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Biological Conservation 75 (1996) 93-101

ELSEVIER

0006-3207(95)00031-3

© 1995 Elsevier Science Limited Printed in Great Britain. All rights reserved 0006-3207/96515.00+.00

CONSERVATION IMPLICATIONS OF LONG-TERM POPULATION TRENDS, ENVIRONMENTAL CORRELATES AND PREDICTIVE MODELS FOR NAMAQUA SANDGROUSE Pterocles namaqua Robin M. Little, Timothy M. Crowe FitzPatrick Institute, University of Cape Town, Rondebosch 7700, South Africa

&

Carlos A. Villacastin-Herrero Department of Zoology, University of Cape Town, Rondebosch 7700, South Africa (Received 27 May 1994; accepted 25 January 1995)

Abstract Hunting bag data for Namaqua sandgrouse Pterocles namaqua from an estate near Kimberley, South Africa, for the period 1907-1992 were analysed to investigate population trends, environmental correlates and to develop predictive models for population fluctuations. An apparent population decline between 1950 and 1992 may be an artifact of increased sandgrouse dispersion in response to an increased number of artificial watering points on the estate, and in the surrounding area, during this latter period Peaks in sandgrouse abundance are significantly negatively correlated with December rainfall and significantly positively correlated with March rainfall. Furthermore, annual sandgrouse abundance and March rainfall showed similar peaks at four-year cycles during 1909-1939. Therefore, a rainfall 'score' which takes cognizance of both December and March rainfall is useful for predicting annual sandgrouse abundance before the forthcoming hunting season. However, correct predictions of low sandgrouse abundance were more common than correct predictions of high sandgrouse abundance. These environmental correlates and predictive models are useful for forecasting the annual viability of commercial hunting of these populations.

Pteroclidae) (Crowe & Little, 1993). Professional gamebird hunting 'industries' have long been lucrative enterprises which contribute to the development of rural communities and the conservation of biodiversity in many parts of the northern hemisphere. However, many of these First-World industries are on the wane because of the transformation of natural habitats and incompatible agricultural practices. This creates opportunities for enterprising African entrepreneurs to develop an African gamebird hunting industry which can service both national and international clients (Little & Crowe, 1993a). The scientific challenge to African gamebird biologists is to identify key ecological factors which influence annual variations in gamebird populations and to develop simple models for forecasting the sustainable use of these gamebirds. Some processes in ecological systems can be detected and identified only by long-term data series, as the ability to manage or manipulate any system depends upon recognizing and ultimately understanding the full gamut of these processes (Dunnet, 1991; Moss & Watson, 1991). Long-term studies of natural populations are necessary for practical conservation to determine population 'danger' zones requiring conservation action (Pienkowski, 1991). Long-term data sets have also been used to develop predictive models of population fluctuations (Cole, 1951, 1954), to understand the effects of environmental influences (Garsd & Howard, 1981; Bautista et al., 1992), to predict annual yields (e.g. for the fur trade (Elton & Nicholson, 1942; Moran, 1953; Bulmer, 1974)), and to maintain sustainable populations, e.g. of red grouse Lagopus lagopus scotieus (Watson et aL, 1988). In contrast to the temperate ecosystems of the northern hemisphere, gamebird populations in the Afrotropics are more regularly 'event-driven' (Berry & Crowe, 1985), and density-independent factors may be more important than density-dependent factors. More

Keywords: Namaqua sandgrouse, Pterocles namaqua, population trends, environmental correlates, predictive models, sustainable use. INTRODUCTION With its burgeoning, increasingly urbanized, human population and generally declining GNP, Africa faces many daunting scientific, educational, socioeconomic and conservation challenges. One promising approach to meeting a range of these challenges is the sustainable use of African gamebirds (e.g. guineafowl Numididae, francolins Phasianidae, ducks Anatidae and sandgrouse 93

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R. M. Little, T. M. Crowe, C. A. Villacastin-Herrero

specifically, fire and the frequency and intensity of rainfall play major roles in the dynamics of animal populations (Crowe et al., 1981), and particularly in the seasonal breeding activities and population fluctuations of passerine birds, e.g. the quelea Quelea quelea (Lack, 1966), and gamebirds (Crowe, 1978; Berry & Crowe, 1985; Little & Crowe, 1993b). These environmental influences are often relatively much more variable, and therefore less predictable, than those in the temperate northern systems (Onesta & Verhoeff, 1976). In practice, fluctuations in natural populations in the Afrotropics are more often referred to as regular irruptions rather than true cycles, e.g. for the quelea and brown locust Locustana pardalina (Lea, 1970). The Namaqua sandgrouse Pterocles namaqua is endemic to the semi-arid regions of southwestern Africa (Cade & Maclean, 1967; Thomas, 1984a,b; Maclean, 1985). It feeds exclusively on small, hard seeds of ephemeral plants (Maclean & Fry, 1986), and needs high-quality drinking water (Knight, 1989; Little et aL, 1993). Populations in the Northern Cape Province breed mostly in the western portion of their range during the early austral summer (September-November), and migrate eastwards for the non-breeding season (April-August) (Malan et aL, 1994). They have reportedly experienced regular large population fluctuations (Maclean, 1968) and a possibly general decline since the late 1940s (Berry & Crowe, 1985). Because of their propensity to congregate in large flocks at traditional watering sites, particularly during the non-breeding season (McLachlan, 1985), Namaqua sandgrouse have attracted considerable attention from local bird hunters (Lynn-Allen, 1951; Malan et al., 1993). Daily hunting bags at such sites have been recorded in a hunting book since the beginning of the 20th century at Rooipoort (28°45'S, 24°05'E), a private nature reserve about 60 km west of Kimberley in the Northern Cape Province, South Africa. The aims of this study were to investigate these bag data to determine long-term fluctuations in sandgrouse populations and develop predictive models useful for forecasting annual offtakes for commercial hunting of these populations.

METHODS Sandgrouse records Indices of relative annual abundance of sandgrouse were calculated for each hunting season at Rooipoort as the total season's bag divided by the number of days on which sandgrouse were hunted (Appendix 1). We use these hunting bag indices as population estimates, assuming that more birds are shot primarily because there are more birds to hunt (Potts et aL, 1984; Little & Crowe, 1993c; Little et al., 1993). Obviously, variations in hunting intensity and methods, and in shooting ability, are potential sources of bias in these indices. However, hunting at Rooipoort is limited to a few hunts each season (and hence is a sampling rather than a depleting activity), the composition of hunting parties

tends to be similar from year to year, and there are no significant between-year differences in hunting effort or hunter efficiency (Malan et al., 1993). Sandgrouse bag data for the years 1908, 1940-1944 and 1947-1949 were not available. Two subsets were therefore used to study long-term trends and cycles for 31 seasons, 1909-1939, and for the 43 seasons, 1950-1992. Data partitioning was desirable, because pre-1950 data reflect a more 'natural' situation at Rooipoort, since several watering points were added during the post-1949 period. Data for the 77 seasons for which bag data were available (i.e. including 1907, 1945 and 1946, Appendix 1) were used for the comparison of annual sandgrouse abundance and annual rainfall variation.

Rainfall data Rainfall data for Rooipoort are not available for the entire period covered by sandgrouse hunting records. Rainfall (mm) and rainfall frequency (number of days on which there was measurable rainfall) data were therefore extracted from the nearest source of continuous data, the De Beers Consolidated Mines treatment plant (1907-1946) and the Kimberley Airport (1947-1992), both located in Kimberley, about 60 km to the east of Rooipoort. Rooipoort and Kimberley are both in the Nama-karoo biome (Rutherford & Westfall, 1986). Furthermore, Berry and Crowe (1985) compared rainfall data for 1923-1939 from Rooipoort with those for the same 17-year period from Kimberley and found no significant difference (p > 0.05) with a slope not different from unity (mean annual rainfall was 395 mm for Rooipoort and 388 mm for Kimberley). Statistical analyses Long-term trends in sandgrouse abundance and rainfall Statistical analyses of time series have received extensive theoretical and practical development and are useful for describing wildlife population fluctuations (Garsd & Howard, 1981; Tapper, 1983). The two subsets of sandgrouse abundance data were log transformed (Y -log(I + 1) where I is our abundance index) to stabilize the variance (Zar, 1984), because annual changes are more likely to be multiplicative than additive. The autoregressive structures of all series were examined using correlograms (Chatfield, 1978) and these were used to identify short- and long-term cyclicity as well as long-term trends. Those data sets that showed indications of a trend were analysed using standard least squares techniques. Thereafter, autoregressive structure was isolated, explained and removed to achieve 'white noise series' (Box & Jenkins, 1976). Spectral analysis (Panofsky & Brier, 1968) was performed on all data series to demonstrate the amount of variability in the data that was accounted for at the different frequencies (periodogram). The resultant peaks are independent and have the asymptote distribution approximately equal to the Chi-square distribution. Furthermore, we looked for evidence of similar

Population trends in Namaqua sandgrouse periodicities between the sandgrouse abundance index and the rainfall data sets by determining the total covariation that occurs at the same frequencies (coherence) and the nature of the lag between them (phase).

Comparison of sandgrouse abundance and rainfall We conducted preliminary comparisons between annual sandgrouse abundance indices and four series of rainfall data (total monthly rainfall, number of days on which measurable rain fell per month, number of days on which > 5 mm of rain fell per month, and the highest number of consecutive days on which rain fell per month). Since the sandgrouse hunting season is May-July, monthly rainfall and frequency were calculated for the 12 months prior to the start of each season (May-April). Total December rainfall and the number of days of heavy rainfall > 5 mm during March were selected because they were most highly correlated with sandgrouse abundance in both simple and stepwise multiple regression analyses (Programme BMDP-2R; Dixon et al., 1990). The total December rainfall was then scored inversely according to 10 classes (i.e. classes 9-0) at 14-ram intervals to standardize with the range in heavy March rainfall days, and that value added to the number of days of heavy rainfall in March (which ranged from 0 to 9) (see Appendix 1). Standard bootstrap procedures (Efron, 1979) were used to assess the precision of predicting sandgrouse abundance with decision criteria of 25 and 50 for the mean daily bag per season (cutoffs for good or poor hunting) at various levels of combined annual rainfall scores (0-18). The decision criteria of 25 and 50 were chosen as approximations of the mean daily bag per season, during the 1907-1939 period (g -- 47-2, SD -75-8, n = 32) and during the 1950-1992 period (~ = 23-9, SD = 32-8, n = 43). Thereafter, December and March rainfall data were used separately in similar bootstrap analyses to determine the proportion of correct prediction decisions for high and low annual sandgrouse abundance. Predictions for high sandgrouse abundance were correct when December rainfall was less than the assigned cutoff criterion (e.g. 5 mm in March was greater than the cutoff criterion (e.g. >4), and the model predicted high sandgrouse abundance coinciding with high observed sandgrouse abundance (e.g. _>25 or > 50). Predictions of low sandgrouse abundance were correct when December rainfall was greater than the cutoff criterion and the number of days of high rainfall during March was less than the cutoff criterion, and the model predicted low sandgrouse abundance coinciding with low observed sandgrouse abundance. The bootstrap program was run for 1000 simulations for all analyses.

sandgrouse abundance index during 1909-1939. There was, however, a significant decline in this index during 1950-1992 (r = -0.40, p 0-05) between annual sandgrouse abundance indices and December rainfall during either period (Fig. 3). However, similar analyses with March rainfall showed a high coherence (C --- 0.92) at

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Fig. 1. Annual Namaqua sandgrouse Pterocles namaqua population abundance index (mean daily hunting bag) during 1909-1939 and 1950-1992 recorded in the Northern Cape Province, South Africa.

R. M. Little, T. M. Crowe, C. A. Villacastin-Herrero

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Fig. 2. Logarithmic spectral density functions for the annual Namaqua sandgrouse Pterocles namaqua population abundance index (mean daily hunting bag), the total December rainfall for the year preceding the hunting season, and the number of days of heavy rainfall (> 5 mm) during the March prior to the hunting season. Note: the scale on the x-axis is not linear.

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e 8 10 12 14 le 18 Annual rainfall scores Fig. 4. Estimates of mean precision for predicting high or low Namaqua sandgrouse Pterocles namaqua population abundance indices (mean daily hunting bag) using bootstrap analysis (1000 simulations) and with annual rainfall scores calculated from a combination of the total December rainfall for the year preceding the hunting season, and the number of days of heavy rainfall (> 5 mm) during the March prior to the hunting season, and using decision criteria of 25 and 50 for the cutoff of good and poor hunting.

four-year cycles during 1909-1939, though no significant coherence during 1950-1992 (Fig. 3).

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C o m p a r i s o n o f sandgrouse abundance and rainfall

Annual sandgrouse abundance was not significantly correlated with December rainfall in the previous year (r = ~). 17, p > 0.05), but was positively correlated with the number of days of heavy rainfall in March (r = 0.29, p < 0.05), and with the annual rainfall score (r = 0.35, p 5 mm

Annual rainfall score

1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992

18.4 30.3 1.0 2.0 4-3 31.0 0.0 39.9 1.4 28-4 6.8 10.0 21.2 0.8 2.0 0.0 17-2 14.0 15.0 17.8 28.9 17.1 22.4 27.7 0.0

30 50 24 74 61 11 60 17 100 75 20 38 0 50 106 18 24 28 95 19 53 63 36 77 42

7 6 8 4 5 9 5 8 2 4 8 7 9 6 2 8 8 8 3 8 6 5 7 4 7

4 2 1 5 5 4 5 7 6 5 4 2 5 5 3 2 3 3 3 4 6 1 3 5 1

11 8 9 9 10 14 10 15 8 9 12 9 14 11 5 10 11 11 6 12 12 6 10 9 8

aSandgrouse abundance index and total December rainfall data were extracted from Berry and Crowe (1985). bDecember rainfall values are for the previous year. nd. no data available.