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29 March 2022

Dozens of unidentified bat species likely live in Asia — and could host new viruses

Study suggests some 40% of horseshoe bats in the region have yet to be formally described.

Smriti Mallapaty

Smriti Mallapaty

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There could be more species of horseshoe bat than previously thought.Credit: Chien Lee/Nature Picture Library

A genomic analysis suggests that there are probably dozens of unknown species of horseshoe bats in southeast Asia1. Horseshoe bats (Rhinolophidae) are considered the reservoir of many zoonotic viruses — which jump from animals to people — including the close relatives of the viruses that caused severe acute respiratory syndrome and COVID-19. Identifying bat species correctly might help pinpoint geographical hotspots with a high risk of zoonotic disease, says Shi Zhengli, a virologist at the Wuhan Institute of Virology in China. “This work is important,” she says. The study was published in Frontiers in Ecology and Evolution on 29 March.Better identification of unknown bat species could also support the search for the origins of SARS-CoV-2 by narrowing down where to look for bats that may harbour close relatives of the virus, says study co-author Alice Hughes, a conservation biologist at the University of Hong Kong. The closest known relatives of SARS-CoV-2 have been found in Rhinolophus affinis bats in Yunnan province, in southwestern China2, and in three species of horseshoe bat in Laos3.Cryptic speciesHughes wanted to better understand the diversity of bats in southeast Asia and find standardized ways of identifying them. So she and her colleagues captured bats in southern China and southeast Asia between 2015 and 2020. They took measurements and photographs of the bats’ wings and noseleaf — “the funky set of tissue around their nose”, as Hughes describes it — and recorded their echolocation calls. They also collected a tiny bit of tissue from the bats’ wings to extract genetic data.To map the bats’ genetic diversity, the team used mitochondrial DNA sequences from 205 of their captured animals, and another 655 sequences from online databases — representing a total of 11 species of Rhinolophidae. As a general rule, the greater the difference between two bats’ genomes, the more likely the animals represent genetically distinct groups, and therefore different species.The researchers found that each of the 11 species were probably actually multiple species, possibly including dozens of hidden species across the whole sample. Hidden, or ‘cryptic’, species are animals that seem to belong to the same species but are actually genetically distinct. For example, the genetic diversity of Rhinolophus sinicus suggests that the group could be six separate species. Overall, they estimated that some 40% of the species in Asia have not been formally described.“It’s a sobering number, but not terribly surprising,” says Nancy Simmons, a curator at the American Museum of Natural History in New York City. Rhinolophid bats are a complex group and there has been only a limited sampling of the animals, she says.However, relying on mitochondrial DNA could mean that the number of hidden species is an overestimate. That is because mitochondrial DNA is inherited only from the mother, so could be missing important genetic information, says Simmons. Still, the study could lead to a burst of research into naming new bat species in the region, she says.Further evidenceThe findings corroborate other genetic research suggesting that there are many cryptic species in southeast Asia, says Charles Francis, a biologist at the Canadian Wildlife Service, Environment and Climate Change Canada, in Ottawa, who studies bats in the region. But, he says, the estimates are based on a small number of samples.Hughes’ team used the morphological and acoustic data to do a more detailed analysis of 190 bats found in southern China and Vietnam and found that it supported their finding that many species had not been identified in those regions. The study makes a strong argument for “the use of multiple lines of evidence when delineating species”, says Simmons.Hughes says her team also found that the flap of tissue just above the bats’ nostrils, called the sella, could be used to identify species without the need for genetic data. Gábor Csorba, a taxonomist at the Hungarian Natural History Museum in Budapest, says this means that hidden species could be identified without doing intrusive morphology studies or expensive DNA analyses.

doi: https://doi.org/10.1038/d41586-022-00776-2

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Our results show that the average diel activity patterns of Ae. aegypti populations in both Miami, FL and in Brownsville, TX were very similar; they both had two peaks, one in the early morning and the other in the evening, and the average host-seeking peaks are between 7:00 and 8:00 and between 19:00 and 20:00 (Fig. 4). Similar observations were previously reported by several investigators3,4,10,11,12 and the bimodal diel activity pattern is the most frequently reported for Ae. aegypti populations worldwide. However, variations between peak activity have been detected between populations. In East Africa, for instance, Trpis et al.3 reported peak activity at 7:00 and at 19:00, whereas McClelland10 reported peak activity two or three hours after sunrise (9:00 or 10:00) and one or two hours before sunset (17:00 or 16:00). Similarly, in the United States, Smith et al.7 observed a bimodal diel activity pattern for Ae. aegypti, but the evening peak was earlier, between 17:00 and 19:00. Despite these variations, the spacing of the peaks is similar in all these studies despite the fact that these studies were conducted in ecologically and climatically diverse locations.The activity patterns observed at site 3 in Brownsville (Fig. 2) and at site 1 in Miami (Fig. 1) were trimodal. In Brownsville, the trimodal activity peaks were between 6:30 and 7:30, 9:30 and 10:30, and 18:30 and 19:30 (Fig. 2), and in Miami the trimodal peaks were between 7:00 and 8:00, 9:00 and 10:00 and between 19:00 and 20:00 (Fig. 1). Interestingly, the timing of the third peak was similar in both Brownsville site 3 and Miami site 1 suggesting similar underlying factors despite geographic distance, different ecology, and different climate. Brownsville, Texas, is in the Lower Rio Grande Alluvial Floodplain ecoregion. The climate is humid subtropical and urbanization has removed most of the indigenous palm trees and floodplain forests vegetation (https://www.epa.gov/sites/default/files/2018-05/documents/brownsvilletx.pdf). Miami is in the Tropical Florida Ecoregion. Similar to Brownsville, Texas, urbanization and agriculture has replaced most of the indigenous Pine Rockland vegetation. Trimodal biting patterns for Ae. aegypti have been observed before in Trinidad by Chadee and Martinez4, but the middle peak was observed at 11:00 which is half an hour to an hour later than what we observed in Miami and Brownsville, respectively (Figs. 1 and 2). While the morning and evening peaks coincide with human outdoor activity, the middle peak occurs during high heat conditions and the factors that lead to this peak or its importance in the epidemiology of Ae. aegypti-borne arboviral diseases are currently not known. The studies by McClelland13 observed multiple activity peaks in an East African population of Ae. aegypti. The significance of the different activity patterns to the epidemiology of Ae. aegypti-borne arboviral diseases are currently unknown and we think they need more investigation especially since Ae. aegypti-borne arboviral infections have been rising in the recent past14,15.We observed that the host-seeking activity peaks were consistent between 5:45 and 7:30 and between 18:00 and 20:45 (Figs. 1 and 2). These observations are important in planning and conducting control operations directed at the adult Ae. aegypti female populations. During the 2016 Zika outbreak, there was no specific information on the host-seeking activity patterns of Ae. aegypti in Miami Dade County and the adulticide treatment implemented as part of an integrated approach targeted the morning activity16. The integrated approach effectively reduced the vector population and interrupted the transmission of the Zika virus; however, it highlighted the need for site-specific information on the diel activity patterns of Ae. aegypti in Miami Dade County in particular and the CONUS in general. There have been sporadic Ae. aegypti-borne arboviral disease outbreaks in Miami Dade County, FL and the city of Brownsville, TX17,18,19,20,21, in the future we will be better prepared to conduct effective adulticide applications with the current knowledge of the diel activity patterns of Ae. aegypti in these areas. Furthermore, we are now better equipped to educate the public on how to minimize exposure to Ae. aegypti-borne arboviral diseases by avoiding outdoor activities during peak biting activity periods.In our studies, we used BG-Sentinel 2 traps and monitored them every hour, twenty-four hours a day over 96 h, a method with some similarities to that used by Smith et al.7. In the past, diel biting activity studies were carried out using human landing catches following the methods primarily established by Haddow22. To our knowledge, only two studies have previously used sampling procedures not based on human landing catches to study the biting activity patterns of Ae. aegypti; the study by Ortega-Lopez et al.6 used mosquito electrocuting traps, and the study by Smith et al.7 used a mechanical rotator mosquito trap. In the present study, the use of BG-Sentinel II traps had the advantage that it was specifically designed to capture female host-seeking Ae. aegypti8,9. In addition, attached BG-Counter devices can keep track of the number of mosquitoes captured per specified unit time and environmental conditions, and store the information in a cloud server. However, the BG-Sentinel 2 traps collected a wide variety of mosquito species, (Table 1), and to keep track of specific species captured each hour, we had to monitor them every hour.Overall, we present data on the diel activity of Ae. aegypti populations in two cities in the southern United States. In both cities the activity patterns were bimodal; there were peaks of activity in the mornings and the evenings. The significance of these observations is that these peaks can be targeted to improve the effectiveness of adulticide treatments aimed at controlling Ae. aegypti adult populations. Using BG-Sentinel 2 traps eliminates individual variations associated with human landing catches and the associated danger of infections from wild mosquitoes especially during ongoing outbreaks. More

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BioassaysSalmon-louse bioassays were performed by the BC Centre for Aquatic Health Sciences (CAHS) as described in Saksida et al.10. Briefly, motile (i.e., pre-adult and adult) L. salmonis were collected from 11 salmon farms in the Broughton Archipelago (BA) between 2010 and 2021 and transported to CAHS in Campbell River, BC. Within 18 h of collection, healthy lice were separated by sex and randomly placed into petri dishes each containing approximately 10 lice (mean ± SD = 9.6 ± 1.1) and subjected to one of six EMB concentrations (either 0, 31.3, 62.5, 125, 250, and 500 ppb or 0, 62.5, 125, 250, 500, and 1000 ppb, depending on suspected variation in EMB sensitivity11). Each collection corresponded to one bioassay, and each bioassay contained roughly four replicates for each sex (4.0 ± 1.3 for females and 3.6 ± 0.9 for males). After 24 h of EMB exposure, lice were classified as alive if they could swim and attach to the petri dish, or moribund/dead otherwise. Lice were kept at 10 °C throughout the process. In total, 34 bioassays were conducted from 11 farms between October 2010 and November 2021.We analysed the proportion of lice that survived exposure to EMB, using standard statistical descriptions that accounted for within-assay dependencies (generalized linear mixed models (GLMMs) with logit link functions, fitted separately to the data from each bioassay). The models included fixed effects for EMB concentration, sex, and the interaction between the two, as well as a random intercept for petri dish. For each analysis, we centered concentration values and scaled them by one standard deviation. We used the GLMM fits to calculate the effective concentrations at which 50% of the lice survived (EC50) in each bioassay. The GLMM for one bioassay produced a singular fit because there was not enough variation in the female survival data to warrant the random-effects structure. We retained the EC50 values resulting from this singular fit because re-fitting without the random intercept yielded identical EC50 values, and removing the entire bioassay from the overall dataset did not qualitatively affect the subsequent analysis.To assess whether the sensitivity of salmon lice to EMB has decreased over time, we fitted a set of five standard GLMs with gamma error distributions and log link functions to the maximum-likelihood EC50 estimates. Each of these five models included binary effects for sex and for whether the farm’s stock had previously been treated, since both affect EMB sensitivity in lice10. The first model included only these two effects and served as a null model that assumed lice did not evolve EMB resistance over time. The second model added a fixed effect for time (i.e., the number of days since January 1, 2010), while the third model included an interaction between time and sex. The fourth and fifth models were identical to the second and third, but with a quadratic effect for time, to account for possible first-order nonlinearity. We were unable to add an effect for farm due to small sample sizes. We performed model selection using the Akaike Information Criterion penalized for small sample sizes AICc25, treating AICc differences of less than two as being indistinguishable in terms of statistical support and selecting the least complex model when that was the case26. The ΔAICc values for the EC50 models were 48.1, 6.1, 4.9, 0, 1.75, respectively.Field efficacyWe used relative salmon-louse counts after EMB treatment (i.e., the post-treatment count divided by the pre-treatment count) as our measure of EMB field resistance between 2010 and 2021 (higher relative counts imply lower treatment efficacy). We defined “pre-treatment” as one month prior to treatment and “post-treatment” as three months after treatment (roughly when one would expect to find the lowest counts in louse populations previously unexposed to EMB), as in Saksida et al.10. We excluded EMB treatments for which an additional, non-EMB treatment was performed within the following three months. In total, there were 73 EMB treatments for which we were able to calculate relative post-treatment counts.Salmon-louse counts were performed by farm staff as described by Godwin et al.27. In short, salmon-louse counts were usually performed at least one per month by capturing 20 stocked fish in each of three net pens using a box seine net, then placing the fish in an anesthetic bath of tricaine methanesulfonate (TMS, or MS-222) and assessing the fish for motile (i.e., pre-adult and adult) L. salmonis by eye.The treatment dataset included the date and type of every treatment that has been performed on a BA farm (i.e., not just the 11 farms with bioassay data). In total, 88 EMB treatments were conducted between 2010 and 2021, of which we were able to calculate relative post-treatment counts for 73 because some months lacked counts or had a non-EMB treatment performed within the following three months. An additional 22 non-EMB treatments (e.g., freshwater and hydrogen baths) were performed, all since the beginning of 2019, but we excluded these data from our analysis.To determine whether field efficacy of EMB treatments has decreased over time, we used GLM-based “hurdle models”—standard statistical descriptions used to accommodate an over-abundance of zeroes in data being analysed. A hurdle model uses two components—one model for whether a count is nonzero and another for the value of the nonzero count—to predict overall mean count. To this end, we fitted three binomial GLMs paired with three gamma GLMs to the relative-count data, each of the paired models being structurally identical in terms of predictors. All of these submodels included a binary fixed effect for previous treatment, as in the EC50 models. The null pair of submodels included no additional terms, the second pair of submodels included a fixed effect for time (i.e., the number of days since January 1, 2010), and the third pair of submodels included a quadratic effect of time (again, to account for possible first-order deviations nonlinearity). We were unable to add an effect for farm due to small sample sizes. We performed model selection of the hurdle models, again using the Akaike Information Criterion penalized for small sample sizes. The ΔAICc values for the three hurdle models were 39.6, 18.3, and 0, respectively. We performed our analyses in R 3.6.028, using the lme4 package29. More

Base run simulationFigure 6 shows the results of the base run simulation. In this scenario, strong vegetation declines over time, while the empty area and flammable vegetation have increasing trends. As such, more fuel would be available for burning, and the wildfire can burn broader areas. Panel (a) shows an oscillatory trend for the burn rate with an average upward trend (To make sure the oscillatory behavior of the model does not fade, Appendix 4 shows the simulation result for 100 years). The observed pattern in the burn rate can be traced back to the patterns of human ignition (Panel b), and the growing trend of vulnerable properties (Panel c). In addition, the results show the long-term declining trend of strong vegetation in our base line simulation (Panel d); over time, stronger vegetation is replaced by flammable vegetation which can lead to more fire. This change in vegetation composition effectively increases the average burn rate. Over time, with more flammable vegetation and with the expansion of vulnerable properties, the likelihood of human-made ignition increases.Figure 6Base run simulation for a 20-year run of the model.Full size imageCoupling effectsFigure 7 shows how the relation between perceived fire risk and the burn rate influences the system. The black line is the base run simulation for comparison. The blue dashed line depicts the condition in which risk perception changes extremely slowly, and the human system is almost disconnected from the natural system. In this situation, if humans underestimate the fire potential, the system burns down nature, resulting in a catastrophic environmental outcome as depicted in panel (a). Panel (a) shows that the burn rate overshoots in the short term but relatively declines due to less remaining natural resources to burn.Figure 7Coupling effect analysis for 20 years. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanel (b) displays the total burn rate throughout the study time to cast further insight into the burn rate sensitivity to perceived risk. The overall burn rate does not significantly change when the risk perception changes from 0.5 to 2, indicating the difference among burn rates in panel (a) is more about the fluctuation timing, but not the size. However, an additional rise in the sense of risk greatly raises the overall burn rate, as seen in panel (a).In the case of prolonged change in risk perception, human ignition continues to increase (panel c) as the perceived risk changes slowly. Furthermore, vulnerable properties are being built faster than their demolition (panel d). A slighter delay in perception leads to a higher frequency of oscillation as depicted in the graphs by the red dashed lines and a longer delay in a lower frequency oscillation, as shown by the purple graphs. Overall, the results are not much different from the base run. We are losing forests (panel e) and have periodic burn rates of increasing magnitude over time.Policy experimentsHere we examine the impact of implementing four proposed policies introduced in Table 2. To prevent the initial condition and transition periods affecting our comparison of proposed policies, we imposed each policy at the fifth year and compared the total burn rates between 10 and 20 years. Figure 8 shows the effect of these policies on different variables. Figure 8Policy implementation. Note: P1: limits vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanels (a) and (b) show the burn rate over time and cumulative, respectively. All four policies reduce the burn-rate magnitude compared to the base run. P3 is more effective in early burning-rate reduction compared to other policies, but they ultimately result in similar behavior. It is worth noticing that P1 has the most effect on long-run fluctuation reduction, although its total effect in the time span is less than P3. It seems that firefighting is more effective in the short run, but it fails to dampen the fluctuation and instead limits its growth. This is partly because of the increase in human ignition and settlement due to the success of firefighting in the short run. As a result, people perceive less fire danger and continue to engage in high-risk activities and expand housing in the WUI. The result is further fluctuation in the burn rate even when P3 is implemented. On the other hand, the WUI expansion limitation policy can effectively reduce the burn-rate fluctuation in a timely manner. Implementing P4 causes a reduction in strong vegetation, which leads to flammable vegetation increase. As flammable vegetation is the main fuel for wildfire, this policy cause increase in fuel availability and an increase in the burning rate.Change in human ignition is provided in panel (c). Different levels of human-made ignition are observable, and the reason is that people adjust their high-risk behavior with burn rate, and not with the number of fires. In the firefighting policy, as for a given level of ignition, the burn rate declines, we observe more risky behavior and more human-made ignition. It is interesting to note that, as panel (c) shows, we end up with more WUI under policies 2, 3, and 4. In fact, the reason is that the firefighting, prescribed burning and clear cutting only affect natural sector of the model, decrease burn rate, which decreases risk perception and in turn result in more WUI development. On the other hand, P1 directly targets WUIs.Panel (e) displays the change in strong vegetation, which shows that P4 causes the most reduction in forest tree cover as it directly removes strong vegetation. P2 also causes a decrease in strong vegetation compared to the base run. The reason is that burning flammable vegetation damages young trees and prevents them from developing into solid vegetation. On the other hand, P3 has the least effect on strong vegetation by slowing the damage to young trees and confining the fire. Panel (f) shows the flammable vegetation dynamic after imposing each policy. P3 and P2 reduce flammable vegetation more than P1. However, there is an important difference in how these policies cause the reduction in flammable vegetation. In comparing panels (a) and (b), we see that while P3 causes further increases in the strong vegetation, P2 causes an increase in the empty area. P4 is the only policy that increases flammable vegetation by removing the strong vegetation and providing an empty area to be filled with young vegetation.Overall, it looks like each policy has some marginal effect on containing wildfire, though the magnitudes of effect are not considerable.Replication of United States dataFor model validation, we investigate its ability to fit a single case, United States’ wildfires from 1996 to 2015. We utilize the United States Department of Agriculture’s wildfire database for the conterminous United States (Short, 2017). The results are shown in Fig. 9. In this figure, simulation of burning rate and human ignition (continuous lines, in black) closely follows the real-world data (dotted lines, in red), and the model fairly replicates the historical trends.Figure 9Burning rate and human ignition per unit of forest area. The black line represents the model result, and the red dotted line represents the historical wildfire activity in the conterminous United States.Full size imageCombination policy implementation analysisTo better understand the impacts of our policies, we run different pairs of policies simultaneously. The results illustrate the nonlinear incremental impacts between policies. Simply put, it appears that the impact of several policies is enforced when combined synergistically. In other words, applying several policies might have a greater overall impact than the sum of the policies’ individual effects and suggests that policymakers should avoid searching for a panacea and adopt a broad range of approaches thoughtfully.The results of multiple policy implementations along with single ones are presented in Fig. 10. For example, P1 and P2 each reduce the total burn rate by 4.9% and 4.5%, respectively. While the summation of these effects is 9.4%, simultaneously implementing P1 and P2 lead to a 13.6% burn-rate reduction—P1 controls the human ignition, and P2 reduces the flammable vegetation stock—together, the burn rate is more affected than if implemented separately. The case is more interesting when P1 and P3 are imposed together. The result is a 38% burn-rate reduction compared to 13.9%, which is the sum of solely implementing each policy. The synergic effect happens because P3 lets the flammable vegetation (mainly young trees) age and become strong vegetation. Furthermore, the P1 also prevents human ignition from growing as fast as a single P3 implementation.Figure 10The nonlinear effect of policies. The benefits of implementing multiple policies differ from the sum of the effect of policies. The figure shows the percent of burn rate reduction. Note: P1: limit vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting.Full size imageAn interesting case happens when P2 and P3 are implemented together. The synergic effect is less than the sum of separate implementation, mainly because both policies affect the vegetation dynamic and not the human factor in the wildfire. P2 and P3 both cause a lower initial burn rate, but due to the reduction in perceived risk of wildfire and expansion of WUI, this effect quickly disappears. This is another evidence for the importance of considering the problem as an interconnected natural and human system, where effective policies should address both sides.Finally, an interesting result emerges when all policies impose together. Surprisingly, imposing all policies together does not have the most impact on the total burn rate (32.5%), which is less than the P1 and P3 effect (38.0%). The reason relates mainly to the fact P2 and P4 both cause increase in flammable vegetation after empty area filled, which lead to more burning rate after a delay.Sensitivity analysisWe conducted a series of sensitivity analysis to check the model’s robustness to our assumptions. Specifically, we conducted a Monte-Carlo analysis and changed several parameter values to determine the range of outcomes. The results are reported in Appendix 2. In summary, the focus was on parameters that can take on substantially different values from those assumed in the model, including parameters used for risk perception formulation, its effect on human behavior, such as time to perceive risk and time to change behavior, in addition to fractional burning rate per ignition, average s burning, initial flammable vegetation, initial strong vegetation, human ignition multiplier, and initial vulnerable property. As described in the Appendix, for most of these variables, we changed the corresponding variable up to double its base run value. Moreover, we test different values for initial strong vegetation and initial flammable vegetation changing them between zero and their base run values. Each sensitivity test is the outcome of 2000 simulation runs using a uniformly distributed random distribution of the parameters within the specified intervals. The results are qualitatively robust, and their variability is within reasonable limits (See Figure A1). More

Marine Ecology Research Group and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New ZealandMads S. Thomsen, Luca Mondardini, David R. Schiel & Alfonso SicilianoDepartment of Bioscience, Aarhus University, 4000, Roskilde, DenmarkMads S. ThomsenSmithsonian Tropical Research Institute, Apartado, 0843-03092, Balboa, Ancon, Republic of PanamaAndrew H. Altieri, Viktoria M. M. Frühling, Seamus B. Harrison & Gerhard ZotzEnvironmental Engineering Sciences, University of Florida, Gainesville, FL, USAAndrew H. Altieri & Christine AngeliniDepartment of Biological Sciences, Macquarie University, Sydney, NSW, AustraliaMelanie J. Bishop & Semonn OleksynDipartimento di Biologia, Università di Pisa, CoNISMa, Via Derna 1, 56126, Pisa, ItalyFabio Bulleri & Joachim LangeneckMarine Sciences, University of Georgia, Athens, GA, USARoxanne FarhanCentre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamSydney Institute of Marine Science, Chowder Bay Road, Mosman, 2088, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamCoastal Ecology Lab, MOE Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, 2005 Songhu Road, 200438, Shanghai, ChinaQiang HeInstitute for Biology and Environmental Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, GermanyMoritz Klinghardt, Tristan Schneider & Gerhard ZotzSchool of Biological Sciences and UWA Oceans Institute, University of Western Australia, Perth, WA, AustraliaYannick Mulders & Thomas WernbergDepartment of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USAAaron P. RamusNicholas School of the Environment, Duke University, 135 Duke Marine Lab Road, Beaufort, NC, USABrian R. Silliman & Stacy ZhangMarine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UKDan A. SmaleCawthron Institute, Nelson, New ZealandPaul M. South More

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