Green infrastructure sustains the food-energy-water-habitat nexus

The ecosystem service potential of urban green infrastructure (GI) is increasingly appreciated, yet its underpinning role in the food-energy-water-habitat (FEWH) nexus is unclear. In order to explore the positive and negative impacts of GI on the FEWH nexus, this study asked three questions: 1) What are the research hotspots in FEWH for GI and what are the trends over time? 2) What ecosystem services can GI provide in terms of FEWH? 3) Can we quantify the ecosystem service potential of GI, and what are the synergies and trade-offs among the service types? By collating the research evidence which supports the ecosystem service potential of GI to contribute to FEWH, we developed a matrix to score the potential and to assess the synergies and trade-offs among ecosystem services. From this, a conceptual framework of the role of GI in supporting the FEWH nexus was developed. The results show that the potential of GI to sustain the FEWH nexus is significant and that multi-functional GI planning is necessary to minimize the trade-offs between them. This requires the application of new methods, theories, adaptation to new circumstances, and the development of appropriate business models within the planning domain, as well as compliance with policy directions and funding externally

Review of the cultivation program within the National Alliance for Advanced Biofuels and Bioproducts

The cultivation efforts within the National Alliance for Advanced Biofuels and Bioproducts (NAABB)were developed to provide four major goals for the consortium, which included biomass production for downstream experimentation, development of new assessment tools for cultivation, development of new cultivation reactor technologies, and development of methods for robust cultivation. The NAABB consortium test beds produced over 1500 kg of biomass for downstream processing. The biomass production included a number of model production strains, but also took into production some of the more promising strains found through the prospecting efforts of the consortium. Cultivation efforts at large scale are intensive and costly, therefore the consortium developed tools and models to assess the productivity of strains under various environmental conditions, at lab scale, and validated these against scaled outdoor production systems. Two new pond-based bioreactor designs were tested for their ability to minimize energy consumption while maintaining, and even exceeding, the productivity of algae cultivation compared to traditional systems. Also, molecular markers were developed for quality control and to facilitate detection of bacterial communities associated with cultivated algal species, including the Chlorella spp. pathogen, Vampirovibrio chlorellavorus,which was identified in at least two test site locations in Arizona and New Mexico. Finally, the consortium worked on understanding methods to utilize compromised municipal waste water streams for cultivation. This review provides an overview of the cultivation methods and tools developed by the NAABB consortium to produce algae biomass, in robust low energy systems, for biofuel production

Biodiversity Increases the Productivity and Stability of Phytoplankton Communities

<div><p>Global biodiversity losses provide an immediate impetus to elucidate the relationships between biodiversity, productivity and stability. In this study, we quantified the effects of species richness and species combination on the productivity and stability of phytoplankton communities subject to predation by a single rotifer species. We also tested one mechanism of the insurance hypothesis: whether large, slow-growing, potentially-defended cells would compensate for the loss of small, fast-growing, poorly-defended cells after predation. There were significant effects of species richness and species combination on the productivity, relative yield, and stability of phytoplankton cultures, but the relative importance of species richness and combination varied with the response variables. Species combination drove patterns of productivity, whereas species richness was more important for stability. Polycultures containing the most productive single species, <em>Dunaliella</em>, were consistently the most productive. Yet, the most species rich cultures were the most stable, having low temporal variability in measures of biomass. Polycultures recovered from short-term negative grazing effects, but this recovery was not due to the compensation of large, slow-growing cells for the loss of small, fast-growing cells. Instead, polyculture recovery was the result of reduced rotifer grazing rates and persisting small species within the polycultures. Therefore, although an insurance effect in polycultures was found, this effect was indirect and unrelated to grazing tolerance. We hypothesize that diverse phytoplankton assemblages interfered with efficient rotifer grazing and that this “interference effect” facilitated the recovery of the most productive species, <em>Dunaliella</em>. In summary, we demonstrate that both species composition and species richness are important in driving patterns of productivity and stability, respectively, and that stability in biodiverse communities can result from an alteration in consumer functioning. Our findings underscore the importance of predator-prey dynamics in determining the relationships between biodiversity, productivity and stability in producer communities.</p> </div

Classification and key morphological characteristics of the study species.

<p>Species were divided into two functional groups, as indicated. Identification numbers from the National Center for Marine Algae and Microbiota (formerly the CCMP) are listed. Measurements of cell length and width or diameter and height were made on at least 25 cells per species. Size ranges encompass all dimensions.</p

Oxygen production, phytoplankton biovolume and rotifer abundance through time in the different richness treatments.

<p>The top figure (A) shows the mean (± SE) net oxygen production of the different richness treatments. The bottom figures show total phytoplankton biovolume (symbols) and rotifer abundance (bars) of the one- (B), two- (C), four- (D) and six-species (e) treatments before rotifer addition (first three points) and after rotifer addition (last three points). Points and bars in b-e represent means ± standard errors (n = nine for polycultures, 18 for monocultures). The arrow indicates the point of rotifer addition at an initial abundance of one rotifer per mL. Rotifer abundance is shown on a log scale.</p

Net oxygen production (A) and total biovolume (B) of phytoplankton monocultures and polycultures.

<p>Each box displays the experiment-long median (line within box), 25^th and 75^th percentiles (box boundaries), 10^th and 90^th percentiles (lower and upper error bars) and 5^th and 95% percentiles (dots). Medians and percentiles were calculated using all data collected throughout the experiment (n = 54 for polycultures, 108 for monocultures). Net oxygen production in the monocultures was centered on zero due to the consumption of oxygen after rotifer addition (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049397#pone-0049397-g003" target="_blank">Figure 3A</a>).</p

Relative yield of net oxygen production (A) and total biovolume (B).

<p>The main plots compare species combinations whereas the insets compare levels of species richness. Bars represent experiment-long means (± SE) calculated for each replicate and sampling day. Letters above each bar indicate the results of the post-hoc Tukey tests associated with ANOVA. Species combinations are listed with each plot, using the following abbreviations: D – <i>Dunaliella</i>, N – <i>Nannochloris</i>, R – <i>Rhodomonas</i>, Ch – <i>Chaetoceros</i>, Co – <i>Coscinodiscus,</i> M – <i>Melosira</i> and all – all species in the six-species combination.</p

Temporal CV of net oxygen production (A) and total biovolume (B).

<p>The main plots compare species combinations whereas the insets compare levels of species richness. Bars represent experiment-long means (± SE) calculated for each replicate and sampling day. Note that the temporal CV was calculated for single culture tubes, such that the mean values for richness treatments and species combinations are not influenced by the number of replicates per treatment. Letters above each bar indicate the results of the post-hoc Tukey tests associated with ANOVA. Species combinations are listed with each plot, using the following abbreviations: D – <i>Dunaliella</i>, N – <i>Nannochloris</i>, R – <i>Rhodomonas</i>, Ch – <i>Chaetoceros</i>, Co – <i>Coscinodiscus,</i> M – <i>Melosira</i> and all – all species in the six-species combination.</p

Experimental design of richness and species combination treatments, with the number of replicates as indicated.

<p>Experimental design of richness and species combination treatments, with the number of replicates as indicated.</p