Analysis and summary

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timer Asked: Jun 19th, 2018
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Question description

Requirement:

1. Identify a topic from this course that you are interested in learning more about.

2. Find a peer-reviewed article about your topic. IMPORTANT to NOTE: Once you’ve chosen your paper, immediately create your initial post on the ‘Lifelong Learning’ discussion board. Your initial post only needs to state your topic and the full citation of the article you will be reading and writing about. You cannot use the same paper as one of your classmates, so you need to post your article as soon as you choose it (stake your claim) and you need to check the discussion board to be sure that nobody else has already chosen the same paper.

3. Read and take notes on the article you selected

4. Write a brief summary of the article and a commentary on how the article builds on course material, including a statement about something new.

NR 435: Contemporary Conservation Issues (Summer 2018) Full Assignment DUE by 11:59pm on Wednesday, June 20 Page 1 Extra Opportunity for Learning (and Extra Credit J) I’ve decided to offer you an opportunity to expand your learning and to earn some extra credit. If you choose to complete this extra (optional) assignment, you can earn up to 10 extra points that will be added to your exams score for NR 435. By doing this assignment carefully and completely, you may increase one of your exam scores by a full letter grade. I was inspired by several of you who have taken it upon yourselves to gather more information about some of the topics you’ve found particularly interesting in this course. Your extra/optional assignment: 1. Identify a topic from this course that you are interested in learning more about. 2. Find a peer-reviewed article about your topic. IMPORTANT to NOTE: Once you’ve chosen your paper, immediately create your initial post on the ‘Lifelong Learning’ discussion board. Your initial post only needs to state your topic and the full citation of the article you will be reading and writing about. You cannot use the same paper as one of your classmates, so you need to post your article as soon as you choose it (stake your claim) and you need to check the discussion board to be sure that nobody else has already chosen the same paper. 3. Read and take notes on the article you selected 4. Write a brief summary of the article and a commentary on how the article builds on course material, including a statement about something new. 5. Post your work on the ‘Lifelong Learning’ discussion board thread. Pay close attention to the following details. (1) Identify a topic that you are interested in learning more about. Here is a list of sample topics (& possible search terms). You may choose a topic from this list, or you can choose another topic from this course that you find especially interesting. • • • • • • • • • • Biodiversity hotspots Biodiversity cold spots Island Biogeography Theory Edge Effects Soundscape Ecology Eutrophication Cultural eutrophication Desalination Saltwater intrusion Blue Revolution • • • • • • • • Sustainable Food System / Sustainable Intensification in Agriculture Food Waste Biomagnification Endocrine disruption / Endocrine disruptors Climate mitigation Climate adaptation Geoengineering Energy sustainability NR 435: Contemporary Conservation Issues (Summer 2018) Full Assignment DUE by 11:59pm on Wednesday, June 20 Page 2 (2) Find a peer-reviewed article about your topic of interest. You can use the UNH Library online databases to search for a peer-reviewed article, but an easier place to look is Google Scholar. In all honesty, this is where researchers often begin their own literature searching. Note that in the future, if you are hoping to complete an exhaustive search for peer-reviewed literature, you should not limit your search to Google Scholar. However, for this assignment, you should be able to find something good there. https://scholar.google.com/ Google Scholar is just like the Google that you’re familiar with, except that your results will (almost all) be peer-reviewed articles. You may get the occasional book or policy paper, but it’s mostly peer-reviewed literature. Just type in your search terms, and then review your search results until you find something interesting (to you). If your initial search terms don’t get you what you’re looking for, refine them and keep trying. You may want to begin by using some of the terms from the list on page 1 as your search terms in Google Scholar. Search for an article that was published recently (since 2010, if possible). However, if you find an older article that you are especially interested in, that’s okay. I want you to enjoy this experience. Notice that in the upper-left sidebar in Google Scholar, you can limit your search to more recent articles (see below): Specify a date range? For this assignment, you may want to pick a paper with a PDF available online? NR 435: Contemporary Conservation Issues (Summer 2018) Full Assignment DUE by 11:59pm on Wednesday, June 20 Page 3 Don’t just select the first article you find. Skim through a few articles, so you’re sure you’re choosing the best one for this assignment (most interesting and relevant). As soon as you select your article, immediately create your initial post on the ‘Lifelong Learning’ discussion board. In your initial post, you only need to: (1) state your topic; (2) provide the full citation of the article you will be reading and writing about; & (3) attach the PDF of your article. Example: My topic is: Biodiversity in Hot and Cold Spots My article is: Melian, C.J., O. Seehausen, V.M. Eguiluz, M.A. Fortuna, and K. Deiner. 2015. Diversification and biodynamics of hot and cold spots. Ecography, 38: 393-401. + attach PDF of the full article Why is it important to create your initial post on the discussion board as soon as you choose your article? You can’t use the same paper as one of your classmates, so you should post your article as soon as you choose it (stake your claim) & you need to check the discussion board to make sure nobody has already chosen the same paper. After you’ve completed the assignment (below), you must share your assignment in a reply to your initial discussion board post. (3) Read and take notes on the article you selected. Reading scientific articles can be like reading a new language. Don’t be hard on yourself if it feels challenging… it probably will. It is always a challenge, even after you’ve read hundreds of peer-reviewed papers… even after you’ve written them! Don’t get bogged down in the details. There are times when it is critical that you understand all the little details of the methods section of papers like these (when you’re trying to re-create the study, for instance) and there are times when you need to understand the statistical analysis section (this is NOT one of those times J). For this assignment, I want you to focus on (carefully read and re-read) the introduction and the discussion sections, and to skim through the methods and technical results sections. DO try to understand the figures (graphs). You should read the Abstract of your article (this is a summary that is usually printed at the top of the first page of scientific papers), but DO NOT LIMIT YOUR READING TO THE ABSTRACT. Abstracts are intended to help us review a lot of papers quickly – to find the best papers for our purpose. Once you choose a paper you are going to use for research (or for an assignment), you must read the entire paper. NR 435: Contemporary Conservation Issues (Summer 2018) Full Assignment DUE by 11:59pm on Wednesday, June 20 Page 4 At the end of this document, I’ve included a one-pager on “How to Read a Scientific Article” – you may find this helpful for this assignment, and in the future. If you are reading scientific articles for a specific purpose, it’s good practice to remind yourself of your goal before you read an article. In this case (for this assignment), your goal is to understand enough to write a summary of the article and a commentary specific to our course. This goal is articulated below. (4 & 5) Write a brief summary & commentary, & Post your work on the ‘Lifelong Learning’ discussion board (as a reply to your initial post). Write a brief summary of the article and a commentary on how the article builds on course material, including a statement about new information that you can share with us. Begin your follow-up post by reminding us which article you read. Paste in the full citation for the article before you add your Summary and Commentary. Your follow-up post should be posted as a reply to your initial post, and should be structured like this: Melian, C.J., O. Seehausen, V.M. Eguiluz, M.A. Fortuna, and K. Deiner. 2015. Diversification and biodynamics of hot and cold spots. Ecography, 38: 393-401. Summary: Your summary should be ~7-10 sentences telling us what this article is about. Does the paper describe an original experiment, or is it a review of other studies, or an opinion piece? What is the context for the article? What questions did the authors ask? What did the author(s) do? What did they conclude? Commentary: How does this article fit in with our course content? What concepts have we covered in the NR 435 readings/videos/lectures that sparked your interest in this topic? What new information did you learn from this article? And, what questions are you left with after reading the article? (8-12 sentences) If you forgot to attach the PDF to your initial post, then post it with your reply. Annotated for NR 435 / NR 435H, University of New Hampshire How to Read a Scientific Article* Scientific papers report new results/theories and relate these finding to previous knowledge in the field. Since papers are the primary way scientists communicate with one another it is important to be able to understand and critique these reports. Reading scientific papers is partly a matter of experience and familiarity with the field. If you are unfamiliar with the field you can still learn a tremendous amount from an article if you take the time to read it carefully focusing on the main concepts and conclusions. I agree that this is the place to start, but it is not enough. You have to read more than just the abstract to understand You may find the following steps helpful. the .indings and implications. Also note that some articles may not begin with an Abstract (or Summary). 1. Read the title and abstract first. The abstract should concisely state why the research was done, how it was done, and what was found. It is often helpful to make a list of the research question, approach, and findings. 2. Next skim the article, spending most time on the introduction and discussion sections. (If you are unfamiliar with the field, it is best to gain an understanding of the main concepts before tackling the specific methodologies.) 3. Then go back to the beginning and carefully read the introduction. The introduction should explain why this research is important and place it in the context of previous work. a. Is the research descriptive, comparative, or analytical? b. What questions are being addressed? c. Are the hypotheses reasonable? For our purposes, do d. Why is this research important? NOT worry about 4. The materials and methods section is often more detailed than the average reader understanding every requires. Try to get a general sense of the procedures (experimental or statistical). aspect of the If you are more familiar with the field you should be able to determine : ‘Methods’ sections of I sometimes a. Are the procedures appropriate? papers. But, it’s good start by reading b. Where the statistical analyses correctly applied? to at least skim thru the Abstract … this section for and then I go c. Were any interactions or confounding variables overlooked? context (and to 5. Read the results and examine the figures and tables. Try to summarize what each straight to the develop your .igures/graphs, figure/table conveys. understanding of before reading 6. The discussion contains the conclusions that the author(s) would like to draw different Methods). the Introduction. from the data. When reading the discussion it is important to recognize that there Try different may not be a right or wrong answer. You should ask yourself: approaches, and a. Did the author(s) interpret the findings appropriately? see what works You may feel unquali.ied best for you! b. Is the interpretation free of bias? to ask these .irst 2 s questions, but you should c. Are alternative interpretation acknowledged? d. Can you think of any alternative interpretations? spend some time on the last 2 questions. Note: If you are unfamiliar with vocabulary used in the article lookup these words in your textbook or dictionary. *Courtesy of Nat Wheelwright, Bowdoin College * Highlights & Annotations by Dr. Jennifer Purrenhage, University of New Hampshire Yes. Emphatically, YES! You should absolutely look up terms/concepts you are unfamiliar with. I usually write them in the margins of papers I’m reading, for later reference. If you’re reading online, keep a running list in your notes.
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications in the Biological Sciences Papers in the Biological Sciences 2014 Eutrophication weakens stabilizing effects of diversity in natural grasslands Yann Hautier University of Minnesota, hauti001@umnl.edu Eric W. Seabloom University of Minnesota, Seabloom@umn.edu Elizabeth T. Borer University of Minnesota, borer@umn.edu Peter B. Adler Utah State University W. Stanley Harpole Utah State University See next page for additional authors Follow this and additional works at: https://digitalcommons.unl.edu/bioscifacpub Part of the Biology Commons Hautier, Yann; Seabloom, Eric W.; Borer, Elizabeth T.; Adler, Peter B.; Harpole, W. Stanley; Hillebrand, Helmut; Lind, Eric M.; MacDougall, Andrew S.; Stevens, Carly J.; Bakker, Jonathan D.; Buckley, Yvonne M.; Chu, Chengjin; Collins, Scott L.; Daleo, Pedro; Damschen, Ellen I.; Davies, Kendi F.; Fay, Philip A.; Firn, Jennifer; Gruner, Daniel S.; Jin, Virginia L.; Klein, Julia A.; Knops, Johannes M. N.; La Pierre, Kimberly J.; Li, Wei; McCulley, Rebecca L.; Melbourne, Brett A.; Moore, Joslin L.; O’Halloran, Lydia R.; Prober, Suzanne M.; Risch, Anita C.; Sankaran, Mahesh; Schuetz, Martin; and Hector, Andy, "Eutrophication weakens stabilizing effects of diversity in natural grasslands" (2014). Faculty Publications in the Biological Sciences. 332. https://digitalcommons.unl.edu/bioscifacpub/332 This Article is brought to you for free and open access by the Papers in the Biological Sciences at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications in the Biological Sciences by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Authors Yann Hautier, Eric W. Seabloom, Elizabeth T. Borer, Peter B. Adler, W. Stanley Harpole, Helmut Hillebrand, Eric M. Lind, Andrew S. MacDougall, Carly J. Stevens, Jonathan D. Bakker, Yvonne M. Buckley, Chengjin Chu, Scott L. Collins, Pedro Daleo, Ellen I. Damschen, Kendi F. Davies, Philip A. Fay, Jennifer Firn, Daniel S. Gruner, Virginia L. Jin, Julia A. Klein, Johannes M. N. Knops, Kimberly J. La Pierre, Wei Li, Rebecca L. McCulley, Brett A. Melbourne, Joslin L. Moore, Lydia R. O’Halloran, Suzanne M. Prober, Anita C. Risch, Mahesh Sankaran, Martin Schuetz, and Andy Hector This article is available at DigitalCommons@University of Nebraska - Lincoln: https://digitalcommons.unl.edu/bioscifacpub/332 LETTER doi:10.1038/nature13014 Eutrophication weakens stabilizing effects of diversity in natural grasslands Yann Hautier1,2, Eric W. Seabloom1, Elizabeth T. Borer1, Peter B. Adler3, W. Stanley Harpole4, Helmut Hillebrand5, Eric M. Lind1, Andrew S. MacDougall6, Carly J. Stevens7, Jonathan D. Bakker8, Yvonne M. Buckley9,10, Chengjin Chu11, Scott L. Collins12, Pedro Daleo13, Ellen I. Damschen14, Kendi F. Davies15, Philip A. Fay16, Jennifer Firn17, Daniel S. Gruner18, Virginia L. Jin19, Julia A. Klein20, Johannes M. H. Knops21, Kimberly J. La Pierre22, Wei Li23, Rebecca L. McCulley24, Brett A. Melbourne15, Joslin L. Moore25,26, Lydia R. O’Halloran27, Suzanne M. Prober28, Anita C. Risch29, Mahesh Sankaran30,31, Martin Schuetz29 & Andy Hector32 Studies of experimental grassland communities1–7 have demonstrated that plant diversity can stabilize productivity through species asynchrony, in which decreases in the biomass of some species are compensated for by increases in others1,2. However, it remains unknown whether these findings are relevant to natural ecosystems, especially those for which species diversity is threatened by anthropogenic global change8–11. Here we analyse diversity–stability relationships from 41 grasslands on five continents and examine how these relationships are affected by chronic fertilization, one of the strongest drivers of species loss globally8. Unmanipulated communities with more species had greater species asynchrony, resulting in more stable biomass production, generalizing a result from biodiversity experiments to real-world grasslands. However, fertilization weakened the positive effect of diversity on stability. Contrary to expectations, this was not due to species loss after eutrophication but rather to an increase in the temporal variation of productivity in combination with a decrease in species asynchrony in diverse communities. Our results demonstrate separate and synergistic effects of diversity and eutrophication on stability, emphasizing the need to understand how drivers of global change interactively affect the reliable provisioning of ecosystem services in real-world systems. Rapid declines in plant diversity have prompted concern over the consequences for the stability of ecosystem functioning and the reliable provisioning of ecological services7,12,13. The first attempts to address this concern used observational studies of natural variation in diversity14,15 and were limited in their ability to separate effects of diversity from other confounding factors16. In response, more recent studies have directly manipulated diversity in experimentally established communities (that is, biodiversity experiments) to assess its impact on ecosystem functioning, particularly above-ground net primary production (ANPP)1–7. Numerous biodiversity experiments have shown that greater species diversity promotes a greater stability of productivity over time2,3,6, with asynchronous response of species to environmental fluctuations as an important underlying mechanism1,2. There would be no stabilizing effect if species fluctuated in perfect synchrony. However, asynchrony in species response to environmental fluctuations causes declines in the biomass of some species to be compensated for by increases in others, thus buffering temporal fluctuation in the productivity of the whole community. Species asynchrony can increase the stability of aggregate functions in species-rich communities, because compensatory effects are more likely to occur when the species pool is larger and more diverse17–19. Biodiversity experiments, in turn, have their own limitations, and their relevance to natural grassland ecosystems is debated11,12,16. For example, experimental gradients of diversity are usually assembled randomly from a local species pool, whereas in natural systems composition and diversity are influenced by a variety of factors including nutrient availability, climatic conditions and anthropogenic land use9–11. It is also likely that diversity is not the only, or even the primary, driver of the stability of ANPP20,21; however, few experiments have simultaneously addressed changes in both biodiversity and other aspects of global change. In natural grasslands the situation is likely to be complex, because anthropogenic impacts such as climate change and eutrophication are likely to change diversity—with potential consequences for stability— as well as having their own direct effects on stability4,13,22,23. In particular, anthropogenic increases in nutrient inputs into grasslands (through direct organic and inorganic fertilization and atmospheric deposition) affect the structure and functioning of natural ecosystems worldwide8,24,25. For instance, nutrient enrichment usually increases productivity and reduces plant diversity24,25. However, the effect of eutrophication on the stability of productivity in natural grasslands remains unclear. On the basis of theory and results limited to single-site experiments22,23, we expect eutrophication to reduce the stability of productivity, because the well-known negative effects of nutrient enrichment on diversity24,25 could in turn reduce species asynchrony and stability1,6,14,22,26. However, eutrophication may have additional impacts on stability that are independent of any changes in diversity. The temporal stability of ANPP is the ratio of the temporal mean to the temporal standard deviation, so an increase in stability can result from an increase in the mean, a decrease 1 Department of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, Minnesota 55108, USA. 2Institute of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland. 3Department of Wildland Resources and the Ecology Center, Utah State University, Logan, Utah 84322, USA. 4Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa 50011, USA. 5Institute for Chemistry and Biology of the Marine Environment, Carl-von-Ossietzky University Oldenburg, D-26111 Oldenburg, Germany. 6Department of Integrative Biology, University of Guelph, Guelph, Ontario N1G 2W1, Canada. 7Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK. 8School of Environmental and Forest Sciences, University of Washington, Seattle, Washington 98195, USA. 9Australian Research Council Centre of Excellence for Environmental Decisions, School of Biological Sciences, The University of Queensland, Queensland 4072, Australia. 10School of Natural Sciences, Department of Zoology, Trinity College Dublin, Dublin 2, Ireland. 11State Key Laboratory of Grassland and Agro-Ecosystems, Research Station of Alpine Meadow and Wetland Ecosystems, School of Life Sciences, Lanzhou University, Lanzhou 730000, China. 12Department of Biology, MSC03-2020, University of New Mexico, Albuquerque, New Mexico 87131, USA. 13Instituto de Investigaciones Marinas y Costeras (IIMyC) (CONICET-UNMdP), Mar del Plata 7600, Argentina. 14Department of Zoology, University of Wisconsin, Madison, Wisconsin 53706, USA. 15Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado 80309, USA. 16United States Department of Agriculture Agricultural Research Service, Grassland Soil and Water Research Lab, Temple, Texas 76502, USA. 17Queensland University of Technology, School of Biological Sciences, Brisbane 4000, Australia. 18Department of Entomology, University of Maryland, College Park, Maryland 20742, USA. 19United States Department of Agriculture Agricultural Research Service, Agroecosystem Management Research Unit, Lincoln, Nebraska 68583, USA. 20Department of Forest, Rangeland and Watershed Stewardship, Colorado State University, Fort Collins, Colorado 80523, USA. 21School of Biological Sciences, University of Nebraska, Lincoln, Nebraska 68588, USA. 22 Berkeley Initiative for Global Change Biology, University of California, Berkeley, California 94720, USA. 23Yunnan Academy of Biodiversity, Southwest Forestry University, Kunming 650224, China. 24 Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky 40546, USA. 25Australian Research Centre for Urban Ecology, Melbourne, c/o School of Botany, University of Melbourne, Victoria 3010, Australia. 26School of Biological Sciences, Monash University, Victoria 3800, Australia. 27Department of Zoology, Oregon State University, Corvallis, Oregon 97331, USA. 28CSIRO Ecosystem Sciences, Wembley, WA 6913, Australia. 29Swiss Federal Institute for Forest, Snow and Landscape Research, 8903 Birmensdorf, Switzerland. 30School of Biology, University of Leeds, Leeds LS2 9JT, UK. 31National Centre for Biological Sciences, GKVK Campus, Bangalore 560065, India. 32Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK. This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved 0 0 M O N T H 2 0 1 4 | VO L 0 0 0 | N AT U R E | 1 RESEARCH LETTER in the standard deviation, or both. Because eutrophication is expected to increase productivity it may have a stabilizing effect by increasing the temporal mean. However, there is also the potential for effects of eutrophication on stability through changes in the temporal standard deviation, but these are less well understood. We therefore require a better picture of how drivers of global change affect ecosystem stability both through changes in diversity and through other routes. Here we compare the relationship between diversity and stability found in grassland biodiversity experiments with those in fertilized and unfertilized plots in natural grasslands. We also assess the effects of eutrophication on the diversity–stability relationship both through changes in diversity and through other routes. We evaluated the relationships between species diversity, species asynchrony and stability of ANPP across 41 naturally assembled grassland ecosystems on five continents (Extended Data Fig. 1 and Extended Data Table 1), using data from the Nutrient Network (NutNet; http:// www.nutnet.org) collaborative experiment27,28. We used standardized methods to assess plant diversity and ANPP at each site in both unmanipulated controls and experimentally fertilized plots in a well-replicated design. We quantified diversity as the average plant species richness in standard 1-m2 plots over a three-year period. Stability can take a variety of meanings in the ecological literature29,30; here we focus on temporal stability of community-level, above-ground live plant biomass from all species in a plot (a measure of ANPP) over three years. We define a temporal stability for each plot as the temporal mean of ANPP divided by its temporal variability—that is, the temporal standard deviation over a common period (see Methods). Stability of ANPP was positively associated with plant diversity in the unmanipulated communities (Fig. 1a). Using a hierarchical sampling design and statistical model we found that stability increased with diversity consistently within and among sites, resulting in parallel relationships (coloured and black lines, respectively, in Fig. 1a). The consistent relationship between diversity and stability is concordant with experimental results obtained in grasslands across Europe1 and with experiments and observations at single locations2,3,6,21,26. We used multiple regression to evaluate the influence of plant diversity and key biotic and abiotic factors on stability in our 41 grasslands. Stability was still associated with diversity after using covariates to control for differences in average site productivity and climatic conditions including annual trends, seasonality and extreme or limiting environmental factors (Extended Data Tables 1 and 2). Together these results demonstrate that temporal stability of ANPP was positively related to variation in plant diversity in our 41 naturally assembled grassland ecosystems. We determined the role of species asynchrony as a mechanism promoting stability, by using a community-wide measure that allowed direct comparison between communities with different numbers of species17–19. Because the biomass of individual plant species was available at few sites, we used estimates based on our three-year record of the percentage c e d f Stability of ANPP ( μ/σ) 4 3 2 1 0 b 1.00 Species asynchrony 0.75 0.50 0.25 0 0 10 20 30 0 10 20 30 Average species richness (m−2) Figure 1 | Relationships of temporal stability of ANPP (upper row) and species asynchrony (lower row) with species diversity. a–d, Unmanipulated (a, b) and fertilized (c, d) communities of the Nutrient Network. e, f, The BIODEPTH network of grassland biodiversity experiments. Relationships of temporal stability of ANPP (temporal mean/temporal standard deviation; natural log transformed for analysis) of 41 grassland sites of the Nutrient Network were positive in the unmanipulated communities (a, b) (slopes and 95% confidence intervals: 0.028 (0.006 to 0.050) and 0.060 (0.023 to 0.097)), but not detectible in the fertilized communities (c, d) (20.001 (20.025 to 0.022) and 0.008 (20.031 to 0.047)). (e, f) Relationships in the BIODEPTH network 2 | N AT U R E | VO L 0 0 0 | 0 0 M O N T H 2 0 1 4 0 10 20 30 Species richness treatment were positive (0.018 (0.003 to 0.039) and 0.073 (0.053 to 0.093)). Species asynchrony varied from zero (perfect synchrony) to one (perfect asynchrony). Species richness values for the Nutrient Network are average values over the three years of post-treatment data. Points are values for individual plots (n 5 117 for Nutrient Network, n 5 480 for BIODEPTH). Black lines are the back-transformed fixed-effect linear regression slopes between sites from the mixed-effects model; coloured lines show patterns within sites. Dashed lines show regression slopes between sites in the unmanipulated communities of the Nutrient Network. Colours correspond to the ‘colour code’ column in Extended Data Table 1. This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved LETTER RESEARCH cover of individual plant species in each plot (see Methods). Our analysis of potential stability mechanisms showed that species asynchrony was positively related to plant diversity (Fig. 1b) and stability (Fig. 2a), consistent with theory on the stabilizing effects of species asynchrony in species-rich communities. Greater stability at higher diversity can also result from an increase in the temporal mean of ANPP with diversity (a ‘performance-enhancing effect’ that results in a higher ratio of the temporal mean to the temporal variation)1,17. Consistent with earlier NutNet analyses27, we found that the temporal mean of ANPP was not related to plant diversity (Fig. 3a). Although it is an indirect test, our result provides no support for a performance-enhancing effect in stabilizing higher-diversity communities in our study. Instead, we found stronger support for a decrease in temporal variation of ANPP (measured by the standard deviation) with diversity (Fig. 3a). In other words, greater stability at higher diversity resulted because diversity decreased the temporal variation of ANPP relative to its mean, resulting in a more stable mean-to-variance ratio. To compare our results from naturally assembled grasslands with results from artificially assembled biodiversity experiments, we calculated values of species asynchrony from the BIODEPTH experiment1. BIODEPTH— a pan-European network of grassland biodiversity experiments—was conducted at eight field sites with a comparable hierarchical design, plot size and measurements (see Methods). Our results are comparable to those from BIODEPTH because both studies use the same threeyear experimental duration and cover a similar range of diversity levels (although, by design, biodiversity experiments feature many more lowdiversity communities than observational surveys). We found that the sign and slope of the overall relationships between diversity and stability and between diversity and asynchrony from our global multisite study were comparable to those from the BIODEPTH network of grassland biodiversity experiments (Fig. 1e, f; compare the solid and dashed lines). We tested the impact of eutrophication on temporal stability and species asynchrony by using data from NutNet plots that were fertilized for three years with a combination of nitrogen, phosphorus, potassium and micronutrients (see Methods). Fertilization weakened the positive effect of diversity on stability and species asynchrony (Fig. 1c, d; compare the solid and dashed lines). We expected this result on the basis of theory, because nutrient enrichment often reduces diversity24,25, which could in turn reduce species asynchrony and stability1,6,14,22,26. However, although fertilization reduced diversity by an average of 1.3 species (95% confidence interval 0.7–1.9) per site (corresponding to a reduction a of diversity from 2.0% to 16.9% relative to average levels in the control plots ranging from 4.4 to 32.3 species per square metre (Extended Data Table 1)), counter to expectations this loss of diversity did not lead to a reduction of stability through a decrease in species asynchrony (Extended Data Fig. 2). Instead, the lower slope of the diversity–stability relationship in the fertilized communities (Fig. 1c) can be explained by a combination of two factors. First, fertilization increased the temporal variation of ANPP in diverse communities in comparison with unmanipulated communities (Fig. 3a, b; compare the dashed lines). Because fertilization generally increased mean productivity in comparison with unmanipulated communities (Fig. 3a, b; compare the solid lines), this increased variation weakened the positive effect of diversity on stability in comparison with unmanipulated communities. Second, fertilization resulted in a decrease in species asynchrony in diverse communities compared with unmanipulated communities (Fig. 1d; compare the dashed and solid lines). Because fertilization did not alter the positive relationship between species asynchrony and stability (Fig. 2b), this decrease in species asynchrony resulted in decreased stability in diverse communities compared with unmanipulated communities. In total, the results of our fertilization experiment did not show the expected destabilizing effects of diversity loss. Instead, eutrophication affected stability directly through a combination of diversity-dependent effects on species asynchrony and on the temporal variation of productivity. These direct effects of eutrophication on the diversity–stability relationship could not have been predicted from studies of natural or experimental diversity gradients. The results of our observational study of naturally assembled grassland communities are consistent with a stabilizing effect of asynchronous responses of species to environmental fluctuations in more diverse plant communities—a result previously restricted to biodiversity experiments1 and observational studies at single locations21,26. However, the drivers of global change causing a loss of diversity may have additional effects on stability. The results of our fertilization experiment demonstrate impacts on stability that were not caused by changes in diversity but came about through effects of eutrophication on both the temporal variation in production and on species asynchrony. However, although the effects of fertilization on stability were not caused by species loss, the changes in species asynchrony and temporal variation that were responsible were both affected by levels of community diversity. Predicting the effects of drivers of global change therefore requires a better understanding of both their direct effects on ecosystem stability and their indirect effects through changes in diversity. Our results indicate that although b Stability of ANPP ( μ/σ) 3 2 1 0 0 0.25 0.50 0.75 1.00 0 0.25 0.50 0.75 1.00 Species asynchrony Figure 2 | Relationships between temporal stability of ANPP (natural log transformed) and species asynchrony in 41 grassland sites of the Nutrient Network. a, Unmanipulated communities; b, fertilized communities. The temporal stability was greatest in plots in which species fluctuations were asynchronized in both the unmanipulated (slope and 95% confidence intervals: 1.93 (1.70 to 2.16)) and fertilized communities of 41 grassland sites of the Nutrient Network (1.90 (1.58 to 2.21)). Points are values for individual plots (n 5 117). Colours correspond to the ‘colour code’ column in Extended Data Table 1. This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved 0 0 M O N T H 2 0 1 4 | VO L 0 0 0 | N AT U R E | 3 RESEARCH LETTER Temporal mean and standard deviation of ANPP a b 7 6 5 4 3 2 0 10 20 30 0 10 Average species richness (m–2) 20 30 Figure 3 | Relationships of temporal mean and standard deviation of ANPP (natural log transformed) to species diversity in 41 grassland sites of the Nutrient Network. a, Unmanipulated communities; b, fertilized communities. Temporal mean (m; solid line and filled symbols) was not related to species diversity (slope and 95% confidence intervals: 0.01 (20.02 to 0.03)) in the unmanipulated communities (a), and standard deviation (s; dashed line and open symbols) was negatively related to species diversity (20.03 (20.05 to 20.01)), suggesting that greater stability at higher diversity in the unmanipulated communities (Fig. 1a) resulted from a decrease in temporal variation. Both temporal mean (slope and 95% confidence intervals 0.01 (20.02 to 0.03)) and standard deviation (slope and 95% confidence intervals 0.01 (20.02 to 0.04)) were not related to species diversity in the fertilized communities (b). Fertilization increased the temporal variation in diverse communities compared with unmanipulated communities, resulting in a reduced positive effect of diversity on stability in fertilized communities (Fig. 1c). Points are values for individual plots (n 5 117). eutrophication is intended to increase average levels of productivity it can also affect its temporal stability. Sustainable management of grassland ecosystems therefore requires a better understanding of the complex interrelationships between diversity, productivity and stability and how they are affected by fertilization. 2. 3. 4. 5. METHODS SUMMARY The 41 sites are part of the Nutrient Network Global Research Cooperative28 (Extended Data Fig. 1 and Extended Data Table 1) (see NutNet’s website). Experimental plots included untreated controls and plots with nitrogen, phosphorus and potassium and micronutrients added in combination (NPK). The analyses presented here include all sites with the first three years of post-treatment community-level ANPP (g m22 yr21) and species-level ANPP estimates based on percentage cover. We also examined data from BIODEPTH, a consortium of coordinated biodiversity experiments that manipulated plant diversity at eight European grassland sites1. We analysed community and species-level ANPP for the three main years of this project8. Ecosystem temporal stability was defined for each plot as m/s, where m is the temporal mean of ecosystem-level ANPP and s its temporal standard deviation over the three-year period. Species asynchrony was measured for each plot as 1 2 Qb, s2 where Qb is species synchrony and is calculated as 1{Qb ~1{ P 2 , S i~1 si where si is the temporal standard deviation of species i in a plot with S species over the three years18. Thus, stability and species asynchrony are related such that higher levels of species asynchrony are associated with greater stability of the community as a whole14. We modelled relationships with linear mixed-effects models by using the lme function from the nlme library in R 2.15.1. To improve normality, the ecosystem temporal stability and community-wide species asynchrony were log-transformed before analyses. Sites and blocks nested within sites for the NutNet data and sites and species composition nested within sites for BIODEPTH were treated as random effects, allowing both the intercepts and slopes of regression versus diversity to vary between sites if supported by model selection. Online Content Any additional Methods, Extended Data display items and Source Data are available in the online version of the paper; references unique to these sections appear only in the online paper. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. Received 11 October 2013; accepted 10 January 2014. Published online 16 February 2014. 22. 1. 23. Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010). 4 | N AT U R E | VO L 0 0 0 | 0 0 M O N T H 2 0 1 4 Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009). Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006). Reich, P. B. Elevated CO2 reduces losses of plant diversity caused by nitrogen deposition. Science 326, 1399–1402 (2009). Naeem, S., Thompson, L. J., Lawler, S. P., Lawton, J. H. & Woodfin, R. M. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994). Bezemer, T. M. & van der Putten, W. H. Ecology: diversity and stability in plant communities. Nature 446, E6–E7 (2007). Cardinale, B. J. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012). Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997). Selmants, P. C., Zavaleta, E. S., Pasari, J. R. & Hernandez, D. L. Realistic plant species losses reduce invasion resistance in a California serpentine grassland. J. Ecol. 100, 723–731 (2012). Zavaleta, E. S. & Hulvey, K. B. Realistic species losses disproportionately reduce grassland resistance to biological invaders. Science 306, 1175–1177 (2004). Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005). Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001). Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007). Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 165–175 (1994). McNaughton, S. J. Stability and diversity of ecological communities. Nature 274, 251–253 (1978). Huston, M. A. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110, 449–460 (1997). Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999). Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008). Loreau, M. From Populations to Ecosystems: Theoretical Foundations for a New Ecological Synthesis (Princeton Univ. Press, 2010). Sankaran, M. & McNaughton, S. J. Determinants of biodiversity regulate compositional stability of communities. Nature 401, 691–693 (1999). Bai, Y., Han, X., Wu, J., Chen, Z. & Li, L. Ecosystem stability and compenatory effects in the inner Mongolia grassland. Nature 431, 181–184 (2004). Yang, Z. L., van Ruijven, J. & Du, G. Z. The effects of long-term fertilization on the temporal stability of alpine meadow communities. Plant Soil 345, 315–324 (2011). Yang, H. J. et al. Diversity-dependent stability under mowing and nutrient addition: evidence from a 7-year grassland experiment. Ecol. Lett. 15, 619–626 (2012). This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved LETTER RESEARCH 24. Stevens, C. J., Dise, N. B., Mountford, J. O. & Gowing, D. J. Impact of nitrogen deposition on the species richness of grasslands. Science 303, 1876–1879 (2004). 25. Hautier, Y., Niklaus, P. A. & Hector, A. Competition for light causes plant biodiversity loss after eutrophication. Science 324, 636–638 (2009). 26. Tilman, D. Biodiversity: population versus ecosystem stability. Ecology 77, 350–353 (1996). 27. Adler, P. B. et al. Productivity is a poor predictor of plant species richness. Science 333, 1750–1753 (2011). 28. Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014). 29. May, R. M. Stability and Complexity in Model Ecosystems (Princeton Univ. Press, 1973). 30. Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 669–674 (1984). Acknowledgements The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 298935 to Y.H. (with A.H. and E.W.S.). This work was generated using data from the Nutrient Network (http://www.nutnet.org) experiment, funded at the site-scale by individual researchers. Coordination and data management have been supported by funding to E.T.B. and E.W.S. from the National Science Foundation Research Coordination Network (NSF-DEB-1042132), the Long Term Ecological Research (LTER) programme (NSF-DEB-1234162 to Cedar Creek as well as other LTER sites), and the Institute on the Environment at the University of Minnesota (DG-0001-13). We also thank the Minnesota Supercomputer Institute for hosting project data, and the Institute on the Environment for hosting Network meetings. We thank R. S. L. Veiga, F. Isbell, R. K. Didham, H. McGinness and M. O’Brien for suggestions that improved the manuscript. Author Contributions E.W.S., E.T.B., W.S.H. and E.M.L. are Nutrient Network coordinators. Y.H. and A.H. developed and framed research questions. Y.H., E.W.S., E.T.B., P.B.A., W.S.H., H.H., A.S.MD., C.J.S., J.D.B., Y.M.B., C.C., S.L.C., E.I.D., K.F.D., P.A.F., J.F., D.S.G., V.L.J., J.A.K., J.M.H.K., K.J.L., W.L., R.L.McC., B.A.M., J.L.M., S.M.P., A.C.R., M.S., M.S. and A.H. collected the data used in this analysis. Y.H. and A.H. analysed the data. E.W.S., E.T.B., H.H., E.M.L., P.D., K.J.L., J.L.M., L.R.O. and M.S. contributed to data analyses. Y.H. and A.H. wrote the paper with input from all authors. Author Information Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to Y.H. (hauti001@umn.edu). This article is a U.S. government work, and is not subject to copyright in the United States. 0 0 M O N T H 2 0 1 4 | VO L 0 0 0 | N AT U R E | 5 ©2014 Macmillan Publishers Limited. All rights reserved RESEARCH LETTER METHODS Site selection and experimental design. The 41 study sites are part of the Nutrient Network (NutNet) Global Research Cooperative (Extended Data Fig. 1 and Extended Data Table 1) (see also NutNet’s website). See ref. 28 for a complete description of site selection, methods and measurements. To be as representative as possible of realistic grassland ecosystems, our analyses included sites covering a wide range of grassland habitats (for example alpine grassland, prairie, pasture, shrub steppe, savanna and old field). Thus, the between-site variation across NutNet sites captures a globally relevant gradient of fine-scale (1-m2) and site-level variation in factors including above-ground biomass, species richness, land-use history and environmental variables (Extended Data Table 1). In some sites, human land use (grazing, burning and mowing as part of the traditional site management) is currently or has been recently carried on (Extended Data Table 1). However, our analyses were robust to land-use history: effects of species richness were similar after we removed 13 sites with strong anthropogenic influence. All sites included in the analyses presented here included control plots and plots with nitrogen (N), phosphorus (P) and potassium and micronutrients (K) added in combination (NPK) (details are given below). Treatments were randomly assigned to the 25-m2 plots and were replicated in three blocks at most sites, although the number of blocks ranged from one to six between sites (Extended Data Table 1). Treatments and sampling followed a standardized protocol at all sites, detailed in ref. 28. Treatment application started at most sites in 2008, although eight sites started in 2009 and two in 2010. For this study we included all sites with three year of post-treatment data collection. We used data collected during the first three-years of post-treatment data collection so that our results would be independent of the time since the start of treatment application. All of our sites had three years of post-treatment data, although three sites had discontinuous data collection (Extended Data Table 1). Longer time series currently exist for only a limited number of sites, but the results were qualitatively the same when extended to four and five years. Climate data. We quantified precipitation and temperature data using the WorldClim Global Climate database31 (version 1.4; http://www.worldclim.org/). A principal component analysis (PCA) was used to reduce the number of climatic variables, many of which were collinear, resulting in a subset of bioclimatic variables representing annual trends (mean annual temperature (uC) and precipitation (mm)), seasonality (mean annual range in temperature, standard deviation in temperature, coefficient of variation of precipitation) and extreme or limiting environmental factors (mean temperature during the wettest four months). Fertilization. Nitrogen (N), phosphorus (P) and potassium (K) were applied annually to fertilized plots, before the beginning of the growing season, at relatively high rates: 10 g m22 y21. These rates are comparable to those for other grassland experiments that seek to alter diversity32. N was supplied as time-release urea ((NH2)2CO) or ammonium nitrate (NH4NO3) (the form of N did not have differential effects on production28). P was supplied as triple super phosphate (Ca(H2PO4)2), and K as potassium sulphate (K2SO4). In addition, a micronutrient mix (Fe, S, Mg, Mn, Cu, Zn, B and Mo) was applied at 100 g m22 y21 to the K-addition plots, once at the start of the experiment but not in subsequent years to avoid toxicity. Species richness and cover. Diversity was quantified as the average plant species richness in standard 1-m2 plots over the three years of post-treatment data for the analyses. We used species richness as a measure of diversity because species asynchrony in response to environmental fluctuations is the basis for functional compensation between species and stability theory33; decreases in the functioning of some species are partly or wholly compensated for by increases in other species. Cover was estimated independently for each species, so that total summed cover can exceed 100% for multilayer canopies. To better match theory, percentage cover was converted to biomass estimates for each species by assuming that the proportion of total cover for each species was equivalent to its proportion of total aboveground biomass34, because we did not have direct measures of biomass for each individual species. Our results were independent of the measure chosen; results of our analyses using percentage cover data did not differ qualitatively from the results presented in the text using estimated biomass data for species, based on percentage cover. Productivity. We used above-ground live biomass as a measure of primary productivity, which is an effective estimator of above-ground net primary production (ANPP) in herbaceous vegetation35,36. At some sites with strongly seasonal communities, cover and biomass were estimated twice during the year to assemble a complete list of species, and the summed biomass of each species was used in the analyses (Extended Data Table 1). However, our results were retained when we performed analyses excluding these sites. BIODEPTH. The BIODEPTH data used in our analysis are available online (http://www.esapubs.org/archive/ecol/E091/155/) from Ecological Archives1. BIODEPTH comprised a consortium of eight coordinated biodiversity experiments that manipulated plant diversity at different European grassland sites1,37,38. The analyses presented here use data on net above-ground biomass production (g m22 yr21) of species from the experimental plots at each of the eight BIODEPTH field sites for the three main years of the project1,38. The data set comprises information on 480 plots, each containing between 1 and 32 species. In total this produces 1,934 data points per year, with each data point reporting the biomass of a species in an individual plot. Each monoculture or species mixture was replicated in two identical plots (with a few exceptions: five plant assemblages were replicated four times38). Monocultures were removed from the analysis to produce a more comparable range of species richness. Stability. Ecosystem temporal stability was defined for each plot as m/s, where m is the temporal mean of ecosystem-level ANPP and s is its temporal standard deviation over the three-year period. Asynchrony. Species asynchrony was measured for each plot as 1 2 Qb, where Qb s2 2 is species synchrony and is calculated as 1{Qb ~1{ P 2 , where s is the S s i i~1 temporal variance in ecosystem function and si is the temporal standard deviation in function of species i in a plot with S species over the three years18. Thus, stability and species asynchrony are related such that higher levels of species asynchrony are associated with greater stability of the community as a whole18,39,40. Analyses. We modelled the relationships with linear mixed-effects models by using the lme function from the nlme library41 in R 2.15.1 (ref. 42). To improve normality, the temporal stability of ANPP, community-wide species asynchrony, temporal mean of ANPP and temporal standard deviation of ANPP were logtransformed before analyses. Changes in diversity, stability and asynchrony were calculated as the average difference per block between the fertilized and unmanipulated plots of the Nutrient Network. Sites and blocks nested within sites for the NutNet data and sites and species composition nested within sites for BIODEPTH were treated as random effects allowing both the intercepts and slopes of regression versus diversity to vary between sites if supported by model selection. For the fixed-by-random-effects interactions, we used a model-selection approach based on minimization of BIC41, in which we compared models with and without a given random effect to determine which level of variation was required in the model. In every case, model selection for NutNet data retained variation between sites but excluded variation due to blocks, whereas model selection for BIODEPTH data retained variation between sites and species composition. Inference for the fixed effects was based on 95% confidence intervals. 31. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005). 32. Harpole, W. S. & Tilman, D. Grassland species loss resulting from reduced niche dimension. Nature 446, 791–793 (2007). 33. McNaughton, S. J. Diversity and stability of ecological communities: a comment on the role of empiricism in ecology. Am. Nat. 111, 515–525 (1977). 34. Fargione, J. et al. From selection to complementarity: shifts in the causes of biodiversity–productivity relationships in a long-term biodiversity experiment. Proc. R. Soc. Lond. B 274, 871–876 (2007). 35. Lauenroth, W. K., Hunt, H. W., Swift, D. M. & Singh, J. S. Estimating aboveground net primary production in grasslands: a simulation approach. Ecol. Modell. 33, 297–314 (1986). 36. Oesterheld, M. & McNaughton, S. J. in Methods in Ecosystem Science (eds Sala, O. E., Jackson, R. B., Mooney, H. A. & Howarth, R. W.) 151–157 (Springer, 2000). 37. Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999). 38. Spehn, E. M. et al. Ecosystem effects of biodiversity manipulations in European grasslands. Ecol. Monogr. 75, 37–63 (2005). 39. de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecol. Lett. 16, 617–625 (2013). 40. Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013). 41. Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-Plus (Springer, 2000). 42. R Development Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012). This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved LETTER RESEARCH Extended Data Figure 1 | Locations of the 41 Nutrient Network sites included in this study. Numbers correspond to the ‘site code’ column in Extended Data Table 1. This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved RESEARCH LETTER Extended Data Figure 2 | Effect of fertilization-induced changes in diversity on changes in stability of ANPP and changes in species asynchrony. a, Changes in stability of ANPP (slope and 95% confidence intervals: 0.009 (20.048 to 0.030)); b, changes in species asynchrony (0.012 (20.004 to 0.027)). Neither was related to changes in species richness caused by fertilization. Flat lines represent the overall non-significant mean effects. Nutrient-induced changes were calculated as the average difference per block between fertilized and unmanipulated Nutrient Network plots. Colours correspond to the ‘colour code’ column in Extended Data Table 1. Points are values for individual plots (n 5 117). This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved LETTER RESEARCH Extended Data Table 1 | Additional information on the 41 Nutrient Network study sites * Years of data collection used in the analyses. { Number of blocks in each site. { Mean annual temperature (uC) 1 Mean annual precipitation (mm) I Mean annual range in temperature (uC) "Standard deviation in temperature. # Coefficient of variation of precipitation. q Mean temperature during the wettest four months (uC). This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved RESEARCH LETTER Extended Data Table 2 | Multiple regression evaluating the influence of plant diversity and key biotic and abiotic factors, productivity and climate, on stability of ANPP in our 41 grasslands { Mean annual temperature (uC). 1 Mean annual precipitation (mm). I Mean annual range in temperature (uC). " Standard deviation in temperature. # Coefficient of variation of precipitation. q Mean temperature during the wettest four months (uC). This article is a U.S. government work, and is not subject to copyright in the United States. ©2014 Macmillan Publishers Limited. All rights reserved

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toto
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Attached.

Running Head: ANALYSIS AND SUMMARY

Analysis and summary

Name

Institutional Affiliation

1

ANAYSIS AND SUMMARY

2

Summary

The topic that I will focus on is Eutrophication. The paper mainly strives to unearth how
structural changes in the ecosystem such as an increase in aquatic plants, increased production of
algae, and depletion of aquatic species weakens the stability of the natural grasslands. To extract
clear findings, the article “Eutrophication weakens stabilizing effects of diversity in natural
grasslands” by Hautier et al (2014) forms the basis of making a profound judgment. In a study
conducted by Hautier et al (2014), it was revealed that plant diversity stabilizes productivity
through species asynchrony. Stability effect occurs because a decrease in biomass of some plant
species is normally compensated by increases in other biomasses.

Hautier et al (201...

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Wow this is really good.... didn't expect it. Sweet!!!!

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