ABSTRACT
Quantifying the cost-effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost-effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single-season occupancy-detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1-200) and repeat surveys (2-40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24-h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25-ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24-h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.
ABSTRACT
We investigated the utility of near-infrared reflectance spectroscopy (NIRS) as a means of rapidly assaying chemical constituents of Eucalyptus leaves and of directly predicting the intake of foliage from individual trees by greater gliders (Petauroides volans) and common ringtail possums (Pseudocheirus peregrinus). The concentrations of total nitrogen, neutral detergent fiber, condensed tannins and total phenolics could be predicted accurately by partial least squares regression models relating the near-infrared reflectance spectra of foliage samples to analyses performed using standard laboratory procedures. Coefficients of determination (r 2) for all four constituents ranged between 0.88 and 0.98, and standard errors of prediction between 0.80 mg g-1dry matter (DM) for total nitrogen and 5.14 quebracho equivalents g-1DM for condensed tannins. Near-infrared spectral-based models of food intake had r 2 values of 0.90 and 0.95 with a standard error of prediction of 3.4 and 8.3 g DM kg-0.75 day-1 for greater gliders and common ringtail possums respectively. We used the predictive model of food intake for greater gliders to examine the relationship between leaf palatability and documented food preferences of animals in the wild. Ranked differences in leaf palatability across four Eucalyptus species were consistent with documented food preferences of greater gliders in the wild. We conclude that NIRS provides a powerful tool to predict foraging behaviour of herbivores where forage choices are determined by compositional attributes of food.
ABSTRACT
Many ecological studies rely heavily on chemical analysis of plant and animal tissues. Often, there is limited time and money to perform all the required analyses and this can result in less than ideal sampling schemes and poor levels of replication. Near infrared reflectance spectroscopy (NIRS) can relieve these constraints because it can provide quick, non-destructive and quantitative analyses of an enormous range of organic constituents of plant and animal tissues. Near infrared spectra depend on the number and type of C[Formula: see text]H, N[Formula: see text]H and O[Formula: see text]H bonds in the material being analyzed. The spectral features are then combined with reliable compositional or functional analyses of the material in a predictive statistical model. This model is then used to predict the composition of new or unknown samples. NIRS can be used to analyze some specific elements (indirectly - e.g., N as protein) or well-defined compounds (e.g., starch) or more complex, poorly defined attributes of substances (e.g., fiber, animal food intake) have also been successfully modeled with NIRS technology. The accuracy and precision of the reference values for the calibration data set in part determines the quality of the predictions made by NIRS. However, NIRS analyses are often more precise than standard laboratory assays. The use of NIRS is not restricted to the simple determination of quantities of known compounds, but can also be used to discriminate between complex mixtures and to identify important compounds affecting attributes of interest. Near infrared reflectance spectroscopy is widely accepted for compositional and functional analyses in agriculture and manufacturing but its utility has not yet been recognized by the majority of ecologists conducting similar analyses. This paper aims to stimulate interest in NIRS and to illustrate some of the enormous variety of uses to which it can be put. We emphasize that care must be taken in the calibration stage to prevent propagation of poor analytical work through NIRS, but, used properly, NIRS offers ecologists enormous analytical power.
