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This chapter considers all aspects of the study of the breeding biology of wild birds, and how to gain representative measures of nest success and other aspects. It discusses the selection of study areas, measuring the success of individual breeding attempts, and determining the proximate causes of breeding failure from signs left at the nest, using wax or plasticine eggs, cameras, and temperature loggers. It also discusses the use of artificial nests to measure nest success and causes of failure, and methods of measuring overall annual productivity from detailed field studies, or from counts or captures after the breeding season, or from the use of simulation models. Final sections deal with assessments of the timing of breeding, measurements of eggs and chicks, and the use of experiments to disentangle the ultimate and proximate causes of breeding failure.

Measurements of daily energy expenditure and water turnover showed that energy expenditure in cheetahs was not significantly greater than expected, but water turnover was low. There were no sex differences in daily energy expenditure, but when hunting along riverbeds cheetahs used more energy than when hunting in the dunes, probably because they moved further in the riverbeds. There were no differences in daily energy expenditure between females in different stages of reproduction. Energy expended chasing prey differed; small prey being least costly and large species most costly. Analyses of prey chases using both GPS and accelerometer loggers revealed that there were two phases; an initial rapid acceleration to catch up with the prey, followed by a slowing phase as cheetahs followed twists and turns of the prey as the distance between them closed. A visualization of five phases recorded from accelerometer data during a successful steenbok hunt is presented.

Along the signal path from the atmosphere, through the sensors and the data logger to the final archive, the signal quality may be irreversibly comprised. These faults include aliasing caused by poor sampling practice and quantization in an analog to- digital converter. Aliasing and quantization will be defined in this chapter. Drift in some of the system parameters, such as temperature sensitivity, is generally preventable but is not always reversible. Sampling of a signal occurs in the time domain and, frequently, in the space domain with one, two, or three dimensions. In the time domain, the time interval between successive points is called the sampling interval and the data logger controls this interval. When two or more sensors are distributed, vertically, along a mast then the system is sampling both in the time domain and in the space domain. When multiple measurements are arrayed along the surface of the earth, the sampling is occurring in time and in two or three space dimensions. Most meteorological systems are undersampled both in time and space. Space undersampling is an economic necessity. The consequence of undersampling is that frequencies above a certain limit, called the Nyquist frequency, will appear at lower frequencies and this is an irreversible effect. Quantization occurs when the signal is converted from analog to digital in the analog-to-digital converter. Since the range of the converter is expressed in a finite number of digital states, signal amplitudes smaller than this quantity will be lost. This is another irreversible effect. These are not the only irreversible effects. For example, drift is caused by physical changes in a sensor or other component of the measurement system. Drift may have a causal component, such as undocumented temperature sensitivity, and a random component such as wearing of an anemometer bearing. The former is theoretically preventable and reversible, whereas the latter is irreversible. Each element of the system may include some signal averaging, and each element may add bias and gain. As noted in earlier chapters, a sensor is a transducer, a device that changes energy from one form to another.

This chapter looks at thermal ecology and the process of quantifying thermal biology of reptiles. Body temperature influences most physiological processes, including metabolism, digestion, reproductive physiology, and locomotion, therefore an animal’s body temperature has ecological consequences, including its ability to escape predators, capture prey, grow, reproduce, and be susceptible to disease. Reptiles and other ectothermic animals rely on their environments to achieve their body temperature using behaviour to select among thermal patches in the environment, and behaviour (along with their morphology and physiological adjustments) to control heat exchanges within a thermal patch. There are two ways for evaluating thermoregulation in reptiles: computational models and physical models; and while both have their advantages and disadvantages, physical models are not useful for determining thermal transients in particular. However, under certain conditions, data loggers may be used in place of physical models.

We consider three categories of physical variables that can be measured for different freshwater ecosystems: (1) variables measured or described at the catchment or sub-catchment scale (e.g., bathymetry, depth, topography, geology); (2) those in or near to the water (e.g., temperature, turbidity, solar radiation); and (3) variables used to describe the substrate (e.g., particle size, mineral vs. peat). In this chapter we consider the practical aspects of undertaking a freshwater survey that includes measurement of physical variables; the approaches needed to undertake the survey; choosing a sampling strategy or protocol; practical tips on choice of measurement method or sensor, battery type, equipment calibration, resolution, accuracy, and links to literature providing further detail. The final section provides examples from a diversity of freshwaters where physical variables have been measured as part of an ecological survey, forming the evidence-base for management or conservation decisions.