Biomedical subjects

R M Fewster

Publications and source records attributed to R M Fewster.

3 recordsLinked to original sources

PubMed2005

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.

Animals↗

PubMed2003

A spatiotemporal stochastic process model for species spread.

We use a spatiotemporal Markov process to model the spread of an ecological population through its environment over time. Available habitat is divided into sites, and a parametric function of spatial variables is used to model the probability that one site is colonized from another. This allows us both to make predictions about the future spread of a population, and to determine which are the important factors governing colonizations. The model evolves in discrete time, allowing the population distribution to change seasonally in accordance with breeding patterns. Discrete time formulations are natural for ecological populations, but are problematic due to difficulties of fitting and predicting over irregular time intervals. The model described here can accommodate years of missing data and can therefore fit and predict at irregular intervals. Two methods of approximating the likelihood are described and applied to ornithological survey data for the woodlark, Lullula arborea, from Thetford Forest in the U.K.

Animals↗

PubMed2001

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).

Algorithms↗