Implications of Spatial Autocorrelation and Dispersal for the Modeling of Species Distributions

Modeling the geographical distributions of wildlife species is important for ecology and conservation biology. Spatial autocorrelation in species distributions poses a problem for distribution modeling because it invalidates the assumption of independence among sample locations. I explored the prevalence and causes of spatial autocorrelation in data from the Breeding Bird Survey, covering the conterminous United States, using Regression Trees, Conditional Autoregressive Regressions (CAR), and the partitioning of variance. I also constructed a simulation model to investigate dispersal as a process contributing to spatial autocorrelation, and attempted to verify the connection between dispersal and spatial autocorrelation in species\u27 distributions in empirical data, using three indirect indices of dispersal. All 108 bird species modeled showed strong spatial autocorrelation, which was significantly better modeled with CAR models than with traditional regression-based distribution models. Not all autocorrelation could be explained by spatial autocorrelation in the underlying environmental factors, suggesting another process at work, which I hypothesized to be dispersal. In the simulation model, dispersal produced additional autocorrelation in the distribution of population abundances. The effect of dispersal on autocorrelation was modulated by the potential population growth rate, with low growth rates leading to a stronger effect. The effect of dispersal on population sizes was different between populations at the periphery and core of a range. Due to their relative isolation, peripheral populations received fewer immigrants than populations at the core, causing lower population sizes. Dispersal could therefore be an explanation for range structures independent of environmental conditions. The verification of dispersal as a partial cause of autocorrelation failed. The most plausible cause was the indirectness of the indices used to represent dispersal. Distribution modelers should generally include space explicitly in their models, especially for species with low potential population growth rates. Dispersal has a strong potential to shape species distributions and requires more explicit consideration in distribution models and conservation plans. To reach this goal, direct research on dispersal distances and strength is urgently needed. Disruptions in natural dispersal patterns through removal of habitat isolates populations and thus may harm species beyond the effects of only direct habitat removal

Testing the Predictive Performance of Distribution Models

Distribution models are used to predict the likelihood of occurrence or abundance of a species at locations where census data are not available. An integral part of modelling is the testing of model performance. We compared different schemes and measures for testing model performance using 79 species from the North American Breeding Bird Survey. The four testing schemes we compared featured increasing independence between test and training data: resubstitution, random data hold-out and two spatially segregated data hold-out designs. The different testing measures also addressed different levels of information content in the dependent variable: regression R2 for absolute abundance, squared correlation coefficient r2 for relative abundance and AUC/Somer’s D for presence/absence. We found that higher levels of independence between test and training data lead to lower assessments of prediction accuracy. Even for data collected independently, spatial autocorrelation leads to dependence between random hold-out test data and training data, and thus to inflated measures of model performance. While there is a general awareness of the importance of autocorrelation to model building and hypothesis testing, its consequences via violation of independence between training and testing data have not been addressed systematically and comprehensively before. Furthermore, increasing information content (from correctly classifying presence/absence, to predicting relative abundance, to predicting absolute abundance) leads to decreasing predictive performance. The current tests for presence/absence distribution models are typically overly optimistic because a) the test and training data are not independent and b) the correct classification of presence/absence has a relatively low information content and thus capability to address ecological and conservation questions compared to a prediction of abundance. Meaningful evaluation of model performance requires testing on spatially independent data, if the intended application of the model is to predict into new geographic or climatic space, which arguably is the case for most applications of distribution models

Biogeographic Variation in Resistance of the Invasive Plant, Alliaria Petiolata, to a Powdery Mildew Fungus and Effect of Resistance on Competitive Dynamics

Garlic mustard is an invasive Eurasian biennial that has spread throughout the eastern United States and southern Canada. Populations of this plant vary in their susceptibility to Erysiphe cruciferarum, a causal agent of powdery mildew disease in Brassicaceous plants. We examined whether there were biogeographic patterns in the distribution of resistance in invasive North American and native European populations of this plant. We grew plants from 78 invasive and 20 native populations and screened them for powdery mildew resistance in the greenhouse. We found that populations were mostly monomorphic for either resistance or susceptibility but that some polymorphic populations were found from both continents. The proportion of populations showing resistance versus susceptibility was similar in both Europe and North America. Within continents, the spatial distribution of resistant and susceptible populations did not deviate significantly from random. We also examined whether the possession of the resistance trait alter intraspecific competitive dynamics. In two trials, we competed plants from resistant and susceptible populations in a target-neighbor design in the presence and absence of powdery mildew inoculum and examined the growth of the target plant. Target plants from resistant populations were overall larger than target plants from susceptible populations. Target plants were overall larger when grown in competition with susceptible neighbors. Further, resistant target plants showed a greater degree of release from competition when grown with a susceptible neighbor versus a resistant neighbor than the degree of release shown by susceptible target plants. This suggests a benefit of possessing the resistance trait with little apparent costs which should promote selection for this trait within plant populations

The Impact of Study Strategies on Knowledge Growth and Summative Exam Performance in the First Year of Medical School

Although the distinction between deep and surface processing strategies, their potential to differentially impact learning, and data supporting the superiority of deep processing strategies on summative exam scores are well supported by the literature, more work is needed to understand: (1) how medical students combine study strategies into learning practices, and (2) the effectiveness of these learning practices in facilitating knowledge gains as measured by standardized test scores

Exploring cellular markers of metabolic syndrome in peripheral blood mononuclear cells across the neuropsychiatric spectrum

Recent evidence suggests that comorbidities between neuropsychiatric conditions and metabolic syndrome may precede and even exacerbate long-term side-effects of psychiatric medication, such as a higher risk of type 2 diabetes and cardiovascular disease, which result in increased mortality. In the present study we compare the expression of key metabolic proteins, including the insulin receptor (CD220), glucose transporter 1 (GLUT1) and fatty acid translocase (CD36), on peripheral blood mononuclear cell subtypes from patients across the neuropsychiatric spectrum, including schizophrenia, bipolar disorder, major depression and autism spectrum conditions (n = 25/condition), relative to typical controls (n = 100). This revealed alterations in the expression of these proteins that were specific to schizophrenia. Further characterization of metabolic alterations in an extended cohort of first-onset antipsychotic drug-naïve schizophrenia patients (n = 58) and controls (n = 63) revealed that the relationship between insulin receptor expression in monocytes and physiological insulin sensitivity was disrupted in schizophrenia and that altered expression of the insulin receptor was associated with whole genome polygenic risk scores for schizophrenia. Finally, longitudinal follow-up of the schizophrenia patients over the course of antipsychotic drug treatment revealed that peripheral metabolic markers predicted changes in psychopathology and the principal side effect of weight gain at clinically relevant time points. These findings suggest that peripheral blood cells can provide an accessible surrogate model for metabolic alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic complications following antipsychotic therapy.This work was supported by grants from the Stanley Medical Research Institute (SMRI); the Engineering and Physical Sciences Research Council UK (EPSRC); the Dutch Government-funded Virgo consortium (ref. FES0908); the Netherlands Genomics Initiative (ref. 050-060-452); the European Union FP7 funding scheme: Marie Curie Actions Industry Academia Partnerships and Pathways (ref. 286334, PSYCH-AID project); SAF2016-76046-R and SAF2013-46292-R (MINECO) and PI16/00156 (isciii and FEDER)

Exploring the neuropsychiatric spectrum using high-content functional analysis of single-cell signaling networks.

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable overlap between different neuropsychiatric diseases and heterogeneously expressed at the level of each individual patient. Reconstruction of the diagnostic categories from the altered nodes revealed an overlapping neuropsychiatric spectrum extending from MDD on one end, through BD and SCZ, to ASC on the other end. Network analysis showed that although the pathway structure of the epitopes was broadly preserved across the clinical groups, there were multiple discrete alterations in network connectivity, such as disconnections within the antigen/integrin receptor pathway and increased negative regulation within the Akt1 pathway in CD4+ T cells from ASC and SCZ patients, in addition to increased correlation of Stat1 (pY701) and Stat5 (pY694) responses in B cells from BD and MDD patients. Our results support the "dimensional" approach to neuropsychiatric disease classification and suggest potential novel drug targets along the neuropsychiatric spectrum

Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions