Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.

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Correlation and process in species distribution models: bridging a dichotomy

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions vi...

Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE

https://www.perinatology.theclinics.com/article/S0095-5108(12)00105-4/pdf

Physiology of Transition from Intrauterine to Extrauterine Life

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This paper provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM).

Programming with models: writing statistical algorithms for general model structures with NIMBLE

Plasmids are important drivers of bacterial evolution, but it is challenging to understand how plasmids persist over the long term because plasmid carriage is costly. Classical models predict that horizontal transfer is necessary for plasmid persistence, but recent work shows that almost half of plasmids are non-transmissible. Here we use a combination of mathematical modelling and experimental evolution to investigate how a costly, non-transmissible plasmid, pNUK73, can be maintained in populations of Pseudomonas aeruginosa. Compensatory adaptation increases plasmid stability by eliminating the cost of plasmid carriage. However, positive selection for plasmid-encoded antibiotic resistance is required to maintain the plasmid by offsetting reductions in plasmid frequency due to segregational loss. Crucially, we show that compensatory adaptation and positive selection reinforce each other's effects. Our study provides a new understanding of how plasmids persist in bacterial populations, and it helps to explain why resistance can be maintained after antibiotic use is stopped.

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Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids

Maximum likelihood estimation for Generalized Linear Mixed Models (GLMM), an important class of statistical models with substantial applications in epidemiology, medical statistics, and many other fields, poses significant computational difficulties. In this article, we use data cloning, a simple computational method that exploits advances in Bayesian computation, in particular the Markov Chain Monte Carlo method, to obtain maximum likelihood estimators of the parameters in these models. This method also leads to a simple estimator of the asymptotic variance of the maximum likelihood estimators. Determining estimability of the parameters in a mixed model is, in general, a very difficult problem. Data cloning provides a simple graphical test to not only check if the full set of parameters is estimable but also, and perhaps more importantly, if a specified function of the parameters is estimable. One of the goals of mixed models is to predict random effects. We suggest a frequentist method to obtain predict...

Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning

Background—Lung hypoplasia and persistent pulmonary hypertension of the newborn limit survival in congenital diaphragmatic hernia (CDH). Unlike other diseases resulting in persistent pulmonary hypertension of the newborn, infants with CDH are refractory to inhaled nitric oxide (NO). Nitric oxide mediates pulmonary vasodilatation at birth in part via cyclic GMP production. Phosphodiesterase type 5 (PDE5) limits the effects of NO by inactivation of cyclic GMP. Because of the limited success in postnatal management of CDH, we hypothesized that antenatal PDE5 inhibition would attenuate pulmonary artery remodeling in experimental nitrofen-induced CDH. Methods and Results—Nitrofen administered at embryonic day 9.5 to pregnant rats resulted in a 60% incidence of CDH in the offspring and recapitulated features seen in human CDH, including structural abnormalities (lung hypoplasia, decreased pulmonary vascular density, pulmonary artery remodeling, right ventricular hypertrophy), and functional abnormalities (decre...

Antenatal Sildenafil Treatment Attenuates Pulmonary Hypertension in Experimental Congenital Diaphragmatic Hernia

We developed three models of daily human- and lightning-caused fire occurrence to support fire management preparedness and detection planning in the province of British Columbia, Canada, using a lasso-logistic framework. Novel aspects of our work involve (1) using an ensemble of models that were created using 500 datasets balanced (through response-selective sampling) to have equal numbers of fire and non-fire observations; (2) the use of a new ranking algorithm to address the difficulty in interpreting variable importance in models with a large number of covariates. We also introduce the use of cause-specific average spatial daily fire occurrence, termed baseline risk, as a covariate for missing or poorly estimated factors that influence human and lightning fire occurrence. All three models have strong predictive ability, with areas under the Receiver Operator Characteristic curve exceeding 0.9.

/pdf/mesoscale-spatiotemporal-predictive-models-of-daily-human-25s4nnyxj6.pdf

Mesoscale spatiotemporal predictive models of daily human- and lightning-caused wildland fire occurrence in British Columbia

Population viability analysis (PVA) entails calculation of extinction risk, as defined by various extinction metrics, for a study population. These calculations strongly depend on the form of the population growth model and inclusion of demographic and/or environmental stochasticity. Form of the model and its parameters are determined based on observed population time series data. A typical population time series, consisting of estimated population sizes, inevitably has some observation error and likely has missing observations. In this paper, we present a likelihood based PVA in the presence of observation error and missing data. We illustrate the importance of incorporation of observation error in PVA by reanalyzing the population time series of song sparrow Melospiza melodia on Mandarte Island, British Columbia, Canada from 1975-1998. Using Akaike information criterion we show that model with observation error fits the data better than the one without observation error. The extinction risks predicted by with and without observation error models are quite different. Further analysis of possible causes for observation error revealed that some component of the observation error might be due to unreported dispersal. A complete analysis of such data, thus, would require explicit spatial models and data on dispersal along with observation error. Our conclusions are, therefore, two-fold: 1) observation errors in PVA matter and 2) integrating these errors in PVA is not always enough and can still lead to important biases in parameter estimates if other processes such as dispersal are ignored.

Likelihood based population viability analysis in the presence of observation error

Nonparametric statistical procedures are commonly used in analyzing for trend in water resources time series (Chapter 23, Hipel and McLeod in Time series modelling of water resources and environmental systems. Elsevier, New York, 2005 [10]). One popular procedure is the seasonal Mann–Kendall tau test for detecting monotonic trend in seasonal time series data with serial dependence (Hirsch and Slack in Water Resour Res 20(6):727–732, 1984 [12]). However there is little rigorous discussion in the literature about its validity and alternatives. In this paper, the asymptotic normality of a seasonal Mann–Kendall test is determined for a large family of absolutely regular processes, a bootstrap sampling version of this test is proposed and its performance is studied through simulation. These simulations compare the performance of the traditional test, the bootstrapped version referred to above, as well as a bootstrapped version of Spearman’s rho partial correlation. The simulation results indicate that both bootstrap tests perform comparably to the traditional test when the seasonal effect is deterministic, but the traditional test can fail to converge to the nominal levels when the seasonal effect is stochastic. Both bootstrapped tests perform similarly to each other in terms of accuracy and power.

Khurram Nadeem

Papers

Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning

Antenatal Sildenafil Treatment Attenuates Pulmonary Hypertension in Experimental Congenital Diaphragmatic Hernia

Mesoscale spatiotemporal predictive models of daily human- and lightning-caused wildland fire occurrence in British Columbia

Likelihood based population viability analysis in the presence of observation error

Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series