Erratum to: What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder

Risk-adjusted survival for adults following in-hospital cardiac arrest by day of week and time of day: Observational cohort study

Background: Internationally, hospital survival is lower for patients admitted at weekends and at night. Data from the UK National Cardiac Arrest Audit (NCAA) indicate that crude hospital survival was worse after in-hospital cardiac arrest (IHCA) at night versus day, and at weekends versus weekdays, despite similar frequency of events. Objective: To describe IHCA demographics during three day/time periods - weekday daytime (Monday to Friday, 08:00 to 19:59), weekend daytime (Saturday and Sunday, 08:00 to 19:59) and night-time (Monday to Sunday, 20:00 to 07:59) - and to compare the associated rates of return of spontaneous circulation (ROSC) for >20 min (ROSC>20 min) and survival to hospital discharge, adjusted for risk using previously developed NCAA risk models. To consider whether any observed difference could be attributed to differences in the case mix of patients resident in hospital and/or the administered care. Methods: We performed a prospectively defined analysis of NCAA data from 27 700 patients aged ≥16 years receiving chest compressions and/or defibrillation and attended by a hospital-based resuscitation team in response to a resuscitation (2222) call in 146 UK acute hospitals. Results: Risk-adjusted outcomes (OR (95% CI)) were worse (p20 min 0.88 (0.81 to 0.95); hospital survival 0.72 (0.64 to 0.80)), and nighttime (ROSC>20 min 0.72 (0.68 to 0.76); hospital survival 0.58 (0.54 to 0.63)) compared with weekday daytime. The effects were stronger for non-shockable than shockable rhythms, but there was no significant interaction between day/ time of arrest and age, or day/time of arrest and arrest location. While many daytime IHCAs involved procedures, restricting the analyses to IHCAs in medical admissions with an arrest location of ward produced results that are broadly in line with the primary analyses. Conclusions: IHCAs attended by the hospital-based resuscitation team during nights and weekends have substantially worse outcomes than during weekday daytimes. Organisational or care differences at night and weekends, rather than patient case mix, appear to be responsible

Potential Tetany Hazard of N-Fertilized Bromegrass as Indicated by Chemical Composition

The objective of this field experiment was to determine the effect of N fertilization on yield and chemical composition of smooth bromegrass (Bromus inermis L.) and the potential for grass tetany hazard in the northern Great Plains as indicated by chemical composition of bromegrass forage. Chemical components of forage considered in relation to the hazard of tetany (a metabolic disorder of ruminants resulting from forage with low Mg availability) were inorganic cations, organic anions, aconitate, and % total N/% total water-soluble carbohydrate ratio (N/TWSC). Soil was Parshall fine sandy loam, a pachic haploborall. Yields and chemical composition of oven dried forage from plots not previously harvested were determined at approximately 3-week intervals beginning May 9. Differences between the sum (in meq/kg) of inorganic cations (Na+, K+, Ca²+, Mg²+) and inorganic anions (Cl-, No?-, H?PO?-, SO?²-) in forage was defined as the concentration of organic anions (C-A). Mature forage yield obtained from the unfertilized check plot treatment on July 29 was only 29 and 22% of yields obtained from plot treatments fertilized with 90 and 270 kg N/ha, respectively. The K/(Ca+Mg) ratios and K concentrations increased during May and early June, resulting in a K/(Ca+Mg) ratio near or above 2.2 during June and early June in oven dried forage from fertilized treatments. Potassium, expressed as a fraction K/C of the total cations (C), accounted for 35 to 74% of the cationic charge. Fertilization with N increased total N and K concentration and K/C in the forage. As K/C increased, Mg/C and Ca/C decreased and K/(Ca+Mg) increased. Aconitate and C-A concentration correlated highly with K concentration and were increased by N fertilization. Aconitate levels exceeded 1% on May 28; the 270 kg N-treatment remained above 1% through July. Nitrogen fertilizer increased N/TWSC in spring-harvested forage, compared to unfertilized forage, and greatly accentuated the peak N/TWSC values occurring in late spring samples. This study indicated that although potential for increased forage and livestock-carrying capacity with N fertilization is tremendous, N-fertilization may result in a potential tetany hazard to ruminants. Therefore, management practices are needed which minimize tetany hazard while bromegrass yields are increased by N fertilization

Individual isotopic specializations predict subsequent inter-individual variation in movement in a freshwater fish

Despite many similarities and intuitive links between individual dietary specialization and behavioral inter-individual variation, these phenomena have been studied in isolation, and empirical data confirming relationships between these intraspecific variance sources are lacking. Here we use stable isotope analysis and acoustic telemetry to test the hypothesis that individual specialization in trophic (d15N) and littoral/pelagic prey reliance (d13C) covary with inter-individual variation in movement in a group of 34 free-swimming burbot (Lota lota). By performing stable isotope analysis on tissues with differing isotopic turnover rates (anal fin and dorsal muscle), in 24 lethally sampled burbot, we demonstrate that individual specialization in trophic niche (d15N) and li

The templated growth of a chiral transition metal chalcogenide

We demonstrate that an intrinsically chiral, high Miller index surface of an achiral metal can be used to template the enantioselective growth of chiral transition metal chalcogenide films. Specifically, Cu(643)R can be used as a template for the enantioselective growth of a chiral copper telluride alloy surface. Beyond a critical alloy thickness the chiral influence of the Cu(643)R surface diminishes and an achiral surface forms. Our work demonstrates a new method of producing chiral transition metal chalcogenide surfaces, with potential applications in the study of structurally chiral topological insulators

Monitoring Soil Quality to Assess the Sustainability of Harvesting Corn Stover

Harvesting feedstock for biofuel production must not degrade soil, water, or air resources. Our objective is to provide an overview of field research being conducted to quantify effects of harvesting corn (Zea mays L.) stover as a bioenergy feedstock. Coordinated field studies are being conducted near Ames, IA; St. Paul and Morris, MN; Mead, NE; University Park, PA; Florence, SC; and Brookings, SD., as part of the USDA-ARS Renewable Energy Assessment Project (REAP). A baseline soil quality assessment was made using the Soil Management Assessment Framework (SMAF). Corn grain and residue yield for two different stover harvest rates (∼50% and ∼90%) are being measured. Available soil data remains quite limited but sufficient for an initial SMAF analysis that confirms total organic carbon (TOC) is a soil quality indicator that needs to be closely monitored closely to quantify crop residue removal effects. Overall, grain yields averaged 9.7 and 11.7 Mg ha−1 (155 and 186 bu acre−1) in 2008 and 2009, values that are consistent with national averages for both years. The average amount of stover collected for the 50% treatment was 2.6 and 4.2 Mg ha−1 for 2008 and 2009, while the 90% treatment resulted in an average removal of 5.4 and 7.4 Mg ha−1, respectively. Based on a recent literature review, both stover harvest scenarios could result in a gradual decline in TOC. However, the literature value has a large standard error, so continuation of this long-term multi-location study for several years is warranted