Point transect sampling along linear features.

Distance sampling is a widely used methodology for assessing animal abundance. A key requirement of distance sampling is that samplers (lines or points) are placed according to a randomized design, which ensures that samplers are positioned independently of animals. Often samplers are placed along linear features such as roads, so that bias is expected if animals are not uniformly distributed with respect to distance from the linear feature. We present an approach for analyzing distance data from a survey when the samplers are points placed along a linear feature. Based on results from a simulation study and from a survey of Irish hares in Northern Ireland conducted from roads, we conclude that large bias may result if the position of samplers is not randomized, and analysis methods fail to account for nonuniformity.

Line transect methods for plant surveys.

Interest in surveys for monitoring plant abundance is increasing, due in part to the need to quantify the rate of loss of biodiversity. Line transect sampling offers an efficient way to monitor many species. However, the method does not work well in some circumstances, for example on small survey plots, when the plant species has a strongly aggregated distribution, or when plants that are on the line are not easily detected. We develop a crossed design, together with methods that exploit the additional information from such a design, to address these problems. The methods are illustrated using data on a colony of cowslips.

Hidden process models for animal population dynamics.

Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer-intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter-run chinook salmon (Oncorhynchus tshawytsha).

Monitoring change in biodiversity through composite indices.

The need to monitor trends in biodiversity raises many technical issues. What are the features of a good biodiversity index? How should trends in abundance of individual species be estimated? How should composite indices, possibly spanning very diverse taxa, be formed? At what spatial scale should composite indices be applied? How might change-points--points at which the underlying trend changes--be identified? We address some of the technical issues underlying composite indices, including survey design, weighting of the constituent indices, identification of change-points and estimation of spatially varying time trends. We suggest some criteria that biodiversity measures for use in monitoring surveys should satisfy, and we discuss the problems of implementing rigorous methods. We illustrate the properties of different composite indices using UK farmland bird data. We conclude that no single index can capture all aspects of biodiversity change, but that a modified Shannon index and the geometric mean of relative abundance have useful properties.

Using the EM algorithm to weight data sets of unknown precision when modelling fish stocks.

Stocks of commercial fish are often modelled using sampling data of various types, of unknown precision, and from various sources assumed independent. We want each set to contribute to estimates of the parameters in relation to its precision and goodness of fit with the model. Iterative re-weighting of the sets is proposed for linear models until the weight of each set is found to be proportional to (relative weighting) or equal to (absolute weighting) the set-specific residual invariances resulting from a generalised least squares fit. Formulae for the residual variances are put forward involving fractional allocation of degrees of freedom depending on the numbers of independent observations in each set, the numbers of sets contributing to the estimate of each parameter, and the number of weights estimated. To illustrate the procedure, numbers of the 1984 year-class of North Sea cod (a) landed commercially each year, and (b) caught per unit of trawling time by an annual groundfish survey are modelled as a function of age to estimate total mortality, Z, relative catching power of the two fishing methods, and relative precision of the two sets of observations as indices of stock abundance. It was found that the survey abundance indices displayed residual variance about 29 times higher than that of the annual landings.

Adaptive line transect sampling.

Adaptive line transect sampling offers the potential of improved population density estimation efficiency over conventional line transect sampling when populations are spatially clustered. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of observations. Its disadvantage is that the survey effort required is not known in advance. We develop an adaptive line transect methodology that, by varying the degree of adaptation, allows total effort to be fixed at the design stage. Relative to conventional line transect surveys, it also provides better survey coverage in the event of disruption in survey effort, e.g., due to poor weather. In analysis, sightings from the adaptive sections are downweighted in proportion to the increase in effort. We evaluate the methodology by simulation and report on surveys of harbor porpoise in the Gulf of Maine, in which the approach was compared with conventional line transect sampling.

Similarity indices for spatial ecological data.

We present a method for assessing similarity between species maps of presence and absence or abundance that emphasizes global features while ignoring minor local dissimilarities. The method arranges sites into small groups, or cliques, and allows controlled changes to be made within cliques to reduce the influence of local discrepancies. Resulting measures of similarity are visually more satisfactory than traditional indices. We show that the similarity indices are useful for model selection by comparing observed spatial patterns with those predicted by different fitted models. Examples are provided for spatial distributions of oribatid mites (Acari, Oribatei), woodlarks (Lullula arborea), and red deer (Cervus elaphus).

Wildlife population assessment: past developments and future directions.

We review the major developments in wildlife population assessment in the past century. Three major areas are considered: mark-recapture, distance sampling, and harvest models. We speculate on how these fields will develop in the next century. Topics for which we expect to see methodological advances include integration of modeling with Geographic Information Systems, automated survey design algorithms, advances in model-based inference from sample survey data, a common inferential framework for wildlife population assessment methods, improved methods for estimating population trends, the embedding of biological process models into inference, substantially improved models for conservation management, advanced spatiotemporal models of ecosystems, and greater emphasis on incorporating model selection uncertainty into inference. We discuss the kind of developments that might be anticipated in these topics.

The relative contribution of remobilization and root uptake in supplying nitrogen after defoliation for regrowth of laminae in four grass species.

Miniswards of Lolium perenne, Festuca rubra, Agrostis casteUana and Poa trivialis were grown in a sand-limestone mixture and fed with a complete nutrient solution containing 3 mol m-3 NH4 NO3 . The miniswards were cut weekly to a height of 4 cm until a steady state of growth, based on weight of clippings removed, was achieved. After clipping (day 0) the nutrient solution was replaced by one in which all the nitrogen (N) was enriched with 15 N to 5 atom per cent, but was otherwise identical. This allowed discrimination of remobilization from current root uptake when considering the supply of N for regrowth of laminae. Destructive harvests were made over the following 28-d period, while unharvested plants continued to receive a weekly clip. The proportion of the total uptake of labelled N which appeared in the clipped material was of the order L. perenne > P. trivialis > A. castellana > F. rubra. The change in unlabelled N in the roots plus leaf bases over time was modelled as a proportion of the amount present on day 0. A simple exponential model with a non-zero asymptote was found to model the data; the asymptote was assumed to represent the proportion of unlabelled N not readily available for remobilization. Species differences in the asymptotes were found, with F. rubra having a significantly larger asymptote than A. castellana and P. trivialis, which in turn both had significantly larger asymptotes than L. perenne. All species used both root uptake and remobilization to supply N for post-defoliation regrowth of laminae. However, the relative contribution of each source was species-dependent. The proportion of N in the clipped material derived from remobilization was significantly greater for F. rubra than A. castellana, which in turn had a significantly greater proportion derived from remobilization than P. trivialis and L. perenne.

Perpendicular distance models for line transect sampling.

Perpendicular distance line transect models are examined to assess whether any single model can provide a general procedure for analysing line transect data. Of the two-parameter models considered, the hazard-rate model appears promising, whereas the exponential power series and exponential quadratic models do not. Of the nonparametric models, the Fourier series is the best developed, and is favoured by many researchers as a general model. However, for a given data set, the Fourier series estimate may be highly dependent on the number of terms selected, and so the model is not a clear improvement over the hazard-rate model. A similar variable-term model, using Hermite polynomials, is considered, and is shown to be less dependent on the number of terms selected. There has been some debate about whether the derivative of the density function of perpendicular distances evaluated at 0 should be 0, so that the function has a "shoulder." The problem is examined in detail, and it is argued that reliable estimation is not possible from line transect data unless a shoulder exists. Many data sets appear to exhibit no shoulder; possible reasons are examined.

A note on the Fourier series model for analysing line transect data.

The Fourier series model offers a powerful procedure for the estimation of animal population density from line transect data. The estimate is reliable over a wide range of detection functions. In contrast, analytic confidence intervals yield, at best, 90% confidence for nominal 95% intervals. Three solutions, one using Monte Carlo techniques, another making direct use of replicate lines and the third based on the jackknife method, are discussed and compared.