Graeme Caughley

Directions in conservation biology

Conservation biology has two threads: the small-population paradigm which deals with the erect of smallness on the persistence of a population, and the declining-population paradigm which deals with the cause of smallness and its cure. The processes relevant to the small-population paradigm are amenable to theoretical examination because they generalize across species and are subsumed by an inclusive higher category: stochasticity. In contrast, the processes relevant to the declining-population paradigm are essentially humdrum, being not one but many. So far they have defied tight generalization and hence are of scant theoretical interest. The small-population paradigm has not yet contributed significantly to conserving endangered species in the wild because it treats an erect (smallness) as if it were a cause

Mortality Patterns in Mammals

Methods of obtaining life table data are outlined and the assumptions implicit in such treatment are defined. Most treatments assume a stationary are distribution, but published methods of testing the stationary nature of a single distribution are invalid. Samples from natural populations tend to be biased in the yound age classes and therefore, because it is least affected by bias, the mortality rate curve (qx) is the most efficient life table series for comparing the pattern of mortality with age in different populations. A life table and fecundity table are presented for females of the ungulate Hemitragus jemlahicus, based on a population sample that was first tested for bias. They give estimates of mean generation length as 5.4 yr, annual mortality rate as 0.25, and mean life expectancy at birth as 3.5 yr. The life table for Hemitragus is compared with those of Ovis aries, O. dalli, man, Rattus norvegicus, Micortus agrestis, and M. orcadensis to show that despite taxonomic and ecological differences the life table have common characteristics. This suggests the hypotheses that most mammalian species have life tables of a common form, and that the pattern of age—specific mortality within species assumes an approximately constant form irrespective of the proximate causes of mortality.

Bias in Aerial Survey

Aerial censuses of large mammals are inaccurate because the observer misses a significant number of animals on the transect. The accuracy deteriorates progressively with increasing width of transect, cruising speed, and altitude. Methods of eliminating bias by refining techniques are discussed and rejected; there seems to be no technical solution. An alternative strategy is to measure the bias and correct the estimates accordingly. A method is suggested for estimating bias during an aerial census, the subsequent analysis returning an unbiased estimate of density. No direct measure of true density is needed and little extra effort is involved over that required for a standard aerial survey. J. WILDL. MANAGE. 38(4):927-933 This paper examines the effect of visibility bias, discusses the means by which the bias arises, and suggests methods by which it might be eliminated from aerial survey estimates of density and population size. Aerial survey is, at best, a rough method of estimating the size of a population. Most efforts at refinement have been aimed at raising the precision of the estimate by combining impeccable survey design, high sampling intensity, intricate stratification, and powerful methods of analysis. This trend can be traced back to Siniff and Skoogs (1964) superb paper on an aerial census of caribou (Rangifer tarandus). Their use of stratified random sampling, with sampling effort allocated proportional to density, contrasted markedly with the crudity of previously reported surveys. Subsequently, Jollys (1969a) paper on designs and analyses appropriate to aerial survey has encouraged a rigorous and disciplined application of the method. Recent papers following this lead have tended to treat the difficulties of estimating population size from the air largely as constituting a sampling problem, a survey being rated successful or otherwise according to the size of the estimates standard error. Tacitly, the standard error was treated as a measure of the estimates accuracy rather than of its repeatability. Underlying the preoccupation with precision there often lurked an implicit assumption that the estimate is free of bias, that the observers counted all animals on each sampled

Experiments in Aerial Survey

By use of balanced experiments amenable to analysis of variance we explored the effects of several factors on the accuracy of aerial survey estimates of animal density. Speed, height above ground, transect width, and observers had significant effects, whereas time of day, fatigue of observers, and length of survey were less important. We tested the hypothesis that a regression of observed density on speed, height, and transect width could be extrapolated backward to estimate true density at zero values of these survey variables. The results were generally consistent with this expectation. The uses of this technique are outlined, with examples, in the context of correcting an observed density to an estimate of true density, of calibrating one observer against another, and of comparing the results from aerial surveys flown at different speeds and heights, and with different widths of transect. The field experiments utilized red kangaroos (Megaleia rufa) at unknown densities and domestic sheep at known densities. Laboratory experiments were performed on dots of known density projected onto a screen. Before the model is accepted as generally applicable it must be tested against several other species in a variety of habitats. J. WILDL. MANAGE. 40(2):290-300 During the early stages of World War II, Royal Air Force crews experienced great difficulty in hunting German submarines in the Bay of Biscay. Their flight path seldom passed near a surfaced submarine and even when it did they often failed to see it. This and related problems forced the birth of an infant branch of applied mathematics called operations research. It swiftly reduced the problem of hunting submarines to its theoretical essentials (Morse and Kimball 1960). A similar problem, amenable to similar treatment, is posed by the difficulty of estimating animal populations from the air. Over the last decade evidence has accumulated steadily to show that aerial surveys provide underestimates, often gross underestimates, of animal density. These data from a broad range of species and habitats were summarized by Caughley (1974). While that paper was in press, further evidence appeared (LeResche and Rausch 1974) showing that the accuracy of counting moose (Alces alces) from the air was influenced significantly by a number of factors. Experienced observers counted only 68 percent of moose on a quadrat; inexperienced observers counted 43 percent. This degree of error is completely unacceptable when the aim is to calculate population size as a prelude to estimating a sustained yield, but there are strong grounds for suspecting that the error cannot be eliminated by refining techniques, by elaborating the survey design, or by hoping that a technological solution such as greatly improved infrared sensing is just around the corner. The alternative is to accept that the best of observers, in the most favorable conditions of observation, will fail to observe many of the animals at which he is staring. This was the starting point of an approach introduced previously (Caughley 1974) in which partial regression analysis was suggested as a means of estimating true density. Since sightability declines with increasing speed, height above ground, and transect width, a regression of observed density on these variables should estimate true density as the y-intercept constant a. 1Study supported by the Australian Research Grants Committee. 290 J. Wildl. Manage. 40 (2) :1976 This content downloaded from 157.55.39.255 on Mon, 01 Aug 2016 06:11:50 UTC All use subject to http://about.jstor.org/terms EXPERIMENTS IN AERIAL SURVEY * Caughley et al. 291 Table 1. Experimental design for the field surveys. Area Transect Sampling surveyed Height Speed width unit Expt. (km2) (m) (km/h) (m) (0.5 km2) Species Observer 1 50 46 129 50 858 red kangaroos G.C., R.S. 91 161 100 858 183 193 200 858 2 70 46 161 100 864 red and grey G.C., D.S. 91 193 200 864 (Macropus giganteus) 183 193 200 864 kangaroos 3 16 46 161 100 576 sheep G.C., D.S. 91 193 200 576 183 193 200 576 The purpose of the present paper is to explore this hypothesis in greater depth by determining the relationship between observed density and the survey variables, and the extent to which these relationships change as viewing conditions change. Finally, we will show that factors can be calculated by partial regression to correct standard aerial survey estimates for visibility bias. This paper reports the results from four experiments in aerial survey. Since the design of each, and the need for the experiment itself, was dictated by the results of the previous experiment, these will be reported in chronological order. We are grateful to T. Mathews who piloted for the experiments; to G. Wilson for survey piloting; to R. Goldsby, E. McLaughlin, and N. Crisp for providing us with the facilities of their stations and for their hospitality; and to G. Courtice for assisting in the simulation experiment.

Kangaroos, their ecology and management in the sheep rangelands of Australia

Preface 1. Introduction to the sheep rangelands Graeme Caughley 2. The environment of the Australian sheep rangelands Graham Robertson, Jeff Short and Greg Wellard 3. The effect of weather on soil moisture and plant growth in the arid zone Greg Wellard 4. Plant dynamics Graham Robertson 5. The diet of herbivores in the sheep rangelands R. D. Barker 6. Factors affecting food intake of rangelands herbivores Jeff Short 7. The mobility and habitat utilisation of kangaroos David Priddel 8. Kangaroo dynamics Peter Bayliss 9. Condition and recruitment of kangaroos Neil Shepherd 10. Ecological relationships Graeme Caughley 11. Options for management of kangaroos Neil Shepherd and Graeme Caughley Indices.

Are big mammals simply little mammals writ large

SummaryPopulations are regulated intrinsically (self-regulated) when the animals lower their rate of increase behaviorally or physiologically as a reaction to rising density. They are regulated extrinsically if the equilibrium is a mechanical consequence of interaction between the population and the organisms providing its food. We suggest that, at least for mammalian herbivores, self-regulation is unlikely to evolve unless the populations intrinsic rate of increase exceeds about 0.45 on a yearly basis. That value corresponds to a body weight of about 30 kg, the intrinsic rate being related inversely to body weight by rm=1.5 W-0.36 with W in kg.The two dynamic strategies, self-regulation and extrinsic regulation, should enforce a bimodality of the frequency distribution of observed intrinsic rates of increase. This in turn might be reflected in a bimodality of body sizes, the smaller herbivores constituting the lower mode generally showing intrinsic regulation and the larger herbivores of the upper mode generally being regulated by extrinsic mechanisms. There is some empirical support for these predictions but it is by no means clearcut.Mechanisms of self-regulation can evolve either by individual or group selection. Individual selection may act in two ways. By inhibiting their neighbours with some form of interference, individuals may increase their relative fitness without increasing their reproductive rate. Alternatively, individual selection may raise the absolute fitness of individuals and thereby raise the populationss intrinsic rate of increase. The population is destabilized if that process continues beyond a certain threshold and the population is then at significant risk of extinction at the troughs of the consequent oscillations. Selection between such populations will favour those carrying the beginnings of a self-regulating mechanism, and with that mechanism strengthened and fixed by continuing group selection, individual selection is again freed of the dynamic restraints on raising further the intrinsic rate of increase.

Does dingo predation control the densities of kangaroos and emus

The density of red kangaroos in the sheep country of the north-west corner of New South Wales is much higher now that it was last century. It is also much higher than the present density across the dingo fence in the adjacent cattle country of South Australia and Queensland. The picture is similar for emus. Farther east, about halfway along the New South Wales–Queensland border, no difference in density between the two States could be detected for red kangaroos, grey kangaroos or emus. We examine and discard several hypotheses to account for the density contrasts in the west and the lack of them farther east, deeming it unlikely that the pattern reflects environmental gradients, or differences in plant composition and growth, hunting pressure or availability of water. Instead, we favour this hypothesis: that the past and present patterns of density are attributable directly to predation by dingoes, which can hold kangaroos at very low density in open country if the dingoes have access to an abundant alternative prey.

Rate of Increase

We outline the differences between three notions of rate of increase: r, the observed rate of increase; rs, the rate implied by the prevailing schedules of survival and fecundity, and rm, the maximum rate at which a population with a stable age distribution can increase in a specifie,d environment. Several mammalogists recently calculated r8 for natural populations under the misapprehension that they were calculating rm. However, their calculated values are usually de£ective even as r8 essdmates because they have used life tables constructed from age distributions or from distributions of age at death. The estimates are thereby infiltrated by the unrecognized but implicit assumption that rs = O, and the calculated values of ss are therefore assumptions retrieved as conclusions. If rm, the intrinsic rate of increase, is to be detelmined for a natural population of mammals, it is best calculated either by measuring the rate at which a newly established population initially increases or by fitting a curve to the growth of a population after its density has been artificially reduced. Over about the last 10 years mammalogists have become increasingly interested in population dynamics, and they have begun using concepts and analyses hitherto restricted almost entirely to the demegraphy of man and inselets. This is a welcome trend that should be continued7 but at the same time a warning is necessary against uncritically using special methods of analysis that may not be appropriate to field studies of mammals. By this we do not imply that the demagraphy of mammals differs esslentially from that of insects; we suggest only that some parameters, wholse estimation is relevant to questions that entomalogists ask) may be extremely difficult to estimate for natural populations of mammals. In addition, the paramleters may be irrelevant to the problem in hand. The reverse is also true. Mammalogists ask questions on conservation and harvesting, for example, that entomalogists seldom consider, and they must therefore estimate parameters that entomologists ignore. In this paper we examine some of the ways in which rate of increase can be measured and point out that some mammalolgists, using equations that are valid and widely used in insect ecology, are estimating it incorrectly. 658 KINDS OF RATE OF INCREASE Because populations tend to grow geometrically, rate of increase is best expressed in expl(}nential form. A population of 100 animals ffiat increases to 200 over a year has been multiplied by 2 or increased by 100 percent, however o!ne prefers to express it. Its exponential rate of increase, r, is given by

A Double-Survey Estimate Of Population Size From Incomplete Counts

We sought to estimate the number of crocodile nests on the Liverpool River, Northern Territory, Australia. Two methods of survey were available, aerial survey and ground survey, in each of which sightings could be mapped. Hence, those nests which were detected by both methods could be identified. These counts were used to demonstrate the method by which an estimate of total number could be calculated. The assumptions and limitations are discussed.

Reindeer on South Georgia : the ecology of an introduced population

Preface Acknowledgements Part I. Reindeer in the Arctic and Antarctic: 1. Reindeer and caribou: taxonomy, habitats and ecology 2. The introduction of mammals to new environments 3. South Georgia: the island and its reindeer Part II. Biology of reindeer: 4. Reproduction 5. Food habits 6. Growth and body condition 7. Causes of mortality Part III. Ecology of an introduction: 8. Irruptions of reindeer 9. Impact of reindeer on vegetation 10. Introduced mammals on southern islands Part IV. Overview: References Index.