Baseline study on Cd,Cu and Pb concentrations in Atlantic neuston organisms

Neuston organisms were caught during two Atlantic expeditions (1982 and 1987) between 48°N and 40° S. About 1200 individuals from 19 taxa were analysed for cadmium, copper and lead. The distribution of metal concentrations was log-normal and thus best described by the median and the percentiles. Fourteen taxa with sample numbers larger than 20 were subjected to a logarithmic regression of weight vs metal concentration. The metal concentrations from populations with significant correlations were then weight-corrected: eight populations for lead, five for cadmium and four for copper. When grouped into ecological categories the pleuston organisms displayed significantly higher cadmium concentrations than the euneuston, which in turn had higher values than the pseudo-and/or facultative neuston. The relative deviation from the median was used in an averaging operation to obtain a broader spatial resolution of the neuston-bound metals, based on 16 taxa which were encountered at ten or more stations. The geographical distribution of cadmium and lead can be interpreted in light of chemical oceanographic knowledge and underlines the importance of organisms in the biogeochemical cycling of metals in the sea.     

Determining Optimal Population Monitoring for Rare Butterflies

Abstract: Determining population viability of rare insects depends on precise, unbiased estimates of population size and other demographic parameters. We used data on the endangered St. Francis' satyr butterfly (Neonympha mitchellii francisci) to evaluate 2 approaches (mark–recapture and transect counts) for population analysis of rare butterflies. Mark–recapture analysis provided by far the greatest amount of demographic information, including estimates (and standard errors) of population size, detection, survival, and recruitment probabilities. Mark–recapture analysis can also be used to estimate dispersal and temporal variation in rates, although we did not do this here. Models of seasonal flight phenologies derived from transect counts (Insect Count Analyzer) provided an index of population size and estimates of survival and statistical uncertainty. Pollard–Yates population indices derived from transect counts did not provide estimates of demographic parameters. This index may be highly biased if detection and survival probabilities vary spatially and temporally. In terms of statistical performance, mark–recapture and Pollard–Yates indices were least variable. Mark–recapture estimates were less likely to fail than Insect Count Analyzer, but mark–recapture estimates became less precise as sampling intensity decreased. In general, count‐based approaches are less costly and less likely to cause harm to rare insects than mark–recapture. The optimal monitoring approach must reconcile these trade‐offs. Thus, mark–recapture should be favored when demographic estimates are needed, when financial resources enable frequent sampling, and when marking does not harm the insect populations. The optimal sampling strategy may use 2 sampling methods together in 1 overall sampling plan: limited mark–recapture sampling to estimate survival and detection probabilities and frequent but less expensive transect counts.     

Hierarchical Bayes estimation of hunting success rates

Although most post-season harvest surveys are conducted at the state level, the effective management of wildlife populations often requires estimates of hunting success rate, hunting pressure and harvest at the sub-area (such as management unit, regional, or county) level.Sample sizes for some sub-areas are often very small or even zero. Because of small sample sizes, estimates for small sub-areas often yield unacceptably large standard errors. In this article, a hierarchical Bayes model is used to estimate hunting success rates at the sub-area level from post-season harvest surveys. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method is illustrated using data from the Missouri Turkey Hunting Survey 1994 Spring Season. The Bayesian estimates are close to the frequency estimates for the sub-areas with large sample sizes and more stable than the frequency estimates for those with small sample sizes. The Bayesian estimates will be more useful to wildlife biologists in estab-lishing hunting regulation on small sub-areas at no additional survey cost.     

Evaluation of Local Ecological Knowledge as a Method for Collecting Extensive Data on Animal Abundance

Abstract:  The use of local ecological knowledge (LEK) has been advocated for biodiversity monitoring and management. To date, however, it has been underused in studying wild populations of animals and, particularly, in obtaining quantitative abundance estimates. We evaluated LEK as a tool for collecting extensive data on local animal abundance and population trends. We interviewed shepherds in southeastern Spain, asking them to estimate the local abundance of the terrestrial tortoise Testudo graeca . We quantified reliability of abundance estimates derived from interviews by comparing them with those obtained from standard field-sampling protocols (distance sampling). We also explored the complementarity of these 2 approaches. LEK provided high-quality and low-cost information about both distribution and abundance of T. graeca . Interviews with shepherds yielded abundance estimates in a much wider range than linear transects, which only detected the species in the upper two-thirds of its abundance range. Abundance estimates from both methodologies showed a close relationship. Analysis of confidence intervals indicated local knowledge could be used to estimate mean local abundances and to detect mean population trends. A cost analysis determined that the information derived from LEK was 100 times cheaper than that obtained through linear-transect surveys. Our results should further the use of LEK as a standard tool for sampling the quantitative abundance of a great variety of taxa, particularly when population densities are low and traditional sampling methods are expensive or difficult to implement.     

Public Willingness to Pay for Recovering and Downlisting Threatened and Endangered Marine Species

Abstract: Nonmarket valuation research has produced economic value estimates for a variety of threatened, endangered, and rare species around the world. Although over 40 value estimates exist, it is often difficult to compare values from different studies due to variations in study design, implementation, and modeling specifications. We conducted a stated‐preference choice experiment to estimate the value of recovering or downlisting 8 threatened and endangered marine species in the United States: loggerhead sea turtle (Caretta caretta), leatherback sea turtle (Dermochelys coriacea), North Atlantic right whale (Eubalaena glacialis), North Pacific right whale (Eubalaena japonica), upper Willamette River Chinook salmon (Oncorhynchus tshawytscha), Puget Sound Chinook salmon (Oncorhynchus tshawytscha), Hawaiian monk seals (Monachus schauinslandi), and smalltooth sawfish (Pristis pectinata). In May 2009, we surveyed a random sample of U.S. households. We collected data from 8476 households and estimated willingness to pay for recovering and downlisting the 8 species from these data. Respondents were willing to pay for recovering and downlisting threatened and endangered marine taxa. Willingness‐to‐pay values ranged from $40/household for recovering Puget Sound Chinook salmon to $73/household for recovering the North Pacific right whale. Statistical comparisons among willingness‐to‐pay values suggest that some taxa are more economically valuable than others, which suggests that the U.S. public's willingness to pay for recovery may vary by species.     

The intrasponge fauna of Spheciospongia vesparia (Porifera,Demospongiae) at Curaçao and bonaire

The infauna of 35 individuals of Spheciospongia vesparia (Lamarck, 1814) of different volumes and from different sites and depths have been inventoried and compared. The number of sponge-inhabiting taxa is logarithmically related to sponge volume. Biomass and total number of the animals contained in the sponge are directly proportional to sponge volume. Numerical and taxonomic composition of infaunas from different sampling sites is fairly constant. Biomass and total number of sponge-inhabiting animals is not significantly different for any of the 4 sampling sites. Several taxa, however, are more abundant in sponges from one or more localities. The ratio of total biomass to total number of intrasponge fauna is found to be significantly smaller for sponges collected in deep water than in shallow water. Differences from and similarities with Pearse's results (1932, 1950) on the infauna of the same sponge species at Dry Tortugas and Bimini are discussed. The relation of the number of contained taxa and the volume of a sponge is compared with the relation of island size and number of taxa present according to MacArthur and Wilson's island-theory (MacArthur, 1972). Finally the erratic occurrence of some taxa as opposed to the highly regular occurrence of some other taxa is discussed. It is concluded that the composition of the sponge-infauna in specimens larger than 11 is highly constant and that the sponge-inhabiting fauna constitutes an ecological community.     

Estimating trend precision and power to detect trends across grouped count data

Ecologists commonly use grouped or clustered count data to estimate temporal trends in counts, abundance indices, or abundance. For example, the U.S. Breeding Bird Survey data represent multiple counts of birds from within each of multiple, spatially defined routes. Despite a reliance on grouped counts, analytical methods for prospectively estimating precision of trend estimates or statistical power to detect trends that explicitly acknowledge the characteristics of grouped count data are undescribed. These characteristics include the fact that the sampling variance is an increasing function of the mean, and that sampling and group-level variance estimates are generally estimated on different scales (the sampling and log scales, respectively). We address these issues for repeated sampling of a single population using an analytical approach that has the flavor of a generalized linear mixed model, specifically that of a negative binomial-distributed count variable with random group effects. The count mean, including grand intercept, trend, and random group effects, is modeled linearly on the log scale, while sampling variance of the mean is estimated on the log scale via the delta method. Results compared favorably with those derived using Monte Carlo simulations. For example, at trend = 5% per temporal unit, differences in standard errors and in power were modest relative to those estimated by simulation (< or = /11/% and < or = /16/%, respectively), with relative differences among power estimates decreasing to < or = /7/% when power estimated by simulations was > or = 0.50. Similar findings were obtained using data from nine surveys of fingernail clams in the Mississippi River. The proposed method is suggested (1) where simulations are not practical and relative precision or power is desired, or (2) when multiple precision or power calculations are required and where the accuracy of a fraction of those calculations will be confirmed using simulations.     

A new approach to the "apparent survival" problem: estimating true survival rates from mark-recapture studies

Survival estimates generated from live capture-mark-recapture studies may be negatively biased due to the permanent emigration of marked individuals from the study area. In the absence of a robust analytical solution, researchers typically sidestep this problem by simply reporting estimates using the term "apparent survival." Here, we present a hierarchical Bayesian multistate model designed to estimate true survival by accounting for predicted rates of permanent emigration. Initially we use dispersal kernels to generate spatial projections of dispersal probability around each capture location. From these projections, we estimate emigration probability for each marked individual and use the resulting values to generate bias-adjusted survival estimates from individual capture histories. When tested using simulated data sets featuring variable detection probabilities, survival rates, and dispersal patterns, the model consistently eliminated negative biases shown by apparent survival estimates from standard models. When applied to a case study concerning juvenile survival in the endangered Cape Sable Seaside Sparrow (Ammodramus maritimus mirabilis), bias-adjusted survival estimates increased more than twofold above apparent survival estimates. Our approach is applicable to any capture-mark-recapture study design and should be particularly valuable for organisms with dispersive juvenile life stages.     

Pleistocene speciation of sister taxa in a North Pacific clade of brooding sea stars (<Emphasis Type="Italic">Leptasterias</Emphasis>)

Although numerous coastal marine species show intra-specific lineage splitting and population divergence times that date to the period of glacial cycles during the Pleistocene epoch, reported instances of recent speciation in the coastal marine environment are relatively rare. Marine organisms with brood-protection and other reproductive modes with limited dispersal potential have been suggested to experience more frequent speciation and extinction events than related species with higher dispersal rates, but few studies have actually estimated divergence times of sister species in these organisms. Here, two mitochondrial gene regions (cytochrome oxidase subunit I, putative control region and upstream tRNAs) and a nuclear gene region (Elongation factor 1α subunit intron 4) provide evidence of recent (0.5–1.2 Mya) cladogenetic events in four pairs of putative sister taxa in a predominantly North Pacific brooding subgenus of sea stars (Leptasterias subgenus Hexasterias). Calibration is obtained from a trans-arctic migration in a related clade of sea stars (Leptasterias subgenera Hexasterias and Nesasterias) that is timed to the opening of the Bering Strait at 3.5 ± 0.25 Mya, and uncertainty in the calibration point is accommodated with a normally-distributed Bayesian prior probability. Similar estimates of population splitting times for two of the pairs of putative sister taxa were obtained by a multilocus coalescent analysis. Estimates of mitochondrial mutation rates (0.01/My) were approximately 50% of the values calibrated for sister species pairs in tropical sea stars and sea urchins. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Use of Linkage Disequilibrium Data to Estimate Effective Size of Hatchery and Natural Fish Populations

A primary parameter in the assessment of the viability of a population is its effective population size ( Ne ). Allozyme analysis of four groups of fishes provided data on linkage disequilibrium, which were then used to estimate Ne . The groups included hatchery samples of juvenile white seabass, Atractoscion nobilis , juvenile rainbow trout, Oncorhynchus mykiss , from the Shasta Hatchery, and juvenile chinook salmon, O. tshawytscha , from the Coleman National Fish Hatchery. The fourth sample consisted of juvenile chinook salmon from the threatened winter run in the upper Sacramento River. The groups of fish were chosen to represent different applications of the methodology to conservation of fishes. For a variety of reasons. Ne may be considerably lower than census counts of fish present in the parental populations. The Ne of the hatchery broodstock that produced the sample of juvenile white seabass was estimated to be approximately 10, although 25 adult white seabass were present in a mass spawning tank. Ne estimates for the parental populations of the Shasta and Coleman Hatchery samples were 35.8 and 132.5, respectively. The actual number of fish spawned at the Shasta Hatchery was approximately 40, whereas nearly 10,000 salmon were spawned at the Coleman Hatchery. The threatened winter run of chinook salmon had an estimated Ne of 85.5 and an approximate run size of 2000 salmon. The method of estimating effective population size from linkage disequilibrium data appears to result in realistic estimates of effective population size when adequate sample size and a sufficient number of polymorphic loci are available.     

Estimation of the cost of air pollution control regulation

Estimates of hydrocarbon pollution control costs under the alternative regulatory approaches of individual source or process standards, plant standards, and regionally marketable permits are presented. The estimates are obtained from data supplied by the DuPont Company based on a 1976 in-house engineering study. The estimation procedure is unique in that the data are based on uniform percentage control across sources while the estimated cost equations allow variable control at each source. The cost simulations show that considerable cost savings are available from allowing firms more flexibility in meeting a given environmental standard. The effect of plant relocation and monopoly in the pollution permit market are also investigated.     

Estimating the normal background rate of species extinction

A key measure of humanity's global impact is by how much it has increased species extinction rates. Familiar statements are that these are 100–1000 times pre‐human or background extinction levels. Estimating recent rates is straightforward, but establishing a background rate for comparison is not. Previous researchers chose an approximate benchmark of 1 extinction per million species per year (E/MSY). We explored disparate lines of evidence that suggest a substantially lower estimate. Fossil data yield direct estimates of extinction rates, but they are temporally coarse, mostly limited to marine hard‐bodied taxa, and generally involve genera not species. Based on these data, typical background loss is 0.01 genera per million genera per year. Molecular phylogenies are available for more taxa and ecosystems, but it is debated whether they can be used to estimate separately speciation and extinction rates. We selected data to address known concerns and used them to determine median extinction estimates from statistical distributions of probable values for terrestrial plants and animals. We then created simulations to explore effects of violating model assumptions. Finally, we compiled estimates of diversification—the difference between speciation and extinction rates for different taxa. Median estimates of extinction rates ranged from 0.023 to 0.135 E/MSY. Simulation results suggested over‐ and under‐estimation of extinction from individual phylogenies partially canceled each other out when large sets of phylogenies were analyzed. There was no evidence for recent and widespread pre‐human overall declines in diversity. This implies that average extinction rates are less than average diversification rates. Median diversification rates were 0.05–0.2 new species per million species per year. On the basis of these results, we concluded that typical rates of background extinction may be closer to 0.1 E/MSY. Thus, current extinction rates are 1,000 times higher than natural background rates of extinction and future rates are likely to be 10,000 times higher. Estimación de la Tasa Normal de Extinción de Especies     

A hierarchical model for spatial capture-recapture data

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture-recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.     

Measuring copepod naupliar abundance in a subtropical bay using quantitative PCR

Copepod nauplii are important in plankton food web dynamics, but limited information is available about their ecology due to methodological challenges. Reported here is a new molecular method that was developed, optimized, and tested in laboratory and field samples that uses quantitative PCR (qPCR) to identify and estimate the abundance of nauplii of the planktonic copepod, Parvocalanus crassirostris. The overall approach included collection of bulk zooplankton samples in the field, size fractionation to create artificial cohorts of relatively few developmental stages, obtaining DNA copy number for each size fraction by qPCR amplification of a target gene region, and estimation of the number of animals in each fraction through application of known DNA copy number across developmental stage. Method validation studies found that our qPCR-based approach has comparable accuracy to microscope-based counts of early developmental stages. Naupliar abundance estimates obtained using the two methods on cultured populations were similar; the regression of qPCR estimates on microscope-based counts resulted in a nearly 1:1 ratio (slope = 1.09). The qPCR-based method is superior to traditional identification and quantification methods for nauplii due to its higher taxonomic resolution, sensitive detection over a range of DNA quantities, and relatively high throughput sample processing.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

Space-time modeling for the Missouri Turkey Hunting Survey

The Missouri Turkey Hunting Survey (MTHS) is a postseason mail survey conducted by the Missouri Department of Conservation. The 1996 MTHS provides information concerning the number of turkeys harvested by hunters on each day and the total number of trips made to the counties by these hunters on each day of the hunting season. The success rates are then found from this information. Small sample sizes produce large standard errors for the estimates at the county level. We use a Bayesian hierarchical generalized linear model to estimate daily hunting success rates at the county level. The model includes an autoregressive process for the days of the hunting season and spatially correlated random geographic effects. The computations are performed using Gibbs sampling and adaptive rejection sampling techniques. Results show that there are significant spatial corelations between counties and correlations between days of the hunting season. The estimates are close to the frequency estimates at the state level and much more stable at the county level.     

A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.     

Modeling spatial patterns in fisheries bycatch: improving bycatch maps to aid fisheries management.

Fisheries bycatch, or incidental take, of large vertebrates such as sea turtles, seabirds, and marine mammals, is a pressing conservation and fisheries management issue. Identifying spatial patterns of bycatch is an important element in managing and mitigating bycatch occurrences. Because bycatch of these taxa involves rare events and fishing effort is highly variable in space and time, maps of raw bycatch rates (the ratio of bycatch to fishing effort) can be misleading. Here we show how mapping bycatch can be enhanced through the use of Bayesian hierarchical spatial models. We compare model-based estimates of bycatch rates to raw rates. The model-based estimates were more precise and fit the data well. Using these results, we demonstrate the utility of this approach for providing information to managers on bycatch probabilities and cross-taxa bycatch comparisons. To illustrate this approach, we present an analysis of bycatch data from the U.S. gill net fishery for groundfish in the northwest Atlantic. The goals of this analysis are to produce more reliable estimates of bycatch rates, assess similarity of spatial patterns between taxa, and identify areas of elevated risk of bycatch.     

Inference about density and temporary emigration in unmarked populations

Few species are distributed uniformly in space, and populations of mobile organisms are rarely closed with respect to movement, yet many models of density rely upon these assumptions. We present a hierarchical model allowing inference about the density of unmarked populations subject to temporary emigration and imperfect detection. The model can be fit to data collected using a variety of standard survey methods such as repeated point counts in which removal sampling, double-observer sampling, or distance sampling is used during each count. Simulation studies demonstrated that parameter estimators are unbiased when temporary emigration is either "completely random" or is determined by the size and location of home ranges relative to survey points. We also applied the model to repeated removal sampling data collected on Chestnut-sided Warblers (Dendroica pensylvancia) in the White Mountain National Forest, U.S.A. The density estimate from our model, 1.09 birds/ha, was similar to an estimate of 1.11 birds/ha produced by an intensive spot-mapping effort. Our model is also applicable when processes other than temporary emigration affect the probability of being available for detection, such as in studies using cue counts. Functions to implement the model have been added to the R package unmarked.     

The relationship between bleaching and mortality of common corals

Reef corals are likely to have many subtle but four gross responses to anomalous warm water. These are (1) not bleach and live (mortality <10%), (2) not bleach and die (mortality >20%), (3) bleach and live, and (4) bleach and die. The frequency of these four possible gross responses was determined for 18 common coral taxa over an exceptionally warm 1998 El Niño where intense bleaching was observed, and mortality determined from line transects averaged 41.2±34.7 (±SD). Field studies included (1) recording the loss of color (bleaching) and observing recently dead individuals among 6,803 colonies during five sampling periods and (2) estimating mortality based on 180 m of line-intercept transects completed 4 months before and near the end of the bleaching episode. There was no clear relationship between the loss of color and either direct observation or transect-based estimates of mortality for the 18 taxa. The morphology of the taxa did not influence color loss but branching and encrusting taxa had higher mortality than massive and submassive taxa. Loss of color and mortality are the most common responses to warm water as only Pavona did not lose color or die and only two taxa, Cyphastrea and Millepora, did not significantly lose color but died. Of the 15 taxa that lost color, five taxa, Astreopora, Favia, Favites, Goniopora, and Leptoria, did not die. These taxa are those most likely to have reduced potential mortality by the loss of pigments and associated algal symbionts. Death of the branching taxa was detected reasonably by direct field observation but some taxa were underestimated when compared with mortality estimates based on line transects. Death of encrusting and massive taxa including Echinopora, Galaxea, Hydnophora, Montipora, Platygyra, and massive Porites was poorly detected from direct observations but they proved to have modest to high mortality (20–80%) based on line transects. There was no single response of these common corals to warm water but these data, collected during an extreme warm-water anomaly, indicate that the loss of color is most frequently a sign of morbidity, particularly for branching and encrusting taxa.Communicated by P.W. Sammarco, Chauvin