13, 73-76. Barker, A. M., Brown, N. J. & Reynolds, C. J. M. (1999) Do host-plant requirements and mortality from soil cultivation determine the distribution of graminivorous sawflies on farmland? Journal of Applied Ecology 36




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    1. References

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Moreby, S.J. & Southway, S. (2002) Cropping and year effects on the availability of invertebrate groups important in the diet of nestling farmland birds. Aspects of Applied Biology, 67, 107-112.

Moreby, S.J. & Stoate, C. (2001) Relative abundance of invertebrate taxa in the nestling diet of three farmland passerine species, Dunnock Prunella modularis, Whitethroat Sylvia communis and Yellowhammer Emberiza citrinella in Leicestershire, England. Agriculture, Ecosystems & Environment, 86, 125-134.

Moreby, S.J., Aebischer, N. J., Southway, S. E., & Sotherton, N. W. (1994) A comparison of the flora and arthropod fauna of organically and conventionally grown winter-wheat in southern England. Annals of Applied Biology, 125, 13-27.

Pascual, J.A., Hart, A.D.M., Saunders, P. J., McKay, H. V., Kilpatrick, J. & Prosser, P. (1999a) Agricultural methods to reduce the risk to birds from cereal seed treatments. I. Sowing depth manipulation. Agriculture, Ecosystems and Environment, 72, 59-73.

Pascual, J.A., Saunders, P.J., Hart, A.D.M. & Mottram, J. (1999b) Agricultural methods to reduce the risk to birds from cereal seed treatments. II. Rolling and harrowing as post-sowing cultivations. Agriculture, Ecosystems and Environment, 72, 75-86.

Powell, W., Walton, M.P. & Jervis, M.A. (1996) Populations and Communities. Insect natural enemies. Practical approaches to their study and evaluation (eds M. Jervis & N. Kidd). Chapman & Hall, London.

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Robinson, R.A. & Sutherland, W.J. (1997) The feeding ecology of seed-eating birds on farmland in winter. The ecology and conservation of cornbuntings (eds Donald P.F. & Aebischer N.J.), UK Nature conservation, 13, 162-169.

Robinson, R.A. & Sutherland, W.J. (1999) The winter distribution of seed eating birds: habitat structure, seed density and seasonal depletion. Ecography, 22, 447-454.

Sheldon, R.D., Chaney, K. & Tyler, G. (2002) Lapwings, earthworms and agriculture. Aspects of Applied Biology, 67, 93-106.

Snoo, G.R. d. & de Leeuw, J. (1996) Non-target insects in unsprayed cereal edges and aphid dispersal to the adjacent crop. Journal of Applied Entomology, 120, 501-504.

Southwood, T.R.E. (1978) Ecological Methods. Chapman & Hall, London.

Standen, V. (2000) The adequacy of collecting techniques for estimating species richness of grassland invertebrates. Journal of Applied Ecology, 37, 884-893.

Stewart, A.J.A. & Wright, A.F. (1995) A new inexpensive suction apparatus for sampling arthropods in grassland. Ecological Entomology, 20, 98-102.

Stoate, C., Moreby, S.J. & Szczur, J. (1998) Breeding ecology of farmland Yellowhammers Emberiza citrinella. Bird Study, 45, 109-121.

Sunderland, K. D. & Topping, C. J. (1995) Estimating population densities of spiders in cereals. Arthropod natural enemies in arable land, I. Density, spatial heterogeneity and dispersal (eds S. Toft & W. Riedel). Acta Jutlandica, 9, 13-22.

Sunderland, K.D., De Snoo, G.R., Dinter, A., Hance, T., Helenius J., Jepson, P., Kromp, B. Lys, J.-A., Samu, F., Sotherton, N.W., Toft S. & Ulber, B. (1995) Density estimation for invertebrate predators in agroecosystems. Arthropod natural enemies in arable land. I. Density, spatial heterogeneity and dispersal. (eds S. Toft & W. Riedel) Acta Jutlandica ,70, 133-162.

Thornhill, E.W. (1978) A motorised insect sampler. Pest articles and News Summaries, 24, 205-207.

Tones, S.J., Ellis, S.E., Oakley, J.N., Powell, W., Stevenson, A. & Walters, K. (2000) Use of unsprayed buffer zones in arable crops to protect terrestrial non-target invertebrates in field margins against spray-drift effects. MAFF Report PS0418.

Tucker, G.M. (1992) Effects of agricultural practices on field use by invertebrate feeding birds in winter. Journal of Applied Ecology, 29, 779-790.

Village, A. & Westwood, N.J. (1994) The relationship of lapwing Vanellus vanellus numbers and feeding rates to earthworm numbers in arable and pasture fields in autumn and winter. The ecology and conservation of lapwings Vanellus vanellus (eds G.M. Tucker, S.M. Davies & R.J. Fuller), pp. 47-48. Joint Nature Conservation Committee, Peterborough (UK Nature Conservation, No 9).

Wakeham-Dawson, A. & Aebischer, N.J. (1998) Factors determining winter densities of birds on Environmentally Sensitive Area arable reversion grassland in Southern England, with special reference to skylarks (Alauda arvensis). Agriculture, Ecosystems & Environment, 70, 189-201.

Watson, A., & Rae, S. (1997) Preliminary results from a study of habitat selection and population size of Corn buntings Miliaria calandra in North Eastern Scotland. The ecology of corn buntings (eds P.F. Donald & N.J. Aebischer), pp. 115-123. UK Nature conservation, 13.

Wilson, B.J., Peters, N.C.B., Wright, K.J. & Atkins, H.A. (1988) The influence of crop competition on the seed production of Lamium purpureum, Viola arvensis and Papaver rhoeas in winter wheat. Aspects of Applied Biology, 18, 71-80.



APPENDIX 7. Construction of models for birds foraging in the breeding season

7.1 Introduction
Depletion of food resources acts to limit the number of individuals that can use an area, as depletion is related to the density of foragers (Sutherland & Anderson 1993). Such models have been used to predict the number of individuals able to use an area (e.g. Sutherland & Allport 1994), but their use has generally been restricted to the non-breeding season.
In the breeding season two extra processes need to be incorporated, resource renewal, for example insects can hatch at a rapid rate, and birds have to provide for their young. It is also necessary to distinguish between species with altricial (young stay in the nest) and precocial (young leave the nest) young. Parents of altricial species return with food to the nest and depletion of food patches close to the nest will require longer travel times to more distant patches. Increased travel time will restrict the feeding rate and potentially impact on chick survival. For precocial species, travel time may be less important as young move with their parents, although there may be a cost to moving between patches. Localised depletion may mean birds have to move more frequently, increasing costs, e.g. mortality due to predation.
The theory for applying depletion models to the breeding season, and to primarily insectivorous foraging, has not been developed, and there are unlikely to be any species for which sufficient data are presently available to enable them to be constructed in detail for particular species. Here we construct an outline framework showing the principles that would need to be developed to apply such models in the breeding season. We do this separately for altricial and precocial species.
7.2 Modelling framework
7.2.1 altricial species
The primary consideration for altricial species is that birds are constrained to forage within a certain distance of the nest. Thus, the foraging behaviour of the adult birds is likely to be that of a central place forager (Charnov 1976), in that foraging rate is not directly maximised, rather the currency of interest is rate of return at the nest (the central place), or the provisioning rate. An outline of the model structure required to link depletion models with reproductive output for a multi-brooded altricial species is presented in Figure 7.1 and a simplified computer program pseudo-code in Table 7.1.
In essence, a bird forages on a renewing food supply over a patchy landscape for the duration of the breeding season, returning to a nest located in the landscape after each foraging trip. Intake during each foraging period is characterised by a functional response (Holling 1959), which relates density of food in the patch to intake rate, as a function of the bird’s search efficiency and the handling time for each item. This food is brought back to the nest and, after the adult’s dietary requirements are satisfied, fed to the young enabling them to grow. The final weight and condition of the chicks may influence their chances of surviving during the period following fledging. For the

Figure 7. 1. Outline of depletion model linking food resources to reproductive output in an altricial species.

duration of the breeding attempt, the nest has a certain chance of daily failure, through for example predation or disturbance from agricultural operations. Once one breeding attempt has finished, the birds commence another. The number and/or condition of chicks produced over the season can then be assessed. A number of additional factors can easily be incorporated within this framework, such as habitat specific predation, of either nests or adults, and density dependent competition, in the form of other nesting pairs, but these do not fundamentally change the model paradigm.

Many of the key measurements required to fully parameterise these models have not been made; here we briefly outline the major gaps in our knowledge.


Key to the models is knowledge of the distribution and dynamics of the insect prey, particularly that fraction actually taken by birds. Detailed autecological studies of a number of species, e.g. corn bunting Miliaria calandra (Brickle et al. 2000) and linnet Carduelis cannabina (Moorcroft 2001) provide some information on the prey types birds forage on during the breeding season in particular localities. However, little information exists on how this prey choice varies with the availability of different prey types, which is likely to affect foraging patterns. Some information on insect distribution on farmland is available, particularly for conspicuous groups, such as Lepidoptera (Feber et al. 1996). In general, management intensity affects the size and composition of the invertebrate community (Beintema et al. 1990; Lee et al. 2001), but effects are likely to be species-specific and detailed knowledge of how insect populations respond to different management practices is generally lacking (e.g. Holland et al. 1994; Vickery et al. 2001).
The ‘renewal rate’ of local populations of insects is also poorly understood, but may be easier to determine for univoltine or bivoltine species, such as Lepidoptera, the larvae of which are an important prey source for many bird species (Cramp & Perrins 1998). The local renewal rate is also likely to be heavily influenced by stochastic processes, such as weather events.
Where food resources vary in density, a bird’s intake rate is determined by the functional response (Holling 1959). This response is often asymptotic, reflecting a constraint imposed by the speed with which birds can handle individual items. This is most commonly represented by a ‘Type 2’ response, with intake rate in relation to prey density increasing to a constant maximum determined by the handling time. The initial rate of increase is determined by a parameter describing the search efficiency of the predator. The shape of this response for birds foraging on insects is extremely difficult to characterise due to difficulties in assessing local resource density and determining what represents potentially available prey. However, it may be possible to determine the functional response under a limited range of scenarios and generalise this across species, as is currently being done for waders (Stillman in litt.).
In at least some species, birds nesting in areas with greater food resources tend to produce chicks that are in better condition, often heavier (e.g. Brickle et al. 2000) and it is likely that chick condition influences probability of fledging (Sheldon et al. 1997) and subsequent post-fledging survival (Davies 1986; Tinbergen & Boerljist 1990). However, the exact nature of this relationship is largely unknown, and some studies fail to find a correlation between provisioning rate and chick condition (Sundberg & Larsson 1994), or between chick condition and survival (e.g. Nishiumi et al. 1996). This may reflect a greater variability in nutritional content between invertebrate taxa, so there are unlikely to be simple relationships between number of items or biomass brought back to the nest and chick growth. Understanding this relationship is likely to require detailed autecological studies.
If these key parameters can be determined, then such models could be used to determine the effects of proposed management changes on bird’s reproductive output. A simple example of the general nature of the model is shown in Figure 7.2. Although not parameterised with ‘real’ figures for each of the parameters, values were chosen to mimic the addition of some form of improved arable margin management, which increased the insect resources available to a generic passerine. As might be expected, increasing food resources meant that more food was brought back to the nest, and hence the chicks fledged in better condition. However, the increase was not linear. Thus, for example, if agricultural management reduces food resources, initially this may have little detectable effect on reproductive output, but after continued adverse management, chick condition will deteriorate increasingly rapidly, without remedial action.
(a)

(b)



Figure 7.2. Effect of increased food resources on reproductive output of a multi-brooded altricial species. (a) Net daily intake rate of adult birds during the breeding season. Points – low food, open circles – medium food, filled circles – high food (b) Daily chick ‘condition’ measured relative to hatching weight (after 15 days in the egg). Solid line – low food, dashed line – medium food, dotted line – high food. In all cases, food density in ‘fields’ is 1 unit, food density in ‘margins’ is low - 2 units, medium - 5 units and high 8 units.ha-1.
7.2.2 precocial species
A similar modelling framework can be applied to precocial species, which are typically game birds. In Britain, the primary example of such a species is the grey partridge Perdix perdix. Species with precocial young do not forage and return to the nest as the young leave the nest at a very early age, rather adults and young move through the landscape foraging as they go. Thus, a central place foraging model, as used for altricial species, is not appropriate. Because individuals move extensively, the risk of predation is much greater than for altricial species, and this is likely to affect foraging decisions (e.g. Lima & Dill 1990). Consequently, returns from foraging in different patches are expressed not in terms of net intake rate, but rather in terms of overall survival, which represents a trade-off between risk of starvation and risk of predation. Of course if other mortality factors are important, such as risk from disease transmission (e.g. Tompkins et al. 2000), these can be incorporated too.
To demonstrate the application of such a model, we have constructed a model in the form of computer pseudocode for a generic precocial species (Table 7.2), which is based partly on the case of the grey partridge (Potts 1986). Essentially, the adult birds move each time period to the area where they obtain the greatest intake, balanced by the risk of mortality from predation, within a given movement distance. The chicks then forage within a given distance from the adult, again balancing intake rate with risk of predation. Predation risk varies between habitats; in this simplified model, foraging in fields is associated with a higher predation risk than in hedgerows. For simplicity the model considers only the period following fledging when the chicks are dependent on the adults, in this case 100 days, which is the length of time grey partridge chicks take to gain adult weight. As with the altricial species, this simple model serves only to highlight the minimum data requirements to implement depletion models in this context.
Clearly, as with the altricial species model, key to implementing depletion models is knowledge both of prey distribution and the renewal rate of insect populations. Much work has been done in this regard as part of a study on grey partridges (Potts 1986; Aebischer 1991). In fact, this work clearly demonstrates a link between (insect) food supply and chick survival, which has been linked to the decline of the grey partridge (Potts & Aebischer 1995). However, this work has largely been done at a field scale and much finer scale detail would be needed to model foraging behaviour at the individual patch level. Foraging strategy would also need to be quantified. The present model assumes birds simply move directly to the optimal foraging patch, in reality movement is likely to be constrained by landscape features, such as the presence of hedgerows. Due to its status as a game species, some information on chick growth rates of the grey partridge in relation to intake rates have been published and how this related to survival (e.g. Borg & Toft 2000; Southwood & Cross 2002) have been published, allowing models of foraging strategies to be linked to population dynamics.
To illustrate the principle of these models, we constructed a simplified implementation of the pseudocode presented in Table 7.2. As with the model for altricial species, the ‘landscape’ consisted of an array of patches classified simply as ‘hedgerows’ (with high food and low predation risk) or ‘fields’. Adult birds foraged across this landscape, with each chick in the brood foraging within a limited distance of the parent bird. As an example of a management policy, we simulated increased planting of hedgerows, by doubling the presence of these patches in the landscape array. This increased reproductive output as measured by mean chick condition (Figure 7.3), though not proportionately and seemed to have little effect on final brood size. This seemed to be because chicks foraged close to the parent bird, thus much of the increased habitat was effectively unavailable to them. In cases where high predation risk coincided with high food supply, greater differences may have been seen as the distribution of survival probabilities across patches will be more equal.
Figure 7.3. Effect of landscape composition on chick growth of a generic precocial bird. In each case an artificial landscape of 100 x 100 patches was constructed, with patches being classified as ‘field’ (low food, high predation risk) and ‘hedgerow’ (high food, low predation risk). A parent bird with 15 young foraged across this landscape, according to the model outlined in Table 7.2. Open circles, ‘hedgerows’ constitute 19% of the landscape patches; Filled circles ‘hedgerows’ constitute 38% of the landscape patches.


7.3 Conclusions
In this section, we have demonstrated how to construct depletion-based foraging models for generic altricial and precocial birds during the breeding season. By modelling food supply directly, such models are often context independent and can thus be applied under a greater range of conditions than habitat-bird density association models, which often lack transituationality. Additional aspects of biology, such as nest-site choice and vulnerability to predation can be built into these foraging based models. A key future requirement is determining whether depletion, and foraging processes more generally, are important in determining numbers of individuals occurring in an area during the breeding season, or whether other factors, such as nest site limitation are more important.
Although the depletion models are reasonably straightforward to construct in general form, information pertaining to much of the detailed structure and parameter values required is lacking. This brief review highlights key areas where information is lacking and future research could profitably be directed:


  • Invertebrate distribution and renewal rates. Although some information exists, such as the work of the Game Conservancy in Sussex (Aebischer 1991; Ewald & Aebischer 1999) and various studies relating to different farming regimes (e.g. Holland et al. 1994), this has largely been at a field scale, a much finer resolution may be required to implement depletion models effectively. In particular, information on the effects of management practices on invertebrate numbers tends to be scattered (e.g. Wilson et al. 1999; Vickery et al. 2001), with results often depending on particular site characteristics (e.g. Holland & Luff 2000).




  • Dietary requirements of chicks The broad dietary range of chicks is known for most species, at least in Britain (Cramp & Perrins 1998). However, how and whether parents choose between different taxonomic groups of invertebrates (at all levels) and how this relates to the relative availability of each is largely unknown. If preference for individual taxa within broader groups is roughly uniform, or based on some simple criteria, such as size, then implementing depletion models will be much more straightforward.




  • The nature of the functional response This is the critical feature of the depletion modelling paradigm. A few studies have characterised the functional response of farmland birds foraging in winter, as food supply (seeds) and foraging behaviour are relatively easy to quantify (Barnard 1980; Robinson 2001). Although quantifying this response in the breeding season is likely to be more challenging, developing general functional response models is likely to lead to a much improved capacity to predict the effects of novel management practices (e.g. Watkinson et al. 2000).




  • Chick growth responses Although food delivery rate must be the key factor determining chick growth rates and condition, amongst passerines many studies find little or no correlation between provisioning rates and measures of chick growth rate or condition. This is likely to related to issues of diet choice and quality discussed above. The relationship between chick condition and survival, both in the nest and, particularly, in the period immediately following fledging is poorly understood. Understanding these processes will be critical to link foraging based models to population dynamics. Largely, because of it’s status as a gamebird, and methodological reasons, such relationships are better understood for the grey partridge.

Depletion models have been used with great success in predicting population sizes in given areas, at least at local regional scales (Sutherland 1996; Gill et al. 2001). The data currently do not exist to fully parameterise a depletion-based model of bird populations during the breeding season. However, although the data required are challenging to generate, some targeted fieldwork, together with a thorough survey of the current literature and some plausible assumptions should enable some simplified models to be constructed in the near future, which could be incorporated into wider population management models.


7.4 References
Aebischer, N.J. (1991) Twenty years of monitoring invertebrates and weeds in cereal fields in Sussex. The ecology of temperate cereal fields (eds L.G. Firbank, N. Carter, J.F. Darbyshire & G.R. Potts), pp. 305-332. Blackwell Scientific Publishers, Oxford.

Barnard, C.J. (1980) Flock feeding and time budgets in the house sparrow (Passer domesticus). Animal Behaviour, 28, 295-309.

Beintema, A.J., Thissen, J.B., Tensen, D. & Visser, G.H. (1990) Feeding ecology of charadrii from chicks in agricultural grassland. Ardea, 79, 31-44.

Borg, C. & Toft, S. (2000) Importance of insect prey quality for grey partridge chicks Perdix perdix: a self-selection experiment. Journal of Applied Ecology, 37, 557-563.

Brickle, N.W., Harper, D.G.C., Aebischer, N.J. & Cockayne, S.H. (2000) Effects of agricultural intensification on the breeding success of corn buntings Miliaria calandra. Journal of Applied Ecology, 37, 742-755.

Charnov, E.L. (1976) Optimal foraging: the marginal value theorem. Theoretical Population Biology, 9, 129-136.

Cramp, S. & Perrins, C.M. (1998) The birds of the Western Palaearctic, Concise Edition. Oxford University Press, Oxford.

Davies, N.B. (1986) Reproductive success of dunnocks, Prunella modularis, in a variable mating system. 1. Factors influencing provisioning rate, nestling weight and fledging success. Journal of Animal Ecology, 55, 123-138.

Ewald, J.A. & Aebischer, N.J. (2000). Trends in pesticide use and efficacy during 26 years of changing agriculture in southern England. Environmental Monitoring and Assessment, 64, 493-539.

Feber, R., Smith, A. & Macdonald, D.W. (1996) The effects on butterfly abundance of the management of uncropped edges of arable fields. Journal of Applied Ecology, 33, 1191-1205.

Holland, J.M. & Luff, M.L. (2000) The effects of agricultural practices on Carabidae in temperate ecosystems. Integrated Pest Management Reviews, 21, 1-21.

Holland, J.M., Frampton, G.K., Çilgi, T. & Wratten, S.D. (1994) Arable acronyms analysed – a review of integrated farming system research in Western Europe. Annals of Applied Biology, 125, 399-438.

Holling, C.S. (1959) Some characteristics of simple types of predation and parasitism. The Canadian Entomologist, 61, 385-398.

Lee, J.C., Menalled, F.D. & Landis, D.A. (2001) Refuge habitats modify impact of insecticide disturbance on carabid beetle communities. Journal of Applied Ecology, 38, 472-483.

Lima, S. L., Dill, L. M., 1990. Behavioral decisions made under the risk of predation: a review and prospectus. Canadian Journal of Zoology, 68, 619-640.

Moorcroft 2001

Nishiumi, I., Ymagishi, S., Maekawa, H. & Shimoda, C. (1996) paternal expenditure is related to to brood sex ratio in polygynous great reed warblers. Behavioural Ecology & Sociobiology, 39, 211-217.

Potts, G.R. (1986) The partridge. Pesticides, predation and conservation. Blackwell Scientific Publications, Oxford.

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Table 7. 1 Pseudocode of depletion model linking food resources to reproductive output in an altricial species.

Define parameters

Clutch Size

Length of nest periods

Unit travel cost

Functional response parameters

Daily energy requirements

Daily nest predation risk

Resource renewal rate
main() {

define and initialise variables

define landscape array and initialise food resources in each cell[i,j]
foreach day {

if ( random() < predation risk )

restart nesting attempt

foreach time period {

max intake = 0

foreach gridsquare {

if ( within foraging distance )

calculate travel cost(nest location, cell[i,j])

else

next gridsquare



calculate_intake rate(a,h,cell[i,j])

if ( intake – travel cost > max intake )

update max intake

else


next gridsquare

}

deplete food in cell[i,j] with max intake



increment food in all cells by renewal rate

if ( in egg stage )

check adult intake sufficient

if ( in young stage )

check adult intake sufficient

feed chicks(net intake)

else

fledge chicks(chick condition)



restart nesting attempt)

}

}



print results (reproductive output)

}
Table 2 Pseudocode of depletion model linking food resources to reproductive output in a precocial species.

Define parameters

Unit travel cost

Functional response parameters

Resource renewal rate


main() {

define and initialise variables

define landscape array and initialise food resources in each cell[i,j]
foreach day {

foreach time period {

max intake = 0

foreach adult {

foreach gridsquare {

if ( within travel distance )

calculate intake rates(a,h,cell[i,j])

calculate survival probability

else

next gridsquare



if ( P[survival] > max P[survival] )

update max P[survival]

else

next gridsquare



}

move birds to cell with max P[survival]

foreach chick {

foreach gridsquare {

if ( within travel distance )

calculate intake rates(a,h,cell[i,j])

calculate survival probability

else


next gridsquare

if ( P[survival] > max P[survival] )

update max P[survival]

else


next gridsquare

}

deplete food in cell[i,j] with max intake



}

}

increment food in all cells by renewal rate



foreach bird and chick

if random() < Psurvival then bird dies

else increment bird growth(intake)

}

}



print results (reproductive output)

}


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