Physical environment, man-made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real-world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man-made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.

/pdf/geographical-detectors-based-health-risk-assessment-and-its-2kb4fe64zr.pdf

Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

Genetical genomics : the added value from segregation

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.12757

How to make more out of community data? A conceptual framework and its implementation as models and software.

Summary
A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co-occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co-occurrence data into a SDM.
JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co-occurrence patterns into components describing shared environmental responses and residual patterns of co-occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia.
Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co-occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities.
Environmental and residual correlations estimated from JSDMs can help indicate whether co-occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12180

Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

The search for general mechanisms of community assembly is a major focus of community ecology. The common practice so far has been to examine alternative assembly theories using dichotomist approaches of the form neutrality versus niche, or compensatory dynamics versus environmental forcing. In reality, all these mechanisms will be operating, albeit with different strengths. While there have been different approaches to community structure and dynamics, including neutrality and niche differentiation, less work has gone into separating out the temporal variation in species abundances into relative contributions from different components. Here we use a refined statistical machinery to decompose temporal fluctuations in species abundances into contributions from environmental stochasticity and inter-/intraspecific interactions, to see which ones dominate. We apply the methodology to community data from a range of taxa. Our results show that communities are largely driven by environmental fluctuations, and that member populations are, to different extents, regulated through intraspecific interactions, the effects of interspecific interactions remaining broadly minor. By decomposing the temporal variation in this way, we have been able to show directly what has been previously inferred indirectly: compensatory dynamics are in fact largely outweighed by environmental forcing, and the latter tends to synchronize the population dynamics.

/pdf/what-drives-community-dynamics-xv8t3sb7fv.pdf

What drives community dynamics

1. In the prevailing context of concerns over climate change and its potential impacts on ecosystems, evaluating ecological consequences of climatic forcing has become a critical issue. 2. Historical data on the abundance of organisms have been extensively used to characterize the ecological effects of climatic forcing through specific weather and/or climatic variables, with most of the studies confined to single population models. 3. However, population responses to environmental fluctuations typically depend upon positive and negative feedbacks induced by interactions with other species. It is therefore important to integrate the insights gained from single population approaches into a multispecies perspective. 4. Here we combine the hierarchical Bayesian modelling approach with the state-space formulation to extend the scope of previously proposed models of population dynamics under climatic forcing to multi-species systems. 5. We use our model to analyse long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey network in the UK, using winter rainfall and winter temperature as environmental covariates. 6. The effects of the two weather variables were consistent across species, being negative for winter rainfall and positive for winter temperature. The two weather variables jointly explained 15-40% of the total environmental variation affecting the dynamics of individual species, and could explain up to 90% of covariances in species dynamics. 7. The contribution of interspecific interactions to community-level variation was found to be weak compared to the contributions of environmental forcing and intraspecific interactions.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2656.2010.01743.x

A multispecies perspective on ecological impacts of climatic forcing

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is “doubly adaptive” and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.

https://www.genetics.org/content/genetics/186/3/1067.full.pdf

Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction.

1.There are tens of thousands of species of phytoplankton found throughout the tree of life. Despite this diversity, phytoplankton are often aggregated into a few functional groups according to metabolic traits or biogeochemical role. We investigate the extent to which phytoplankton species dynamics are neutral within functional groups.

2.Seasonal dynamics in many regions of the ocean are known to affect phytoplankton at the functional group level leading to largely predictable patterns of seasonal succession. It is much more difficult to make general statements about the dynamics of individual species.

3.We use a 7 year time-series at station L4 in the Western English Channel with 57 diatom and 17 dinoflagellate species enumerated weekly to test if the abundance of diatom and dinoflagellate species vary randomly within their functional group envelope or if each species is driven uniquely by external factors.

4.We show that the total biomass of the diatom and dinoflagellate functional groups is well predicted by irradiance and temperature and quantify trait values governing the growth rate of both functional groups. The biomass dynamics of the functional groups are not neutral and each has their own distinct responses to environmental forcing. Compared to dinoflagellates, diatoms have faster growth rates, and grow faster under lower irradiance, cooler temperatures, and higher nutrient conditions.

5.The biomass of most species vary randomly within their functional group biomass envelope, most of the time. As a consequence, modelers will find it difficult to predict the biomass of most individual species. Our analysis supports the approach of using a single set of traits for a functional group and suggests that it should be possible to determine these traits from natural communities.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2435.12641

Ecological equivalence of species within phytoplankton functional groups

https://www.mmab.ca/pubs/mutshinda-2013-j-eco-model.pdf

Crispin M. Mutshinda

Papers

What drives community dynamics

A multispecies perspective on ecological impacts of climatic forcing

Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction.

Ecological equivalence of species within phytoplankton functional groups

Which environmental factors control phytoplankton populations? A Bayesian variable selection approach