Dredging in the Spratly Islands: Gaining Land but Losing Reefs

Coral reefs on remote islands and atolls are less exposed to direct human stressors but are becoming increasingly vulnerable because of their development for geopolitical and military purposes. Here we document dredging and filling activities by countries in the South China Sea, where building new islands and channels on atolls is leading to considerable losses of, and perhaps irreversible damages to, unique coral reef ecosystems. Preventing similar damage across other reefs in the region necessitates the urgent development of cooperative management of disputed territories in the South China Sea. We suggest using the Antarctic Treaty as a positive precedent for such international cooperation.Coral reefs constitute one of the most diverse, socioeconomically important, and threatened ecosystems in the world [1–3]. Coral reefs harbor thousands of species [4] and provide food and livelihoods for millions of people while safeguarding coastal populations from extreme weather disturbances [2,3]. Unfortunately, the world’s coral reefs are rapidly degrading [1–3], with ~19% of the total coral reef area effectively lost [3] and 60% to 75% under direct human pressures [3,5,6]. Climate change aside, this decline has been attributed to threats emerging from widespread human expansion in coastal areas, which has facilitated exploitation of local resources, assisted colonization by invasive species, and led to the loss and degradation of habitats directly and indirectly through fishing and runoff from agriculture and sewage systems [1–3,5–7]. In efforts to protect the world’s coral reefs, remote islands and atolls are often seen as reefs of “hope,” as their isolation and uninhabitability provide de facto protection against direct human stressors, and may help impacted reefs through replenishment [5,6]. Such isolated reefs may, however, still be vulnerable because of their geopolitical and military importance (e.g., allowing expansion of exclusive economic zones and providing strategic bases for military operations). Here we document patterns of reclamation (here defined as creating new land by filling submerged areas) of atolls in the South China Sea, which have resulted in considerable loss of coral reefs. We show that conditions are ripe for reclamation of more atolls, highlighting the need for international cooperation in the protection of these atolls before more unique and ecologically important biological assets are damaged, potentially irreversibly so.Studies of past reclamations and reef dredging activities have shown that these operations are highly deleterious to coral reefs [8,9]. First, reef dredging affects large parts of the surrounding reef, not just the dredged areas themselves. For example, 440 ha of reef was completely destroyed by dredging on Johnston Island (United States) in the 1960s, but over 2,800 ha of nearby reefs were also affected [10]. Similarly, at Hay Point (Australia) in 2006 there was a loss of coral cover up to 6 km away from dredging operations [11]. Second, recovery from the direct and indirect effects of dredging is slow at best and nonexistent at worst. In 1939, 29% of the reefs in Kaneohe Bay (United States) were removed by dredging, and none of the patch reefs that were dredged had completely recovered 30 years later [12]. In Castle Harbour (Bermuda), reclamation to build an airfield in the early 1940s led to limited coral recolonization and large quantities of resuspended sediments even 32 years after reclamation [13]; several fish species are claimed extinct as a result of this dredging [14,15]. Such examples and others led Hatcher et al. [8] to conclude that dredging and land clearing, as well as the associated sedimentation, are possibly the most permanent of anthropogenic impacts on coral reefs.The impacts of dredging for the Spratly Islands are of particular concern because the geographical position of these atolls favors connectivity via stepping stones for reefs over the region [16–19] and because their high biodiversity works as insurance for many species. In an extensive review of the sparse and limited data available for the region, Hughes et al. [20] showed that reefs on offshore atolls in the South China Sea were overall in better condition than near-shore reefs. For instance, by 2004 they reported average coral covers of 64% for the Spratly Islands and 68% for the Paracel Islands. By comparison, coral reefs across the Indo-Pacific region in 2004 had average coral covers below 25% [21]. Reefs on isolated atolls can still be prone to extensive bleaching and mortality due to global climate change [22] and, in the particular case of atolls in the South China Sea, the use of explosives and cyanine [20]. However, the potential for recovery of isolated reefs to such stressors is remarkable. Hughes et al. [20] documented, for instance, how coral cover in several offshore reefs in the region declined from above 80% in the early 1990s to below 6% by 1998 to 2001 (due to a mixture of El Niño and damaging fishing methods that make use of cyanine and explosives) but then recovered to 30% on most reefs and up to 78% in some reefs by 2004–2008. Another important attribute of atolls in the South China Sea is the great diversity of species. Over 6,500 marine species are recorded for these atolls [23], including some 571 reef coral species [24] (more than half of the world’s known species of reef-building corals). The relatively better health and high diversity of coral reefs in atolls over the South China Sea highlights the uniqueness of such reefs and the important roles they may play for reefs throughout the entire region. Furthermore, these atolls are safe harbor for some of the last viable populations of highly threatened species (e.g., Bumphead Parrotfish [Bolbometopon muricatum] and several species of sawfishes [Pristis, Anoxypristis]), highlighting how dredging in the South China Sea may threaten not only species with extinction but also the commitment by countries in the region to biodiversity conservation goals such as the Convention of Biological Diversity Aichi Targets and the United Nations Sustainable Development Goals.Recently available remote sensing data (i.e., Landsat 8 Operational Land Imager and Thermal Infrared Sensors Terrain Corrected images) allow quantification of the sharp contrast between the gain of land and the loss of coral reefs resulting from reclamation in the Spratly Islands (Fig 1). For seven atolls recently reclaimed by China in the Spratly Islands (names provided in Fig 1D, S1 Data for details); the area of reclamation is the size of visible areas in Landsat band 6, as prior to reclamation most of the atolls were submerged, with the exception of small areas occupied by a handful of buildings on piers (note that the amount of land area was near zero at the start of the reclamation; Fig 1C, S1 Data). The seven reclaimed atolls have effectively lost ~11.6 km² (26.9%) of their reef area for a gain of ~10.7 km² of land (i.e., >75 times increase in land area) from February 2014 to May 2015 (Fig 1C). The area of land gained was smaller than the area of reef lost because reefs were lost not only through land reclamation but also through the deepening of reef lagoons to allow boat access (Fig 1B). Similar quantification of reclamation by other countries in the South China Sea (Fig 1Reclamation leads to gains of land in return for losses of coral reefs: A case example of China’s recent reclamation in the Spratly Islands.Table 1List of reclaimed atolls in the Spratly Islands and the Paracel Islands.The impacts of reclamation on coral reefs are likely more severe than simple changes in area, as reclamation is being achieved by means of suction dredging (i.e., cutting and sucking materials from the seafloor and pumping them over land). With this method, reefs are ecologically degraded and denuded of their structural complexity. Dredging and pumping also disturbs the seafloor and can cause runoff from reclaimed land, which generates large clouds of suspended sediment [11] that can lead to coral mortality by overwhelming the corals’ capacity to remove sediments and leave corals susceptible to lesions and diseases [7,9,25]. The highly abrasive coralline sands in flowing water can scour away living tissue on a myriad of species and bury many organisms beyond their recovery limits [26]. Such sedimentation also prevents new coral larvae from settling in and around the dredged areas, which is one of the main reasons why dredged areas show no signs of recovery even decades after the initial dredging operations [9,12,13]. Furthermore, degradation of wave-breaking reef crests, which make reclamation in these areas feasible, will result in a further reduction of coral reefs’ ability to (1) self-repair and protect against wave abrasion [27,28] (especially in a region characterized by typhoons) and (2) keep up with rising sea levels over the next several decades [29]. This suggests that the new islands would require periodic dredging and filling, that these reefs may face chronic distress and long-term ecological damage, and that reclamation may prove economically expensive and impractical.The potential for land reclamation on other atolls in the Spratly Islands is high, which necessitates the urgent development of cooperative management of disputed territories in the South China Sea. First, the Spratly Islands are rich in atolls with similar characteristics to those already reclaimed (Fig 1D); second, there are calls for rapid development of disputed territories to gain access to resources and increase sovereignty and military strength [30]; and third, all countries with claims in the Spratly Islands have performed reclamation in this archipelago (20]. One such possibility is the generation of a multinational marine protected area [16,17]. Such a marine protected area could safeguard an area of high biodiversity and importance to genetic connectivity in the Pacific, in addition to promoting peace in the region (extended justification provided by McManus [16,17]). A positive precedent for the creation of this protected area is that of Antarctica, which was also subject to numerous overlapping claims and where a recently renewed treaty froze national claims, preventing large-scale ecological damage while providing environmental protection and areas for scientific study. Development of such a legal framework for the management of the Spratly Islands could prevent conflict, promote functional ecosystems, and potentially result in larger gains (through spillover, e.g. [31]) for all countries involved.     

Using Sex to Cure the Genome

The diversification of prokaryotes is accelerated by their ability to acquire DNA from other genomes. However, the underlying processes also facilitate genome infection by costly mobile genetic elements. The discovery that cells can uptake DNA by natural transformation was instrumental to the birth of molecular biology nearly a century ago. Surprisingly, a new study shows that this mechanism could efficiently cure the genome of mobile elements acquired through previous sexual exchanges.Horizontal gene transfer (HGT) is a key contributor to the genetic diversification of prokaryotes [1]. Its frequency in natural populations is very high, leading to species’ gene repertoires with relatively few ubiquitous (core) genes and many low-frequency genes (present in a small proportion of individuals). The latter are responsible for much of the phenotypic diversity observed in prokaryotic species and are often encoded in mobile genetic elements that spread between individual genomes as costly molecular parasites. Hence, HGT of interesting traits is often carried by expensive vehicles.The net fitness gain of horizontal gene transfer depends on the genetic background of the new host, the acquired traits, the fitness cost of the mobile element, and the ecological context [2]. A study published in this issue of PLOS Biology [3] proposes that a mechanism originally thought to favor the acquisition of novel DNA—natural transformation—might actually allow prokaryotes to clean their genome of mobile genetic elements.Natural transformation allows the uptake of environmental DNA into the cell (Fig 1). It differs markedly from the other major mechanisms of HGT by depending exclusively on the recipient cell, which controls the expression of the transformation machinery and favors exchanges with closely related taxa [4]. DNA arrives at the cytoplasm in the form of small single-stranded fragments. If it is not degraded, it may integrate the genome by homologous recombination at regions of high sequence similarity (Fig 1). This results in allelic exchange between a fraction of the chromosome and the foreign DNA. Depending on the recombination mechanisms operating in the cell and on the extent of sequence similarity between the transforming DNA and the genome, alternative recombination processes may take place. Nonhomologous DNA flanked by regions of high similarity can be integrated by double homologous recombination at the edges (Fig 1E). Mechanisms mixing homologous and illegitimate recombination require less strict sequence similarity and may also integrate nonhomologous DNA in the genome [5]. Some of these processes lead to small deletions of chromosomal DNA [6]. These alternative recombination pathways allow the bacterium to lose and/or acquire novel genetic information.Open in a separate windowFig 1Natural transformation and its outcomes.The mechanism of environmental DNA uptake brings into the cytoplasm small single-stranded DNA fragments (A). Earlier models for the raison d’être of natural transformation have focused on the role of DNA as a nutrient (B), as a breaker of genetic linkage (C), or as a substrate for DNA repair (D). The chromosomal curing model allows the removal of mobile elements by recombination between conserved sequences at their extremities (E). The model is strongly affected by the size of the incoming DNA fragments, since the probability of uptake of a mobile element rapidly decreases with the size of the element and of the incoming fragments (F). This leads to a bias towards the deletion of mobile elements by recombination, especially the largest ones. In spite of this asymmetry, some mobile elements can integrate the genome via natural transformation, following homologous recombination between large regions of high sequence similarity (G) or homology-facilitated illegitimate recombination in short regions of sequence similarity (H).Natural transformation was the first described mechanism of HGT. Its discovery, in the first half of the 20th century, was instrumental in demonstrating that DNA is the support of genetic information. This mechanism is also regularly used to genetically engineer bacteria. Researchers have thus been tantalized by the lack of any sort of consensus regarding the raison d’être of natural transformation.Croucher, Fraser, and colleagues propose that the small size of recombining DNA fragments arising from transformation biases the outcome of recombination towards the deletion of chromosomal genetic material (Fig 1F). Incoming DNA carrying the core genes that flank a mobile element, but missing the element itself, can provide small DNA fragments that become templates to delete the element from the recipient genome (Fig 1E). The inverse scenario, incoming DNA carrying the core genes and a mobile element absent from the genome, is unlikely due to the mobile element being large and the recombining transformation fragments being small. Importantly, this mechanism most efficiently removes the loci at low frequency in the population because incoming DNA is more likely to lack such intervening sequences when these are rare. Invading mobile genetic elements are initially at low frequencies in populations and will be frequently deleted by this mechanism. Hence, recombination will be strongly biased towards the deletion or inactivation of large mobile elements such as phages, integrative conjugative elements, and pathogenicity islands. Simulations at a population scale show that transformation could even counteract the horizontal spread of mobile elements.An obvious limit of natural transformation is that it can''t cope with mobile genetic elements that rapidly take control of the cell, such as virulent phages, or remain extra-chromosomal, such as plasmids. Another limit of transformation is that it facilitates the acquisition of costly mobile genetic elements [7,8], especially if these are small. When these elements replicate in the genome, as is the case of transposable elements, they may become difficult to remove by subsequent events of transformation. Further work will be needed to quantify the costs associated with such infections.Low-frequency adaptive genes might be deleted through transformation in the way proposed for mobile genetic elements. However, adaptive genes rise rapidly to high frequency in populations, becoming too frequent to be affected by transformation. Interestingly, genetic control of transformation might favor the removal of mobile elements incurring fitness costs while preserving those carrying adaptive traits [3]. Transformation could, thus, effectively cure chromosomes and other replicons of deleterious mobile genetic elements integrated in previous events of horizontal gene transfer while preserving recently acquired genes of adaptive value.Prokaryotes encode an arsenal of immune systems to prevent infection by mobile elements and several regulatory systems to repress their expression [9]. Under the new model (henceforth named the chromosomal curing model), transformation has a key, novel position in this arsenal because it allows the expression of the incoming DNA while subsequently removing deleterious elements from the genome.Mobile elements encode their own tools to evade the host immune systems [9]. Accordingly, they search to affect natural transformation [3]. Some mobile genetic elements integrate at, and thus inactivate, genes encoding the machineries required for DNA uptake or recombination. Other elements express nucleases that degrade exogenous DNA (precluding its uptake). These observations suggest an arms race evolutionary dynamics between the host, which uses natural transformation to cure its genome, and mobile genetic elements, which target these functions for their own protection. This gives further credibility to the hypothesis that transformation is a key player in the intra-genomic conflicts between prokaryotes and their mobile elements.Previous studies have proposed alternative explanations for the evolution of natural transformation, including the possibility that it was caused by selection for allelic recombination and horizontal gene transfer [10], for nutrient acquisition [11], or for DNA repair [12]. The latter hypothesis has recently enjoyed regained interest following observations that DNA-damage agents induce transformation [13,14], along with intriguing suggestions that competence might be advantageous even in the absence of DNA uptake [15,16]. The hypothesis that transformation evolved to acquire nutrients has received less support in recent years.Two key specific traits of transformation—host genetic control of the process and selection for conspecific DNA—share some resemblance with recombination processes occurring during sexual reproduction. Yet, the analogy between the two processes must be handled with care because transformation results, at best, in gene conversion of relatively small DNA fragments from another individual. The effect of sexual reproduction on genetic linkage is thought to be advantageous in the presence of genetic drift or weak and negative or fluctuating epistasis [17]. Interestingly, these conditions could frequently be met by bacterial pathogens [18], which might explain why there are so many naturally transformable bacteria among human pathogens, such as Streptococcus pneumoniae, Helicobacter pylori, Staphylococcus aureus, Haemophilus influenzae, or Neisseria spp. The most frequent criticism to the analogy between transformation and sexual reproduction is that environmental DNA from dead individuals is unlikely to carry better alleles than the living recipient [11]. This difficulty is circumvented in bacteria that actively export copies of their DNA to the extracellular environment. Furthermore, recent theoretical studies showed that competence could be adaptive even when the DNA originates from individuals with lower fitness alleles [19,20]. Mathematically speaking, sexual exchanges with the dead might be better than no exchanges at all.The evaluation of the relative merits of the different models aiming to explain the raison d’être of natural transformation is complicated because they share several predictions. For example, the induction of competence under maladapted environments can be explained by the need for DNA repair (more DNA damage in these conditions), by selection for adaptation (through recombination or HGT), and by the chromosomal curing model because mobile elements are more active under such conditions (leading to more intense selection for their inactivation). Some of the predictions of the latter model—the rapid diversification and loss of mobile elements and their targeting of the competence machinery—can also be explained by models involving competition between mobile elements and their antagonistic association with the host. One of the great uses of mathematical models in biology resides in their ability to pinpoint the range of parameters and conditions within which each model can apply. The chromosomal curing model remains valid under broad ranges of variation of many of its key variables. This might not be the case for alternative models [3].While further theoretical work will certainly help to specify the distinctive predictions of each model, realistic experimental evolutionary studies will be required to test them. Unfortunately, the few pioneering studies on this topic have given somewhat contradictory conclusions. Some showed that natural transformation was beneficial to bacteria adapting under suboptimal environments (e.g., in times of starvation or in stressful environments) [21,22], whereas others showed it was most beneficial under exponential growth and early stationary phase [23]. Finally, at least one study showed a negative effect of transformation on adaptation [24]. Part of these discrepancies might reveal differences between species, which express transformation under different conditions. They might also result from the low intraspecies genetic diversity in these experiments, in which case the use of more representative communities might clarify the conditions favoring transformation.Macroevolutionary studies on natural transformation are hindered by the small number of prokaryotes known to be naturally transformable (82 species, following [25]). In itself, this poses a challenge: if transformation is adaptive, then why does it seem to be so rare? The benefits associated with deletion of mobile elements, with functional innovation, or with DNA repair seem sufficiently general to affect many bacterial species. The trade-offs between cost and benefit of transformation might lead to its selection only when mobile elements are particularly deleterious for a given species or when species face particular adaptive challenges. According to the chromosomal curing model, selection for transformation would be stronger in highly structured environments or when recombination fragments are small. There is also some evidence that we have failed to identify numerous naturally transformable prokaryotes, in which case the question above may lose part of its relevance. Many genomes encode key components of the transformation machinery, suggesting that this process might be more widespread than currently acknowledged [25]. As an illustration, the ultimate model for research in microbiology—Escherichia coli—has only recently been shown to be naturally transformable; the conditions leading to the expression of this trait remain unknown [26].The chromosomal curing model might contribute to explaining other mechanisms shaping the evolution of prokaryotic genomes beyond the removal of mobile elements. Transformation-mediated deletion of genetic material, especially by homology-facilitated illegitimate recombination (Fig 1H), could remove genes involved in the mobility of the genetic elements, facilitating the co-option by the host of functions encoded by mobile genetic elements. Several recent studies have pinpointed the importance of such domestication processes in functional innovation and bacterial warfare [27]. The model might also be applicable to other mechanisms that transfer small DNA fragments between cells. These processes include gene transfer agents [28], extracellular vesicles [29], and possibly nanotubes [30]. The chromosomal curing model might help unravel their ecological and evolutionary impact.     

Response to “Vast (but avoidable) underestimation of global biodiversity”

This Formal Comment provides clarifications on the authors’ recent estimates of global bacterial diversity and the current status of the field, and responds to a Formal Comment from John Wiens regarding their prior work.

We welcome Wiens’ efforts to estimate global animal-associated bacterial richness and thank him for highlighting points of confusion and potential caveats in our previous work on the topic [1]. We find Wiens’ ideas worthy of consideration, as most of them represent a step in the right direction, and we encourage lively scientific discourse for the advancement of knowledge. Time will ultimately reveal which estimates, and underlying assumptions, came closest to the true bacterial richness; we are excited and confident that this will happen in the near future thanks to rapidly increasing sequencing capabilities. Here, we provide some clarifications on our work, its relation to Wiens’ estimates, and the current status of the field.First, Wiens states that we excluded animal-associated bacterial species in our global estimates. However, thousands of animal-associated samples were included in our analysis, and this was clearly stated in our main text (second paragraph on page 3).Second, Wiens’ commentary focuses on “S1 Text” of our paper [1], which was rather peripheral, and, hence, in the Supporting information. S1 Text [1] critically evaluated the rationale underlying previous estimates of global bacterial operational taxonomic unit (OTU) richness by Larsen and colleagues [2], but the results of S1 Text [1] did not in any way flow into the analyses presented in our main article. Indeed, our estimates of global bacterial (and archaeal) richness, discussed in our main article, are based on 7 alternative well-established estimation methods founded on concrete statistical models, each developed specifically for richness estimates from multiple survey data. We applied these methods to >34,000 samples from >490 studies including from, but not restricted to, animal microbiomes, to arrive at our global estimates, independently of the discussion in S1 Text [1].Third, Wiens’ commentary can yield the impression that we proposed that there are only 40,100 animal-associated bacterial OTUs and that Cephalotes in particular only have 40 associated bacterial OTUs. However, these numbers, mentioned in our S1 Text [1], were not meant to be taken as proposed point estimates for animal-associated OTU richness, and we believe that this was clear from our text. Instead, these numbers were meant as examples to demonstrate how strongly the estimates of animal-associated bacterial richness by Larsen and colleagues [2] would decrease simply by (a) using better justified mathematical formulas, i.e., with the same input data as used by Larsen and colleagues [2] but founded on an actual statistical model; (b) accounting for even minor overlaps in the OTUs associated with different animal genera; and/or (c) using alternative animal diversity estimates published by others [3], rather than those proposed by Larsen and colleagues [2]. Specifically, regarding (b), Larsen and colleagues [2] (pages 233 and 259) performed pairwise host species comparisons within various insect genera (for example, within the Cephalotes) to estimate on average how many bacterial OTUs were unique to each host species, then multiplied that estimate with their estimated number of animal species to determine the global animal-associated bacterial richness. However, since their pairwise host species comparisons were restricted to congeneric species, their estimated number of unique OTUs per host species does not account for potential overlaps between different host genera. Indeed, even if an OTU is only found “in one” Cephalotes species, it might not be truly unique to that host species if it is also present in members of other host genera. To clarify, we did not claim that all animal genera can share bacterial OTUs, but instead considered the implications of some average microbiome overlap (some animal genera might share no bacteria, and other genera might share a lot). The average microbiome overlap of 0.1% (when clustering bacterial 16S sequences into OTUs at 97% similarity) between animal genera used in our illustrative example in S1 Text [1] is of course speculative, but it is not unreasonable (see our next point). A zero overlap (implicitly assumed by Larsen and colleagues [2]) is almost certainly wrong. One goal of our S1 Text [1] was to point out the dramatic effects of such overlaps on animal-associated bacterial richness estimates using “basic” mathematical arguments.Fourth, Wiens’ commentary could yield the impression that existing data are able to tell us with sufficient certainty when a bacterial OTU is “unique” to a specific animal taxon. However, so far, the microbiomes of only a minuscule fraction of animal species have been surveyed. One can thus certainly not exclude the possibility that many bacterial OTUs currently thought to be “unique” to a certain animal taxon are eventually also found in other (potentially distantly related) animal taxa, for example, due to similar host diets and or environmental conditions [4–7]. As a case in point, many bacteria in herbivorous fish guts were found to be closely related to bacteria in mammals [8], and Song and colleagues [6] report that bat microbiomes closely resemble those of birds. The gut microbiome of caterpillars consists mostly of dietary and environmental bacteria and is not species specific [4]. Even in animal taxa with characteristic microbiota, there is a documented overlap across host species and genera. For example, there are a small number of bacteria consistently and specifically associated with bees, but these are found across bee genera at the level of the 99.5% similar 16S rRNA OTUs [5]. To further illustrate that an average microbiome overlap between animal taxa at least as large as the one considered in our S1 Text (0.1%) [1] is not unreasonable, we analyzed 16S rRNA sequences from the Earth Microbiome Project [6,9] and measured the overlap of microbiota originating from individuals of different animal taxa. We found that, on average, 2 individuals from different host classes (e.g., 1 mammalian and 1 avian sample) share 1.26% of their OTUs (16S clustered at 100% similarity), and 2 individuals from different host genera belonging to the same class (e.g., 2 mammalian samples) share 2.84% of their OTUs (methods in S1 Text of this response). A coarser OTU threshold (e.g., 97% similarity, considered in our original paper [1]) would further increase these average overlaps. While less is known about insect microbiomes, there is currently little reason to expect a drastically different picture there, and, as explained in our S1 Text [1], even a small average microbiome overlap of 0.1% between host genera would strongly limit total bacterial richness estimates. The fact that the accumulation curve of detected bacterial OTUs over sampled insect species does not yet strongly level off says little about where the accumulation curve would asymptotically converge; rigorous statistical methods, such as the ones used for our global estimates [1], would be needed to estimate this asymptote.Lastly, we stress that while the present conversation (including previous estimates by Louca and colleagues [1], Larsen and colleagues [2], Locey and colleagues [10], Wiens’ commentary, and this response) focuses on 16S rRNA OTUs, it may well be that at finer phylogenetic resolutions, e.g., at bacterial strain level, host specificity and bacterial richness are substantially higher. In particular, future whole-genome sequencing surveys may well reveal the existence of far more genomic clusters and ecotypes than 16S-based OTUs.     

Peer-Led Team Learning Helps Minority Students Succeed

Active learning methods have been shown to be superior to traditional lecture in terms of student achievement, and our findings on the use of Peer-Led Team Learning (PLTL) concur. Students in our introductory biology course performed significantly better if they engaged in PLTL. There was also a drastic reduction in the failure rate for underrepresented minority (URM) students with PLTL, which further resulted in closing the achievement gap between URM and non-URM students. With such compelling findings, we strongly encourage the adoption of Peer-Led Team Learning in undergraduate Science, Technology, Engineering, and Mathematics (STEM) courses.Recent, extensive meta-analysis of over a decade of education research has revealed an overwhelming consensus that active learning methods are superior to traditional, passive lecture, in terms of student achievement in post-secondary Science, Technology, Engineering, and Mathematics (STEM) courses [1]. In light of such clear evidence that traditional lecture is among the least effective modes of instruction, many institutions have been abandoning lecture in favor of “flipped” classrooms and active learning strategies. Regrettably, however, STEM courses at most universities continue to feature traditional lecture as the primary mode of instruction.Although next-generation active learning classrooms are becoming more common, large instructor-focused lecture halls with fixed seating are still the norm on most campuses—including ours, for the time being. While there are certainly ways to make learning more active in an amphitheater, peer-interactive instruction is limited in such settings. Of course, laboratories accompanying lectures often provide more active learning opportunities. But in the wake of commendable efforts to increase rigorous laboratory experiences at the sophomore and junior levels at Syracuse University, a difficult decision was made for the two-semester, mixed-majors introductory biology sequence: the lecture sections of the second semester course were decoupled from the laboratory component, which was made optional. There were good reasons for this change, from both departmental and institutional perspectives. However, although STEM students not enrolling in the lab course would arguably be exposed to techniques and develop foundational process skills in the new upper division labs, we were concerned about the implications for achievement among those students who would opt out of the introductory labs. Our concerns were apparently warranted, as students who did not take the optional lab course, regardless of prior achievement, earned scores averaging a letter grade lower than those students who enrolled in the lab. However, students who opted out of the lab but engaged in Peer-Led Team Learning (PLTL) performed at levels equivalent to students who also took the lab course [2].Peer-Led Team Learning is a well-defined active learning model involving small group interactions between students, and it can be used along with or in place of the traditional lecture format that has become so deeply entrenched in university systems (Fig 1, adapted from [3]). PLTL was originally designed and implemented in undergraduate chemistry courses [4,5], and it has since been implemented in other undergraduate science courses, such as general biology and anatomy and physiology [6,7]. Studies on the efficacy of PLTL have shown improvements in students’ grade performance, attitudes, retention in the course [6–11], conceptual reasoning [12], and critical thinking [13], though findings related to the critical thinking benefits for peer leaders have not been consistent [14].Open in a separate windowFig 1The PLTL model.In the PLTL workshop model, students work in small groups of six to eight students, led by an undergraduate peer leader who has successfully completed the same course in which their peer-team students are currently enrolled. After being trained in group leadership methods, relevant learning theory, and the conceptual content of the course, peer leaders (who serve as role models) work collaboratively with an education specialist and the course instructor to facilitate small group problem-solving. Leaders are not teachers. They are not tutors. They are not considered to be experts in the content, and they are not expected to provide answers to the students in the workshop groups. Rather, they help mentor students to actively construct their own understanding of concepts.     

Orienting the Interaction Compass: Resource Availability as a Major Driver of Context Dependence

Life on earth is enormously diverse, in part because each individual engages in countless interactions with its biotic and abiotic environment during its lifetime. Not only are there many such interactions, but any given interaction of each individual with, say, its neighbor or a nutrient could lead to a different effect on its fitness and on the dynamics of the population of which it is a member. Predicting those effects is an enduring challenge to the field of ecology. Using a simple laboratory system, Hoek and colleagues present evidence that resource availability can be a primary driver of variation between interactions. Their results suggest that a complex continuum of interaction outcomes can result from the simple combined effects of nutrient availability and density-dependent population dynamics. The future is rich with potential to integrate tractable experimental systems like theirs with hypotheses derived from studies of interactions in natural communities.The science of ecology is plagued or elevated (depending on your perspective) by the tendency for interactions between organisms and their environment to vary in space and time, with differing consequences for behavior, physiology, and/or fitness. This variation, known as context dependence, affects biotic and abiotic interactions alike and frustrates predictive efforts. For example, to determine how herbivory affects a plant population, you have to predict not only (a) how tissue loss will affect plant fitness but also (b) how much tissue will be lost (which depends on the density and identity of herbivores) and (c) how difficult that tissue will be to replace (which depends on resource availability). Herbivore communities and resource availability thus provide the “context” for plant tissue loss, such that herbivory can matter "hardly at all" or "a whole lot" depending on where you are and when you look.Mutualisms—i.e., interactions with reciprocal fitness benefits (Box 1)—were early poster children for context dependence [1], in part because it seemed difficult to reconcile cooperative behavior with the selective pressure to minimize interaction costs [2]. Such selection can destabilize mutualism by favoring the evolution of exploiters (or "cheaters"), whose effects on their interacting partners are dampened or even reversed (i.e., resulting in parasitism; Box 1). Compounding this paradox still further, some mutualisms occur within a trophic level, where substantial niche overlap between partners also renders them potential competitors. Recent work on microbes nevertheless suggests that mutualism readily evolves between partners at the same trophic level under certain environmental conditions [3]. Work on positive interactions between plants had in fact previously suggested a general "stress gradient hypothesis" for predicting these context-dependent outcomes: interactions should transition from negative to positive along gradients of increasing environmental stress [4]. Although derived from plant community ecology, the stress gradient hypothesis has recently gained traction in diverse mutualisms [5–8]. So far, this concordance is more about pattern than process; elucidating process is, however, ultimately essential to understanding context dependence.

Box 1. The Interaction Compass

Interactions are usually defined by the direction in which they affect the interactors, be they species, strains, or individuals. Even as variation in interspecific interactions first came into focus [1,9], it was clear that both the strength and the sign of interactions shifted back and forth along a continuum (Fig 1). The center of the interaction compass (see [10,11]) is sometimes called neutralism, but this box classifies any interaction where a fitness effect does not occur. Although the interaction compass is typically shown with only two species for the purposes of illustration, all species are involved in networks of interactions, and indirect interactions—defined where one species affects another by way of a third species or pathway—are ubiquitous in ecological communities and can rival direct interactions in their strength [e.g., 12]. The variety of terms and their distinct historical origins can lead to some ambiguity, as is the case with mutualism and facilitation [13]. Facilitation does not appear in the interaction compass (Fig 1), but it is associated with some of the earliest research on positive interactions across environmental gradients and with the stress gradient hypothesis in particular [4]. The term arises from 20th-century plant community ecology and refers either to any interaction where one species modifies the environment in a way that is positive for a neighboring species or specifically to positive interactions within a trophic level. Relevant here, until the recent surge of interest in microbe-microbe interactions, the term mutualism typically referred to interactions between trophic levels, where the competition outcome (––) is unlikely because the interactors do not overlap substantially in their niche requirements. It is common to speak instead of the mutualism-parasitism continuum. Although microbes fit perhaps only uncomfortably into the trophic boxes defined on the basis of macroorganism interactions, most cross-feeding mutualisms occur within a trophic level and thus could be thought of as examples of both mutualism and facilitation, with outcomes ranging around the full compass, from mutualism to competition and back to mutualism again.Open in a separate windowFig 1The interaction compass.A two-species interaction is illustrated with the terms defining each of the differently signed outcomes; the signs indicate individual fitness or population growth rate. A positive (+) sign thus indicates a positive effect of the interaction on the individual or population, a zero (0) sign indicates no effect, and a negative (–) sign indicates a negative effect. Moving away from the center increases the magnitude of the net effect of the interaction.One relatively straightforward path by which an increase in stress can lead to stronger mutualism is when the interaction involves a direct exchange of the environmentally limiting resource. In North American grasslands, grasses associate with arbuscular mycorrhizal fungi, which exchange soil nutrients for carbon fixed by the grass. The fungi can deliver both phosphorus and nitrogen, increasing grass uptake of whichever nutrient is least available in a given soil [5]. Although this seems like a good trick, we cannot characterize the outcome of the grass''s interaction with the fungi on the basis of nutrient uptake (the benefit) alone. The delivery of carbon by the grass to the fungi (the cost), and the net balance of trade (benefit−cost), is key. In this example, the grass receives a net benefit (increased biomass) from interacting with the fungi in phosphorus-poor soil but not in nitrogen-poor soil [5]. The fungi thus seem to be parasites in nitrogen-poor soil, but, interestingly, even that interaction is less negative for grasses in soils with less nitrogen [5]. This example suggests potentially broad relevance for the stress gradient hypothesis across the continuum of interaction types (Box 1) (Fig 1) but also highlights how the balance of trade determines ecological outcomes. To predict the outcome of any given interaction, therefore, we need to understand how both the benefits and the costs of interactions depend on an organism''s environment. This is a challenging task, requiring integrative understanding of organismal physiology, axes of environmental variation, and the nature of biotic interactions, as well as of the feedbacks between organismal ecology and evolution.The diversity and experimental tractability of microorganisms, as well as their fundamental role in life on earth, make them appealing systems for studying context dependence in its multiple dimensions. The potential for mutualism among microbes and between microbes and their multicellular hosts is receiving unprecedented attention as the diverse and important roles of the human gut microbiome come into sharp relief. As field-based studies of macroorganism interactions move past the recognition of context dependence to a deliberate focus on its drivers and mechanisms [14], laboratory-based studies of microbes are, in parallel, moving past debates about the "typical" nature of microbial interactions [15,16] to focus on how and why interaction outcomes vary across environmental gradients [3,17].In this issue of PLOS Biology, Hoek and colleagues show that interactions between two cross-feeding yeast strains can transition across nearly a full continuum of outcomes with simple variation in environmental nutrient concentration [18]. Cross-feeding microbes are those with similar metabolic requirements whose metabolic pathways are complementary, either because of a "leaky" byproduct system whereby some metabolites end up in the environment [15] or because of costly, cooperative exchange [19]. In the Hoek et al. study, the investigators used strains of cross-feeding yeast engineered to differ in amino acid production: one strain lacks leucine production but overproduces tryptophan (Leu⁻), and the other lacks tryptophan production but overproduces leucine (Trp⁻). By varying the quantity of leucine and tryptophan in the environment in a constant ratio, the investigators produced a continuum of interaction outcomes, from low-amino-acid environments that exhibit obligate mutualism to high-amino-acid environments that exhibit strong competition. They go on to show that many of these dynamics can be recovered with a remarkably simple model of each strain''s population growth, primarily depending only on the quantity of environmental amino acids and the population densities of the two strains. The complete range of empirically determined qualitative change in the interaction is mirrored by this simple model, which suggests that the outcomes of interactions that depend on resource exchange (including most mutualisms! [20]) can be predicted to an impressive degree by measuring the availability of that resource, the population densities of the interacting species, and their intrinsic growth rates.Returning to our grass-fungi interaction from above, however, we recall that the benefits of interacting (receiving the missing amino acid in the case of these yeasts) are only one side of the coin. The costs of interacting are what underlie the conflicts of interest that threaten mutualism stability and can lead to increasingly negative interactions over evolutionary time. In the Hoek et al. study, the costs of overproducing the amino acid that is consumed by the other strain are modeled only implicitly in the intrinsic growth rate (r), which is determined by growing each strain in monoculture with unlimited amino acids. The major discrepancy between their model and the empirical data, however, is that the model incorrectly predicts a much larger range of amino-acid concentrations at which the Trp⁻ strain is expected to outperform the Leu⁻ strain in both monoculture and co-culture. Interestingly, the Trp⁻ strain performs particularly poorly when it is co-cultured with the Leu⁻ strain, except at very high levels of environmental amino-acid availability. It is tempting to speculate that this discrepancy is caused by the model''s lack of an explicit density-dependent cost of leucine production, which would be exacerbated when the Leu⁻ strain is performing well. Going forward, it should be possible to merge population dynamic models that include such a cost [21] with an explicit term for resource availability to see how well these models predict dynamics in a variety of empirical systems.Rapid, ongoing global change presents one compelling reason to determine how resource availability underpins interactions, and the Hoek et al. study also sheds some needed light here. Using their model, they determine distinct early-warning "signatures" of imminent population collapse for co-cultures at very low levels of amino acids (think, e.g., drought), in which both strains go extinct upon the collapse of the obligate mutualism, and at very high levels of amino acids (think, e.g., nutrient pollution), in which competitive exclusion leads to the extinction of the slower growing strain. The collapse of populations engaging in obligate mutualism is predicted when the ratio of the population densities of the two strains becomes stable much more quickly than the total population size (particularly at small population sizes) and vice versa for competitive exclusion. In contrast, in healthy populations, the ratio of the strains and the total population size become stable at approximately equal speeds. As the authors note, this result suggests that we can predict how close one or both interacting species are to extinction by monitoring their comparative population dynamics.This is an exciting prospect, but how easy is it to monitor the population dynamics of interacting species outside the laboratory? Monitoring populations of long-lived species in the field is inherently difficult, and, historically, less attention has been paid to determining how the environment affects populations of interacting species than to how it affects individual traits and fitness [14]. But wait, you say, surely individual fitness is the driving force behind population dynamics. Well, yes and no. To assess individual fitness, investigators almost always use one or more proxies, including growth, survival, and reproductive biomass. Population-level studies have shown that a given interacting species can have multiple, frequently opposing effects on these different components of their partner''s fitness, such that an exclusive focus on any one component can be very misleading [22,23]. In addition, population dynamics depend on the probability of successful offspring recruitment; this probability is critical because it itself is also likely to vary along environmental gradients [14]. Population-level approaches thus deserve explicit focus despite their challenges, and this is one area where field studies can be greatly enhanced by both theory and model systems.A more serious problem, perhaps, and one that confronts all manner of approaches and systems, is how to scale up from simplified studies of two interacting species to entire ecological communities [24,25]. To use distinct dynamical signatures to predict population collapse, we need to know what other threats a species faces as well as what other opportunities are available. For example, corals exchange nutrients and protection for fixed carbon from their photosynthetic algal symbionts in the genus Symbiodinium. There are at least four major clades within Symbiodinium that associate with corals, and any given coral can associate with more than one type of algae, either simultaneously or throughout its lifetime. Temperature is an important environmental stress for corals, leading to the well known and increasingly problematic coral bleaching phenomenon, in which corals lose the carbon source provided by their algal symbionts (through loss of the symbionts themselves or of the symbionts'' photosynthetic capacity) as the oceans warm. However, some algal symbionts are more thermally tolerant than others, such that corals under temperature stress may lose their association with one symbiont (i.e., collapse of that obligate mutualism) only to gain assocation with a second, more thermally tolerant symbiont (i.e., formation of a different obligate mutualism) [26]. Various alternative responses to environmental stress can be imagined by considering interactions in a community context (Fig 2), and our understanding of these scenarios in well studied field systems should be mined to generate hypotheses about lesser known microbial interaction networks.Open in a separate windowFig 2Mutualism in a community context.Multispecies interactions can exhibit a greater variety of outcomes than two-way interactions. (A) An interaction in which the mutualism is always obligate (individuals with no mutualist have zero fitness), but some mutualists are better than others at high environmental stress (e.g., as in the coral-algae example). (B) An interaction in which there are multiple mutualists that vary in their cost to the partner. The costly mutualist is more effective, so Mutualist A increases fitness more than Mutualist B when stress is low to intermediate, but the cost of Mutualist A exceeds its benefit when stress is intermediate to high. In this mutualism, unlike in (A), the partner can exist independently of its mutualists and actually does so with higher fitness when environmental stress is very low (resource availability is very high) or stress is very high (resource availability is very low).The challenges to elucidating the drivers and mechanisms of context dependence are real, but the work of Hoek and colleagues reminds us that complex outcomes do not necessarily require complicated explanations. The emerging parallels between the burgeoning study of interactions among microbes and the research on species interactions in the macro-world should not be ignored and should, in fact, be leveraged for additional insight. For example, a functional approach is being pursued in both subfields and may increase our ability to generalize across highly diverse systems [24,27], but the context dependence of such functional types themselves [28] must be recognized and investigated in tandem. Simple systems like these cross-feeding yeasts suggest numerous possible future experiments to study what happens when we add dimensions in the environment or the species pool under high levels of experimental control. This study emphasizes the importance of resource availability for orienting the interaction compass.     

Late Devonian Acritarchs from the Gneudna formation in the Western Carnarvon basin,Western Australia

The Gneudna Formation is a Late Devonian(Frasnian) sequence of marine calcareous sediments that occurs in the Carnarvon Basin, Western Australia. The present palynological study is based upon subsurface silty strata from a borehole (Pelican Hill or Bibbawarra Bore) that was drilled early this century near the western coastal limit of the Carnarvon Basin.The subject strata have previously been attributed to the Gneudna Formation on lithostratigraphic grounds. They contain a rich and varied assemblage of marine microphytoplankton (acritarchs), associated with trilete miospores of which Geminospora lemurataBalme, 1962 is the dominant form. Forty-seven species of acritarchs are recognizable in the palynoflora, which corresponds very closely with that described recently (Playford & Dring, 1981) from the Gneudna Formation in the vicinity of its type section on the opposite (eastern) side of the Carnarvon Basin. The apparently parochial complexion of the Gneudna acritarch suite is probably illusory, insofar as early Late Devonian acritarchs have not been studied extensively or intensively from either the northern or southern hemispheres.The following new species of acritarchs areformally instituted herein: Elektoriskos villosa, Lophosphaeridium pelicanensis, and Pterospermella tenellula.     

Walk on the Wild Side: Estimating the Global Magnitude of Visits to Protected Areas

How often do people visit the world’s protected areas (PAs)? Despite PAs covering one-eighth of the land and being a major focus of nature-based recreation and tourism, we don’t know. To address this, we compiled a globally-representative database of visits to PAs and built region-specific models predicting visit rates from PA size, local population size, remoteness, natural attractiveness, and national income. Applying these models to all but the very smallest of the world’s terrestrial PAs suggests that together they receive roughly 8 billion (8 x 109) visits/y—of which more than 80% are in Europe and North America. Linking our region-specific visit estimates to valuation studies indicates that these visits generate approximately US $600 billion/y in direct in-country expenditure and US $250 billion/y in consumer surplus. These figures dwarf current, typically inadequate spending on conserving PAs. Thus, even without considering the many other ecosystem services that PAs provide to people, our findings underscore calls for greatly increased investment in their conservation.Enjoyment of nature, much of it in protected areas (PAs), is recognised as the most prominent cultural ecosystem service [1–3], yet we still lack even a rough understanding of its global magnitude and economic significance. Large-scale assessments have been restricted to regional or biome-specific investigations [4–8] (but see [9]). There are good reasons for this. Information on visit rates is limited, widely scattered, and confounded by variation in methods [10,11]. Likewise, estimates of the value of visits vary greatly—geographically, among methods, and depending on the component of value being measured [12–14]. Until now, these problems have prevented data-driven analysis of the worldwide scale of nature-based recreation and tourism. But with almost all the world’s governments committed (through the Aichi Biodiversity Targets [15]) to integrating biodiversity into national accounts, policymakers require such gaps in our knowledge of natural capital to be filled.We tackled this shortfall in our understanding of a major ecosystem service by focusing on terrestrial PAs, which cover one-eighth of the land [16] and are a major focus of nature-based recreation and tourism. We compiled data on visit rates to over 500 PAs and built region-specific models, which predicted variation in visitation in relation to the properties of PAs and to local socioeconomic conditions. Next, we used these models to estimate visit rates to all but the smallest of the world’s terrestrial PAs. Last, by summing these estimates by region and combining the totals with region-specific medians for the value of nature visits obtained from the literature, we derived approximate estimates of the global extent and economic significance of PA visitation.Given the scarcity of data on visits to PAs, our approach was to use all available information (although we excluded marine and Antarctic sites, and International Union for Conservation of Nature (IUCN) Category I PAs where tourism is typically discouraged; for further details of data collection and analysis see Materials and Methods). This generated a database of visitor records for 556 PAs spread across 51 countries and included 2,663 records of annual visit numbers over our best-sampled ten-year period (1998–2007) (S1 Table). Mean annual visit rates for individual PAs in this sample ranged from zero to over 10 million visits/y, with a median across all sampled PAs of 20,333 visits/y.We explored this variation by modelling it in relation to a series of biophysical and socioeconomic variables that might plausibly predict visit rates (after refs [6,7,17]): PA size, local population size, PA remoteness, a simple measure of the attractiveness of the PA’s natural features, and national income (see Materials and Methods for a priori predictions). For each of five major regions, we performed univariate regressions (S2 Table) and then built generalised linear models (GLMs) in an effort to predict variation in observed visit rates. While the GLMs had modest explanatory power within regions (S3 Table), together they accounted for 52.9% of observed global variation in visit rates. Associations with individual GLM variables—controlling for the effects of other variables—differed regionally in their strength but broadly matched our predictions (S1 Fig.). Visit rates increased with local population size (in Europe), decreased with remoteness (everywhere apart from Asia/Australasia), increased with natural attractiveness (in North and Latin America), and increased with national income (everywhere else). Controlling for these variables, visit rates were highest in North America, lower in Asia/Australasia and Europe, and lowest in Africa and Latin America.To quantify how often people visit PAs as a whole, we used our region-specific GLMs to estimate visit rates to 94,238 sites listed in the World Database on Protected Areas (WDPA) [18]). We again excluded marine, Antarctic, and Category I PAs, as well as almost 40,000 extremely small sites which were below the size (10 ha) of the smallest PA in our sample (S2 Fig.). The limited power of our GLMs and significant errors in the WDPA mean our estimates of visit rates should be treated with caution for individual sites or (when aggregated to national level) for smaller countries. However, the larger-scale patterns they reveal are marked. Estimated median visit rates per PA (averaged within countries) are lowest in Africa (at around 3,000/y) and Latin America (4,000/y), and greatest in North America (350,000/y) (S3 Table). When visit rates are aggregated across all PAs within a country, pronounced regional differences in the numbers of PAs (with relatively few in Africa and Latin America) magnify these patterns and indicate that while many African countries have <100,000 PA visits/y, PAs in the United States receive a combined total of over 3 billion visits/y (Fig. 1). This variation is underscored when aggregate PA visit rates are standardised by the annual number of non-workdays and total population size of each region: across Europe we reckon there are ~5 PA visits/100 non-work person-days; for North America, the figure is ~10 visits/100 non-work person-days respectively, while for each other region our estimates are <0.3 visits/100 non-work person-days.Open in a separate windowFig 1Estimated total PA visit rates for each country.Totals (which are log₁₀-transformed) were derived by applying the relevant regional GLM (S3 Table) to all of a country’s terrestrial PAs (excluding those <10 ha, and marine and IUCN Category I PAs) listed in the WDPA [18]. Asterisks show countries for which we had visit rate observations.Summing our aggregate estimates of PA visits suggests that between them, the world’s terrestrial PAs receive approximately 8 billion visits/y. Of these, we estimate 3.8 billion visits/y are in Europe (where more than half of the PAs in the WDPA are located) and 3.3 billion visits/y are in North America (S3 Table). These numbers are strikingly large. However, given our confidence intervals (95% CIs for the global total: 5.4–18.5 billion/y) and considering several conservative aspects of our calculations (e.g., the exclusion of ~40,000 very small sites and the incomplete nature of the WDPA), we consider it implausible that there are fewer than 5 billion PA visits worldwide each year. Three national estimates support this view: 2.5 billion visitdays/y to US PAs in 1996 [4], >1 billion visits/y (albeit many of them cultural rather than nature-based) to China’s National Parks in 2006 [19], and 3.2–3.9 billion visits/y to all British “ecosystems” (most of which are not in PAs) in 2010 [7].Finally, what can be inferred about the economic significance of visits on this scale? Economists working on tourism distinguish two main, non-overlapping components of value [12]: direct expenditure by visitors (an element of economic impact, calculated from spending on fees, travel, accommodation, etc.); and consumer surplus (a measure of economic value which arises because many visitors would be prepared to pay more for their visit than they actually have to, and which is defined as the difference between what visitors would be prepared to pay for a visit and what they actually spend; consumer surplus is typically quantified using travel cost or contingent valuation methods). We conducted an extensive literature search to derive median (but conservative) figures for each type of value for each region (S4 Table). Applying these to our corresponding estimates of visit rates and summing across regions yields an estimate of global gross direct expenditure associated with PA visits (within-country only, and excluding indirect and induced expenditure) of ~US $600 billion/y worldwide (at 2014 prices). The corresponding figure for global consumer surplus is ~US $250 billion/y.Such numbers are unavoidably imprecise. Uncertainty in our modelled visit rates and the wide variation in published estimates of expenditure and consumer surplus mean that they could be out by a factor of two or more. However, comparison with calculations that visits to North American PAs alone have an economic impact of $350–550 billion/y [4] and that direct expenditure on all travel and tourism worldwide runs at $2,000 billion/y [20] suggests our figures are of the correct order of magnitude, and that the value of PA visitation runs into hundreds of billions of dollars annually.These results quantify, we believe for the first time, the scale of visits to the world’s PAs and their approximate economic significance. We currently spend <$10 billion/y in safeguarding PAs [21]—a figure which is widely regarded as grossly insufficient [21–25]. Even without considering the many other benefits which PAs provide [22], our estimates of the economic impact and value of PA visitation dwarf current expenditure—highlighting the risks of underinvestment in conservation, and suggesting substantially increased investments in protected area maintenance and expansion would yield substantial returns.     

Fossil Giraffidae (Mammalia,Artiodactyla) from Lee Adoyta,Ledi-Geraru,and Late Pliocene Dietary Evolution in Giraffids from the Lower Awash Valley,Ethiopia

The giraffid fossils recovered from ~ 2.8–2.6 million year old (Ma) sediments from Lee Adoyta, Ledi-Geraru, Ethiopia, are described here. Sivatherium maurusium and Giraffa cf. G. gracilis are the two identified taxa, with the former being more abundant than the latter. We interpret this skew of relative abundance to be of paleoenvironmental significance, as Sivatherium is rare and Giraffa is common in the adjacent, but older sediments of the Hadar Formation at Hadar (~ 3.4 to 2.95 Ma), which was characterized by wooded and well-watered habitats through most of its sequence. Stable carbon isotope analyses show that Giraffa remained an obligate browser throughout the lower Awash Valley (LAV) sequence while Sivatherium underwent a dietary transition from a browser in the Hadar Formation to a grazer at Lee Adoyta. This dietary shift in Sivatherium reflects local environmental change through time in the LAV as open habitats spread during the late Pliocene. A compilation of isotopic data from other sites in eastern Africa shows that the LAV dietary shift in Sivatherium occurred roughly one million years earlier than in the Turkana Basin, Kenya, reflecting a spatiotemporally staggered expansion of C₄ vegetation across eastern Africa.     

Achieving global equity for COVID-19 vaccines: Stronger international partnerships and greater advocacy and solidarity are needed

Peter Figueroa and co-authors advocate for equity in the worldwide provision of COVID-19 vaccines.

Many may not be aware of the full extent of global inequity in the rollout of Coronavirus Disease 2019 (COVID-19) vaccines in response to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. As of June 20, 2021, only 0.9% of those living in low-income countries and less than 10% of those in low- and middle-income countries (LMICs) had received at least 1 dose of a COVID-19 vaccine compared with 43% of the population living in high-income countries (HICs) [1] (Fig 1). Only 2.4% of the population of Africa had been vaccinated compared with 41% of North America and 38% of Europe [1,2] (S1 Fig). Primarily due to the inability to access COVID-19 vaccines, less than 10% of the population in as many as 85 LMICs had been vaccinated compared with over 60% of the population in 26 HICs [1]. Only 10 countries account for more than 75% of all COVID-19 vaccines administered [3]. This striking and ongoing inequity has occurred despite the explicit ethical principles affirming equity of access to COVID-19 vaccines articulated in WHO SAGE values framework [4,5] prepared in mid-2020, well prior to the availability of COVID-19 vaccines.Open in a separate windowFig 1Proportion of people vaccinated with at least 1 dose of COVID-19 vaccine by income (April 14 to June 23, 2021).Note: Data on China appeared on the database on June 9, hence the jump in upper middle-income countries. COVID-19, Coronavirus Disease 2019. Source: https://ourworldindata.org/covid-vaccinations.The COVID-19 pandemic highlights the grave inequity and inadequacy of the global preparedness and response to serious emerging infections. The establishment of the Coalition for Epidemic Preparedness Innovations (CEPI) in 2018, the Access to COVID-19 Tools Accelerator (ACT-A), and the COVID-19 Vaccines Global Access (COVAX) Facility in April 2020 and the rapid development of COVID-19 vaccines were all positive and extraordinary developments [6]. The COVAX Facility, as of June 2021, has delivered approximately 83 million vaccine doses to 75 countries, representing approximately 4% of the global supply, and one-fifth of this was for HICs [7]. The COVAX Facility has been challenged to meet its supply commitments to LMICs due to insufficient access to doses of COVID-19 vaccines with the prerequisite WHO emergency use listing (EUL) or, under exceptional circumstances, product approval by a stringent regulatory authority (SRA) [8,9]. Because of the anticipated insufficient COVID-19 vaccine supply through the COVAX Facility, the majority of nonvaccine-producing LMIC countries made the decision, early in the COVID-19 pandemic, to secure and use vaccines produced in China or Russia prior to receipt of WHO EUL or SRA approval. Most of the vaccines used in LMICs as of June 20, 2021 (nearly 1.5 billion doses of the 2.6 billion doses administered) were neither WHO EUL or SRA approved at the time they were given [10]. This may raise possible concerns with respect to the effectiveness, safety, and acceptability of individual vaccines used by many countries [8,9].     

Origin of the Yeast Whole-Genome Duplication

Whole-genome duplications (WGDs) are rare evolutionary events with profound consequences. They double an organism’s genetic content, immediately creating a reproductive barrier between it and its ancestors and providing raw material for the divergence of gene functions between paralogs. Almost all eukaryotic genome sequences bear evidence of ancient WGDs, but the causes of these events and the timing of intermediate steps have been difficult to discern. One of the best-characterized WGDs occurred in the lineage leading to the baker’s yeast Saccharomyces cerevisiae. Marcet-Houben and Gabaldón now show that, rather than simply doubling the DNA of a single ancestor, the yeast WGD likely involved mating between two different ancestral species followed by a doubling of the genome to restore fertility.The unicellular baker’s yeast Saccharomyces cerevisiae was the first eukaryote to have its genome sequenced, using the first generation of automated sequencing machines and before the advent of the whole-genome shotgun approach. The sequencing was done during the period between 1990 and 1996 by an international consortium that included many small European laboratories, one of which was mine. Each laboratory was given a “tranche” of about 30 kb to sequence, and when you had completed that chunk, you could apply for another one. We were paid €2 per base pair. Progress meetings, chaired energetically by André Goffeau [1], were held every six months to ensure that the project remained on track. At these meetings, each group would make a 5-minute presentation about the genes they had found in their current chunk. The presentations were often tedious, enlivened only by the occasional exigency for André to reassign pieces of DNA from the sequencing tortoises to the hares. But as the project progressed, a pattern began to emerge: many of the chunks were similar to other chunks. The first clone that I sequenced happened to contain the centromere of chromosome II, and I noticed that a gene beside it had a paralog beside the centromere of chromosome IV [2]. My second chunk, from chromosome XV, contained four genes that had four paralogs, in the same order, on chromosome I [3].When the complete genome was released in April 1996, we were able to identify 55 large duplicated blocks of this type, ranging in size from three to 18 duplicated genes (Fig 1) [4]. Two observations indicated that the duplications were quite old: the average amino acid sequence identity between the gene pairs was only 63%, and within each block only about 25% of the genes were actually duplicated, the others being single copy. This pattern suggested that the whole block was initially duplicated, and then many individual genes were deleted. Two other observations suggested that the blocks were remnants of duplicated chromosomes that had become rearranged during evolution: there were almost no overlaps between the blocks, and the orientation of each pair of blocks was conserved relative to the centromeres and telomeres. This layout of blocks was consistent with duplication of the whole genome followed by both extensive deletion of single genes and genome rearrangement solely by the process of reciprocal translocation between chromosomes [4]. Under this hypothesis, there had been an ancient whole-genome duplication (WGD), and the 55 blocks that we could identify were simply the most duplicate-dense regions that still survived without evolutionary rearrangement.Open in a separate windowFig 1A simple model of WGD, gene loss, and synteny relationships.The upper panel shows how duplicated blocks were initially identified using only genes that remain in duplicate in S. cerevisiae [4]. The lower panel shows how additional data from non-WGD yeasts such as Lachancea waltii [5] allowed the parts of the genome that were not initially allocated to blocks to be placed into pairs, providing a duplication map that covered the whole S. cerevisiae genome. Letters A–W represent genes, and dots represent centromeres. Only two chromosomes (yellow and brown) are shown.The hypothesis of a WGD in S. cerevisiae was confirmed in 2004 when three groups sequenced the genomes of species that had branched off from this lineage before the WGD occurred [5–7]. These non-WGD genomes had a “double conserved synteny” relationship with the S. cerevisiae genome—that is, instead of each pair of duplicated regions, they had a single region containing all the genes in a merged order (Fig 1). This discovery allowed the entire genome of S. cerevisiae to be mapped into pairs of regions via their double conserved synteny with the non-WGD species, even if the pairs retain no duplicated genes, thus filling the gaps between the initial map of 55 duplicated blocks. These analyses proved that the WGD encompassed the entire genome of S. cerevisiae and showed that its 16 centromeres fall into eight ancestral pairs that are syntenic with centromeres of the non-WGD species. It therefore appeared that the WGD turned an eight-chromosome ancestor into a 16-chromosome descendant. From this complete map, we now know that among the 5,774 protein-coding genes of S. cerevisiae, there are 551 pairs of duplicated genes (ohnologs) that were formed by the WGD and that about 144 chromosomal rearrangements scrambled the genome after the WGD [8,9]. We also know that the WGD is not confined to Saccharomyces but occurred in the common ancestor of six genera, some of which diverged from each at an early stage when more than 4,000 genes were still duplicated, leading to later losses of different gene copies in different lineages [10].What were the molecular events that caused the WGD? It is relatively easy to draw a diagram summarizing the history of each chromosomal region (Fig 2), but it is much more difficult to specify the provenance of the intermediate molecules and the timescales involved. Two alternative scenarios can describe the steps in Fig 2. In both scenarios, event 1 is a DNA replication, and cells W and Z are each capable of mating (they are respectively a non-WGD haploid and a post-WGD haploid). The key question is whether the DNA molecules labeled X and Y existed in (1) two different cells of the same species or (2) two cells of two different species. Scenario 1 is called autopolyploidization, in which case event 1 corresponds to a simple cell division and event 2 is a mating between gametes from the same species or some other form of cell fusion. Scenario 2 is called allopolyploidization or hybridization, in which case event 1 is a speciation and event 2 is an interspecies mating or cell fusion. If event 2 was a mating, then an additional step such as deletion of one allele at the MAT locus is necessary to convert cell Z from a nonmating zygote to a mating gamete—but it is not essential that this additional step occurred immediately after event 2. In fact, a long delay in which cell Z replicated mitotically for many generations could be useful because it could allow reproductive isolation from cells of type W to build up. Eventually (event 3), mating between two post-WGD haploid cells of type Z can produce a post-WGD diploid like cell ZZ, which is the state in which S. cerevisiae is normally found in nature.Open in a separate windowFig 2Tracing the history of a single chromosomal region.See text for details. In an allopolyploidization, the red and blue chromosomes are called homeologs.The major difference between these two scenarios is the amount of time (T) that elapsed between events 1 and 2: was it a few generations or millions of years? In scenario 1, molecules X and Y must be identical, whereas in scenario 2 they could have any level of sequence divergence from minimal to extensive, and they could also differ by chromosomal rearrangements. It has been difficult to design tests that could differentiate between these scenarios, but an analysis of the inferred order of genes along molecules X and Y did not find any rearrangements and so did not rule out scenario 1 [9]. However, it has been frustrating that we could not pin down the details of this crucial phase of yeast evolution, which gave birth to many pairs of genes with substantially divergent functions [11–15].In this issue of PLOS Biology, Marcet-Houben and Gabaldón now report strong evidence in support of interspecies hybridization (scenario 2) as the source of the two subgenomes in post-WGD species [16]. By phylogenetic analysis using state-of-the-art methods, they show that molecules X and Y have phylogenetic affinities to two different non-WGD lineages that they call the KLE and ZT clades. The KLE clade (Kluyveromyces, Lachancea, and Eremothecium) is the group of non-WGD species that was sequenced in 2004 [5–7]. The ZT clade (Zygosaccharomyces and Torulaspora) is a separate, more recently studied non-WGD lineage [17,18]. Previous phylogenetic studies using supertrees or concatenated data suggested that the ZT clade is sister to the post-WGD clade, with the KLE clade being an out-group to them both [17,19,20]. The new analysis [16] made trees for each gene individually and found that, although the majority of genes in post-WGD species do cluster phylogenetically with the ZT clade as expected, a significant minority (about 30%) instead either cluster with the KLE clade or form an outgroup to a KLE + ZT clade. This phylogenetic heterogeneity was not noticed before because the KLE signal is only present in a minority of genes, and it is swamped by the ZT signal in methods that try to place the post-WGD clade at a single point on the tree.Marcet-Houben and Gabaldón interpret this phylogenetic heterogeneity as evidence that the two post-WGD subgenomes have separate origins, one from the ZT clade and the other from an unidentified lineage that is an outgroup to KLE + ZT. Under the simplest hypothesis of hybridization, we might then expect that phylogenetic trees constructed from ohnolog pairs should show one S. cerevisiae gene grouping with the ZT clade and the other grouping with the KLE clade, but in fact, most ohnolog pairs group with each other, with the ZT clade as their closest relative [16]. The authors’ explanation for these two results—an excess of ZT-like ohnolog pairs, and an excess of ZT-like genes in the whole genome (which is mostly singletons)—is that the post-WGD genomes have been affected by biased gene conversion that preferentially replaced some KLE-derived sequences with copies of the homeologous ZT-derived sequences, homogenizing these regions and obliterating their signal of KLE ancestry.The hybridization proposed by Marcet-Houben and Gabaldón makes a lot of sense in terms of what we know about the biology of yeast interspecies hybrids. Many yeast strains, most notably those used in commercial settings where stress tolerance is important, have turned out to be interspecies hybrids. For instance, the yeast used to brew lager (S. pastorianus) is a hybrid between S. cerevisiae and S. eubayanus [21,22], and many other combinations of genomes from different species of Saccharomyces have been found in nature [23]. These interspecies hybrids are usually infertile (unable to sporulate) because the two copies (homeologs) of each chromosome that they contain are too dissimilar to pair properly during meiosis [24–26]. One simple way to restore fertility is to double the genome, allowing each chromosome to pair with an identical partner instead of trying to pair with the homeolog. In this model, cell Z changes from being a nonmater (effectively diploid) to a mater (effectively haploid—perhaps by deletion of a MAT allele), then two cells of type Z mate to produce cell ZZ (diploid), and cell ZZ is able to go through meiosis and make spores with twice the DNA content of cell W. Thus, one hypothesis that Marcet-Houben and Gabaldón propose is that event 2 was an interspecies mating and event 3 was a restoration of fertility by genome doubling, with a possible interval of many mitotic generations between these two events. Alternatively, they hypothesize that event 2 may have been an interspecies fusion of diploid cells, obviating the need for a separate event 3.The obscuring of the phylogenetic signal of hybridization by subsequent gene conversions [16] is consistent with the known genome structures of some interspecies hybrids. The yeasts Pichia sorbitophila [27] and Candida orthopsilosis [28] are both interspecies hybrids, but in each case extensive homogenization of parts of the genome has occurred. This process of homogenization has been called overwriting, loss of heterozygosity, or gene conversion by different groups. It leaves the number of chromosomes unchanged (equal to the sum of the numbers of chromosomes in the two incoming subgenomes) but involves the replacement of sequences in one subgenome by sequences copied from the other subgenome (cell H in Fig 2). Homogenization could occur on scales as small as a few hundred base pairs (gene conversion) or as large as whole chromosome arms (break-induced replication). In the latter case, even differences in gene order such as inversions between the parental species could be ironed out.The discovery that the yeast WGD was an allopolyploidization adds complexity to what initially seemed to be a simple story of duplication. If an interspecies hybrid such as P. sorbitophila with a partly homogenized genome developed two mating types and these could mate to form a diploid that could sporulate efficiently, the result would be a species with a genome resembling the inferred progenitor of the post-WGD clade (cell HH in Fig 2). Allopolyploidy answers some old questions about why genes were retained in duplicate if their sequences were identical (answer: they weren’t identical), what the immediate selective advantage of the post-WGD cell was (answer: hybrid vigor), and how the post-WGD lineage became reproductively isolated from the pre-WGD lineage (answer: delay between events 2 and 3). But it also raises new questions about homogenization (how much of the genome? how often? why is it biased?) and about the mechanism of restoration of fertility (why is event 3 so rare, apparently happening only once in the budding yeast family even though event 2 happened quite often?).Ancient WGDs have been detected right across the eukaryotic tree of life, including in animals, ciliates, fungi, and, most prominently, plants [29–32]. If extensive gene conversion can obscure the traces of allopolyploidization in yeast genomes, one might wonder how many of these other ancient WGDs also began as interspecies hybridizations. In fact, there is evidence from plants that gene conversion acts continually to homogenize ohnolog pairs [32,33] and that hybrid plants can show preferential retention of DNA from one parent over the other [34] similar to the situation in P. sorbitophila [27]. Detecting the yeast hybridization in the presence of these obscuring factors required both good luck and good timing: good luck that a reference species closer to one parent than to the other had been sequenced and good timing that the hybrid was sampled before all traces of its hybrid origin had faded away. These fortunate circumstances may not hold for ancient hybridizations in other eukaryotes, but as a famous golfer once said, “The harder I practice, the luckier I get.” Detecting that they are hybridizations may become possible with exhaustive sampling of possible parental lineages and the use of sensitive phylogenomic methods of the type introduced by the authors [16].     

Bacterial Ventures into Multicellularity: Collectivism through Individuality

Multicellular eukaryotes can perform functions that exceed the possibilities of an individual cell. These functions emerge through interactions between differentiated cells that are precisely arranged in space. Bacteria also form multicellular collectives that consist of differentiated but genetically identical cells. How does the functionality of these collectives depend on the spatial arrangement of the differentiated bacteria? In a previous issue of PLOS Biology, van Gestel and colleagues reported an elegant example of how the spatial arrangement of differentiated cells gives rise to collective behavior in Bacillus subtilus colonies, further demonstrating the similarity of bacterial collectives to higher multicellular organisms.Introductory textbooks tend to depict bacteria as rather primitive and simple life forms: the billions of cells in a population are all supposedly performing the exact same processes, independent of each other. According to this perspective, the properties of the population are thus nothing more than the sum of the properties of the individual cells. A brief look at the recent literature shows that life at the micro scale is much more complex and far more interesting. Even though cells in a population share the same genetic material and are exposed to similar environmental signals, they are individuals: they can greatly differ from each other in their properties and behaviors [1,2].One source of such phenotypic variation is that individual cells experience different microenvironments and regulate their genes in response. However, and intriguingly, phenotypic differences can also arise in the absence of environmental variation [3]. The stochastic nature of biochemical reactions makes variation between individuals unavoidable: reaction rates in cells will fluctuate because of the typical small number of the molecules involved, leading to slight differences in the molecular composition between individual cells [4]. While cells cannot prevent fluctuations from occurring, the effect of these extracellular and intracellular perturbations on a cell’s phenotype can be controlled by changing the biochemical properties of molecules or the architecture of gene regulatory networks [4,5]. The degree of phenotypic variation could thus evolve in response to natural selection. This raises the question of whether the high degree of phenotypic variation observed in some traits could offer benefits to the bacteria [5].One potential benefit of phenotypic variation is bet hedging. Bet hedging refers to a situation in which a fraction of the cells express alternative programs, which typically reduce growth in the current conditions but at the same time allow for increased growth or survival when the environment abruptly changes [6–8]. Another potential benefit can arise through the division of labor: phenotypic variation can lead to the formation of interacting subpopulations that specialize in complementary tasks [9]. As a result, the population as a whole can perform existing functions more efficiently or attain new functionality [10]. Division of labor enables groups of bacteria to engage in two tasks that are incompatible with each other but that are both required to attain a certain biological function.One of the most famous examples of division of labor in bacteria is the specialization of multicellular cyanobacteria into photosynthesizing and nitrogen-fixing subpopulations [11]. Here, the driving force behind the division of labor is the biochemical incompatibility between photosynthesis and nitrogen fixation, as the oxygen produced during photosynthesis permanently damages the enzymes involved in nitrogen fixation [12]. Other examples include the division of labor between two subpopulations of Salmonella Typhimurium (Tm) during infections [9] and the formation of multicellular fruiting bodies in Myxococcus xanthus [13]. Division of labor is not restricted to interactions between only two subpopulations; for example, the soil-dwelling bacteria Bacillus subtilis can differentiate into at least five different cell types [14]. Multiple types can simultaneously be present in Bacillus biofilms and colonies, each contributing different essential tasks [14,15].An important question is whether a successful division of labor requires the different subpopulations to coordinate their behavior and spatial arrangement. For some systems, it turns out that spatial coordination is not required. For example, the division of labor in clonal groups of Salmonella Tm does not require that the two cell types are spatially arranged in a particular way [9]. In other systems, spatial coordination between the different cell types seems to be beneficial. For example, differentiation into nitrogen-fixing and photosynthetic cells in multicellular cyanobacteria is spatially regulated in a way that facilitates sharing of nitrogen and carbon [16]. In general, when cell differentiation is combined with coordination of behavior between cells, this can allow for the development of complex, group-level behaviors that cannot easily be deduced from the behavior of individual cells [17–20]. In these cases, a population can no longer be treated as an assembly of independent individuals but must be seen as a union that together shows collective behavior.The study by van Gestel et al. [21] in a previous issue of PLOS Biology offers an exciting perspective on how collective behavior can arise from processes operating at the level of single cells. Van Gestel and colleagues [21] analyzed how groups of B. subtilis cells migrate across solid surfaces in a process known as sliding motility. The authors found that migration requires both individuality—the expression of different phenotypes in clonal populations—and spatial coordination between cells. Migration depends critically on the presence of two cell types: surfactin-producing cells, which excrete a surfactant that reduce surface tension, and matrix-producing cells, which excrete extracellular polysaccharides and proteins that form a connective extracellular matrix (Fig 1B) [14]. These two cell types are not randomly distributed across the bacterial group but are rather spatially organized (Fig 1C). The matrix-producing cells form bundles of interconnected and highly aligned cells, which the authors refer to as “van Gogh” bundles. The surfactant producing cells are not present in the van Gogh bundle but are essential for the formation of the bundles [21].Open in a separate windowFig 1Collective behavior through the spatial organization of differentiated cells.(A) Initially cells form a homogenous population. (B) Differentiation: cells start to differentiate into surfactin- (orange) and matrix- (blue) producing cells. The two cell types perform two complementary and essential tasks, resulting in a division of labor. (C) Spatial organization: the matrix-producing cells form van Gogh bundles, consisting of highly aligned and interconnected cells. Surfactin-producing cells are excluded from the bundles and have no particular spatial arrangement. (D) Collective behavior: growth of cells in the van Gogh bundles leads to buckling of these bundles, resulting in colony expansion. The buckling and resulting expansion depend critically on the presence of the two cell types and on their spatial arrangement.The ability to migrate is a collective behavior that can be linked to the biophysical properties of the multicellular van Gogh bundles. The growth of cells in these bundles causes them to buckle, which in turn drives colony migration (Fig 1D) [21]. This is a clear example of an emergent (group-level) phenotype: the buckling of the van Gogh bundles and the resulting colony motility cannot easily be deduced from properties of individual cells. Rather, to understand colony migration we have to understand the interactions between the two cells type as well as their spatial organization. Building on a rich body of work on the regulation of gene expression and cellular differentiation in Bacillus [14], van Gestel et al. [21] are able to show how these molecular mechanisms lead to the formation of specialized cell types that, through coordinated spatial arrangement, provide the group the ability to move (Fig 1). The study thus uniquely bridges the gap between molecular mechanisms and collective behavior in bacterial multicellularity.This study raises a number of intriguing questions. A first question pertains to the molecular mechanisms underlying the spatial coordination of the two cell types. Can the spatial organization be explained based on known mechanisms of the regulation of gene expression in this organism or does the formation of these patterns depend on hitherto uncharacterized gene regulation based on spatial gradients or cell–cell interaction? A second question is about the selective forces that lead to the evolution of collective migration of this organism. The authors raise the interesting hypothesis that van Gogh bundles evolved to allow for migration. Although this explanation is very plausible, it also raises the question of how selection acting on a property at the level of the group can lead to adaptation at the individual cell level. Possible mechanisms for such selective processes have been described within the framework of multilevel selection theory. However, there are still many questions regarding how, and to what extent, multilevel selection operates in the natural world [22–24]. The system described by van Gestel and colleagues [21] offers exciting opportunities to address these questions using a highly studied and experimentally amenable model organism.Bacterial collectives (e.g., colonies or biofilms) have been likened to multicellular organisms partly because of the presence of cell differentiation and the importance of an extracellular matrix [25,26]. Higher multicellular organisms share these properties; however, they are more than simple lumps of interconnected, differentiated cells. Rather, the functioning of multicellular organisms critically depends on the precise spatial organization of these cells [27]. Even though spatial organization has been suggested before in B. subtilis biofilms [28], there was a gap in our understanding of how spatial organization of unicellular cells can lead to group-level function. The van Gogh bundles in the article by van Gestel et al. [21] provide direct evidence on how differentiated cells can spatially organize themselves to give rise to group-level behavior. This shows once more that bacteria are not primitive “bags of chemicals” but rather are more like us “multicellulars” than we might have expected.     

Assisted reproduction technology and long-term cardiometabolic health in the offspring

Ronald Ma and co-authors discuss Emma Norrman and colleagues’ accompanying research study on the health of children born with assisted reproductive technology.

Since the birth of the first baby with the aid of in vitro fertilization (IVF) in July 1978, more than 9 million children have since been born through IVF or other assisted reproduction technology (ART). From a report covering around 2/3 of world ART activity, it was estimated that more than 4.4 million ART cycles have been initiated between 2008 and 2010, which resulted in 1.14 million births during that period [1]. From 1997 to 2016, the numbers of recorded ART treatment have increased by 5.3-fold in Europe, 4.6-fold in the United States of America, and 3.0-fold in Australia and New Zealand [1]. In an accompanying study in PLOS Medicine, Emma Norrman and colleagues address the health of babies born after ART [2].While initially met with considerable skepticism and controversy, the large number of healthy babies born over the last 4 decades is testament to the success and safety of IVF, which has transformed the lives of many couples and families. Nevertheless, given the appreciation of the Developmental Origins of Health and Disease (DOHaD) hypothesis, which posits that insults during critical times of development (including in utero or early life) may modify an individual’s phenotype and alter later risk of disease in adulthood, as well as previous demonstration of potential epigenetic changes following ART, there has been rekindled interest in the potential long-term effects of ART on offspring health [3]. In a large retrospective Nordic population-based cohort study of all children born after ART between 1982 and 2007, there was no significant increase in overall cancer rates among children born after ART, compared to children born after spontaneous conception (SC) [4]. Questions have also been raised about the long-term cardiovascular health of offspring born after ART, as several mechanisms have been postulated to potentially contribute to impaired cardiovascular health, including suboptimal culture conditions, ART-induced epigenetic changes, as well as indirect effects through low birthweight, thereby contributing to altered cardiovascular phenotype [5] (Fig 1). A systematic review and meta-analysis did not show evidence of increased cardiovascular risk or diabetes for women following ART, though there was comparatively less data to address offspring risk [6]. In line with a recent systematic review and meta-analysis [7], previous small studies have found increased adiposity, cardiometabolic risk, and blood pressure among offspring born after ART, potentially due to altered gene expression [8,9]. However, issues regarding potential selection bias have been raised for the small studies included.Open in a separate windowFig 1A DOHaD perspective on the potential relationship between ART and later risk of T2D and CVD.The putative link is highlighted by the dotted outline. ART, assisted reproduction technology; CVD, cardiovascular disease; DOHaD, Developmental Origins of Health and Disease; EDCs, endocrine-disrupting chemicals; GDM, gestational diabetes; ICSI, intracytoplasmic sperm injection; IVF, in vitro fertilization; ncRNA, noncoding RNA; PCOS, polycystic ovary syndrome; T2D, type 2 diabetes. (adapted with permission from Ma and colleagues [13]).Norrman and colleagues conducted a large population-based study from the Committee of Nordic ART and Safety (CoNARTaS) cohort, which included all individuals born in Norway, Sweden, Finland, and Denmark between 1984 and 2015, including 122,429 children born after ART, and more than 7.5 million children born after SC, to investigate the risk of cardiovascular disease (CVD), diabetes, and obesity following ART compared to SC. Offspring were followed for a mean 8.6 years in children born after ART and 14.0 years for children born following SC. Although the crude rates for CVD and type 2 diabetes (T2D) were higher among children born after ART, there were no significant difference in rates after adjustment for measured confounders. The study noted a significant increase in the risk of obesity among children born to ART, though the risk was modest, with adjusted HR 1.14 (CI 1.06 to 1.23, p = 0.001). The design of the study also meant that it could not address whether any increased risk in the offspring might be related to maternal causes of infertility (such as polycystic ovary syndrome), rather than the ART. In contrast to the previous systematic review that suggested significant increase in cardiometabolic risk factors in ART offspring, the authors concluded that the cardiometabolic outcomes in ART children are, in general, reassuring. However, further studies with longer follow-up are needed.The study provided much-needed medium-term outcome data addressing this important question of long-term cardiometabolic risk in children born after ART. By combining high-quality Nordic registers, Norrman and colleagues have been able to create a uniquely large cohort of ART children in order to compare their risk of cardiovascular health with children born after SC. Such population-based design provided high coverage rate and high validity, such that missing data and the risk of selection bias can be minimized. However, there were some notable limitations of the study, including the relatively short follow-up period, especially among children born to ART. The number of clinical outcomes of interest was limited, hence restricting statistical power to detect differences in outcome. Although the use of national registries has minimized any risk of selection bias, the definitions of outcomes were based on inpatient and outpatient attendance and may be associated with some ascertainment bias, especially in relation to capturing obesity outcomes. There is also a significant proportion with missing maternal BMI, paternal characteristics, or other covariates, which posed limitations on the analyses. Another important point to note is that the impact of different ART factors on health of offspring has not been addressed. Over the years, ART practices and technologies have continued to evolve, for example, the increasing use of oocyte freezing and embryo biopsy for genetic testing and the shift of slow freezing to vitrification method for gamete or embryo freezing. It has been shown that singleton babies conceived from fresh embryo transfers of IVF are associated with increased risks of low birthweight and preterm delivery, while ART involving frozen embryos are associated with higher incidences of large babies, macrosomia, and hypertensive disorders of pregnancy [10]. Conversely, both low birthweight, intrauterine growth restriction (IUGR), as well as macrosomia have been linked with increased risk of later T2D and CVD [11]. These differential outcomes illustrate that the different ART techniques may have different safety profiles and exert different impacts on the long-term health of offspring. Of note, the study by Norrman and colleagues included relatively few births from frozen embryos, and these have not been compared to births by SC for later risk of diabetes or CVD.This important study highlights some of the challenges in ascertaining long-term effects of ART, or other early life exposures, for that matter. The establishment of ART registries including the important exposure factors may be an important component for the way forward, especially given the long-term follow-up required. There are important challenges, including those around confidentiality, but also the increasingly diverse and complex treatment protocols, as well as innovative technologies, which may be associated with different long-term outcomes. There is currently limited understanding of the long-term outcome of some of these novel techniques. There are also new challenges given the globalization of healthcare delivery, with increasing cross-border reproductive care [12]. The increasing cryopreservation of gametes, gonadal tissues, and embryos will pose new challenges on tracking the outcomes of ART births. More population-based long-term studies are warranted, and establishing the infrastructure that would facilitate anonymous linkage of ART registers, birth records with national diabetes and other disease registers that facilitate tracking of long-term health may be one way forward. Nevertheless, given the sensitivities around the data involved, such analyses may be difficult to perform in some areas, and population-based analyses, wherever possible, will continue to contribute much-needed data to this discussion.     

The Hologenome Concept: Helpful or Hollow?

With the increasing appreciation for the crucial roles that microbial symbionts play in the development and fitness of plant and animal hosts, there has been a recent push to interpret evolution through the lens of the “hologenome”—the collective genomic content of a host and its microbiome. But how symbionts evolve and, particularly, whether they undergo natural selection to benefit hosts are complex issues that are associated with several misconceptions about evolutionary processes in host-associated microbial communities. Microorganisms can have intimate, ancient, and/or mutualistic associations with hosts without having undergone natural selection to benefit hosts. Likewise, observing host-specific microbial community composition or greater community similarity among more closely related hosts does not imply that symbionts have coevolved with hosts, let alone that they have evolved for the benefit of the host. Although selection at the level of the symbiotic community, or hologenome, occurs in some cases, it should not be accepted as the null hypothesis for explaining features of host–symbiont associations.The ubiquity and importance of microorganisms in the lives of plants and animals are ever more apparent, and increasingly investigated by biologists. Suddenly, we have the aspiration and tools to open up a new, complicated world, and we must confront the realization that almost everything about larger organisms has been shaped by their history of evolving from, then with, microorganisms [1]. This development represents a dramatic shift in perspective—arguably a revolution—in modern biology.Do we need to revamp basic tenets of evolutionary theory to understand how hosts evolve with associated microorganisms? Some scientists have suggested that we do [2], and the recently introduced terms “holobiont” and “hologenome” encapsulate what has been described as an “emerging postmodern synthesis” [3]. Holobiont was initially used to refer to a host and a single inherited symbiont [4] but was later extended to a host and its community of associated microorganisms, specifically for the case of corals [5]. The idea of the holobiont is that a host and its associated microorganisms must be considered as an integrated unit in order to understand many biological and ecological features.The later introduction of the term hologenome [2,6,7] sought to describe a holobiont by its genetic composition. The term has been used in different ways by different authors, but in most contexts a hologenome is considered a genetic unit that represents the combined genomes of a host and its associated microorganisms [8]. This non-controversial definition of hologenome is linked to the idea that this entity has a role in evolution. For example, Gordon et al. [1,9] state, "The genome of a holobiont, termed the hologenome, is the sum of the genomes of all constituents, all of which can evolve within that context." That last phrase is sufficiently general that it can be interpreted in any number of ways. Like physical conditions, associated organisms can be considered as part of the environment and thus can be sources of natural selection, affecting evolution in each lineage.But a more sweeping and problematic proposal is given by originators of the term, which is that "the holobiont with its hologenome should be considered as the unit of natural selection in evolution" [2,7] or by others, that “an organism’s genetics and fitness are inclusive of its microbiome” [3,4]. The implication is that differential success of holobionts influences evolution of participating organisms, such that their observed features cannot be fully understood without considering selection at the holobiont level. Another formulation of this concept is the proposal that the evolution of host–microbe systems is “most easily understood by equating a gene in the nuclear genome to a microbe in the microbiome” [8]. Under this view, interactions between host and microbial genotypes should be considered as genetic epistasis (interactions among alleles at different loci in a genome) rather than as interactions between the host’s genotype and its environment.While biologists would agree that microorganisms have important roles in host evolution, this statement is a far cry from the claim that they are fused with hosts to form the primary units of selection, or that hosts and microorganisms provide different portions of a unified genome. Broadly, the hologenome concept contends, first, that participating lineages within a holobiont affect each other’s evolution, and, second, that that the holobiont is a primary unit of selection. Our aim in this essay is to clarify what kinds of evidence are needed for each of these claims and to argue that neither should be assumed without evidence. We point out that some observations that superficially appear to support the concept of the hologenome have spawned confusion about real biological issues (Box 1).

Box 1. Misconceptions Related to the Hologenome Concept

Misconception #1: Similarities in microbiomes between related host species result from codiversification. Reality: Related species tend to be similar in most traits. Because microbiome composition is a trait that involves living organisms, it is tempting to assume that these similarities reflect a shared evolutionary history of host and symbionts. This has been shown to be the case for some symbioses (e.g., ancient maternally inherited endosymbionts in insects). But for many interactions (e.g., gut microbiota), related hosts may have similar effects on community assembly without any history of codiversification between the host and individual microbial species (Fig 1B).Open in a separate windowFig 1Alternative evolutionary processes can result in related host species harboring similar symbiont communities.Left panel: Individual symbiont lineages retain fidelity to evolving host lineages, through co-inheritance or other mechanisms, with some gain and loss of symbiont lineages over evolutionary time. Right panel: As host lineages evolve, they shift their selectivity of environmental microbes, which are not evolving in response and which may not even have been present during host diversification. In both cases, measures of community divergence will likely be smaller for more closely related hosts, but they reflect processes with very different implications for hologenome evolution. Image credit: Nancy Moran and Kim Hammond, University of Texas at Austin. Misconception #2: Parallel phylogenies of host and symbiont, or intimacy of host and symbiont associations, reflect coevolution. Reality: Coevolution is defined by a history of reciprocal selection between parties. While coevolution can generate parallel phylogenies or intimate associations, these can also result from many other mechanisms. Misconception #3: Highly intimate associations of host and symbionts, involving exchange of cellular metabolites and specific patterns of colonization, result from a history of selection favoring mutualistic traits. Reality: The adaptive basis of a specific trait is difficult to infer even when the trait involves a single lineage, and it is even more daunting when multiple lineages contribute. But complexity or intimacy of an interaction does not always imply a long history of coevolution nor does it imply that the nature of the interaction involves mutual benefit. Misconception #4: The essential roles that microbial species/communities play in host development are adaptations resulting from selection on the symbionts to contribute to holobiont function. Reality: Hosts may adapt to the reliable presence of symbionts in the same way that they adapt to abiotic components of the environment, and little or no selection on symbiont populations need be involved. Misconception #5: Because of the extreme importance of symbionts in essential functions of their hosts, the integrated holobiont represents the primary unit of selection. Reality: The strength of natural selection at different levels of biological organization is a central issue in evolutionary biology and the focus of much empirical and theoretical research. But insofar as there is a primary unit of selection common to diverse biological systems, it is unlikely to be at the level of the holobiont. In particular cases, evolutionary interests of host and symbionts can be sufficiently aligned such that the predominant effect of natural selection on genetic variation in each party is to increase the reproductive success of the holobiont. But in most host–symbiont relationships, contrasting modes of genetic transmission will decouple selection pressures.     

Which New Health Technologies Do We Need to Achieve an End to HIV/AIDS?

In the last 15 years, antiretroviral therapy (ART) has been the most globally impactful life-saving development of medical research. Antiretrovirals (ARVs) are used with great success for both the treatment and prevention of HIV infection. Despite these remarkable advances, this epidemic grows relentlessly worldwide. Over 2.1 million new infections occur each year, two-thirds in women and 240,000 in children. The widespread elimination of HIV will require the development of new, more potent prevention tools. Such efforts are imperative on a global scale. However, it must also be recognised that true containment of the epidemic requires the development and widespread implementation of a scientific advancement that has eluded us to date—a highly effective vaccine. Striving for such medical advances is what is required to achieve the end of AIDS.In the last 15 years, antiretroviral therapy (ART) has been the most globally impactful life-saving development of medical research. Antiretrovirals (ARVs) are used with great success for both the treatment and prevention of HIV infection. In the United States, the widespread implementation of combination ARVs led to the virtual eradication of mother-to-child transmission of HIV from 1,650 cases in 1991 to 110 cases in 2011, and a turnaround in AIDS deaths from an almost 100% five-year mortality rate to a five-year survival rate of 91% in HIV-infected adults [1]. Currently, the estimated average lifespan of an HIV-infected adult in the developed world is well over 40 years post-diagnosis. Survival rates in the developing world, although lower, are improving: in sub-Saharan Africa, AIDS deaths fell by 39% between 2005 and 2013, and the biggest decline, 51%, was seen in South Africa [2].Furthermore, the association between ART, viremia, and transmission has led to the concept of “test and treat,” with the hope of reducing community viral load by testing early and initiating treatment as soon as a diagnosis of HIV is made [3]. Indeed, selected regions of the world have begun to actualize the public health value of ARVs, from gains in life expectancy to impact on onward transmission, with a potential 1% decline in new infections for every 10% increase in treatment coverage [2]. In September 2015, WHO released new guidelines removing all limitations on eligibility for ART among people living with HIV and recommending pre-exposure prophylaxis (PrEP) to population groups at significant HIV risk, paving the way for a global onslaught on HIV [4].Despite these remarkable advances, this epidemic grows relentlessly worldwide. Over 2.1 million new infections occur each year, two-thirds in women and 240,000 in children [2]. In heavily affected countries, HIV infection rates have only stabilized at best: the annualized acquisition rates in persons in their first decade of sexual activity average 3%–5% yearly in southern Africa [5–7]. These figures are hardly compatible with the international health community’s stated goal of an “AIDS-free generation” [8,9]. In highly resourced settings, microepidemics of HIV still occur, particularly among gays, bisexuals, and men who have sex with men (MSM) [10]. HIV epidemics are expanding in two geographic regions in 2015—the Middle East/North Africa and Eastern Europe/Central Asia—largely due to challenges in implementing evidence-based HIV policies and programmes [2]. Even for the past decade in the US, almost 50,000 new cases recorded annually, two-thirds among MSM, has been a stable figure for years and shows no evidence of declining [1].While treatment scale-up, medical male circumcision [11], and the implementation of strategies to prevent mother-to-child transmission [12] have received global traction, systemic or topical ARV-based biomedical advances to prevent sexual acquisition of HIV have, as yet, made limited impressions on a population basis, despite their reported efficacy. Factors such as their adherence requirements, cost, potential for drug resistance, and long-term feasibility have restricted the appetite for implementation, even though these approaches may reduce HIV incidence in select populations.Already, several trials have shown that daily oral administration of the ARV tenofovir disoproxil fumarate (TDF), taken singly or in combination with emtricitabine, as PrEP by HIV-uninfected individuals, reduces HIV acquisition among serodiscordant couples (where one partner is HIV-positive and the other is HIV-negative) [13], MSM [14], at-risk men and women [15], and people who inject drugs [16,17] by between 44% and 75%. Long-acting injectable antiretroviral agents such as rilpivirine and cabotegravir, administered every two and three months, respectively, are also being developed for PrEP. All of these PrEP approaches are dependent on repeated HIV testing and adherence to drug regimens, which may challenge effectiveness in some populations and contexts.The widespread elimination of HIV will require the development of new, more potent prevention tools. Because HIV acquisition occurs subclinically, the elimination of HIV on a population basis will require a highly effective vaccine. Alternatively, if vaccine development is delayed, supplementary strategies may include long-acting pre-exposure antiretroviral cocktails and/or the administration of neutralizing antibodies through long-lasting parenteral preparations or the development of a “genetic immunization” delivery system, as well as scaling up delivery of highly effective regimens to eliminate mother-to-child HIV transmission (Fig 1).Open in a separate windowFig 1Medical interventions required to end the epidemic of HIV.Image credit: Glenda Gray.     

Whisking,Sniffing, and the Hippocampal θ-Rhythm: A Tale of Two Oscillators

The hippocampus has unique access to neuronal activity across all of the neocortex. Yet an unanswered question is how the transfer of information between these structures is gated. One hypothesis involves temporal-locking of activity in the neocortex with that in the hippocampus. New data from the Matthew E. Diamond laboratory shows that the rhythmic neuronal activity that accompanies vibrissa-based sensation, in rats, transiently locks to ongoing hippocampal θ-rhythmic activity during the sensory-gathering epoch of a discrimination task. This result complements past studies on the locking of sniffing and the θ-rhythm as well as the relation of sniffing and whisking. An overarching possibility is that the preBötzinger inspiration oscillator, which paces whisking, can selectively lock with the θ-rhythm to traffic sensorimotor information between the rat’s neocortex and hippocampus.The hippocampus lies along the margins of the cortical mantle and has unique access to neuronal activity across all of the neocortex. From a functional perspective, the hippocampus forms the apex of neuronal processing in mammals and is a key element in the short-term working memory, where neuronal signals persist for tens of seconds, that is independent of the frontal cortex (reviewed in [1,2]). Sensory information from multiple modalities is highly transformed as it passes from primary and higher-order sensory areas to the hippocampus. Several anatomically defined regions that lie within the temporal lobe take part in this transformation, all of which involve circuits with extensive recurrent feedback connections (reviewed in [3]) (Fig 1). This circuit motif is reminiscent of the pattern of connectivity within models of associative neuronal networks, whose dynamics lead to the clustering of neuronal inputs to form a reduced set of abstract representations [4] (reviewed in [5]). The first way station in the temporal lobe contains the postrhinal and perirhinal cortices, followed by the medial and lateral entorhinal cortices. Of note, olfactory input—which, unlike other senses, has no spatial component to its representation—has direct input to the lateral entorhinal cortex [6]. The third structure is the hippocampus, which contains multiple substructures (Fig 1).Open in a separate windowFig 1Schematic view of the circuitry of the temporal lobe and its connections to other brain areas of relevance.Figure abstracted from published results [7–15]. Composite illustration by Julia Kuhl.The specific nature of signal transformation and neuronal computations within the hippocampus is largely an open issue that defines the agenda of a great many laboratories. Equally vexing is the nature of signal transformation as the output leaves the hippocampus and propagates back to regions in the neocortex (Fig 1)—including the medial prefrontal cortex, a site of sensory integration and decision-making—in order to influence perception and motor action. The current experimental data suggest that only some signals within the sensory stream propagate into and out of the hippocampus. What regulates communication with the hippocampus or, more generally, with structures within the temporal lobe? The results from studies in rats and mice suggest that the most parsimonious hypothesis, at least for rodents, involves the rhythmic nature of neuronal activity at the so-called θ-rhythm [16], a 5–10 Hz oscillation (reviewed in [17]). The origin of the rhythm is not readily localized to a single locus [10], but certainly involves input from the medial septum [17] (a member of the forebrain cholinergic system) as well as from the supramammillary nucleus [10,18] (a member of the hypothalamus). The medial septum projects broadly to targets in the hippocampus and entorhinal cortex (Fig 1) [10]. Many motor actions, such as the orofacial actions of sniffing, whisking, and licking, occur within the frequency range of the θ-rhythm [19,20]. Thus, sensory input that is modulated by rhythmic self-motion can, in principle, phase-lock with hippocampal activity at the θ-rhythm to ensure the coherent trafficking of information between the relevant neocortical regions and temporal lobe structures [21–23].We now shift to the nature of orofacial sensory inputs, specifically whisking and sniffing, which are believed to dominate the world view of rodents [19]. Recent work identified a premotor nucleus in the ventral medulla, named the vibrissa region of the intermediate reticular zone, whose oscillatory output is necessary and sufficient to drive rhythmic whisking [24]. While whisking can occur independently of breathing, sniffing and whisking are synchronized in the curious and aroused animal [24,25], as the preBötzinger complex in the medulla [26]—the oscillator for inspiration—paces whisking at nominally 5–10 Hz through collateral projections [27]. Thus, for the purposes of reviewing evidence for the locking of orofacial sensory inputs to the hippocampal θ-rhythm, we confine our analysis to aroused animals that function with effectively a single sniff/whisk oscillator [28].What is the evidence for the locking of somatosensory signaling by the vibrissae to the hippocampal θ-rhythm? The first suggestion of phase locking between whisking and the θ-rhythm was based on a small sample size [29,30], which allowed for the possibility of spurious correlations. Phase locking was subsequently reexamined, using a relatively large dataset of 2 s whisking epochs, across many animals, as animals whisked in air [31]. The authors concluded that while whisking and the θ-rhythm share the same spectral band, their phases drift incoherently. Yet the possibility remained that phase locking could occur during special intervals, such as when a rat learns to discriminate an object with its vibrissae or when it performs a memory-based task. This set the stage for a further reexamination of this issue across different epochs in a rewarded task. Work from Diamond''s laboratory that is published in the current issue of PLOS Biology addresses just this point in a well-crafted experiment that involves rats trained to perform a discrimination task.Grion, Akrami, Zuo, Stella, and Diamond [32] trained rats to discriminate between two different textures with their vibrissae. The animals were rewarded if they turned to a water port on the side that was paired with a particular texture. Concurrent with this task, the investigators also recorded the local field potential in the hippocampus (from which they extracted the θ-rhythm), the position of the vibrissae (from which they extracted the evolution of phase in the whisk cycle), and the spiking of units in the vibrissa primary sensory cortex. Their first new finding is a substantial increase in the amplitude of the hippocampal field potential at the θ-rhythm frequency—approximately 10 Hz for the data of Fig 2A—during the two, approximately 0.5 s epochs when the animal approaches the textures and whisks against it. There is significant phase locking between whisking and the hippocampal θ-rhythm during both of these epochs (Fig 2B), as compared to a null hypothesis of whisking while the animal whisked in air outside the discrimination zone. Unfortunately, the coherence between whisking and the hippocampal θ-rhythm could not be ascertained during the decision, i.e., turn and reward epochs. Nonetheless, these data show that the coherence between whisking and the hippocampal θ-rhythm is closely aligned to epochs of active information gathering.Open in a separate windowFig 2Summary of findings on the θ-rhythm in a rat during a texture discrimination task, derived from reference [32]. (A) Spectrogram showing the change in spectral power of the local field potential in the hippocampal area CA1 before, during, and after a whisking-based discrimination task. (B) Summary index of the increase in coherence between the band-limited hippocampal θ-rhythm and whisking signals during approach of the rat to the stimulus and subsequent touch. The index reports 〈sin(ϕH−ϕW)〉2+〈cos(ϕH−ϕW)〉2, where ɸ_H and ɸ_W are the instantaneous phase of the hippocampal and whisking signals, respectively, and averaging is over all trials and animals. (C) Summary indices of the increase in coherence between the band-limited hippocampal θ-rhythm and the spiking signal in the vibrissa primary sensory cortex (“barrel cortex”). The magnitude of the index for each neuron is plotted versus phase in the θ-rhythm. The arrows show the concentration of units around the mean phase—black arrows for the vector average across only neurons with significant phase locking (solid circles) and gray arrows for the vector average across all neurons (open and closed circles). The concurrent positions of the vibrissae are indicated. The vector average is statistically significant only for the approach (p < 0.0001) and touch (p = 0.04) epochs.The second finding by Grion, Akrami, Zuo, Stella, and Diamond [32] addresses the relationship between spiking activity in the vibrissa primary sensory cortex and the hippocampal θ-rhythm. The authors find that spiking is essentially independent of the θ-rhythm outside of the task (foraging in Fig 2C), similar to the result for whisking and the θ-rhythm (Fig 2B). They observe strong coherence between spiking and the θ-rhythm during the 0.5 s epoch when the animal approaches the textures (approach in Fig 2C), yet reduced (but still significant) coherence during the touch epoch (touch in Fig 2C). The latter result is somewhat surprising, given past work from a number of laboratories that observe spiking in the primary sensory cortex and whisking to be weakly yet significantly phase-locked during exploratory whisking [33–37]. Perhaps overtraining leads to only a modest need for the transfer of sensory information to the hippocampus. Nonetheless, these data establish that phase locking of hippocampal and sensory cortical activity is essentially confined to the epoch of sensory gathering.Given the recent finding of a one-to-one locking of whisking and sniffing [24], we expect to find direct evidence for the phase locking of sniffing and the θ-rhythm. Early work indeed reported such phase locking [38] but, as in the case of whisking [29], this may have been a consequence of too small a sample and, thus, inadequate statistical power. However, Macrides, Eichenbaum, and Forbes [39] reexamined the relationship between sniffing and the hippocampal θ-rhythm before, during, and after animals sampled an odorant in a forced-choice task. They found evidence that the two rhythms phase-lock within approximately one second of the sampling epoch. We interpret this locking to be similar to that seen in the study by Diamond and colleagues (Fig 2B) [32]. All told, the combined data for sniffing and whisking by the aroused rodent, as accumulated across multiple laboratories, suggest that two oscillatory circuits—the supramammillary nucleus and medial septum complex that drives the hippocampal θ-rhythm and the preBötzinger complex that drives inspiration and paces the whisking oscillator during sniffing (Fig 1)—can phase-lock during epochs of gathering sensory information and likely sustain working memory.What anatomical pathway can lead to phase locking of these two oscillators? The electrophysiological study of Tsanov, Chah, Reilly, and O’Mara [9] supports a pathway from the medial septum, which is driven by the supramammillary nucleus, to dorsal pontine nuclei in the brainstem. The pontine nucleus projects to respiratory nuclei and, ultimately, the preBötzinger oscillator (Fig 1). This unidirectional pathway can, in principle, entrain breathing and whisking. Phase locking is not expected to occur during periods of basal breathing, when the breathing rate and θ-rhythm occur at highly incommensurate frequencies. However, it remains unclear why phase locking occurs only during a selected epoch of a discrimination task, whereas breathing and the θ-rhythm occupy the same frequency band during the epochs of approach, as well as touch-based target selection (Fig 2A). While a reafferent pathway provides the rat with information on self-motion of the vibrissae (Fig 1), it is currently unknown whether that information provides feedback for phase locking.A seeming requirement for effective communication between neocortical and hippocampal processing is that phase locking must be achieved at all possible phases of the θ-rhythm. Can multiple phase differences between sensory signals and the hippocampal θ-rhythm be accommodated? Two studies report that the θ-rhythm undergoes a systematic phase-shift along the dorsal–ventral axis of the hippocampus [40,41], although the full extent of this shift is only π radians [41]. In addition, past work shows that vibrissa input during whisking is represented among all phases of the sniff/whisk cycle, at levels from primary sensory neurons [42,43] through thalamus [44,45] and neocortex [33–37], with a bias toward retraction from the protracted position. A similar spread in phase occurs for olfactory input, as observed at the levels of the olfactory bulb [46] and cortex [47]. Thus, in principle, the hippocampus can receive, transform, and output sensory signals that arise over all possible phases in the sniff/whisk cycle. In this regard, two signals that are exactly out-of-phase by π radians can phase-lock as readily as signals that are in-phase.What are the constraints for phase locking to occur within the observed texture identification epochs? For a linear system, the time to lock between an external input and hippocampal theta depends on the observed spread in the spectrum of the θ-rhythm. This is estimated as Δf ~3 Hz (half-width at half-maximum amplitude), implying a locking time on the order of 1/Δf ~0.3 s. This is consistent with the approximate one second of enhanced θ-rhythm activity observed in the study by Diamond and colleagues (Fig 2A) [32] and in prior work [39,48] during a forced-choice task with rodents.Does the θ-rhythm also play a role in the gating of output from the hippocampus to areas of the neocortex? Siapas, Lubenov, and Wilson [48] provided evidence that hippocampal θ-rhythm phase-locks to electrical activity in the medial prefrontal cortex, a site of sensory integration as well as decision-making. Subsequent work [49–51] showed that the hippocampus drives the prefrontal cortex, consistent with the known unidirectional connectivity between Cornu Ammonis area 1 (CA1) of the hippocampus and the prefrontal cortex [11] (Fig 1). Further, phase locking of hippocampal and prefrontal cortical activity is largely confined to the epoch of decision-making, as opposed to the epoch of sensory gathering. Thus, over the course of approximately one second, sensory information flows into and then out of the hippocampus, gated by phase coherence between rhythmic neocortical and hippocampal neuronal activity.It is of interest that the medial prefrontal cortex receives input signals from sensory areas in the neocortex [52] as well as a transformed version of these input signals via the hippocampus (Fig 1). Yet it remains to be determined if this constitutes a viable hub for the comparison of the original and transformed signals. In particular, projections to the medial prefrontal cortex arise from the ventral hippocampus [2], while studies on the phase locking of hippocampal θ-rhythm to prefrontal neocortical activity were conducted in dorsal hippocampus, where the strength of the θ-rhythm is strong compared to the ventral end [53]. Therefore, similar recordings need to be performed in the ventral hippocampus. An intriguing possibility is that the continuous phase-shift of the θ-rhythm along the dorsal to the ventral axis of the hippocampus [40,41] provides a means to encode the arrival of novel inputs from multiple sensory modalities relative to a common clock.A final issue concerns the locking between sensory signals and hippocampal neuronal activity in species that do not exhibit a continuous θ-rhythm, with particular reference to bats [54–56] and primates [57–60]. One possibility is that only the up and down swings of neuronal activity about a mean are important, as opposed to the rhythm per se. In fact, for animals in which orofacial input plays a relatively minor role compared to rodents, such a scheme of clocked yet arrhythmic input may be a necessity. In this case, the window of processing is set by a stochastic interval between transitions, as opposed to the periodicity of the θ-rhythm. This may imply that up/down swings of neuronal activity may drive hippocampal–neocortical communications in all species, with communication mediated via phase-locked oscillators in rodents and via synchronous fluctuations in bats and primates. The validity of this scheme and its potential consequence on neuronal computation remains an open issue and a focus of ongoing research.     

Evaluation of portable devices for medicine quality screening: Lessons learnt,recommendations for implementation,and future priorities

Céline Caillet and co-authors discuss a Collection on use of portable devices for the evaluation of medicine quality and legitimacy.

Summary points

• Portable devices able to detect substandard and falsified medicines are vital innovations for enhancing the inspection of medicines in pharmaceutical supply chains and for timely action before they reach patients. Such devices exist, but there has been little to no independent scientific evidence of their accuracy and cost-effectiveness to guide regulatory authorities in choosing appropriate devices for their settings.
• We tested 12 portable devices, evaluated their diagnostic performances and the resources required to use each device in a laboratory.
• We then assessed the utility and usability of the devices in medicine inspectors’ hands in a pharmacy mimicking a real-life Lao pharmacy.
• We then assessed the health and economic benefits of using portable devices compared to not using them in a low- to middle-income setting.
• Here, we discuss the conclusions and practical implications of the multiphase study discussed in this Collection. We discuss the results, highlight the evidence gaps, and provide recommendations on the key aspects to consider in the implementation of portable devices and their main advantages and limitations.

Global concerns over the quality of medicines, especially in low- and middle-income countries (LMICs) are exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic [1,2]. The World Health Organisation (WHO) estimated that 10.5% of medicines in LMICs may be substandard or falsified (SF) [3]. “Prevention, detection, and response” to SF medical products are strategic priorities of WHO to contribute to effective and efficient regulatory systems [4]. Numerous portable medicine screening devices are available on the market, holding great hope for detection of SF medicines in an efficient and timely manner, and, therefore, might serve as key detection tools to inform prevention and response [5,6]. Screening devices have the potential to rapidly identify suspected SF medical products, giving more objective selection for reference assays, reducing the financial and technical burden. However, little is known regarding how well the existing devices fulfil their functions and how they could be deployed within risk-based postmarketing surveillance (rb-PMS) systems [5–7].We conducted, during 2016 to 2018, a collaborative multiphase exploratory study aimed at comparing portable screening devices. This paper accompanies 4 papers in this PLOS Collection “A multiphase evaluation of portable screening devices to assess medicines quality for national Medicines Regulatory Authorities.” The first article introduced the multiphase study [8]. In brief, 12 devices (S1 Table) were first evaluated in a laboratory setting [9], to select the most field-suitable devices for further evaluation of their utility/usability by Lao medicines inspectors [10]. Cost-effectiveness analysis of their implementation for rb-PMS in Laos was also conducted [11]. The results of these 3 phases were discussed in a multistakeholder meeting in 2018 in Vientiane, Lao PDR (S1 Text). The advantages/disadvantages, cost-effectiveness, and optimal use of screening devices in medicine supply chains were discussed to develop policy recommendations for medicines regulatory authorities (MRAs) and other institutions who wish to implement screening technologies. A summary of the main results of the multiphase study is presented in S2 Table.As far as we are aware, this is the first independent investigation comparing the accuracy and practical use from a public health perspective, of a diverse set of portable medicine quality screening devices. The specific objective(s) for which the portable screening technologies are implemented, their advantages/limitations, costs and logistics, and the development of detailed standard operating procedures and training programmes are key points to be carefully addressed when considering selection and deployment of screening technologies within specific rb-PMS systems (Fig 1).Open in a separate windowFig 1Major proposed considerations for the selection and implementation of medicine quality screening device.Each circle represents a key consideration when purchasing a screening device, grouped by themes (represented by heptagons). When the shapes overlap, the considerations are connected. For example, standard operating procedures are needed for the implementation of devices and should include measures for user safety. The circle diameters are illustrative.Here, we utilise this research and related literature to discuss the evidence, gaps, and recommendations, complementary to those recently published by the US Pharmacopeial Convention [12]. These discussions can inform policy makers, non-governmental organisations, wholesalers/distributors, and hospital pharmacies considering the implementation of such screening devices. We discuss unanswered research questions that require attention to ensure that the promise these devices hold is realised.     

Honing the Priorities and Making the Investment Case for Global Health

In the aftermath of the Ebola crisis, the global health community has a unique opportunity to reflect on the lessons learned and apply them to prepare the world for the next crisis. Part of that preparation will entail knowing, with greater precision, what the scale and scope of our specific global health challenges are and what resources are needed to address them. However, how can we know the magnitude of the challenge, and what resources are needed without knowing the current status of the world through accurate primary data? Once we know the current status, how can we decide on an intervention today with a predicted impact decades out if we cannot project into that future? Making a case for more investments will require not just better data generation and sharing but a whole new level of sophistication in our analytical capability—a fundamental shift in our thinking to set expectations to match the reality. In this current status of a distributed world, being transparent with our assumptions and specific with the case for investing in global health is a powerful approach to finding solutions to the problems that have plagued us for centuries.When we have proactively set our sights on large and defined obstacles to human wellness, the global health community has been able to chart a course toward lasting, widespread impact. However, few would argue that the global health community’s response to Ebola—while ultimately effective—was the optimal way to anticipate and address a global health crisis. Comprehensive analyses have been conducted on what worked well and what didn’t [1]. Despite all the failings that led to over 11,000 deaths and an estimated US$1.6 billion in costs to the economy in Guinea, Sierra Leone, and Liberia, the global community did come together and help turn the tide against the epidemic—albeit more slowly than what could have been possible with a better-prepared world [2,3]. Major funding commitments were made when the reality and urgency of the epidemic became evident [4]. Another point that may not be widely known is that the private sector responded to the challenge by directing significant resources to develop vaccines, drugs, and diagnostics at an unprecedented pace. As a result, we now have four vaccine candidates, three therapeutics in Phase III clinical trials, and six diagnostics authorized for emergency use by WHO [5].At the turn of the millennium, the global community sought to address the far more complex problem of vaccination coverage. In 2000, the glaring disparity in vaccine access between wealthy and developing nations led to the formation of Gavi, the Vaccine Alliance [6]. After 15 years, Gavi has helped create a roadmap for countries to ramp up their immunization programs—reaching nearly half a billion additional children with vaccines [7]. Through its multisector partnership, Gavi not only addressed the huge challenge of improving childhood vaccination coverage, but it also provided certainty to the private sector, encouraging it to manufacture products for developing country markets and to make them affordable. In 2015, donors came together again and made US$7.5 billion in pledges, the largest ever financial commitment to support childhood immunization [8].We can find a comparable example in the sobering problem of tuberculosis (TB), in which the battle is being fought with a decades-old and unwieldy six-month treatment regimen of diminishing efficacy due to multidrug resistance. In 2013, TB made an estimated 9 million people sick, and 1.5 million people died from the disease [9]. However, the fight against TB is being reinvigorated. The TB Drug Accelerator (TBDA) is a groundbreaking partnership among eight pharmaceutical companies, seven research institutions, and a product development partnership funded by the Bill & Melinda Gates Foundation [10]. By driving collaboration and data sharing atypical of its partners, the TBDA’s overall goal is to create a new TB drug regimen that cures patients in only one month, replacing the outmoded intervention we have today. While the structure and purpose of the TBDA took rigorous iteration to get where it is today, the data and expertise shared among its partners has already identified compounds that could potentially lead to a more effective treatment.Although it might seem that the motivations behind the investments in these three cases are different—a potential regional or global health catastrophe in the case of Ebola, a humanitarian imperative underlying Gavi, and a dual global drug resistance threat and humanitarian basis for the TBDA—there is a common thread. These examples show that when there is imminent and clear need, we have been able to mobilize resources and construct creative partnerships to create an impact. Summers et al. make the compelling case for investing in global health by showing the general economic benefit of those investments [11]. Similarly, others have claimed a substantial return on investment in specific areas of health science as a motivation for future investments [12]. These cases are fairly general in their content, and we see modern investors in global health (whether countries or philanthropists) as being much more demanding in terms of wanting to know exactly how their resources are deployed and the impact that could be expected.At the Bill & Melinda Gates Foundation, we are exploring a set of approaches that start with our current (and improving) knowledge of the state of the burden of diseases relevant to low- and middle-income countries (LMICs) and comparing the potential interventions we have to reduce this burden along a number of dimensions. The ultimate objective is to arrive at a view of the actionable priorities that we can support at any one time in order to maximize our impact on health and wellbeing in communities with the highest burden. This approach has some parallels with portfolio analysis in the biopharmaceutical industry [13], but the very sparse and poor quality of the underlying primary data in global health means that, at best, we can rely on this as a rough guide and a mechanism for exposing outliers in cost, effectiveness, and impact (Fig 1). This approach provides a framework for comparison across diverse categories through a metric that is understandable. More importantly, it forces us to state our assumptions explicitly for debate and reconciliation. However, we also need to be cautious about any notion that the complex sociopolitical environments we work in and the fluctuating humanitarian crises that arise can ever be reduced to simple algorithms for decision-making.Open in a separate windowFig 1Portfolio analysis for global health impact.Cost per disability-adjusted life year (DALY) averted is the incremental cost to deliver incremental DALY savings versus only the standard of care. Probability of success is the estimate of probability of technical and regulatory success (PTRS) informed by industry benchmarks and expert opinion. NRRV: Non-replicating rotavirus vaccine. Both the cost and the probability of success are dynamic values and subject to change with information that is constantly evolving.Although our understanding of the burden of disease has improved tremendously at a national and subnational level for important pathogens, as evidenced by a recent integration of our knowledge of the spatial distribution of the risk of malaria in sub-Saharan Africa [14], we need to invest much more heavily in obtaining better primary data. We therefore recently launched CHAMPS, the Child Health and Mortality Prevention Surveillance Network [15]. CHAMPS will be a network of disease surveillance sites in LMICs that will help gather accurate data about how, where, and why children are getting sick and dying. For the first time in history, pathology-based surveillance will be used to track the causes of childhood mortality, complementing and improving upon existing cause-of-death information from verbal autopsy surveys and vital statistics. Through geospatial modeling and mapping, these new surveillance data will provide an increasingly broad and accurate picture to guide more effective use of the scarce resources for prevention and treatment.Improved data can also help drive progress against less familiar health challenges such as neglected tropical diseases (NTDs) [16]. Until recently, little was known about the geographical distribution of NTDs. Because of weak surveillance systems, the scarcity of geospatial mapping was greatest in sub-Saharan Africa, which has hampered deployment of effective programs. To address this, a WHO African Region-led effort has conducted thousands of field surveys using mobile phone data capture to complete the picture of NTDs across Africa. In addition to guiding disease control efforts, such as targeting of mass drug administrations only to places that need them, this mapping provides, for the first time, the necessary central database to allow analysis of program performance and to make projections of likely outcomes, including the probability of disease elimination.The framework we use to evaluate this data has a few simple dimensions: cost per disability-adjusted life year (DALY) averted, probability of technical success, and, at a more strategic level, whether our resources fill a real gap in the funding landscape (Fig 1). There are many alternative metrics, but we have chosen this scheme for its simplicity and augment it with additional analyses when these make sense. An important part of the framework related to work that might only be completed well into the future is understanding the spectrum of potential trajectories for future disease burden. Thus, forecasting becomes an essential element of decision-making; for this, we need to go beyond only linearly extrapolating future outcomes based on past trends. Much more sophisticated forecasting that integrates all significant covariates of the main outcome is becoming available [17] and will be increasingly useful for decision-making and the longitudinal evaluation of projects to assess whether interventions are shifting the envelope of outcomes in a positive direction.The system described above, which is already in use across parts of our global health portfolio, makes us optimistic that we can, in the near future, expand this approach to the entire portfolio and arrive at a more systematic way of understanding the inherent values and risks of a given intervention. This will also give us a clear picture of the huge and urgent problems in global health, paired with more deeply evaluated and cost-specific solutions, as well as a forecast of the negative consequences of inaction. Much as the world, or parts thereof, were mobilized by the Ebola crisis, the childhood vaccination gap, and the TB epidemic, we would then have maximized the likelihood of accessing new resources for potential solutions to ongoing global health crises.     

Characterizing the Transmission Dynamics and Control of Ebola Virus Disease

Carefully calibrated transmission models have the potential to guide public health officials on the nature and scale of the interventions required to control epidemics. In the context of the ongoing Ebola virus disease (EVD) epidemic in Liberia, Drake and colleagues, in this issue of PLOS Biology, employed an elegant modeling approach to capture the distributions of the number of secondary cases that arise in the community and health care settings in the context of changing population behaviors and increasing hospital capacity. Their findings underscore the role of increasing the rate of safe burials and the fractions of infectious individuals who seek hospitalization together with hospital capacity to achieve epidemic control. However, further modeling efforts of EVD transmission and control in West Africa should utilize the spatial-temporal patterns of spread in the region by incorporating spatial heterogeneity in the transmission process. Detailed datasets are urgently needed to characterize temporal changes in population behaviors, contact networks at different spatial scales, population mobility patterns, adherence to infection control measures in hospital settings, and hospitalization and reporting rates.Ebola virus disease (EVD) is caused by an RNA virus of the family Filoviridae and genus Ebolavirus. Five different Ebolavirus strains have been identified, namely Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Tai Forest ebolavirus (TAFV), Bundibugyo ebolavirus (BDBV), and Reston ebolavirus (RESTV). The great majority of past Ebola outbreaks in humans have been linked to three Ebola strains: EBOV, SUDV, and BDBV [1]. The Ebola virus ([EBOV] formerly designated Zaire ebolavirus) derived its name from the Ebola River, located near the epicenter of the first outbreak identified in 1976 in Zaire (now the Democratic Republic of Congo). EVD outbreaks among humans have been associated with direct human exposure to fruit bats—the most likely reservoir of the virus—or through contact with intermediate infected hosts, which include gorillas, chimpanzees, and monkeys. Outbreaks have been reported on average every 1.5 years [2]. Past EVD outbreaks have occurred in relatively isolated areas and have been limited in size and duration (Fig. 1). It has been recently estimated that about 22 million people living in areas of Central and West Africa are at risk of EVD [3].Open in a separate windowFigure 1Time series of the temporal progression of four past EVD outbreaks in Congo (1976, 1995, 2014) [4–6] and Uganda (2000) [7].An epidemic of EVD (EBOV) has been spreading in West Africa since December 2013 in Guinea, Liberia, and Sierra Leone [8]. A total of 18,603 cases, with 6,915 deaths, have been reported to the World Health Organization as of December 17, 2014 [9]. While the causative strain associated with this epidemic is closely related to that of past outbreaks in Central Africa [10], three key factors have contributed disproportionately to this unprecedented epidemic: (1) substantial delays in detection and implementation of control efforts in a region characterized by porous borders; (2) limited public health infrastructure including epidemiological surveillance systems and diagnostic testing [11], which are necessary for the timely diagnosis of symptomatic individuals, effective isolation of infectious individuals, contact tracing to rapidly identify new cases, and providing supportive care to increase the chances of survival to EVD infection; and (3) cultural practices that involve touching the body of the deceased and the association of illness with witchcraft or conspiracy theories.EBOV is transmitted by direct human-to-human contact via body fluids or indirect contact with contaminated surfaces, but it is not spread through the airborne route. Individuals become symptomatic after an average incubation period of 10 days (range 2–21 days) [12], and infectiousness is increased during the later stages of disease [13]. The characteristic symptoms of EVD are nonspecific and include sudden onset of fever, weakness, vomiting, diarrhea, headache, and a sore throat, while only a fraction of the symptomatic individuals present with hemorrhagic manifestations [14]. The case fatality risk (CFR), calculated as the proportion of deaths among the total number of EVD cases with known outcomes, has been estimated from data of the first 9 months of the epidemic in West Africa at 70.8% (95% CI 68.6–72.8), in broad agreement with estimates from past outbreaks [12].Two important quantities to understand in the transmission dynamics of EVD are the serial interval and the basic reproduction number. The serial interval is defined as the time from illness onset in a primary case to illness onset in a secondary case [15] and has been estimated at 15 days on average for the ongoing epidemic [12]. The basic reproduction number, R ₀, quantifies transmission potential at the beginning of an epidemic and is defined as the average number of secondary cases generated by a typical infected individual during the early phase of an epidemic, before interventions are put in place [16]. If R ₀ < 1, transmission is not sufficient to generate a major epidemic. In contrast, a major epidemic is likely to occur whenever R ₀ > 1. When transmission potential is measured over time t, the effective reproduction number R_t, can be helpful to quantify the time-dependent transmission potential resulting from the effect of control interventions and behavior changes [17]. Estimates of R ₀ for the ongoing epidemic in West Africa have fluctuated around 2 with some uncertainty (e.g., [12, 18–22]), which are in good agreement with estimates from past EVD outbreaks [23]. R ₀ could also vary across regions as a function of the local public health infrastructure (e.g., availability of health care settings and infection control protocols), such that an outbreak may be very unlikely to unfold in developed countries simply as a result of baseline infection control measures in place (i.e., R ₀ < 1) while poor countries with extremely weak or absent public health systems may be unable to control an Ebola outbreak (i.e., R ₀ > 1).Mathematical models of disease transmission have proved to be useful tools to characterize the transmission dynamics of infectious diseases and evaluate the effects of control intervention strategies in order to inform public health policy [16, 24, 25]. There are a limited number of mathematical models for the transmission and control of EVD, but a number of efforts are underway in the context of the epidemic in West Africa. The transmission dynamics of EVD have been modeled on the basis of the simple compartmental susceptible-exposed-infectious-removed (SEIR) model that assumes a homogenously mixed population [23]. The modeled population can be structured according to the contributions of community, hospital, and unsafe burials to transmission as EVD transmission has been amplified in health care settings with ineffective infection control measures and during unsafe burials [23]. A schematic representation of the main transmission pathways of EVD is shown in Fig. 2.Open in a separate windowFigure 2Schematic representation of the transmission dynamics of Ebola virus disease.A recent study published in PLOS Biology by Drake and colleagues [26] presents an interesting and flexible modeling framework for the transmission and control of EVD in Liberia. Their framework is based on a multi-type branching process model in which “multi-type” refers to the consideration of two types of settings where transmission can occur, while “branching process” is the mathematical term to specify a probabilistic model. For instance, in the case of a single-type branching process, the transmission dynamics are simply described using a single reproduction number, i.e., the average number of secondary cases produced by a single primary case. However, when two types of hosts are considered in the transmission process, two reproduction numbers are needed to characterize within-group mixing (e.g., within-hospital and within-community transmission) and two reproduction numbers characterize transmission between groups (e.g., transmission from hospital to community and vice versa).Drake and colleague’s elegant modeling approach describes EVD transmission according to infection generations by calculating probability distributions of the number of secondary cases that arise in the community via nursing care or during unsafe burials and in health care settings via infections to health care workers and visitors. The model explicitly accounts for the hospitalization rate—the fraction of infectious individuals in the community seeking hospitalization (estimated in this study at 60%). However, the number of effectively isolated infectious individuals is constrained by the number of available beds in treatment centers—which are assumed in this study to operate at twice their regular capacity. It is important to note that the number of beds available to treat EVD patients was severely limited in Liberia prior to mid August 2014 (Fig. 1 in [26]). Moreover, the rate of safe burials that reduces the force of infection is included in their model as an increasing function of time. The model was calibrated by tuning six parameters to fit the trajectories of the number of reported cases in the community and among health care workers during the period 4 July to 2 September 2014 for a total of four infection generations during which the effective reproduction number was estimated to decline on average from about 2.8 to 1.4. The model was able to effectively capture heterogeneity in transmission of EVD in both the community and hospital settings.Drake and colleagues [26] employed their calibrated model to forecast the epidemic trajectory in Liberia from 3 September to 31 December 2014 under different scenarios that account for an increasing fraction of cases seeking hospitalization and a surge in the number of beds available to isolate and treat EVD patients. Their results indicate that allocating 1,700 additional beds (100 new beds every 4 days) in new Ebola treatment centers committed by US aid reduces the mean epidemic size to ~51,000 (60% reduction with respect to the baseline scenario), while epidemic control by mid-March is only plausible through a 4-fold increase in the number of beds committed by US aid and enhancing the hospitalization rate from 60% to 99% for a final epidemic size of 12,285. Moreover, an additional epidemic forecast incorporating data up to 1 December 2014 indicated that containment could be achieved between March and June 2015.Other interventions were not explicitly incorporated in their model because it is difficult to parameterize them in the absence of datasets that permit statistical estimation of their impact on the transmission dynamics. These additional interventions include the use of household protection kits, designed to reduce transmission in the community; improvements in infection control protocols in health care settings that reduce transmission among health care workers; and the impact of rapid diagnostic kits in Ebola treatment centers, which reduce the time to isolation for infectious individuals seeking hospitalization. Increasing awareness and education of the population about the disease could have also yielded further reductions in case incidence by reducing the size of the at-risk susceptible population (Fig. 3) [27]. Nevertheless, some of these effects could have been indirectly captured implicitly by the time-dependent safe burial rate parameter in their model.Open in a separate windowFigure 3Contrasting epidemic growth in the presence and absence of behavior changes that reduce the transmission rate.Importantly, prior models of EVD transmission [23, 28, 29] and the model by Drake and colleagues have not incorporated spatial heterogeneity in the transmission dynamics. In particular, the EVD epidemic in West Africa can be characterized as a set of asynchronous local (e.g., district) epidemics that exhibit sub-exponential growth, which could be driven by a highly clustered underlying contact network or population behavior changes induced by the accumulation of morbidity and mortality rates (see Fig. 4 and [30]). EVD contagiousness is most pronounced in the later and more severe stages of Ebola infection when infectious individuals are confined at home or health care settings and mostly exposed to caregivers (e.g., health care workers, family members) [30]. This characterization would lead to EVD transmission over a network of contacts that is highly clustered (e.g., individuals are likely to share a significant fraction of their contacts), which is associated with significantly slower spread relative to the common random mixing assumption as illustrated in Fig. 5. The development of transmission models that incorporate spatial heterogeneity (e.g., by modeling spatial coupling or human migration) is currently limited by the shortage of detailed datasets from the EVD-affected areas about the geographic distribution of households, health care settings, reporting and hospitalization rates across urban and rural areas, and patterns of population mobility in the region. Some of these limitations may be overcome in the near future. For instance, cell phone data could provide a basis to characterize population mobility in the region at a refined spatial scale.Open in a separate windowFigure 4Representative time series of the cumulative number of EVD cases (in log scale) at the district level in Guinea, Sierra Leone, and Liberia.Open in a separate windowFigure 5Epidemic growth in two populations characterized by two different underlying contact networks.The ongoing epidemic in West Africa offers a unique opportunity to improve our current understanding of the transmission characteristics of EVD in humans. To achieve this goal, it is crucial to collect spatial-temporal data on population behaviors, contact networks, social distancing measures, and education campaigns. Datasets comprising detailed demographic, socio-economic, contact rates, and population mobility estimates in the region (e.g., commuting networks, air traffic) need to be integrated and made publicly available in order to develop highly resolved transmission models, which could guide control strategies with greater precision in the context of the EVD epidemic in West Africa. Although recent data from Liberia indicates that the epidemic is on track for eventual control, the epidemic in Sierra Leone continues an increasing trend, and in Guinea, case incidence roughly follows a steady trend. The potential impact of vaccines should also be incorporated in future modeling efforts as these pharmaceutical interventions are expected to become available in the upcoming months.