprising “All birds” caught in orchards, and for the sub-population “Orchard consumers only” that excludes birds that never used orchard habitat during radio-tracking.
Table 2. Cereals . Estimation of 90th and 95th percentile PT values. For each percentile, the median estimate is shown together with the lower and upper bounds of the 90% confidence interval. Estimates are made separately for the population comprising “All birds” caught in the arable landscape, and for the sub-population “Crop consumers only” that excludes birds that never used cereal fields during radio-tracking.
37
Beet-Potatoes
All birds PT 90th percentile 95th percentile N Sample
Table 3. Beet & Potatoes . Estimation of 90th and 95th percentile PT values. For each percentile, the median estimate is shown together with the lower and upper bounds of the 90% confidence interval. Estimates are made separately for the population comprising “All birds” caught in the arable landscape, and for the sub-population “Crop consumers only” that excludes birds that never used beet or potato fields during radio-tracking.
38
OSR
All birds PT 90th percentile 95th percentile N Sample
Table 4. OSR. Estimation of 90th and 95th percentile PT values. For each percentile, the median estimate is shown together with the lower and upper bounds of the 90% confidence interval. Estimates are made separately for the population comprising “All birds” caught in the arable landscape, and for the sub-population “Crop consumers only” that excludes birds that never used beet or oilseed rape fields during radio-tracking.
39
Figure 18. Cereals: skylark. Data are plotted as cumulative distribution functions, so that for any
percentile of interest along the Y axis, median value of PT and the 90% confidence bounds can be read off directly from the X-axis. Figure 18. Cereals: Skylark
