NOAA Office of National Marine Sanctuaries

BACKGROUND: The rising temperature of the world's oceans has become a major threat to coral reefs globally as the severity and frequency of mass coral bleaching and mortality events increase. In 2005, high ocean temperatures in the tropical Atlantic and Caribbean resulted in the most severe bleaching event ever recorded in the basin. METHODOLOGY/PRINCIPAL FINDINGS: Satellite-based tools provided warnings for coral reef managers and scientists, guiding both the timing and location of researchers' field observations as anomalously warm conditions developed and spread across the greater Caribbean region from June to October 2005. Field surveys of bleaching and mortality exceeded prior efforts in detail and extent, and provided a new standard for documenting the effects of bleaching and for testing nowcast and forecast products. Collaborators from 22 countries undertook the most comprehensive documentation of basin-scale bleaching to date and found that over 80% of corals bleached and over 40% died at many sites. The most severe bleaching coincided with waters nearest a western Atlantic warm pool that was centered off the northern end of the Lesser Antilles. CONCLUSIONS/SIGNIFICANCE: Thermal stress during the 2005 event exceeded any observed from the Caribbean in the prior 20 years, and regionally-averaged temperatures were the warmest in over 150 years. Comparison of satellite data against field surveys demonstrated a significant predictive relationship between accumulated heat stress (measured using NOAA Coral Reef Watch's Degree Heating Weeks) and bleaching intensity. This severe, widespread bleaching and mortality will undoubtedly have long-term consequences for reef ecosystems and suggests a troubled future for tropical marine ecosystems under a warming climate.

Many marine scientists have concluded that coral reefs are moving toward or are locked into a seaweed-dominated state. However, because there have been no regional- or global-scale analyses of such coral reef "phase shifts," the magnitude of this phenomenon was unknown. We analyzed 3581 quantitative surveys of 1851 reefs performed between 1996 and 2006 to determine the frequency, geographical extent, and degree of macroalgal dominance of coral reefs and of coral to macroalgal phase shifts around the world. Our results indicate that the replacement of corals by macroalgae as the dominant benthic functional group is less common and less geographically extensive than assumed. Although we found evidence of moderate local increases in macroalgal cover, particularly in the Caribbean, only 4% of reefs were dominated by macroalgae (i.e., > 50% cover). Across the Indo-Pacific, where regional averages of macroalgal cover were 9-12%, macroalgae only dominated 1% of the surveyed reefs. Between 1996 and 2006, phase shift severity decreased in the Caribbean, did not change in the Florida Keys and Indo-Pacific, and increased slightly on the Great Barrier Reef due to moderate coral loss. Coral reef ecosystems appear to be more resistant to macroalgal blooms than assumed, which has important implications for reef management.

Abstract High‐resolution data collected over the past 60 years by a single family of Siberian scientists on Lake Baikal reveal significant warming of surface waters and long‐term changes in the basal food web of the world's largest, most ancient lake. Attaining depths over 1.6 km, Lake Baikal is the deepest and most voluminous of the world's great lakes. Increases in average water temperature (1.21 °C since 1946), chlorophyll a (300% since 1979), and an influential group of zooplankton grazers (335% increase in cladocerans since 1946) may have important implications for nutrient cycling and food web dynamics. Results from multivariate autoregressive (MAR) modeling suggest that cladocerans increased strongly in response to temperature but not to algal biomass, and cladocerans depressed some algal resources without observable fertilization effects. Changes in Lake Baikal are particularly significant as an integrated signal of long‐term regional warming, because this lake is expected to be among those most resistant to climate change due to its tremendous volume. These findings highlight the importance of accessible, long‐term monitoring data for understanding ecosystem response to large‐scale stressors such as climate change.

What should be the role of P-values and confidence intervals in the interpretation of scientific results? This question is not new 1 and our field of epidemiology is far from alone in struggling with it. 2,3 I have four suggestions for authors and readers. The first is quite broad, so I offer that one before describing current practices. I then turn to the other three. My remarks are confined to settings in which P-values and confidence intervals accompany estimates of effect measures, such as the relative risk. Briefly, here are my suggestions. One, we should work harder than ever to avoid strict or exact interpretations of P-values and confidence intervals in observational research, where these statistics lack a theoretical basis. Two, we should stop interpreting P-values and confidence intervals as though they measure the probability of hypotheses. Three, when we want to know the probability of hypotheses, we should use Bayesian methods, which are designed expressly for that purpose. Four, we should get serious about precision and look for narrow confidence intervals instead of low P-values to identify results that are least influenced by random error. Real Life Is Not Randomized When treatment or exposure is randomized, we have a solid theoretical basis, testable in simulations, for the probability models from which P-values, confidence intervals, and likelihoods are deduced. In observational research, all we can do is hope that the social, behavioral, and physical processes by which people become exposed to risk factors in the unrandomized real world do not differ too greatly from randomization. 4 Unfortunately, each time we find that risk factors are associated with each other in observational studies, we find evidence against that hope. We cannot remind ourselves too often of this fundamental problem. At the very least, it should cause us to avoid hairsplitting interpretations of probabilistic statistics in observational research, where they are intrinsically fuzzy. Contemporary Uses of P-Values and Confidence Intervals Significance testing unquestionably dominates epidemiology today. In attempting to refrain from this practice over the past 17 years, 5 I have often been expected, assumed, encouraged, and sometimes even forced to engage in it by editors, reviewers, colleagues, professors, students, funding sources, regulators, attorneys, and journalists. It is not easy to be a non-tester in a testing world. After Rothman’s highly influential 1978 essay, “A Show of Confidence,”6 an immense and easily documented shift in reporting style took place. 7 Whereas P-values or “S” (significant) and “NS” (not significant) once were reported exclusively, the reporting of confidence intervals has now become accepted practice, with or without P- value accompaniment. Confidence intervals have a survival advantage for the tiny non-testing minority to which I belong. They enable us to gauge the precision of estimates easily, but without depriving the established majority of its beloved tests. Epidemiologists who see no purpose to a confidence interval other than its use in significance testing sometimes wonder why this shift in reporting practice has occurred. The P-value provides the information they desire more efficiently and exactly. Some are vaguely aware that confidence intervals supposedly convey information that P-values do not, but are unsure what that extra information is and even less sure how it might be useful. The word “precision” seems to be used with increasing regularity nowadays, and confidence intervals are occasionally described as “wide,” but “wide” and “imprecise” often seem nothing more than code words for “includes the null value” and hence for “not statistically significant.” Improbable Observations Do Not Imply Improbable Hypotheses When we estimate a parameter such as the relative risk, each possible value of that parameter is the expected value under some hypothesis, and each hypothesis has a P-value. 8,9 What we call “the”P-value is the P-value for the null hypothesis. Approximately, each P-value is the probability of obtaining an estimate at least as far from a specified value as the estimate we have obtained, if that specified value were the true value. It follows that no P-value, for the null hypothesis or any other, is the probability that the specified hypothesis is true. As an obvious example, the hypothesis corresponding to the point estimate has a (two-sided) P-value of 1.0. However, we do not treat our point estimates as absolutely certain to be true. Neither is the point estimate, in general, the most probable value. For a given estimate, the 95% confidence interval is the set of all parameter values for which P ≥ 0.05. For the value at each limit of a 95% confidence interval, P = 0.05 (two-sided). Thus, if either of the 95% confidence limits for a relative risk estimate equals 1.0 (the null value of this parameter), we can infer that the null P-value is 0.05. From this link between confidence intervals and P-values, it follows that a 95% confidence interval is not a range of values within which the unknown true value lies with 95% probability. The well-known “coverage probability” of confidence intervals pertains to a parameter value that is known to be true and the probability that an as yet unknown confidence interval will contain it. Coverage probability does not pertain to a known confidence interval and an unknown true value. To interpret a given 95% confidence interval as having a 95% probability of including the unknown true value is to mistake a frequentist confidence interval for a Bayesian probability interval. 10 This error is merely an extension of the logical fallacy of mistaking the null P-value for the probability that the null hypothesis is true. Why do we turn probability logic on its head in this way? We very much want to know the probabilities of hypotheses, which require Bayesian methods to determine, but our biostatistical teachers give us the P-values and confidence intervals of frequentist statistics. We are thus led into a basic fallacy, by which the probability of A given B is mistaken for the probability of B given A. 10 A P-value of 0.04 tells us that, if the null hypothesis were true, an association at least as strong as the one we observed would occur with a probability of 4%. We find it quite natural to reverse the terms, and conclude mistakenly that the probability of the null hypothesis is 4%, given the association we observed. The null hypothesis or any other hypothesis can be highly probable even though its P-value is less than 0.05. The null hypothesis or any other hypothesis can have a low probability even though its P- value is greater than 0.05. A relative risk can be highly probable even though it lies outside a 95% confidence interval. A relative risk can be highly improbable even though it lies inside a 95% confidence interval. The indispensable role of hypotheses in the computation of P-values and confidence intervals, with each hypothesis assigning a probability to each estimate we might possibly obtain, means that these measures are not the descriptive statistics they are sometimes said to be. 11P-values and confidence intervals are inferential statistics, but the flow of the inference is a deductive flow, in which hypotheses confer probability “down” to estimates . 12,13 Inductive statistical inference, in which the direction of the probability flow is from estimates back “up” to hypotheses, properly takes place only when prior probabilities are updated with new data, by means of Bayes’s theorem, to form posterior probabilities. 10,13 The only way we can determine the probability of the null hypothesis, or a range of values within which the true value lies with a given level of probability, is by using Bayesian methods. 10,13–15 Bayesian methods cannot be employed without the specification of prior probabilities for the hypothetical values of interest (eg, all possible values of relative risk, from zero to infinity). Since we do not specify prior probability distributions when we compute conventional (frequentist) confidence intervals, those intervals have no generally valid interpretation as Bayesian probability intervals. Many familiar expressions - some employing probabilistic language, others avoiding it - have the effect of leading us into this misinterpretation. It has been said that being located inside a 95% confidence interval makes values plausible, probable, likely, reasonably included by the data, or even possible. Values exterior to 95% confidence intervals have been said to be implausible, improbable, unlikely, reasonably excluded by the data, or even ruled out. None of these variations on a rhetorical theme can change a simple fact of statistical life: If we want to know which values are more and less likely, more and less plausible, etc., we must specify prior probabilities for those values and use Bayes’s theorem to update those probabilities when new data are in hand. It has become increasingly clear that the null P-value (hereafter called “the”P-value) does not do a very good job of the task for which it was originally intended: to quantify the statistical evidence against the null hypothesis. The reason is simple. The familiar Type I and Type II error rates upon which Neyman and Pearson taught us to focus 16,17 beg vitally important questions. One minus the Type I error rate is the specificity of a significance test: the probability of not declaring “significance” when the null hypothesis is true. One minus the Type II error rate is the test’s power or sensitivity: the probability of declaring “significance” when the alternative hypothesis is true. No informed patient would be satisfied with a diagnostic test result knowing only the test’s specificity and sensitivity. That patient would want to know the test’s predictive value (positive or negative, depending on the result). Significance tests are no different. In the same frequency terms that Neyman and Pearson used, 16,17 the researcher who wishes to be fully informed should be interested in questions such as the following: How often is the null hypothesis true when we fail to reject it? When we do reject the null hypothesis, how often is the alternative hypothesis true? These are the probabilities of ultimate concern in significance testing – the predictive values of “NS” and “S.” There is no way to determine them without postulating (stated again in frequency terms) how often the null and alternative hypotheses are true. The interest many epidemiologists express in how low the P-value is, if it is lower than 0.05, 18 raises still other questions. How much evidence against the null hypothesis do we have when P = 0.04, or when P = 0.001? To answer these questions, we need to consider the probabilities under the null and alternative hypotheses of obtaining these particular P-values, not just the probabilities of obtaining P < 0.05. Statisticians who have examined these questions in detail 19–26 have found, under widely ranging conditions, that P-values on the order of 0.05, 0.01, and even lower provide much less evidence against the null hypothesis than they appear to provide at face value. As a general matter, P- values in the vicinity of 0.05 provide almost no evidence against the null hypothesis at all. P = 0.04, for instance, is typically found to be almost equally probable under the null and alternative hypotheses. One upshot of this work has been a statistical research program devoted to calibrating, standardizing, conditioning, or adjusting low P-values to make them higher, so that they reflect more realistically the limited statistical evidence they provide against the null hypothesis. 19–26 Now that Bayesian methods are computationally feasible, one wonders whether these efforts to patch up P-values will ultimately be viewed a transitional stopgap. Taking Precision Seriously Transitional stopgaps should not be dismissed lightly, especially when the transitions in question take decades to unfold. Stopgaps can be particularly valuable when it seems that the only alternative is to cry in the (frequentist) wilderness for a (Bayesian) revolution. In epidemiology, the advent of confidence intervals creates an opportunity to take another small step toward more widespread use of Bayesian methods, while at the same time improving overall interpretation. This step is merely to take precision seriously. Epidemiologists have many reasons to emphasize certain results over others. Some results may pertain to particularly topical research questions. Some may be more valid than others. And some may be less influenced by random error. This last consideration seems to be an important one to many epidemiologists, who regularly use P-values to determine the degree to which chance influences their results. They believe that the lower the P-value, the less the influence of chance. Unfortunately, this extremely common use of the P-value is a misuse and an abuse of that statistic. The estimates least influenced by chance are not those with low P-values, but those with narrow confidence intervals. Consider the four hypothetical relative risk estimates in Table 1. The ratio of the upper to lower 95% confidence limits (CLR) is a handy measure of confidence interval width, and thus of precision. (For a difference measure such as the risk difference, the difference between the upper and lower confidence limits would serve the same purpose.) The example was devised to dramatize four clear-cut combinations of statistical “significance” and precision. Table 1: Results from a Hypothetical Study of a Single Binary Exposure and Four Diseases or of a Single Disease and Four Binary ExposuresTo the extent that the role of chance would be taken into account in deciding which of these results to emphasize, the conventional choices would be the statistically “significant” estimates B and C. These would be the “associations unlikely to be due to chance alone.” But one of them, estimate C, is very unstable. That estimate is influenced much more by random error, and from that standpoint is much less dependable, than estimate B. Of equal importance, when C is compared with D, estimate C is influenced much more by chance and in that regard is much less trustworthy, even though estimate C is statistically “significant” and estimate D is not. Estimates B and D – not B and C – are this study’s most precise estimates. Estimates B and D stand the best chance of holding up, conditional on their validity, in the context of existing and future research. Estimates B and D would weigh more heavily into meta-analyses and would exert stronger influences on probability distributions in properly conducted Bayesian analyses. Estimates B and D are the results that should be put forth for emphasis as the most statistically stable results this study has to offer. It is sometimes said that confidence intervals are especially valuable, and that increases in sample size and statistical efficiency are particularly needed, when statistical “significance” has not been attained. To the contrary, an estimate that has a wide confidence interval is imprecise and unstable no matter how low its P-value. Based solely on the results in Table 1, larger sample sizes, special study populations and statistically more efficient designs would be particularly desirable for A and C, regardless of the fact that one of these estimates is statistically “significant” and the other is not. Some epidemiologists wonder what all the fuss over P-values and confidence intervals is about. This hypothetical example shows how an emphasis on precision rather than statistical “significance” can affect which results we may choose to highlight. I invite the reader to examine published research reports in which the estimates with the lowest P-values have been singled out for emphasis, and to imagine how differently those papers would read if the estimates with the narrowest confidence intervals had been highlighted instead. CONCLUSION Our results that deserve the greatest reliance are those that are most stable and trustworthy. With regard to random error, a very poor way of identifying dependable results is to select associations with impressively low P-values. Inference and decision-making would be far better served by choosing estimates with narrow confidence intervals, which are least vulnerable to the play of chance. These are the results for which, by virtue of intentional or accidental features of our research methods, our studies provide the most evidence (as distinguished from the most valid evidence). By taking precision seriously, we can easily identify those research questions on which our studies provide the greatest quantity of statistical evidence, and those questions for which larger and more statistically efficient studies are needed. In terms of resistance to random error, our most durable results are our most precise estimates - however unspectacular, unsensational, and “non-significant” many of those estimates might be.

Using ecological criteria as a theoretical framework, we describe the steps involved in designing a network of marine reserves for conservation and fisheries management. Although we describe the case study of the Channel Islands, the approach to marine reserve design may be effective in other regions where traditional management alone does not sustain marine resources. A group of agencies, organizations, and individuals established clear goals for marine reserves in the Channel Islands, including conservation of ecosystem biodiversity, sustainable fisheries, economic viability, natural and cultural heritage, and education. Given the constraints of risk management, experimental design, monitoring, and enforcement, scientists recommended at least one, but no more than four, reserves in each biogeographic region. In general, the percentage of an area to be included in a reserve network depends on the goals. In the Channel Islands, after consideration of both conservation goals and the risk from human threats and natural catastrophes, scientists recommended reserving an area of 30–50% of all representative habitats in each biogeographic region. For most species of concern, except pinnipeds and seabirds, information about distributions, dispersal, and population growth was limited. As an alternative to species distribution information, suitable habitats for species of concern were used to locate potential reserve sites. We used a simulated annealing algorithm to identify potential reserve network scenarios that would represent all habitats within the smallest area possible. The analysis produced an array of potential reserve network scenarios that all met the established goals.

Recent transitions from coral to macroalgal dominance on some tropical reefs have engendered debate about their causes and effects. A widely accepted view is that reef environments support stable, alternative coral or non-coral assemblages, despite the lack of evidence to support this hypothesis. Confusion in the literature stems from (1) misunderstanding theory; and (2) conflating a switch between alternative stable states with a shift in the phase portrait of a single equilibrial system caused by a persistent change, or trend, in the environment. In the present paper we outline the conceptual derivation of the hypothesis of alternative stable states, distinguish it from the phase-shift hypothesis, and discuss the evidence required to support each one. For cases in which firm conclusions can be drawn, data from fossil and modern reefs overwhelmingly support the phase-shift hypothesis rather than the hypothesis of alternative stable states. On tropical reefs, a given environment evidently supports at most a single stable community. Corals dominate environments that are disturbed primarily by natural events and have small anthropogenic impacts. In such environments, macroalgae dominate a stage during some successional trajectories to the stable, coral-dominated community. In anthropogenically perturbed environments, the resilience of the coral-dominated community is lost, precipitating phase shifts to communities dominated by macroalgae or other noncoral taxa. The implication for reef management and restoration is both substantial and optimistic. To the extent that the environments of degraded reefs are restored, either passively or actively, the communities should return to coral dominance.

BACKGROUND: Coral reefs are facing increasing pressure from natural and anthropogenic stressors that have already caused significant worldwide declines. In January 2010, coral reefs of Florida, United States, were impacted by an extreme cold-water anomaly that exposed corals to temperatures well below their reported thresholds (16°C), causing rapid coral mortality unprecedented in spatial extent and severity. METHODOLOGY/PRINCIPAL FINDINGS: Reef surveys were conducted from Martin County to the Lower Florida Keys within weeks of the anomaly. The impacts recorded were catastrophic and exceeded those of any previous disturbances in the region. Coral mortality patterns were directly correlated to in-situ and satellite-derived cold-temperature metrics. These impacts rival, in spatial extent and intensity, the impacts of the well-publicized warm-water bleaching events around the globe. The mean percent coral mortality recorded for all species and subregions was 11.5% in the 2010 winter, compared to 0.5% recorded in the previous five summers, including years like 2005 where warm-water bleaching was prevalent. Highest mean mortality (15%-39%) was documented for inshore habitats where temperatures were <11°C for prolonged periods. Increases in mortality from previous years were significant for 21 of 25 coral species, and were 1-2 orders of magnitude higher for most species. CONCLUSIONS/SIGNIFICANCE: The cold-water anomaly of January 2010 caused the worst coral mortality on record for the Florida Reef Tract, highlighting the potential catastrophic impacts that unusual but extreme climatic events can have on the persistence of coral reefs. Moreover, habitats and species most severely affected were those found in high-coral cover, inshore, shallow reef habitats previously considered the "oases" of the region, having escaped declining patterns observed for more offshore habitats. Thus, the 2010 cold-water anomaly not only caused widespread coral mortality but also reversed prior resistance and resilience patterns that will take decades to recover.

During the late summer to fall of 1987, Caribbean reef corals experienced an intense and widespread discoloration event described as bleaching. Contrary to initial predictions, most bleached corals did not die. However, energy input from zooxanthellae decreased, as estimated from: (i) delta(13)C values, a measure of the discrimination against (13)C in (12)C/(13)C assimilation, of skeletal aragonite; (ii) in situ photosynthesis-irradiance measurements; (iii) and tissue biomass parameters of Montastraea annularis and Agaricia lamarcki. The delta(18)O signal, a measure of the discrimination against (18)O in (16)O/(18)O assimilation, from M. annularis skeletons demonstrated that this event coincided with abnormally elevated water temperatures.

This is the second part of an extended review of the etiology and treatment of chronic back pain (CBP). The first paper dealt with somatic factors and interventions, this paper will examine psychological theories on the etiology of CBP and psychological treatments for CBP. Finally common problems of both the somatic and the psychological approaches will be discussed and suggestions for treatment and research will be made.

A coral disease characterized by a novel pattern of rapid tissue destruction first appeared on reefs of the middle Florida Keys in lune 1995. Between lune and October 1995 the disease infected 17 species of scleractinian corals and the hydrocoral Millepora alcicornis. Localized populations of Dichocoenia stokesi, the species most affect­ed, revealed up to 38% mortality. Many colonies exhibited complete tissue loss within days as the disease moved across colonies at rates of up to 2 cm per 24 hr. Typically tissue loss was initiated at the base of the colony and moved upward. At times disease progression halted and colonies retained partial tissue resembling a cap on the top of an otherwise denuded colony. Laboratory cultures of samples from the disease line revealed a dominant bacterium that, when isolated and characterized using genetic and metabolic techniques, most closely matched the genus Sphingomonas. Pure laboratory cultures of the bacterium produced disease in freshly collected coral colonies incu­bated in laboratory aquaria. The disease that we call plague type II appeared on different reefs of sou Florida and the Florida Keys in 1996 and 1997. While coral mortality associated with each of the three outbreaks was regionally confined and did no! recur in subsequent years on the same reefs, the high mortality rates distinguish this disease as one of the most serious yet documented.

The effects of chronic exposure to increasing levels of human-induced underwater noise on marine animal populations reliant on sound for communication are poorly understood. We sought to further develop methods of quantifying the effects of communication masking associated with human-induced sound on contact-calling North Atlantic right whales (Eubalaena glacialis) in an ecologically relevant area (~10,000 km(2) ) and time period (peak feeding time). We used an array of temporary, bottom-mounted, autonomous acoustic recorders in the Stellwagen Bank National Marine Sanctuary to monitor ambient noise levels, measure levels of sound associated with vessels, and detect and locate calling whales. We related wind speed, as recorded by regional oceanographic buoys, to ambient noise levels. We used vessel-tracking data from the Automatic Identification System to quantify acoustic signatures of large commercial vessels. On the basis of these integrated sound fields, median signal excess (the difference between the signal-to-noise ratio and the assumed recognition differential) for contact-calling right whales was negative (-1 dB) under current ambient noise levels and was further reduced (-2 dB) by the addition of noise from ships. Compared with potential communication space available under historically lower noise conditions, calling right whales may have lost, on average, 63-67% of their communication space. One or more of the 89 calling whales in the study area was exposed to noise levels ≥120 dB re 1 μPa by ships for 20% of the month, and a maximum of 11 whales were exposed to noise at or above this level during a single 10-min period. These results highlight the limitations of exposure-threshold (i.e., dose-response) metrics for assessing chronic anthropogenic noise effects on communication opportunities. Our methods can be used to integrate chronic and wide-ranging noise effects in emerging ocean-planning forums that seek to improve management of cumulative effects of noise on marine species and their habitats.

Abstract Selection of the appropriate management unit is critical to the conservation of animal populations. Defining such units depends upon knowledge of population structure and upon the timescale being considered. Here, we examine the trajectory of eleven subpopulations of five species of baleen whales to investigate temporal and spatial scales in management. These subpopulations were all extirpated by commercial whaling, and no recovery or repopulation has occurred since. In these cases, time elapsed since commercial extinction ranges from four decades to almost four centuries. We propose that these subpopulations did not recover either because cultural memory of the habitat has been lost, because widespread whaling among adjacent stocks eliminated these as sources for repopulation, and/or because segregation following exploitation produced the abandonment of certain areas. Spatial scales associated with the extirpated subpopulations are frcequently smaller than those typically employed in management. Overall, the evidence indicates that: (1) the time frame for management should be at most decadal in scope ( i.e. , &lt;100 yr) and based on both genetic and nongenetic evidence of population substructure, and (2) at least some stocks should be defined on a smaller spatial scale than they currently are.

Marine biodiversity of the United States (U.S.) is extensively documented, but data assembled by the United States National Committee for the Census of Marine Life demonstrate that even the most complete taxonomic inventories are based on records scattered in space and time. The best-known taxa are those of commercial importance. Body size is directly correlated with knowledge of a species, and knowledge also diminishes with distance from shore and depth. Measures of biodiversity other than species diversity, such as ecosystem and genetic diversity, are poorly documented. Threats to marine biodiversity in the U.S. are the same as those for most of the world: overexploitation of living resources; reduced water quality; coastal development; shipping; invasive species; rising temperature and concentrations of carbon dioxide in the surface ocean, and other changes that may be consequences of global change, including shifting currents; increased number and size of hypoxic or anoxic areas; and increased number and duration of harmful algal blooms. More information must be obtained through field and laboratory research and monitoring that involve innovative sampling techniques (such as genetics and acoustics), but data that already exist must be made accessible. And all data must have a temporal component so trends can be identified. As data are compiled, techniques must be developed to make certain that scales are compatible, to combine and reconcile data collected for various purposes with disparate gear, and to automate taxonomic changes. Information on biotic and abiotic elements of the environment must be interactively linked. Impediments to assembling existing data and collecting new data on marine biodiversity include logistical problems as well as shortages in finances and taxonomic expertise.

Long-term ecological data sets present opportunities for identifying drivers of community dynamics and quantifying their effects through time series analysis. Multivariate autoregressive (MAR) models are well known in many other disciplines, such as econometrics, but widespread adoption of MAR methods in ecology and natural resource management has been much slower despite some widely cited ecological examples. Here we review previous ecological applications of MAR models and highlight their ability to identify abiotic and biotic drivers of population dynamics, as well as community-level stability metrics, from long-term empirical observations. Thus far, MAR models have been used mainly with data from freshwater plankton communities; we examine the obstacles that may be hindering adoption in other systems and suggest practical modifications that will improve MAR models for broader application. Many of these modifications are already well known in other fields in which MAR models are common, although they are frequently described under different names. In an effort to make MAR models more accessible to ecologists, we include a worked example using recently developed R packages (MAR1 and MARSS), freely available and open-access software.

Biotic resistance, the process by which new colonists are excluded from a community by predation from and/or competition with resident species, can prevent or limit species invasions. We examined whether biotic resistance by native predators on Caribbean coral reefs has influenced the invasion success of red lionfishes (Pterois volitans and Pterois miles), piscivores from the Indo-Pacific. Specifically, we surveyed the abundance (density and biomass) of lionfish and native predatory fishes that could interact with lionfish (either through predation or competition) on 71 reefs in three biogeographic regions of the Caribbean. We recorded protection status of the reefs, and abiotic variables including depth, habitat type, and wind/wave exposure at each site. We found no relationship between the density or biomass of lionfish and that of native predators. However, lionfish densities were significantly lower on windward sites, potentially because of habitat preferences, and in marine protected areas, most likely because of ongoing removal efforts by reserve managers. Our results suggest that interactions with native predators do not influence the colonization or post-establishment population density of invasive lionfish on Caribbean reefs.

The decline and potential recovery of Caribbean reefs has been the subject of intense discussion and is of great interest to reef ecologists and managers. The recent return of Diadema antillarum sea urchins at some Caribbean locations and the concomitant changes in coral cover and recruitment provide a new perspective on the reversibility of Caribbean coral reef decline. This study examined the influence of recovering populations of Diadema and the subsequent formation of dense urchin zones on the growth and density of newly settled juvenile scleractinian corals. In these urchin zones, where Diadema graze algae, we documented higher growth rates of juvenile corals, and higher densities of small juvenile recruits (likely to be important precursors to reef recovery). Coral survivorship was higher for juvenile corals living in urchin versus algal zones. Roughly 83% of the juvenile corals in urchin zones survived over the 2 yr period of the study, while ~69% survived in the algal zones. Corals in the urchin zones increased in major diameter by an average of 75 7% from 2001 to 2003 versus 24 4% for corals in the algal zones during the same time period. The relatively abrupt decrease in macroalgal cover and the signs of increasing coral cover along the north coast of Jamaica following the return of Diadema, reported here and by other authors, suggest that these reefs have undergone rapid phase shifts, rather than being constrained to alternate stable states. In the Caribbean, it appears that Diadema are effective at enhancing scleractinian coral recruitment and growth and thus could be used as an important manipulative tool for returning reefs to a coral dominated state, especially on reefs that are severely overfished.

The present study examined the reliability and validity of a daily activity diary for chronic pain patients. The diary assesses various postures and activities including time spent lying, sitting, or standing/walking, the use of pain relief devices, time spent in pain relief activities, and the use of analgesic medications. The methodology compared patient self-report on the daily activity diary to spouse observations of the same activities. In addition, patient self-report of uptime/downtime was compared to the objective assessment of uptime/downtime by an automated electromechanical device. Reliability coefficients for the daily activity diary categories were all positive and statistically reliable as were correlations between patient and spouse ratings on lying down time, time spent standing/walking, and pain intensity. Also, patient self-report of medication use correlated significantly with spouse pill count. Finally, the correlation between patient report of lying down time and downtime as measured by the electromechanical monitor was also positive and highly significant. These results indicate that the daily activity diary is a reliable and valid instrument for the assessment of daily activity patterns of chronic pain patients in their natural environment. These results are discussed in relation to other research and the use of daily diaries for assessment and treatment outcome research with this population.

Marine protected areas (MPAs) provide place-based management of marine ecosystems through various degrees and types of protective actions. Habitats such as coral reefs are especially susceptible to degradation resulting from climate change, as evidenced by mass bleaching events over the past two decades. Marine ecosystems are being altered by direct effects of climate change including ocean warming, ocean acidification, rising sea level, changing circulation patterns, increasing severity of storms, and changing freshwater influxes. As impacts of climate change strengthen they may exacerbate effects of existing stressors and require new or modified management approaches; MPA networks are generally accepted as an improvement over individual MPAs to address multiple threats to the marine environment. While MPA networks are considered a potentially effective management approach for conserving marine biodiversity, they should be established in conjunction with other management strategies, such as fisheries regulations and reductions of nutrients and other forms of land-based pollution. Information about interactions between climate change and more "traditional" stressors is limited. MPA managers are faced with high levels of uncertainty about likely outcomes of management actions because climate change impacts have strong interactions with existing stressors, such as land-based sources of pollution, overfishing and destructive fishing practices, invasive species, and diseases. Management options include ameliorating existing stressors, protecting potentially resilient areas, developing networks of MPAs, and integrating climate change into MPA planning, management, and evaluation.

Previous work in this laboratory has indicated that the transplantation of adrenal medullary tissue into the subarachnoid space of the rat spinal cord can reduce pain sensitivity to acute noxious stimuli, particularly following stimulation by nicotine. This most likely results from the stimulated release of opioid peptides and catecholamines from the transplanted chromaffin cells. However, chronic pain models may more closely resemble human clinical pain, and the arthritic rat model has been used for screening potential therapeutic strategies. The purpose of the present study was to assess the potential for adrenal medullary tissue implanted into the spinal subarachnoid space to alleviate chronic pain. Adrenal medullary tissue was implanted into adjuvant-induced arthritic rats, and changes in body weight and vocalization responses were monitored over the 10 week course of the disease. Results indicate that the severe weight reduction normally associated with this inflammatory arthritis was attenuated by adrenal medullary, but not control, implants. In addition, vocalizations were reduced in animals implanted with adrenal medullary, but not control tissue following nicotine stimulation. This reduction was blocked by the opiate antagonist, naloxone, and partially attenuated by the alpha-adrenergic antagonist, phentolamine. Together, these results suggest that the transplantation of adrenal medullary tissue into the subarachnoid space of the spinal cord may provide a local source of opioid peptides and catecholamines for the reduction of chronic pain.

Stellwagen Bank National Marine Sanctuary is located in Massachusetts Bay off the densely populated northeast coast of the United States; subsequently, the marine inhabitants of the area are exposed to elevated levels of anthropogenic underwater sound, particularly due to commercial shipping. The current study investigated the alteration of estimated effective communication spaces at three spawning locations for populations of the commercially and ecologically important fishes, Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus). Both the ambient sound pressure levels and the estimated effective vocalization radii, estimated through spherical spreading models, fluctuated dramatically during the three-month recording periods. Increases in sound pressure level appeared to be largely driven by large vessel activity, and accordingly exhibited a significant positive correlation with the number of Automatic Identification System tracked vessels at the two of the three sites. The near constant high levels of low frequency sound and consequential reduction in the communication space observed at these recording sites during times of high vocalization activity raises significant concerns that communication between conspecifics may be compromised during critical biological periods. This study takes the first steps in evaluating these animals' communication spaces and alteration of these spaces due to anthropogenic underwater sound.