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.

The probability of detecting an animal in a sampled area during a survey consists of 2 components: 1) the probability of an animal being available for detection (availability), which can be highly variable in heterogeneous environments; and 2) the probability of an animal being detected, conditional on its being available for detection (perception). Many surveys only estimate the latter probability because modeling the availability process requires information collected external to the survey. We illustrate estimation of both probabilities in an application to aerial surveys of dugongs (Dugong dugon) in Northern Australian coastal waters where water clarity varies greatly over relatively small spatial scales. Using artificial dugong models and timed depth recorders deployed on 15 wild dugongs to obtain dive profiles, we carried out experiments to determine zones of detectability for dugongs at the range of depths, turbidities, and sea states that spanned the environmental heterogeneity encountered on dugong surveys. Resulting probability estimates were heterogeneous and dependent on the measured conditions. To estimate perception probability, we used a tandem team of 2 observers on either side of the aircraft. This permitted fitting generalized Lincoln–Petersen models with Program MARK. We then used the generalized Horvitz–Thompson estimator, based on the overall detection probability for each individual dugong, to generate population estimates. We also developed a new simulation‐based method for estimating standard errors and confidence intervals. We contrast absolute abundance estimates of dugongs in the Torres Strait and Northern Great Barrier Reef regions using both the new and original approaches (Marsh and Sinclair 1989a). For Torres Strait, the new method produced a substantially smaller estimate (11,956 vs. 14,106 dugongs) and a very much smaller standard error (1,189 vs. 2,314 dugongs), whereas the new method produced slightly larger estimates (mean 9,855 vs. 9,193 dugongs, standard error 1,184 vs. 917 dugongs) for the Northern Great Barrier Reef survey.

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Traditional approaches to the question of the effects of plant secondary metabolites on the feeding choices of folivores of Eucalyptus have focused on the tree species level, although numerous field studies of foraging behaviour have identified selection at the level of the individual trees. Attempts to relate these decisions to deterrency resulting from secondary leaf chemistry have been inconclusive because assays used have focused on broad groups of compounds such as "total" phenolics. In this study we have conducted no-choice feeding trials with two arboreal mammalian folivores, the common ringtail possum (Pseudocheirus peregrinus) and the koala (Phascolarctos cinereus), to measure deterrency of individual trees of two species of Eucalyptus, E. ovata and E. viminalis. Average daily intakes of E. ovata foliage by common ringtail possums ranged from 2.5 to 50 g kg body mass. Koala intakes of foliage from the same individual trees ranged from 22.4 to 36.3 g kg body mass. When fed foliage from different individual E. viminalis trees, common ringtail possums ate between 1.26 and 6.28 g kg body mass while koalas ate from 14.3 to 45.9 g kg body mass. Correlative analyses showed no relationships between feeding and several measures of nutritional quality, nor with total phenolics or condensed tannins. They did, however, identify two groups of plant secondary metabolites that may cause deterrency: terpenes, and a defined group of phenolic compounds, the diformylphloroglucinols (DFPs). Further bioassay experiments with common ringtail possums showed that only the DFPs could cause the effects seen with the foliage experiments at concentrations similar to those found in the leaves. We argue that, when in sufficiently high concentrations, DFPs determine the level of food intake by these animals irrespective of other questions of nutritional quality of the leaves.

Seedlings of Eucalyptus tereticornis (Smith) were grown under two levels of availability each of CO (352 and 793 µmol mol), soil nutrients (1/24 and 1/4 Hoagland's solution) and light (full and 30% sunlight). Low soil nutrient availability or high light increased the C:N ratio of leaves, leading to lower leaf nitrogen concentrations, higher leaf specific weights and higher levels of both total phenolics and condensed tannins. These results were consistent with other studies of the effect of environmental resource availability on foliage composition. Similar results were observed when the C:N ratio of leaves was increased under elevated CO. The changes in leaf chemistry induced by the treatments affected the performance of 4th-instar larvae of Chrysophtharta flaveola (Chapuis) fed on the leaves. Increased C:N ratios of leaves reduced digestive efficiencies and pupal body sizes and increased mortality. Below a threshold nitrogen concentration of approximately 1% dry mass, severe reductions in the performance of larvae were recorded. Such changes may have significant consequences for herbivores of Eucalyptus, particularly in view of projected increases in atmospheric CO.

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Strip-transect aerial surveys of Shark Bay, Ningaloo Reef and Exmouth Gulf were conducted during the winters of 1989 and 1994. These surveys were designed primarily to estimate the abundance and distribution of dugongs, although they also allowed sea turtles and dolphins, and, to a lesser extent, whales, manta rays and whale sharks to be surveyed. Shark Bay contains a large population of dugongs that is of international significance. Estimates of approximately 10000 dugongs resulted from both surveys. The density of dugongs is the highest recorded in Australia and the Middle East, where these surveys have been conducted. Exmouth Gulf and Ningaloo Reef are also important dugong habitats, each supporting in the order of 1000 dugongs. The estimated number of turtles in Shark Bay is comparable to the number in Exmouth Gulf plus Ningaloo Reef (7000–9000). The density of turtles in Ningaloo Reef and, to a lesser extent, Exmouth Gulf is exceptionally high compared with most other areas that have been surveyed by the same technique. Shark Bay supports a substantial population of bottlenose dolphins (2000–3000 minimum estimate). Exmouth Gulf and Ningaloo Reef were not significant habitats for dolphins during the winter surveys. Substantial numbers of whales (primarily humpbacks) and manta rays occur in northern and western Shark Bay in winter. Ningaloo Reef is an important area for whale sharks and manta rays in autumn and winter. The Shark Bay Marine Park excludes much of the winter habitats of the large vertebrate fauna of Shark Bay. In 1989 and 1994, more than half of all the dugongs were seen outside the Marine Park (57·4 and 50·7%, respectively). Approximately one-third to one-half of turtles and dolphins were seen outside the Marine Park (in 1989 and 1994 respectively: turtles, 43 and 27%; dolphins, 47 and 32%). Almost all the whales and most of the manta rays were seen outside the Marine Park. Expansion of the Shark Bay Marine Park, to bring it into alignment with the marine section of the Shark Bay World Heritage Area, would facilitate the appropriate management of these populations. This would also simplify the State– Commonwealth collaboration necessary to meet the obligations of World Heritage listing. The coastal waters of Western Australia north of the surveyed area (over 6000 km of coastline) are relatively poorly known and surveys of their marine megafauna are required for wise planning and management.