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.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions

Over the past three decades, the decline and altered spatial distribution of the western stock of Steller sea lions (Eumetopias jubatus) in Alaska have been attributed to changes in the distribution or abundance of their prey due to the cumulative effects of fisheries and environmental perturbations. During this period, dietary prey occurrence and diet diversity were related to population decline within metapopulation regions of the western stock of Steller sea lions, suggesting that environmental conditions may be variable among regions. The objective of this study, therefore, was to examine regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions within the context of recent measures of diet diversity and population trajectories. Habitat use was assessed by deploying satellite-depth recorders and satellite relay data loggers on juvenile Steller sea lions (n = 45) over a five-year period (2000–2004) within four regions of the western stock, including the western, central, and eastern Aleutian Islands, and central Gulf of Alaska. Areas used by sea lions during summer months (June, July, and August) were demarcated using satellite telemetry data and characterized by environmental variables (sea surface temperature [SST] and chlorophyll a [chl a]), which possibly serve as proxies for environmental processes or prey. Spatial patterns of SST diversity and Steller sea lion population trends among regions were fairly consistent with trends reported for diet studies, possibly indicating a link between environmental diversity, prey diversity, and distribution or abundance of Steller sea lions. Overall, maximum spatial heterogeneity coupled with minimal temporal variability of SST appeared to be beneficial for Steller sea lions. In contrast, these patterns were not consistent for chl a, and there appeared to be an ecological threshold. Understanding how Steller sea lions respond to measures of environmental heterogeneity will ultimately be useful for implementing ecosystem management approaches and developing additional conservation strategies.

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.

Temporal and Spatial Variations of Ions, Isotopes and Agricultural Contaminants in Surface Waters and Groundwater of Nebraska's Rainwater Basin Wetland Region

The wetlands of south-central Nebraska’s Rainwater Basin region are considered of international importance as a habitat for millions of migratory birds, but are being endangered by agricultural practices. The Rainwater Basin extends across 17 counties and covers 4,000 square miles. The purpose of this study was to assemble baseline chemical data for several representative wetlands across the Rainwater Basin region, and determine the use of these chemical data for investigating groundwater recharge. Eight representative wetlands were chosen across the Rainwater Basin to monitor surface and groundwater chemistry. At each site, a shallow well and deep well were installed and sampled once in the summer of 2009 and again in the spring of 2010. Wetland surface water was sampled monthly from April, 2009 to May, 2010. Waters were analyzed for major ions, nutrients, pesticides and oxygen-18 and deuterium isotopes at the University of Nebraska Water Sciences Laboratory. Geochemical analysis of surface waters presents a range of temporal and spatial variations. Wetlands had variable water volumes, isotopic compositions, ion chemistries and agricultural contaminant levels throughout the year and, except for a few trends, theses variations cannot be predicted with certainty year-to-year or wetland-to-wetland. Isotopic compositions showed evaporation was a contributor to water loss, and thus, did impact water chemistry. Surface water nitrate concentrations ranged from <0.10 to 4.04 mg/L. The nitrate levels are much higher in the groundwater, ranging from <0.10 to 18.4 mg/L, and are of concern because they are found above the maximum contaminant level (MCL) of 10 mg/L. Atrazine concentrations in surface waters ranged from <0.05 to 10.3 ppb. Groundwater atrazine concentrations ranged from <0.05 to 0.28 ppb. The high atrazine concentrations in surface waters are of concern as they are above the MCL of 3 ppb, and the highest levels occur during the spring bird migration. Most sampled groundwaters had detectable tritium indicating a mix of modern (<5 to 10 years old) and submodern (older than 1950s) recharge. The groundwater also had differences in chemical and isotope composition, and in some cases, increased nitrate concentrations, between the two sampling periods. Modern groundwater tritium ages and changes in groundwater chemical and isotopic compositions may indicate connections with surface waters in the Rainwater Basin.

Contributions of demography and dispersal parameters to the spatial spread of a stage-structured insect invasion

Stage-structured models that integrate demography and dispersal can be used to identify points in the life cycle with large effects on rates of population spatial spread, information that is vital in the development of containment strategies for invasive species. Current challenges in the application of these tools include: (1) accounting for large uncertainty in model parameters, which may violate assumptions of ‘‘local’’ perturbation metrics such as sensitivities and elasticities, and (2) forecasting not only asymptotic rates of spatial spread, as is usually done, but also transient spatial dynamics in the early stages of invasion. We developed an invasion model for the Diaprepes root weevil (DRW; Diaprepes abbreviatus [Coleoptera: Curculionidae]), a generalist herbivore that has invaded citrus-growing regions of the United States. We synthesized data on DRW demography and dispersal and generated predictions for asymptotic and transient peak invasion speeds, accounting for parameter uncertainty. We quantified the contributions of each parameter toward invasion speed using a ‘‘global’’ perturbation analysis, and we contrasted parameter contributions during the transient and asymptotic phases. We found that the asymptotic invasion speed was 0.02–0.028 km/week, although the transient peak invasion speed (0.03– 0.045 km/week) was significantly greater. Both asymptotic and transient invasions speeds were most responsive to weevil dispersal distances. However, demographic parameters that had large effects on asymptotic speed (e.g., survival of early-instar larvae) had little effect on transient speed. Comparison of the global analysis with lower-level elasticities indicated that local perturbation analysis would have generated unreliable predictions for the responsiveness of invasion speed to underlying parameters. Observed range expansion in southern Florida (1992–2006) was significantly lower than the invasion speed predicted by the model. Possible causes of this mismatch include overestimation of dispersal distances, demographic rates, and spatiotemporal variation in parameter values. This study demonstrates that, when parameter uncertainty is large, as is often the case, global perturbation analyses are needed to identify which points in the life cycle should be targets of management. Our results also suggest that effective strategies for reducing spread during the asymptotic phase may have little effect during the transient phase. Includes Appendix.

Spatial analysis of egg distribution and geographic changes in the spawning habitat of the Brazilian sardine Sardinella brasiliensis

This paper establishes the spawning habitat of the Brazilian sardine Sardinella brasiliensis and investigates the spatial variability of egg density and its relation with oceanographic conditions in the shelf of the south-east Brazil Bight (SBB). The spawning habitats of S. brasiliensis have been defined in terms of spatial models of egg density, temperature-salinity plots, quotient (Q) analysis and remote sensing data. Quotient curves (Q(C)) were constructed using the geographic distribution of egg density, temperature and salinity from samples collected during nine survey cruises between 1976 and 1993. The interannual sea surface temperature (SST) variability was determined using principal component analysis on the SST anomalies (SSTA) estimated from remote sensing data over the period between 1985 and 2007. The spatial pattern of egg occurrences in the SBB indicated that the largest concentration occurred between Paranagua and Sao Sebastiao. Spawning habitat expanded and contracted during the years, fluctuating around Paranagua. In January 1978 and January 1993, eggs were found nearly everywhere along the inner shelf of the SBB, while in January 1988 and 1991 spawning had contracted to their southernmost position. The SSTA maps for the spawning periods showed that in the case of habitat expansion (1993 only) anomalies over the SBB were zero or slightly negative, whereas for the contraction period anomalies were all positive. Sardinella brasiliensis is capable of exploring suitable spawning sites provided by the entrainment of the colder and less-saline South Atlantic Central Water onto the shelf by means of both coastal wind-driven (to the north-east of the SBB) and meander-induced (to the south-west of the SBB) upwelling.

SPATIAL AND TEMPORAL DISTRIBUTION OF FISH IN PALMAS BAY, UBATUBA, BRAZIL

The aim of this study was to analyze the distribution and abundance of the fish fauna of Palmas bay on Anchieta Island in southeastern Brazil. Specimens were caught in the summer and winter of 1992, using an otter trawl at three locations in the bay. The specimens were caught in both the nighttime and daytime. Data on the water temperature and salinity were recorded for the characterization of the predominant water mass in the region, and sediment samples were taken for granulometric analysis. A total of 7 656 specimens (79 species), with a total weight of approximately 300 kg, were recorded. The most abundant species were Eucinostomus argenteus, Ctenosciaena gracilicirrhus, Haemulon steindachneri, Eucinostomus gula and Diapterus rhombeus, which together accounted for more than 73% of the sample. In general, the ecological indices showed no differences in the composition of species for the abiotic variables analyzed. The multivariate analysis showed that the variations in the distribution of the fish fauna were mainly associated with intra-annual differences in temperature and salinity, resulting from the presence of South Atlantic Central Water (SACW) in the area during the summer. The analysis also showed an association with the type of bottom and a lesser association with respect to the night/day periods.

Spatial and Temporal Variation of Archaeal, Bacterial and Fungal Communities in Agricultural Soils

Background: Soil microbial communities are in constant change at many different temporal and spatial scales. However, the importance of these changes to the turnover of the soil microbial communities has been rarely studied simultaneously in space and time. Methodology/Principal Findings: In this study, we explored the temporal and spatial responses of soil bacterial, archaeal and fungal beta-diversities to abiotic parameters. Taking into account data from a 3-year sampling period, we analyzed the abundances and community structures of Archaea, Bacteria and Fungi along with key soil chemical parameters. We questioned how these abiotic variables influence the turnover of bacterial, archaeal and fungal communities and how they impact the long-term patterns of changes of the aforementioned soil communities. Interestingly, we found that the bacterial and fungal b-diversities are quite stable over time, whereas archaeal diversity showed significantly higher fluctuations. These fluctuations were reflected in temporal turnover caused by soil management through addition of N-fertilizers. Conclusions: Our study showed that management practices applied to agricultural soils might not significantly affect the bacterial and fungal communities, but cause slow and long-term changes in the abundance and structure of the archaeal community. Moreover, the results suggest that, to different extents, abiotic and biotic factors determine the community assembly of archaeal, bacterial and fungal communities.

Suicide rates and income in Sao Paulo and Brazil: a temporal and spatial epidemiologic analysis from 1996 to 2008

Background: In a classical study, Durkheim noted a direct relation between suicide rates and wealth in the XIX century France. Since that time, several studies have verified this relationship. It is known that suicide rates are associated with income, although the direction of this association varies worldwide. Brazil presents a heterogeneous distribution of income and suicide across its territory; however, evaluation for an association between these variables has shown mixed results. We aimed to evaluate the relationship between suicide rates and income in Brazil, State of Sao Paulo (SP), and City of SP, considering geographical area and temporal trends. Methods: Data were extracted from the National and State official statistics departments. Three socioeconomic areas were considered according to income, from the wealthiest (area 1) to the poorest (area 3). We also considered three regions: country-wide (27 Brazilian States and 558 Brazilian micro-regions), state-wide (645 counties of SP State), and city-wide (96 districts of SP city). Relative risks (RR) were calculated among areas 1, 2, and 3 for all regions, in a cross-sectional approach. Then, we used Joinpoint analysis to explore the temporal trends of suicide rates and SaTScan to investigate geographical clusters of high/low suicide rates across the territory. Results: Suicide rates in Brazil, the State of SP, and the city of SP were 6.2, 6.6, and 5.4 per 100,000, respectively. Taking suicide rates of the poorest area (3) as reference, the RR for the wealthiest area was 1.64, 0.88, and 1.65 for Brazil, State of SP, and city of SP, respectively (p for trend <0.05 for all analyses). Spatial cluster of high suicide rates were identified at Brazilian southern (RR = 2.37), state of SP western (RR = 1.32), and city of SP central (RR = 1.65) regions. A direct association between income and suicide were found for Brazil (OR = 2.59) and the city of SP (OR = 1.07), and an inverse association for the state of SP (OR = 0.49). Conclusions: Temporospatial analyses revealed higher suicide rates in wealthier areas in Brazil and the city of SP and in poorer areas in the State of SP. We further discuss the role of socioeconomic characteristics for explaining these discrepancies and the importance of our findings in public health policies. Similar studies in other Brazilian States and developing countries are warranted.

Engineering geological data in support of municipal land use planning-a case study in Analandia, southeast Brazil

This paper presents a method for transforming the information of an engineering geological map into useful information for non-specialists involved in land-use planning. The method consists of classifying the engineering geological units in terms of land use capability and identifying the legal and the geologic restrictions that apply in the study area. Both informations are then superimposed over the land use and a conflict areas map is created. The analysis of these data leads to the identification of existing and forthcoming land use conflicts and enables the proposal of planning measures on a regional and local scale. The map for the regional planning was compiled at a 1:50,000 scale and encompasses the whole municipal land area where uses are mainly rural. The map for the local planning was compiled at a 1:10,000 scale and encompasses the urban area. Most of the classification and operations on maps used spatial analyst tools available in the Geographical Information System. The regional studies showed that the greater part of Analandia's territory presents appropriate land uses. The local-scale studies indicate that the majority of the densely occupied urban areas are in suitable land. Although the situation is in general positive, municipal policies should address the identified and expected land use conflicts, so that it can be further improved.

Admissions due to pneumonia and biomass burning: a spatial approach

Objective: To identify spatial patterns in rates of admission for pneumonia among children and relate them to the number of fires reported in the state of Mato Grosso, Brazil. Methods: We conducted an ecological and exploratory study of data from the state of Mato Grosso for 2008 and 2009 on hospital admissions of children aged 0 to 4 years due to pneumonia and on fires in the same period. Admission rates were calculated and choropleth maps were plotted for rates and for fire outbreaks, Moran's I was calculated and the kernel estimator used to identify "hotspots." Data were analyzed using TerraView 3.3.1. Results: Fifteen thousand six hundred eighty-nine children were hospitalized (range zero to 2,315), and there were 161,785 fires (range 7 to 6,454). The average rate of admissions per 1,000 inhabitants was 2.89 (standard deviation [SD] = 5.18) and the number of fires per 1,000 inhabitants was 152.81 (SD = 199.91). Moran's I for the overall number of admissions was I = 0.02 (p = 0.26), the index for rate of admission was I = 0.02 (p = 0.21) and the index for the number of fires was I = 0.31 (p < 0.01). It proved possible to identify four municipalities with elevated rates of admissions for pneumonia. It was also possible to identify two regions with high admission densities. A clustering of fires was evident along what is known as the "arc of deforestation." Conclusions: This study identified municipalities in the state of Mato Grosso that require interventions to reduce rates of admission due to pneumonia and the number fires.

Spatial analysis of the social vulnerability of adolescent pregnancy

The purpose of this study was to present a spatial analysis of the social vulnerability of teenage pregnancy by geoprocessing data on births and deaths present on the Brazilian Ministry of Health databases in order to support intersectoral management actions and strategies based on spatial analysis in neighborhood areas. The thematic maps of the educational, occupational, birth and marital status of mothers, from all births and deaths in the city, presented a spatial correlation with teenage pregnancy. These maps were superimposed to produce social vulnerability map of adolescent pregnancy and women in general. This process presents itself as a powerful tool for the study of social vulnerability.