Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Population Growth of Antarctic Fur Seals: Limitation by a Top Predator, The Leopard Seal?

Antarctic fur seals (Arctocephalus gazella) in the South Shetland Islands are recovering from 19th-century exploitation more slowly than the main population at South Georgia. To document demographic changes associated with the recovery in the South Shetlands, we monitored fur seal abundance and reproduction in the vicinity of Elephant Island during austral summers from 1986/1987 through 1994/1995. Total births, mean and variance of birth dates, and average daily mortality rates were estimated from daily live pup counts at North Cove (NC) and North Annex (NA) colonies on Seal Island. Sightings of leopard seals (Hydrurga leptonyx) and incidents of leopard seal predation on fur seal pups were recorded opportunistically during daily fur seal research at both sites. High mortality of fur seal pups, attributed to predation by leopard seals frequently observed at NC, caused pup numbers to decline rapidly between January and March (i.e., prior to weaning) each year and probably caused a long-term decline in the size of that colony. The NA colony, where leopard seals were never observed, increased in size during the study. Pup mortality from causes other than leopard seal predation appeared to be similar at the two sites. The number of pups counted at four locations in the Elephant Island vicinity increased slowly, at an annual rate of 3.8%, compared to rates as high as 11% at other locations in the South Shetland Islands. Several lines of circumstantial evidence are consistent with the hypothesis that leopard seal predators limit the growth of the fur seal population in the Elephant Island area and perhaps in the broader population in the South Shetland Islands. The sustained growth of this fur seal population over many decades rules out certain predator–prey models, allowing inference about the interaction between leopard seals and fur seals even though it is less thoroughly studied than predator–prey systems of terrestrial vertebrates of the northern hemisphere. Top-down forces should be included in hypotheses for future research on the factors shaping the recovery of the fur seal population in the South Shetland Islands.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.