Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly $37{,}000$ one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only $28$ of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Landscape dynamics of northeastern forests

This project involves collaborative research with Stephen W. Pacala and Simon A. Levin of Princeton University to calibrate, test, and analyze models of heterogeneous forested landscapes containing a diverse array of habitats. The project is an extension of previous, NASA-supported research to develop a spatially-explicit model of forest dynamics at the scale of an individual forest stand (hectares to square kilometer spatial scales). That model (SORTIE) has been thoroughly parameterized from field studies in the modal upland environment of western Connecticut. Under our current funding, we are scaling-up the model and parameterizing it for the broad range of upland environments in the region. Our most basic goal is to understand the linkages between stand-level dynamics (as revealed in our previous research) and landscape-level dynamics of forest composition and structure

Identifying hotspots for plant invasions and forecasting focal points of further spread

1. To ensure the successful detection, control and eradication of invasive plant species, we need information that can identify areas prone to invasions and criteria that can point out which particular populations may become foci of further spread. Specifically, our work aimed to develop statistical models that identify hotspots of invasive plant species and evaluate the conditions that give rise to successful populations of invasive species. 2. We combined extensive data sets on invasive species richness and on species per cent ground cover, together with climate, local habitat and land cover data. We then estimated invasive species richness as a function of those environmental variables by developing a spatially explicit generalized linear model within a hierarchical Bayesian framework. In a second analysis, we used an ordinal logistic regression model to quantify invasive species abundance as a function of the same set of predictor variables. 3. Our results show which locations in the studied region, north-eastern USA, are prone to plant species invasions given the combination of climatic and land cover conditions particular to the sites. Predictions were also generated under a range of climate scenarios forecasted for the region, which pointed out at an increase in invasive species incidence under the most moderate forecast. Predicted abundance for some of the most common invasive plant species, Berberis thumbergii , Celastrus orbiculatus , Euonymus alata , Elaeagnus umbellata and Rosa multiflora , allowed us to identify the specific conditions that promote successful population growth of these species, populations that could become foci of further spread. 4. Synthesis and applications. Reliable predictions of plants’ invasive potential are crucial for the successful implementation of control and eradication management plans. By following a multivariate approach the parameters estimated in this study can now be used on targeted locations to evaluate the risk of invasions given the local climate and landscape structure; they can also be applied under different climate scenarios and changing landscapes providing an array of possible outcomes. In addition, this modelling approach can be easily used in other regions and for other species.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78698/1/j.1365-2664.2009.01736.x.pd

Beyond seasonal climate: statistical estimation of phenological responses to weather

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/117250/1/eap20142471793.pd

Scaling up: linking field data and remote sensing with a hierarchical model

Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we use a Hierarchical Bayesian model to assess the relationship between measurements collected on the ground at centimeter scales nested within 2 × 3 m quadrats, which are in turn nested within much larger (0.1-12 ha) plots. We compare these measurements with the Normalized Difference Vegetation Index (NDVI) derived from radiometrically and geometrically corrected 30-m resolution LANDSAT ETM+ data to assess the NDVI-Biomass relationship in the Cape Floristic Region of South Africa. Our novel modeling approach allows the data observed at submeter scales to be incorporated directly into the model and thus all the data (and variability) collected at finer scales are represented in the estimates of biomass at the LANDSAT scale. The model reveals that there is a strong correlation between NDVI and biomass, which supports the use of NDVI in spatiotemporal analysis of vegetation dynamics in Mediterranean shrubland ecosystems. The methods developed here can be easily generalized to other ecosystems and ecophysiological parameters

Explaining species distribution patterns through hierarchical modeling

Understanding spatial patterns of species diversity and the distri- butions of individual species is a consuming problem in biogeography and con- servation. The Cape Floristic Region (CFR) of South Africa is a global hotspot of diversity and endemism, and the Protea Atlas Project, with some 60,000 site records across the region, provides an extraordinarily rich data set to analyze bio- diversity patterns. Analysis for the region is developed at the spatial scale of one minute grid-cells ( 37; 000 cells total for the region). We report on results for 40 species of a owering plant family Proteaceae (of about 330 in the CFR) for a de ned subregion. Using a Bayesian framework, we develop a two stage, spatially explicit, hierar- chical logistic regression. Stage one models the suitability or potential presence for each species at each cell, given species attributes along with grid cell (site-level) climate, precipitation, topography and geology data using species-level coe cients, and a spatial random e ect. The second level of the hierarchy models, for each species, observed presence=absence at a sampling site through a conditional speci- cation of the probability of presence at an arbitrary location in the grid cell given that the location is suitable. Because the atlas data are not evenly distributed across the landscape, grid cells contain variable numbers of sampling localities. Indeed, some grid cells are entirely unsampled; others have been transformed by human intervention (agriculture, urbanization) such that none of the species are there though some may have the potential to be present in the absence of distur- bance. Thus the modeling takes the sampling intensity at each site into account by assuming that the total number of times that a particular species was observed within a site follows a binomial distribution.In fact, a range of models can be examined incorporating di erent rst and second stage speci cations. This necessitates model comparison in a misaligned multilevel setting. All models are tted using MCMC methods. A best" model is selected. Parameter summaries o er considerable insight. In addition, results are mapped as the model-estimated potential presence for each species across the domain. This probability surface provides an alternative to customary empiri- cal \range of occupancy" displays. Summing yields the predicted species richness over the region. Summaries of the posterior for each environmental coe cient show which variables are most important in explaining species presence. Other biodi- versity measures emerge as model unknowns. A considerable range of inference is available. We illustrate with only a portion of the analyses we have conducted, noting that these initial results describe biogeographical patterns over the modeled region remarkably well

Biological, clinical and population relevance of 95 loci for blood lipids.

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD

Changing lenses to assess biodiversity: patterns of species richness in sub-Antarctic plants and implications for global conservation

Article discussing patterns of species richness in sub-Antarctic plants and implications for global conservation