Da Ratkowsky

Global diversity and geography of soil fungi

Introduction The kingdom Fungi is one of the most diverse groups of organisms on Earth, and they are integral ecosystem agents that govern soil carbon cycling, plant nutrition, and pathology. Fungi are widely distributed in all terrestrial ecosystems, but the distribution of species, phyla, and functional groups has been poorly documented. On the basis of 365 global soil samples from natural ecosystems, we determined the main drivers and biogeographic patterns of fungal diversity and community composition. Direct and indirect effects of climatic and edaphic variables on plant and fungal richness. Line thickness corresponds to the relative strength of the relationships between the variables that affect species richness. Dashed lines indicate negative relationships. MAP, mean annual precipitation; Fire, time since last fire; Dist. equator, distance from the equator; Ca, soil calcium concentration; P, soil phosphorus concentration; pH, soil pH. Rationale We identified soil-inhabiting fungi using 454 Life Sciences (Branford, CN) pyrosequencing and through comparison against taxonomically and functionally annotated sequence databases. Multiple regression models were used to disentangle the roles of climatic, spatial, edaphic, and floristic parameters on fungal diversity and community composition. Structural equation models were used to determine the direct and indirect effects of climate on fungal diversity, soil chemistry, and vegetation. We also examined whether fungal biogeographic patterns matched paradigms derived from plants and animals—namely, that species’ latitudinal ranges increase toward the poles (Rapoport’s rule) and diversity increases toward the equator. Last, we sought group-specific global biogeographic links among major biogeographic regions and biomes using a network approach and area-based clustering. Results Metabarcoding analysis of global soils revealed fungal richness estimates approaching the number of species recorded to date. Distance from equator and mean annual precipitation had the strongest effects on richness of fungi, including most fungal taxonomic and functional groups. Diversity of most fungal groups peaked in tropical ecosystems, but ectomycorrhizal fungi and several fungal classes were most diverse in temperate or boreal ecosystems, and many fungal groups exhibited distinct preferences for specific edaphic conditions (such as pH, calcium, or phosphorus). Consistent with Rapoport’s rule, the geographic range of fungal taxa increased toward the poles. Fungal endemicity was particularly strong in tropical regions, but multiple fungal taxa had cosmopolitan distribution. Conclusions Climatic factors, followed by edaphic and spatial patterning, are the best predictors of soil fungal richness and community composition at the global scale. Richness of all fungi and functional groups is causally unrelated to plant diversity, with the exception of ectomycorrhizal root symbionts, suggesting that plant-soil feedbacks do not influence the diversity of soil fungi at the global scale. The plant-to-fungi richness ratio declined exponentially toward the poles, indicating that current predictions—assuming globally constant ratios—overestimate fungal richness by 1.5- to 2.5-fold. Fungi follow similar biogeographic patterns as plants and animals, with the exception of several major taxonomic and functional groups that run counter to overall patterns. Strong biogeographic links among distant continents reflect relatively efficient long-distance dispersal compared with macro-organisms. Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework. Global metagenomics detects hotspots of fungal diversity and macroecological patterns and indicates that plant and fungal diversity are uncoupled. [Also see Perspective by Wardle and Lindahl] Assessing fungal diversity worldwide Fungi are hyperdiverse but poorly known, despite their ecological and economic impacts. Tedersoo et al. collected nearly 15,000 topsoil samples from 365 sites worldwide and sequenced their genomes (see the Perspective by Wardle and Lindahl). Overall, they found a striking decline in fungal species richness with distance from the equator. For some specialist groups though, diversity depended more on the abundance of host plants than host diversity or geography. The findings reveal a huge gap between known and described species and the actual numbers of distinct fungi in the worlds soils. Science, this issue 10.1126/science.1256688; see also p. 1052

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

ABSTRACT Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains ofListeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into “growth is possible” or “growth is not possible” classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773–1779, 1998). We used “nonlinear logistic regression” to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenesreported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes.

Modelling the bacterial growth/no growth interface

A logistic regression model is proposed which enables one to model the boundary between growth and no growth for bacterial strains in the presence of one or more growth controlling factors such as temperature, pH and additives such as salt and sodium nitrite. The form of the expression containing the growth limiting factors may be suggested by a kinetic model, while the response at a given combination of factors may either be presence/absence (i.e. growth/no growth) or probabilistic (i.e. r successes in n trials). The approach described represents an integration of the probability and kinetic aspects of predictive microbiology, and a unification of predictive microbiology and the hurdle concept. The model is illustrated using data for Shigella flexneri.

Problems of hypothesis testing of regressions with multiple measurements from individual sampling units

Abstract Problems with hypothesis testing arise when regression analysis is applied to data sets which contain multiple measurements from individual sampling units. These sampling units might be individual trees from each of which several measurements were taken at different positions on each tree, or they might be individual plots in each of which many trees were measured, or they might be individual plots each of which was measured at several ages. The problems arise because application of ordinary least-squares regression to such data sets leads to underestimates of the covariance matrix of the parameter estimates and the residual variance of the regression equation. Thus, it would not be possible to carry out properly the statistical tests involved in matters such as a covariance analysis or the determination of the most appropriate form of the equation to be fitted to a data set. Theory already exists to solve these problems in cases where the measurements are all made at the same set of conditions in each sampling unit: this is often the case with data from designed experiments. Forestry data sets are often not of this nature and these solutions are generally inappropriate. Using a simple example, the present work explains how problems of hypothesis testing of regressions arise with these data sets. Six theoretical attempts to solve the problems are reviewed. All these theories apply only asymptotically, that is when the number of sampling units is very large. Their small sample behaviour is unknown and their usefulness is therefore questioned. Practical methods for handling such data sets are suggested. In particular, a technique to analyze data in two stages has been found most useful. A number of examples of forestry problems from the literature are described to demonstrate the range of circumstances under which these difficulties occur.

Modelling the effects of temperature, water activity, pH and lactic acid concentration on the growth rate of Escherichia coli

An extended square root-type model describing Escherichia coli growth rate was developed as a function of temperature (7.63-47.43 degrees C), water activity (0.951-0.999, adjusted with NaCl), pH (4.02-8.28) and lactic acid concentration (0-500 mM). The new model, based on 236 growth rate data, combines and extends previously published square root-type models and incorporates terms for upper and lower limiting temperatures, upper and lower limiting pH, minimum inhibitory concentrations of dissociated and undissociated lactic acid and lower limiting water activity. A term to describe upper limiting water activity was developed but could not be fitted to the E. coli data set because of the difficulty of generating data in the super-optimal water activity range (i.e. >0.998). All data used to generate the model are presented. The model provides an excellent description of the experimental data.

Quantifying the hurdle concept by modelling the bacterial growth/no growth interface.

The hurdle concept described eloquently over many years by Professor Leistner and his colleagues draws attention to the interaction of factors that affect microbial behaviour in foods. Under some circumstances these effects are additive. Under others the implication is that synergistic interactions lead to a combined effect of greater magnitude than the sum of constraints applied individually. Predictive modelling studies on the combined effects of temperature and water activity and temperature and pH suggest that the effect of these combinations on growth rate is independent. Where the effect of the two factors is interactive rather than independent is at the point where growth ceases--the growth/no growth interface. An interesting and consistent observation is that a very sharp cut off occurs between conditions permitting growth and those preventing growth, allowing those combinations of factors to be defined precisely and modelled. Growth/no growth interface models quantify the effects of various hurdles on the probability of growth and define combinations at which the growth rate is zero or the lag time infinite. Increasing the stringency of one or more hurdles at the interface by only a small amount will significantly decrease the probability of an organism growing. Understanding physiological processes occurring near the growth/no growth interface and changes induced by moving from one side of the interface to the other may well provide insights that can be exploited in a new generation of food preservation techniques with minimal impact on product quality.

Modelling the combined temperature and salt (NaCl) limits for growth of a pathogenic Escherichia coli strain using nonlinear logistic regression

A broth-based method is used to determine if exponential phase Escherichia coli R31, an STEC, is able to grow within 50 days under various combinations of sub-optimal temperatures and salt concentrations. From these data, the growth limits for combinations of temperature (7.7-37.0 degrees C) and water activity (0.943-0.987; NaCl as humectant) are defined and modelled using a nonlinear logistic regression model. That form of model is able to predict the combinations of salt concentration/water activity and temperature that will prevent the growth of E. coli R31 with selected levels of confidence. The model fitted the data with an approximate concordance rate of 97.3%. The minimum water activity that permitted growth occurred in the range 25-30 degrees C, the temperature range which optimises cell yield. At temperatures below this range the minimum water activity which allowed growth increased with decreasing temperature.

Universality of Thermodynamic Constants Governing Biological Growth Rates

Background Mathematical models exist that quantify the effect of temperature on poikilotherm growth rate. One family of such models assumes a single rate-limiting ‘master reaction’ using terms describing the temperature-dependent denaturation of the reactions enzyme. We consider whether such a model can describe growth in each domain of life. Methodology/Principal Findings A new model based on this assumption and using a hierarchical Bayesian approach fits simultaneously 95 data sets for temperature-related growth rates of diverse microorganisms from all three domains of life, Bacteria, Archaea and Eukarya. Remarkably, the model produces credible estimates of fundamental thermodynamic parameters describing protein thermal stability predicted over 20 years ago. Conclusions/Significance The analysis lends support to the concept of universal thermodynamic limits to microbial growth rate dictated by protein thermal stability that in turn govern biological rates. This suggests that the thermal stability of proteins is a unifying property in the evolution and adaptation of life on earth. The fundamental nature of this conclusion has importance for many fields of study including microbiology, protein chemistry, thermal biology, and ecological theory including, for example, the influence of the vast microbial biomass and activity in the biosphere that is poorly described in current climate models.

Some examples of, and some problems with, the use of nonlinear logistic regression in predictive food microbiology.

A new technique, nonlinear logistic regression, is described for modelling binomially distributed data, i.e., presence/absence data where growth is either observed or not observed, for applications in predictive food microbiology. Some examples of the successful use of this technique are presented, where the controlling factors are temperature, water activity, pH and the concentration of lactic acid, a weakly dissociating organic acid. Generally speaking, good-fitting models were obtained, as evidenced using various performance measures and goodness-of-fit statistics. As may be expected with a new statistical technique, some problems were encountered with the implementation of the modelling approach and these are discussed.

Antibacterial metabolites from Australian macrofungi from the genus Cortinarius

In this study, ethyl acetate and aqueous fractions from 117 collections of Australian macrofungi belonging to the mushroom genus Cortinarius were screened for antimicrobial activity against Staphylococcus aureus and Pseudomonas aeruginosa. Overall, the lipophilic fractions were more active than the aqueous fractions. The ethyl acetate fractions of most or all collections of 13 species, namely Cortinarius ardesiacus, C. archeri, C. austrosaginus, C. austrovenetus, C. austroviolaceus, C. coelopus, C. [Dermocybe canaria](2), C. clelandii, C. [D. kula], C. memoria-annae, C. persplendidus, C. sinapicolor, C. vinosipes and forty seven collections of un-described Cortinarius species exhibited IC(50) values of 0.09 mg/mL against S. aureus. In contrast, most or all collections of only four species, namely C. abnormis, C. austroalbidus, C. [D. kula], C. persplendidus, and eleven un-described Cortinarius collections exhibited similar effects against P. aeruginosa (IC(50) <or= 0.09 mg/mL). Anthraquinonoid pigments isolated from C. basirubescens together with emodin physcion and erythrogluacin were assessed for their antimicrobial activity. The fungal octaketides austrocortilutein, austrocortirubin, torosachrysone, physcion and emodin were found to strongly inhibit the growth of S. aureus (IC(50) 0.7-12 microg/mL) whereas only physcion and emodin exhibited potency against P. aeruginosa (IC(50) 1.5 and 2.0 microg/mL, respectively).