A Discriminant Analysis Model of Alaskan Biomes Based on Spatial Climatic and Environmental Data

Classification of high-latitude landscapes into their appropriate biomes is important for many climate and global change-related issues. Unfortunately, large-scale, high-spatial-resolution observations of plant assemblages associated with these regions are generally unavailable, so accurate modeling of plant assemblages and biome boundaries is often needed. We built different discriminant analysis models and used them to “convert” various combinations of spatial climatic data (surface temperature and precipitation) and spatial environmental data (topography, soil, permafrost) into a biome-level map of Alaska. Five biomes (alpine tundra and ice fi elds, Arctic tundra, shrublands, boreal forest, and coastal rainforest) and one biome transition zone are modeled. Mean annual values of climatic variables were less useful than their annual extrema in this context. A quadratic discriminant analysis, combined with climate, topography, permafrost, and soil information, produced the most accurate Alaskan biome classification (skill = 74% when compared to independent data). The multivariate alteration detection transformation was used to identify Climatic Transition Zones (CTZs) with large interannual variability, and hence, less climatic consistency than other parts of Alaska. Biome classification was the least accurate in the CTZs, leading to the conclusion that large interannual climatic variability does not favor a unique biome. We interpret the CTZs as “transition biome areas” or ecotones between the five “core biomes” cited above. Both disturbance events (e.g., fires and subsequent plant succession sequences) and the partial intersection of the environmental variables used to characterize Alaskan biomes further complicate biome classification. Alaskan results obtained from the data-driven quadratic discriminant model compare favorably (based on Kappa statistics) with those produced by an equilibrium-based biome model for regions of Canada ecologically similar to the biomes we studied in Alaska. Climatic statistics are provided for each biome studied. Le classement des paysages de hautes latitudes dans les biomes adéquats revêt de l'importance dans le cadre de nombreux enjeux relatifs aux changements climatiques et à d'autres changements d'envergure mondiale. Malheureusement et en règle générale, il n'existe pas d'observations spatiales de haute résolution et à grande échelle pour ce qui est des assemblages de végétaux pour ces régions. C'est pourquoi il faut souvent procéder à la modélisation des assemblages de végétaux et des limites des biomes. Nous avons élaboré différents modèles d'analyses discriminantes dont nous nous sommes servis pour « transformer » divers ensembles de données climatiques spatiales (température de la surface et précipitation) et diverses données sur l'environnement spatial (topographie, sol, pergélisol) en carte des biomes de l'Alaska. La modélisation porte sur cinq biomes (toundra alpine et champs de glace, toundra arctique, arbustaie, forêt boréale et forêt pluviale côtière) et sur une zone de transition de biome. Les valeurs moyennes annuelles des variables climatiques ont été moins utiles que leurs extremas annuels dans ce contexte. Une analyse discriminante quadratique, combinée aux données relatives au climat, à la topographie, au pergélisol et au sol, a permis d'aboutir au classement de biomes alaskiens le plus précis (habileté = 74 % lorsque comparé aux données indépendantes). Nous avons recouru à la transformation de la détection de l'altération à variables multiples (multivariate alteration detection transformation) pour identifi er les zones de transition climatique (ZTC) ayant une importante variabilité interannuelle et, par conséquent, une moins grande uniformité climatique que d'autres parties de l'Alaska. Le classement des biomes était moins précis dans les ZTC, ce qui nous a amenés à conclure que l'importante variabilité climatique interannuelle ne favorise pas un biome unique. Nous interprétons les ZTC comme des « régions de biomes de transition » ou des écotones entre les cinq « biomes principaux » dont il est question ci-dessus. Les deux perturbations (c'est-à-dire les incendies et les séquences subséquentes des végétaux) et l'intersection partielle des variables environnementales utilisées pour caractériser les biomes alaskiens compliquent davantage le classement des biomes. Les résultats alaskiens obtenus à partir du modèle discriminant quadratique dérivant des données se comparent favorablement (en fonction des statistiques kappa) à ceux obtenus par un modèle de biome en équilibre pour des régions du Canada similaires du point de vue écologique aux biomes que nous avons étudiés en Alaska. Des statistiques climatiques sont fournies pour chaque biome étudié

Assessing the potential impact of environmental land management schemes on emergent infection disease risks

Financial incentives are provided by governments to encourage the plantation of new woodland to increase habitat, biodiversity, carbon sequestration, and other economic benefits for landowners. Whilst these are largely positive effects, it is worth considering that greater biodiversity and presence of wildlife species in proximity to agricultural holdings may pose a risk of disease transmission between wildlife and livestock. Wildlife transmission and the provision of a reservoir for infectious disease is particularly important in the transmission dynamics of bovine tuberculosis. In this paper we develop an economic model for changing land use due to forestry subsidies. We use this asses the impact on wild deer populations in the newly created woodland areas and the emergent infectious disease risk arising from the proximity of new and existing wild deer populations and existing cattle holdings. We consider an area in the South-West of Scotland, having existing woodland, deer populations, and extensive and diverse cattle farm holdings. In this area we find that, with a varying level of subsidy and plausible new woodland creation, the contact risk between areas of wild deer and cattle increases between 26% and 35% over the contact risk present with zero subsidy. This model provides a foundation for extending to larger regions and for examining potential risk mitigation strategies, for example the targeting of subsidy in low risk areas or provisioning for buffer zones between woodland and agricultural holdings

Action Boundary Proximity Effects on Perceptual-Motor Judgments

INTRODUCTION: Designed as a more ecological measure of reaction times, the Perception-Action Coupling Task (PACT) has shown good reliability and within-subject stability. However, a lengthy testing period was required. Perceptual-motor judgments are known to be affected by proximity of the stimulus to the action boundary. The current study sought to determine the effects of action boundary proximity on PACT performance, and whether redundant levels of stimuli, eliciting similar responses, can be eliminated to shorten the PACT.METHODS: There were 9 men and 7 women who completed 4 testing sessions, consisting of 3 familiarization cycles and 6 testing cycles of the PACT. For the PACT, subjects made judgments on whether a series of balls presented on a tablet afford "posting" (can fit) through a series of apertures. There were 8 ratios of ball to aperture size (B-AR) presented, ranging from 0.2 to 1.8, with each ratio appearing 12 times (12 trials) per cycle. Reaction times and judgment accuracy were calculated, and averaged across all B-ARs. Ratios and individual trials within each B-AR were systematically eliminated. Variables were re-averaged, and intraclass correlation coefficients (ICC) and coefficients of variation (CVTE) were calculated in an iterative manner.RESULTS: With elimination of the 0.2 and 1.8 B-ARs, the PACT showed good reliability (ICC = 0.81-0.99) and consistent within-subject stability (CVTE = 2.2-14.7%). Reliability (ICC = 0.81-0.97) and stability (CVTE = 2.6-15.6%) were unaffected with elimination of up to 8 trials from each B-AR.DISCUSSION: The shortened PACT resulted in an almost 50% reduction in total familiarization/testing time required, significantly increasing usability.Johnson CD, LaGoy AD, Pepping G-J, Eagle SR, Beethe AZ, Bower JL, Alfano CA, Simpson RJ, Connaboy C. Action boundary proximity effects on perceptual-motor judgments. Aerosp Med Hum Perform. 2019; 90(12):1000-1008

A Natural Plasmid Uniquely Encodes Two Biosynthetic Pathways Creating a Potent Anti-MRSA Antibiotic

Background Understanding how complex antibiotics are synthesised by their producer bacteria is essential for creation of new families of bioactive compounds. Thiomarinols, produced by marine bacteria belonging to the genus Pseudoalteromonas, are hybrids of two independently active species: the pseudomonic acid mixture, mupirocin, which is used clinically against MRSA, and the pyrrothine core of holomycin. Methodology/Principal Findings High throughput DNA sequencing of the complete genome of the producer bacterium revealed a novel 97 kb plasmid, pTML1, consisting almost entirely of two distinct gene clusters. Targeted gene knockouts confirmed the role of these clusters in biosynthesis of the two separate components, pseudomonic acid and the pyrrothine, and identified a putative amide synthetase that joins them together. Feeding mupirocin to a mutant unable to make the endogenous pseudomonic acid created a novel hybrid with the pyrrothine via “mutasynthesis” that allows inhibition of mupirocin-resistant isoleucyl-tRNA synthetase, the mupirocin target. A mutant defective in pyrrothine biosynthesis was also able to incorporate alternative amine substrates. Conclusions/Significance Plasmid pTML1 provides a paradigm for combining independent antibiotic biosynthetic pathways or using mutasynthesis to develop a new family of hybrid derivatives that may extend the effective use of mupirocin against MRSA

Profile likelihood analysis for a stochastic model of diffusion in heterogeneous media

We compute profile likelihoods for a stochastic model of diffusive transport motivated by experimental observations of heat conduction in layered skin tissues. This process is modelled as a random walk in a layered one-dimensional material, where each layer has a distinct particle hopping rate. Particles are released at some location, and the duration of time taken for each particle to reach an absorbing boundary is recorded. To explore whether this data can be used to identify the hopping rates in each layer, we compute various profile likelihoods using two methods: first, an exact likelihood is evaluated using a relatively expensive Markov chain approach; and, second we form an approximate likelihood by assuming the distribution of exit times is given by a Gamma distribution whose first two moments match the expected moments from the continuum limit description of the stochastic model. Using the exact and approximate likelihoods we construct various profile likelihoods for a range of problems. In cases where parameter values are not identifiable, we make progress by re-interpreting those data with a reduced model with a smaller number of layers.Comment: 41 pages, 11 figure

Comparing Maps of Mean Monthly Surface Temperature and Precipitation for Alaska and Adjacent Areas of Canada Produced by Two Different Methods

Maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada, produced by Oregon State University’s Spatial Climate Analysis Service (SCAS) and the Alaska Geospatial Data Clearinghouse (AGDC), were analyzed. Because both sets of maps are generally available and in use by the community, there is a need to document differences between the processes and input data sets used by the two groups to produce their respective set of maps and to identify similarities and differences between the two sets of maps and possible reasons for the differences. These differences do not affect the observed large-scale patterns of seasonal and annual variability. Alaska is divided into interior and coastal zones, with consistent but different variability, separated by a transition region. The transition region has high interannual variability but low long-term mean variability. Both data sets support the four major ecosystems and ecosystem transition zone identified in our earlier work. Differences between the two sets of maps do occur, however, on the regional scale; they reflect differences in physiographic domains and in the treatment of these domains by the two groups (AGDC, SCAS). These differences also provide guidance for an improved observational network for Alaska. On the basis of validation with independent in situ data, we conclude that the data set produced by SCAS provides the best spatial coverage of Alaskan long-term mean monthly surface temperature and precipitation currently available.On a analysé des cartes représentant les moyennes mensuelles des précipitations et des températures de l’air en surface pour l’Alaska et les zones contiguës du Canada. Ces cartes avaient été établies par le service d’analyse du climat spatial (SCAS) de l’université de l’Oregon et le centre d’échange de données géospatiales de l’Alaska (AGDC). Vu qu’en général le public peut se procurer les deux ensembles de cartes et qu’il les utilise, il est nécessaire de documenter les différences entre les processus et les jeux de données d’entrée utilisés par les deux groupes pour créer leur propre ensemble de cartes, ainsi que de dégager les similarités et les différences entre les deux ensembles de cartes et les raisons possibles de ces différences. Ces dernières n’affectent pas les schémas de variabilité saisonnière et annuelle observés à grande échelle. L’Alaska est divisé en zones intérieures et zones côtières, possédant une variabilité constante mais différente, séparées par une région de transition. Celle-ci possède une grande variabilité interannuelle mais une faible variabilité à long terme de la moyenne. Les deux jeux de données sont compatibles avec les quatre grands écosystèmes et leurs zones de transition que nous avions identifiés dans nos travaux antérieurs. Il y a cependant des différences à l’échelle régionale entre les deux ensembles de cartes; elles témoignent de différences dans les domaines physiographiques et dans le traitement que font les deux groupes (AGDC et SCAS) de ces domaines. Ces différences offrent également une piste pour l’établissement d’un réseau d’observation amélioré pour l’Alaska. En nous basant sur une validation fondée sur des données indépendantes recueillies in situ, nous concluons que le jeu de données produit par SCAS représente actuellement la meilleure couverture spatiale disponible pour les moyennes mensuelles à long terme des précipitations et des températures de l’air en surface en Alaska

Efficient inference and identifiability analysis for differential equation models with random parameters

Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.Comment: Minor changes to text. Additional results in supplementary material. Additional statistics regarding results given in main and supplementary materia