Mangrove Restoration: Do We Know Enough?

Mangrove restoration projects have been attempted, with mixed results, throughout the world. In this paper, I first examine goals of existing mangrove restoration projects and determine whether these goals are clear and adequate, and whether or not they account for the full range of biological diversity and ecological processes of mangrove ecosystems. Many restored mangrove forests resemble forest plantations rather than truly integrated ecosystems, but mangrove plantations can be a first step toward mangrove rehabilitation. Mangrove restoration projects that involve associated aquaculture or mariculture operations tend to be more likely to approximate the biological diversity and ecological processes of undisturbed mangrove ecosystems than are projects that focus only on the trees. These integrated restoration projects also provide a higher economic return than do silvicultural projects alone. Second, I briefly assess whether existing ecological data are sufficient to undergird successful restoration of mangal and define criteria for determining whether or not a mangrove ecosystem has been restored successfully. These criteria include characteristics of vegetation (forest) structure, levels of primary production, composition of associated animal communities, and hydrology. Finally, I suggest ways to improve mangrove restoration projects and identify key research needs required to support these efforts. Ecological theories derived from other wetland and upland systems rarely have been applied to either “basic” or “applied” mangrove forest studies, to the detriment of restoration projects, whereas lessons from restoration of the relatively species‐poor mangrove ecosystems could be beneficially applied to restoration projects in other contexts. An international database of mangrove restoration projects would reduce the likelihood that unsuccessful restoration projects would be repeated elsewhere. Clear criteria for evaluating success, greater accessibility of information by managers in the developing world, intensified international cooperation, and application of relevant ecological theories will improve the success rate of mangrove restoration projects.     

What Do We Know About Metal Recycling Rates?

The recycling of metals is widely viewed as a fruitful sustainability strategy, but little information is available on the degree to which recycling is actually taking place. This article provides an overview on the current knowledge of recycling rates for 60 metals. We propose various recycling metrics, discuss relevant aspects of recycling processes, and present current estimates on global end‐of‐life recycling rates (EOL‐RR; i.e., the percentage of a metal in discards that is actually recycled), recycled content (RC), and old scrap ratios (OSRs; i.e., the share of old scrap in the total scrap flow). Because of increases in metal use over time and long metal in‐use lifetimes, many RC values are low and will remain so for the foreseeable future. Because of relatively low efficiencies in the collection and processing of most discarded products, inherent limitations in recycling processes, and the fact that primary material is often relatively abundant and low‐cost (which thereby keeps down the price of scrap), many EOL‐RRs are very low: Only for 18 metals (silver, aluminum, gold, cobalt, chromium, copper, iron, manganese, niobium, nickel, lead, palladium, platinum, rhenium, rhodium, tin, titanium, and zinc) is the EOL‐RR above 50% at present. Only for niobium, lead, and ruthenium is the RC above 50%, although 16 metals are in the 25% to 50% range. Thirteen metals have an OSR greater than 50%. These estimates may be used in considerations of whether recycling efficiencies can be improved; which metric could best encourage improved effectiveness in recycling; and an improved understanding of the dependence of recycling on economics, technology, and other factors.     

Two Decades of River Restoration in California: What Can We Learn?

As part of the National River Restoration Science Synthesis (NRRSS), we developed a summary database of 4,023 stream restoration projects built in California since 1980, from which we randomly selected 44 records for in‐depth interviews with project managers. Despite substantial difficulties in gathering the data, we were able to draw conclusions about current design, implementation, monitoring, and evaluation practices used in California projects and compare them with national trends. Although more than half of the projects for which we conducted interviews were located in watersheds for which a management or assessment plan had been prepared, these plans had a limited impact on site selection. We also found that the state lacks a consistent framework for design, monitoring, and reporting restoration projects, and that although monitoring is far more widespread than the information in the NRRSS summary database would suggest, there are still problems with the type, duration, and reporting of monitoring. The general lack of systematic, objective assessment of completed projects hinders the advance of restoration science.     

What Can Article-Level Metrics Do for You?

Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.The scientific impact of a particular piece of research is reflected in how this work is taken up by the scientific community. The first systematic approach that was used to assess impact, based on the technology available at the time, was to track citations and aggregate them by journal. This strategy is not only no longer necessary—since now we can easily track citations for individual articles—but also, and more importantly, journal-based metrics are now considered a poor performance measure for individual articles [1],[2]. One major problem with journal-based metrics is the variation in citations per article, which means that a small percentage of articles can skew, and are responsible for, the majority of the journal-based citation impact factor, as shown by Campbell [1] for the 2004 Nature Journal Impact Factor. Figure 1 further illustrates this point, showing the wide distribution of citation counts between PLOS Biology research articles published in 2010. PLOS Biology research articles published in 2010 have been cited a median 19 times to date in Scopus, but 10% of them have been cited 50 or more times, and two articles [3],[4] more than 300 times. PLOS Biology metrics are used as examples throughout this essay, and the dataset is available in the supporting information (Data S1). Similar data are available for an increasing number of other publications and organizations.Open in a separate windowFigure 1Citation counts for PLOS Biology articles published in 2010.Scopus citation counts plotted as a probability distribution for all 197 PLOS Biology research articles published in 2010. Data collected May 20, 2013. Median 19 citations; 10% of papers have at least 50 citations.Scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator [2],[5],[6]. To this end, PLOS has collected and displayed a variety of metrics for all its articles since 2009. The array of different categorised article-level metrics (ALMs) used and provided by PLOS as of August 2013 are shown in Figure 2. In addition to citations and usage statistics, i.e., how often an article has been viewed and downloaded, PLOS also collects metrics about: how often an article has been saved in online reference managers, such as Mendeley; how often an article has been discussed in its comments section online, and also in science blogs or in social media; and how often an article has been recommended by other scientists. These additional metrics provide valuable information that we would miss if we only consider citations. Two important shortcomings of citation-based metrics are that (1) they take years to accumulate and (2) citation analysis is not always the best indicator of impact in more practical fields, such as clinical medicine [7]. Usage statistics often better reflect the impact of work in more practical fields, and they also sometimes better highlight articles of general interest (for example, the 2006 PLOS Biology article on the citation advantage of Open Access articles [8], one of the 10 most-viewed articles published in PLOS Biology).Open in a separate windowFigure 2Article-level metrics used by PLOS in August 2013 and their categories.Taken from [10] with permission by the authors.A bubble chart showing all 2010 PLOS Biology articles (Figure 3) gives a good overview of the year''s views and citations, plus it shows the influence that the article type (as indicated by dot color) has on an article''s performance as measured by these metrics. The weekly PLOS Biology publication schedule is reflected in this figure, with articles published on the same day present in a vertical line. Figure 3 also shows that the two most highly cited 2010 PLOS Biology research articles are also among the most viewed (indicated by the red arrows), but overall there isn''t a strong correlation between citations and views. The most-viewed article published in 2010 in PLOS Biology is an essay on Darwinian selection in robots [9]. Detailed usage statistics also allow speculatulation about the different ways that readers access and make use of published literature; some articles are browsed or read online due to general interest while others that are downloaded (and perhaps also printed) may reflect the reader''s intention to look at the data and results in detail and to return to the article more than once.Open in a separate windowFigure 3Views vs. citations for PLOS Biology articles published in 2010.All 304 PLOS Biology articles published in 2010. Bubble size correlates with number of Scopus citations. Research articles are labeled green; all other articles are grey. Red arrows indicate the two most highly cited papers. Data collected May 20, 2013.When readers first see an interesting article, their response is often to view or download it. By contrast, a citation may be one of the last outcomes of their interest, occuring only about 1 in 300 times a PLOS paper is viewed online. A lot of things happen in between these potential responses, ranging from discussions in comments, social media, and blogs, to bookmarking, to linking from websites. These activities are usually subsumed under the term “altmetrics,” and their variety can be overwhelming. Therefore, it helps to group them together into categories, and several organizations, including PLOS, are using the category labels of Viewed, Cited, Saved, Discussed, and Recommended (Figures 2 and ​and4,4, see also [10]).Open in a separate windowFigure 4Article-level metrics for PLOS Biology.Proportion of all 1,706 PLOS Biology research articles published up to May 20, 2013 mentioned by particular article-level metrics source. Colors indicate categories (Viewed, Cited, Saved, Discussed, Recommended), as used on the PLOS website.All PLOS Biology articles are viewed and downloaded, and almost all of them (all research articles and nearly all front matter) will be cited sooner or later. Almost all of them will also be bookmarked in online reference managers, such as Mendeley, but the percentage of articles that are discussed online is much smaller. Some of these percentages are time dependent; the use of social media discussion platforms, such as Twitter and Facebook for example, has increased in recent years (93% of PLOS Biology research articles published since June 2012 have been discussed on Twitter, and 63% mentioned on Facebook). These are the locations where most of the online discussion around published articles currently seems to take place; the percentage of papers with comments on the PLOS website or that have science blog posts written about them is much smaller. Not all of this online discussion is about research articles, and perhaps, not surprisingly, the most-tweeted PLOS article overall (with more than 1,100 tweets) is a PLOS Biology perspective on the use of social media for scientists [11].Some metrics are not so much indicators of a broad online discussion, but rather focus on highlighting articles of particular interest. For example, science blogs allow a more detailed discussion of an article as compared to comments or tweets, and journals themselves sometimes choose to highlight a paper on their own blogs, allowing for a more digestible explanation of the science for the non-expert reader [12]. Coverage by other bloggers also serves the same purpose; a good example of this is one recent post on the OpenHelix Blog [13] that contains video footage of the second author of a 2010 PLOS Biology article [14] discussing the turkey genome.F1000Prime, a commercial service of recommendations by expert scientists, was added to the PLOS Article-Level Metrics in August 2013. We now highlight on the PLOS website when any articles have received at least one recommendation within F1000Prime. We also monitor when an article has been cited within the widely used modern-day online encyclopedia, Wikipedia. A good example of the latter is the Tasmanian devil Wikipedia page [15] that links to a PLOS Biology research article published in 2010 [16]. While a F1000Prime recommendation is a strong endorsement from peer(s) in the scientific community, being included in a Wikipedia page is akin to making it into a textbook about the subject area and being read by a much wider audience that goes beyond the scientific community. PLOS Biology is the PLOS journal with the highest percentage of articles recommended in F1000Prime and mentioned in Wikipedia, but there is only partial overlap between the two groups of articles because they focus on different audiences (Figure 5). These recommendations and mentions in turn show correlations with other metrics, but not simple ones; you can''t assume, for example, that highly cited articles are more likely to be recommended by F1000Prime, so it will be interesting to monitor these trends now that we include this information.Open in a separate windowFigure 5 PLOS Biology articles: sites of recommendation and discussion.Number of PLOS Biology research articles published up to May 20, 2013 that have been recommended by F1000Prime (red) or mentioned in Wikipedia (blue).With the increasing availability of ALM data, there comes a growing need to provide tools that will allow the community to interrogate them. A good first step for researchers, research administrators, and others interested in looking at the metrics of a larger set of PLOS articles is the recently launched ALM Reports tool [17]. There are also a growing number of service providers, including Altmetric.com [18], ImpactStory [19], and Plum Analytics [20] that provide similar services for articles from other publishers.As article-level metrics become increasingly used by publishers, funders, universities, and researchers, one of the major challenges to overcome is ensuring that standards and best practices are widely adopted and understood. The National Information Standards Organization (NISO) was recently awarded a grant by the Alfred P. Sloan Foundation to work on this [21], and PLOS is actively involved in this project. We look forward to further developing our article-level metrics and to having them adopted by other publishers, which hopefully will pave the way to their wide incorporation into research and researcher assessments.     

What Do We Study in Evolutionary Ethnobiology? Defining the Theoretical Basis for a Research Program

Many scientific fields investigate the relationship between humans and nature from different perspectives and with a wide range of questions. These fields include, for example, human ecology, human behavioral ecology, and evolutionary psychology, and they are linked through the fact that they all adopt ecological and/or evolutionary hypotheses to understand this phenomenon. In this paper, we define for the first time the field of evolutionary ethnobiology, which seeks to understand these relationships in a broad perspective, considering an interdisciplinary program that integrates the advances, tools, and insights from different scientific fields. This theoretical and methodological integration is necessary for the formation of theoretical bases in ethnobiology and in other areas that investigate the relationship between humans and nature.     

What Do We Really Know about Cellulose Biosynthesis in Higher Plants?

Cellulose biosynthesis is one of the most important biochemical processes in plant biology. Despite the considerable progress made during the last decade, numerous fundamental questions related to this key process in plant development are outstanding. Numerous models have been proposed through the years to explain the detailed molecular events of cellulose biosynthesis. Almost all models integrate solid experimental data with hypotheses on several of the steps involved in the process. Speculative models are most useful to stimulate further research investigations and bring new exciting ideas to the field. However, it is important to keep their hypothetical nature in mind and be aware of the risk that some undemonstrated hypotheses may progressively become admitted. In this review, we discuss the different steps required for cellulose formation and crystallization, and highlight the most important specific aspects that are supported by solid experimental data.     

What Do We Really Know about Cellulose Biosynthesis in Higher Plants?

Cellulose biosynthesis is one of the most important biochemical processes in plant biology. Despite the considerable progress made during the last decade, numerous fundamental questions related to this key process in plant development are outstanding. Numerous models have been proposed through the years to explain the detailed molecular events of cellulose biosynthesis. Almost all models integrate solid experimental data with hypotheses on several of the steps involved in the process. Speculative models are...     

Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers—landscape features which impede movement between ecologically important areas—where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options.     

What about cultural ecosystems? Opportunities for cultural considerations in the “International Standards for the Practice of Ecological Restoration”

The Society for Ecological Restoration's 2016 (SER) “International Standards for the Practice of Ecological Restoration” is a living document intended to guide restoration projects “anywhere in the world.” Given its intended global scope and in hopes of informing future editions, this document is critically assessed in light of the role people have played in ecosystems around the world. We argue that the Standards has an underlying nature–culture dichotomization that limits its applicability; in qualifying what it calls “cultural ecosystems” for rehabilitation, rather than restoration, the Standards privileges colonial visions of ecological restoration. We also discuss the Standards' representation of the ecological impacts and practices of indigenous groups. Whereas the Standards claims that preindustrial cultural ecosystems exist in states similar to unmodified areas, many historians, anthropologists, and paleoecologists would point out that preindustrial people sometimes had massive environmental impacts through agriculture, hydrological engineering, over‐hunting, living in dense urban environments, transporting species, burning on a scale capable of changing the climate, and other practices. Furthermore, the Standards does not discuss how the cultural goals of indigenous groups fit into the overall picture of ecological restoration. Future drafts of the Standards should more accurately frame the diverse roles people play in nature, and create global standards that account for the validity of cultural goals for ecological restoration.     

Do We Feel the Same Empathy for Loved and Hated Peers?

Empathy allows us to understand and react to other people''s feelings and sensations; we can more accurately judge another person’s situation when we are aware of his/her emotions. Empathy for pain is a good working model of the behavioral and neural processes involved in empathy in general. Although the influence of perspective-taking processes (notably "Self" vs. "Other") on pain rating has been studied, the impact of the degree of familiarity with the person representing the “Other” perspective has not been previously addressed. In the present study, we asked participants to adopt four different perspectives: "Self", "Other-Most-Loved-Familiar", "Other-Most-Hated-Familiar" and "Other-Stranger". The results showed that higher pain ratings were attributed to the Other-Most-Loved-Familiar perspective than to the Self, Other-Stranger and Other-Most-Hated-Familiar perspectives. Moreover, participants were quicker to rate pain for the Other-Most-Loved-Familiar perspective and the Self-perspective than for the other two perspectives. These results for a perspective-taking task therefore more clearly define the role of familiarity in empathy for pain.     

Vitiligo: Where Do We Stand?

Vitiligo is a puzzling disorder characterized by a disappearance of epidermal and/or follicular melanocytes by unknown mechanisms. This very common disorder involving 1–4% of the world population is thus of great importance for the practicing dermatologist. The cellular and molecular mechanisms leading to the destruction of melanocytes in this disorder have not yet been elucidated, making it of major interest for the cell biologist involved in melanocyte research. Recent advances in this field, due largely to the availability of techniques for culturing normal human melanocytes, opened new perspectives in the understanding of vitiligo. Although vitiligo has long been considered a disorder confined to the skin, there is now good evidence that it also involves the extracutaneous compartment of the “melanocyte organ.” It is also clear that vitiligo is not only a melanocyte disorder, but that it also involves cells, such as keratinocytes and Langerhans cells, found in the epidermis and follicular epithelium. The three prevailing theories of the pathogenesis of vitiligo are the immune hypothesis, the neural hypothesis, and the self-destruct hypothesis. New hypotheses suggest that vitiligo may be due to (1) a deficiency in an unidentified melanocyte growth factor, (2) an intrinsic defect of the structure and function of the rough endoplasmic reticulum in vitiligo melanocytes, (3) abnormalities in a putative melatonin receptor on melanocytes and (4) a breakdown in free radical defense in the epidermis. None of these hypotheses has been demonstrated, and according to the available data, it is likely that the loss of epidermal and follicular melanocytes in vitiligo may be the result of several different pathogenetic mechanisms.