Spatial analyses of groundwater level differences using geostatistical modeling

The purpose of this study was to determine and evaluate the spatial changes in the depletion of groundwater level differences by using geostatistical methods based on data from 58 groundwater wells during the period from April 1999 to April 2008 in the study area. Geostatistical methods have been used widely as a convenient tool to make decision on the management of groundwater levels. To evaluate the spatial changes in the level of the groundwater, geographic information system is used for the application of universal kriging method with cross-validation leading to the estimation of groundwater levels. The resulting prediction mappings identify the locations of groundwater level fluctuations of the study area. The average range of variogram (spherical model) for the spatial analysis is about 9,200 m. Results of universal kriging for groundwater level differences drops were underestimated by 15 %. Cross-validation errors are within an acceptable level. The maps show that this area of high decrease of groundwater level is located at the southwest. Kriging model helps also to detect sensitively risk prone areas for groundwater withdrawing. Such areas must be protected with an effective management procedure for future groundwater exploitations.     

Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling

Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian framework to (1) estimate model parameters, (2) implement a model selection procedure to evaluate competing hypotheses about causal mechanisms, and (3) assess the fit of models to data using posterior predictive checks. We illustrate the value of our approach using two case studies on wild bird populations. We first show that CR-SEM can be useful to quantify the action of selection on a set of phenotypic traits with an analysis of selection gradients on morphological traits in Common Blackbirds (Turdus merula). In a second case study on Blue Tits (Cyanistes caeruleus), we illustrate the use of CR-SEM to study evolutionary trade-offs in the wild, while accounting for varying environmental conditions.     

Spatial heterogeneity in distribution and ecology of Western Palearctic birds

Species vary in abundance and heterogeneity of spatial distribution, and the ecological and evolutionary consequences of such variability are poorly known. Evolutionary adaptation to heterogeneously distributed resources may arise from local adaptation with individuals of such locally adapted populations rarely dispersing long distances and hence having small populations and small overall ranges. We quantified mean population density and spatial heterogeneity in population density of 197 bird species across 12 similarly sized regions in the Western Palearctic. Variance in population density among regions differed significantly from a Poisson distribution, suggesting that random processes cannot explain the observed patterns. National estimates of means and variances in population density were positively correlated with continental estimates, suggesting that means and variances were maintained across spatial scales. We used Morisita's index of population abundance as an estimate of heterogeneity in distribution among regions to test a number of predictions. Heterogeneously distributed passerine bird species as reflected by Morisita's index had small populations, low population densities, and small breeding ranges. Their breeding populations had been consistently maintained at low levels for considerable periods of time, because the degree of genetic variation in a subsample of non-passerines and passerines was significantly negatively related to heterogeneity in distribution. Heterogeneously distributed passerine species were not more often habitat specialists than homogeneously distributed species. Furthermore, heterogeneously distributed passerine species had high annual adult survival rates but did not differ in annual fecundity from homogeneously distributed species. Heterogeneously distributed passerine species rarely colonized urban habitats. Finally, homogeneously distributed bird species were hosts to a greater diversity of blood parasite species than heterogeneously distributed species. In conclusion, small breeding ranges, population sizes, and population densities of heterogeneously distributed passerine bird species, combined with their low degree of genetic variability, and their inability to colonize urban areas may render such species particularly susceptible to human-influenced global climatic changes.     

A generalized approach to modeling and estimating indirect effects in ecology

The need to model and test hypotheses about complex ecological systems has led to a steady increase in use of path analytical techniques, which allow the modeling of multiple multivariate dependencies reflecting hypothesized causation and mechanisms. The aim is to achieve the estimation of direct, indirect, and total effects of one variable on another and to assess the adequacy of whole models. Path analytical techniques based on maximum likelihood currently used in ecology are rarely adequate for ecological data, which are often sparse, multi-level, and may contain nonlinear relationships as well as nonnormal response data such as counts or proportion data. Here I introduce a more flexible approach in the form of the joint application of hierarchical Bayes, Markov chain Monte Carlo algorithms, Shipley's d-sep test, and the potential outcomes framework to fit path models as well as to decompose and estimate effects. An example based on the direct and indirect interactions between ants, two insect herbivores, and a plant species demonstrates the implementation of these techniques, using freely available software.     

Hierarchical spatial modeling for estimation of population size

Estimation of population size has traditionally been viewed from a finite population sampling perspective. Typically, the objective is to obtain an estimate of the total population count of individuals within some region. Often, some stratification scheme is used to estimate counts on subregions, whereby the total count is obtained by aggregation with weights, say, proportional to the areas of the subregions. We offer an alternative to the finite population sampling approach for estimating population size. The method does not require that the subregions on which counts are available form a complete partition of the region of interest. In fact, we envision counts coming from areal units that are small relative to the entire study region and that the total area sampled is a very small proportion of the total study area. In extrapolating to the entire region, we might benefit from assuming that there is spatial structure to the counts. We implement this by modeling the intensity surface as a realization from a spatially correlated random process. In the case of multiple population or species counts, we use the linear model of coregionalization to specify a multivariate process which provides associated intensity surfaces hence association between counts within and across areal units. We illustrate the method of population size estimation with simulated data and with tree counts from a Southwestern pinyon-juniper woodland data set.     

Foraging ecology of leatherback sea turtles in the Western North Atlantic determined through multi-tissue stable isotope analyses

Leatherback turtles, Dermochelys coriacea, are highly migratory, spending most of their lives submerged or offshore where their feeding habits are difficult to observe. In order to elucidate the foraging ecology of leatherbacks off Massachusetts, USA, stable isotope analyses were performed on leatherback tissues and prey collected from 2005 to 2009. Stable isotope ratios of nitrogen and carbon were determined in whole blood, red blood cells, blood plasma, muscle, liver, and skin from adult male, female, and subadult leatherbacks. Isotopic values were analyzed by body size (curved carapace length) and grouped by sex, and groups were tested for dietary differences. Gelatinous zooplankton samples were collected from leatherback foraging grounds using surface dip nets and stratified net tows, and prey contribution to leatherback diet was estimated using a two-isotope Bayesian mixing model. Skin and whole blood δ¹³C values and red blood cell δ¹⁵N values were correlated with body size, while δ¹³C values of red blood cells, whole blood, and blood plasma differed by sex. Mixing model results suggest that leatherbacks foraging off Massachusetts primarily consume the scyphozoan jellyfishes, Cyanea capillata and Chrysaora quinquecirrha, and ctenophores, while a smaller proportion of their diet comes from holoplanktonic salps and sea butterflies (Cymbuliidae). Our results are consistent with historical observations of leatherback turtles feeding on scyphozoan prey in this region and offer new insight into size- and sex-related differences in leatherback diet.     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

The effect of neglecting spatial variations of the parameters in pollutant transport modeling in rivers

Environmental Fluid Mechanics - For many environmental projects and plans, it is necessary to model pollutant transport in rivers. Pollutant transport modeling is a complex phenomenon with multiple...     

The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages

Benthic habitats are known to influence the abundance and richness of demersal fish assemblages; however, little is known about how habitat structure and composition influences these distributions at very fine scales. We examined how the benthic environment structures marine fish assemblages using high-resolution bathymetry and accurate predicted benthic habitat maps. Areas characterised by a mosaic of habitat patches supported the highest richness of demersal fishes. A total of 37.4% of the variation in the distribution of the fish assemblage was attributed to 6 significant variables. Depth explained 23.0% of the variation, with the boulders explaining 12.6% and relief 1.4%. The remaining measures (seawhips, light/exposure and solid reef) provided a small (<1.0%) but significant contribution. Identifying components of the benthic environment important in structuring fish assemblages and understanding how they influence the spatial distribution of marine fishes is imperative for better management of demersal fish populations.     

试论生态学的一门分支学科:环境耐性生态学

针对目前对生态学的学科分支情况,及生态学的发展趋势和环境灾害的启示,提出了"环境耐性生态学"这一生态学的分支学科.初步明确了这一分支学科的概念,提出了它的3个研究层次的研究框架,即耐性特征、耐性规律及提高耐性的方法与途径,指出了它的核心思想是:把自然环境看成是具有"生命"的系统,有一定的耐性能力和自我恢复能力,也具有自然免疫的特征和能力.同时,指出了当前急需确定的3个生态环境耐性指标,即容许侵蚀量、容许施肥量和容许承载量.     

Spatial surplus production modeling of Atlantic tunas and billfish

We formulate and simulation-test a spatial surplus production model that provides a basis with which to undertake multispecies, multi-area, stock assessment. Movement between areas is parameterized using a simple gravity model that includes a "residency" parameter that determines the degree of stock mixing among areas. The model is deliberately simple in order to (1) accommodate nontarget species that typically have fewer available data and (2) minimize computational demand to enable simulation evaluation of spatial management strategies. Using this model, we demonstrate that careful consideration of spatial catch and effort data can provide the basis for simple yet reliable spatial stock assessments. If simple spatial dynamics can be assumed, tagging data are not required to reliably estimate spatial distribution and movement. When applied to eight stocks of Atlantic tuna and billfish, the model tracks regional catch data relatively well by approximating local depletions and exchange among high-abundance areas. We use these results to investigate and discuss the implications of using spatially aggregated stock assessment for fisheries in which the distribution of both the population and fishing vary over time.     

Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units

In this article, we describe a parallel agent-based model of spatial opinion diffusion that is driven by graphics processing units (GPUs). Modeling opinion exchange and diffusion across landscapes often involves the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation requires substantial computational power. GPU-enabled computing resources provide a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform holds considerable promise for meeting the computing requirement of agent-based models of spatial problems. In this article, we focus on the parallelization of an agent-based spatial opinion model using GPU technologies. We discussed key algorithms designed for parallel agent-based opinion modeling: including domain decomposition and mutual exclusion. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models of large-scale opinion exchange among individual decision makers.     

Effects of connectivity and spatial resolution of analyses on conservation prioritization across large extents

The outcome of analyses that prioritize locations for conservation on the basis of distributions of species, land cover, or other elements is influenced by the spatial resolution of data used in the analyses. We explored the influence of data resolution on prioritization of Finnish forests with Zonation, a software program that ranks the priority of cells in a landscape for conservation. We used data on the distribution of different forest types that were aggregated to nine different resolutions ranging from 0.1 × 0.1 km to 25.6 × 25.6 km. We analyzed data at each resolution with two variants of Zonation that had different criteria for prioritization, with and without accounting for connectivity and with and without adjustment for the effect on the analysis of edges between areas at the project boundary and adjacent areas for which data do not exist. Spatial overlap of the 10% of cells ranked most highly when data were analyzed at different resolutions varied approximately from 15% to 60% and was greatest among analyses with similar resolutions. Inclusion of connectivity or edge adjustment changed the location of areas that were prioritized for conservation. Even though different locations received high priority for conservation in analyses with and without accounting for connectivity, accounting for connectivity did not reduce the representation of different forest types. Inclusion of connectivity influenced most the outcome of fine-resolution analyses because the connectivity extents that we based on dispersal distances of typical forest species were small. When we kept the area set aside for conservation constant, representation of the forest types increased as resolution increased. We do not think it is necessary to avoid use of high-resolution data in spatial conservation prioritization. Our results show that large extent, fine-resolution analyses are computationally feasible, and we suggest they can give more flexibility to implementation of well-connected reserve networks.     

Feeding ecology of juvenile rockfishes off Oregon and Washington based on stomach content and stable isotope analyses

The feeding habits of pelagic, juvenile rockfishes (Sebastes spp.) collected off Oregon in 2002, and Oregon and Washington in 2006, were examined using stomach content and stable isotope analyses. Sampling occurred along a series of transects across the shelf between Crescent City, California (Lat. 41°54.0′), and Newport, Oregon (Lat. 44°39.0′), in 2002, and off Willapa Bay, Washington (Lat. 46°40.0′), and the Columbia River, Oregon (Lat. 46°10.0′), in 2006. Species composition varied both years with distance from shore, but the predominant species were darkblotched (Sebastes crameri), canary (S. pinniger), yellowtail (2006 only; S. flavidus), and widow (S. entomelas) rockfishes. Stomach content analysis revealed that darkblotched rockfish had highly variable diets, and canary, yellowtail, and widow rockfishes exhibited a high degree of overlap in 2006. Multivariate analysis showed significant differences in diet based on distance from shore where caught, fish size, and species. Stable isotope analysis indicated that all species were feeding at about the same trophic level within each year, with a 1.5 ‰ difference in δ¹⁵N between years and regions. The difference in δ¹⁵N values may indicate a greater contribution of mesotrophic zooplankton such as euphausiids, hyperiid amphipods, and chaetognaths to fish diets in 2006. Depleted ¹³C values were indicative of diets based on primary production from a more offshore origin, suggesting that these rockfish had previously inhabited offshore waters. These results add to our understanding of some of the important environmental factors that affect young-of-the-year rockfishes during their pelagic phase.