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SID 5
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Research Project Final Report
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SID 5 (Rev. 3/06)
Project identification
PS2330
Improved estimates of food and water intake for risk
assessment
Contractor
organisation(s)
Central Science Laboratory
Sand Hutton
York
North Yorkshire
YO41 1LZ
54. Total Defra project costs
(agreed fixed price)
5. Project:
Page 1 of 23
£
17,085
start date ................
15 January 2007
end date .................
13 July 2007
6. It is Defra’s intention to publish this form.
Please confirm your agreement to do so. ................................................................................... YES
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(a) When preparing SID 5s contractors should bear in mind that Defra intends that they be made public. They
should be written in a clear and concise manner and represent a full account of the research project
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Executive Summary
7.
The executive summary must not exceed 2 sides in total of A4 and should be understandable to the
intelligent non-scientist. It should cover the main objectives, methods and findings of the research, together
with any other significant events and options for new work.
1. Methods of estimating water intake in birds for risk assessment were reviewed in project PS2327.
It was recommended that drinking water estimates should be based on estimates of water influx
rate (WIR) from doubly labelled water (DLW) studies combined with estimates of water in food and
metabolic water production to calculate drinking water requirements.
2. Given the relative lack of actual measured WIR values for UK species, and the large number of
DLW studies conducted since the late eighties when the existing allometric equations for water
flux were developed, it was also recommended that these are updated to include this new
information.
3. Given the need to review the DLW literature to update the water intake estimates, and the ongoing
revision of the current SANCO bird and mammal risk assessment guidance document, it was
decided that recent information about energy requirements should be gathered so that these
estimates should also be updated to ensure that any new guidance was based on the most up to
date information available. These data can also be incorporated, where appropriate, into the
WEBFRAM models of pesticide risk assessment.
4. Literature from DLW studies were reviewed to develop an up to date database of energy and
water requirements for birds. Recent data on energy requirements of mammals were also
collated. These were then be used to generate allometric equations like those of Nagy and
Peterson (1988) for water intake and Crocker et al. (2002) for energy requirements.
5. A total of 739 values for water flux for fully-grown birds from 92 species were collected from 114
studies. This allowed a significant expansion of the dataset that was available when the existing
allometric equations were generated. Allometric equations were developed based on data from all
birds and a separate equation for passerines. Information about estimation of metabolic water
production was also provided.
6. A total of 1290 energy values for 134 bird species from 157 studies were collated. These were
then used to produce updated allometric equations for Daily Energy Expenditure (DEE) that could
be combined with data on food energy content, assimilation efficiency and food water content to
calculate food intake requirements. Separate allometric equations for passerines and nonpasserines suitable for use in risk assessments were developed.
SID 5 (Rev. 3/06)
Page 2 of 23
7. A total of 608 values for 115 mammal species were collected from 117 studies. Allometric
equations for DEE were updated as for birds. This information was then analysed as for data on
birds in PN0908 to establish the relationship between DEE and bodyweight within a species.
Based on this analysis it was considered unwise to attempt use differences in bodyweight
between individuals of a species to predict individual differences in energy expenditure.
8. Despite the increase in the amount of data used to produce the allometric equations in this study,
it is still clear that there is a lack of information on relevant species at different times of year. There
is therefore a research need for more doubly labelled water studies to be conducted at different
times of year and preferably covering the range of potential diets (e.g. seeds, insects, fruit, soil
invertebrates).
Project Report to Defra
8.
As a guide this report should be no longer than 20 sides of A4. This report is to provide Defra with
details of the outputs of the research project for internal purposes; to meet the terms of the contract; and
to allow Defra to publish details of the outputs to meet Environmental Information Regulation or
Freedom of Information obligations. This short report to Defra does not preclude contractors from also
seeking to publish a full, formal scientific report/paper in an appropriate scientific or other
journal/publication. Indeed, Defra actively encourages such publications as part of the contract terms.
The report to Defra should include:
 the scientific objectives as set out in the contract;
 the extent to which the objectives set out in the contract have been met;
 details of methods used and the results obtained, including statistical analysis (if appropriate);
 a discussion of the results and their reliability;
 the main implications of the findings;
 possible future work; and
 any action resulting from the research (e.g. IP, Knowledge Transfer).
SID 5 (Rev. 3/06)
Page 3 of 23
INTRODUCTION
Methods of estimating water intake in birds for risk assessment were reviewed in project PS2327.
Current methods of estimating the drinking water requirements of birds were considered to be
unsatisfactory as they do not take account of the water in the food so that a bird eating dry seeds would
appear to have the same drinking water requirements as a bird feeding on leaves. It was recommended
that drinking water estimates should be based on estimates of daily water influx from doubly labelled
water (DLW) studies combined with estimates of water in food and metabolic water production to
calculate drinking water requirements. This provides a more realistic estimate of drinking water
requirements than the existing method, and is similar to the approach used for estimating daily energy
expenditure (DEE) and food requirements in project PN0908.
It was recommended that where water flux estimates from studies on the species of concern are
available, these should preferably be used in any risk assessment. Where estimates for the species of
concern are not available (which is the case for most UK species), estimates should be based on the
appropriate allometric equation from Nagy and Peterson (1988), which allow predictions to be made for
birds of different sizes and types. However, given the relative lack of actual measured water flux values
for UK species, and the large number of DLW studies conducted since the late eighties when the
existing allometric equations for water flux were developed, it was also recommended that these are
updated to include this new information.
Given the need to review the DLW literature to update the water intake estimates, and the ongoing
revision of the current SANCO bird and mammal risk assessment guidance document, it was decided
that recent information about energy requirements should be gathered so that these estimates could also
be updated. This was done to ensure that any new guidance was based on the most up to date
information available. These data can also be incorporated, where appropriate, into the WEBFRAM
models of pesticide risk assessment published on the internet in which external users can carry out
probabilistic assessments of the effects pesticides on wildlife.
Also, as part of PN0908, it was shown that the relationship between avian body mass and energy
expenditure within a species was often quite different from that between species: a fat blue tit may not
use the same energy as a thin great tit. It was therefore considered necessary to check for similar
relationships among mammal species. And because risk analyses are usually focused at the level of the
individual bird or mammal, it is appropriate, especially for probabilistic assessments, to collect
information on individual variation in energy expenditure.
Literature from DLW studies were reviewed to develop an up to date database of energy and water
requirements for birds. Recent data on energy requirements of mammals were also collated. These were
then be used to generate allometric equations like those of Nagy and Peterson (1988) for water intake
and Crocker et al. (2002) for energy requirements.
OBJECTIVES
1.
2.
3.
4.
Provide improved methods of estimating daily water intake by birds for use in risk assessments by
including data from doubly-labelled water (DLW) studies conducted since the publication of Nagy
and Peterson (1988).
Update the existing estimates of energy and food requirements of birds and mammals by including
data from DLW studies conducted since PN0908.
Investigate the relationship between bodyweight and measured energy expenditure within species
for mammals.
Present the above information to PSD in a form that can be of immediate use in risk assessments
(e.g. spreadsheets, tables etc.) as was done in project PN0908.
SID 5 (Rev. 3/06)
Page 4 of 23
PROGRESS
Objective 1. Provide improved methods of estimating daily water intake by birds for use in risk
assessments by including data from DLW studies conducted since the publication of Nagy and Peterson
(1988).
Estimating drinking water requirements
A bird can obtain water from sources other than drinking (Table 1) and the relative amounts obtained
from each will be different for different species and diets. For example birds feeding on large quantities
of succulent food will have far less need for drinking water than one that is feeding on dry seeds.
Table 1. Water intake and loss in birds.
Water in
Water out
Water in food
Faeces
Metabolic water
Pulmocutaneous evaporation
Drinking water
Where an estimate of total daily water flux can be made it is therefore possible to combine this with data
on preformed water in the diet and metabolic water production to determine how much water a bird
would need to drink to achieve water balance.
e.g.
Drinking water (ml/d) = Total water flux – [Food water + Metabolic water]
This approach has been used in several isotope studies to estimate the need for drinking water (Ambrose
et al. 1996, Degen et al. 1983, Dykstra and Karasov 1993, Goldstein and Nagy 1985, Kam et al. 1987,
Weathers et al. 2001, Weathers and Stiles 1989, Williams et al. 1995, Williams and Dwinnel 1990).
Such an approach has also been used to estimate the diet of desert birds necessary to explain observed
water flux in the absence of drinking water (Alkon et al. 1985, Anava et al. 2000, Williams 2001,
Williams and Duplessis 1996), or confirm estimates of energy intake by seabirds (Costa and Prince
1987, Gabrielsen et al. 1987, Gabrielsen et al. 1991, Mehlum et al, 1993, Nagy and Obst 1992, Nagy et
al. 1984, Obst et al. 1987, Roby and Ricklefs 1986). In these cases it is assumed that the only water
available to the birds comes from food (food water and metabolic water).
Improved estimates of daily water flux
Published papers on doubly-labelled water studies on wild birds from projects PN0908 and PS2327
were examined to extract any data on water flux. These were supplemented with data from more recent
papers from an online search conducted by the CSL Information Centre. This was combined with
suitable data reported in Nagy and Peterson 1988 (e.g. adult bird data only) to produce an up to date
database of water flux values for free-living birds which could be used to calculate updated allometric
equations using the linear regression methods reported in PN0908. As far as the published data allowed,
we collected data on individual birds’ water flux, which could form the basis of a probabilistic risk
assessment in which the daily variation and uncertainty in water intake could be simulated for individual
birds, and estimates made of reasonable worst case individual consumption.
A total of 739 values for water flux for fully-grown birds from 92 species were collected from 114
studies. This allowed a significant expansion of the dataset that was available when the existing
SID 5 (Rev. 3/06)
Page 5 of 23
allometric equations were generated by Nagy and Peterson (1988). That study used 62 values for 27
species used to develop the equations, some of which (6 values for three species) were for nestling
rather than adult birds.
The vast majority of these values were from DLW studies where DEE data for the same individuals or
group of birds was also reported. A small number of values came from tritiated water studies where only
water flux was measured and these were also included (as in the earlier study by Nagy and Peterson
1988).
Allometric equations for water flux in birds
The relationship between daily water flux and bodyweight for all species is shown in Figure 1.
Figure 1. Water flux data plotted against bodyweight for 92 species of birds.
Water Flux v Body Weight for 92 bird species
log10(Water Flux) = 0.1830 + 0.7184 log10(Body Weight)
Regression
95% PI
Water Flux (mL/day)
10000
S
R-Sq
R-Sq(adj)
1000
0.270459
87.6%
87.4%
100
10
1
1
10
100
1000
Body weight (g)
10000
100000
Adjusted r2 for this regression was 0.874 and this increased to 0.901 when ‘group’ (desert,
hummingbird, terrestrial non-passerine, passerine, or seabird) was also included. The data for each
group and fitted lines are shown in Figure 2.
SID 5 (Rev. 3/06)
Page 6 of 23
Figure 2. Water flux data for different species groups plotted against bodyweight.
Water Flux v Body Weight for 92 bird species
Water Flux (mL/day)
10000
Desert
Hummingbird
Terrestrial non-Passerine
Terrestrial Passerine
Seabird
1000
100
10
1
1
10
100
1000
Body Weight (g)
10000
100000
Data for 26 species of passerine were available for analysis and the relationship between water flux and
bodyweight for this group is shown in Figure 3.
Figure 3. Water flux plotted against bodyweight for passerines.
Water Flux v Body Weight for 26 Passerine bird species
log10(Water Flux) = - 0.1945 + 1.003 log10(Body Weight)
Regression
95% PI
Water Flux (mL/day)
100
S
R-Sq
R-Sq(adj)
50
0.186820
62.3%
60.7%
20
10
1
10
20
Body Weight (g)
50
100
The new allometric equations for estimating water flux in each group of birds are shown in Table 2.
SID 5 (Rev. 3/06)
Page 7 of 23
Table 2. Birds. Relationship between body weight (g) and Daily Water Flux (ml) in birds for selected
groups of avian species. The general form of equation is: Log(DEE) = Log a + b  (log Body weight).
Insert log10 a and b from the table to obtain the specific equation for the relevant species group. Also
shown are the standard errors for a and b (SE), the number of species in each group (N), and the
proportion of variation explained by each equation (r2).
Group
Desert
Hummingbirds
Other
Passerine*
Seabird
b
SE b
N
r2
0.121
0.320
0.423
0.195
0.115
0.735
1.174
0.548
1.003
0.616
0.057
0.425
0.173
0.159
0.040
15
5
7
26
39
0.923
0.624
0.601
0.607
0.859
0.065
0.718
0.029
92
0.874
Log10 a
SE Log10 a
-0.098
0.111
0.289
-0.195
0.601
all birds
0.183
*excluding marine and desert passerines
The group ‘other’ indicates terrestrial non-passerines that are not hummingbirds or desert species and
this may appear the equation of choice for estimating water flux in non-passerines for risk assessment.
Unfortunately the fitted line for this group is not very useful due to the small number of species in this
category and the species composition (six species from the parrot family and one owl) and it would
seem more appropriate to use the ‘all birds’ equation for non-passerines as was done in project PS2327.
For example, a skylark weighing 37g may be expected to have a Daily Water Flux of
Log10(Water Flux) = -0.195 + 1.003*Log10(37)
Log10(Water Flux) = 1.378
Water Flux = 101.378
Water Flux = 23.9 ml/day
Similarly a goose weighing 3kg might be expected to show a Daily Water Flux of
Log10(Water Flux) = 0.183 + 0.718*Log10(3000)
Water Flux = 478.2 ml/day
Water in food
To determine how much of a birds daily water requirement might be obtained from its food, it is
necessary to determine how much food is eaten in a day and combine this with the fractional water
content. Methods for estimating food intake have already been developed (Crocker et al. 2002) based on
daily energy expenditure (DEE) estimates from allometric equations, energy contents of different foods
and assimilation efficiency. Data on the moisture content of foods is used to calculate the wet weight
daily food requirements. The best approach would therefore be to use the output of these calculations to
determine the amount of water that may be obtained from food.
SID 5 (Rev. 3/06)
Page 8 of 23
e.g.
Food water (g) = Daily food intake (g) x Fractional water content
For a mixed diet it would be necessary to calculate the water content for each type and sum to estimate
total daily food water intake.
Metabolic water
Different food constituents (fats, proteins, carbohydrates) produce different amounts of water when
metabolised (Table 3).
Table 3. Energy and metabolic water values for food constituents adapted from Schmidt-Nielsen (1979)
using a conversion of 1 kcal = 4.184kJ.
Water formed
Metabolic energy value
Foodstuff
(ml water/g food)
(kJ/g)
Starch (carbohydrates)
0.56
17.57
Fat
1.07
39.33
Protein (urea excretion)
0.39
17.99
Protein (uric acid excretion)
0.5
18.41
Water formed
(ml H2O/kJ)
0.0319
0.0272
0.0217
0.0272
While different food constituents yield different amounts of water per g of food metabolised, these
differences are reduced when the water produced per kJ is considered. This also simplifies the
calculation of metabolic water produced as it could be estimated directly from the estimate of DEE.
Ideally this would be estimated based on the relative amounts of carbohydrate, fat and protein in the diet
under consideration (e.g. Williams and Prints, 1986). In the absence of such detailed information about
dietary composition then it may be appropriate to use a mean value (0.0278 g water/kJ) or, more
conservatively, the lowest value (average protein value 0.0244 g water/kJ). Given that birds excrete
nitrogen mainly as uric acid with a small proportion of urea this may slightly underestimate water
produced by metabolism of proteins. However, using the uric acid value alone may overestimate water
production, which would be less conservative.
e.g.
Metabolic water (ml) = DEE (kJ) x 0.0278 (ml/kJ)
(using mean value)
Alternatively, it would be possible to estimate metabolic water production from daily food intake
provided energy content, fractional water content and assimilation efficiency are known.
e.g.
where:
Metabolic water (ml) = DFI x [1 – FWC] x AE x EC x MWP
DFI
FWC
AE
EC
MWP
= Daily food intake (g wet weight)
= Fractional water content of food (unitless proportion)
= Assimilation efficiency (unitless proportion)
= Energy content of food (kJ/g dry weight)
= Metabolic water production (ml/kJ see above)
Where detailed information about dietary composition is available (% carbohydate, % fat, % protein)
then metabolic water production can be estimated from the data on production per unit dry weight
metabolised (ml/g).
e.g. Metabolic water (ml) = (g carbohydrate x 0.56) + (g fat x 1.07) + (g protein x 0.0244)
SID 5 (Rev. 3/06)
Page 9 of 23
Note this should be estimated using the dry weight of food that is metabolised.
e.g.
DEE/energy content (kJ/g dry weight of food)
or
Total food intake (dry weight in g) x Assimilation Efficiency
Example of values for metabolic water production used in isotope studies to estimate water intake for
different diets are shown in Table 4.
Table 4. Values of metabolic water production (MWP) used in doubly-labelled and tritiated water
studies to estimate total water intake.
Species
Diet
MWP
(ml/kJ)
Reference
Adelie penguin
Krill
0.024
Chappell et al (1993)
Dune Larks
Millet*
0.0301
Williams (2001)
Dune Larks
Insects**
0.0272
Williams (2001)
House wren (nestling)
Insects
0.026
Dykstra & Karasov (1993)
Gambel’s quail
Seeds
0.0297
Goldstein & Nagy (1985)
Chukar/Sand partridge
Seeds
0.0301
Kam et al. (1987)
Chukar/Sand partridge
Vegetation
0.0294
Kam et al. (1987)
Chukar/Sand partridge
Insects
0.0257
Kam et al. (1987)
Adelie penguin
Krill
0.024
Nagy and Obst (1992)
Emperor penguins
Fish/Squid
0.0272
Robertson & Newgrain (1996)
Crowned woodnymph
Nectar/Insects***
0.0309
Weathers & Stiles (1989)
Black-rumped waxbill
Seeds
0.0269
Weathers & Nagy (1984)
Sociable weavers
Seeds
0.03
Williams & Duplessis (1996)
Savannah sparrows
Insects
0.024
Williams & Dwinnel (1990)
*
assuming 13.5% protein, 5.1% lipid and 81.4% carbohydrate
**
assuming 62% protein, 14.9% lipid and 15.0% carbohydrates
***
90% nectar, 10% insects
Some of these values are based on actual estimates of dietary constituents (e.g. Williams 2001) or make
clear distinctions between values for different diets (e.g. Kam et al. 1987) making use of different
estimates of MWP for seeds, insects and vegetation. Others appear to use a value (0.024 ml/kJ) close to
the mean value for proteins suggested above.
Some of these values may also be used to estimate metabolic water production. For example, the mean
values for seeds and insects may be appropriate (Table 5).
Table 5. Mean values for metabolic water production for two food types based on these used in other
studies.
Food type
Seeds
Insects
SID 5 (Rev. 3/06)
Number of studies
4
4
Mean MWP (ml/kJ)
0.0294
0.0257
Page 10 of 23
For a mixed diet it would be best to calculate the metabolic water content production for each type of
food (if sufficient data is available on the dietary composition of each food type is available) and sum
them to estimate total daily food water intake. Otherwise the total DEE estimate could be used with a
single value for metabolic water production as indicated above.
Metabolic water can therefore be estimated in at least three ways depending on the data available and
the degree of precision required e.g.
1. Use DEE and mean (0.0278 ml/kJ) or lowest (0.0244 ml/kJ) value for MWP.
2. Calculate from carbohydrate, fat and protein values (ml/g) where data on dietary composition
and food intake is available.
3. Use values from previous studies where available (e.g. for seeds and insects).
Limitations of the method for estimating drinking water requirements
This method of estimating drinking water requirements provides a useful indication of those
species/food type combinations that present the most risk. However, the lack of actual daily water flux
data for most of the relevant species that might be considered in risk assessment leads to a heavy
reliance on allometric equations This has some shortcomings that were discussed in detail in project
PS2327. In addition, measured values of water flux may be affected by the specific circumstances under
which they were collected such as time of year or diet. For example, high values for water flux for a
given species may reflect the fact that the birds were feeding on succulent materials when measured and
therefore had a relatively high volume of water passing through the body. As long as estimates of water
requirements are based on the same diet this should not present a problem. However, if water
requirements measured when a bird was feeding on a relatively moist diet (e.g. insects) are used to
estimate water intake of the same species at a different time of year when the diet was mostly seeds,
then water requirements may be overestimated. Use of fitted lines that include both types of diet may
lessen this effect but if data for an individual species is used (where available) it would be best to only
use it for the season/diet combination for which it was collected.
Objective 2. Update the existing estimates of energy and food requirements of birds and mammals by
including data from DLW studies conducted since PN0908.
Updating the existing dataset for DEE in birds
The literature gathered as in Objective 1 was examined and any new data on the daily energy
expenditure of wild birds not in the existing database gathered and added. A total of 1290 values for
134 species from 157 studies were collated. These were then used to produce updated allometric
equations for energy intake that could be combined with data on food energy content, assimilation
efficiency and food water content to calculate food intake requirements. Again, as far as possible, we
collected data on individual variation as a foundation for probabilistic simulations.
Allometric equations for DEE in birds
The relationship between DEE and bodyweight for all species is shown in Figure 4.
SID 5 (Rev. 3/06)
Page 11 of 23
Figure 4. Daily energy expenditure plotted against bodyweight for 134 species of birds.
DEE v Body Weight for 134 bird species
log10(DEE) = 1.019 + 0.6705 log10(Body Weight)
Daily Energy Expenditure (kJ/day)
50000
Regression
95% PI
10000
S
R-Sq
R-Sq(adj)
0.179381
92.5%
92.4%
1000
100
10
1
10
100
1000
Body Weight (g)
10000
100000
Adjusted r2 for this regression was 0.924 and this increased to 0.949 when ‘group’ (same as used in the
analysis of water flux data) was also included. The data for each group and fitted lines are shown in
Figure 5.
Figure 5. Daily energy expenditure plotted against bodyweight for different groups of species.
Daily Energy Expenditure (kJ/day)
DEE v Body Weight for 134 bird species
Desert
Hummingbird
Terrestrial non-Passerine
Terrestrial Passerine
Seabird
10000
1000
100
10
1
10
100
1000
Body Weight (g)
10000
100000
The relationship between DEE and bodyweight for passerines and terrestrial non-passerines are shown
in Figures 6 and 7.
SID 5 (Rev. 3/06)
Page 12 of 23
Figure 6. Daily energy expenditure plotted against bodyweight for passerines only.
DEE v Body Weight for 44 Passerine bird species
Daily Energy Expenditure (kJ/day)
log10(DEE) = 1.032 + 0.6760 log10(Body Weight)
Regression
95% PI
S
R-Sq
R-Sq(adj)
200
0.0769012
84.3%
83.9%
100
50
20
10
10
20
50
Body Weight (g)
100
Figure 7. Daily energy expenditure plotted against bodyweight for terrestrial non-passerines.
DEE v Body Weight for 18 non-Passerine bird species
Daily Energy Expenditure (kJ/day)
log10(DEE) = 0.8387 + 0.6694 log10(Body Weight)
Regression
95% PI
5000
S
R-Sq
R-Sq(adj)
2000
1000
0.177467
87.6%
86.8%
500
200
100
50
20
10
20
50
100 200
500 1000 2000
Body Weight (g)
5000
The appropriate allometric equations for estimating DEE each group of birds are shown in Table 6.
SID 5 (Rev. 3/06)
Page 13 of 23
Table 6. Birds. Relationship between body weight (g) and Daily Energy Expenditure (DEE (kJ)) in birds
for selected groups of avian species. The general form of equation is: Log(DEE) = Log a + b  (log
Body weight). Insert log10 a and b from the table to obtain the specific equation for the relevant species
group. Also shown are the standard errors for a and b (SE), the number of species in each group (N), and
the proportion of variation explained by each equation (r2).
Group
Desert
Hummingbirds
Terrestrial (non passerine)
Passerine*
Seabird
b
SE b
N
r2
0.099
0.082
0.161
0.058
0.077
0.684
1.206
0.669
0.676
0.632
0.048
0.109
0.063
0.045
0.027
14
5
18
44
53
0.941
0.968
0.868
0.839
0.911
0.037
0.671
0.017
134
0.924
Log10 a
SE Log10 a
0.762
0.749
0.839
1.032
1.219
All birds
1.019
*excluding marine and desert passerines
It is recommended that the ‘passerine‘ and ‘terrestrial – non-passerine’ equations are used in risk
assessment as appropriate.
For example, a skylark weighing 37g may be expected to have a Daily Energy expenditure of
Log10(DEE)=1.032 +0.676* Log10(37)
Log10(DEE)=2.092
DEE= 102.092
DEE=123.6 kJ/day
Similarly a goose weighing 3kg might be expected to show a DEE of
Log10(DEE)=0.839 +0.669* Log10(3000)
DEE= 1463 kJ/day
Objective 3. Investigate the relationship between bodyweight and measured energy expenditure
within species for mammals.
Updating the existing dataset for DEE in mammals
Literature reporting DEE for a range of bodyweights for mammals of a given species from studies
identified in PN0908 and more recent studies found in the literature search were reviewed, data collated
and added to the existing database. A total of 608 values for 115 species were collected from 117
studies. This information was then analysed as for data on birds in PN0908 to establish the relationship
between DEE and bodyweight within a species. For some bird species such as the great tit, it has been
shown that the relationship between DEE and bodyweight within a species is much steeper than that
between species (Tinbergen and Dietz, 1994): it is energetically costly to be an over-weight great tit.
SID 5 (Rev. 3/06)
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Should trends be identified in mammals, recommendations will be made as to how this could be used to
improve estimates of daily energy expenditure.
Allometric equations for mammals
The relationship between DEE and bodyweight for all mammal species is shown in Figure 8.
Figure 8. Daily energy expenditure plotted against bodyweight for all mammal species for which data
was collected.
DEE v Body Weight for 115 mammal species
Daily Energy Expenditure (kJ/day)
log10(DEE) = 0.7037 + 0.7188 log10(Body Weight)
Regression
95% PI
100000
S
R-Sq
R-Sq(adj)
10000
0.212711
95.1%
95.0%
1000
100
10
10
100
1000
10000
Body Weight (g)
100000
The best predictor of DEE is Body weight (r2 = 0.950). The habitat/taxonomic grouping adds a very
small (0.2%) albeit significant improvement (r2 = 0.952). The relationship between DEE and
bodyweight for different groups of species is shown in Figure 9. The term ‘eutherian’ refers to placental
mammals as distinguished from marsupials (e.g. possum, kangaroo) and egg-layers (e.g. platypus and
echidna). ‘Eutherian – other’ includes all terrestrial mammals (e.g. bat, rat) requiring relatively moist
habitats.
SID 5 (Rev. 3/06)
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Figure 9. Daily energy expenditure plotted against bodyweight for different groups of mammal species.
DEE v Body Weight for 115 mammal species
Daily Energy Expenditure (kJ/day)
100000
Eutherian - desert
Eutherian - marine
non-Eutherian
Eutherian - other
10000
1000
100
10
10
100
1000
10000
Body Weight (g)
100000
The relationship between bodyweight and DEE for the habitat/taxonomic grouping most relevant to risk
assessment (eutherian mammals not living in deserts or the sea) is shown in Figure 10.
Figure 10. Daily energy expenditure plotted against bodyweight for eutherian mammals not living in
deserts or the sea.
DEE v Body weight for 46 non-marine, non-desert Eutherian mammals
log10(DEE) = 0.8136 + 0.7149 log10(Body Weight)
Daily Energy Expenditure (kJ/day)
100000
Regression
95% PI
S
R-Sq
R-Sq(adj)
10000
1000
100
10
10
SID 5 (Rev. 3/06)
100
1000
10000
Body Weight (g)
Page 16 of 23
100000
0.152069
96.8%
96.8%
Allometric equations for calculation of DEE from bodyweight for all mammals and each
habitat/taxonomic grouping are shown in Table 7.
Table 7. Mammals. Relationship between body weight (g) and Daily Energy Expenditure (DEE (kJ)) in
mammals for five groups of mammalian species. The general form of equation is: Log(DEE) = log a + b
 (log Body weight). Insert log10 a and b from the table to obtain the specific equation for the relevant
species group. Also shown are the standard errors for a and b (SE), the number of species in each group
(N), and the proportion of variation explained by each equation (r2).
Group
Non-eutherians
All eutherians
Desert eutherians
Marine eutherians
Other eutherians*
b
SE b
N
r2
0.070
0.045
0.075
1.055
0.046
0.593
0.762
0.785
0.640
0.715
0.022
0.016
0.030
0.215
0.019
32
83
29
8
46
0.958
0.964
0.960
0.528
0.968
0.044
0.719
0.015
115
0.950
Log10 a
SE Log10 a
0.957
0.647
0.451
1.373
0.814
All mammals
0.704
* excluding desert and marine eutherians
It is recommended that the ‘other eutherians’ equation is used for typical risk assessment scenarios.
Relationships between DEE and bodyweight within species
The results above demonstrate a strongly significant relationship between body weight and Daily
Energy Expenditure between different mammalian species. For the purposes of risk assessment we
typically consider individuals within a single focal species. We investigated whether there were any
broad trends indicating that the relationship between bodyweight and DEE scaled differently within a
species according to the habitat grouping or according to species bodyweight.
Of the 115 mammal species for which we have estimates of DEE, there were 58 species with 3 or more
data points enabling us to estimate the relationship between the DEE-bodyweight relationship within a
species and the habitat group to which the species belonged, and also to the mean weight of the species.
Analysis of variance indicated no significant differences between the different habitat groupings (Noneutherian, desert, marine & other) in the slope of DEE-bodyweight regression and the habitat group.
Neither was there any relationship between the slope (based on individual bodyweights) and the mean
bodyweight for the species. Figure 11 below shows the relationship between these within-species slopes
and the mean weight of the species.
SID 5 (Rev. 3/06)
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Figure 11. Relationship between within species slope and bodyweight for 58 species of mammal.
Slope v Body weight for 58 species where a within species
regression of DEE on body weight could be calculated
10
8
6
4
Slope
2
0
-2
0
1
2
3
4
5
6
-4
-6
-8
-10
Log Body Weight (g)
The figure shows that, averaged across species, the slope between individual body weight and DEE was
close to 0. However for smaller mammal species (less than 100g) some species had steep positive
relationships between Body Weight and DEE and others had steep negative relationships such that
heavier individuals expended less energy lighter individuals.
Of the 58 slopes shown in Figure 11, only 14 were statistically significant. These are plotted in Figure
12. Again this suggests that for lighter mammal species there may be a more pronounced relationship
between individual body weights and DEE but the direction of correlation is uncertain.
Figure 12. Relationship between statistically significant within-species slopes and body weight for 14
species of mammal.
Slope v Body weight for 14 species where slope was
statistically siginificant
8
6
Slope
4
2
0
0
1
2
3
-2
-4
-6
Log Body Weight (g)
SID 5 (Rev. 3/06)
Page 18 of 23
4
5
6
On balance it would be unwise to attempt use differences in body weight between individuals of a
species to predict individual differences in energy expenditure. For heavier species there is a relatively
weak relationship between individual bodyweights and DEE. For lighter species there may be strong
relationship within a species that may differ significantly from that between different species, but it is
not possible to make generalizations about the nature of that relationship.
Objective 4. Present the above information to PSD in a form that can be of immediate use in risk
assessments.
Use of results of objectives in risk assessment
The information provided above under Objective 1 can be used to estimate drinking water requirements
for birds of different types (passerines and non-passerines). Further background for this approach can be
obtained from the report on project PS2327.
The information provided under Objectives 2 and 3 can be used to estimate DEE for birds and mammals
as is done currently (using information from project PN0908) but updating the allometric equations with
those developed here. The estimated DEE and food intake information can be used to estimate food
water and metabolic water production values necessary to estimate drinking water requirements.
The new data obtained in this study will be available for use in the WEBFRAM project as appropriate.
Estimates of drinking water requirements for current exposure scenarios
The following are the exposure scenarios for birds described in the current mammals and birds guidance
document (Table 8).
Table 8. Indicator bird species for crops/stages from the birds and mammals guidance document
(adapted from Anon 2002).
Crop
Crop stage
Indicator species
Example
Grassland
-
Large herbivorous bird – 3000g
Goose
Insectivorous bird – 10g
Wren, tit
Large herbivorous bird – 3000g
Goose
Insectivorous bird – 10g
Wren, tit
Late
Insectivorous bird – 10g
Wren, tit
Early / late
Medium herbivorous bird – 300g Partridge, pigeon
Cereals
Leafy crops
Early
Insectivorous bird – 10g
Wren, tit
Orchard / vine / hops
Early / late
Insectivorous bird – 10g
Wren, tit
Seed treatment
-
Granivorous bird – 15g
Linnet
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Methods for estimating food intake are well established and the only change necessary is to adopt the
new allometric equations developed in this study. The following demonstrates how the proposed
methods for estimating drinking water intake are carried out and the results obtained for the currently
used scenarios.
The necessary data required to calculate estimates of drinking water rate are shown in Table 9, and the
estimated drinking water requirements of the indicator species are shown in Table 10. The equations
used for DEE and Water Flux are those developed in this study (see Tables 6 and 2).
Table 9. Data used to calculate drinking water rate (DWR) for indicator species of bird in the birds and
mammal guidance document (Anon 2002).
Species
DEE
Food type
Energy
content - dry
(kJ/g)
Equation
(kJ/d)
Partridge,
pigeon
Terrestrial
(non pass.)
313.5
Non-grass
herbs
Goose
Terrestrial
(non pass.)
1462.8
Wren
Passerine
Linnet
Passerine
Assimilation Efficiency
Water flux
equation
Metabolic water
production
Estimate (ml/kJ)
Group
Value
17.98
Fowl
0.42
All birds
Mean
0.0278
Grasses,
cereal shoots
17.96
Ducks &
Geese
0.41
All birds
Mean
0.0278
51.1
Arthropods
22.60
Passerine
0.76
Passerine
Insect*
0.0257
67.1
Cereal seeds
17.27
Passerine
0.80
Passerine
Seed*
0.0294
* see Table 5.
Table 10. Drinking water rate (DWR) for indicator species of bird in the birds and mammal guidance
document (Anon 2002).
Indicator
species
Example
Body
weight
(g)
Food type FIR (fresh Moisture Food
Water flux
Metabolic DWR DWR/bw
material)
water
water
(g/day)
(%)
(g) Equation Flux
(ml)
(ml/day)
(ml/day)
Non-grass
231.9
82.1
190.4 All birds
91.5
8.7
-107.6
-0.36
herbs
Medium
herbivorous
bird
Partridge,
pigeon
300
Large
herbivorous
bird
Goose
3000
Grasses,
cereal
shoots
841.8
76.4
Insectivorous
bird
Granivorous
bird
Wren
10
Arthropods
10.0
70.3
7.0
Linnet
15
Cereal
seeds
5.6
13.2
0.7
643.1 All birds
478.2
40.7
-205.7
-0.07
Passerine
6.4
1.3
-1.9
-0.19
Passerine
9.7
2.0
6.9
0.46
This indicates that herbivorous birds are unlikely to require drinking water while feeding on plant
material and so an assessment of exposure via this route would not be necessary. The calculation for the
insectivorous bird also suggests that sufficient water could be obtained from food alone, but only just.
This would leave the small granivorous birds as the only scenario where birds would need to find
drinking water based on these estimates. This could be done on the basis of the above crop/species
combination but given the known risk to granivorous birds drinking from some leafy crops (Hommes et
SID 5 (Rev. 3/06)
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al 1990) it would be appropriate to consider these species as well where it is considered that the crop
could provide an attractive source of drinking water (e.g. following irrigation during dry weather).
RECOMMENDATIONS
The allometric equations developed in this study should be used to estimate water flux and DEE as
described above unless suitable data is available for the species under consideration. Despite the
increase in the amount of data used to produce the allometric equations in this study, it is still clear that
there is a lack of information on relevant species at different times of year. For water flux, this is further
complicated by the potential effect of dietary water content on the value measured. This is particularly
important for animals that may feed on a variety of foods at different times of year. There is therefore a
research need for more doubly labelled water studies to be conducted at different times of year and
preferably covering the range of potential diets (e.g. seeds, insects, fruit, soil invertebrates).
It would also be useful to collate data on the composition of wild animal foods (carbohydrate, fat,
protein) and calculate values of metabolic water production for each type. This would allow production
of tables of values such as those available for energy content, moisture content and assimilation
efficiency.
References to published material
9.
This section should be used to record links (hypertext links where possible) or references to other
published material generated by, or relating to this project.
SID 5 (Rev. 3/06)
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REFERENCES
Alkon P U, Degen A A, Pinshow B and Shaw P J (1985) Phenology, diet, and water turnover
rates of Negev desert chukars. Journal of Arid Environments. 9:51-61
Ambrose S J, Bradshaw S D, Withers P C and Murphy D P (1996) Water and energy-balance of
captive and free-ranging spinifexbirds (Eremiornis carteri) north (Aves, Sylviidae) on Barrow
Island, Western Australia. Australian Journal Of Zoology 44:107-117.
Anava A, Kam M, Shkolnik A and Degen A A (2000) Seasonal field metabolic rate and dietary
intake in Arabian Babblers (Turdoides squamiceps) inhabiting extreme deserts. Functional
Ecology 14:607-613
Anon. (2002) Working document Guidance document on Risk Assessment for Birds and
Mammals Council Directive 91/414/EEC SANCO/4145/2002.
(http://europa.eu.int/comm/food/fs/ph_ps/pro/wrkdoc/wrkdoc19_en.pdf)
Costa D P and Prince P A (1987) Foraging energetics of gray-headed albatrosses Diomedeachrysostoma at Bird Island South Georgia south Atlantic Ocean. Ibis 129:149-158.
Crocker D, Hart A, Gurney J and McCoy C. (2002) Methods for estimating daily food intake of
wild birds and mammals.
http://www.pesticides.gov.uk/uploadedfiles/Web_Assets/PSD/Research_PN0908.pdf
Degen A A, Pinshow B and Alkon P U (1983) Summer water turnover rates in free-living
chukars and sand partridges in the Negev desert. Condor 85:333-337.
Dykstra C R and Karasov W H (1993) Nesting energetics of house wrens (Troglodytes-aedon) in
relation to maximal rates of energy-flow. Auk, 110:481-491.
Gabrielsen G W, Taylor J R E, Konarzewski M and Mehlum F (1991) Field and laboratory
metabolism and thermoregulation in dovekies (alle-alle). Auk 108:71-78.
Gabrielsen G W, Mehlum,F and Nagy K A (1987) Daily energy expenditiure and energy
utilization of free ranging Black-legged Kittiwakes (Rissa tridactyla). Condor 89:126-132.
Goldstein D L and Nagy K A (1985) Resource utilisation by desert quail: time, energy, food and
water. Ecology 66:378-387.
Hommes V M, Buchs W, Joermann G and Siebers J. (1990) Vogelgefährdung durch
Planzenschutzmittelrückstände in Blattpfützen von Gemüsekohl (Poisoning risk of birds by
residues of pesticides in leaf puddles of cole crops). Nachrichtenbl. Deut. Pflanzenschutzd,
42:113-117.
Kam M, Degen A A and Nagy K A (1987) Seasonal energy water and food consumption of
Negev chukars and sand partridges. Ecology 68:1029-1037.
Mehlum F, Gabrielsen G W and Nagy K A (1993) Energy-expenditure by black guillemots
(Cepphus-grylle) during chick- rearing. Colonial Waterbirds 16:45-52.
Nagy K A and Peterson C C. (1988) Scaling of Water Flux Rate in Animals. University of
California Press. Berkeley.
Nagy K A, Siegfried W R and Wilson R P (1984) Energy utilization in free-ranging Jackass
penguins, Spheniscus demersus. Ecology 65:1648-1655.
SID 5 (Rev. 3/06)
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Nagy K A and Obst B S (1992) Food and energy-requirements of adelie penguins (Pygoscelisadeliae) on the Antarctic peninsula. Physiological Zoology 65:1271-1284.
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