Abstract. We critically evaluated population-monitoring programs for three endangered species of Australian honeyeater: the regent honeyeater, Xanthomyza ...
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Wildlife Research, 2003, 30, 427–435
Assessing programs for monitoring threatened species – a tale of three honeyeaters (Meliphagidae) Rohan H. ClarkeA, Damon L. OliverB, Rebecca L. BoultonA,C, Phillip CasseyD and Michael F. ClarkeA A
Department of Zoology, La Trobe University, Bundoora, Vic. 3086, Australia. Threatened species unit, NSW NPWS Western Directorate, PO Box 2111, Dubbo, NSW 2830, Australia. C Current address: Ecology Group, Massey University, Private Bag 11-222, Palmerston North, New Zealand. D Laboratoire d’Ecologie, Ecole Normale Superieure, 46 rue d’Ulm 75230, Paris cedex 05, France.
B
Abstract. We critically evaluated population-monitoring programs for three endangered species of Australian honeyeater: the regent honeyeater, Xanthomyza phrygia, the black-eared miner, Manorina melanotis, and the helmeted honeyeater, Lichenostomus melanops cassidix (Meliphagidae). Our results challenge the common assumption that meaningful monitoring is possible in all species within the five-year lifetime of recovery plans. We found that the precision achievable from monitoring programs not only depends on the monitoring technique applied but also on the species’ biology. Relevant life-history attributes include a species’ pattern of movement, its home-range size and its distribution. How well understood and predictable these attributes are will also influence monitoring precision. Our results highlight the large degree of variability in precision among monitoring programs and the value of applying power analysis before continuing longer-term studies. They also suggest that managers and funding agencies should be mindful that more easily monitored species should not receive preferential treatment over species that prove more difficult to monitor. WR02056 RAe.taslHe.sCilanrgeksuc es ofmonitoring porgarms
Introduction Population monitoring is central to the identification and management of threatened species (Male 1994; Martin 1994). In the first instance, population estimates and monitoring of population trends are required to identify species under threat. Indeed, such information underpins many of the IUCN criteria needed to list taxa as threatened (IUCN 2001). This information is then fed into recovery action planning and the development of formal recovery plans for priority species (Foin et al. 1998). Following this, reliable methods for evaluating population trends over time are necessary to gauge the effectiveness of any recovery actions undertaken. Monitoring is also required for a pre-determined time after any actions have been deemed a success to ensure they have had a lasting effect (Male 1994). Therefore, because the results of monitoring have so many applications in threatened species management, recovery plans for threatened species invariably place a high priority upon actions that seek to assess the current population size © CSIRO 2003
and monitor population trends (e.g. Garnett and Crowley 2000). The results of monitoring programs also have broader application, as they are often used by environmental management agencies to determine whether the recovery effort as a whole is a success or failure. For example, as part of a process of assessment the Australian federal funding agency, Environment Australia, requests an ‘Endangered Species Progress Report’ from all programs receiving federal funding. Within each submission, threatened species managers are asked to report by what percentage the population has increased or decreased. The reality is that those programs that can demonstrate a population increase through monitoring will invariably be deemed a greater success than those that are unable to demonstrate such a trend. However, an inability to demonstrate an increase (or decrease) does not necessarily mean that the size of the population has remained unchanged (and by inference earlier management actions were ineffectual). The monitoring 10.1071/WR02056
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program may simply be too weak to detect a change, be it through poor survey design and testing, or insurmountable challenges posed by the focal species’ demographics, biology and ecology. Knowledge of the precision of the monitoring program is therefore essential information that allows researchers, managers and funding agencies to interpret results of monitoring and better evaluate a population’s current status. Unfortunately, although it is standard practice to calculate the level of variability for results being reported in the scientific literature, reporting the level of certainty for the results of monitoring programs is often overlooked. The common assumption is that meaningful monitoring is always possible within the lifetime of a recovery plan and that the results obtained accurately reflect the population trend. Given that outcomes of monitoring programs have so many applications, and in extreme cases can effectively determine the likelihood of ongoing funding (Taylor and Muller 1995), this assumption requires further scrutiny. Therefore, the aim of this study was to evaluate monitoring programs for threatened-species populations and test the assumption that meaningful monitoring is possible within the five-year lifetime of recovery plans. We compare the results of monitoring programs for three species of Australian honeyeaters (Meliphagidae) (regent honeyeater, black-eared miner and helmeted honeyeater), all of which are classified as endangered or critically endangered (Garnett and Crowley 2000). The species selected for inclusion in this study have different population sizes, ranges, movement patterns, social organisation and breeding seasonality. This allows the exploration of how these differences may influence our ability to obtain meaningful results from monitoring programs within realistic time and budgetary constraints. Study Species and Methods Regent honeyeater The regent honeyeater has an estimated population of ~1500 individuals (Menkhorst et al. 1998). Despite a relatively small population, the species occupies a vast range of some 300000 km2 in eastern Australia (Garnett and Crowley 2000). The regent honeyeater is a seasonal breeder and, when breeding, the species’ distribution is very localised. Indeed, researchers are aware of only four key areas where the species regularly breeds. In contrast, when not breeding, most of the population disperses to places unknown. Monitoring efforts presented here were undertaken in the Bundarra/Barraba region of northern New South Wales, at one of the regent honeyeater’s few regular breeding sites. Regent honeyeater surveys consisted of fixed transects 50 m wide and 200 m long (1 ha). The same person (DLO) was responsible for all surveys. All birds seen or heard within 25 m either side of the transect were recorded in a 20-min period. All data were converted to units of birds per hectare surveyed. Ten sites were routinely surveyed for regent honeyeaters. Data were gathered in the months of September, November and January of 1995–97 (Oliver 1998; Oliver et al. 1999). The maximum number of regent honeyeaters detected at each site across these months was the focus of analyses. The average survey
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result across months was not used, as results would have been meaningless and misleading because of the frequent occurrence of zeros in the datasets. These maxima were then averaged across sites within a breeding season. As the maxima themselves were not normally distributed, a non-parametric Freidman test was used to determine whether there was a significant change across years. Black-eared miner The black-eared miner has a total population estimated to be at least 1000 individuals (R. Clarke, unpublished data). A single viable population persists in the Bookmark Biosphere Reserve, north of the Murray River in South Australia, where the colonies are spread over 450 km2 of mallee habitat (Garnett and Crowley 2000). The black-eared miner breeds opportunistically at any time of the year when conditions are suitable. At such times it is relatively easy to locate and monitor. When not breeding, colonies appear to remain site-faithful but occupy a larger home range of >10 km2, making them much more difficult to locate (R. Clarke, unpublished data). Because there are at least 1000 black-eared miners, a monitoring program was established for a subset of the population. Further, because the movement patterns of the species are associated with an opportunistic and unpredictable breeding strategy, the population was monitored twice yearly in the hope that at least one of these monitoring periods coincided with a breeding event. Survey locations were selected at sites at which black-eared-miner colonies had been observed breeding at some time in the preceding 18 months. At each site, standardised point counts, incorporating call playback at five points each 250 m apart and with a search radius of 30 m, were undertaken. A total area of 1.4 ha was therefore surveyed at each colony site. Surveys took ∼1 h to complete and were always conducted within 6 h of sunrise by either RHC or RLB. Surveys were conducted at 22 colonies within the Bookmark region in autumn and spring for a period of 2.5 years between 1998 and 2000. Helmeted honeyeater The helmeted honeyeater is restricted to a single site, Yellingbo Nature Reserve, north-east of Melbourne, Victoria, where fewer than 100 individuals occupy an area of 5 km2 (Menkhorst and Middleton 1991; Garnett and Crowley 2000). The helmeted honeyeater is sedentary, breeding seasonally in narrow stretches of riparian habitat (Franklin et al. 1995, 1999; Pearce and Minchin 2001). All individuals within the population were marked with unique combinations of colour-bands at the time of fledging and life histories were documented through intensive, often daily, observation through until the individual’s death. Where an individual’s fate was unknown because it disappeared, this was treated as a death (McCarthy et al. 1994; McCarthy 1996). The snapshot of population size presented here was taken in September of each year (1990–94), at a time when all free-flying individuals were at least eight months old and therefore probably sexually mature (Franklin et al. 1995; Menkhorst et al. 1999). Power analysis One approach to assessing the capabilities of monitoring regimes is to estimate the statistical power of monitoring programs. That is, how likely is it for the monitoring program to detect a decline or increase of a given magnitude in the population over a given time? Although the application of retrospective power analysis has met with some resistance (Reed and Blaustein 1997; Thomas 1997; Gerard et al. 1998, Hoenig and Heisey 2001), the application of power analysis in the design stages has, in recent years, been more universally recommended (Conroy et al. 1995; Thomas 1997; Gerard et al. 1998). With such an approach, four elements can be computed: power (1 – β), probability of a Type 1 error (α), required sample size (n) and effect size. In situations where power analysis is applied to research planning, appropriate values for α, β and effect size are identified and n is calculated as a
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N=
2 2
t
2
α,n −1
,
e – where x is the mean, s is the standard deviation of the sample population, t is the value of t from a t distribution for a sample size of n and a reliability of 1 – α, and e is the error one is prepared to tolerate (Burgman and Lindenmayer 1998). We set a range of effect sizes rather than use the observed effect size (Thomas 1997), as such values are of little relevance to determining the adequacy of the monitoring program. For the purposes of this study α was set at 0.1. Although this is a departure from the more conventional α of 0.05, in studies involving threatened species decreasing the risk of rejecting a hypothesis when it is correct (Type 1 error) has merit (Fairweather 1991; Mapstone 1995). It is arguably far less damaging to take action in halting a decline for a threatened species when one has not occurred, rather than risk taking no action because any decline that has occurred has gone undetected. In keeping with Cohen (1988), results for each species are presented to demonstrate what monitoring effort (i.e. number of samples) would be required to be 90% confident of detecting a small (20%), medium (40–60%) and large (80%) change in the respective population sizes. As is typical of monitoring programs for threatened species, datasets obtained in this study were small and in some instances over-dispersed. Rather than calculate the mean and standard deviation from the raw data, we therefore calculated means and standard deviations for 1000 bootstrap samples generated using the program R (Version 1.7.0 2003).
8
6 Number of adults
(s x )
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4
2
0 N =
10 Sep 95 - Jan 96
(b)
10 Sep 96 - Jan 97
30
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function of these. The results of pilot studies provide the means by which estimates of the variances involved in the proposed investigation are obtained (Otis 1995). Power analysis was applied to survey data collected during monitoring programs, in order to estimate the sampling effort required to ensure adequate statistical power in future monitoring of the three endangered honeyeaters. In all power calculations the following formula was used:
20
10
0 N =
22
22
22
A/W 98* S/S 98* A/W 99
Results
22
22
S/S 99 A/W 00*
(c) 80
Regent honeyeater
80
74 68
Number of adults
Surveys were undertaken between September 1995 and January 1997 (Fig. 1a). In total, 8 field-person-days were invested in monitoring of regent honeyeaters each year. There were no significant changes in the number of individuals detected across the years of survey effort (Wilcoxon signed-ranks test, Z = 0.521, P = 0.357). However, this test had only a 35% chance of detecting a large-scale change (i.e. 50% reduction) across years had one occurred during the study period. Power analysis showed that 12 years of survey effort equal to that which was being undertaken would have been required to detect a population change of 80%. To be confident of detecting moderate (40–60%) to small (20%) population changes, survey effort would have had to span decades (21–188 years) (Fig. 2a). The current monitoring program incorporating 10 sites per monitoring period would only be capable of detecting massive year-to-year population fluctuations of 80%. To be capable of detecting moderate (40–60%) to small (20%) population changes from one year to the next 15–133 sites would have to be surveyed (Fig. 2b).
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Fig. 1. Results of honeyeater-monitoring programs, showing (a) box-and-whisker plot of the mean maximum number of regent honeyeaters recorded at 10 sites in two survey periods, (b) box-and-whisker plot of the mean number of black-eared miners recorded across 22 sites in each of five survey periods, and (c) the adult helmeted honeyeater population on 1 September over five years (data presented in Menkhorst et al. 1999). A/W, autumn/winter period and S/S, spring/summer period. 䊉 and 䊊 indicate outliers and extremes respectively. The asterisk indicates the season when black-eared miners breed
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(a)
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Fig. 2. The estimated survey effort required for the regent honeyeater population in the Bundarra/Barraba region of New South Wales to detect population changes of various magnitudes expressed as (a) the number of years of survey effort (n = 10 sites), and (b) the number of survey sites to detect changes on a year-to-year basis.
Black-eared miner Five surveys of black-eared-miner colonies were conducted over a 2.5-year period from 1998 to 2000 (Fig. 1b). In total, 17 field-person-days were invested in monitoring black-eared miners in each year. There were significant differences in the number of individuals detected across all survey periods (Freidman test, χ2 = 12.3, d.f. = 4, α = 0.05, P = 0.015) but not across breeding periods (χ2 = 3.84, d.f. = 2, α = 0.05, P = 0.147) (Fig. 1b). However, the nonsignificant test for breeding periods had only a 54% chance of detecting a large-scale change (i.e. 50% reduction) across years had one occurred in the study period. The number of years of future survey effort (equal to that being undertaken) required to detect population changes of various magnitudes, as determined by power analysis, are shown in Fig. 3a;
0.4
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Fig. 3. The estimated survey effort required for the black-eared miner population in the Bookmark Biosphere Reserve, South Australia, to detect population changes of various magnitudes expressed as (a) the number of surveys per site (n = 22 sites), and (b) the number of survey sites to detect changes between survey periods. 䊏, breeding and non-breeding periods combined; 䊐, breeding periods only.
similarly, the number of survey sites required to detect population changes of various magnitudes from year to year are shown in Fig. 3b. In both instances separate analyses were conducted on (1) all surveys combined, and (2) only those monitoring periods when breeding was detected. In both approaches, survey power was found to be greater when analysis was restricted to breeding periods. Nevertheless, massively increased monitoring either in terms of duration (10–42 years) or coverage (35–140 sites per year) was needed to be confident of detecting subtle or even moderate (0.2–0.4) changes in population size. Helmeted honeyeater Over a period of five years (1990–94) of intensive monitoring the helmeted honeyeater population ranged from
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58 to 80 adults (Fig. 1c). No power analysis was undertaken as the intensive monitoring methods used ensured that researchers were confident that no adults were overlooked and total population census was achieved. This position is supported by the fact that unmarked adults were never located within the population during this and subsequent periods of intensive monitoring (I. Smales, personal communication). As abundance was determined with no error, power was therefore equal to 1.0. Approximately 320 field-person-days per year were invested in monitoring. However, this figure should be treated as a guide only as field efforts were never solely, or even primarily, directed towards producing the results presented here. Rather, fieldwork was undertaken to provide information on many aspects of the species’ biology and recovery, of which census results on 1 September were a part. Over the five-year period of intensive monitoring it was possible to demonstrate a total increase of 38% or an average annual increase of 9.5% in the population of adult helmeted honeyeaters. Discussion Those responsible for managing the recovery of threatened species should be unsettled to learn that current monitoring programs for many species are incapable of producing meaningful results for years to come. Indeed, to monitor some species effectively, the sampling effort would have to be increased enormously, either in terms of the number of sites used or the frequency with which they are visited. For many threatened species this is simply not possible (e.g. Forcada 2000). The relative abundance of black-eared miners could be determined with only moderate precision within the five-year life of a recovery plan. More sobering are the measures of relative abundance for the regent honeyeater, which were capable of detecting only a catastrophic decline (or a massive increase!) in the population. Unfortunately, for the regent honeyeater, population demographics limit the number of available monitoring sites. While for the black-eared miner the number of field-person-days needed to significantly increase monitoring precision would curtail many other recoveryprogram actions for which there is real promise (e.g. Clarke et al. 2002). Only the monitoring program for the helmeted honeyeater was capable of accurately detecting trends on an annual basis. However, the very small and localised population of helmeted honeyeaters, which facilitated accurate monitoring, also means that this species is exposed to a very high risk of extinction (Garnett and Crowley 2000). It would be of great concern if only those species that display critically small, localised populations could be effectively monitored within the usual lifetime of a recovery program. Clearly, monitoring of any particular species poses unique challenges. However, monitoring ability is not affected only
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by population size but also by differences in other population demographics and species ecology. Patterns of movement influence our ability to monitor populations effectively (Table 1). The resident helmeted honeyeater population provided fewer challenges to monitoring than did the locally dispersive black-eared miner population, and monitoring precision for both these species was far greater than that obtained for the nomadic regent honeyeater. How predictable an animal’s pattern of movement proves to be will also have an impact on our monitoring precision. Those species that display well defined seasonal movements and site faithfulness between seasons are likely to provide fewer challenges than species that display opportunistic movements in response to factors that we cannot predict. The regent honeyeater monitoring program lacks precision, in part because the species’ movement patterns are largely unpredictable (Franklin et al. 1987, 1989; Oliver 1998). The area of occupancy or home-range size of a species also influences monitoring precision (Table 1). Indeed, many previous studies have highlighted the difficulty of monitoring species that occur at low density (e.g. Green and Young 1993; Taylor and Gerrodette 1993; Thompson et al. 2000). When breeding, black-eared miners occupy small home ranges of several hundred hectares. However, when not breeding home ranges can be greater than 10 km2 (R. Clarke, unpublished data). The decrease in survey precision between breeding and non-breeding periods reported here is, in part, due to the researchers’ inability to find many birds when they occupy a larger home range. Taylor and Gerrodette (1993) demonstrated that the density of northern spotted owls, Strix occidentalis caurina, influenced the precision of estimates for each population. They found that at low densities estimates of birth and death rates had greater power when determining population trends than did more direct estimates using line transects. In contrast, they found that at higher densities the reverse was true. Species that aggregate in response to environmental attributes may also promote precision in monitoring programs (Table 1). For example, shorebirds roosting as tight flocks provide opportunities to monitor populations that would be logistically more challenging at other times (e.g. Morrison et al. 1994). In the case of the helmeted honeyeater, habitat preferences (Pearce et al. 1994; Moysey 1997; Pearce 2000) limit the species’ area of occupancy, meaning that researchers have a clearly defined area in which to conduct monitoring. Likewise, cohesive behaviours, such as colonial social systems, present opportunities for monitoring that would otherwise be more difficult to undertake. Black-eared miners form a socially cohesive colony when breeding (Backhouse et al. 1997) but these social bonds are apparently weaker when the birds are breeding; when combined with the larger home ranges
Large
Environmental variability – magnitude Environmental variability – predictability
Highly variable
Poorly known
Knowledge of species biology
Knowledge of species distribution Poorly known
Movement pattern – scale Individual detectability
Movement pattern – timing
Night parrot, Pezoporus occidentalis (Garnett and Crowley 2000); star finch, Neochmia ruficauda ruficauda (Garnett and Crowley 2000) Spectacled porpoise, Australophocoena dioptrica (Bannister et al. 1996); beaked whales , Ziphiidae (Bannister et al. 1996) Painted snipe, Rostratula benghalensis (ephemeral wetlands) (Garnett and Crowley 2000) Black-eared miner (this study); painted burrowing frog, Neobatrachus pictus (only detectable after rain) (NPWS 2000)
Very low density Forrest’s mouse, Leggadina forresti (NPWS 2002c); Coxen’s fig parrot, Cyclopsitta diophthalma coxeni (NPWS 2002a) Unpredictable Regent honeyeater (this study); beaked whales family, Ziphiidae (on current knowledge) (Bannister et al. 1996) Broad Regent honeyeater (this study) Highly cryptic Pale-headed snake, Hoplocephalus bitorquatus (RACD 2002); brush-tailed rock-wallaby, Petrogale penicillata (trap shy) (NPWS 2001c)
Population density
Examples
More difficult
Highly predictable
Small
Well known
Well known
Local Readily detected
Highly predictable
High density
Less difficult
Royal penguin, Eudyptes schlegeli (Marchant and Higgins 1990); Christmas Island white-eye, Zosterops natalis (Stokes 1988; Garnett and Crowley 2000) Gould’s petrel, Pterodroma leucoptera (when breeding) (Priddel and Carlile 1997); orange-bellied parrot, Neophema chrysogaster (when breeding) (OBPRT 1998) Helmeted honeyeater (resident) (Franklin et al. 1995; 1999) Southern bell frog, Litoria raniformis (G. Pyke, unpublished); glossy black cockatoo, Calyptorhynchus lathami (central NSW near water) (Dubbo Field Naturalists, M. Cameron, unpublished) Mountain pygmy possum, Burramys parvus (NPWS 2001a); Lord Howe Island woodhen, Gallirallus sylvestris (NPWS 2002b) Helmeted honeyeater (Franklin et al. 1995, 1999); southern corroboree frog, Pseudophryne corroboree (very specific habitat) (NPWS 2001b) Bent-wing bat, Miniopterus schreibersii (Baudinette et al. 1994) Helmeted honeyeater (Franklin et al. 1995, 1999)
Examples
Ecological and biological attributes that influence the ease with which threatened species can be monitored Note: examples may refer to threatened populations of species that are not listed as globally threatened
Attribute
Table 1.
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discussed above, this results in the survey precision being lower in the non-breeding period. Even within species, local environmental variability means that multiple, same-species monitoring programs may still achieve different levels of power at different sites. In addition to the regent honeyeater data presented, two other sites were also the focus of intensive monitoring (Regent Honeyeater Recovery Team, unpublished data). Sites are at least 500 km apart, meaning that each site has its own environmental predictability that influences the populations’ behaviour (e.g. timing of breeding). Although methodologies were essentially the same, results of power analysis showed highly variable precision across sites (R. and M. Clarke, unpublished data). Unfortunately, increasing the number of sites surveyed to maintain power while reducing the duration of the monitoring period is not possible for any of the regent honeyeater populations that are being monitored. There are simply no more known sites at which regent honeyeaters occur that could be usefully added to the monitoring program. As such, the Regent Honeyeater Recovery Team is unable to determine whether a key assessment criterion, ‘that population numbers and patterns of usage at existing sites at least remain at current levels’ (Menkhorst 1997), has been met. Thus, although power analysis of a monitored population may demonstrate that suitable precision can be obtained, given the environmental variability, it would be inappropriate to assume that similar precision will be achieved when monitoring other populations of the same species. Given high variability in monitoring-program precision among and within species, critical evaluation of all monitoring-program outcomes is required. In the absence of knowledge of monitoring precision there is a very real danger that program managers may make judgments based on ‘apparent’ population trends when such estimates are at best uncertain and at worst misleading. Therefore, monitoring precision needs to be taken into consideration when setting biological priorities within a recovery program. Additionally, before any long-term programs are implemented, managers must ensure that the program will be sufficiently powerful to detect changes of a minimum useful magnitude. An awareness of monitoring-program precision allows managers to better assess the progress of the recovery effort as a whole and choose future directions. One can determine whether actions that have been implemented have had a positive impact on population trends and how these results will feed into an adaptive management framework. Critical evaluation of monitoring-program results through power analysis can also alert managers to instances where monitoring techniques may simply be unsuitable (Forcada 2000). Application of power analysis can also help avoid a scenario where sampling effort is greater than needed to be suitably confident in the conclusions, and thus help avoid wasting valuable resources (Otis 1995).
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Only when managers know the strengths and weaknesses of a monitoring program can they act. There is little value in monitoring if the precision is such that the results are biologically meaningless. Although every effort should be made to develop monitoring programs with adequate power for the circumstances, other approaches to assess a program’s success or failure must be explored, if this is not achievable. While we recognise that monitoring programs may provide additional and valuable benefits to a recovery program (e.g. collection of opportunistic data or increased community involvement), these benefits should not be achieved under the guise of monitoring, rather they should be specifically targeted and adequately budgeted. In an age of limited budgets and funding uncertainty where financial considerations often influence managerial decisions (Haight et al. 2000), monitoring precision also needs to be taken into consideration when setting future budgetary priorities. As demonstrated, often there will be trade-offs between the level of achievable monitoring precision and the cost of such monitoring. However, a program that has expended vast resources to achieve a high level of statistical precision may have wasted funds that would have been better directed elsewhere. By contrast, a program that has chosen to suspend monitoring after pilot data demonstrate that the monitoring program produced results with biologically meaningless precision will have made a commendable decision. In short, if a ‘level playing field’ is to be achieved, threatened species that, because of their biology, are easily monitored should not receive preferential treatment by funding agencies over species that prove more difficult to monitor. Conclusion Given the apparent lack of critical evaluation of threatened-species monitoring programs in Australia, we make several recommendations. Monitoring programs take time to develop, implement and conduct. So as not to hinder the recovery process, monitoring should therefore be implemented as soon as is practicable. Before monitoring commences the aims of the program need to be clearly identified. The time that a program will run before it is critically assessed also needs to be specified. Factored into this decision is a requirement that the pilot period span multiple breeding periods, seasons or other temporal factors that are likely to influence monitoring precision so that any estimate of variance is obtained from a biologically meaningful time-frame. Additionally, where it is unclear what the most suitable monitoring technique may be, pilot monitoring periods present an opportunity to coordinate different monitoring methodologies concurrently so that the technique offering the most suitable precision can be selected for future monitoring without delay. Once a pilot period of monitoring is completed, the results require critical evaluation to determine whether the level of precision
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achieved is useful. Here, we recommend the application of prospective power analysis. If monitoring precision is adequate for the intended purposes, then monitoring should continue. However, if the achievable level of precision proves to be biologically meaningless then that monitoring program should cease and more targeted monitoring or novel approaches to program assessment should be developed. Acknowledgments The authors thank D. Baker-Gabb, J. Ewen, D. Franklin and P. Menkhorst for commenting on a draft of this manuscript. Eileen Collins, Scott Jessup, Kris French and their volunteers, and members of the Helmeted Honeyeater, Regent Honeyeater and Black-eared Miner Recovery Teams are thanked for their input and provision of data. Monitoring of helmeted honeyeaters was funded by Environment Australia (EA) and the Victorian Department of Natural Resources and Environment (DNRE). Monitoring of black-eared miners was funded by EA, DNRE and the South Australian Department of Environment and Heritage. Monitoring of regent honeyeaters was funded by the Murray–Darling Basin Commission and EA. References Backhouse, G., McLaughlin, J., Clarke, M., and Copley, P. (1997). ‘Recovery Plan for the black-eared miner Manorina melanotis 1997–2000.’ (Environment Australia: Canberra.) Bannister, J. L., Kemper, C. M., and Warneke, R. M. (1996). ‘The Action Plan for Australian Cetaceans.’ (Biodiversity Group, Environment Australia: Canberra.) Baudinette, R. V., Wells, R. T., Sanderson, K. J., and Clark, B. (1994). Microclimatic conditions in maternity caves of the bent wing bat Miniopterus schreibersii: an attempted restoration of a former maternity site. Wildlife Research 21, 607–619. Burgman, M. A., and Lindenmayer, D. B. (1998). ‘Conservation Biology for the Australian Environment.’ (Surrey Beatty: Sydney.) Clarke, R. H., Boulton, R. L., and Clarke, M. F. (2002). Translocation of the socially complex black-eared miner Manorina melanotis: a trial using hard and soft release techniques. Pacific Conservation Biology 8, 223–234. Cohen, J. (1988). ‘Statistical Power Analysis for the Behavioural Sciences.’ 2nd Edn. (Lawrence Erlbaum: Hillside, NJ.) Conroy, M. J., Samuel, M. D., and White, G. C. (1995). Journal News. Journal of Wildlife Management 59, 196–197. Fairweather, P. G. (1991). Statistical power and design requirements for environmental monitoring. Australian Journal of Marine and Freshwater Research 42, 555–567. Foin, T. C., Riley, S. P. D., Pawley, A. L., Ayres, D. R., Carlsen, T. M., Hodum, P. J., and Switzer, P. V. (1998). Improving recovery planning for threatened and endangered species. Bioscience 48, 177–184. Forcada, J. (2000). Can population surveys show if the Mediterranean monk seal colony at Cap Blanc is declining in abundance? Journal of Applied Ecology 37, 171–181. Franklin, D. C., Menkhorst, P., and Robinson, J. (1987). Field surveys of the regent honeyeater Xanthomyza phrygia in Victoria. Australian Bird Watcher 12, 91–95. Franklin, D. C., Menkhorst, P., and Robinson, J. (1989). Ecology of the regent honeyeater Xanthomyza phrygia. Emu 89, 140–154.
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Manuscript received 17 July 2002; accepted 24 June 2003
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