Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

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Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse

Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a single a priori hypothesis test. This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified. We show that the probability of finding at least one ‘significant’ effect is high, even if all null hypotheses are true (e.g. 40% when starting with four predictors and their two-way interactions). This probability is close to theoretical expectations when the sample size (N) is large relative to the number of predictors including interactions (k). In contrast, type I error rates strongly exceed even those expectations when model simplification is applied to models that are over-fitted before simplification (low N/k ratio). The increase in false-positive results arises primarily from an overestimation of effect sizes among significant predictors, leading to upward-biased effect sizes that often cannot be reproduced in follow-up studies (‘the winner's curse’). Despite having their own problems, full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone. We favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results

Multi-Scale Sampling to Evaluate Assemblage Dynamics in an Oceanic Marine Reserve

To resolve the capacity of Marine Protected Areas (MPA) to enhance fish productivity it is first necessary to understand how environmental conditions affect the distribution and abundance of fishes independent of potential reserve effects. Baseline fish production was examined from 2002–2004 through ichthyoplankton sampling in a large (10,878 km2) Southern Californian oceanic marine reserve, the Cowcod Conservation Area (CCA) that was established in 2001, and the Southern California Bight as a whole (238,000 km2 CalCOFI sampling domain). The CCA assemblage changed through time as the importance of oceanic-pelagic species decreased between 2002 (La Niña) and 2003 (El Niño) and then increased in 2004 (El Niño), while oceanic species and rockfishes displayed the opposite pattern. By contrast, the CalCOFI assemblage was relatively stable through time. Depth, temperature, and zooplankton explained more of the variability in assemblage structure at the CalCOFI scale than they did at the CCA scale. CalCOFI sampling revealed that oceanic species impinged upon the CCA between 2002 and 2003 in association with warmer offshore waters, thus explaining the increased influence of these species in the CCA during the El Nino years. Multi-scale, spatially explicit sampling and analysis was necessary to interpret assemblage dynamics in the CCA and likely will be needed to evaluate other focal oceanic marine reserves throughout the world

Behavioural Thermoregulatory Tactics in Lacustrine Brook Charr, Salvelinus fontinalis

The need to vary body temperature to optimize physiological processes can lead to thermoregulatory behaviours, particularly in ectotherms. Despite some evidence of within-population phenotypic variation in thermal behaviour, the occurrence of alternative tactics of this behaviour is rarely explicitly considered when studying natural populations. The main objective of this study was to determine whether different thermal tactics exist among individuals of the same population. We studied the behavioural thermoregulation of 33 adult brook charr in a stratified lake using thermo-sensitive radio transmitters that measured hourly individual temperature over one month. The observed behavioural thermoregulatory patterns were consistent between years and suggest the existence of four tactics: two “warm” tactics with both crepuscular and finer periodicities, with or without a diel periodicity, and two “cool” tactics, with or without a diel periodicity. Telemetry data support the above findings by showing that the different tactics are associated with different patterns of diel horizontal movements. Taken together, our results show a clear spatio-temporal segregation of individuals displaying different tactics, suggesting a reduction of niche overlap. To our knowledge, this is the first study showing the presence of behavioural thermoregulatory tactics in a vertebrate

From spatial ecology to spatial epidemiology: Modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165thPCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r 2= 0.579). Conclusions: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information

Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.

Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and nonaquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics estimating predictability, and we provide some guidelines for this approach

The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China.

Bacteria play key roles in the ecology of both aquatic and terrestrial ecosystems; however, little is known about their diversity and biogeography, especially in the rare microbial biosphere of inland freshwater ecosystems. Here we investigated aspects of the community ecology and geographical distribution of abundant and rare bacterioplankton using high-throughput sequencing and examined the relative influence of local environmental variables and regional (spatial) factors on their geographical distribution patterns in 42 lakes and reservoirs across China. Our results showed that the geographical patterns of abundant and rare bacterial subcommunities were generally similar, and both of them showed a significant distance-decay relationship. This suggests that the rare bacterial biosphere is not a random assembly, as some authors have assumed, and that its distribution is most likely subject to the same ecological processes that control abundant taxa. However, we identified some differences between the abundant and rare groups as both groups of bacteria showed a significant positive relationship between sites occupancy and abundance, but the abundant bacteria exhibited a weaker distance-decay relationship than the rare bacteria. Our results implied that rare subcommunities were mostly governed by local environmental variables, whereas the abundant subcommunities were mainly affected by regional factors. In addition, both local and regional variables that were significantly related to the spatial variation of abundant bacterial community composition were different to those of rare ones, suggesting that abundant and rare bacteria may have discrepant ecological niches and may play different roles in natural ecosystems

Colorful Niches of Phytoplankton Shaped by the Spatial Connectivity in a Large River Ecosystem: A Riverscape Perspective

Large rivers represent a significant component of inland waters and are considered sentinels and integrators of terrestrial and atmospheric processes. They represent hotspots for the transport and processing of organic and inorganic material from the surrounding landscape, which ultimately impacts the bio-optical properties and food webs of the rivers. In large rivers, hydraulic connectivity operates as a major forcing variable to structure the functioning of the riverscape, and–despite increasing interest in large-river studies–riverscape structural properties, such as the underwater spectral regime, and their impact on autotrophic ecological processes remain poorly studied. Here we used the St. Lawrence River to identify the mechanisms structuring the underwater spectral environment and their consequences on pico- and nanophytoplankton communities, which are good biological tracers of environmental changes. Our results, obtained from a 450 km sampling transect, demonstrate that tributaries exert a profound impact on the receiving river’s photosynthetic potential. This occurs mainly through injection of chromophoric dissolved organic matter (CDOM) and non-algal material (tripton). CDOM and tripton in the water column selectively absorbed wavelengths in a gradient from blue to red, and the resulting underwater light climate was in turn a strong driver of the phytoplankton community structure (prokaryote/eukaryote relative and absolute abundances) at scales of many kilometers from the tributary confluence. Our results conclusively demonstrate the proximal impact of watershed properties on underwater spectral composition in a highly dynamic river environment characterized by unique structuring properties such as high directional connectivity, numerous sources and forms of carbon, and a rapidly varying hydrodynamic regime. We surmise that the underwater spectral composition represents a key integrating and structural property of large, heterogeneous river ecosystems and a promising tool to study autotrophic functional properties. It confirms the usefulness of using the riverscape approach to study large-river ecosystems and initiate comparison along latitudinal gradients