Clinical assessment of the MOD-MEM cancer test in controls with non-malignant diseases.

A control series of 105 patients in hospital with non-malignant diseases was used in a limited clinical assessment of the MOD-MEM test. Twenty-seven positive results could be explained on the basis of destruction of nervous parenchyma, tissue necrosis, tuberculosis, malignant disease, etc. The remaining 13 unexplained positives showed a sex and age distribution in agreement with that predicted from cancer registration statistics if the MOD-MEM test detects cancer about 16 years before the clinical appearance of the disease

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions

Active adaptive conservation of threatened species in the face of uncertainty

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives

Nitrogen Increases Early-Stage and Slows Late-Stage Decomposition Across Diverse Grasslands

To evaluate how increased anthropogenic nutrient inputs alter carbon cycling in grasslands, we conducted a litter decomposition study across 20 temperate grasslands on three continents within the Nutrient Network, a globally distributed nutrient enrichment experiment We determined the effects of addition of experimental nitrogen (N), phosphorus (P) and potassium plus micronutrient (Kμ) on decomposition of a common tree leaf litter in a long-term study (maximum of 7 years; exact deployment period varied across sites). The use of higher order decomposition models allowed us to distinguish between the effects of nutrients on early- versus late-stage decomposition. Across continents, the addition of N (but not other nutrients) accelerated early-stage decomposition and slowed late-stage decomposition, increasing the slowly decomposing fraction by 28% and the overall litter mean residence time by 58%. Synthesis. Using a novel, long-term cross-site experiment, we found widespread evidence that N enhances the early stages of above-ground plant litter decomposition across diverse and widespread temperate grassland sites but slows late-stage decomposition. These findings were corroborated by fitting the data to multiple decomposition models and have implications for N effects on soil organic matter formation. For example, following N enrichment, increased microbial processing of litter substrates early in decomposition could promote the production and transfer of low molecular weight compounds to soils and potentially enhance the stabilization of mineral-associated organic matter. By contrast, by slowing late-stage decomposition, N enrichment could promote particulate organic matter (POM) accumulation. Such hypotheses deserve further testing

Drivers of the microbial metabolic quotient across global grasslands

Aim: The microbial metabolic quotient (MMQ; mg CO2-C/mg MBC/h), defined as the amount of microbial CO2 respired (MR; mg CO2-C/kg soil/h) per unit of microbial biomass C (MBC; mg C/kg soil), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (nitrogen + phosphorus + potassium + micronutrients) and herbivore exclusion on MR, MBC and MMQ across 23 sites (five continents). Our sites encompassed a wide range of edaphoclimatic conditions; thus, we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Location: Australia, Asia, Europe, North/South America. Time period: 2015–2016. Major taxa: Soil microbes. Methods: Soils were collected from plots with established experimental treatments. MR was assessed in a 5-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using linear mixed effects models (LMMs) and structural equation models (SEMs), we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. Results: MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity and lower soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (< 1.7%). Main conclusions: Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies

Environmental heterogeneity modulates the effect of plant diversity on the spatial variability of grassland biomass

Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.Fil: Daleo, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Chaneton, Enrique Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Iribarne, Oscar Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Tognetti, Pedro Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Borer, Elizabeth. University of Minnesota; Estados UnidosFil: Bruschetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: MacDougall, Andrew S.. University Of Guelph. Department Of Integrative Biology.; CanadáFil: Pascual, Jesus Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Sankaran, Mahesh. University of Leeds; Reino Unido. Tata Institute of Fundamental Research; IndiaFil: Seabloom, Eric. University of Minnesota; Estados UnidosFil: Wang, Shaopeng. Peking University; ChinaFil: Bagchi, Sumanta. Indian Institute of Science; IndiaFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Catford, Jane A.. University of Melbourne; Australia. Kings College London (kcl);Fil: Dickman, Chris R.. The University Of Sydney; AustraliaFil: Dickson, Tymothy L.. University of Nebraska; Estados UnidosFil: Donohue, Ian. Trinity College Dublin; Reino UnidoFil: Eisenhauer, Nico. Universitat Leipzig; Alemania. German Centre for Integrative Biodiversity Research; AlemaniaFil: Gruner, Daniel S.. University of Maryland; Estados UnidosFil: Haider, Sylvia. German Centre for Integrative Biodiversity Research; Alemania. Martin Luther University Halle-Wittenberg; Alemania. Leuphana University of Lüneburg; AlemaniaFil: Jentsch, Anke. University of Bayreuth; AlemaniaFil: Knops, Johannes M. H.. Xi’an Jiaotong-Liverpool University; ChinaFil: Lekberg, Ylva. University of Montana; Estados UnidosFil: McCulley, Rebecca L.. University of Kentucky; Estados UnidosFil: Moore, Joslin L.. University of Melbourne; Australia. Monash University; Australia. Arthur Rylah Institute for Environmental Research; AustraliaFil: Mortensen, Brent. Benedictine College; Estados UnidosFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de la Patagonia Austral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rocca, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentin

Linking changes in species composition and biomass in a globally distributed grassland experiment

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonisation and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e. local extinctions) were balanced by gains (i.e. colonisation). Under fertilisation, the decline in species richness resulted from increased species loss and decreases in species gained. Biomass increase under fertilisation resulted mostly from species that persist and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained or persisting.Fil: Ladouceur, Emma. Martin Luther University Halle-Wittenberg; Alemania. Universitat Leipzig; Alemania. German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena; AlemaniaFil: Blowes, Shane A.. Martin Luther University Halle-Wittenberg; Alemania. German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena; AlemaniaFil: Chase, Jonathan M.. German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena; Alemania. Martin Luther University Halle-Wittenberg; AlemaniaFil: Clark, Adam T.. Martin Luther University Halle-Wittenberg; Alemania. German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena; Alemania. University of Graz; AustriaFil: Garbowski, Magda. German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena; Alemania. Universitat Leipzig; AlemaniaFil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Arnillas, Carlos Alberto. University of Toronto; CanadáFil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Barrio, Isabel C.. Agricultural University of Iceland; IslandiaFil: Bharath, Siddharth. Atria University; IndiaFil: Borer, Elizabeth. University of Minnesota; Estados UnidosFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Cadotte, Marc W.. University of Toronto; CanadáFil: Chen, Qingqing. Peking University; ChinaFil: Collins, Scott L.. University of New Mexico; Estados UnidosFil: Dickman, Christopher R.. The University Of Sydney; AustraliaFil: Donohue, Ian. Trinity College Dublin; IrlandaFil: Du, Guozhen. Lanzhou University; ChinaFil: Ebeling, Anne. Universitat Jena; AlemaniaFil: Eisenhauer, Nico. Martin Luther University Halle—Wittenberg; Alemania. German Centre For Integrative Biodiversity Research (idiv) Halle-jena-leipzig; AlemaniaFil: Fay, Philip A.. USDA-ARS Grassland Soil and Water Research Lab; Estados UnidosFil: Hagenah, Nicole. University Of Pretoria; SudáfricaFil: Hautier, Yann. University of Utrecht; Países BajosFil: Jentsch, Anke. University of Bayreuth; AlemaniaFil: Jónsdóttir, Ingibjörg S.. University of Iceland; IslandiaFil: Komatsu, Kimberly J.. Smithsonian Environmental Research Center; Estados UnidosFil: MacDougall, Andrew. University of Guelph; CanadáFil: Martina, Jason P.. Texas State University; Estados UnidosFil: Moore, Joslin L.. Arthur Rylah Institute For Environmental Research; Australia. Monash University; AustraliaFil: Morgan, John W.. La Trobe University; AustraliaFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Grassland productivity limited by multiple nutrients

Terrestrial ecosystem productivity is widely accepted to be nutrient limited1. Although nitrogen (N) is deemed a key determinant of aboveground net primary production (ANPP)2,3, the prevalence of co-limitation by N and phosphorus (P) is increasingly recognized4,​5,​6,​7,​8. However, the extent to which terrestrial productivity is co-limited by nutrients other than N and P has remained unclear. Here, we report results from a standardized factorial nutrient addition experiment, in which we added N, P and potassium (K) combined with a selection of micronutrients (K+μ), alone or in concert, to 42 grassland sites spanning five continents, and monitored ANPP. Nutrient availability limited productivity at 31 of the 42 grassland sites. And pairwise combinations of N, P, and K+μ co-limited ANPP at 29 of the sites. Nitrogen limitation peaked in cool, high latitude sites. Our findings highlight the importance of less studied nutrients, such as K and micronutrients, for grassland productivity, and point to significant variations in the type and degree of nutrient limitation. We suggest that multiple-nutrient constraints must be considered when assessing the ecosystem-scale consequences of nutrient enrichment