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Experimental Evaluation of Koala Scat Persistence and Detectability with Implications for Pellet-Based Fauna Census

Experimental Evaluation of Koala Scat Persistence and Detectability with Implications for... Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 631856, 12 pages doi:10.1155/2012/631856 Research Article Experimental Evaluation of Koala Scat Persistence and Detectability with Implications for Pellet-Based Fauna Census 1, 2 3 1, 4 Romane H. Cristescu, Klara Goethals, Peter B. Banks, 2 5 Frank N. Carrick, and Celine ´ Frer ` e School of Biological, Earth and Environmental Sciences, The University of New South Wales, Kensington, NSW 2052, Australia Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia Department of Comparative Physiology and Biometrics, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium School of Biological Sciences, The University of Sydney, Camperdown, NSW 2006, Australia School of Land, Crop and Food Sciences, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia Correspondence should be addressed to Romane H. Cristescu, romromfr@yahoo.fr Received 1 May 2012; Revised 30 July 2012; Accepted 14 August 2012 Academic Editor: Stephen Secor Copyright © 2012 Romane H. Cristescu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Establishing species distribution and population trends are basic requirements in conservation biology, yet acquiring this fundamental information is often difficult. Indirect survey methods that rely on fecal pellets (scats) can overcome some difficulties but present their own challenges. In particular, variation in scat detectability and decay rate can introduce biases. We studied how vegetation communities affect the detectability and decay rate of scats as exemplified by koalas Phascolarctos cinereus:scat detectability was highly and consistently dependent on ground layer complexity (introducing up to 16% non-detection bias); scat decay rates were highly heterogeneous within vegetation communities; exposure of scats to surface water and rain strongly accelerated scat decay rate and finally, invertebrates were found to accelerate scat decay rate markedly, but unpredictably. This last phenomenon may explain the high variability of scat decay rate within a single vegetation community. Methods to decrease biases should be evaluated when planning scat surveys, as the most appropriate method(s) will vary depending on species, scale of survey and landscape characteristics. Detectability and decay biases are both stronger in certain vegetation communities, thus their combined effect is likely to introduce substantial errors in scat surveys and this could result in inappropriate and counterproductive management decisions. 1. Introduction to determine habitat preferences and predict habitat quality (e.g., [10, 11]) and are thus commonly used for monitoring Knowledge of species abundance and distribution must endangered wildlife [12] or managing game species [13]. underpin rational conservation and management decisions However, variability of both scat detectability and decay rate [1]. However, acquiring such critical information is far from has led to the expression of concerns regarding the reliability a trivial undertaking [2, 3]. This is particularly true for of such surveys [14]. cryptic animals (especially when they occur at low densities) Some sources of biases due to detectability and decay [4], for which there is often a need to use indirect survey have been widely studied, and methods have been developed methods [5]. These indirect methods include, but are not to compensate for them. For instance, scat detectability varies limited to, sign surveys [6, 7]; of which scat (fecal pellet) between observers but can be standardized by developing survey is one of the oldest and most widely used indirect personal correction factors or eliminated by using the same methods [8, 9]. More specifically, scat surveys are often used observer [15]. Scat decay can vary widely between seasons 2 International Journal of Zoology and can generally be reduced by restricting the performance of surveys to certain periods of the year [16, 17]. One critical source of bias seems unavoidable, however: environmental heterogeneity is present to some degree within almost all study sites (e.g., vegetation types, microclimate, presence and density of decomposers). The significance of the extent to which variability of environmental factors within study sites can influence scat detectability and decay rate remains unresolved [5]; thus since the occurrence of such environ- mental heterogeneity cannot be avoided, it is critical that the variability in scat detectability and decay rates between environments is quantified and accounted for. (a) Koalas, Phascolarctos cinereus,provide agood modelto investigate whether and how different environmental factors influence scat detectability and decay rate. Koalas use a variety of environments (vegetation communities, exposure, soil types, etc.) and are difficult to survey directly because of their cryptic, nocturnal habits and their low population density. Scat surveys have thus been widely used in studies of koala distribution [18, 19], habitat use [20, 21], and abundance [22], as well as frequently forming the basis for management [23]. One source of inaccuracy reported in other species is variability of scat decay linked with diet variability [24]; this should be negligible for koalas which are folivores with a relatively homogeneous diet all (b) year round, relying mainly on a few genera in the Family Myrtaceae, predominantly Eucalyptus, Corymbia, Melaleuca Figure 1: Example of two sites showing plot, cap, and bag, on two and Lophostemon [25, 26]. Thus environmental sources of different ground layer types. (a) Complex three-dimensional litter, variability in scat decay rates can be examined without the (b) simple Allocasuarina sp. litter. confounding effects of changes in diet. Previous reports have identified the need to incorpo- rate consideration of how koala scat decay rate [28]and were compared between scats protected or unprotected from detectability [22] influence the interpretation of scat surveys. the decomposing action of invertebrates. Rhodes et al. [28] focused on seasonal and geographical dif- ferences in climate, which they found significantly influenced the rate of scat decay but also emphasized that most of the 2. Materials and Methods observed variation in scat decay rates remained unexplained 2.1. Study Site. Thefieldworkwas conductedonNorth and highlighted the need for more research. Robust scat ◦  ◦ Stradbroke Island (NSI), Australia (27 23 /27 45 S, survey methodology is particularly critical because land use ◦  ◦ 153 23 /153 33 E) which has an area of approximately planning and management decisions often rely on such 27,500 ha. Koalas occupy a mosaic of vegetation comm- surveys (e.g., [21, 29]). These decisions affect localized unities (classification based on regional ecosystems [27]) extinction risk [30] and the fate of a species is often found on the island. Six remnant regional ecosystems were determined by the sum of these local extinctions [31]. Such selected on the basis of their use by koalas on NSI [34]: management decisions will especially impact on fauna that mallee Eucalyptus spp. low woodland; wetlands containing are not well represented in reserves due to competing land Eucalyptus, Lophostemon and Melaleuca spp.; Corymbia spp. use priorities, such as fauna favoring land that is level and/or open to low closed forest; E. racemosa woodland; Melaleuca at low elevation and/or composed of highly productive soils quinquenervia open forest to woodland; E. pilularis open [32]. Land use management agencies thus have a key role forest. In addition, two disturbed vegetation communities in species conservation [33], and they must, therefore, be (mine rehabilitation) were differentiated: one characterized confident that survey methodologies underpinning their by a complex litter layer, the other by a simple litter layer decisions are reliable. (see full description below and Figure 1). Three experiments In this study, we conducted three experiments to inves- were conducted as described below. tigate the effect of different environments on detectability and decay rate of koala scats. Firstly, the influence of ground layer complexity on scat detectability was investigated. Next 2.2. Experiment 1: Fecal Pellet Detectability in Litter Layers of scat decay rates in different vegetation communities used Different Complexities. In ordertomeasure variationinthe by koalas were analyzed, as was the relative influence on detectability of scats associated with vegetation communities decay rate of local environmental variables identified from of varying ground layer complexity, 30 plots (1 × 5m) were the literature as potentially important. Finally decay rates established. One researcher dispersed a random number of International Journal of Zoology 3 scats (1–22) in each plot. We used old scats collected from pseudo replicates (50 m apart) were laid, amounting to 48 the field, as fresh pellets have a more conspicuous color and sitesintotal.Eachscatgroup wasplacedina10 × 10 cm plot patina that makes them easier to find. This fresh condition on the ground directly below the canopy of potential koala lasts only a few days, consequently it does not characterize fodder or roosting trees of genera Eucalyptus, Corymbia, most scats naturally found during surveys (RC, personal Melaleuca,or Lophostemon. observation). A second researcher (RC), who conducted all During the first 48 hours after scat placement, the study searches to eliminate observer bias [15], searched the plot sites were struck by unusually heavy rainfall. Sites were without prior knowledge of the number of scats in the plot checked the next day and some scats had already disappeared. (zero scat was a possible outcome). The search had no time It was feared that the rain had either soaked and disintegrated limit and ended when the second researcher was confident some scats or washed them away. Consequently, 10 randomly the plot had been thoroughly searched. The total time taken selected new scats were added in an additional plot next to search the plot to achieve this level of confidence was to each of the initial 48. Hereafter, the initial plots will be recorded, as well as the percentage of scats found and the referred to as rained plots and the ones deployed just after the time needed to find each scat. rain will be simply referred to as plots. High rainfall events Plots were established in vegetation communities classi- were not recorded again during the rest of the experiment. fied in three groups on the basis of ground layer complexity: Scat locations were visited once a week, when the number (1) the simple litter group (N = 10) which had a flat litter and condition of remaining scats were recorded and the layer composed of Allocasuarina needles, with little to no condition of scats described (from intact to scat almost plants or woody debris (<5%) and varying amounts of bare unrecognizable, Table 1). When the weekly observations ground (0 to 20%); (2) the complex litter group (N = 10) indicated decay rate had slowed down, scats were checked which was composed of a three-dimensional litter of leaves every two weeks, then less often. The survey lasted 36 weeks and bark, no bare ground, and some plants and woody debris in total, by which time 50% of the scats had disappeared. (between 20% and 90%, Figure 1); (3) the highly complex Again, counts and classifications of the condition of scats litter group (N = 10), in which the substrate was mostly were conducted by a single researcher to eliminate observer covered with plants and woody debris (>90%). For the bias [15]. simple and complex litter groups, all plots were searched for Variables characterizing the local environment in which scats beforehand to ensure that no scat was present prior to the scats were observed were also recorded at each visit. Scat the experiment. For the highly complex litter group, however, moisture (wet or dry), accumulation of litter fallen on top presearching the plots would have disturbed the plots to an of scats (presence/absence), and the activity of detritivores extent that would have compromised the experiment; thus (defined here as the presence/absence of invertebrates or they were not presearched. In order to minimize unwanted fungal-type organisms) on scats were recorded as binary presence of scats prior to our experiment in the highly scores. At the end of the experiment the results were complex plots, we located the plots outside the known koala averaged. Site elevations were extracted using Terramodel distribution on the island but with substrates comparable to Version 10.61 from a 2008 airborne laser scan of the island the highly complex litter substrates in areas known to be used (Sibelco, unpublished data). A final environmental variable by koalas. Each of the three litter groups was replicated at two was flooded. The flooded variable indicated, for individual locations (five plots at each location). At each location, plots plots, the occurrence of surface water in the vicinity of scats were placed 50 m apart from one another. at any time during the experiment. Although, as indicated above, in some species scat decay rate can vary with diet, we did not expect that scats 2.3. Experiment 2: Scat Decay Rate in Different Vegetation from hospitalized koalas would decay significantly differently Communities. Fresh scats (1,980 in total) were collected from scats of free-ranging koalas. Indeed, the food items from 15 females and 31 males aged from 1 to 10 years and consumed were closely related, even if differences in the housed at the Australian Wildlife Hospital; these were placed particular tree species eaten occurred (Table 2). However, in different vegetation communities, and their decay rate was we tested this assumption by comparing the decay rate recorded. The koalas had been in the hospital for less than of scats from hospitalized koalas and free-ranging koalas from NSI. Scats from free-ranging koalas were collected 2.5 months, and none had received treatments that could alter scat decay rate (e.g., worm treatment). Hospital cages from two females. Groups of ten randomly selected scats are cleaned daily so all scats used in this experiment were were deposited beside plots representing six out of the eight vegetation communities previously described, since it was less than 24 h old. Scats from all 46 koalas, were mixed and ten scats were randomly selected to form a group. Each scat not possible to collect sufficient fresh scats from wild koalas group was weighed and groups <6g or >10 g were discarded to include all eight communities. to ensure homogeneity of groups. On 14 and 15 February 2010 (scats were stored in a refrigerator overnight), scat groups were placed in each 2.4. Experiment 3: Variability of Scat Decay Rate in Relation to of the eight different vegetation communities previously Invertebrates. Lepidopteron larvae develop in and consume described, with three replicates per vegetation community koala scats, while adult Coleoptera also exploit koala scats (24 locations in total). Replicates were several kilometers as a food source [35–37]. To investigate the effect of apart (mean = 6.6 km, SD = 3.9) and at each location, two invertebrates on scat decay rate, we partially or totally 4 International Journal of Zoology Table 1: Condition of scats at Week 1 and Week 12 in the different treatments. Week 1 Week 12 Treatments Intact Fibrous Surface eaten Part eaten Mass of fibre Melted Buried Part eaten Half eaten Fibrous Melted ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Plot 270 107 32 63 1 07 220 114 25 0 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Rained plots 132 129 44 113 18 50 165 118 19 0 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Caps 217 72 27 93 4 19 241 111 3 4 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Bags 272 183 6 14 2 10 454 23 1 1 Test statistics 32.1 17.6 14.3 43.1 14.3 6.2 7.5 88.6 44.9 16.5 2.0 P values <0.001 0.001 0.002 <0.001 0.002 0.102 0.057 <0.001 <0.001 0.001 0.570 Plot 56.3 22.3 6.7 13.1 0.2 0 1.5 61.3 31.8 7.0 0 Rained plots 29.9 29.3 10.0 25.6 4.1 1.1 0.0 54.6 39.1 6.3 0 Caps 51.3 17.0 6.4 22.0 0.9 0.2 2.1 67.1 30.9 0.8 1.1 Bags 56.9 38.3 1.3 2.9 0.4 0.2 0.0 94.8 4.8 0.2 0.2 ∗∗ ∗ Scat number: in bold, results are significantly different across treatments, Kruskal-Wallis tests (df = 3): P ≤ 0.001, P < 0.01. Condition of scats are classified as (1) intact: the scat was complete; (2) surface eaten: the scat presented a rough surface; (3) partly eaten: parts of the scat were missing; (4) half eaten: at least half of the scat had disappeared; (5) fibrous: the inner matrix had disappeared, only fibers remained visible, but the scat shape remained present; (6) mass of fiber: scats constituted of a shapeless mass of fiber. Rare states of scats were (7) scats “melted” to a shapeless entity or (8) partially buried by invertebrates. Table 2: Eucalypt species fed to hospitalised koalas between 1 and 6 days [26] before their scats were collected in comparison to species found in the diet of wild koalas on NSI (only E. tereticornis, E. robusta, and E. resinifera are present on NSI, their percentages are taken from Cristescu et al. [34]). Common name Scientific name % fed between last 1 to 6 days % eaten by NSI wild koalas Grey gum E. propinqua, E. punctata, E. major 41% Blue gum E. tereticornis 23% 12% River red gum E. camaldulensis 17% White gum E. dunnii 5% Swamp mahogany E. robusta 5% 4% Flooded gum E. grandis 4% Red stringybark E. resinifera 2% 11% Ironbark E. crebra 1% Mountain Blue gum E. deanei 1% Spotted gum E. maculata 1% protected some scats from their influence. Next to the 48 2.5. Data Analysis. All variables were tested for normality plots described earlier, two groups of 10 random scats were and homoscedasticity (Levene’s test) and appropriate para- added (Figure 1). One group of 10 scats was placed on the metric or nonparametric tests were performed in PAWS ground, covered with an insect screen (1 mm mesh, which Statistics 18.0 [38]. Significance was taken to be P< was expected to be fine enough to impair lepidopteron and 0.05 (except when accounting for Bonferroni’s adjustment), coleopteran access) secured into the ground to protect the standard error of mean (SEM) and standard deviation (SD) scats from ground-surface-dwelling and flying invertebrates. are given as appropriate [39]. These protected scat groups are referred to as caps.The Relationships between response and explanatory vari- second groups of 10 scats (referred to as bags) were placed ables were investigated using a survival model for interval- into a sealed insect screen bags and placed directly on the censored data [40], with the 48 sites treated as random effect litter. This protected the scats from any invertebrate damage (more often referred to as a frailty effect in survival analysis, (i.e., no invertebrate was observed inside the bags during any [41, 42]). Our data were analyzed using the proportional visits, while some invertebrates were recorded on the scats hazards model proposed by Bellamy et al. [41]. A Weibull in the plots, see Experiment 2). All the scats in caps and distribution [43] was assumed for the event times and a bags were deposited prior to the heavy rainfall event. Scats lognormal distribution for the frailties [41]. in caps and bags were checked at Week 1 and Week 12 and For model selection, an apriori model approach was the numbers of scats remaining and their condition were used so as to avoid data dredging [44, 45]and to reduce compared to the unprotected scats in the rained-plots and the possible selection of noise variables [46]. Apriori models plots. Scats in caps and bags were observed to be wet and thus, were chosen to investigate the main variable of interest as anticipated, the treatments did not seem to have protected (vegetation communities) and to see if any other envi- the scats from the rain. ronmental variables had an additional effect, while taking Percentage Scat number International Journal of Zoology 5 Table 3: Percentage of scats detected and time needed to detect them, in three different ground layer complexities. Mean time taken Mean percentage Characteristics of Mean percentage of Time of search (min) Mean time to find by scat (sec) found at 2 min search scats found (± SEM) the 1st scat (sec) Mean Minimum Maximum Simple litter 100.0 3.4 2.1 6.2 0.73 87.3 22 Complexlitter 97.7(±1.1) 15.8 7.1 25.2 1.05 30.3 10.1 Highly complexlitter 83.9(±4.4) 34.3 15.4 48.6 2.61 11.2 17.5 SEM: standard error of mean. into account the known effect of rain [47]. Environmental variables chosen based on the literature [17, 48, 49]were: detritivores, litter, elevation, and flooded (scat moisture was measured but not included, as it was correlated with other 0.8 explanatory variables). Models were based on the two main 12.2.6 Rehab variables (vegetation communities and rain) separately or complex in combination, then each combination of the four other 12.2.5 0.6 environmental variables was added. 12.2.10 Rehab simple Data were graphically analyzed for skewed explanatory 12.2.8 variables and none was observed. Explanatory variables were 12.2.7 standardized (z-transformed) to allow comparisons of model 0.4 parameter estimates [50] and collinearity was tested with a variance inflation factor (VIF). As reported above, moisture level had to be excluded as it was correlated with other 0.2 explanatory variables. Models were fitted with the nlmixed 12.2.15 (wetland) procedure in SAS 9.2 for Windows and were ranked on the basis of AICc, the Akaike’s information criterion corrected for small sample size. Multimodel inference methods were used to determine 0 10 20 30 40 the relative importance of explanatory variables based on Weeks our set of models. Based on AICc, Akaike differences (Δ) between each model and the most parsimonious model Figure 2: Kaplan-Meier survival curves of scats, displayed by veg- were calculated, as well as Akaike weights, a measure of etation community. NB: vegetation communities (12.2.10: mallee Eucalyptus spp. low woodland, 12.2.15: wetlands, 12.2.5: Corymbia the weight of evidence of each model; and the evidence spp. open to low closed forest, 12.2.6: E. racemosa woodland, 12.2.7: ratios. To account for model uncertainty, the model average Melaleuca quinquenervia open forest to woodland, and 12.2.8: E. parameter estimates and the unconditional standard error of pilularis open forest) follow Queensland Herbarium [27]exceptfor each estimate were calculated. the addition of simple and complex rehabilitation. 3. Results 3.2. Experiment 2: Scat Decay Rate in Different Vegetation 3.1. Experiment 1: Fecal Pellet Detectability in Litter Layers Communities. Before performing any analyses, we con- of Different Complexities. The percentage of scats found firmed that the average weight of each group of 10 scats was decreased with increased litter complexity (Kruskal-Wallis = not different between and within vegetation communities 14.85, P = 0.001); from 100% of scats found in simple litter (ANOVA: rained-plots: F = 1.007, P = 0.441; plots: F 7,47 7,47 to 97.7% (SEM = 1.1) in complex litter and down to 83.9% = 2.107, P = 0.065). Next it was confirmed that the origin (SEM = 4.4) in highly complex litter (Table 3). The total of scats (free-ranging/hospitalized koalas) had no significant time taken to search the plot increased with litter complexity effect on scat decay rate (survival model, β = −0.14; SE = (Kruskal-Wallis = 24.56, P< 0.001). It took an average of 0.20; P = 0.51). 3.4 minutes (SD = 1.2) to search plots in simple litter, 15.8 Survival curves [51] showed that scats placed in wetlands minutes (SD = 6.6) in complex litter, and up to 34.3 minutes decayed faster than scats placed in any other vegetation (SD = 10.0) in highly complex litter. The mean time taken to community investigated (Figure 2). Within each vegetation find a scat increased with litter complexity (Kruskal-Wallis community, the number of scats remaining after 36 weeks = 16.29,P< 0.001), while the mean percentage of scats found was highly variable (every possibility between zero and 10 after searching for 2 min decreased with litter complexity scats, Figure 3(a)). When averaged across the 36 weeks (Kruskal-Wallis = 23.06, P< 0.001). However, the mean duration of the experiment, all vegetation communities had a time to find the first scat was similar across the three litter median of between 7 and 9 remaining scats, except wetlands complexities (Kruskal-Wallis = 0.01, P = 0.995). which had a median of 5 (Figure 3(b)). Survival 6 International Journal of Zoology The most parsimonious survival model contained rain, flooded, and litter (Table 4). This model indicated that both rain and flood increased scat decay rate and that litter 8 ∗ decreased decay rate (Table 5); however, four of the 48 models were well supported (<2ΔAIC). The three variables 6 characterizing the most parsimonious model were also present in all other models within 2ΔAIC. The “vegetation” variable was not incorporated in the four best models and only one vegetation type was associated with a significant influence on scat decay in our study: wetlands were found to increase decay rate (Table 5). It is noteworthy that most plots in wetlands had been consistently flooded (all but one of the wetland plots was flooded, whereas only one plot outside wetlands, in Melaleuca quinquenervia open forest to woodland, was flooded). (a) 3.3. Experiment 3: Variability of Scat Decay Rate in Relation to Invertebrates. After one week, the number of scats remaining in the rained plots was similar to the number of scats protected from invertebrates by caps placed above them (Mann-Whitney test = 932.0, P = 0.077); both were lower than the number of scats protected in bags (see Table 6 for details). After 12 weeks, rained plots contained significantly fewer scats than found in caps, which in turn contained fewer scats than remained in bags (see Table 6 for values and P values). The condition of the scats also varied with 0 the treatments; with the best preserved scats being the ones in the bags, while the most deteriorated scats were in the rained plots (Table 1). Overall, the scats protected from all invertebrate activity in bags were best preserved in terms of both quantity and quality, while scats in the rained plots were the worst preserved on both accounts. (b) Figure 3: Boxplots (minimum, first quartile, median, third quar- tile, and maximum, with outliers ◦ and extreme values ∗) show-ing 4. Discussion the variability of the average number of scats remaining by vege- tation community (a) after 36 weeks, (b) all 36 weeks combined 4.1. Scat Detectability and Bias. Most study sites encompass (grey lines in (b) include max and min medians of all but wetlands). different vegetation types with potentially variable ground Boxplots are ordered from the vegetation community with the least layers. Here we demonstrated that scat detectability varied remaining scats (12.2.15) to the one with most remaining scats with ground layer complexity, with up to 16% variation in (12.2.6). NB: vegetation communities (12.2.10: mallee Eucalyptus the proportion of scats detected between plots of different spp. low woodland, 12.2.15: wetlands, 12.2.5: Corymbia spp. open ground layer complexities. The measured 16% variation in to low closed forest, 12.2.6: E. racemosa woodland, 12.2.7: Melaleuca scat detectability most likely underestimated the bias ratio quinquenervia open forest to woodland, and 12.2.8: E. pilularis open that would be present in many other scat survey studies: (a) forest) follow Queensland Herbarium [27] except for the addition the time dedicated to searching for scats (up to 48 minutes) of simple and complex rehabilitation. was much higher in our study than would be provided for in most scat surveys which usually have to be performed under arbitrary time and budget constraints; (b) no maximum time limit was imposed for scat searches; (c) limited search the robustness of scat surveys requires standardizing the time would most likely decrease the probability of finding search area (plots of a fixed size) rather than standardizing scats, especially at sites with high ground layer complexities. search time. Nevertheless, the majority of studies relying on scat surveys This detectability limitation might be lessened because for koalas use some variation of the Spot Assessment the SAT search is terminated when the first scat is found. Technique (SAT) [52, 53], where the search for scats lasts for In all the plots in our study, the first scat was found within a maximum of 2 minutes (or until the first scat is found). Our the first two minutes of search, and the time to find the first results demonstrate that with a constant amount of search scat appeared not to be correlated with the complexity of time per tree, proportionately more scats would be missed in the substrate. Nonetheless, it is likely that the search time complex ground layers and thus a detectability bias would be to find the first scat would be increased in many real-world introduced. Given this potential detectability bias, improving situations, in contrast to our survey sites where scats were Scats Scats 12.2.15 12.2.15 (wetland) (wetland) 12.2.7 12.2.7 12.2.8 12.2.8 Rehab simple Rehab simple 12.2.10 12.2.10 Rehab complex Rehab complex 12.2.5 12.2.5 12.2.6 12.2.6 International Journal of Zoology 7 Table 4: Model selection for explaining scat decay rate during 36 weeks, all models were fitted with interval censored survival model with the 48 sites as frailty effect. Models AICc ΔAICc AICc weight Evidence ratio Rain + litter + flooded 3633.4 0 0.259 1 Rain + litter + flooded + elevation 3633.5 0.1 0.246 1.05 Rain + litter + detritivores + flooded + elevation 3633.6 0.2 0.234 1.11 Rain + litter + detritivores + flooded 3633.9 0.5 0.202 1.28 Vegetation + rain + litter + detritivores + flooded 3639.6 6.2 0.012 22.20 Vegetation + rain + litter + detritivores + flooded + elevation 3639.8 6.4 0.011 24.53 Rain + detritivores + flooded 3639.9 6.5 0.010 25.79 Vegetation + rain + litter + flooded 3640.5 7.1 0.007 34.81 Rain + detritivores + flooded + elevation 3640.6 7.2 0.007 36.60 Vegetation + rain + litter + flooded + elevation 3641.3 7.9 0.005 51.94 Vegetation + rain + detritivores + flooded + elevation 3644.3 10.9 0.001 232.76 Vegetation + rain + detritivores + flooded 3644.9 11.5 0.001 314.19 Rain + flooded 3644.9 11.5 0.001 314.19 Vegetation + rain + litter + detritivores + elevation 3645.3 11.9 0.001 383.75 Rain + litter + elevation 3645.7 12.3 0.001 468.72 AICc: akaike information criterion corrected for small sample size, ΔAICc: AICc differences, see descriptions in text (models with a weight <0.001 are not shown). Table 5: Relative variable importance (by decreasing importance) across all models searching for environmental variables explaining koala scat decay. Variable Model-averaged estimate Unconditional standard error estimator Relative variable importance Rain 1.02 0.26 0.99 Flooded 0.67 0.10 0.99 Litter −1.06 0.35 0.98 Elevation −0.01 0.00 0.50 Detritivores −0.97 0.72 0.48 Rehabilitation −0.10 0.47 0.03 RE 12.2.5 0.55 0.57 0.03 RE 12.2.6 0.14 0.56 0.03 RE 12.2.7 0.12 0.41 0.03 RE 12.2.8 0.56 0.42 0.03 RE 12.2.10 0.45 0.48 0.03 RE 12.2.15 0.94 0.60 0.03 Table 6: Number of scats remaining at week 1 and week 12 in the different treatments (N = 48 per treatment, see text) and statistical differences between treatments (P values and Mann-Whitney U test statistics, Bonferroni’s adjustment α = 0.004). Rained plot Cap Bag Mean SD Min Max UP value UP value UP value Plot 9.85 0.46 8 10 612.5 <0.001 845.5 0.002 1078.0 0.229 Rained plot 8.17 2.43 0 10 932.0 0.077 557.0 <0.001 Week 1 Cap 8.75 2.37 2 10 781.0 <0.001 Bag 9.96 0.2 9 10 Plot 7.63 2.66 0 10 680.0 <0.001 1055.0 0.467 438.0 <0.001 Rained plot 5.94 2.75 0 10 643.0 <0.001 93.5 <0.001 Week 12 Cap 7.67 3.12 0 10 575.5 <0.001 Bag 9.75 0.93 5 10 Min: minimum number of scats, max: maximum number of scats. 8 International Journal of Zoology deposited just before the search (for example, this precluded ground layer complexity based on available studies (e.g., the the pellets being obscured by litter fall). Perhaps even more present study and see also [22]). importantly, a major component of the present study was to investigate the recovery rate of scats from different substrates, thus the numbers of scats deposited in the plots were higher 4.2. Scat Decay Rate and Bias. In comparison to scat than would be found naturally in most circumstances. From detectability, variability in decay rates was not consistently the data collected from 36 plots on North Stradbroke Island associated with particular vegetation communities (Figure 3, [54], the average natural scat density was just under 1,100 Table 4). For instance, extreme outcomes were seen for per hectare, whilst in the experimental plots in this paper, the two plots inside the same vegetation community after 36 (artificial) scat density was over 27,300 per hectare. Thus the weeks, despite those plots being only 50 m apart: one time to find the first scat during searches could potentially had 100% scats remaining and the other had 0%. Similar be around 25 times longer in natural conditions than in our results were also found in a study by Rhodes et al. [28], experiment; hence it will often require much more than two where a high and unexplained proportion of decay rate minutes to find the first scat in routine surveys. variability was associated with the variation between plots The detectability bias determined in the present study of within a site. In the current study, it is worth noting up to 16% (likely to be even higher in the context of SAT- that the treatments preventing invertebrates from accessing type approaches) has major implications for indirect fauna the scats greatly influenced scat decay rate. The fine-scale surveys. Even slight variations in detection probability across variation of invertebrate densities and the stochastic chances different vegetation communities can seriously skew study of invertebrates finding scats could explain much of the results [55, 56]. A simulation study modeling species occur- small-scale heterogeneity of scat decay rate. Whatever the rence as a function of habitat covariates evaluated the errors reasons, the lack of consistency in scat decay rate makes the resulting from imperfect detections [55]. A detectability bias development of a correction factor difficult. One possible of 15 to 20% (as found in our study) resulted in up to 200% way to compensate for variability in scat decay rate could be relative bias when estimating parameters (i.e., an over or to increase the intensity of sampling within each vegetation under estimation of 200% of the effect of a habitat variable community in order to average out the fine-scale heterogene- on a species’ occupancy). ity [28, 63]. A widely used and efficient technique to account for One exception to the inconsistency in decay rate relates imperfect detection is distance sampling, a method where to the vegetation community associated with wetlands. In perpendicular distance from a transect to a fecal mass is used wetlands, the decay rate was consistently faster than that to estimate a detection function [57]. Detection functions observed in other vegetation communities (Figures 2 and 3). account for a greater chance of imperfect detection further Plots in wetlands were consistently and selectively flooded, away from the transect and can be derived for observers or which was found to accelerate scat decay rate (Table 5). habitats. Distance sampling has, for instance, been widely This suggests that strong biases could be introduced when used in dung surveys of elephant Loxodonta sp. populations landscapes with surface water present (or probably even very [58]. However, this method is not feasible for koalas given moist substrates) are being surveyed. Koalas actually tend the difficulty of locating their scats: indeed, the probability to favor wet habitats [21] and so for this species, there is of detection for koala scats does not equal one even on the the paradoxical likelihood that there will be a low density of transect and thus transgresses a fundamental assumption of detectable scats present in areas supporting a relatively high distance sampling [57]. density of koalas. Despite this, few studies have attempted to Other methods have been developed to deal with control for decay bias in koala scat surveys and even these imperfect detection using zero-inflated processes [2, 59– studies have tried to account for scat decay by excluding 61]. These methods rely on repeated surveys over a short scats older than a certain threshold [20, 22, 64]. Scat aging, time [56, 62]. While these methods are based on direct however, has been found to be generally unreliable [48]or animal surveys, they should also be applicable to indirect at best limited to highly skilled and experienced researchers animal surveys. Some of these approaches even allow the [65]. Nevertheless, some species have had more objective probability of detection to vary with characteristics of the criteria developed such as in the case of the gorilla Gorilla vegetation communities being surveyed [59]. As suggested gorilla, for which dung age has been reliably correlated with by MacKenzie [56], repeated samples could also be collected dung pile height [12]. For species where objective criteria to by multiple observers carrying out searches on the same plot age scats are not yet available, developing correction factors and comparing scat detection [15], as long as interobserver by measuring scat decay rates in sites where an accelerated variability is modeled to reduce or eliminate bias from this decay rate can be expected is strongly indicated.The most source. useful method for developing these factors is retrospective As a last resort, consistency in detectability bias could estimation of decay rate, where fresh scats are recorded in allow for the determination and incorporation of a correc- different habitats at different times prior to the survey. At the tion factor. If researchers and managers are using a one off time of the survey, all the previously marked scat locations scat survey, they will need to incorporate methods to account are revisited, and the percentage of decayed scats per habitat for the effects of variation in ground complexity in their is calculated [66]. This accounts for specific influences (e.g., protocols. For instance, in distribution models based on scat temperature, precipitation) that the surveyed scats and the presence/absence, the results could be weighted for variable marked scats share (see an example for deer in [67]). A International Journal of Zoology 9 Table 7: Comparison of the percentage of predicted scat recovery in two extreme vegetation communities based on observed detectability and decay rates. % scats remaining due to decay % scats found due to both biases Habitat % detected 3 months 9 months 3 months 9 months No rain Rain No rain Rain No rain Rain No rain Rain Wetland 83.9 40.0 40.0 18.3 10.0 33.6 33.6 15.4 8.4 Simple litter rehab 100.0 73.3 50.0 68.3 42.9 73.3 50.0 68.3 42.9 retrospective estimate for koala scats presents two main most other vegetation communities. For the same density of difficulties. (1) It is recommended for the retrospective scats deposited in wetlands and in rehabilitated areas with method that the scats be surveyed long enough for 90% of simple litter ground layers, the proportion of scats found in a scats to have decayed. Given that after 9 months only 50% survey would vary widely (Table 7). In the worst case, after of our scats had disappeared, a retrospective survey for scat 9 months, a survey would detect 8% of the total original decay would mean going in the field, in each different habitat, scats in wetlands against 42% in rehabilitated vegetation. for an extensive amount of time prior to the survey. (2) On In this example, if the actual koala utilization rate of both each of these occasions, koalas would have to be found and areas were equal, wetlands would be wrongly classified as fresh scats detected and marked for the study. Koalas are five times less used by koalas. A bias of this magnitude, if difficult and time consuming to find which is, of course, not able to be corrected, would invalidate any interpretation why indirect methods are used in the first place. However, of koala distribution or habitat preference. The potential for the use of fresh scats obtained from captive or radiotracked such biases to exist more generally, seriously questions the koalas and distributed to establish a surrogate for randomly validity of scat surveys which fail to determine whether those located pellets might help resolve that second difficulty. biases are actually present. This time-consuming aspect of the retrospective scat decay In this paper, we present the first comprehensive study analysis approach prior to the actual scat survey needs to be of two significant biases of fecal pellet surveys, detectability taken into account when evaluating and selecting between and decay of scats, which were found to be a function different survey options. Furthermore, as there might be of the different environments present within a study site. considerable interannual variations in many parameters Biases are often acknowledged and occasionally dealt with influencing scat decay (e.g., temperature, precipitation, or when comparing studies being undertaken in different even insect abundance), deriving a correction from a single geographical locations, climates, or seasons. On the other survey might not be appropriate to rely upon to correct hand, the biases within a particular study area are still often for bias in subsequent surveys. Thus the time-consuming deemed negligible and dismissed without any real evidence. retrospectiveestimateofscatdecay priortoeachscatsurvey However, our study demonstrates the presence of biases might be warranted. inherent in using indirect signs of a species’ presence for Another method proposed to limit decay bias is to clear assessing the species’ distribution or habitat preferences, plots yearly [48]. However, we observed substantial decay let alone confounding estimation of its abundance. Scat heterogeneity over a period much shorter than one year. On detectability bias can be controlled because of its consistency the basis of our results, clearing plots five weeks prior to the and the existence of appropriate methodologies, thus it survey could remove bias arising from heterogeneous decay should be accounted for in future scat survey studies. Scat rates. This method has been used in a study of macropods, decaybiasactsinmorecomplex ways andmay be an even where plots were cleared one month before the survey [68]. more problematic issue. In extreme cases, scat decay bias For fauna found typically at low density, however, clearing might erroneously decrease the estimated value of what is sites one month prior to surveys might result in scats not actually prime habitat. This was a real effect observed in being deposited in the interval between clearing and survey. our case study, where the focal species (the koala) prefers In any case, it might not be possible to remove 100% of the types of habitats where there is also a higher scat decay rate. scats when clearing plots, resulting in further errors ([69], Further studies to determine exactly how to use additional but see avenues for potential solutions in [28]). corrections to decrease scat decay bias to an acceptable level (e.g., sampling effort, whether retrospective estimate of decay rate can be reused) are still required [28]. But it is clear from the present study that wet and dry sites will differ 4.3. Combination of Detectability and Decay Rate Variability. significantly in scat decay rate and until similar studies have The percentages of scats that would be found in a survey after been replicated in a variety of different environments, the accounting for detectability and decay biases were compared conservative assumption must be that there will be a high between two vegetation communities (Table 7). On the one degree of site specificity of this parameter and each distinct hand, wetlands have the worst detectability owing to very location will require calibration (with its own retrospective dense ground vegetation and the quickest decay rate owing to estimate of decay rate). their proneness to flooding. On the other hand, rehabilitated The magnitude of potential detectability and decay biases vegetation communities with simple litter recorded the best combined could seriously impede scat survey reliability in detectability and a decay rate not significantly different from 10 International Journal of Zoology any species. These biases can occur even at a relatively gorillas and dung height,” Ecological Applications, vol. 17, no. 8, pp. 2403–2414, 2007. small scale because most study areas are heterogeneous. [13] C. C. Webbon, P. J. Baker, and S. Harris, “Faecal density counts Such biases associated with particular habitats are likely to for monitoring changes in red fox numbers in rural Britain,” introduce errors in scat surveys which in turn could lead to Journal of Applied Ecology, vol. 41, no. 4, pp. 768–779, 2004. inappropriate management decisions. [14] W. M. Block, A. B. Franklin, J. P. Ward, J. L. Ganey, and G. C. 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Experimental Evaluation of Koala Scat Persistence and Detectability with Implications for Pellet-Based Fauna Census

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Abstract

Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 631856, 12 pages doi:10.1155/2012/631856 Research Article Experimental Evaluation of Koala Scat Persistence and Detectability with Implications for Pellet-Based Fauna Census 1, 2 3 1, 4 Romane H. Cristescu, Klara Goethals, Peter B. Banks, 2 5 Frank N. Carrick, and Celine ´ Frer ` e School of Biological, Earth and Environmental Sciences, The University of New South Wales, Kensington, NSW 2052, Australia Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia Department of Comparative Physiology and Biometrics, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium School of Biological Sciences, The University of Sydney, Camperdown, NSW 2006, Australia School of Land, Crop and Food Sciences, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia Correspondence should be addressed to Romane H. Cristescu, romromfr@yahoo.fr Received 1 May 2012; Revised 30 July 2012; Accepted 14 August 2012 Academic Editor: Stephen Secor Copyright © 2012 Romane H. Cristescu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Establishing species distribution and population trends are basic requirements in conservation biology, yet acquiring this fundamental information is often difficult. Indirect survey methods that rely on fecal pellets (scats) can overcome some difficulties but present their own challenges. In particular, variation in scat detectability and decay rate can introduce biases. We studied how vegetation communities affect the detectability and decay rate of scats as exemplified by koalas Phascolarctos cinereus:scat detectability was highly and consistently dependent on ground layer complexity (introducing up to 16% non-detection bias); scat decay rates were highly heterogeneous within vegetation communities; exposure of scats to surface water and rain strongly accelerated scat decay rate and finally, invertebrates were found to accelerate scat decay rate markedly, but unpredictably. This last phenomenon may explain the high variability of scat decay rate within a single vegetation community. Methods to decrease biases should be evaluated when planning scat surveys, as the most appropriate method(s) will vary depending on species, scale of survey and landscape characteristics. Detectability and decay biases are both stronger in certain vegetation communities, thus their combined effect is likely to introduce substantial errors in scat surveys and this could result in inappropriate and counterproductive management decisions. 1. Introduction to determine habitat preferences and predict habitat quality (e.g., [10, 11]) and are thus commonly used for monitoring Knowledge of species abundance and distribution must endangered wildlife [12] or managing game species [13]. underpin rational conservation and management decisions However, variability of both scat detectability and decay rate [1]. However, acquiring such critical information is far from has led to the expression of concerns regarding the reliability a trivial undertaking [2, 3]. This is particularly true for of such surveys [14]. cryptic animals (especially when they occur at low densities) Some sources of biases due to detectability and decay [4], for which there is often a need to use indirect survey have been widely studied, and methods have been developed methods [5]. These indirect methods include, but are not to compensate for them. For instance, scat detectability varies limited to, sign surveys [6, 7]; of which scat (fecal pellet) between observers but can be standardized by developing survey is one of the oldest and most widely used indirect personal correction factors or eliminated by using the same methods [8, 9]. More specifically, scat surveys are often used observer [15]. Scat decay can vary widely between seasons 2 International Journal of Zoology and can generally be reduced by restricting the performance of surveys to certain periods of the year [16, 17]. One critical source of bias seems unavoidable, however: environmental heterogeneity is present to some degree within almost all study sites (e.g., vegetation types, microclimate, presence and density of decomposers). The significance of the extent to which variability of environmental factors within study sites can influence scat detectability and decay rate remains unresolved [5]; thus since the occurrence of such environ- mental heterogeneity cannot be avoided, it is critical that the variability in scat detectability and decay rates between environments is quantified and accounted for. (a) Koalas, Phascolarctos cinereus,provide agood modelto investigate whether and how different environmental factors influence scat detectability and decay rate. Koalas use a variety of environments (vegetation communities, exposure, soil types, etc.) and are difficult to survey directly because of their cryptic, nocturnal habits and their low population density. Scat surveys have thus been widely used in studies of koala distribution [18, 19], habitat use [20, 21], and abundance [22], as well as frequently forming the basis for management [23]. One source of inaccuracy reported in other species is variability of scat decay linked with diet variability [24]; this should be negligible for koalas which are folivores with a relatively homogeneous diet all (b) year round, relying mainly on a few genera in the Family Myrtaceae, predominantly Eucalyptus, Corymbia, Melaleuca Figure 1: Example of two sites showing plot, cap, and bag, on two and Lophostemon [25, 26]. Thus environmental sources of different ground layer types. (a) Complex three-dimensional litter, variability in scat decay rates can be examined without the (b) simple Allocasuarina sp. litter. confounding effects of changes in diet. Previous reports have identified the need to incorpo- rate consideration of how koala scat decay rate [28]and were compared between scats protected or unprotected from detectability [22] influence the interpretation of scat surveys. the decomposing action of invertebrates. Rhodes et al. [28] focused on seasonal and geographical dif- ferences in climate, which they found significantly influenced the rate of scat decay but also emphasized that most of the 2. Materials and Methods observed variation in scat decay rates remained unexplained 2.1. Study Site. Thefieldworkwas conductedonNorth and highlighted the need for more research. Robust scat ◦  ◦ Stradbroke Island (NSI), Australia (27 23 /27 45 S, survey methodology is particularly critical because land use ◦  ◦ 153 23 /153 33 E) which has an area of approximately planning and management decisions often rely on such 27,500 ha. Koalas occupy a mosaic of vegetation comm- surveys (e.g., [21, 29]). These decisions affect localized unities (classification based on regional ecosystems [27]) extinction risk [30] and the fate of a species is often found on the island. Six remnant regional ecosystems were determined by the sum of these local extinctions [31]. Such selected on the basis of their use by koalas on NSI [34]: management decisions will especially impact on fauna that mallee Eucalyptus spp. low woodland; wetlands containing are not well represented in reserves due to competing land Eucalyptus, Lophostemon and Melaleuca spp.; Corymbia spp. use priorities, such as fauna favoring land that is level and/or open to low closed forest; E. racemosa woodland; Melaleuca at low elevation and/or composed of highly productive soils quinquenervia open forest to woodland; E. pilularis open [32]. Land use management agencies thus have a key role forest. In addition, two disturbed vegetation communities in species conservation [33], and they must, therefore, be (mine rehabilitation) were differentiated: one characterized confident that survey methodologies underpinning their by a complex litter layer, the other by a simple litter layer decisions are reliable. (see full description below and Figure 1). Three experiments In this study, we conducted three experiments to inves- were conducted as described below. tigate the effect of different environments on detectability and decay rate of koala scats. Firstly, the influence of ground layer complexity on scat detectability was investigated. Next 2.2. Experiment 1: Fecal Pellet Detectability in Litter Layers of scat decay rates in different vegetation communities used Different Complexities. In ordertomeasure variationinthe by koalas were analyzed, as was the relative influence on detectability of scats associated with vegetation communities decay rate of local environmental variables identified from of varying ground layer complexity, 30 plots (1 × 5m) were the literature as potentially important. Finally decay rates established. One researcher dispersed a random number of International Journal of Zoology 3 scats (1–22) in each plot. We used old scats collected from pseudo replicates (50 m apart) were laid, amounting to 48 the field, as fresh pellets have a more conspicuous color and sitesintotal.Eachscatgroup wasplacedina10 × 10 cm plot patina that makes them easier to find. This fresh condition on the ground directly below the canopy of potential koala lasts only a few days, consequently it does not characterize fodder or roosting trees of genera Eucalyptus, Corymbia, most scats naturally found during surveys (RC, personal Melaleuca,or Lophostemon. observation). A second researcher (RC), who conducted all During the first 48 hours after scat placement, the study searches to eliminate observer bias [15], searched the plot sites were struck by unusually heavy rainfall. Sites were without prior knowledge of the number of scats in the plot checked the next day and some scats had already disappeared. (zero scat was a possible outcome). The search had no time It was feared that the rain had either soaked and disintegrated limit and ended when the second researcher was confident some scats or washed them away. Consequently, 10 randomly the plot had been thoroughly searched. The total time taken selected new scats were added in an additional plot next to search the plot to achieve this level of confidence was to each of the initial 48. Hereafter, the initial plots will be recorded, as well as the percentage of scats found and the referred to as rained plots and the ones deployed just after the time needed to find each scat. rain will be simply referred to as plots. High rainfall events Plots were established in vegetation communities classi- were not recorded again during the rest of the experiment. fied in three groups on the basis of ground layer complexity: Scat locations were visited once a week, when the number (1) the simple litter group (N = 10) which had a flat litter and condition of remaining scats were recorded and the layer composed of Allocasuarina needles, with little to no condition of scats described (from intact to scat almost plants or woody debris (<5%) and varying amounts of bare unrecognizable, Table 1). When the weekly observations ground (0 to 20%); (2) the complex litter group (N = 10) indicated decay rate had slowed down, scats were checked which was composed of a three-dimensional litter of leaves every two weeks, then less often. The survey lasted 36 weeks and bark, no bare ground, and some plants and woody debris in total, by which time 50% of the scats had disappeared. (between 20% and 90%, Figure 1); (3) the highly complex Again, counts and classifications of the condition of scats litter group (N = 10), in which the substrate was mostly were conducted by a single researcher to eliminate observer covered with plants and woody debris (>90%). For the bias [15]. simple and complex litter groups, all plots were searched for Variables characterizing the local environment in which scats beforehand to ensure that no scat was present prior to the scats were observed were also recorded at each visit. Scat the experiment. For the highly complex litter group, however, moisture (wet or dry), accumulation of litter fallen on top presearching the plots would have disturbed the plots to an of scats (presence/absence), and the activity of detritivores extent that would have compromised the experiment; thus (defined here as the presence/absence of invertebrates or they were not presearched. In order to minimize unwanted fungal-type organisms) on scats were recorded as binary presence of scats prior to our experiment in the highly scores. At the end of the experiment the results were complex plots, we located the plots outside the known koala averaged. Site elevations were extracted using Terramodel distribution on the island but with substrates comparable to Version 10.61 from a 2008 airborne laser scan of the island the highly complex litter substrates in areas known to be used (Sibelco, unpublished data). A final environmental variable by koalas. Each of the three litter groups was replicated at two was flooded. The flooded variable indicated, for individual locations (five plots at each location). At each location, plots plots, the occurrence of surface water in the vicinity of scats were placed 50 m apart from one another. at any time during the experiment. Although, as indicated above, in some species scat decay rate can vary with diet, we did not expect that scats 2.3. Experiment 2: Scat Decay Rate in Different Vegetation from hospitalized koalas would decay significantly differently Communities. Fresh scats (1,980 in total) were collected from scats of free-ranging koalas. Indeed, the food items from 15 females and 31 males aged from 1 to 10 years and consumed were closely related, even if differences in the housed at the Australian Wildlife Hospital; these were placed particular tree species eaten occurred (Table 2). However, in different vegetation communities, and their decay rate was we tested this assumption by comparing the decay rate recorded. The koalas had been in the hospital for less than of scats from hospitalized koalas and free-ranging koalas from NSI. Scats from free-ranging koalas were collected 2.5 months, and none had received treatments that could alter scat decay rate (e.g., worm treatment). Hospital cages from two females. Groups of ten randomly selected scats are cleaned daily so all scats used in this experiment were were deposited beside plots representing six out of the eight vegetation communities previously described, since it was less than 24 h old. Scats from all 46 koalas, were mixed and ten scats were randomly selected to form a group. Each scat not possible to collect sufficient fresh scats from wild koalas group was weighed and groups <6g or >10 g were discarded to include all eight communities. to ensure homogeneity of groups. On 14 and 15 February 2010 (scats were stored in a refrigerator overnight), scat groups were placed in each 2.4. Experiment 3: Variability of Scat Decay Rate in Relation to of the eight different vegetation communities previously Invertebrates. Lepidopteron larvae develop in and consume described, with three replicates per vegetation community koala scats, while adult Coleoptera also exploit koala scats (24 locations in total). Replicates were several kilometers as a food source [35–37]. To investigate the effect of apart (mean = 6.6 km, SD = 3.9) and at each location, two invertebrates on scat decay rate, we partially or totally 4 International Journal of Zoology Table 1: Condition of scats at Week 1 and Week 12 in the different treatments. Week 1 Week 12 Treatments Intact Fibrous Surface eaten Part eaten Mass of fibre Melted Buried Part eaten Half eaten Fibrous Melted ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Plot 270 107 32 63 1 07 220 114 25 0 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Rained plots 132 129 44 113 18 50 165 118 19 0 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Caps 217 72 27 93 4 19 241 111 3 4 ∗∗ ∗∗ ∗ ∗∗ ∗ ∗∗ ∗∗ ∗∗ Bags 272 183 6 14 2 10 454 23 1 1 Test statistics 32.1 17.6 14.3 43.1 14.3 6.2 7.5 88.6 44.9 16.5 2.0 P values <0.001 0.001 0.002 <0.001 0.002 0.102 0.057 <0.001 <0.001 0.001 0.570 Plot 56.3 22.3 6.7 13.1 0.2 0 1.5 61.3 31.8 7.0 0 Rained plots 29.9 29.3 10.0 25.6 4.1 1.1 0.0 54.6 39.1 6.3 0 Caps 51.3 17.0 6.4 22.0 0.9 0.2 2.1 67.1 30.9 0.8 1.1 Bags 56.9 38.3 1.3 2.9 0.4 0.2 0.0 94.8 4.8 0.2 0.2 ∗∗ ∗ Scat number: in bold, results are significantly different across treatments, Kruskal-Wallis tests (df = 3): P ≤ 0.001, P < 0.01. Condition of scats are classified as (1) intact: the scat was complete; (2) surface eaten: the scat presented a rough surface; (3) partly eaten: parts of the scat were missing; (4) half eaten: at least half of the scat had disappeared; (5) fibrous: the inner matrix had disappeared, only fibers remained visible, but the scat shape remained present; (6) mass of fiber: scats constituted of a shapeless mass of fiber. Rare states of scats were (7) scats “melted” to a shapeless entity or (8) partially buried by invertebrates. Table 2: Eucalypt species fed to hospitalised koalas between 1 and 6 days [26] before their scats were collected in comparison to species found in the diet of wild koalas on NSI (only E. tereticornis, E. robusta, and E. resinifera are present on NSI, their percentages are taken from Cristescu et al. [34]). Common name Scientific name % fed between last 1 to 6 days % eaten by NSI wild koalas Grey gum E. propinqua, E. punctata, E. major 41% Blue gum E. tereticornis 23% 12% River red gum E. camaldulensis 17% White gum E. dunnii 5% Swamp mahogany E. robusta 5% 4% Flooded gum E. grandis 4% Red stringybark E. resinifera 2% 11% Ironbark E. crebra 1% Mountain Blue gum E. deanei 1% Spotted gum E. maculata 1% protected some scats from their influence. Next to the 48 2.5. Data Analysis. All variables were tested for normality plots described earlier, two groups of 10 random scats were and homoscedasticity (Levene’s test) and appropriate para- added (Figure 1). One group of 10 scats was placed on the metric or nonparametric tests were performed in PAWS ground, covered with an insect screen (1 mm mesh, which Statistics 18.0 [38]. Significance was taken to be P< was expected to be fine enough to impair lepidopteron and 0.05 (except when accounting for Bonferroni’s adjustment), coleopteran access) secured into the ground to protect the standard error of mean (SEM) and standard deviation (SD) scats from ground-surface-dwelling and flying invertebrates. are given as appropriate [39]. These protected scat groups are referred to as caps.The Relationships between response and explanatory vari- second groups of 10 scats (referred to as bags) were placed ables were investigated using a survival model for interval- into a sealed insect screen bags and placed directly on the censored data [40], with the 48 sites treated as random effect litter. This protected the scats from any invertebrate damage (more often referred to as a frailty effect in survival analysis, (i.e., no invertebrate was observed inside the bags during any [41, 42]). Our data were analyzed using the proportional visits, while some invertebrates were recorded on the scats hazards model proposed by Bellamy et al. [41]. A Weibull in the plots, see Experiment 2). All the scats in caps and distribution [43] was assumed for the event times and a bags were deposited prior to the heavy rainfall event. Scats lognormal distribution for the frailties [41]. in caps and bags were checked at Week 1 and Week 12 and For model selection, an apriori model approach was the numbers of scats remaining and their condition were used so as to avoid data dredging [44, 45]and to reduce compared to the unprotected scats in the rained-plots and the possible selection of noise variables [46]. Apriori models plots. Scats in caps and bags were observed to be wet and thus, were chosen to investigate the main variable of interest as anticipated, the treatments did not seem to have protected (vegetation communities) and to see if any other envi- the scats from the rain. ronmental variables had an additional effect, while taking Percentage Scat number International Journal of Zoology 5 Table 3: Percentage of scats detected and time needed to detect them, in three different ground layer complexities. Mean time taken Mean percentage Characteristics of Mean percentage of Time of search (min) Mean time to find by scat (sec) found at 2 min search scats found (± SEM) the 1st scat (sec) Mean Minimum Maximum Simple litter 100.0 3.4 2.1 6.2 0.73 87.3 22 Complexlitter 97.7(±1.1) 15.8 7.1 25.2 1.05 30.3 10.1 Highly complexlitter 83.9(±4.4) 34.3 15.4 48.6 2.61 11.2 17.5 SEM: standard error of mean. into account the known effect of rain [47]. Environmental variables chosen based on the literature [17, 48, 49]were: detritivores, litter, elevation, and flooded (scat moisture was measured but not included, as it was correlated with other 0.8 explanatory variables). Models were based on the two main 12.2.6 Rehab variables (vegetation communities and rain) separately or complex in combination, then each combination of the four other 12.2.5 0.6 environmental variables was added. 12.2.10 Rehab simple Data were graphically analyzed for skewed explanatory 12.2.8 variables and none was observed. Explanatory variables were 12.2.7 standardized (z-transformed) to allow comparisons of model 0.4 parameter estimates [50] and collinearity was tested with a variance inflation factor (VIF). As reported above, moisture level had to be excluded as it was correlated with other 0.2 explanatory variables. Models were fitted with the nlmixed 12.2.15 (wetland) procedure in SAS 9.2 for Windows and were ranked on the basis of AICc, the Akaike’s information criterion corrected for small sample size. Multimodel inference methods were used to determine 0 10 20 30 40 the relative importance of explanatory variables based on Weeks our set of models. Based on AICc, Akaike differences (Δ) between each model and the most parsimonious model Figure 2: Kaplan-Meier survival curves of scats, displayed by veg- were calculated, as well as Akaike weights, a measure of etation community. NB: vegetation communities (12.2.10: mallee Eucalyptus spp. low woodland, 12.2.15: wetlands, 12.2.5: Corymbia the weight of evidence of each model; and the evidence spp. open to low closed forest, 12.2.6: E. racemosa woodland, 12.2.7: ratios. To account for model uncertainty, the model average Melaleuca quinquenervia open forest to woodland, and 12.2.8: E. parameter estimates and the unconditional standard error of pilularis open forest) follow Queensland Herbarium [27]exceptfor each estimate were calculated. the addition of simple and complex rehabilitation. 3. Results 3.2. Experiment 2: Scat Decay Rate in Different Vegetation 3.1. Experiment 1: Fecal Pellet Detectability in Litter Layers Communities. Before performing any analyses, we con- of Different Complexities. The percentage of scats found firmed that the average weight of each group of 10 scats was decreased with increased litter complexity (Kruskal-Wallis = not different between and within vegetation communities 14.85, P = 0.001); from 100% of scats found in simple litter (ANOVA: rained-plots: F = 1.007, P = 0.441; plots: F 7,47 7,47 to 97.7% (SEM = 1.1) in complex litter and down to 83.9% = 2.107, P = 0.065). Next it was confirmed that the origin (SEM = 4.4) in highly complex litter (Table 3). The total of scats (free-ranging/hospitalized koalas) had no significant time taken to search the plot increased with litter complexity effect on scat decay rate (survival model, β = −0.14; SE = (Kruskal-Wallis = 24.56, P< 0.001). It took an average of 0.20; P = 0.51). 3.4 minutes (SD = 1.2) to search plots in simple litter, 15.8 Survival curves [51] showed that scats placed in wetlands minutes (SD = 6.6) in complex litter, and up to 34.3 minutes decayed faster than scats placed in any other vegetation (SD = 10.0) in highly complex litter. The mean time taken to community investigated (Figure 2). Within each vegetation find a scat increased with litter complexity (Kruskal-Wallis community, the number of scats remaining after 36 weeks = 16.29,P< 0.001), while the mean percentage of scats found was highly variable (every possibility between zero and 10 after searching for 2 min decreased with litter complexity scats, Figure 3(a)). When averaged across the 36 weeks (Kruskal-Wallis = 23.06, P< 0.001). However, the mean duration of the experiment, all vegetation communities had a time to find the first scat was similar across the three litter median of between 7 and 9 remaining scats, except wetlands complexities (Kruskal-Wallis = 0.01, P = 0.995). which had a median of 5 (Figure 3(b)). Survival 6 International Journal of Zoology The most parsimonious survival model contained rain, flooded, and litter (Table 4). This model indicated that both rain and flood increased scat decay rate and that litter 8 ∗ decreased decay rate (Table 5); however, four of the 48 models were well supported (<2ΔAIC). The three variables 6 characterizing the most parsimonious model were also present in all other models within 2ΔAIC. The “vegetation” variable was not incorporated in the four best models and only one vegetation type was associated with a significant influence on scat decay in our study: wetlands were found to increase decay rate (Table 5). It is noteworthy that most plots in wetlands had been consistently flooded (all but one of the wetland plots was flooded, whereas only one plot outside wetlands, in Melaleuca quinquenervia open forest to woodland, was flooded). (a) 3.3. Experiment 3: Variability of Scat Decay Rate in Relation to Invertebrates. After one week, the number of scats remaining in the rained plots was similar to the number of scats protected from invertebrates by caps placed above them (Mann-Whitney test = 932.0, P = 0.077); both were lower than the number of scats protected in bags (see Table 6 for details). After 12 weeks, rained plots contained significantly fewer scats than found in caps, which in turn contained fewer scats than remained in bags (see Table 6 for values and P values). The condition of the scats also varied with 0 the treatments; with the best preserved scats being the ones in the bags, while the most deteriorated scats were in the rained plots (Table 1). Overall, the scats protected from all invertebrate activity in bags were best preserved in terms of both quantity and quality, while scats in the rained plots were the worst preserved on both accounts. (b) Figure 3: Boxplots (minimum, first quartile, median, third quar- tile, and maximum, with outliers ◦ and extreme values ∗) show-ing 4. Discussion the variability of the average number of scats remaining by vege- tation community (a) after 36 weeks, (b) all 36 weeks combined 4.1. Scat Detectability and Bias. Most study sites encompass (grey lines in (b) include max and min medians of all but wetlands). different vegetation types with potentially variable ground Boxplots are ordered from the vegetation community with the least layers. Here we demonstrated that scat detectability varied remaining scats (12.2.15) to the one with most remaining scats with ground layer complexity, with up to 16% variation in (12.2.6). NB: vegetation communities (12.2.10: mallee Eucalyptus the proportion of scats detected between plots of different spp. low woodland, 12.2.15: wetlands, 12.2.5: Corymbia spp. open ground layer complexities. The measured 16% variation in to low closed forest, 12.2.6: E. racemosa woodland, 12.2.7: Melaleuca scat detectability most likely underestimated the bias ratio quinquenervia open forest to woodland, and 12.2.8: E. pilularis open that would be present in many other scat survey studies: (a) forest) follow Queensland Herbarium [27] except for the addition the time dedicated to searching for scats (up to 48 minutes) of simple and complex rehabilitation. was much higher in our study than would be provided for in most scat surveys which usually have to be performed under arbitrary time and budget constraints; (b) no maximum time limit was imposed for scat searches; (c) limited search the robustness of scat surveys requires standardizing the time would most likely decrease the probability of finding search area (plots of a fixed size) rather than standardizing scats, especially at sites with high ground layer complexities. search time. Nevertheless, the majority of studies relying on scat surveys This detectability limitation might be lessened because for koalas use some variation of the Spot Assessment the SAT search is terminated when the first scat is found. Technique (SAT) [52, 53], where the search for scats lasts for In all the plots in our study, the first scat was found within a maximum of 2 minutes (or until the first scat is found). Our the first two minutes of search, and the time to find the first results demonstrate that with a constant amount of search scat appeared not to be correlated with the complexity of time per tree, proportionately more scats would be missed in the substrate. Nonetheless, it is likely that the search time complex ground layers and thus a detectability bias would be to find the first scat would be increased in many real-world introduced. Given this potential detectability bias, improving situations, in contrast to our survey sites where scats were Scats Scats 12.2.15 12.2.15 (wetland) (wetland) 12.2.7 12.2.7 12.2.8 12.2.8 Rehab simple Rehab simple 12.2.10 12.2.10 Rehab complex Rehab complex 12.2.5 12.2.5 12.2.6 12.2.6 International Journal of Zoology 7 Table 4: Model selection for explaining scat decay rate during 36 weeks, all models were fitted with interval censored survival model with the 48 sites as frailty effect. Models AICc ΔAICc AICc weight Evidence ratio Rain + litter + flooded 3633.4 0 0.259 1 Rain + litter + flooded + elevation 3633.5 0.1 0.246 1.05 Rain + litter + detritivores + flooded + elevation 3633.6 0.2 0.234 1.11 Rain + litter + detritivores + flooded 3633.9 0.5 0.202 1.28 Vegetation + rain + litter + detritivores + flooded 3639.6 6.2 0.012 22.20 Vegetation + rain + litter + detritivores + flooded + elevation 3639.8 6.4 0.011 24.53 Rain + detritivores + flooded 3639.9 6.5 0.010 25.79 Vegetation + rain + litter + flooded 3640.5 7.1 0.007 34.81 Rain + detritivores + flooded + elevation 3640.6 7.2 0.007 36.60 Vegetation + rain + litter + flooded + elevation 3641.3 7.9 0.005 51.94 Vegetation + rain + detritivores + flooded + elevation 3644.3 10.9 0.001 232.76 Vegetation + rain + detritivores + flooded 3644.9 11.5 0.001 314.19 Rain + flooded 3644.9 11.5 0.001 314.19 Vegetation + rain + litter + detritivores + elevation 3645.3 11.9 0.001 383.75 Rain + litter + elevation 3645.7 12.3 0.001 468.72 AICc: akaike information criterion corrected for small sample size, ΔAICc: AICc differences, see descriptions in text (models with a weight <0.001 are not shown). Table 5: Relative variable importance (by decreasing importance) across all models searching for environmental variables explaining koala scat decay. Variable Model-averaged estimate Unconditional standard error estimator Relative variable importance Rain 1.02 0.26 0.99 Flooded 0.67 0.10 0.99 Litter −1.06 0.35 0.98 Elevation −0.01 0.00 0.50 Detritivores −0.97 0.72 0.48 Rehabilitation −0.10 0.47 0.03 RE 12.2.5 0.55 0.57 0.03 RE 12.2.6 0.14 0.56 0.03 RE 12.2.7 0.12 0.41 0.03 RE 12.2.8 0.56 0.42 0.03 RE 12.2.10 0.45 0.48 0.03 RE 12.2.15 0.94 0.60 0.03 Table 6: Number of scats remaining at week 1 and week 12 in the different treatments (N = 48 per treatment, see text) and statistical differences between treatments (P values and Mann-Whitney U test statistics, Bonferroni’s adjustment α = 0.004). Rained plot Cap Bag Mean SD Min Max UP value UP value UP value Plot 9.85 0.46 8 10 612.5 <0.001 845.5 0.002 1078.0 0.229 Rained plot 8.17 2.43 0 10 932.0 0.077 557.0 <0.001 Week 1 Cap 8.75 2.37 2 10 781.0 <0.001 Bag 9.96 0.2 9 10 Plot 7.63 2.66 0 10 680.0 <0.001 1055.0 0.467 438.0 <0.001 Rained plot 5.94 2.75 0 10 643.0 <0.001 93.5 <0.001 Week 12 Cap 7.67 3.12 0 10 575.5 <0.001 Bag 9.75 0.93 5 10 Min: minimum number of scats, max: maximum number of scats. 8 International Journal of Zoology deposited just before the search (for example, this precluded ground layer complexity based on available studies (e.g., the the pellets being obscured by litter fall). Perhaps even more present study and see also [22]). importantly, a major component of the present study was to investigate the recovery rate of scats from different substrates, thus the numbers of scats deposited in the plots were higher 4.2. Scat Decay Rate and Bias. In comparison to scat than would be found naturally in most circumstances. From detectability, variability in decay rates was not consistently the data collected from 36 plots on North Stradbroke Island associated with particular vegetation communities (Figure 3, [54], the average natural scat density was just under 1,100 Table 4). For instance, extreme outcomes were seen for per hectare, whilst in the experimental plots in this paper, the two plots inside the same vegetation community after 36 (artificial) scat density was over 27,300 per hectare. Thus the weeks, despite those plots being only 50 m apart: one time to find the first scat during searches could potentially had 100% scats remaining and the other had 0%. Similar be around 25 times longer in natural conditions than in our results were also found in a study by Rhodes et al. [28], experiment; hence it will often require much more than two where a high and unexplained proportion of decay rate minutes to find the first scat in routine surveys. variability was associated with the variation between plots The detectability bias determined in the present study of within a site. In the current study, it is worth noting up to 16% (likely to be even higher in the context of SAT- that the treatments preventing invertebrates from accessing type approaches) has major implications for indirect fauna the scats greatly influenced scat decay rate. The fine-scale surveys. Even slight variations in detection probability across variation of invertebrate densities and the stochastic chances different vegetation communities can seriously skew study of invertebrates finding scats could explain much of the results [55, 56]. A simulation study modeling species occur- small-scale heterogeneity of scat decay rate. Whatever the rence as a function of habitat covariates evaluated the errors reasons, the lack of consistency in scat decay rate makes the resulting from imperfect detections [55]. A detectability bias development of a correction factor difficult. One possible of 15 to 20% (as found in our study) resulted in up to 200% way to compensate for variability in scat decay rate could be relative bias when estimating parameters (i.e., an over or to increase the intensity of sampling within each vegetation under estimation of 200% of the effect of a habitat variable community in order to average out the fine-scale heterogene- on a species’ occupancy). ity [28, 63]. A widely used and efficient technique to account for One exception to the inconsistency in decay rate relates imperfect detection is distance sampling, a method where to the vegetation community associated with wetlands. In perpendicular distance from a transect to a fecal mass is used wetlands, the decay rate was consistently faster than that to estimate a detection function [57]. Detection functions observed in other vegetation communities (Figures 2 and 3). account for a greater chance of imperfect detection further Plots in wetlands were consistently and selectively flooded, away from the transect and can be derived for observers or which was found to accelerate scat decay rate (Table 5). habitats. Distance sampling has, for instance, been widely This suggests that strong biases could be introduced when used in dung surveys of elephant Loxodonta sp. populations landscapes with surface water present (or probably even very [58]. However, this method is not feasible for koalas given moist substrates) are being surveyed. Koalas actually tend the difficulty of locating their scats: indeed, the probability to favor wet habitats [21] and so for this species, there is of detection for koala scats does not equal one even on the the paradoxical likelihood that there will be a low density of transect and thus transgresses a fundamental assumption of detectable scats present in areas supporting a relatively high distance sampling [57]. density of koalas. Despite this, few studies have attempted to Other methods have been developed to deal with control for decay bias in koala scat surveys and even these imperfect detection using zero-inflated processes [2, 59– studies have tried to account for scat decay by excluding 61]. These methods rely on repeated surveys over a short scats older than a certain threshold [20, 22, 64]. Scat aging, time [56, 62]. While these methods are based on direct however, has been found to be generally unreliable [48]or animal surveys, they should also be applicable to indirect at best limited to highly skilled and experienced researchers animal surveys. Some of these approaches even allow the [65]. Nevertheless, some species have had more objective probability of detection to vary with characteristics of the criteria developed such as in the case of the gorilla Gorilla vegetation communities being surveyed [59]. As suggested gorilla, for which dung age has been reliably correlated with by MacKenzie [56], repeated samples could also be collected dung pile height [12]. For species where objective criteria to by multiple observers carrying out searches on the same plot age scats are not yet available, developing correction factors and comparing scat detection [15], as long as interobserver by measuring scat decay rates in sites where an accelerated variability is modeled to reduce or eliminate bias from this decay rate can be expected is strongly indicated.The most source. useful method for developing these factors is retrospective As a last resort, consistency in detectability bias could estimation of decay rate, where fresh scats are recorded in allow for the determination and incorporation of a correc- different habitats at different times prior to the survey. At the tion factor. If researchers and managers are using a one off time of the survey, all the previously marked scat locations scat survey, they will need to incorporate methods to account are revisited, and the percentage of decayed scats per habitat for the effects of variation in ground complexity in their is calculated [66]. This accounts for specific influences (e.g., protocols. For instance, in distribution models based on scat temperature, precipitation) that the surveyed scats and the presence/absence, the results could be weighted for variable marked scats share (see an example for deer in [67]). A International Journal of Zoology 9 Table 7: Comparison of the percentage of predicted scat recovery in two extreme vegetation communities based on observed detectability and decay rates. % scats remaining due to decay % scats found due to both biases Habitat % detected 3 months 9 months 3 months 9 months No rain Rain No rain Rain No rain Rain No rain Rain Wetland 83.9 40.0 40.0 18.3 10.0 33.6 33.6 15.4 8.4 Simple litter rehab 100.0 73.3 50.0 68.3 42.9 73.3 50.0 68.3 42.9 retrospective estimate for koala scats presents two main most other vegetation communities. For the same density of difficulties. (1) It is recommended for the retrospective scats deposited in wetlands and in rehabilitated areas with method that the scats be surveyed long enough for 90% of simple litter ground layers, the proportion of scats found in a scats to have decayed. Given that after 9 months only 50% survey would vary widely (Table 7). In the worst case, after of our scats had disappeared, a retrospective survey for scat 9 months, a survey would detect 8% of the total original decay would mean going in the field, in each different habitat, scats in wetlands against 42% in rehabilitated vegetation. for an extensive amount of time prior to the survey. (2) On In this example, if the actual koala utilization rate of both each of these occasions, koalas would have to be found and areas were equal, wetlands would be wrongly classified as fresh scats detected and marked for the study. Koalas are five times less used by koalas. A bias of this magnitude, if difficult and time consuming to find which is, of course, not able to be corrected, would invalidate any interpretation why indirect methods are used in the first place. However, of koala distribution or habitat preference. The potential for the use of fresh scats obtained from captive or radiotracked such biases to exist more generally, seriously questions the koalas and distributed to establish a surrogate for randomly validity of scat surveys which fail to determine whether those located pellets might help resolve that second difficulty. biases are actually present. This time-consuming aspect of the retrospective scat decay In this paper, we present the first comprehensive study analysis approach prior to the actual scat survey needs to be of two significant biases of fecal pellet surveys, detectability taken into account when evaluating and selecting between and decay of scats, which were found to be a function different survey options. Furthermore, as there might be of the different environments present within a study site. considerable interannual variations in many parameters Biases are often acknowledged and occasionally dealt with influencing scat decay (e.g., temperature, precipitation, or when comparing studies being undertaken in different even insect abundance), deriving a correction from a single geographical locations, climates, or seasons. On the other survey might not be appropriate to rely upon to correct hand, the biases within a particular study area are still often for bias in subsequent surveys. Thus the time-consuming deemed negligible and dismissed without any real evidence. retrospectiveestimateofscatdecay priortoeachscatsurvey However, our study demonstrates the presence of biases might be warranted. inherent in using indirect signs of a species’ presence for Another method proposed to limit decay bias is to clear assessing the species’ distribution or habitat preferences, plots yearly [48]. However, we observed substantial decay let alone confounding estimation of its abundance. Scat heterogeneity over a period much shorter than one year. On detectability bias can be controlled because of its consistency the basis of our results, clearing plots five weeks prior to the and the existence of appropriate methodologies, thus it survey could remove bias arising from heterogeneous decay should be accounted for in future scat survey studies. Scat rates. This method has been used in a study of macropods, decaybiasactsinmorecomplex ways andmay be an even where plots were cleared one month before the survey [68]. more problematic issue. In extreme cases, scat decay bias For fauna found typically at low density, however, clearing might erroneously decrease the estimated value of what is sites one month prior to surveys might result in scats not actually prime habitat. This was a real effect observed in being deposited in the interval between clearing and survey. our case study, where the focal species (the koala) prefers In any case, it might not be possible to remove 100% of the types of habitats where there is also a higher scat decay rate. scats when clearing plots, resulting in further errors ([69], Further studies to determine exactly how to use additional but see avenues for potential solutions in [28]). corrections to decrease scat decay bias to an acceptable level (e.g., sampling effort, whether retrospective estimate of decay rate can be reused) are still required [28]. But it is clear from the present study that wet and dry sites will differ 4.3. Combination of Detectability and Decay Rate Variability. significantly in scat decay rate and until similar studies have The percentages of scats that would be found in a survey after been replicated in a variety of different environments, the accounting for detectability and decay biases were compared conservative assumption must be that there will be a high between two vegetation communities (Table 7). On the one degree of site specificity of this parameter and each distinct hand, wetlands have the worst detectability owing to very location will require calibration (with its own retrospective dense ground vegetation and the quickest decay rate owing to estimate of decay rate). their proneness to flooding. 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