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1.
We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R2. We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998). 相似文献
2.
Zero-inflated models with application to spatial count data 总被引:1,自引:2,他引:1
Deepak K. Agarwal Alan E. Gelfand Steven Citron-Pousty 《Environmental and Ecological Statistics》2002,9(4):341-355
Count data arises in many contexts. Here our concern is with spatial count data which exhibit an excessive number of zeros. Using the class of zero-inflated count models provides a flexible way to address this problem. Available covariate information suggests formulation of such modeling within a regression framework. We employ zero-inflated Poisson regression models. Spatial association is introduced through suitable random effects yielding a hierarchical model. We propose fitting this model within a Bayesian framework considering issues of posterior propriety, informative prior specification and well-behaved simulation based model fitting. Finally, we illustrate the model fitting with a data set involving counts of isopod nest burrows for 1649 pixels over a portion of the Negev desert in Israel. 相似文献
3.
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. 相似文献
4.
Xuying Ma Ian Longley Jennifer Salmond Jay Gao 《Frontiers of Environmental Science & Engineering》2020,14(3):44
5.
A Poisson process is a commonly used starting point for modeling stochastic variation of ecological count data around a theoretical expectation. However, data typically show more variation than implied by the Poisson distribution. Such overdispersion is often accounted for by using models with different assumptions about how the variance changes with the expectation. The choice of these assumptions can naturally have apparent consequences for statistical inference. We propose a parameterization of the negative binomial distribution, where two overdispersion parameters are introduced to allow for various quadratic mean-variance relationships, including the ones assumed in the most commonly used approaches. Using bird migration as an example, we present hypothetical scenarios on how overdispersion can arise due to sampling, flocking behavior or aggregation, environmental variability, or combinations of these factors. For all considered scenarios, mean-variance relationships can be appropriately described by the negative binomial distribution with two overdispersion parameters. To illustrate, we apply the model to empirical migration data with a high level of overdispersion, gaining clearly different model fits with different assumptions about mean-variance relationships. The proposed framework can be a useful approximation for modeling marginal distributions of independent count data in likelihood-based analyses. 相似文献
6.
How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-organization in ecology: a spiral-wave forming predator-prey model, a model of pattern formation in arid vegetation, and a model of disturbance in mussel beds on rocky shores. Using these models, we show that the solutions of models on large spatial grids can be obtained on graphics processors with up to two orders of magnitude reduction in simulation time relative to normal pc processors. This allows for efficient simulation of very large spatial grids, which is crucial for, for instance, the study of the effect of spatial heterogeneity on the formation of self-organized spatial patterns, thereby facilitating the comparison between theoretical results and empirical data. Finally, we show that large-scale spatial simulations are preferable over repetitions at smaller spatial scales in identifying the presence of scaling relations in spatially self-organized ecosystems. Hence, the study of scaling laws in ecology may benefit significantly from implementation of ecological models on graphics processors. 相似文献
7.
Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions
outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which
the species range is determined by a spatial probit model, including physical variables as covariates. Within that range,
species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference
for this model is illustrated using data on oak seedling counts.
Received: May 2004 / Revised: December 2004 相似文献
8.
In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM10 levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003). 相似文献
9.
10.
Spatially constrained clustering and upper level set scan hotspot detection in surveillance geoinformatics 总被引:1,自引:0,他引:1
G. P. Patil R. Modarres W. L. Myers P. Patankar 《Environmental and Ecological Statistics》2006,13(4):365-377
We discuss upper level set (ULS) scan as a type of spatially constrained clustering in relation to two ways of imposing the
spatial constraint, retrospectively versus progressively. We show that ULS scan produces the same results both ways; whereas
two popular clustering techniques, single-linkage and K-means, can yield different results when spatial constraints are imposed
retrospectively versus progressively. The ULS scan approach examines spatially connected components of a tessellation as a
threshold is moved from the highest level (value) in the data to the lowest level. When the variable of interest on the tessellation
is a rate of incidence, then a significance test is available based on binomial or Poisson null models and Monte Carlo techniques.
This is a common context for detecting hotspots of diseases in epidemiological work. We also discuss an approach for extending
the univariate methodology to accommodate multivariate contexts.
Received: September 2005 / Revised: February 2006
This material is based upon work supported by (i) the National Science Foundation under Grant No. 0307010, (ii) the United
States Environmental Protection Agency under Grant No. CR-83059301 and (iii) the Pennsylvania Department of Health using Tobacco
Settlement Funds under Grant No. ME 01324. Any opinions, findings, and conclusions or recommendations expressed in this material
are those of the author(s) and do not necessarily reflect the views of the agencies. 相似文献
11.
The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling. 相似文献
12.
André L. F. Cançado Cibele Q. da-Silva Michel F. da Silva 《Environmental and Ecological Statistics》2014,21(4):627-650
The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisson processes. However, real data may present substantial departure from the underlying Poisson process. One of the possible departures has to do with zero excess. Some studies point out that when applied to data with excess zeros, the spatial scan statistic may produce biased inferences. In this work, we develop a closed-form scan statistic for cluster detection of spatial zero-inflated count data. We apply our methodology to simulated and real data. Our simulations revealed that the Scan-Poisson statistic steadily deteriorates as the number of zeros increases, producing biased inferences. On the other hand, our proposed Scan-ZIP and Scan-ZIP+EM statistics are, most of the time, either superior or comparable to the Scan-Poisson statistic. 相似文献
13.
Effects of preference heterogeneity among landowners on spatial conservation prioritization
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Anne Sofie Elberg Nielsen Niels Strange Hans Henrik Bruun Jette Bredahl Jacobsen 《Conservation biology》2017,31(3):675-685
The participation of private landowners in conservation is crucial to efficient biodiversity conservation. This is especially the case in settings where the share of private ownership is large and the economic costs associated with land acquisition are high. We used probit regression analysis and historical participation data to examine the likelihood of participation of Danish forest owners in a voluntary conservation program. We used the results to spatially predict the likelihood of participation of all forest owners in Denmark. We merged spatial data on the presence of forest, cadastral information on participation contracts, and individual‐level socioeconomic information about the forest owners and their households. We included predicted participation in a probability model for species survival. Uninformed and informed (included land owner characteristics) models were then incorporated into a spatial prioritization for conservation of unmanaged forests. The choice models are based on sociodemographic data on the entire population of Danish forest owners and historical data on their participation in conservation schemes. Inclusion in the model of information on private landowners’ willingness to supply land for conservation yielded at intermediate budget levels up to 30% more expected species coverage than the uninformed prioritization scheme. Our landowner‐choice model provides an example of moving toward more implementable conservation planning. 相似文献
14.
Modeling Species' Distributions to Improve Conservation in Semiurban Landscapes: Koala Case Study 总被引:2,自引:0,他引:2
JONATHAN R. RHODES†‡†† THORSTEN WIEGAND‡ CLIVE A. MCALPINE† JOHN CALLAGHAN§ DANIEL LUNNEY MICHIALA BOWEN HUGH P. POSSINGHAM† 《Conservation biology》2006,20(2):449-459
Abstract: Models of species' distributions are commonly used to inform landscape and conservation planning. In urban and semiurban landscapes, the distributions of species are determined by a combination of natural habitat and anthropogenic impacts. Understanding the spatial influence of these two processes is crucial for making spatially explicit decisions about conservation actions. We present a logistic regression model for the distribution of koalas ( Phascolarctos cinereus ) in a semiurban landscape in eastern Australia that explicitly separates the effect of natural habitat quality and anthropogenic impacts on koala distributions. We achieved this by comparing the predicted distributions from the model with what the predicted distributions would have been if anthropogenic variables were at their mean values. Similar approaches have relied on making predictions assuming anthropogenic variables are zero, which will be unreliable if the training data set does not include anthropogenic variables close to zero. Our approach is novel because it can be applied to landscapes where anthropogenic variables are never close to zero. Our model showed that, averaged across the study area, natural habitat was the main determinant of koala presence. At a local scale, however, anthropogenic impacts could be more important, with consequent implications for conservation planning. We demonstrated that this modeling approach, combined with the visual presentation of predictions as a map, provides important information for making decisions on how different conservation actions should be spatially allocated. This method is particularly useful for areas where wildlife and human populations exist in close proximity. 相似文献
15.
Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds >100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal. 相似文献
16.
Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0–10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an arid and semi-arid area of northwest China, and the performance of the model for estimating daily 0–10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r2) between model-predicted 0–10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation. 相似文献
17.
Andrew M Latimer Shanshan Wu Alan E Gelfand John A Silander 《Ecological applications》2006,16(1):33-50
Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process. 相似文献
18.
A. S. KALLIMANIS‡ W. E. KUNIN† J. M. HALLEY S. P. SGARDELIS 《Conservation biology》2005,19(2):534-546
Abstract: Recent extinction models generally show that spatial aggregation of habitat reduces overall extinction risk because sites emptied by local extinction are more rapidly recolonized. We extended such an investigation to include spatial structure in the disturbance regime. A spatially explicit metapopulation model was developed with a wide range of dispersal distances. The degree of aggregation of both habitat and disturbance pattern could be varied from a random distribution, through the intermediate case of a fractal distribution, all the way to complete aggregation (single block). Increasing spatial aggregation of disturbance generally increased extinction risk. The relative risk faced by populations in different landscapes varied greatly, depending on the disturbance regime. With random disturbance, the spatial aggregation of habitat reduced extinction risk, as in earlier studies. Where disturbance was spatially autocorrelated, however, this advantage was eliminated or reversed because populations in aggregated habitats are at risk of mass extinction from coarse-scale disturbance events. The effects of spatial patterns on extinction risk tended to be reduced by long-distance dispersal. Given the high levels of spatial correlation in natural and anthropogenic disturbance processes, population vulnerability may be greatly underestimated both by classical (nonspatial) models and by those that consider spatial structure in habitat alone. 相似文献
19.
Spatial autocorrelation in wildlife observation data arises when extrinsic environmental processes and patterns that influence the spatial distribution of wildlife are themselves spatially structured, or when species are subject to intrinsic population processes, causing contagion or dispersion effects. Territoriality, Allee effects, dispersal limitations, and social clustering are examples of intrinsic processes. Both forms of autocorrelation can violate the assumptions of generalized linear regression models, resulting in biased estimation of model coefficients and diminished predictive performance. Such consequences may be avoided for extrinsic autocorrelation when autocorrelated environmental variables are available for use as model covariates, whereas intrinsic spatial autocorrelation requires an alternative modeling approach. The autologistic model provides an approach suited to the binary observations often obtained in wildlife surveys, but its performance has not been tested across widely varying sampling intensities or strengths of intrinsic spatial structure. Here we use simulated data to test the autologistic model under a range of sampling conditions. The autologistic model obtains better fits and substantially better predictive performance than the standard logistic regression model over the full range of sampling designs and intensities tested. We provide a simple Bayesian implementation of the autologistic model, which until now has not been achieved with standard statistical software alone. A step-by-step procedure is given for characterizing and modeling spatial autocorrelation in binary observation data, along with computer code for fitting autologistic models in WinBUGS, a freeware Bayesian analysis package. This approach avoids normal approximations to the pseudo-likelihood, in contrast to previous Bayesian applications of the autologistic model. We provide a sample application of the autologistic model, fitted to survey data for a gliding marsupial in southeastern Australia. 相似文献
20.
Modelling spatial zero-inflated continuous data with an exponentially compound Poisson process 总被引:1,自引:1,他引:0
Sophie Ancelet Marie-Pierre Etienne Hugues Benoît Eric Parent 《Environmental and Ecological Statistics》2010,17(3):347-376
A parsimonious model is presented as an alternative to delta approaches to modelling zero-inflated continuous data. The data model relies on an exponentially compound Poisson process,
also called the law of leaks (LOL). It represents the process of sampling resources that are spatially distributed as Poisson
distributed patches, each containing a certain quantity of biomass drawn from an exponential distribution. In an application
of the LOL, two latent structures are proposed to account for spatial dependencies between zero values at different scales
within a hierarchical Bayesian framework. The LOL is compared to the delta-gamma (ΔΓ) distribution using bottom-trawl survey data. Results of this case study emphasize that the LOL provides slightly
better fits to learning samples with a very high proportion of zero values and small strictly positive abundance data. Additionally,
it offers better predictions of validation samples. 相似文献