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

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

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe

The antiquity of Madagascar's grasslands and the rise of C 4 grassy biomes

ABSTRACT Aim Grasslands and savannas, which make up > 75% of Madagascar's land area, have long been viewed as anthropogenically derived after people settled on the island c. 2 ka. We investigated this hypothesis and an alternative -that the grasslands are an insular example of the post-Miocene spread of C 4 grassy biomes world-wide. Location Madagascar, southern Africa, East Africa. Methods We compared the number of C 4 grass genera in Madagascar with that in southern and south-central African floras. If the grasslands are recent we would expect to find fewer species and genera in Madagascar relative to Africa and for these species and genera to have very wide distribution ranges in Madagascar. Secondly, we searched Madagascan floras for the presence of endemic plant species or genera restricted to grasslands. We also searched for evidence of a grassland specialist fauna with species endemic to Madagascar. Plant and animal species endemic to C 4 grassy biomes would not be expected if these are of recent origin

2007. Partitioning of understorey light and dry-season soil moisture gradients among seedlings of four rain-forest tree species in

Abstract: Resource partitioning has been hypothesized to play a role in the maintenance of tree diversity in tropical forests. We looked for evidence of light and soil moisture partitioning among seedlings of four native Malagasy tree species, the pioneer, gap-adapted species Harungana madagascariensis and the three shade-tolerant species Ocotea cymosa, Stephanostegia capuronii and Uapaca ferruginea. Four hundred and eighty seedlings were transplanted in experimental plots in the Tampolo coastal forest and grown for 2 y. Growth rates increased with increasing light availability for all species, and with increasing dry-season soil moisture for H. madagascariensis. With increasing light availability, survival increased for H. madagascariensis, S. capuronii and U. ferruginea but decreased for O. cymosa. While dry-season soil moisture did not influence the growth or survival of the shade-tolerant species, it interacted with understorey light in its effect on the performance of H. madagascariensis, which performed better in wet soils at high light than in dry soils in shade. Rank reversals in species performance suggested that three of the four tree species partition resource gradients as seedlings, mostly light and secondarily dry-season soil moisture. There was only partial agreement between the performance of transplanted seedlings and the distribution of natural seedlings of the same four species with respect to light and soil moisture, suggesting that the success of tropical tree regeneration can only be partly accounted for by seedling performance across resource gradients

Data from: Processes of community assembly in an environmentally heterogeneous, high biodiversity region

Despite decades of study, the relative importance of niche-based versus neutral processes in community assembly remains largely ambiguous. Recent work suggests niche-based processes are more easily detectable at coarser spatial scales, while neutrality dominates at finer scales. Analyses of functional traits with multi-year multi-site biodiversity inventories may provide deeper insights into assembly processes and the effects of spatial scale. We examined associations between community composition, species functional traits, and environmental conditions for plant communities in the Kouga-Baviaanskloof region, an area within South Africa's Cape Floristic Region (CFR) containing high α and β diversity. This region contains strong climatic gradients and topographic heterogeneity, and is comprised of distinct vegetation classes with varying fire histories, making it an ideal location to assess the role of niche-based environmental filtering on community composition by examining how traits vary with environment. We combined functional trait measurements for over 300 species with observations from vegetation surveys carried out in 1991/1992 and repeated in 2011/2012. We applied redundancy analysis, quantile regression, and null model tests to examine trends in species turnover and functional traits along environmental gradients in space and through time. Functional trait values were weakly associated with most spatial environmental gradients and only showed trends with respect to vegetation class and time since fire. However, survey plots showed greater compositional and functional stability through time than expected based on null models. Taken together, we found clear evidence for functional distinctions between vegetation classes, suggesting strong environmental filtering at this scale, most likely driven by fire dynamics. In contrast, there was little evidence of filtering effects along environmental gradients within vegetation classes, suggesting that assembly processes are largely neutral at this scale, likely the result of very high functional redundancy among species in the regional species pool

Ten Principles for Biocultural Conservation at the Southern Tip of the Americas: the Approach of the Omora Ethnobotanical Park

Although there is general agreement among conservation practitioners about the need for (1) social involvement on the part of scientists; (2) interdisciplinary approaches; (3) working on local, regional, and global levels; and (4) implementing international agreements on biodiversity and environmental protection, a major challenge we face in conservation today is how to integrate and implement these multiple dimensions. Few researchers have actually offered hands-on examples for showing in practical terms how such integration can be accomplished. To address this challenge we present an innovative case study: the Omora Ethnobotanical Park, a long-term biocultural conservation initiative at the southern extreme of the Americas. Located near Puerto Williams (55º S), Cape Horn Archipelago region, Chile, the Omora Park is a public-private reserve that provides material and conceptual foundations for three complementary conservation actions: (1) interdisciplinary scientific research; (2) informal and formal education, i.e., school, university, and training courses; and (3) biocultural conservation. The latter entails an actual reserve that protects biodiversity and the water quality of Puerto Williams' watershed, as well as programs on Yahgan traditional ecological knowledge and interdisciplinary activities, such as "field environmental ethics" and ecotourism, carried out in the reserve. Being at the "end of the world," and within one of the most remote and pristine ecoregions on the planet, Omora Park offers a "bio-cultural treasure." At the same time, its geographical and technological isolation presents a challenge for implementing and sustaining conservation actions. To achieve the general conservation goals, we have defined 10 principles that have guided the actions of Omora: (1) interinstitutional cooperation, (2) a participatory approach, (3) an interdisciplinary approach, (4) networking and international cooperation, (5) communication through the media, (6) identification of a flagship species, (7) outdoor formal and informal education, (8) economic sustainability and ecotourism, (9) administrative sustainability, and (10) research and conceptual sustainability for conservation. These principles have been effective for establishing the long-term Omora initiative, as well as involving multiple actors, disciplines, and scales. Upon these foundations, the Omora initiative has extended its local goals to the regional level through a successful 5-yr process in cooperation with the Chilean government to create the Cape Horn Biosphere Reserve, designated by UNESCO in June 2005, with the goal of establishing a long-term institutional-political framework that promotes social well-being and biocultural conservation at the southernmost tip of the Americas

Supplement 1. R code and fynbos data set.

<h2>File List</h2><blockquote> <p> <a href="Merow_et_al_R_code.r">Merow_et_al_R_code.r</a> -- R code for all analyses </p> <p> <a href="fynbos_abundance_matrix.csv">fynbos_abundance_matrix.csv</a> -- matrix of relative abundance (rows) by sites (columns) </p> <p> <a href="fynbos_trait_matrix.csv">fynbos_trait_matrix.csv</a> -- matrix of species (rows) by traits (columns) </p> </blockquote><h2>Description</h2><blockquote> <p> The file Merow_et_al_R_code.r contains R code for constructing the EM models shown in the main text. This includes the basic EM model, calculating the Hellinger fit metric, Lagrange multipliers and plotting results for both local and regional communities. We also include code for predicting community-aggregated traits from splines, cross validation, generating informative priors and permutation tests. The file fynbos_abundance_matrix.csv contains site by site relative abundance data for the eight elevational communities we used (derived from 43 5 × 10 m releves) from the Baviaanskloof Mountains, South Africa. The file fynbos_trait_matrix.csv contains data for each species on the following traits: graminoid (binary), succulent (binary), maximum height, leaf longevity (ordinal 1–3, 1 is lowest), flowering duration, pubescence (binary), leaf width, leaf perimeter^2/leaf area, leaf area/basal diameter, stem length/stem basal diameter^(2/3). These traits have been rescaled to lie on the interval [0,1]. </p> <p> Checksum values are as follows: </p> <p> For fynbos_trait_matrix.csv the columns sum to: col 1 (life.form.graminoid) = 19, col 2 = 2, col. 3 = 5.84708, col. 4 = 24, col. 5 = 18.09093, col. 6 = 14, col. 7 = 7.75024, col. 8 = 8.16681, col. 9 = 6.7815, and last col. = 8.2096. </p> <p> For fynbos_abundance_matrix.csv, all columns sum to 1. </p> </blockquote