The power of monitoring programs to detect biological change: some examples from Kakadu and Litchfield National Parks, Northern Territory

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1 The power of monitoring programs to detect biological change: some examples from Kakadu and Litchfield National Parks, Northern Territory Owen Price, Alaric Fisher, Jeremy Russell-Smith, John Woinarski, Martin Armstrong Tropical Savannas Cooperative Research Centre & NT Department of Infrastructure, Planning and Environment PO Box 496, Palmerston, NT, 0831 Introduction The goal of biodiversity monitoring programs is generally to detect changes in plant and/or animal populations over time, typically from counts of individuals from repeated sampling of plots over time. An effective monitoring program must be able to separate real trends from noise arising from seasonal variation and other stochastic variation in count data, as well as sampling error and bias. The ability to reliably detect trends is encapsulated in the power of a monitoring program. Statistically defined, power is 1 - (beta), where beta is the probability of wrongly accepting a false null hypothesis (Type II error). Power is thus the likelihood of correctly rejecting the null hypothesis (typically for monitoring, that there is no change) when it is actually false. Power is influenced by many factors, including spatial and temporal variation in the system being monitored, the objectives of the monitoring program, sample design and sampling effort. Only some of these factors are liable to manipulation, the most critical being sample design (in order to minimise count variation), the number of samples, and the frequency of sampling. While the design of effective monitoring programs is a critical issue, with the requirement for robust and statistically defensible inference balanced against inevitable restrictions on resources (money, time and/or staff), the issue of power is often not addressed explicitly in the early stages of program design. Where it is not, there is a real danger of sampling effort being insufficient to detect trends or (probably less likely), resources wasted by collecting more data than required. There are few examples of terrestrial biodiversity monitoring programs in Australia that consider multiple species over landscape scales, and fewer still in northern Australia. The most ambitious example are probably the fire monitoring programs established in Kakadu National Park and subsequently in other large conservation reserves in the Northern Territory (Litchfield, Nitmiluk and Gregory National Parks), which seek to assess (amongst other things) the impacts of fire regimes on the biota of these parks. The Kakadu monitoring program is described in detail by Edwards et al (in press) and we briefly summarise it here as an interesting example of a landscape-scale biodiversity monitoring scheme. However, the main purpose of this paper is to use some data from these programs to investigate issues relating to power and sampling adequacy (and which which may prompt useful discussion in the workshop). In particular, we: - use vegetation data from the large number of monitoring sites in Kakadu National Park to assess the power of the program to reliably detect change in the abundance of individual plant species - use data from repeated fauna surveys in Litchfield Park to undertake a similar analysis. For various reasons, the Litchfield example does not necessarily represent a good model power of monitoring programs 1

2 for vertebrate fauna monitoring programs. However, we use this data in simulations to explore the relative influence of a number of factors on power and required sampling effort, and this may usefully inform the development of such programs. The authors do not claim a high level of statistical expertise, and the power analyses we present are somewhat simplistic, using software freely available on the Web. We also note that multiple-species monitoring programs may adopt more complex multivariate approaches to examining trends in community composition. Nevertheless, the general principles discussed here have wide relevance, and any attempts to explore the adequacy of existing or proposed biodiversity monitoring programs should be encouraged. Methods Kakadu monitoring program A major focus of biodiversity monitoring activities in Kakadu is the impacts of fire regimes on biodiversity. The focus on fire reflects the role of fire as a major driver of environmental change in northern Australia; the importance of fire as a management tool (both to traditional owners and rangers) within the Park; the acknowledged disruption of fire regimes that has occurred in the past century; observed changes in some biota attributed to this disruption; and the presence of fire-sensitive vegetation communities and species in the Park. The fire monitoring program comprises two complementary components: satellite mapping of fire events and on-ground assessment of biota at a large series of permanent plots. The annual cost of the program is estimated to be $138,300 (Edwards et al. in press). Satellite imagery has been used to compile a 21-year fire history across the entire Park area, which is validated by stratified aerial assessments. During the period , the mean annual extent was 40.3%, a slightly lower percentage than for the previous 15 years. The proportional area burnt varies between the main environments in the park, with highest fire frequencies in lowland woodland and open forest. In 1995, a network of 134 permanent (40mx20m) quadrats was established across the Park, representing all major environments. In positioning the sites, priority was given to areas likely to reveal environmental dynamics, such as ecotones and patches of fire-sensitive vegetation, as well as intensively-managed areas associated with Park infrastructure. At each plot, detailed information on vegetation structure and composition is recorded, including counts of all species within three height strata (number of individuals for trees and shrubs, and mean cover and frequency for ground-layer species, estimated using 1m 2 sub-plots). All plots were sampled in 1995 and re-sampled in 2000, with further resampling proposed for (at least) 2005 and Sampling of vertebrate fauna has also been recently added to the monitoring program, based partly on the fire plots. Fauna data is not discussed here, but further details are given by Edwards et al (in press) and.. power of monitoring programs 2

3 Litchfield fauna surveys An extensive vertebrate fauna survey was carried out in Litchfield National Park in 1995/6 (Griffiths et al 1997), with the aim of describing the fauna of the Park and providing a baseline against which to assess the effects of management practices. During this survey, vertebrate fauna were sampled at 116, 1ha sites representing the major environments of the Park, using a widely applied vertebrate survey methodology (eg. Woinarski ). In 2001/2, 46 sites were resampled using the same methodology. 40 of these sites overlapped the fire monitoring plots established in a program mirroring that described above for Kakadu National Park. In the Litchfield program there is a single fire plot within each of the most extensive land units described for the Park, and the plots are generally in areas likely to be affected by fire management regimes imposed by Park staff. The fauna sampling methodology involves estimating the relative abundance of bird, reptile, mammal and frog species in a 1 hectare site over a three day period, using a combination of visual census, Elliott traps, cage traps, pit buckets with drift fences, timed searches and spotlighting. It should be noted that this methodology was developed for inventory surveys rather than specifically for monitoring purposes. However, as is the case here, the large number of lots sampled in many areas of the Top End provide considerable scope for resurveys aimed at assessing changes in vertebrate fauna over time (eg ). It is instructive therefore, to examine the likely adequacy of such data to reveal trends. Power analysis Power analysis was undertaken using the (freeware) software MONITOR (Gibbs & Melvin 1997). MONITOR uses Monte-Carlo simulations to model count surveys over time, then generates observed detection rates derived from route-regression analyses. A large number of variables can be controlled, including the number of plots, the duration of monitoring, the interval of monitoring, the significance level associated with trend detection, the likely shape of population trends, the magnitude of counts per plot, count variation and trend variation. The latter two are important variables that describe the noise inherent in the biotic data: count variation is the variation observed in counts on each plot (ie. temporal variation on the same plot); trend variation is the degree to which trends vary between multiple plots. These variables can be derived from the monitoring plot data (as is the case here), from pilot studies specifically undertaken prior to establishing the program (the ideal situation), or extrapolated from other studies (a list and bibliography for measured count variation for a range of vertebrate species is listed at.) A more detailed analysis of data from the Kakadu monitoring program is presented by Edwards et al (in press). Monitoring power was not investigated for each species separately. Rather, count variation and trend variation were derived from the data for each species, and then mean values of these variables calculated for trees, shrubs and groundlayer species. The mean values were taken as representative of all species in that stratum, and used to investigate monitoring power for typical species. The number of sites required to detect a 2%, 5% and 10% annual decline with a minimum acceptable reliability (power) of was calculated, assuming that sites were monitored four times, with a fiveyear interval between samples (ie. the current situation). power of monitoring programs 3

4 For the Litchfield fauna data, count variation and trend variation were similarly estimated for each species, and then mean values of these variables used to represent typical species. The relationship between number of sites and power to detect change was then modeled for a number of scenarios (see Table 5 for details): - increasing the mean count within each plot - decreasing count variation - reducing or increasing trend variation - applying more or less stringent criteria for type 1 error rate (alpha) - reducing the period between samples - doing repeat sampling in the year of sampling Unless otherwise indicated, simulations assumed sites were monitored four times, with a five-year interval between samples (no repeat sampling within the year), with linear trends and an alpha of 0.1. Results Vegetation in Kakadu fire plots A total of 963 plant taxa were recorded from the two samples, representing c. 50% of the known taxa within the Alligator Rivers region. Only 7% of species occurred in at least 10% of site, and 37% of tree species, 48% of shrub species and 47% of ground-layer species were recorded from a single site. Site variance and trend variance were fairly low for trees and higher for shrubs and groundlayer species (Table 1), and were reasonably consistent within each group. For a minimum acceptable power of, an annual decline of 5% could be detected for a tree species occurring in 4 sites, or a shrub species occurring in 8 plots (Table 2). However, many more sites were needed for ground layer species, especially if cover or frequency data was used. 35% of tree species recorded during the surveys occurred in sufficient sites to be monitored reliably (Table 3). For shrubs, this percentage falls to 19% and for ground-layer species to 2.4% (presence-absence data) or 1% (cover or frequency data). Of all plant species recorded, only 10% occurred in sufficient sites to reliably monitor decline. Only one of 11 threatened plant species known to occur in Kakadu was recorded from a monitoring plot, and this species occurred in a single plot. Of the 344 obligate seeding (fire-sensitive) species recorded, only ten were recorded from sufficient plots to monitor reliably. A number of vegetation structure variables were quantified for each site (Edwards et al in press). Variation in structure variables was generally lower than that for individual plant species, and a value for these variables could be calculated for every plot. As a result, power to detect structural change was relatively high: only 4 plots were required to detect a 5% annual decline in tree count and 15 plots to detect a 5% decline in forb cover. power of monitoring programs 4

5 Vertebrates in Litchfield fire plots A total of 184 vertebrate species (19 frogs, 86 birds, 25 mammals, 54 reptiles) were recorded during the 2 surveys. Many species were infrequently recorded, with 39 species occurring at only one site, and 96 species at less than 5 sites. There was considerable variation between the two surveys. 141 species were recorded in the first, and 153 in the second, with 54 species recorded during only one survey. Particularly notable is the variation between surveys in composition of the same sites, with only 76 species reoccurring in the second survey at one or more sites, and only 22 species reoccurring at 5 or more sites. Of the 88 species occurring in at least 5 sites (both surveys combined), the majority reoccurred at less than 20% of those sites, and 24 did not reoccur at any site. While a mean of 17 species were recorded at each site, a mean 5.3 species were recorded from each site during both surveys. In fact, mean site richness (all vertebrates) varied significantly between the two surveys (15.0 and 19.0 species) this variation was due to an increased richness of birds in the second survey, and can probably be attributed to observer bias. An analysis of the change in frequency or abundance of individual species (Armstrong et al in prep) suggests a significant (p<0.1) change between surveys for 28 species (13 birds, 11 reptiles, 4 frogs, 4 mammals) of which 19 have increased and 9 decreased. Power analysis For tehmore frequent species, mean abundance and mean count variation did not vary greatly between taxonomic groups (Table 4). Count variation was considerably lower when the analysis was restricted to sites where the species was recorded during both surveys. With the current survey design, a very large number of plots is needed to reliably detect (power=) change for a typical species (site variance=125%): 80 plots for a 5% annual decrease and >200 plots for a 2% annual decrease (Fig 1). The power analysis is markedly asymmetric for increases and decreases, with only 16 plots are required to confidently detect a 5% increase. Increasing the mean abundance of a species without changing the count variance has virtually no influence on power (Fig. 2). However, a large improvement in power is achieved by reducing count variation (Fig 3): when this is reduced to 50%, a 5% annual decline may be reliably detected from only 12 plots. Power is maximised when trends are consistent across sites (ie. a trend variation of 0). However, the influence of trend variation on power is relatively subdued (Fig 4). The effect of varying the acceptable type I error rate is somewhat greater, but remains far less important than variation in counts (Fig 5). Fewer sites are required if the sites are sampled more frequently, either by replicating the sample within a year, or decreasing the number of years between samples (Fig 6). However, the reduction in number of sites (17-33%) is smaller than the increased investment in sampling (50-100%). Using the data from the Litchfield survey, only 8 sites are required to reliably detect a 5% annual decrease in site richness and 17 sites to detect a 2% decrease (Fig 7). However, power of monitoring programs 5

6 richness is in itself a particularly blunt measure of change, as significant compositional change may occur while site richness remains relatively constant. Discussion [to be completed]. Major points: the Kakadu monitoring program is very effective for tracking changes in vegetation structure, which may reflect both fire and other (eg. climate) effects the fire plots can successfully monitor plant species that are relatively common and stable in their pattern of occurrence, but this is a minority of species. It also includes very few species of special management significance. Other more specific programs are needed if these species are to be targeted [Nevertheless, the program has shown dramatic change sin abundance in a 5-year period for many shrub and ground-layer species]. both examples illustrate the problem of rare/sparse species. Approaches that groups species into functional groups need to be considered? the analysis method used here does not deal well with the situation of many plots having counts of zero. More thought about a different approach to this situation is required the Litchfield example shows that the inventory fauna survey methodology is not very suitable for monitoring purposes (particularly following trends in individual species). The low sample intensity and small site size contribute to the high number of sparse species and low reoccurrence rate, which is reflected in the high count variation. Resampling of survey sites may be more relevant to assessing changes in the frequency of species amongst sites within a region the analyses emphasise the importance of minimising count variation. Relative to the methodology used in Litchfield, this might be achieved by using larger sites, sampled over a longer period, pooling sites within the same habitat type the Litchfield example is useful for illustrating the influence of changing sampling frequency. In this case, greater gains could be achieved from sampling more plots or sampling each plot more intensively, than from more frequent samples. Power analysis is particularly useful in teasing this out. Note that power will also increase (or fewer plots are required) with an increase in the overall timeframe of the program. However, for management purposes, probably need to detect changes in a?10-year timeframe [note that a 5% annual change represents a very large change over 15 years, so you d hope that this sort of change could be reliably detected]. some note about the costs of each scheme and implications if all Parks were to be similarly monitored it should be recognised that monitoring programs in Parks (and elsewhere) have important collateral value in involving managers and helping them recognise the consequences of management action, so that detecting change is only one consideration in designing the program Acknowledgements - many people involved in establishing monitoring programs and collecting the data References - to be added power of monitoring programs 6

7 Table 1: Extent of change in vegetation parameters between the baseline and first resampling of plots, for different vegetation strata and measures of occurrence (for ground layer species). Stratum no. of species in 5 or more plots count variation mean % and (stdev) trend variation mean % and (stdev) trees (12.7) 18.4 (12.8) shrubs (19.1) 38.4 (13.8) ground layer cover (18.4) 42.8 (19.9) ground layer frequency ground layer presence/absence 62.4 (22.0) 64.4 (2) 47.9 (27.7) 61.6 (20.7) Table 2. Results from the power analysis of Kakadu plant data : the minimum frequency of plots from which a plant species needs to be reported in order to detect a decline. Stratum minimum plot occurrence needed to detect decline extent of decline 2% 5% 10% trees shrubs ground layer - cover > ground layer frequency ground layer presence-absence Table 3. Results from the power analysis of Kakadu plant data: the number of plant species that can be monitored reliably (power=). stratum number of species (A) number for which 5% decline detectable (B) B as a % of A trees shrubs ground layer: cover or frequency ground layer: presence-absence power of monitoring programs 7

8 Table 4. Mean site richness and temporal variation in site richness for the Litchfield fauna data. Only species occurring in at least 5 sites were included. Mean CV is equivalent to count variation. all sites where species occurs at least once only sites where species occurs twice mean abn mean CV mean abn mean CV birds mammals reptiles frogs Table 5. Inputs to MONITOR used to test the influence of each variable on power. The final column indicates which figure illustrates the comparison. The line labelled default is included in all subsequent comparisions. mean abun. site variance (%) trend variance (%) a (type I error rate) no. of surveys years between surveys resamples per survey Fig Fig Fig Fig Fig 3 default Fig Fig Fig Fig Fig Fig 6 power of monitoring programs 8

9 Fig 1. Litchfield fauna data relationship between number of sites and power for a typical vertebrate species (site variance=125%). See Table 5 for configuration of other variables. analysis 1 power 5% decrease 2% decrease 2% increase 5% increase number of sites Fig 2. Influence of mean abundance on power (curves for decrease only shown) changing mean abundance 5% decrease abn=2 2% decrease abn=2 5% decrease abn=10 2% decrease abn= n power of monitoring programs 9

10 Fig 3. Influence of site variance (curves for 5% decrease only shown) varying site variance (5% decrease) power cv = 125% cv = 100% cv = 75% cv = 50% number of sites Fig 4. Influence of trend variance (curves for 5% decrease only shown) changing trend cv (5% decrease) power trend cv = 25% trend cv = 0% trend cv = 50% number of sites power of monitoring programs 10

11 Fig 5. Influence of Type I error rate (curves for 5% decrease only shown) changing a (5% decrease) a = 0.1 a = a = n Fig 6. Influence of sample frequency (curves for 5% decrease only shown) changing sampling frequency (15 years, 5% decrease) power 1 sample, 5 year interval 1 sample, 3 year interval 2 samples, 5 year interval number of sites power of monitoring programs 11

12 Fig 7. Relationship between number of sites and power for vertebrate site richness, using Litchfield fauna data vert species richness n 5% decrease 2% decrease 2% increase 5% increase power of monitoring programs 12

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